Genome-Wide Selection Program for Improvement of

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Genome-Wide Selection Program for Improvement of Indigenous Pigs in

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Thesis · January 2012

DOI: 10.13140/2.1.3287.1367

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Genome-Wide Selection Program for Improvement of Indigenous Pigs

in Tropical Developing Countries

by

Everestus Chima Akanno

A Thesis

presented to

The University of Guelph

In partial fulfillment of requirements

for the degree of

Doctor of Philosophy

in

Animal and Poultry Science

Guelph, Ontario, Canada

© Everestus Chima Akanno, January, 2012

ABSTRACT

GENOME-WIDE SELECTION PROGRAM FOR IMPROVEMENT OF

INDIGENOUS PIGS IN TROPICAL DEVELOPING COUNTRIES

Everestus Chima Akanno Advisor

University of Guelph, 2012 Dr. J. A. B. Robinson

Genetic improvement of indigenous pig populations in tropical developing

countries can make a significant contribution to the conservation and utilization of local

genetic resources. Designing a swine breeding program requires knowledge of genetic

parameters for economically important traits. A meta-analysis of genetic parameters

determined under tropical conditions and published from 1974 to 2009 was carried out to

provide consensus estimates of genetic parameters. Given that the data recording and

analysis infrastructure for implementing the conventional best linear unbiased prediction

(BLUP) methods is generally lacking in developing countries, Genome-wide selection

(GS) provides an approach for achieving faster genetic progress without developing a

pedigree recording system. A simulation study was carried out to evaluate the option of

using available 60 K single nucleotide polymorphism marker panel. The observed levels

of linkage disequilibrium (LD) in the tropical pig populations were simulated and

utilized. Genomic predictions were from ridge regression analysis. The results showed

that expected accuracies of genomic breeding values (GBV) were in the range of 0.31 -

0.86 for the validation set. Genome-wide selection improved accuracy of GBVs over

conventional BLUP method for traits with low heritability and in young animals with no

performance data. Crossbred training populations had higher accuracy than purebred

training populations. An assessment of the opportunities for GS in tropical pig breeding

was conducted. Genome-wide selection performed better than conventional methods by

increasing genetic gain and maintaining genetic variation while lowering inbreeding

especially for traits with low heritability, by exploiting LD and the Mendelian sampling

effects. Combining GS with repeated backcrossing of crossbreds to the selected exotic

population in moderate LD promises faster improvements of the commercial population.

A two-step selection strategy that involves the use of GS to pre-select candidates that

entered the performance test station and for selecting replacement candidates in a nucleus

swine breeding program was evaluated and compared to other conventional approaches.

Genome-wide selection generated an increase of about 38% to 172% in annual returns

compared to other conventional approaches for previously selected population in

moderate LD and about 2% to 50% increases in return for unselected population in low

LD.

iv

ACKNOWLEDGEMENT

Sincere appreciation and gratitude is extended to my advisor, Dr. J.A.B.

Robinson, for his constructive criticism, insightful guidance and immense help and

support throughout the preparation and completion of this Doctoral thesis. Thanks also go

to other members of my advisory committee, Dr. F. Schenkel, Dr. R. Friendship, for their

support and helpful discussions during my programs and more importantly to Dr. M.

Sargolzaei for developing the QMSim software which accelerated my study. Special

thanks go to Dr. L. Schaeffer for mentoring me in the course of my studies. Computing

and statistical support from Bill Szkotnicki and Dr. M. Quinton are gratefully

acknowledged. I would also like to thank all the faculty, staff and graduate students of the

Centre for Genetic Improvement of Livestock, Department of Animal and Poultry

Science, for providing a pleasant and stimulating environment over these past 4+ years.

Data assistance from the Canadian Centre for Swine Improvement is acknowledged.

The full financial support provided by Canadian Commonwealth Scholarship and

Fellowship program under the Department of Foreign Affairs and International Trade

(DFAIT), Canada, Bursary awards received from the University of Guelph and partial

funding from Federal University of Technology, Owerri, Nigeria are gratefully

acknowledged.

I thank my wife, Vivian Akanno, for continued support, encouragement, patience

and housework and my son, Great Chidubem Chukwunonye for permitting me to be

away when he needed me most. I am also grateful to my father Emmanuel Akanno, my

mother Augustina Akanno and all my siblings for being a source of special inspiration

v

and strength towards the successful completion of my studies. Support from all my

friends is appreciated.

Finally, I thank the Almighty GOD for His Grace and Blessings without which

this program would have been impossible. To Him I say ―Thus Far the Lord has helped

me‖.

Many thanks to all and may GOD bless all of you.

v

This thesis is dedicated to my son Great Chidubem Chukwunonye

vi

TABLE OF CONTENTS

Chapter 1 General introduction 1

1.1 Preamble 2

1.2 Study objective 3

1.3 Justification of the study 3

1.4 Outline of thesis 4

Chapter 2 Swine genetic improvement in tropical developing countries:

Problems and Prospects 6

2.0 Abstract 7

2.1 Introduction 8

2.2 Swine genetic improvement programs in the tropics 11

2.3 Advances in swine genetic improvement 15

2.4 Genome-wide selection: a new method 17

2.5 Conclusions 28

Chapter 3 Meta-analysis of genetic parameter estimates for reproduction,

growth and carcass traits of pigs in the tropics 31

3.0 Abstract 32

3.1 Introduction 33

3.2 Materials and Methods 35

3.3 Results and Discussion 42

3.4 Conclusions and Recommendations 53

3.5 Appendix 67

vii

Chapter 4 Accuracies of genomic breeding values for simulated tropical pig

populations 83

4.0 Abstract 84

4.1 Introduction 85




Chapter 5 Opportunities for genome-wide selection for pig breeding in

developing countries 120

5.0 Abstract 121

5.1 Introduction 122




Chapter 6 Relative economic returns from selection schemes for a nucleus swine

breeding program 154

6.0 Abstract 155

6.1 Introduction 156




viii

Chapter 7 General Discussion and Conclusion 181

7.1 Summary 182

7.2 Recommendations 185

7.3 Future directions 186

References 190

ix

LIST OF TABLES

Table 2.1 Ranges of accuracies of genomic breeding values across various

sizes of reference population and using different statistical methods

in real dataset

30

Table 3.1 List of traits studied, abbreviations used and units of measurement

55

Table 3.2 Number of literature estimates (N), weighted least square means

(Mean±S.E), standard deviation (SD) and coefficient of variation

(CV) for reproduction, growth and carcass traits of pigs in the

tropics

56

Table 3.3 Number of literature estimates (N), combined (exotic and

indigenous) weighted mean heritability (h2), between study variance

(σ2

bs), Q statistics and 95% confidence interval (CI) for economic

traits of pigs in the tropics

57

Table 3.4 Number of literature estimates (N), weighted mean heritability for

indigenous (h2

I) and exotic (h2

E) breeds of pigs, maternal heritability

(h2

M), correlation between direct and maternal genetic effects (ram)

and common litter effect (l2) for reproduction traits of pigs in the

tropics

58

Table 3.5 Number of literature estimates (N), weighted mean heritability for

indigenous (h2

I) and exotic (h2

E) breeds of pigs, maternal heritability

(h2

M), correlation between direct and maternal genetic effects (ram)

and common litter effect (l2) for growth and carcass traits of pigs in

the tropics

59

Table 3.6 Levels of significance for the fixed factors in the analysis of

heritability estimates of some traits of pigs in the tropics

60

Table 3.7 Weighted means of estimates from literature for genetic (above

diagonal) and phenotypic (below diagonal) correlations among

reproduction traits of pigs in the tropics with the number of

estimates in parenthesis ( ) and the 95% confidence interval in

brackets [ ]

62

Table 3.8 Weighted means of genetic (above diagonal) and phenotypic (below

diagonal) correlations among growth traits of pigs in the tropics

63

x

with the number of estimates in parenthesis ( ) and the 95%

confidence interval in brackets [ ]

Table 3.9 Weighted means of genetic (above diagonal) and phenotypic (below

diagonal) correlations among carcass traits of pigs in the tropics



64

Table 3.10 Weighted means of estimates from literature for genetic correlations

among reproduction, growth and carcass traits of pigs in the tropics



65

Table 3.11 Weighted means of estimates from literature for phenotypic

correlations among reproduction, growth and carcass traits of pigs

in the tropics with the number of estimates in parenthesis ( ) and the

95% confidence interval in brackets [ ]

66

Table 4.1 The parameters used in the simulation program

108

Table 4.2 Correlations (r) between estimated breeding values ( ) and true

breeding values ( ) in the training (TP) and validation (VP)

populations

115

Table 4.3 Average accuracy of genomic breeding values when training and

validation population differ in structure

116

Table 4.4 Average linkage disequilibrium between adjacent marker pairs in

training and validation1 populations

117


146

Table 5.2 Average cumulative responses (standard deviation units) after five

and ten generations of selection using a pedigree or genome-based

methods for different traits and breeding programs

148

Table 5.3 Average cumulated inbreeding and genetic variance after five and

ten generations of selection using a pedigree or genome-based

methods for different traits and breeding programs

149

xi

Table 6.1 Traits in the breeding objective and their parameters

175

Table 6.2 Accuracy of estimated breeding values of pigs in the training and

validation sets for traits in the selection index

176

Table 6.3 Accuracy of selection indices (rHI) for two steps selection strategies

using different methods and for different populations in a nucleus

swine breeding program

177

Table 6.4 Expected genetic change per year for different selection schemes

and populations

178

Table 6.5 Differential returns from different selection methods and for a range

of genotyping cost in a nucleus breeding program

179

Table 6.6 Differential returns from selection when GS scheme is based on pre-

selection accuracy in a nucleus breeding program

180

xii

LIST OF FIGURES

Figure 2.1 Trends in pig production in developed and developing countries

29

Figure 3.1 Funnel plot of heritability estimates for (A) LSF showing no

evidence of publication bias and (B) ADG showing classic

asymmetry suggesting the presence of a publication bias

61

Figure 4.1 Schematic representation of the simulated population structure

109

Figure 4.2 Distribution of true allelic effects simulated for the three traits

studied

110

Figure 4.3 Distribution of average minor allele frequency (MAF) for the SNP

markers in the founder populations

111

Figure 4.4 Average linkage disequilibrium as measured by r2 against distance

(cM) between a pair of marker in different founder populations

112

Figure 4.5 Effect of training population size and structure on accuracy and

persistency of accuracy following ten generations of random

mating in selected exotic (top) and unselected indigenous (bottom)

populations and for NBA

113

Figure 4.6 Effect of training population size and structure on accuracy and

persistency of accuracy following ten generations of random

mating in selected exotic (top) and unselected indigenous (bottom)

populations and for BFT

114

Figure 4.7 Decay of accuracy of GBVs in different populations and traits

under random mating for ten generations. The straight line is the

accuracy level based on trait heritability

118

Figure 4.8 Influence of trait heritability on persistency of accuracy of GBVs in

different populations under random mating

119


147

Figure 5.2 Cumulative response (A), inbreeding (B), LD (C) and realised

accuracy (D) for various selection methods in an unselected

indigenous population and for ADG

150

xiii

Figure 5.3 Cumulative response (A), inbreeding (B), LD (C) and realised

accuracy (D) for different breeding programs using genome-wide

selection and for ADG

151

Figure 5.4 Cumulative responses (A), inbreeding (B), LD (C) and realised

accuracy (D) for different traits in a genome-wide selection scheme

for unselected indigenous population

152

Figure 5.5 Average minor allele frequency (MAF) changes due to (A)

different selection methods in an unselected indigenous population

for ADG, (B) different breeding programs in a genome-wide

selection scheme and for ADG and (C) different traits in a genome-

wide selection scheme for unselected indigenous population

153

Figure 6.1 Decision point in a nucleus swine breeding program 174

xiv

Glossary of Terms

BLUP Best linear unbiased predictions

DNA Deoxyribonucleic acid

GBV Genomic breeding value

GS Genome-wide selection

HWE Hardy-Weinberg equilibrium

LD Linkage disequilibrium

MAS Marker assisted selection

MAI Marker assisted introgression

MDE Mutation drift equilibrium

MAF Minor allele frequency

Ne Effective population size

NBS Nucleus breeding scheme

QTL Quantitative trait loci

SNP Single nucleotide polymorphism

1

CHAPTER 1

GENERAL INTRODUCTION

E. C. Akanno

Centre for Genetic Improvement of Livestock,

Department of Animal & Poultry Science, University of Guelph, ON, Canada

2

1.1 PREAMBLE

Genome-wide selection (GS) is currently revolutionizing livestock breeding

programs around the globe (Nejati-Javaremi, et al., 1997; Meuwissen et al., 2001).

Genome-wide selection aims to estimate the effects of dense genetic markers

simultaneously, and summing up these effects across all marker loci to predict the

genomic breeding values (GBV) of an individual. Selection decisions can then be based

on the GBV. This method relies heavily on the existence of linkage disequilibrium (LD)

between markers and putative quantitative trait loci (QTL) influencing traits of economic

importance in livestock (Habier et al., 2007; Toosi et al., 2010).

Genome-wide selection will be useful in the genetic improvement of indigenous

pigs in tropical developing countries where the infrastructures for the implementation of

conventional methods are lacking. Development of pedigree recording systems may no

longer be required but this does not remove the need to collect phenotypic data because

substantial datasets with both genotypes and phenotypes are required for training the

prediction model. The reference population could be managed under low-input conditions

and estimates of marker effects derived from this population would be used to obtain

GBV for selection candidates in a nucleus breeding herd.

Alternatively, the indigenous and exotic purebreds are crossed to produce

crossbred reference population for training marker effects and prediction of GBV in the

purebreds, thereby improving commercially relevant traits. Cryptic or transgressive

alleles (Notter, 1999) in the unselected indigenous breeds can be captured easily using

GS and their frequencies increased in this population. Other relevant adaptive genes in

3

the indigenous population necessary for pork production under tropical conditions will be

identified and incorporated into commercial population.

1.2 STUDY OBJECTIVE

The overall goal of this thesis was to evaluate the use of genome-wide selection

programs for genetic improvement of production traits in breeding programs involving

indigenous and exotic pigs in tropical developing countries. There were four specific

objectives:

1) To provide consensus estimates of genetic parameters for pig production traits

determined under tropical conditions

2) To evaluate the accuracies of genomic breeding values for simulated tropical pig

population structure

3) To assess the opportunities for genome-wide selection for pig breeding in

developing countries

4) To evaluate the relative returns from selection schemes for nucleus swine

breeding program

1.3 JUSTIFICATION OF THE STUDY

Designing an experiment to evaluate the option of implementing GS in tropical

pig breeding systems will require housing and managing a large number of reference pig

populations under tropical conditions and collecting phenotypic data on traits of interest.

These animals will need to be genotyped for thousands of marker loci based on an

existing dense marker panel. Many of these marker genotypes may not be informative

4

and some will not be segregating at all in this population. Another set of animals will

need to be produced and genotyped for the same set of markers as in the reference

population. These will be used to validate the accuracy of GBV. Currently, the cost of

genotyping for 60 K single nucleotide polymorphism (SNP) markers is still within the

neighbourhood of $120 per animal at DNA Landmarks (M. McNairnay, personal

communication, 2011) hence, implementation of GS on real populations can be very

costly.

Computer simulation can be used to conduct this experiment in a relatively short

period of time with minimum cost. Its usefulness depends on the completeness of the

assumed model and the accuracy of the parameters that go into the simulation program.

Usually, the assumed model is only an approximation of the real life situation. Therefore,

a simulation approach was chosen to evaluate the option of GS for tropical pig breeding

systems.

1.4 OUTLINE OF THESIS

The current states of knowledge about the development of genetic improvement

programs for pigs in tropical developing countries are reviewed in Chapter 2. The use of

genetic markers in pig breeding and their potential in contributing to a sustainable pig

improvement program under low-input production systems are discussed. Chapter 3

presents estimates of genetic and phenotypic parameters for pig production traits

determined under tropical conditions. This is the first time a meta-analysis of genetic

parameters was conducted for pig production traits in tropical developing countries.

Chapter 4 focuses on accuracies of GBV in populations with different breeding

5

programs and selection history. Analysis was based on simulated data assuming tropical

pig population structure. In Chapter 5, the application of genome-wide selection scheme

was evaluated and compared to conventional methods. In addition, implications for

genetic response, inbreeding rate, realized accuracy, LD and QTL minor allele frequency

change are discussed. Chapter 6 attempts to evaluate the relative returns from the

implementation of GS in a nucleus swine breeding program. A summary of all findings

from these studies are presented in Chapter 7 with some recommendations for the future.

6

CHAPTER 2

SWINE GENETIC IMPROVEMENT IN TROPICAL DEVELOPING

COUNTRIES: PROBLEMS AND PROSPECTS

E. C. Akanno, F. S. Schenkel and J. A. B. Robinson



7

2.0 ABSTRACT

The increase in productivity per pig to cushion the effect of animal protein

shortage and improve the livelihood of pig producers in developing countries depends

among other factors on the development of a sustainable genetic improvement program.

This paper reviewed the current challenges limiting the implementation of swine genetic

improvement programs in tropical developing countries. The application of a genome-

wide selection program (GS), as opposed to the development of infrastructure for

implementing the traditional selection scheme, is suggested. Genome-wide selection has

the potential to increase genetic gain by increasing accuracy of genomic breeding values

for selection candidates. Genome-wide selection can be adapted to existing tropical pig

breeding system. This paper calls for the evaluation of this option using computer

simulation of pig breeding scenarios in the tropics because implementation of GS on real

populations can be very costly.

Keywords: Genetic improvement, genome-wide selection, tropical pig, developing

countries

8

2.1 INTRODUCTION

A growing concern in many tropical developing countries is the low per capita

meat consumption level created by population growth, increased climate variability, and

persistent low investment in animal production research, among others (The World Bank,

2007). Owing to recent increases in household income and associated rise in standard of

living, meat consumption in developing countries has been projected to increase from

52% currently to 63% of the world total consumption by the year 2020 (Delgado, 2003).

About 44% of this increase will be derived from pork and pork products alone (FAO,

2001).

2.1.1 Pig production in the tropics

The pig is one of the earliest domesticated animals and plays an integral part in a

number of peasant economies in the tropics. In several African and Southeast Asian

countries such as Nigeria and Thailand, intensive pig production has played a significant

role in the overall economic growth and the feeding of an expanding urban population.

Furthermore, although in some circumstances pigs do compete with humans for food

resources, they are also very versatile at utilizing byproducts and wastes that are not

suitable for human consumption (Eusebio, 1984).

Currently, exotic pigs originally imported from Europe and North America are

used in conjunction with indigenous population for commercial pork production (Olivier

et al., 2002). Importations of exotic pigs into many developing countries are known to

have occurred before 1970 (Blench, 2000). The primary reason for this importation was

to improve the performance of the indigenous pigs under tropical conditions. Forsberg et

9

al. (2005) showed that pig populations in developing countries have continued to increase

at a linear rate of 10 percent per year since early 1970s while pig populations in

developed countries have remained comparatively constant (Figure 2.1). This increase

may be connected with the introduction of exotic breeds of pigs into many tropical

developing countries around this period.

In addition to pork production, pig rearing is considered as a form of financial

security and source of independent income especially for smallholder farmers (Lekule

and Kyvsgaard, 2003). Pigs are also commonly used in social-cultural functions such as

marriage and religious ceremonies. Because an increase in the number of pigs will occur,

a major effort should be made to increase the productivity per pig while lowering the cost

per unit of production. This will help to alleviate poverty and improve the livelihoods of

resource-poor farmers in the developing world (Garett, 2000).

2.1.2 Constraints to swine production under tropical conditions

The development of a swine production system requires an understanding of the

constraints on productivity and profitability. Studies have shown that inadequate

nutrition, variable management practices, poor genetic stock, heat stress and disease

challenge are among the prime factors limiting the growth of the swine industry in

developing countries (Muys and Westenbrink, 1995; Olivier et al., 2002; van der Waaij et

al., 2002; Lekule and Kyvsgaard, 2003; Beek, 2007; Mohanty and Chhabra, 2007).

Exotic pigs require sophisticated high-input feeding and management systems in order

for the pigs to fully express their superior genetic merit for faster growth rate, larger litter

size at birth and weaning, and good carcass quality. However, medium- to low-input

10

production systems tend to dominate the swine industry in most tropical locations

(Madalena et al., 2002; Olivier et al., 2002).

In these locations, ambient temperature is frequently greater than 22 , the

evaporative critical temperature for swine (Quiniou and Noblet, 1999), and in intensive

production systems, buildings are usually open or semi-open to the outdoor ambient

environment. Also, the climate is very suitable for both bacterial multiplication and

parasite survival (Kunavongkrit and Heard, 2000). Under these conditions, the exotic pig

breeds suffer from heat stress and disease challenge resulting in lower performance than

averages reported for their counterparts in temperate environments.

Another implication of this outcome is the huge investment in housing, high

quality feed and medication which tends to compromise with profitability of pork

production in the tropics. Attempts have been made to manipulate productivity of exotic

pigs through cross-breeding with the low yielding indigenous breeds (Nguyen et al.,

1996; Lakhani and Jogi, 1999; Oseni, 2005).

2.1.3 Development of indigenous pig populations

The swine production system in tropical areas often operates on a subsistence

level that is characterised by medium- to low-inputs and a stressful environment. The

local pig population has been adapted to these harsh conditions through generations of

natural selection. This has resulted in indigenous breeds and strains that survive and

produce under the prevailing environmental stresses of the tropics (King, 1987;

Kanengoni et al., 2004). Also, this population could be a potential source of the so-called

11

transgressive or cryptic alleles. These alleles are superior genes for some productive traits

supposedly ―hidden‖ in unselected breeds that are inferior for these traits (Notter, 1999).

There is evidence that indigenous breeds of pigs are less susceptible to common

diseases and parasites than the exotic pigs (Zanga et al., 2003), and have the ability to

survive long periods of feed and water deprivation (Anderson, 2003). They are less

reliant on external inputs, unlike exotic breeds and are, therefore, a potential part of a

sustainable agricultural system for the resource-poor farmers (Chiduwa et al., 2008).

They are generally hardy, can survive and reproduce on a low plane of nutrition under

extensive production systems (Holness, 1976). Despite these attributes, there is still

neglect in the utilization of these breeds for pork production leading to a decline in their

numbers, which puts them at risk of extinction (FAO, 1998; Lekule and Kyvsgaard,

2003).

2.2 SWINE GENETIC IMPROVEMENT PROGRAMS IN THE TROPICS

2.2.1 Nucleus breeding schemes

Nucleus breeding scheme (NBS) represents a breeding program in which

selection is carried out on a small number of elite breeding animals in a nucleus that

produces germ-plasm or breeding stock for use in the field. This method has been

proposed to overcome the technical constraints limiting the implementation of genetic

improvement programs for low-input production systems in developing countries (Smith,

1988). The genetic superiority generated in the nucleus is disseminated to the whole

population. The rate of genetic progress depends, among other things, on the size of the

nucleus and the effectiveness of selection within the nucleus (Weigel, 2001). Opening the

12

nucleus to replacements from the village herds might improve the genetic progress and,

more importantly, its chances of success because it encourages more farmer participation

(Bondoc and Smith, 1993).

Although there has been some success recorded for NBS, these programmes are

often faced with extensive genotype by environmental interactions and lack of acceptance

of nucleus breeding stock in the field. A genome based selection program offers

opportunities to overcome these limitations by collecting phenotypes and

deoxyribonucleic acid (DNA) from a large sample of animals in the field, without

requiring them to be pedigreed. The resulting estimates could be used to either select

within a nucleus herd or to identify superior breeding stock in the field on the basis of

their single nucleotide polymorphism (SNP) genotypes (Dekkers, 2010).

2.2.2 Cross-breeding programs

Cross-breeding is a successful management practice for increasing pork

production in swine. By exploiting heterosis, cross-breeding is known to improve

reproduction and growth traits (Adebambo, 1986; Gerasimov and PronAE, 2006;

Nwakpu and Onu, 2007), and for breed (or line) complementarities. Generally, those

traits that suffer from inbreeding depression or have low heritability are improved

through crossbreeding (Falconer and Mackay, 1996). In practice, there are many crossing

designs employed, from two-breed crosses and backcrosses, reciprocal crosses or three-

way-terminal crosses to crosses involving more than four lines (Oseni, 2005).

Results of several cross-breeding studies in the tropics involving exotic and

indigenous breeds of pigs have indicated varying levels of heterosis (Adebambo, 1985;

13

Meade Moreno, 1987; Kanengoni et al., 2004; Oke et al., 2006; Nwakpu and Onu, 2007).

In addition to the advantage of utilising heterosis, different breeds or lines may be kept to

breed for different objectives, for example specialised sire and dam lines selected for lean

growth rate and reproduction, respectively (Visscher et al., 2000). Selection can also

proceed on the crossbred population in order to conserve useful genetic material for pork

production in this environment.

Furthermore, the heterozygosity observed across loci with the attendant linkage

disequilibrium (LD) in crossbred population has led to the mapping of loci that affects

traits of economic importance in swine (Rothschild et al., 2007). Crosses between

divergent lines have become a mating design of choice in the search for quantitative trait

loci (QTL). Consequently, F2 and backcross designs are employed in experiments aimed

at identifying and mapping QTL that associates with traits of economic importance in

swine (see for example King et al., 2003; Mote and Rothschild, 2006 ).

2.2.3 Traditional genetic improvement methods

Genetic improvement of pigs in the tropics is centred on improving reproduction,

growth and carcass quality traits with little emphasis on disease resistance due to the

difficulty associated with measuring this trait (Adebambo, 1986; Rohilla et al., 2000;

Rinaldo et al., 2003; Oseni, 2005; Chimonyo and Dzama, 2007). The approach used is

the traditional quantitative genetic method based on infinitesimal model assumptions

(Fisher, 1918). Reliable estimates of genetic and phenotypic parameters for all traits of

economic importance are required as priors in order to predict responses to selection, to

choose among various breeding plans, to estimate economic returns and to predict best

14

linear unbiased predictions (BLUP) of breeding values (Henderson, 1975) for selection

candidates.

A limitation to the successful implementation of traditional BLUP method in

developing countries is the lack of extensive and reliable pedigree information to

facilitate gene tracking (Pathiraja, 1987; Kahi et al., 2005; Chimonyo and Dzama, 2007),

inaccurate performance recording and the dearth of genetic parameter estimates for

economic traits of pigs under tropical conditions (Kahi et al., 2005). Genetic parameters

are known to be population specific and are often not estimated very precisely, even in

large datasets. A mistake often made by breeders in the tropics is to use estimates of

genetic parameters determined in a temperate environment when constructing the genetic

variance-covariance matrix required in animal genetic evaluation. When this is the case,

accuracy is compromised and genetic progress is delayed (Visscher et al., 2008).

2.2.4 Genetic and phenotypic parameters

Over the last four decades, several estimates of genetic and phenotypic parameters

for pig production traits determined under tropical conditions have been published in the

literature. These estimates were derived using a variety of methods, are of variable

quality in terms of sample size used to estimate them, and have contradictory results.

Under these circumstances, swine breeders and researchers may have difficulty making

sense of the literature estimates or taking stock of the knowledge base. This can have

negative consequences on breeding decisions and can hinder genetic progress.

An effort to harmonise these estimates by pooling results from different

populations is intuitively appealing, particularly if differences between populations are

15

mainly due to sampling error (Koots, 1994). If estimates are systematically affected by

identifiable factors, pooled estimates can be adjusted by applying appropriate statistical

models. A statistical method that is currently used for synthesis of results is ―meta-

analysis‖ (Sutton et al., 2000). This method has been used to summarise genetic

parameter estimates for beef cattle (Koots et al., 1994; Giannotti et al., 2005) and sheep

(Safari et al., 2005) production traits.

2.3 ADVANCES IN SWINE GENETIC IMPROVEMENT

2.3.1 Use of genetic markers

The advent of molecular genetics technology driven by the human genome project

has led to the study of variation at the DNA level, and the ability to generate large

numbers of polymorphic genetic markers in any species of livestock. The SNP appears to

be a marker of choice because of its abundant coverage of the genome and ease of

genotyping (Visscher et al., 2000). These markers are variations in the nucleotide base

sequence at the same point in the genome among individuals or between an individual‘s

chromosome pairs. Methods have been developed for high-throughput genotyping of

thousands of SNPs per individual animal and the cost of genotyping per individual

marker genotype is currently less than one cent (Weller, 2010). More so, a high density

porcine SNP chip (~60 K) for pigs developed by Illumina Inc in collaboration with the

Swine Genome Sequencing Consortium (SGSC) already exists (Ramos et al., 2009).

The application of the new marker technology to pig genetic improvement stems

from the search for chromosomal regions in the swine genome that are linked to putative

QTLs influencing traits of economic importance (Visscher and Haley, 1995). To date,

16

about 6344 significant (P < 0.05) QTLs have been identified in the pig, influencing over

593 traits (Retrieved April 28, 2011 from pigQTLdb: www.animalgenome.org/cgi-

bin/QTLdb/SS/summary). Chromosome 1 tends to have the highest number of QTLs

identified (about 1391) and these QTLs significantly affect most of the important

economic traits including litter size, average daily gain and back-fat thickness (Malek et

al., 2001; Milan et al., 2002; Evans et al., 2003; Kim et al., 2005; Buske et al., 2006).

Other QTLs affecting economic traits in pigs are summarised by Dekkers (2004).

2.3.2 Marker-assisted selection and introgression

Research towards utilising detected QTLs in swine breeding programmes using

marker-assisted selection (MAS) or marker-assisted introgression (MAI) has been

extensive in developed countries (Lande and Thompson, 1990; Hayes and Goddard,

2003; Dekkers and Chakraborty, 2004; Frisch and Melchinger, 2005; Hospital, 2005),

but implementation has been limited and increases in genetic gain have been small

(Dekkers, 2004). The weaknesses in the methodology of MAS and MAI lies in their

requirements for 1) QTL identification followed by estimation of effects on phenotype; 2)

linkage phase between a marker and QTL which persists only within families; 3)

decisions on how many markers to be included in genetic evaluation; and 4) use of

stringent significance tests in QTL detection.

As most polygenic traits are controlled by many genes, tracking a small number

of these through a few markers will only explain a small proportion of the genetic

variance (Goddard and Hayes, 2007). Methods that use all markers across the genome

simultaneously may therefore result in higher reliabilities of predictions of total genetic

http://www.animalgenome.org/cgi-bin/QTLdb/SS/summary

http://www.animalgenome.org/cgi-bin/QTLdb/SS/summary

17

merit, indicating that a larger proportion of the genetic variance is explained (Meuwissen

et al., 2001; Gianola et al., 2003).

2.4 GENOME-WIDE SELECTION: A NEW METHOD

Genome-wide selection (GS) uses a ―training population‖ of individuals with

phenotypic and genotypic records to develop a prediction model that takes genotypic data

from a ―candidate population‖ of untested individuals and produces genomic breeding

values (GBV) of an individual using the estimated additive marker effects as priors

(Meuwissen et al., 2001). This means that the infinitesimal model assumption (Fisher,

1918) implied in the prediction of BLUP breeding values may no longer hold and that a

finite number of loci underlie the variation in quantitative traits (Ewing and Green, 2000).

Using GS in animal genetic improvement will yield a considerable increase in

selection response for juvenile animals that do not have phenotypic records (Meuwissen

et al., 2001; Gianola et al., 2003; Hayes et al., 2009) and potentially can reduce the cost

of breeding program (Schaeffer, 2006). Genome-wide selection is also believed to be

advantageous for sex-limited traits, traits that are difficult or expensive to measure like

disease traits and traits with a low heritability (Goddard and Hayes, 2007).

2.4.1 Methods for genomic breeding value prediction

The development of statistical methods for the prediction of GBV is an ongoing

endeavour ( Nejati-Javaremi, et al., 1997; Whittaker et al., 1999; Meuwissen et al., 2001;

Xu, 2003; Fernando et al., 2007; Gianola et al., 2006; Hayes et al., 2009; VanRaden et

al., 2009). Several methods have been proposed to deal with the problem of over-

18

parameterization and multicollinearity between the predictors when using high density

markers as fixed effects in the prediction model. These methods differ in the prior

assumptions that they make about the distribution of marker effects, which in turn reflects

the distribution of QTL effects and has been shown to affect accuracy of GBV. In the

section that follows, three of these statistical methods that are widely used in the

implementation of GS are described briefly. In these methods, a linear mixed effect

model for the analysis of genomic data with bi-allelic genotypes can be defined as:

yi = μ + ∑j zijβjδj + ei 2.1

where yi is the phenotypic observation on an individual i, μ is the overall mean of y, zij is

0, 1, or 2 depending on the SNP genotype at the marker locus j in individual i, βj is the

allele substitution effect associated with marker j, δj is a 1 or 0 indicator variable for the

inclusion or exclusion of marker j in the estimation of breeding values, and ei is a

residual.

2.4.1.1 Ridge regression

The application of ridge regression in the analysis of marker data was first

proposed by Whittaker et al. (1999), and modified by Meuwissen et al. (2001) for

analysis of markers across the genome with a slight adjustment for the variance of the

marker loci made by Fernando et al. (2007). In general, ridge regression is a variant of

ordinary multiple linear regression where the goal is to circumvent the problem of

instability arising from collinearity of the predictors and over-parameterization. The

estimates of the regression coefficients are shrunk towards zero through the use of a

regularization parameter (λ) that controls the amount of shrinkage.

19

The ridge regression method assumes that δj given in equation 2.1 is always 1 and

that βj ~ N (0, σ2

β) for all markers. The marker variance, σ2

β, is estimated from the data by

a maximum likelihood method. The solution to equation 2.1 can be obtained by solving

the mixed model equations (Henderson, 1984) given below:

2.2

The value of λ was given by Meuwissen et al. (2001) as a function of the prior variances

and the total number of marker loci (m) expressed as while Fernando et al. (2007)

modified this expression by using the mean heterozygosity of the markers across the

genome given as . This method also assumes that marker effects are distributed

with equal variance and are somewhat similar to the traditional BLUP method except that

the later equations are based on individual animals and their relationships.

Genomic evaluation using ridge regression approach is simple and fast to

implement with minimal computations compared to other methods. Habier et al. (2007)

showed that ridge regression is more effective at capturing genetic relationships among

genotyped individuals because it fits more markers into the prediction model. In addition,

there are freely available computer programs for implementing ridge regression method

with the functionality of handling large genomic dataset (Sargolzaei et al., 2009).

2.4.1.2 Bayesian methods

Meuwissen et al. (2001) proposed two variants of the Bayesian approach for

estimating marker allele effects. They called these methods BayesA and BayesB. The

Bayesian approach attempts to model the data at two levels namely; at the level of the

20

data and at the level of the variances of the marker loci. The data model is analogous to

ridge regression except that the variances of the marker loci (σ2

βj) differ at every position

and are estimated using the combined information from the prior distribution of variances

and from the data.

The prior distribution of the variances of the marker loci was the scaled inverted

chi-square distribution, χ-2

(v, S), where S is a scale parameter and v is the degrees of

freedom. Meuwissen et al. (2001) showed how these parameters can be derived. The

difference between the two Bayesian methods lies in the assumptions about the

distribution of the SNP effects and their implementation. BayesA assumes that all marker

loci affect the traits with different variances and all loci are included in the model for

estimation, i.e. δj in equation 2.1 is always equal to 1. Gibbs sampling algorithm is

convenient for estimating effects and variances for this method.

BayesB assumes that there are many loci with zero genetic variance and a few

segregating loci affecting the trait of interest. The marker effects are modelled by

excluding those loci that are not affecting the trait from the estimation model and

therefore uses a prior that has a high density, π, at σ2

βj = 0 and has an inverted chi-square

distribution for σ2

βj > 0. This means that δj is equal to zero with probability π when σ2

βj =

0 and δj is equal to one with probability 1 – π when σ2

βj > 0. The probability π of non-

segregating loci is assumed to be known a priori and a Metropolis-Hasting algorithm is

used to determine for each locus whether it has an effect on the phenotype or not. If it has

not, the effect of that locus is set to zero (Meuwissen et al., 2001).

In a related study by Kizilkaya et al. (2010), another Bayesian technique called

BayesCπ was used. As in BayesB, in BayesCπ, δj is equal to zero with probability π but π

21

is estimated assuming a uniform prior distribution between 0 and 1. In addition, BayesCπ

assumes that the prior variance for the effects of all markers for which δ j = 1 is equal.

That is, the effect βj is zero when δj = 0 or βj ~ N (0, σ2

β) when δj = 1.

The implementation of the Bayesian procedures like BayesB in genomic

evaluation have resulted in slightly higher accuracy than ridge regression (Meuwissen et

al., 2001) because BayesB is more effective at capturing LD between markers and QTL

that are sufficiently strong to allow their accuracy to persist longer than those from ridge

regression (Habier et al., 2007). In addition, the over-parameterization problem of ridge

regression is smaller with BayesB and different variances are assumed for each marker.

More so, BayesB requires more computer time than ridge regression and the assumption

of markers with zero effect is equivalent to a decrease in the genome size and

concentration of responses from a few QTLs which results in higher accuracy in this

method (Smaragdov, 2009).

The marker effects estimated by either ridge regression or the Bayesian methods

can be applied to the SNP genotypes of selection candidates to predict their GBV using

the standard model:

GBVi = 2.3

2.4.1.3 GBLUP method

An alternative for implementing genomic selection to the methods described

above is to predict breeding values using a genomic relationship matrix in place of the

pedigree derived relationship matrix (see for example Habier et al., 2007; Hayes et al.,

2009; VanRaden et al., 2009; Hayes and Goddard, 2010). This model is equivalent to

22

predicting individual SNP effects and calculating GBV as the sum of these effects,

provided the SNP effects are assumed to be normally distributed.

The model in equation 2.1 can be re-written in matrix form assuming that δj = 1 as

follow:

y = 1nμ + Zg + e 2.4

where y is a vector of phenotypes of size n, μ is the overall mean, 1n is a vector of ones,

Z is a design matrix allocating records to breeding values, g is a vector of breeding values

and e is a vector of random errors ~ N (0, σ2

e). Then g = Wu where uj is the effect of jth

SNP, and variance V (g) = WW´σ2

u. Elements of matrix W are wij for the ith animal and

jth SNP, where wij = 0 – 2pj if the animal is homozygous 11 at the jth SNP, 1 – 2pj if the

animal is heterozygous 12 or 21 at the jth SNP, and 2 – 2pj if the animal is homozygous

22 at the jth SNP. The diagonal elements of WW´ will be , where m is

the number of SNPs. If WW´ is scaled such that G = nWW´/ then V (g) = G

σ2

g. The breeding values for both phenotyped and non-phenotyped individuals can then

be predicted by solving the equations:

2.5

Using this method in animal genetic evaluation requires replacing the additive

relationship matrix with the genomic relationship matrix in the existing genetic

evaluation program (Hayes and Goddard, 2010). The method can also be useful for

countries without a good pedigree recording system as the genomic relationship matrix

will capture this relationship information among the genotyped individuals.

23

2.4.2 Comparison of methods based on accuracies of genomic predictions

The accuracies of genomic predictions in simulation studies utilizing the

statistical methods described above ranged from 0.46 – 0.88 for the ridge regression

method (Meuwissen et al., 2001; Muir, 2007; Habier et al., 2007; Fernando et al., 2007;

Saatchi et al., 2010), 0.56 – 0.98 using different versions of the Bayesian methods

(Meuwissen et al., 2001; Solberg et al., 2008; Habier et al., 2009; Ibanz-Escriche et al.,

2009; Kizilkaya et al., 2010), and 0.59 – 0.96 using GBLUP (VanRaden, 2008;

Christensen and Lund, 2010) for a range of trait heritabilities. These results suggest

similarity in the expected accuracy of GBV based on different methods of estimation.

Consideration of appropriate method for use in genetic evaluation will depend on the

persistency of these accuracies across multiple populations and generations,

computational requirements of the method and time constraint.

Table 2.1 gives a summary of ranges of GBV prediction accuracies obtained from

studies that use real dataset from dairy cattle and swine reference population across

different traits and statistical methods. The accuracies of GBV observed from these

empirical studies are moderate to high and somewhat comparable to results from

simulation studies. Methods that assume all marker effects are distributed with equal

variance like ridge regression are as accurate as methods that assume some markers do

not explain any variance (e.g BayesB). Note that assuming all marker effects are drawn

from the same distribution does not mean the effects are all equal but that they are all

equally shrunken towards zero (Jannink et al., 2010).

The GBLUP method appears to have higher and slightly narrower range of GBV

accuracies than other methods across several traits. This could justify the higher number

24

of studies that used this method in genetic evaluation. More so, the GBLUP method

allows you to use existing genetic evaluation programs by replacing the additive

relationship matrix with the genomic relationship matrix and the estimates of GBV from

this method are BLUP (Hayes and Goddard, 2010).

Beside statistical methods used in estimating marker effects, other factors known

to affect accuracies of GBV are the marker types and density (Solberg et al., 2008), the

size and number of generations in the reference population (Muir, 2007), the population

structure and history, the trait heritability and the LD between markers and QTL across

generations (Habier et al., 2007; Toosi et al., 2010).

2.4.3 LD and accuracy of GBV across populations and generations

The accuracies of GBV rely heavily on the existence of LD between QTL and

markers in the population of interest and on the strength of predicted individuals‘

relatedness to the reference population, much as it will do when using pedigree

information to perform predictions. As the population diverges, accuracy of GBV

declines due to recombination between QTL and marker across multiple populations

unless dense markers are used to maintain sufficient LD (de Roos et al., 2009).

Conceptually assuming large reference population, the more dense the markers and

greater the population-wide LD, the better the method works.

The declining effective population size (Ne < 100) and strong directional selection

that occurs in exotic pig populations have resulted in high LD between QTL and markers.

For two SNPs separated by 1 cM distance, the squared correlation of the SNP alleles (r2)

averaged over all possible intervals across the genome ranged from 0.20 – 0.35

25

(Harmegnies et al., 2006; Du et al., 2007; Amaral et al., 2008; Jafarikia et al., 2010;

Huisman et al., 2010) in the exotic population which means that approximately 2,300

evenly spaced markers are required in order to capture QTLs in a genome scan utilizing

LD assuming an estimated porcine genome size of approximately 2,300 cM (Rohrer et

al., 1996). However, Meuwissen (2009) showed by simulation that, to achieve very

accurate GBV, 10NeL markers are required, where L is the length of the genome in

Morgans. Assuming a porcine genome size of 23 Morgans and approximately Ne = 100,

then a marker density of 23,000 SNPs is required to implement GS within breed in swine.

Indigenous pig populations on the other hand are seldom researched. Effective

population size may be larger in this type of population than that observed in exotic pig

populations and LD may be very small creating a need for more markers in order to apply

GS. In a study with indigenous cattle populations in West Africa, Thévenon et al. (2007)

observed Ne of >1000 with average r2 of 0.01 for markers < 3 cM apart. They concluded

that a large number of markers will be required for a whole genome scan in this

population. Moreover, preliminary assessment of LD in indigenous Chinese breeds of

pigs showed that LD of 0.3 magnitude occurred between 0.005 – 0.05 cM distance on

average (Amaral et al., 2008). This suggests mild selection pressures in the indigenous

pig populations and confirms that more markers will be required to implement GS in

those populations.

To implement GS in any given population, it is important to assess the extent of

LD in that population. The most commonly used measure of LD for SNP markers is the

squared correlation of alleles at two locations in the genome (r2) as proposed by Hill and

Robertson (1968). This is defined as:

26

2.6

where DAB = DA1B1 = pA1B1 – pA1pB1, pA1 is the frequency of allele A1 at locus A, pB1 is the

frequency of allele B1 at the locus B and pA1B1 is the frequency of haplotype A1B1 in the

population. The breakup of LD between markers and QTL caused by recombination is

expected to lead to a reduction of accuracy of GBV across generations of selection. This

is one factor that determines the optimal frequency to re-estimate SNP effects (Calus

2010). Some studies showed that depending on the frequency of re-estimating the effects,

some genomic evaluation methods may be more suitable to obtain SNP effects whose

accuracy is more persistent across generations. For example, methods that can exploit LD

like the Bayesian approach showed better persistency of GBV accuracy across

generations (Habier et al., 2007; Zhong et al., 2009; Calus, 2010).

2.4.4 Evaluation of genome-wide selection in tropical swine breeding

Computer simulations have proved to be a powerful and effective tool for

investigating the design and efficiency of breeding programs (Hill and Caballero, 1992).

In reality, because of the cost of maintaining a breeding population for multiple

generations, the need for replicated tests and the risk of sub-optimal genetic improvement

of livestock, it is prudent to simulate any proposed breeding method or technique prior to

implementation on real populations. Consequently, several simulation studies evaluating

approaches for implementing GS in livestock populations have been reported

(Meuwissen et al., 2001; Muir, 2007; Odegard et al., 2009).

In general, these studies have evaluated GS assuming a single population that is

either derived from mutation drift equilibrium (MDE; Meuwissen et al., 2001) or under

27

Hardy-Weinberg equilibrium (HWE; Muir, 2007). Commercial swine populations

typically fall between the extreme states of MDE and HWE (Muir, 2007). Genome-wide

selection will be efficient in populations under HWE but unstable if Ne becomes small

(Smaragdov, 2009). For populations starting at MDE, Ne value is not important and

accuracy of GS will be stable for many generations (Habier et al., 2007). Other studies

have considered a multiple population context (de Roos et al., 2009; Ibánẽz-Escriche et

al., 2009; Toosi et al., 2010) but these studies did not take into account the impact of

evolutionary pressures such as selection on long term genetic response.

The application of GS for genetic improvement of pigs in tropical developing

countries has been proposed (Goddard and Hayes, 2007). Genome-wide selection will be

useful to tropical pig breeding programs in a number of ways. Development of a pedigree

recording system may no longer be required (Habier et al., 2007), but this does not

remove the need to collect phenotypic data because substantial datasets are required for

training a prediction model. The reference population could be managed under low-input

conditions and estimates of marker effects derived from this population are used to

predict GBV for selection candidates in a nucleus breeding herd (Dekkers, 2010).

Alternatively, the indigenous and exotic pigs found in the tropics can be crossed

to produce crossbred reference population used for estimating marker effects and

predicting GBV of purebreds for selection (Toosi et al., 2010), thereby improving

commercially relevant traits. Cryptic alleles in the unselected indigenous population can

be captured in a selective sweep (Dekkers, 2010) of QTLs in LD with markers using GS

and their frequencies increased in this population. Other relevant adaptive genes in the

28

tropical indigenous population will be identified by genome analysis and incorporated

into the commercial population.

As large numbers of SNPs are being discovered, cost effective methods to

genotype these SNPs have become commercially available (Ramos et al., 2009), so

genomic selection will become feasible for the average pig breeder and the genetic gain

expected will compensate for any additional cost.

2.5 CONCLUSIONS

Improving the productivity of pigs in tropical developing countries by genetically

making them more efficient for the continuously changing conditions is important.

Contrary to conventional wisdom, it may be reasonable to bypass the development of

traditional BLUP method to implement a genome-wide selection program. Published

results indicate that GS promises to increase genetic gain by increasing accuracy of GBV

for selection candidates.

Considerable challenges are expected for implementing GS in breeding programs

involving indigenous and exotic pigs under tropical conditions. These include the

applicability of available 60 K porcine SNP panel, lack of infrastructure for swine genetic

improvement, possible cross-breeding between existing populations and computational

challenges. Given the cost associated with implementing GS on real population, it is

important to evaluate this option with computer simulation of existing scenarios.

29

Figure 2.1 Trends in pig production in developed and developing countries

Source: FAO statistics, 2002 In Forsberg et al. 2005

30

Table 2.1 Ranges of accuracies of genomic breeding values across various sizes of reference population and using different statistical

methods in real dataset

Statistical methods Reference size No. of SNPs used Dairy Cattle Swine References

Ridge regression 1945 7372 0.56 to 0.71 Moser et al. (2009)

Ridge regression 1847 42576 0.15 to 0.64 Moser et al. (2010)

Ridge regression 332-637 3090-4369 0.42 to 0.73 Hayes et al. (2009)

BayesA 332-637 3090-4369 0.37 to 0.74 Hayes et al. (2009)

BayesA 1945 7372 0.56 to 0.71 Moser et al. (2009)

BayesA 2000 46741 0.50 to 0.82 Cleveland et al. (2010)

BayesA 781 39048 0.44 to 0.71 Hayes et al. (2009)

BayesA 287 39048 0.37 to 0.67 Hayes et al. (2009)

BayesB 250 18991 0.13 to 0.61 Luan et al. (2009)

BayesB 781 39048 0.44 to 0.70 Hayes et al. (2009)

BayesB 287 39048 0.37 to 0.65 Hayes et al. (2009)

BayesCπ 2000 46741 0.33 to 0.61 Cleveland et al. (2010)

Bayesian 3600 48000 0.52 to 0.82 de Ross et al. (2009)

Bayesian 1238 38055 0.55 to 0.85 Lund and Su (2009)

GBLUP 5335 38416 0.44 to 0.79 VanRaden et al. (2009)

GBLUP 781 39048 0.44 to 0.62 Hayes et al. (2009)

GBLUP 287 39048 0.41 to 0.72 Hayes et al. (2009)

GBLUP 2490 44146 0.63 to 0.82 Harris et al. (2008)

GBLUP 3966-4127 38416 0.44 to 0.79 Schenkel et al. (2009)

GBLUP 596 42598 0.56 to 0.71 Berry et al. (2009)

GBLUP 250 18991 0.13 to 0.61 Luan et al. (2009)

31

CHAPTER 3

META-ANALYSIS OF GENETIC PARAMETER ESTIMATES FOR

REPRODUCTION, GROWTH AND CARCASS TRAITS OF PIGS IN

THE TROPICS

E. C. Akanno1, F. S. Schenkel

1, V. M. Quinton

1, R. M. Friendship

2 and J. A. B.

Robinson1

1Centre for Genetic Improvement of Livestock,


2Department of Population Medicine, Ontario Veterinary College,

University of Guelph, Guelph, ON, Canada

32

3.0 ABSTRACT

The design of swine breeding programs for the tropics requires knowledge of

genetic parameters for economically important traits determined under tropical

conditions. A literature review found a total of 468 heritability (h2) estimates, 368 genetic

correlations and 254 phenotypic correlations for 35 traits across 117 peer-reviewed

articles published from 1974 to 2009 and covering tropical Africa, Southeast Asia, the

Caribbean and Latin America. A model that incorporated between and within study

variance component was used to obtain weighted means and variances for all parameter

estimates. Weighted means and standard errors of direct and maternal heritability,

common litter effects and the correlation between direct and maternal effects are

presented for various reproduction, growth and carcass traits. The weighted means and

confidence intervals for the genetic and phenotypic correlations between these traits are

also presented. Weighted least-squares analyses of the h2 estimates were performed by

fitting a number of fixed effects and covariates for each trait as appropriate for the data.

Breed, data origin, estimation method, data age and location of study were found to be

significant (P < 0.05) for at least one of the traits analysed. These results indicate the

relevance of having local, population-specific genetic parameter estimates for the tropics.

The weighted mean estimates of genetic parameters presented here are recommended for

use when reliable estimates are not available for a specific tropical pig population. This is

quite appropriate because it uses the vast resource of published genetic parameter

estimates effectively.

Keywords: Meta-analysis, heritability, genetic and phenotypic correlations, pigs, tropics

33

3.1 INTRODUCTION

Genetic parameters like heritability, genetic and phenotypic correlations and other

components of total phenotypic variance have wide application in genetic and animal

breeding research. Accurate estimation of genetic parameters in every population of

interest requires large across-generation data sets with reliable pedigree information

which are not always available. The development of animal models using REML and

Bayesian procedures (Sorensen and Gianola, 2002; Thompson, 2008) and the advances in

computing capacity have all resulted in the publication of what are expected to be better

estimates of these parameters. The advances in procedures have allowed for estimation of

maternal heritability, common litter effects and the correlation between direct and

maternal genetic effects for many traits.

Over the last four decades, several estimates of genetic and phenotypic parameters

for pig production traits determined under tropical conditions have been published. These

estimates were derived using a variety of methods, are of variable quality in terms of

sample size used to estimate them, and have contradictory results. Under these

circumstances, swine breeders and researchers may have difficulty making sense of the

literature estimates or taking stock of the knowledge base. This can have negative

consequences on breeding decisions and can hinder genetic progress.

The motivation for this study was the need for a quantitative synthesis of previous

findings in order to provide summary estimates of genetic parameters for the

development of breeding objectives for swine genetic improvement in tropical

developing countries. A statistical method known as ―meta-analysis‖ was utilized (Sutton

et al., 2000). Koots et al. (1994a) summarised estimates of genetic parameters for beef

34

production traits using a fixed effects meta-analysis method and identified significant

study characteristics that affect estimates. A random effect meta-analysis model was used

to obtain weighted estimates of genetic parameters in sheep (Safari et al., 2005) and in

beef cattle (Giannotti et al., 2005). No such review has yet been carried out for pigs

raised in tropical environments.

Using a fixed-effect method to combine estimates of genetic parameters makes

the assumption that each published estimate is estimating the same underlying true

parameter (i.e. no heterogeneity between published estimates (Barron et al., 2008) and

any difference observed is due to chance factors or sampling variation within each study.

If this assumption is invalid, as shown by Koots et al. (1994a), then a preferred

alternative method would be to fit a random effects meta-analysis model. A random-

effects model assumes that each estimate from the various studies is estimating different

underlying true parameters, and takes into account the extra variation implied in making

this assumption. This underlying true parameter is assumed to vary at random and its

effect is believed to be normally distributed. Hence, this model accounts for the between

and within-study sources of variation and has been proposed as a more conservative

method of meta-analysis that yields results that can be generalised (Berkey et al., 1995;

Sutton et al., 2000; Safari et al., 2005; Barron et al., 2008).

The objective of this study was to use a random-effect meta-analysis model to

arrive at consensus estimates of genetic and phenotypic parameters of economically

important traits of pigs determined under tropical conditions and to test for factors that

can affect these parameters in the tropics.

35

3.2 MATERIALS AND METHODS

3.2.1 Description of study scope and traits

A systematic search of the literature using electronic and non-electronic media

was conducted to identify all references reporting estimates of genetic and phenotypic

parameters for pig production traits in the tropics. A total of 117 peer-reviewed articles

with full description of methods and published between 1974 and 2009 were reviewed.

These papers reported original research that was carried out in Southeast Asia, Latin

America, The Caribbean and Tropical Africa. These regions lie in the tropics between

latitude 23.5 north and latitude 23.5 south of the equator. The climate is hot and humid

all year round with daytime temperature above 25 and relative humidity of 85%, on

average. The annual rainfall usually exceeds 2000 mm.

Traits included in the study were those considered to be economically important

for pork production in the tropics and are likely to be included in breeding objectives.

These traits were grouped into three broad categories: reproduction, growth and carcass.

A comprehensive list of the traits, units of measurement and abbreviations used are

presented in Table 3.1. Reproduction traits included litter size at farrowing (LSF),

number born alive (NBA), litter size at 21 days (LS21), litter size at weaning (LSW),

litter weight at farrowing (LWF), litter weight at 21 days (LW21), litter weight at

weaning (LWW), pre-weaning average daily gain (PDG), maternal ability (MA

= ), survival rate (SRT = ), age at first farrowing (AFF),

age at first conception (AFC), gestation length (GLT), weaning to service interval (WSI),

farrowing interval (FIT), pre-weaning mortality (MOR) and daily feed intake during

36

lactation (DFI). Weaning of piglets occurred between 5 – 8 weeks of age for all studies

with this information.

Growth traits were defined as weights at various ages: individual birth weight

(IBW), individual weight at 21 days (IW21), individual weight at weaning (IWW, 5-8

weeks of age), post-weaning individual weight (PIW, up to 6 months of age), mature

weight (MWT, > 6 months of age) and average daily gain (ADG). Other growth traits

included are days to 90 kg weight (D90), feed conversion ratio (FCR) and average daily

feed intake (ADFI).

Carcass traits included back-fat thickness on live animals using ultrasound

devices (BFU), adjusted for live weight (BFA) and on carcasses (BFT). Most fat

measurements were taken at the P2 position which is about 65 mm from the dorsal

midline along the last rib. Estimates for carcass weight (CWT), lean meat yield (LEAN),

dressing percentage (DPT), carcass length (CLT), loin eye area (LEA) and ham yield

(HYD) were also included.

3.2.2 Data recorded and analyses

The dataset constructed from the literature included direct and maternal

heritabilities, common litter effects as a proportion of the total variance, correlations

between direct and maternal genetic effects when available for some traits, as well as

genetic and phenotypic correlations among traits and published standard errors of

parameter estimates. Other information recorded were trait means and phenotypic

standard deviations, coefficients of variation reported or derived from the trait means and

37

standard deviations, number of records used, number of parents (sires and dams), author

reference and a variety of fixed factors (See model below).

In the case where estimates from the same population were reported in different

publications only the most recent was included in the analysis. All published estimates

including negative heritabilities and those published in languages other than English were

extracted and meta-analysed as a check against publication bias (Fleiss and Gross, 1991).

The number of estimates of genetic parameters published during this period in the tropics

may be greater than that available for review. Owing to the fact that most tropical

countries are still developing, this information may not be available to the public.

All parameter estimates recorded for all traits in this review were originally

analysed assuming normality. This assumption was investigated for a number of traits

with >25 estimates and their distributions were approximately normal. Potential outliers

introduced during data entry or from other sources were identified and corrected during

analysis. A meta-analysis of published results generally proceeds by weighting each

estimate of a parameter from the various studies by the amount of information used in

estimating them. Two weighting factors namely; sample size and inverse of sampling

variance were compared for a few traits using a random- effect meta-analysis model and

the results showed that both methods gave similar weighted mean heritability as observed

by Safari et al. (2005), but the standard error of estimates were smaller when the inverse

of sampling variances was used. Hedges and Olkin (1985) and Marin-Martinez and

Sanchez-Meca (2010) have shown that the optimal weight for a meta-analysis is the

inverse of the sampling variance. In addition, the inverse of sampling variance may have

38

some advantages because the statistical significance of the estimates for each trait is

determined not only by the sample size, but also by the level of variation in the trait.

Weighted mean for the trait means, standard deviations and coefficient of

variation were obtained for all traits using sample size as weights while the inverse of the

sampling variance were used as weights for obtaining weighted mean heritability,

maternal heritability and common litter effects. For some published estimates no standard

errors were reported. Approximate standard errors were derived for these estimates by

using the combined variance method (Sutton et al., 2000). First, a weighted mean

standard deviation (SDW) was obtained from the reported standard errors using Equation

3.1, where si is the standard error and ni is the number of records for the ith estimate (i =

1, 2, . . ., y). The predicted standard error for estimates with no reported standard error

was calculated by dividing SDW by the square root of the number of records in the

estimate. The number of records was available for all studies.

3.1

In order to calculate a weighted estimate of phenotypic and genetic correlations

(r) among a combination of traits and the correlation between direct and maternal genetic

effects, the published values were first transformed to an approximate normal scale by

using a Fisher‘s Z transformation (Steel and Torrie, 1960) in Equation 3.2, with the

standard error from Equation 3.3, where n is the number of records for a phenotypic

correlation and the number of sires for a genetic correlation. The weighted mean for the Z

transformed correlations was calculated across studies and back transformed using

Equation 3.4, where rw is the weighted mean correlation (phenotypic or genetic) and z is

the weighted mean for the Z transformed correlations.

39

3.2

3.3

3.4

The standard errors generated using Equation 3.3 were highly correlated with those in

studies that reported standard errors for genetic and phenotypic correlations. Similar

observations were made by other researchers (Koots et al., 1994b; Safari et al., 2005).

3.2.3 Test of heterogeneity of estimates

The first analyses performed for the published genetic parameter estimates was a

test of heterogeneity of the estimates i.e. a test of the null hypothesis that the true

parameter estimate is the same in all studies versus the

alternative (H1) that at least one of the estimates differs from the rest. Essentially, this is

testing whether it is reasonable to assume that all the studies combined are estimating a

single underlying population parameter, and whether variation in study estimates is likely

to be random. The Q test statistic devised by Cochran (1954) and described below was

used for this purpose.

3.5

where k is the number of parameter estimates ( ), and

is the weighting factor taken as the inverse of the sampling variance (s2)

(Hedges and Olkin, 1985). Q is approximately distributed as a χ2 distribution with k - 1

degrees of freedom under H0.

3.2.4 Estimation of weighted means of genetic parameters

40

The weighted means of the parameter estimates were obtained by fitting a

random-effect model for all traits studied. As mentioned earlier, this model allowed

estimates to represent random sub-populations with differing underlying true parameters

drawn from the overall population. This took into account the extra variation implied in

this assumption by including between study and within study components of variance and

used Residual Maximum Likelihood approach (ASREML program, Gilmour et al., 2009)

to determine the relative contribution of the between and within study variances to the

weights.

The random-effect model was:

θi = µ + ui + ei 3.6

where θi is the estimate of a parameter in the ith study, µ is the population mean

(weighted mean of the estimates), ui is the between study component of the deviation

from the mean and ei is the within study component due to sampling error in the ith

estimate. The components ui and ei are normally distributed with mean zero and variances

and V, respectively. For the trait means, standard deviation and coefficient of

variation, V = where ni is the number of records. For direct and maternal

heritability, and common environmental effects, V = where wi = .

For the correlations V = , where ni is the number of sires for genetic

correlation and the number of records for phenotypic correlation and correlation between

direct and maternal genetic effects. The standard errors for the weighted means and the

95% confidence intervals of the estimates were derived from V, with those for

correlations being back transformed using Equation 3.4.

41

3.2.5 Weighted least squares analyses

A weighted least squares analysis was performed for those traits with sufficient

data in order to identify important study characteristics that may affect heritability

estimates in the tropics. The fixed linear model was:

= Bi + Dj + Mk + Ll + Am + eijklmn 3.7

where is the published estimate of heritability which is assumed to be

independently and identically normally distributed (µ, V), Bi (i = 1, 2) is the breed of sire

effect grouped as exotic breeds (such as Large White, Landrace, Duroc and Hampshire

breeds) and indigenous breeds (consisting of a variety of local pigs that are indigenous to

the tropics), Dj (j = 1, 2) is the effect of data origin such as data from experimental

stations and field data, Mk (k = 1, 2) is the effect of estimation methods grouped as intra –

class correlation of half-sibs procedure or the animal model, Ll is the absolute latitude

which represents the distance of a study location from the equator and was fitted as a

covariate that accounts for variation in rainfall, humidity, environmental mycobacteria

that may produce natural immunity, temperature, and other factors that may have

biological implications on the performance of pigs under tropical conditions (Berkey et

al., 1995), Am is the age of data effect (a covariate that was obtained as number of years

of the data used in the study relative to 2009), and eijklmn is the random error also

assumed to be independently and identically normally distributed (0, V), where V =

and is . The PROC GLM procedure of the Statistical Analysis System

(SAS Institute Inc., 2009) was used to test the significance of fixed effects and covariates.

42

3.3 RESULTS AND DISCUSSION

3.3.1 Summary statistics

The weighted descriptive statistics for 35 traits considered in this review are

presented in Table 3.2. In general, litter traits such as litter size and weight at farrowing

and weaning, and some growth traits as well as back fat thickness on carcass are all

equally likely to be recorded which suggest the importance of these traits in swine

production systems in the tropics. Exotic breeds of pigs have more estimates of these

statistics than indigenous breeds. Their performance for many of these traits was two

times greater and differed significantly (P < 0.05) from that reported for indigenous

breeds. This may be connected with the general acceptability of exotic breeds of pigs for

commercial pork production in the tropics (Egbunike, 1986).

The weighted standard deviation (SD) and coefficient of variation (CV) shows

more variability in reproduction and growth traits than in carcass traits (Table 3.2).

Carcass traits are less affected by environmental variations and have high heritability.

The SD and CV for litter traits tend to increase as the animal ages but this trend is in the

opposite directions for growth traits. This may be due to competition among piglets as

lactation advances which introduces lots of variability within litter. Because the statistics

presented in Table 3.2 are subjected to wide environmental variation, they may not be

appropriate for breeding decisions unless there is stability in management and uniformity

in environmental effects across generations, populations and countries which are

43

unrealistic in the tropics. Local means and phenotypic standard deviations are therefore

recommended for use where possible.

3.3.2 Heritability and 95% confidence intervals

The number of contributing estimates, weighted mean heritability and standard

error (h2±S.E), between study variance (σ

2bs) expressed as a percentage of the total

variance, Q statistics and 95% confidence intervals (CI) for 35 traits studied are showed

in Table 3.3. Litter traits, average daily gain, and individual weight at farrowing and

weaning and back-fat thickness all have > 10 estimates of heritability contributing to the

weighted mean. Reproduction traits had low (<0.15) heritability estimates for ten traits,

moderate (>0.15 to <0.40) heritability estimates for six traits and high (>0.40) estimates

of heritability for one trait. In the growth category, one trait had a low estimate of

heritability while the rest in the group were moderately heritable. Carcass traits have high

heritability estimates except for two traits that were moderate. The standard errors of the

weighted mean heritability for many of the traits in this study were low and consistent

with the results obtained by Safari et al. (2005) in a related study involving sheep. This

means that the weighted mean heritability estimates from this study are precise.

There were significant differences (P < 0.05) among published estimates of

heritability for 21 traits with appreciable amounts of between study variations (Table

3.3). This suggests the importance of accounting for this variation in the model used to

estimate weighted means in order to obtain estimates that are at least 95% precise. The

confidence intervals for the weighted mean heritability were however, wider than when a

fixed effect model was used. This was due to the inclusion of additional sources of

44

variation in deriving these intervals. In some cases, these intervals were outside the

boundary of expected intervals of heritability estimates.

The low heritability estimates observed for most reproduction traits were

consistent with our knowledge about the inheritance of these traits. These traits are

largely influenced by uterine (Lee and Haley, 1995) and maternal environment effects

(Chimonyo et al., 2008) as well as management practices rather than an individual‘s

additive genetic value. For many of the traits in this category, the heritability estimates

presented in Table 3.3 were within the range reported for reproduction traits in a review

by Rothschild (1996). Growth traits in general were moderately heritable (0.15±0.023 –

0.38±0.039) except for heritability of individual weight at 21 days which was low

(0.08±0.029). The heritability estimates for body weight at various ages tended to

increase from birth to adult (Table 3.3) approximating to a growth curve. The increase in

heritability with age has been attributed to certain genes being turned on and off as an

animal ages (Meyer, 1998). Similar estimates of heritability for some of the growth traits

and the observed trend have been reported in a review by Clutter and Brascamp (1998)

and also in a related study in sheep (Safari et al., 2005).

The weighted mean heritability for carcass traits in this study was moderate to

highly heritable and range from 0.33±0.045 to 0.60±0.117. The estimates for these traits

were in close agreement with the values reported in a review by Sellier (1998). The high

heritability estimates of carcass traits observed in this study means that relatively small

numbers of estimates are required for precision of genetic merit and an individual‘s

phenotypic performance can be a good predictor of its genetic background. Moreover,

measurement of carcass traits often requires the slaughter of animals resulting in fewer

45

estimates of genetic parameters in literature. In recent times, the use of ultrasound

(Wilson, 1992) for predicting back-fat thickness in live animals has increased the

frequency of evaluating for this trait. The values of heritability estimates of back-fat

thickness obtained by this method (BFU, 0.33±0.045) were slightly lower than that

obtained on carcass (BFT, 0.44±0.037). Adjustment of live back-fat thickness (BFA) for

live weight considerably increased the heritability estimates on live animals to

0.46±0.050.

3.3.3 Heritability within breed, maternal heritability and common environmental

effects

The weighted mean heritability obtained within breed groups for all traits studied

are presented in Tables 3.4 and 3.5. Exotic breeds of pigs have higher heritability

estimates for litter size at various ages than indigenous breeds while the reverse is the

case for litter weight at various ages. The numbers of estimates contributing to the mean

heritability for reproduction traits in indigenous breeds are fewer. The exotic breed group

includes Large White and Landrace which are known for high reproduction performance

in the tropics (Dan and Summers, 1996). In addition, indigenous breeds have

demonstrated good mothering ability which could lead to heavier litters in later lactations

(Adebambo and Dettmers, 1982).

Heritability estimate was low for IBW (0.10±0.016) but high for PIW

(0.49±0.199), MWT (0.41±0.046) and FCR (0.42±0.020) in the indigenous breeds while

exotic breeds had moderate heritability estimates for growth traits except for IW21 which

was low (0.02±0.014). The high heritability for FCR in the indigenous breeds supports

46

the finding that they are better converters of feed to pork than exotic breeds (Mushandu et

al., 2005), although this estimate came from only two studies. Estimate of heritability for

ADG were higher in indigenous (0.37±0.073) than in exotic (0.28±0.022) breeds. The

higher value of heritability estimate for ADG observed in the indigenous breeds may be

connected to the limited selection for this trait that have occurred in this population and

suggest room for genetic improvement. Also, their inherent ability to use less feed for

pork production is an economic advantage that can be exploited.

For the various breed groups, heritability estimates of carcass traits tend to be

higher for indigenous breeds (0.36±0.089 – 0.82±0.092) than for their exotic counterparts

(0.25±0.018 – 0.49±0.082). More importantly is the higher inherent tendency for

indigenous breeds to lay down body fat (0.63±0.208) which brings to question the quality

of their carcass given the health implication of fat to humans. Genetic improvement

programs aimed at reducing back-fat thickness in this population have been strongly

advocated (Adebambo, 1986; Chimonyo and Dzama, 2007).

The weighted maternal heritability estimates for reproduction traits were low and

ranged from 0.01±0.005 in LW21 to 0.13±0.049 in PDG (Table 3.4). For live weight at

various ages, maternal heritability tended to increase with age contrary to what is

expected (Table 3.5). For single studies that included maternal genetic effect in the model

for evaluating carcass traits, the estimates were low and ranged from 0.03±0.002 in CWT

to 0.11±0.001 in CLT. The correlations between direct and maternal genetic effects in

this study were mostly negative and strong for most traits analysed (Tables 3.4 and 3.5)

except for strong and positive correlations observed for LSF (0.64±1.407), NBA

(0.73±0.837), LW21 (0.85±2.530) and PDG (0.44±1.220), respectively. However, these

47

estimates were associated with large standard errors. A similar observation was made by

Safari et al. (2005) in sheep.

Maternal genetic effects define the influence of the dam‘s genotype on the

expression of its progeny phenotypes. In an animal model context maternal estimate

depends largely on the data structure and the model used for analysis (Meyer, 1992).

Several workers have reported an inflated direct heritability for litter traits in swine when

maternal effects were included in the model (Irgang et al., 1994; Chimonyo et al., 2008) .

Also, inclusion of maternal genetic effects for the evaluation of carcass traits was studied

by Chimonyo and Dzama (2007). The increase in maternal heritability with age of

progeny as observed for growth traits in this study (Table 3.5) may be due to data

structure used in the primary studies. A negative correlation between direct and maternal

genetic effects has been shown to be a product of both actual genetic antagonism and sire

by year interaction (Konstantinov and Brien, 2003; Maniatis and Pollott, 2003) and can

be influenced by data structure (Maniatis and Pollott, 2003).

Tables 3.4 and 3.5 also showed significant common litter effects for many of the

traits analysed. Litter-bearing species usually have a large number of non-additive

relationships (Norris et al., 2007) and common litter effects should be accounted for in

the model for analysis of traits (Chimonyo et al., 2006; Chimonyo and Dzama, 2007;

Ilatsia et al., 2008).

3.3.4 Factors affecting heritability estimates

The result of the weighted least squares analysis of heritability estimates for nine

traits with sufficient data are presented in Table 3.6. Data origin, breed group, data age,

48

absolute latitude and estimation method significantly (P < 0.05) affected most of the traits

analysed. The R2 values for the model ranged from 0.25 – 0.84. Data origin affected

heritability estimates for NBA, LW21, ADG and BFT. This was expected for some of

these traits because environmental variation is better accounted for and controlled in

experimental data than in field data (Baker et al., 1984). Breed group affected LW21,

IBW and BFT. Exotic breeds of pigs such as Large White are known to produce heavier

litters at birth while indigenous breeds generally produce fatty carcasses (Adebambo,

1986). Methods for estimating heritability affected LS21, LWW and IWW. Animal

model methodology yielded better estimates than paternal half sib correlation because it

utilises full pedigree information, allows for isolation of other sources of variation and

also accounts for possible selection in the population (Thompson, 2008).

The inclusion of covariates such as data age relative to 2009 and location of a

study defined by absolute latitude in the model for analysis significantly (P < 0.05)

affected heritability estimates for most traits analysed. Usually, an estimate of heritability

is defined for a particular trait measurement, taken at a particular time or age, in a given

population. Variation in any of these measurement standards will affect heritability

estimates. Differences in estimates may also be connected with the level of technological

advancement in each location, management practices or other environmental variation.

Use of weighted mean heritability estimates for genetic evaluation will be

appropriate if factors contributing to variation in heritability estimates are essentially

random and unidentifiable. However, a random effect model as used in this study

accounts for the existence of other sources of variation besides random error. The

49

weighted mean heritability estimates in Table 3.3 are therefore, recommended for use

because they give the most weight to estimates with the highest accuracy.

3.3.5 Genetic and phenotypic correlation

The weighted mean genetic and phenotypic correlations among all traits studied

are presented in Tables 3.7 to 3.11. Of the important trait combinations, a large number

of correlations were not available in the literature. The number of correlation estimates

contributing to the weighted mean genetic and phenotypic correlation for the available

trait pairs ranged from 1 – 22. There were more estimates contributing to genetic

correlations than phenotypic correlations. The various trait categories showed varying

degrees of positive genetic correlations among themselves than between groups.

Reproduction vs. growth tended to have more positive and high genetic correlations

among pairs of traits than growth vs. carcass and reproduction vs. carcass which had

negative and low estimates, on average. The trend was similar for phenotypic correlation

estimates.

Genetic and phenotypic correlations among important trait combinations are

required for constructing complete variance – (co)variance matrices for joint genetic

evaluation of reproduction, growth and carcass traits. The numbers of estimates for

possible pairs of traits available in the literature for tropical pigs were very few. About

one-fifth of the estimates came from a single study. Correlations among reproduction

traits tend to have more estimates followed by growth traits and then carcass traits.

The weighted genetic and phenotypic correlations among reproduction traits

ranged from -0.96 to 0.87 and -0.67 to 0.98, respectively (Table 3.7). Similar results have

50

been observed for mean correlations among reproduction traits in sheep (Safari et al.,

2005) and beef cattle (Koots et al., 1994b). Important genetic correlations were those

between LSF and LS21 (0.80), LS21 and LSW (0.87), and LW21 and LWW (0.76)

which were high and positive. These estimates suggest that selecting for litter size at

farrowing will lead to higher numbers of post-natal live piglets as well as more viable

piglets at weaning. At 21 days a pig starts to develop active immunity against diseases in

its production environment (Kyriazakis and Whittemore, 2006) which has a huge bearing

on its performance at weaning and post-weaning.

Growth traits had high estimates of genetic (-1.00 – 1.00) and phenotypic (-0.97 –

0.88) correlations among themselves (Table 3.8). This was expected because many of the

growth traits were measures of growth at different ages. The weighted genetic

correlations were higher for live weights at adjacent age classes and increased with age

from birth to adult. Weighted phenotypic correlations follow a similar trend. Individual

birth weight had a high genetic correlation with weights at 21 days (0.44) and at weaning

(0.53), while association with post-weaning individual weight was moderate (0.19). An

exception to the genetic correlations among growth traits is that between ADG and FCR

(-0.78) and between ADFI and FCR (0.68). The genetic correlation was negative and

high, and positive and high, respectively, because an autocorrelation between a ratio and

its denominator is expected to yield negative values and that between a ratio and its

numerator are positive (Pearson, 1900). In addition, fast growing animals are generally

more feed efficient.

Carcass traits tend to have more negative genetic and phenotypic correlations

among member traits (Table 3.9). Most negative correlations occurred between BFT and

51

other carcass traits. The ranges for weighted mean genetic and phenotypic correlations

between BFT and other carcass traits were -0.82 to 0.31 and -0.48 to 0.03, respectively.

BFT had negative correlation with LEAN (-0.82), CWT (-0.18) and HYD (-0.11) but

positively correlated with DPT (0.31) and LEA (0.21). DPT was highly and positively

correlated to CWT (0.91), LEA (0.68) and CLT (0.56). The weighted mean phenotypic

correlations were generally similar or lower than the corresponding genetic correlations.

These findings were in agreement with comparable traits in previous studies (Koots et al.,

1994b; Sellier, 1998; Safari et al., 2005)

The weighted genetic correlations among reproduction vs. growth traits were

mostly negative for litter size and live weights at various ages but positive for litter

weight and live weights at various ages (Table 3.10). The genetic antagonism between

litter size and live weights at various ages means that selecting for increased litter size at

farrowing will lead to lower live weight at later ages. However, because litter size is

largely controlled by environment, the reduction in live weight at later ages may be

amenable to management practices. The association between ADG and reproduction

traits were weak suggesting independence of these traits. The correlations among

reproduction vs. carcass traits were few in the literature. The available genetic

correlations were those that occurred between PDG and BFT (0.05), PDG and CWT

(0.08), and PDG and CLT (0.35) (Table 3.10). The phenotypic correlations among groups

of traits are presented in Table 3.11 and were low and negligible for many of the trait

combinations. The phenotypic correlations between growth and carcass traits ranged

from -0.50 to 0.68. The genetic and phenotypic correlations observed between growth

52

and carcass traits in this study suggest that these traits should be considered

simultaneously in a selection index.

The confidence intervals for the weighted mean genetic and phenotypic

correlation estimates (Tables 3.7 to 3.11) were wide for many of the trait pairs. This

reflects the general large variation between published estimates and the relative small

datasets that resulted in large standard errors for the individual estimates.

3.3.6 Examination of publication bias in heritability estimates

Publication bias is a term used to define what happens when a sample of

published estimates of a given parameter from the literature are not true representatives

of all possible estimates from completed studies. This may have an adverse effect on the

validity of the results from a meta-analytic review (Rothstein et al., 2005). In the case of

a genetic parameter like heritability, this bias may arise from several sources among

which includes; (1) non-acceptance by reviewers and journal editors of genetic parameter

estimates from small samples; (2) non-publication by authors of estimates with negative

variance components often referred to as ‗the file drawer problem‘; 3) publication of

results in a non-conventional language (language bias); (4) non-publication of results

from PhD and MSc dissertations and results presented at conferences (Sutton et al.,

2000); and (5) non-publication of results that are against the interest of funding agencies.

In this study, effort was made to identify and include in the meta-analysis,

estimates of heritability from a range of sources that were judged to have been peer-

reviewed and including conference proceedings if appropriate. Negative heritability

estimates were also included. Usually negative variance components will arise from

53

methods that are not constrained like ML and REML procedures to yield positive values

within the parameter space. Figures 3.1 gives a visual assessment of the presence or

absence of publication bias for heritability estimates involving two traits with sufficient

data for assessment using a funnel plot (Sutton et al., 2000) . Heritability estimates were

plotted against the inverse of sampling variance to give a shape like a funnel, if there is

no publication bias (Figure 3.1A; Macaskill and Walter, 2001). Generally, estimates from

smaller samples will be more widely spread around the mean estimate because of large

random errors. A skewed or asymmetric shape of the funnel suggests a likely publication

bias (Figure 3.1B). Because the existence of a bias in this study was negligible no effort

was made to investigate them.

3.4 CONCLUSIONS AND RECOMMENDATIONS

Among the important population parameters, heritability and genetic correlations

are the most difficult to estimate due to their requirements for sophisticated computing

techniques. The problem of variations in literature estimates of these parameters for

animal genetic evaluation has been stressed. Improving the precision of parameter

estimates by using a meta-analysis is possible. In most tropical developing countries,

there is an urgent need to provide accurate estimates of genetic parameters and in

particular correlations among groups of production traits with high economic value, such

as reproduction, growth and carcass traits. These parameters are required for accurate

genetic evaluation of pigs and the development of an optimum breeding objective and

selection indices that will have outcome that can be predicted with confidence. The

54

results from this study will address some of these needs and will serve as a basis for

comparing genetic parameter estimates in the tropics in the future.

The weighted mean estimates of genetic parameters presented here are

recommended for use when reliable estimates are not available for a specific tropical pig

population. Where estimates within a population are available, these could be combined

with the weighted literature average by simply weighting each estimate by the inverse of

the sampling variance. This would yield a population specific estimate that incorporates

prior values. The result presented in this study should allow researchers in developing

countries to extract genetic parameter estimates suitable for a wide range of tropical

conditions. The data base of published parameters from this environment and the

accompanying references are available for extraction of specific estimates in portable

format from the author.

55

Table 3.1 List of traits studied, abbreviations used and units of measurement

Traits Abbreviations Units

Reproduction

Litter size at farrowing LSF piglets

Number born alive NBA piglets

Litter size at 21 days LS21 piglets

Litter size at weaning (5 – 8 weeks) LSW piglets

Litter weight at farrowing LWF kg

Litter weight at 21 days LW21 kg

Litter weaning weight (5 – 8 weeks) LWW kg

Pre-weaning daily gain PDG g/d

Pre-weaning mortality MOR piglets

Maternal ability MA ratio

Survival rate SRT %

Age at first farrowing AFF d

Age at first conception AFC d

Gestation length GLT d

Weaning to service interval WSI d

Farrowing interval FIT d

Daily feed intake during lactation DFI Kg/d

Growth

Average daily gain ADG g/d

Individual birth weight IBW kg

Individual weight at 21 days IW21 kg

Individual weaning weight (5 – 8 weeks) IWW kg

Post weaning individual weight ( 6 months) PIW kg

Mature weight (> 6 months) MWT kg

Days to 90 kg weight D90 d

Feed conversion ratio FCR ratio

Average daily feed intake ADFI kg/d

Carcass

Back fat ultrasonically measured BFU mm

Back fat adjusted for live weight BFA mm

Back fat thickness on carcass BFT mm

Percent lean meat LEAN %

Carcass weight CWT kg

Carcass length CLT cm

Dressing percentage DPT %

Loin eye area LEA cm2

Ham yield HYD kg

56

Table 3.2 Number of literature estimates (N), weighted least square means (Mean±S.E1),

standard deviation (SD) and coefficient of variation (CV) for reproduction, growth and carcass


Traits Indigenous breed Exotic breed Overall

N Mean±S.E N Mean±S.E N Mean SD CV±S.E (%)

Reproduction LSF 2 7.35±0.97

a 23 9.77±0.16b 25 9.94 2.88 28.71±1.53

NBA 1 8.17±2.24 17 9.24±0.14 18 9.23 2.78 29.64±0.82

LS21 - - 15 9.17±0.16 15 9.17 2.52 27.55±1.58

LSW 3 8.90±0.43 17 8.19±0.28 20 8.39 1.80 24.44±3.33

LWF 2 7.18±2.16a 15 14.74±0.48

b 17 14.38 4.93 32.12±1.51

LW21 1 18.66±20.03 14 51.41±1.71 15 51.17 11.48 25.74±2.96

LWW 3 39.44±18.24 11 62.01±6.25 14 59.64 16.43 29.24±3.81

PDG 2 156.53±21.09 6 199.91±6.96 8 195.64 56.31 29.82±6.26

MA 1 0.13 1 0.90 2 0.60 0.10 16.03±0.64

SRT - - 1 84.60 1 84.60 21.18 25.03

AFF - - 1 387.01 1 387.01 3.11 0.80

AFC - - 2 254.43±7.64 2 254.43 23.17 5.93±4.76

GLT - - 3 114.50±1.00 3 114.50 4.50 3.93±1.62

WSI - - 6 7.16±0.89 6 7.16 5.98 85.70±4.95

FIT 1 140.90±13.75 3 169.26±2.84 4 168.10 47.34 28.02±3.58

DFI - - 2 5.51±0.55 2 5.51 1.37 25.94±1.95

Growth

ADG 2 335.43±171.58a 16 792.51±29.06

b 18 779.77 101.79 15.48±1.97

IBW 5 0.69±0.05a 19 1.43±0.03

b 24 1.28 0.34 32.86±9.09

IW21 1 2.20±0.95a 4 5.64±0.26

b 5 5.39 1.05 20.72±3.96

IWW 5 6.26±0.71a 18 10.23±0.46

b 23 9.03 2.04 24.46±2.32

PIW 1 19.23±6.33a 5 45.99±4.09

b 6 38.10 8.67 24.26±3.61

MWT - - 4 94.91±7.70 4 94.91 10.77 17.67±3.00

D90 - - 7 156.99±8.87 7 156.99 14.00 8.74±0.61

FCR - - 4 2.86±0.04 4 2.86 0.35 11.65±1.37

ADFI - - 1 2.71 1 2.71 0.04 1.40

Carcass

BFT 2 21.47±8.09 11 18.58±1.46 13 18.68 4.57 25.62±2.59

BFA - - 8 11.63±0.85 8 11.63 2.79 23.36±3.84

BFU 1 13.15±0.55 2 12.11±0.41 3 12.47 1.90 16.02±1.42

LEAN - - 3 54.25±2.99 3 54.25 3.63 6.53±0.11

CWT 2 23.81±4.74 1 48.90±14.42 3 26.26 3.85 10.73±5.08

CLT 3 52.55±2.20a 4 96.99±2.32

b 7 73.58 3.67 5.90±1.82

DPT 3 63.74±2.54a 2 80.91±2.37

b 5 72.92 3.92 5.91±2.87

LEA 2 21.37±2.46a 4 33.71±1.24

b 6 31.22 3.85 12.28±1.92

HYD 1 15.86±18.36 3 18.56±6.22 4 18.28 1.44 10.30±4.76 ab

means within row differ significantly (P < 0.05); 1S.E = standard error

57

Table 3.3 Number of literature estimates (N), combined (exotic and indigenous)

weighted mean heritability (h2), between study variance (σ

2bs), Q statistics and 95%

confidence interval (CI) for economic traits of pigs in the tropics

Traits N h2±S.E σ

2bs

1 Q 95% CI for h

2

Reproduction

LSF 32 0.08±0.015 0.23 142.91*** 0.05 to 0.11

NBA 29 0.08±0.008 0.83 91.44*** 0.06 to 0.10

LS21 19 0.07±0.014 0.06 28.29ns

0.04 to 0.10

LSW 19 0.07±0.012 0.49 27.99ns

0.04 to 0.10

LWF 23 0.11±0.024 0.45 53.97*** 0.06 to 0.16

LW21 19 0.13±0.014 0.18 84.97*** 0.10 to 0.16

LWW 14 0.07±0.024 0.00 31.02** 0.02 to 0.12

PDG 9 0.19±0.053 1.13 155.10*** 0.06 to 0.32

MA 2 0.27±0.308 9.09 3.97* -

SRT 3 0.62±0.177 0.00 1.55ns

-

AFF 2 0.23±0.203 9.09 2.58ns

-

AFC 1 0.21±0.030 - - -

GLT 4 0.25±0.024 0.00 1.48ns

0.17 to 0.33

WSI 6 0.08±0.028 1.99 24.01*** 0.01 to 0.15

FIT 4 0.02±0.003 0.00 0.65ns

0.01 to 0.03

DFI 3 0.08±0.037 9.10 7.12* -

MOR 3 0.20±0.070 - 9.29** -

Growth

ADG 26 0.28±0.020 0.32 79.00*** 0.24 to 0.32

IBW 38 0.15±0.023 0.22 173.17*** 0.10 to 0.20

IW21 9 0.08±0.029 0.25 136.35*** 0.01 to 0.15

IWW 32 0.22±0.025 0.33 195.87*** 0.17 to 0.27

PIW 11 0.38±0.039 0.00 4.17ns

0.29 to 0.47

MWT 8 0.23±0.041 0.00 8.19ns

0.13 to 0.33

D90 9 0.28±0.035 4.70 38.48*** 0.20 to 0.36

FCR 11 0.32±0.039 - 25.12** 0.23 to 0.41

ADFI 1 0.37±0.090 - - -

Carcass

BFT 17 0.44±0.037 0.00 37.63** 0.36 to 0.52

BFU 10 0.33±0.045 1.09 73.14*** 0.23 to 0.43

BFA 8 0.46±0.050 0.03 153.77*** 0.34 to 0.58

LEAN 4 0.42±0.023 0.00 0.49ns

0.35 to 0.49

CWT 4 0.41±0.128 0.00 7.63ns

0.00 to 0.82

CLT 10 0.39±0.054 - 31.52*** 0.27 to 0.51

DPT 6 0.60±0.117 - 10.96ns

0.30 to 0.90

LEA 9 0.51±0.062 4.24 6.82ns

0.37 to 0.65

HYD 7 0.45±0.075 - 37.04*** 0.27 to 0.63 1Expressed as a percentage of the total variation;

ns non-significant (P > 0.05); * P < 0.05; ** P <

0.01;*** P < 0.001

58

Table 3.4 Number of literature estimates (N), weighted mean heritability for indigenous (h2

I) and exotic (h2

E) breeds of pigs, maternal

heritability (h2

M), correlation between direct and maternal genetic effects (ram) and common litter effect (l2) for reproduction traits of

pigs in the tropics

Traits N h2

I±S.E N h2

E±S.E N h2

M±S.E N ram±S.E N l2±S.E

Reproduction

LSF 2 0.02±0.001 30 0.09±0.017 5 0.01±0.005 5 0.64±1.407 3 0.06±0.027

NBA 3 0.07±0.020 26 0.08±0.008 8 0.03±0.004 7 0.73±0.837 3 0.01±0.012

LS21 - - 19 0.07±0.014 8 0.01±0.005 8 -0.66±0.855 8 0.04±0.014

LSW 3 0.06±0.031 16 0.07±0.012 1 0.03±0.020 - - - -

LWF 2 0.22±0.049 21 0.11±0.026 4 0.03±0.011 3 -0.98±3.074 3 0.12±0.010

LW21 1 0.18±0.119 18 0.13±0.014 3 0.01±0.003 3 0.85±2.530 3 0.30±0.244

LWW 3 0.19±0.028 11 0.07±0.026 1 0.06±0.002 - - - -

PDG 2 0.18±0.032 7 0.22±0.070 5 0.13±0.049 4 0.44±1.220 4 0.03±0.010

MA 1 0.05±0.013 1 0.71±0.330 - - - - - -

SRT - - 3 0.62±0.177 - - - - -

AFF - - 2 0.23±0.203 - - - - - -

AFC - - 1 0.21±0.030 - - - - - -

GLT - - 4 0.25±0.024 - - - - - -

WSI - - 6 0.08±0.028 - - - - - -

DFI - - 3 0.08±0.037 - - - - - -

MOR - - 3 0.20±0.070 - - - - - -

59

Table 3.5 Number of literature estimates (N), weighted mean heritability for indigenous (h2

I) and exotic (h2

E) breeds of pigs, maternal

heritability (h2

M), correlation between direct and maternal genetic effects (ram) and common litter effect (l2) for growth and carcass


Traits N h2

I±S.E N h2

E±S.E N h2

M±S.E N ram±S.E N l2±S.E

Growth

ADG 4 0.37±0.073 22 0.28±0.022 4 0.02±0.004 4 -0.35±0.110 6 0.12±0.033

IBW 8 0.10±0.016 30 0.18±0.032 4 0.03±0.017 4 -0.19±0.647 4 0.08±0.044

IW21 2 0.17±0.034 7 0.02±0.014 4 0.06±0.031 3 -0.01±2.859 4 0.06±0.031

IWW 8 0.18±0.027 24 0.24±0.036 3 0.23±0.014 2 0.09±0.678 2 0.09±0.050

PIW 3 0.49±0.199 8 0.36±0.038 1 0.32±0.001 1 -0.01±5.920 1 0.06±0.040

MWT 2 0.41±0.046 6 0.22±0.044 - - - - 2 0.20±0.050

D90 - - 9 0.28±0.035 1 0.09±0.001 - - 2 0.18±0.090

FCR 2 0.42±0.020 9 0.30±0.045 - - - - - -

ADFI - - 1 0.37±0.090 - - - - - -

Carcass

BFT 3 0.63±0.208 14 0.44±0.039 - - - - 2 0.05±0.045

BFU 3 0.52±0.001 7 0.25±0.018 1 0.04±0.001 1 -0.28±0.006 2 0.06±0.050

BFA - - 8 0.46±0.050 1 0.08±0.001 - - 1 0.08±0.065

LEAN - - 4 0.42±0.023 - - - - - -

CWT 2 0.66±0.340 2 0.34±0.070 1 0.03±0.002 1 -0.28±0.047 1 0.18±0.002

CLT 4 0.36±0.089 6 0.42±0.067 1 0.11±0.001 1 -0.48±0.009 1 0.04±0.001

DPT 3 0.82±0.092 3 0.42±0.113 - - - - - -

LEA 3 0.58±0.099 6 0.49±0.082 - - - - - -

HYD 3 0.48±0.014 4 0.42±0.136 - - - - - -

60

Table 3.6 Levels of significance for the fixed factors in the analysis of heritability estimates

of some traits of pigs in the tropics

Traits N R2 Data Origin Breed Group Method Data Age Latitude

LSF 32 0.25 *

NBA 29 0.53 ** * ***

LS21 19 0.57 * ** *

LW21 19 0.83 *** * ** **

LWW 14 0.84 ** * ***

ADG 26 0.61 * * *

IBW 38 0.28 * *

IWW 32 0.54 ** *

BFT 17 0.50 ‡ ‡ ‡

‡ P < 0.1; * P < 0.05; ** P < 0.01; *** P < 0.001

61

Table 3.7 Weighted means of estimates from literature for genetic (above diagonal) and phenotypic (below diagonal) correlations among reproduction

traits of pigs in the tropics with the number of estimates in parenthesis ( ) and the 95% confidence interval in brackets [ ]

Traits LSF LS21 LSW LWF LW21 LWW WSI MOR PDG AFF AFC GLT DFI MA SRT

LSF 0.80 (6)

[0.62, 0.90]

0.70 (11)

[0.54, 0.81]

0.55 (11)

[-0.16, 0.89]

0.55 (10)

[0.33, 0.71]

0.08 (3)

[0.03, 0.14]

0.05 (3)

[-0.04, 0.14]

0.64 (5)

[-0.23, 0.94]

0.42

(1)

0.19

(1)

-0.48

(1)

-0.17

(1)

0.19

(1)

- -

LS21 0.82 (2)

[-1.00, 1.00]

0.87 (3)

[-0.91, 1.00]

0.69 (4)

[0.45, 0.83]

0.85 (4)

[0.78, 0.90]

0.52 (1) - -0.21 (1) - - - - - - -

LSW 0.74 (7)

[0.21, 0.93]

0.98 (3)

[0.89, 1.00]

0.55 (5)

[0.45, 0.64]

0.74 (5)

[-0.30, 0.98]

0.52 (5)

[-0.58, 0.95]

0.29 (1) -0.35 (4)

[-0.87, 0.58]

- - - 0.34

(1)

0.27

(1)

0.62

(1)

0.37 (2)

[-0.46,

0.86]

LWF 0.73 (7)

[0.55, 0.86]

0.50 (2)

[0.21, 0.71]

0.68 (3)

[0.37, 0.85]

0.65 (8)

[0.56, 0.73]

0.11 (3)

[-0.24, 0.43]

0.09 (1) 0.19 (4)

[-0.36, 0.64]

- 0.14

(1)

- - - -0.10

(1)

-

LW21 0.59 (3)

[0.21, 0.82]

0.81 (3)

[0.49, 0.94]

0.76 (2)

[-1.00, 1.00]

0.62 (3)

[0.40, 0.77]

0.76 (2)

[-0.91, 1.00]

- -0.64 (4)

[-0.92, 0.07]

- - - - - - -

LWW 0.54 (5)

[0.10, 0.81]

0.81 (1) 0.86 (4)

[0.75, 0.93]

0.46 (3)

[-0.43, 0.90]

0.81 (2)

[-0.78, 1.00]

0.05 (1) -0.48 (1)

- - - - - -0.21

(1)

0.42 (2)

[0.30,

0.54]

WSI 0.03 (2)

[-0.28, 0.33]

- 0.00 (1) 0.01 (1) - 0.00 (1) - - - - - - - -

MOR 0.17 (1) -0.59 (1) -0.28 (1) -0.28 (1) -0.65 (1) -0.67 (1) - - - - - - - -

PDG - - - - - - - 0.35

(1)

- -0.96

(1)

- -

AFF -0.03 (1) - - 0.00 (1) - - - - - - - - -

AFC - -0.05 (1) - - - 0.07 (1) - - - - - - -

GLT -0.06 (1) - 0.05 (1) - - - - - - - - 0.10

(1)

- -

DFI 0.02 (1)

- 0.11 (1) - - - - - 0.33

(1)

- - 0.02

(1)

- -

MA - - -0.34 (1) - -0.19 (1) -0.26 (1) - - - - - - - -

SRT - - 0.48 (2)

[0.11, 0.73]

- - 0.48 (2)

[-0.61, 0.94]

- - - - - - - -

62

Table 3.8 Weighted means of genetic (above diagonal) and phenotypic (below diagonal) correlations among growth traits of pigs in the

tropics with the number of estimates in parenthesis ( ) and the 95% confidence interval in brackets [ ]

Traits ADG IBW IW21 IWW PIW MWT D90 FCR ADFI

ADG 0.65 (1) - 0.87 (2)

[-1.00, 1.00]

- 1.00 (1) -1.00 (1) -0.78 (10)

[-0.91,-0.54]

0.27 (1)

IBW - 0.44 (4)

[-0.01, 0.74]

0.53 (13)

[0.13, 0.78]

0.19 (8)

[-0.21, 0.54]

0.44 (7)

[0.17, 0.65]

- - -

IW21 - 0.45 (3)

[-0.24, 0.84]

0.86 (3)

[0.45, 0.97]

0.78 (2)

[-1.00, 1.00]

0.95 (1) - - -

IWW 0.11 (1) 0.43 (12)

[0.34, 0.51]

0.71 (3)

[0.19, 0.92]

0.67 (8)

[0.48, 0.81]

0.58 (7)

[0.10, 0.84]

- - -

PIW - 0.23 (8)

[-0.06, 0.47]

0.66 (2)

[-1.00, 1.00]

0.49 (8)

[0.22, 0.70]

0.86 (6)

[0.56, 0.96]

- - -

MWT 0.98 (1) 0.19 (7)

[-0.01, 0.36]

0.88 (1) 0.43 (7)

[0.13, 0.66]

0.69 (6)

[0.39, 0.86]

-1.00 (1) - -

D90 -0.96 (1 - - - - -0.97 (1) - -

FCR -0.83 (4)

[-0.97, -0.79]

- - - - - - 0.68 (1)

ADFI 0.63 (1) - - - - - - 0.34 (1)

63

Table 3.9 Weighted means of genetic (above diagonal) and phenotypic (below diagonal) correlations among carcass traits of pigs in

the tropics with the number of estimates in parenthesis ( ) and the 95% confidence interval in brackets [ ]

Traits BFT LEAN CWT CLT DPT LEA HYD

BFT -0.82 (4)

[-0.94, -0.51]

-0.18 (2)

[-0.89, 0.89]

0.03 (6)

[-0.35, 0.41]

0.31 (3)

[-0.81, 0.90]

0.21 (4)

[-0.91, 0.96]

-0.11 (3)

[-0.44, 0.25]

LEAN -0.48 (3)

[-0.74, -0.10]

- 0.31 (2)

[-1.00, 1.00]

- 0.41 (1) -0.23 (1)

CWT - - 0.50 (2)

[-0.98, 0.99]

0.91 (1) 0.70 (2)

[-1.00, 1.00]

-

CLT -0.47 (1) 0.15 (1) - 0.56 (3)

[-0.63, 0.96]

0.25 (5)

[-0.44, 0.75]

-0.03 (2)

[-0.39, 0.34]

DPT - -0.05 (1) - - 0.68 (4)

[0.19, 0.90]

-

LEA -0.33(2)

[-1.00, 1.00]

0.48 (1) - -0.30 (2)

[-1.00, 0.99]

- 0.30 (2)

[-0.49, 0.81]

HYD 0.03 (2)

[-0.22, 0.27]

0.25 (1) - -0.35 (2)

[-0.62, 0.00]

- 0.00 (2)

[-1.00, 1.00]

64

Table 3.10 Weighted means of estimates from literature for genetic correlations among reproduction, growth and carcass traits of pigs in the

tropics with the number of estimates in parenthesis ( ) and the 95% confidence interval in brackets [ ]

Traits ADG IBW IW21 IWW BFT CWT CLT LEAN DPT LEA HYD

LSF -0.04 (1) -0.66 (4)

[-0.87, -0.23]

-0.70 (1) -0.53 (4)

[-0.73, -0.25]

-0.02 (1) - - - - - -

LS21 - -0.25 (1) 0.04 (1) -0.55 (1) - - - - - - -

LSW - -0.45 (4)

[-0.67, -0.16]

0.34 (1) -0.24 (5)

[-0.43, -0.03]

- - - - - - -

LWF - 0.31 (2)

[-0.92, 0.98]

0.03 (2)

[-0.10, 0.16]

-0.36 (1) - - - - - - -

LW21 - 0.11 (2)

[-0.58, 0.71]

0.53 (2)

[-0.44, 0.93]

0.15 (1) - - - - - - -

LWW - 0.04 (1) 0.58 (1) 0.29 (3)

[-0.01, 0.54]

- - - - - - -

PDG 0.38 (2)

[-0.99, 1.00]

0.87 (1) 0.85 (1) - 0.05 (1) 0.08 (1) 0.35 (1) - - - -

SRT - - - -0.60 (2)

[-1.00, 1.00]

- - - - - - -

AFC -0.04 (1) - - -0.02 (1) - - - - - -

WSI -0.08 (1) - - -0.27 (1) - - - - - -

MOR - -0.21 (2)

[-1.00, 1.00]

-0.78 (1) -0.30 (1) - - - - - - -

ADG - - - - -0.35 (22)

[-0.47, -0.21]

0.57 (1) 0.58 (1) 0.50 (1) 0.00 (1) 0.02 (4)

[-0.60, 0.62]

0.14 (3)

[-0.27, 0.51]

MWT - - - - 0.16 (2)

[-1.00, 1.00]

- 0.43 (1)

- - - -

D90 - - - - 0.18 (7)

[0.06, 0.29]

- - - - - -

FCR - - - - 0.22 (10)

[0.10, 0.36]

- 0.06 (2)

[-0.61, 0.68]

-0.53 (1) - -0.04 (2)

[-0.50, 0.50]

-0.07 (2)

[-0.31, 0.18]

ADFI - - - - 0.49 (2)

[-0.96, 0.99]

- - - - - -

65

Table 3.11 Weighted means of estimates from literature for phenotypic correlations among reproduction, growth and carcass traits of pigs in

the tropics with the number of estimates in parenthesis ( ) and the 95% confidence interval in brackets [ ]

Traits ADG IBW IW21 IWW BFT CWT CLT LEAN DPT LEA HYD

LSF -0.04 (1) -0.35 (4)

[-0.83, 0.41]

0.27 (1) -0.54 (3)

[-0.83, -0.03]

-0.01 (1) - - - - - -

LS21 - -0.10 (1) -0.03 (1) -0.09 (1) - - - - - - -

LSW - -0.30 (4)

[-0.53, -0.03]

0.01 (1) -0.24 (5)

[-0.52, 0.07]

- - - - - - -

LWF - -0.20 (1) -0.08 (1) -0.11 (1) - - - - - - -

LW21 - 0.07 (1) 0.33 (1) -0.14 (1) - - - - - - -

LWW - 0.23 (1) - 0.22 (3)

[0.04, 0.38]

- - - - - - -

PDG -0.09 (1) - - - -0.01 (1) 0.01 (1) 0.04 (1) - - - -

SRT - - - 0.33 (2)

[0.06, 0.56]

- - - - - - -

AFC -0.04 (1) - - -0.02 (1) - - - - - -

WSI -0.04 (1) - - -0.06 (1) - - - - - -

MOR - -0.22 (1) -0.23 (1) -0.31 (1) - - - - - - -

ADG - - - - 0.09 (13)

[-0.03, 0.22]

0.68 (1) 0.07 (4)

[-0.62, 0.69]

0.09 (1) -0.08

(1)

0.00 (3)

[-0.22, 0.23]

0.23 (3)

[-0.57, 0.81]

MWT - - - - 0.00 (1) - - - - - -

D90 - - - - 0.27 (3)

[-0.35, 0.73]

- - - - - -

FCR - - - - 0.06 (5)

[-0.13, 0.25]

- 0.12 (2)

[-1.00, 1.00]

-0.09 (1) - -0.22 (2)

[-0.97, 0.93]

-0.50 (2) [-1.00, 1.00]

ADFI - - - - 0.27 (2)

[-0.80, 0.93]

- - - - - -

66

Figure 3.1 Funnel plot of heritability estimates for (A) LSF showing no evidence of

publication bias and (B) ADG showing classic asymmetry suggesting the presence of a

publication bias

A

B

67

3.5 Appendix

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83

CHAPTER 4

ACCURACIES OF GENOMIC BREEDING VALUES FOR

SIMULATED TROPICAL PIG POPULATIONS


1, M. Sargolzaei

13, R. M. Friendship

2 and J. A. B.

Robinson1


Department of Animal & Poultry Science, University of Guelph, Guelph, ON, Canada



3L'Alliance Boviteq, Saint-Hyacinthe, Quebec, Canada

84

4.0 ABSTRACT

Genetic improvement of pigs in tropical developing countries has focused on

imported exotic populations subjected to intensive breeding. A declining effective

population size (Ne) resulted in moderate linkage disequilibrium (LD) in this population.

Presently, indigenous pig population with limited selection and low LD are being

considered for improvement. Given that the infrastructure for genetic improvement using

the conventional BLUP selection methods are lacking, a genome-wide selection (GS)

program was proposed for developing countries. A simulation study was conducted to

evaluate the option of using 60 K single nucleotides polymorphism panel and observed

level of LD in the indigenous and exotic pig populations. Several scenarios were

evaluated, including different trait heritability, different size and structure of training and

validation populations and different selection methods. The training set included

previously selected exotic pigs, unselected indigenous pigs and their crossbreds. The

ridge regression method was used to train the prediction model. The results showed that

accuracies of genomic breeding values (GBVs) in the range of 0.31 - 0.86 for different

trait heritabilities are expected for validation populations with varying LD levels as long

as high density marker panels are utilized. The GS method improved accuracy of GBVs

better than pedigree-based selection for traits with low heritability and in young animal

with no performance data. Crossbred training set had higher accuracy of GBVs than

purebreds. Genome-wide selection holds promise for genetic improvement of pigs in the

tropics.

Keywords: Genome-wide selection, accuracy, tropical pigs, developing countries

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4.1 INTRODUCTION

Genetic improvement of pigs in tropical developing countries has focused on

imported exotic populations subjected to intensive breeding. A declining effective

population size (Ne) and strong directional selection resulted in moderate linkage

disequilibrium (LD) between quantitative trait loci (QTL) and close by markers (Henson,

1992). In practice, indigenous pig populations that are bred in extensive system or under

medium- to low-input and stressful environments are also used for pork production. As

expected, Ne in the indigenous populations is larger than that observed in commercial

populations resulting in very low LD across the genome (Thévenon et al., 2007; Amaral

et al., 2008). Moreover indigenous pig populations have been mildly selected due to their

poor performance for economic traits (Shenglin, 2007).

Although genetic improvement is desired in indigenous pig populations, a

limitation to this effort in developing countries is the lack of necessary infrastructure, i.e.

lack of programs that record phenotype on pedigreed animals in an extensive breeding

system (Kahi et al., 2005). A common approach to genetic improvement of pigs is to

carry out selection in nucleus herds and to combine the indigenous and exotic populations

in crossbreeding programs in order to produce crossbred market pigs (Oseni, 2005;

Nwakpu and Onu, 2007).

Meuwissen et al. (2001) proposed a method for genetic improvement of livestock

which was called genome-wide selection (GS). Owing to recent developments in high

throughput, high density single nucleotide polymorphism (SNP) panel technology, GS

can currently be applied. Genome-wide selection uses a ―training population‖ of

individuals with phenotypic and genotypic information to develop a prediction model.

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The prediction model then uses genotypic data from a ―candidate population‖ of untested

individuals and produces genomic breeding values (GBV) of an individual by using the

estimated additive marker effects from the training population. Selection decisions are

based on the GBV. Using GS in swine genetic improvement is expected to yield a

considerable increase in selection responses for juvenile animals that do not have

phenotypic records (Meuwissen et al., 2001; Gianola et al., 2003; Hayes et al., 2009) and

potentially can reduce the cost of breeding programs (e.g. Schaeffer, 2006). Genome-

wide selection is also believed to be advantageous for sex-limited traits, traits that are

difficult to measure like disease and longevity traits with low heritability and traits

requiring sacrifice of the animal (Goddard and Hayes, 2007).

The success of GS depends mainly on the prediction accuracy of GBVs which is

directly proportional to the response to selection (Falconer and Mackay, 1996). Several

authors have reported high accuracies of GBV in the range of 0.45 – 0.88 in theoretical

(Meuwissen et al., 2001; Muir, 2007; de Roos et al., 2009; Toosi et al., 2010) and

empirical studies (Hayes et al., 2009; VanRaden et al., 2009; Cleveland et al., 2010) in

livestock species. Accuracy of GBV relies on the existence of LD between QTL and

markers in the population of interest and on the strength of predicted individuals‘

relatedness to the reference population, much as it will do when using pedigree

information to perform predictions (Jannink et al., 2010). As the population diverges

from the training set, accuracy of GBV declines because of recombination between QTL

and markers unless densely spaced markers are used to maintain sufficient LD (de Roos

et al., 2009). Conceptually assuming a large reference population, the more dense the

marker spacing and greater the population-wide LD, the better GS works.

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Statistical methods for training prediction models are being investigated

(Whittaker et al., 1999; Meuwissen et al., 2001; Xu, 2003; Gianola et al., 2006; Fernando

et al., 2007). Methods that assume all marker effects are distributed with equal variance,

like ridge regression (Whittaker et al., 1999), have been found to be as accurate as

methods that assume some markers do not explain any variance (e.g. BayesB; Meuwissen

et al., 2001). Assuming all marker effects are drawn from the same distribution does not

mean all the effects are equal but that they are all equally shrunken toward zero (Jannink

et al., 2010). Habier et al. (2007) also showed that ridge regression was more effective at

capturing genetic relationships among families because it fits more markers into the

prediction model.

Therefore, the objective of this study was to evaluate long term application of GS

in populations characterised by small LD, large Ne and mild selection pressure, and in

constant hybridization with populations of opposite characteristics, assuming truncated

selection pressure. This allows the assessment of the long term application of GS in

swine genetic improvement in tropical developing countries based on the availability of a

60 K SNP chip panel for this environment. Accuracies of genomic predictions were

investigated and compared for different training datasets, traits and breeding structures.

Accuracy of a genome-based model was contrasted to a pedigree-based model for

breeding value prediction and the decay of accuracy of GBV under random mating was

also evaluated. The study was based on computer simulations of relevant tropical pig

population structures.

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4.2.1 Simulation software

The QMSim simulation software developed by Sargolzaei and Schenkel (2009)

was used for this study. This forward-in-time simulation program was designed to

simulate large genomes and complex pedigree structures that mimic existing livestock

populations. In this program, breeding values can be estimated using different approaches

and various selection events can be imposed on the populations. Table 4.1 gives a

description of the parameters used for the simulation.

4.2.2 Population structure

All pigs found in tropical developing countries were assumed to come from a

common base of unrelated individuals which served as ancestors to the exotic and

indigenous populations. The common base population was mated based on a union of

gametes randomly sampled from the male and female gametic pools for 1,000

generations with Ne of 5,000 breeding individuals (Figure 4.1). This ancestral population

was later contracted to Ne of 1,050 individuals consisting of 300 breeding males at the

end of another 1000 generations in order to generate an initial historic LD. Effective

population size in livestock populations generally shows a declining trend due to

domestication (Henson, 1992). The ancestral population was split into 2 unequal sized

purebred subpopulations (referred to as exotic and indigenous populations) at generation

2001.

The exotic pig population was created by drawing the top ranking 50 males and

top ranking 250 females based on their phenotypes from the common base population

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resulting in Ne of 167 and subjected to phenotypic selection for the trait under

consideration for 50 generations with an intensity of 10% for males and 50% for females.

Each female produced 4 offspring (2 males and 2 females) resulting in 1,000 breeding

animals in each generation from which selection was made. The remaining members of

the common base population consisting of 250 males and 500 females were allowed to

mate at random concurrently for 50 generations resulting in the indigenous population

with Ne of 667. The Ne assumed in this study was chosen to yield a simulated level of LD

observed in real life. The actual Ne might differ slightly.

At generation 2049 in the simulation, randomly selected sires from the exotic

population were mated to randomly selected dams from the indigenous population to

produce F1 crossbreds and their reciprocals which were also generated by selecting sires

from the indigenous population and dams from the exotic population and randomly

mating them. Therefore, at generation 2050, four different populations namely: exotic,

exotic x indigenous, indigenous x exotic and indigenous were available to conduct the

selection experiment. The impact of these assumptions on the level and pattern of LD

will be addressed in the discussion section.

The resulting populations in generation 2050 were used in the different breeding

schemes in the ten subsequent generations (T1 – T10) that were produced (Figure 4.1).

Pure-breeding was carried out for the exotic and indigenous populations separately. The

synthetic population was produced by mating F1 sires to their reciprocal crossbred dams

followed by inter se mating. For the backcross breeding program, repeated backcrossing

was conducted for generations T1 - T5, followed by inter se mating of the backcross

population from generations T6 – T10. All lines in the breeding schemes were kept at a

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constant size of 1,000 breeding candidates and 5% of males and 50% of females were

randomly selected in each line per generation. Throughout the selection experiment, all

sires and dams were replaced each generation thus discrete generations were assumed.

The breeding structure described above allowed for the evaluation of GS in previously

selected and unselected pure lines and their crosses used for training and validation. The

goal was to mimic the selected exotic and unselected indigenous pig populations

currently used for pork production in tropical developing countries.

4.2.3 Genome structure

The simulated genome consisted of 5 chromosomes of 150 cM each with 250

multi-allelic segregating QTL and a marker density of 22 bi-allelic loci per cM evenly

spaced across the genome (Table 4.1). This was chosen to make the simulation

computationally tractable and in such a way that the marker density represents

approximately 60 K SNP chip currently available (Ramos et al. 2009). To establish

mutation drift equilibrium (MDE) in the ancestral population after 2,000 generations,

about 3 and 20 times as many markers and QTL, respectively, were simulated with equal

starting allele frequencies and a recurrent mutation rate of 2.5 x 10-3

for markers and

infinite-allele mutation rate of 2.5 x 10-5

for QTL.

The required number of QTL and markers were drawn at random from

segregating loci with a minor allele frequency (MAF) of ≥ 0.05 at generation 2000 before

creating the exotic and indigenous populations. No new mutation was simulated in the

recent populations, resulting in many of the markers and QTL not segregating in the

exotic population after 50 generations of phenotypic selection. Mutation rate per

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generation in mammals is estimated to be about 2.5 x 10-9

(Kumar and Subramanian,

2002) and may not have resulted in a significant number of new mutants after 50

generations in real populations. The Haldane mapping function with no interference and

Mendelian inheritance of all loci was assumed. The allelic effects of the QTL including

original and new mutations were sampled from a gamma distribution with a shape

parameter of 0.4 (Hayes and Goddard, 2001) and scaled such that the sum of the QTL

variances in the last historical generation equals the input QTL variance. The QTL was

randomly distributed across the genome. Figure 4.2 showed an L-shaped gamma

distribution of the true QTL effects for the three traits studied.

The magnitude and extent of LD in the founder populations at generation 2000

and 2050 were estimated as average correlations between pairs of marker alleles (r2)

4.1

(Hill and Robertson, 1968), where DAB = DA1B1 = pA1B1 – pA1pB1, pA1 is the frequency of

allele A1 at locus A, pB1 is the frequency of allele B1 at the locus B and pA1B1 is the

frequency of haplotype A1B1 in the population. Only markers with a MAF ≥ 0.10 were

included in this analysis. The r2 value averaged across the genome was compared with

their expectation based on Sved 1971: E (r2) , where c was the

recombination rate calculated as and

Ne was as before.

4.2.4 Phenotypes

To simulate data for each animal with a record at generation 2000, a finite

additive locus model was assumed such that the true breeding value of an individual was

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equal to the sum of the 250 QTL effects scaled on the basis of the specified heritability

(h2) and phenotypic variance (σ

2p). Given the genetic variance, environmental effects

were generated from a normal distribution with a mean of zero and a constant variance

set to give the desired h2. The phenotype was assumed to be the sum of the genotypic and

environmental effects, i.e. no pleiotropy or genotype-environment interactions were

simulated. For the traits considered, h2 and σ

2p used in the simulation were obtained from

a previous meta-analysis study carried out for the tropical environment (Chapter 3).

Three traits, namely number born alive (NBA), average daily gain (ADG) and back-fat

thickness (BFT), were simulated independently assuming a normal distribution. All

individuals within the various genetic groups in the last 10 generations were genotyped

for the available markers. In addition, for training the prediction models, individuals in

the training dataset were assumed to be recorded for all traits studied.

4.2.5 Model for comparison

The phenotypic information from the training population was used to calculate the

estimated breeding values (EBV) for the pedigree-based model according to Henderson

(1984). The model used was

y = μ1n + Za + e 4.2

where y is observed phenotype of size n, μ is the overall mean, 1n is a vector of n ones, a

is a vector of random additive genetic effects of each individual ~N (0, Aσ2

a), A is the

additive relationship matrix, Z is a design matrix for the additive genetic effect and e is a

vector of random residuals ~N (0, Iσ2

e). The variance ratio required in the mixed model

equations was (1-h2)/h

2 where the true heritability at each generation was used.

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For the genome-based model, the marker effects were estimated using ridge

regression described in Meuwissen et al. (2001) and extended by Fernando et al. (2007).

The statistical model was

y = μ1n + Wg + e 4.3

The mixed model equations (MME) were:

4.4

where y is observed phenotype of size n, g is a vector of allele substitution effects due to

the ith genotype ~N (0, I ), W is a design matrix that has a 0, 1, and 2 for the number

of alleles of type gi present in the jth animal, p is the base allele frequency at ith locus, q

is (1-p), σ2g is the true genetic variance at each generation and m is the total number of

markers simulated. The other parameters are as describe previously.

Once estimates of marker effects were obtained from the training data set, the

genomic breeding values of kth animal (GBVk) in both the training and validation data set

and for the future selection candidates in the genomic selection scheme was computed as

GBVk = 4.5

where and are the recorded genotype and the estimated marker allele substitution

effect at the ith locus, respectively, and m is the total number of segregating markers. For

estimating GBV of an individual in all of the different populations simulated, about

14,500 segregating markers distributed across five chromosomes with MAF ≥ 0.10 and

based on the exotic population marker panel were used.

Five sets of training data from the previously selected and unselected populations

of size 275, 775, 1,000, 1,275 and 2,275, and consisting of a single and multi-

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generational population structure made up of males and females were initially used to

train the prediction model in order to evaluate the required reference population size and

structure for implementing GS and the persistency of the accuracy of GBV. In the pure

breeding scenario, the validation population consisted of the generation following the

training population and were made up of 1,000 breeding candidates while in another

scenario both the training and validation sets were of different population structures.

4.2.6 Analysis of results

Each scenario was replicated 10 times starting each replicate from the same base

population up to generation 2050. Comparisons between methods were based on the

correlation between the estimated and true breeding values. Standard errors were

computed as the standard deviation of average accuracy across the 10 replicates, divided

by . All analyses were coded in R statistical software (Ihaka and Gentleman, 1996)

and the Statistical Analysis System (SAS Institute Inc., 2009) was used for least squares

analysis of estimates.


4.3.1 Simulation assumptions

Table 4.1 shows the parameters used to simulate the datasets for this study. As

described in the methods section, these parameters were chosen to make the simulation

computationally tractable, while providing a realistic dataset for the target populations.

The assumption of 5 chromosomes rather than 18 in the case of swine are not expected to

change substantially the main conclusions of the research as long as the number of

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markers used are dense enough to represent the 60 K SNP markers evenly distributed

across the 18 pairs of chromosome and the number of QTL affecting the trait are fairly

large. The results from this study were generally similar to the case where only one

chromosome was simulated with the same number of markers and QTL (data not shown).

In reality, the number of QTL contributing to a given trait will not be known in advance.

Also, using a dense marker map has an important effect on the accuracy of GBVs

because a higher number of markers, when equally distributed across the genome, will

increase the probability that each QTL is in high LD with at least one marker (Calus et

al., 2008; Goddard, 2009)

The distributions of the true additive QTL effects for various traits studied are

presented in Figure 4.2. In general, there were many QTLs with small effects and a few

QTL with large effects which follows the L-shaped gamma distribution that Hayes and

Goddard (2001) suggest as the true distribution of QTL effects. The implication of this

assumption on the method used for estimating marker effects based on marker-QTL

association will be addressed later. Non-additive genetic effects were not simulated in

this study, but they play a role in a litter-bearing species like swine.

The scenarios simulated in this study mimics the exotic and indigenous pig

populations that are used for pork production in tropical developing countries (Pathiraja,

1986; Pathiraja, 1987). The exotic pigs were originally imported into these regions to

upgrade the genetic merit of the indigenous population (Blench, 2000). Due to the

improved performance of exotic pig under tropical conditions they have continued to

dominate the swine industry with intensive breeding. Because the conditions in the

tropics have failed to improve, the performance of the exotic pigs is dwindling

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necessitating the integration of the indigenous pigs in pork production (Chimonyo and

Dzama, 2007). However, as the infrastructure for genetic improvement of indigenous pig

using the traditional method is lacking in developing countries (Kahi et al., 2005), the

goal of this study was to evaluate a scenario where GS was applied and compared with

the results from a conventional approach.

4.3.2 Extent of LD in founders

The MAF distributions for the SNP markers in the founder populations are given

in Figure 4.3. This distribution of MAF showed that about 30% of all loci in the exotic

founders have become fixed for alternate SNP alleles while the majority of the loci are

still segregating in the indigenous and crossbred founders. Consequently, evaluation of

LD in the founders and genomic predictions using the SNP markers was carried out for

MAF ≥ 0.10.

In this study, divergence between the founder populations was created by drift

and differential selection pressures imposed on the founders (Figure 4.1). The extent of

LD in the founders used to produce all training populations showed that the decay of

average r2 with genomic distance differed between them (Figure 4.4). LD was

significantly higher on short distances (0 – 2 cM) for the exotic founders compared to the

indigenous, whereas the long range LD (>10 cM) was quite similar for both groups. The

LD levels used in this simulation study are consistent with observations in real swine data

after several generations of divergence, domestication and selection events (Harmegnies

et al., 2006; Du et al., 2007; Amaral et al., 2008; Jafarikia et al., 2010).

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Note that for the exotic and indigenous founders, average LD was similar to the

expected LD based on Sved‘s (1971) formula for effective population sizes of 167 and

667, respectively. In Sved (1971), the expected level of LD assumes a closed random

mating population with constant historical effective population size, which are not the

same assumptions in the current study. Nevertheless, the estimated constants give a good

indication that the effective number of founders and, therefore, the extent and decline of

LD with distance, differed substantially between the exotic and indigenous populations.

4.3.3 Requirements for training marker effects

The implementation of GS requires a reference population (RP) with phenotypic

and genotypic data for training the prediction model. For a breeding system that uses

populations with differing selection histories, there is a need to know the appropriate

breeding structure and size of the RPs. The RP should provide estimates of marker effects

for predicting GBVs in different populations with high accuracy and with minimal

frequency of re-estimation of marker effects.

Accuracies of GBVs with alternative sizes and structure of RP and tested in

previously selected exotic and unselected indigenous populations were investigated and

the results are given in Figure 4.5. Accuracy increased as RP size increased and improved

with a multigenerational data structure. An exciting observation is that GS works well for

traits with low heritability both for a previously selected population in moderate LD and

for an unselected population in low LD. From Figure 4.5, a trait of low heritability in a

previously selected population can achieve accuracy greater than the heritability of the

trait for at least three generations with a multi-generational training population of 2,275

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individuals. An unselected population in low LD achieves accuracy greater than the

heritability of the trait for at least five generations with a single progeny generation of

1,000 individuals.

For a trait with high heritability in both exotic and indigenous populations (Figure

4.6), regardless of the size and structure of the RP, the accuracy cannot exceed the

heritability in the generation used for training. For similar heritability, Meuwissen et al.

(2001) found an accuracy of 0.732 using the same method of estimation and RP of 2,200

phenotypes/genotypes. Accuracies of this magnitude or higher were only observed in the

unselected indigenous population in low LD. The selected exotic population due to

previous selection events have dissipated much of the genetic variability. Thus, in the

presence of moderate LD in the exotic population, accuracies of GBVs were lower than

expected. Accuracy of GBVs is not only a function of how close the markers are to the

QTL to be able to explain the variation in the trait, but also a function of how much of the

genetic variability in the trait of interest is left to be explained. In addition, moderate to

large-sized QTL effect would be in high frequency of the favourable allele or even fixed,

making more difficult the predictions.

Another striking observation from Figures 4.5 and 4.6 is that, regardless of RP

size and structure, the decay of accuracy over generations under random mating is faster

for an unselected population in low LD level than for a selected population in moderate

LD which strengthens the importance of LD in GS schemes. Clearly, accuracy of GBVs

using a multigenerational RP with size of not less than 2,000 individuals is superior to a

single generation RP. Similar conclusions have been made by other researchers

(Meuwissen et al., 2001; Muir, 2007). However, if resources are a limitation in choosing

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appropriate RP, then a population size of not less than 1,000 individuals should be

utilized if a considerable level of accuracy is desired, but this will require re-estimation of

marker effects after 2 generations of selection.

4.3.4 Accuracy of GBVs in training and validation populations

Although, the training population size of 2,275 individuals showed better

accuracy of GBVs (Figures 4.5 and 4.6), the results of genomic evaluation presented

from this section forward were based on a training set of 1,275 individuals chosen

because of the anticipated cost of genotyping. The correlations between estimated and

true breeding values of individuals in the different training populations (TP) and in their

first generation progeny population used for validation (VP) and for different traits and

methods of EBV estimation are presented in Table 4.2. When validation of GS was in the

first generation progeny of the training individuals, accuracy of GBVs was reduced by

3% in exotic, 10% in indigenous, 10% in synthetic and 27% in backcross populations for

a trait with low heritability. For a trait with high heritability, accuracies of GBVs were

reduced in the different validation populations except for the synthetic population where

accuracy was increased by 3%, on average. Accuracy of GBVs was higher in the

unselected indigenous purebreds than in the previously selected exotic purebreds for all

traits. More so, as the proportion of the exotic alleles in the crossbred individuals

increased, accuracy of GS increased substantially. Backcross populations had higher

accuracy for GS than the synthetic and purebred lines.

A change of selection method from a pedigree-based approach to GS resulted in

an increase in accuracy of breeding values by 11% in the TP and 61% in the VP, on

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average (Table 4.2). In traits with low heritability where the pedigree-based scheme

needed help, accuracy of GS was increased by 16% in the TP and 82% in the VP, on

average. The gain in accuracy for a trait with high heritability was 7% for TP and 48%

for VP, on average. The application of a genome-based scheme in a crossbred

populations resulted in an increase in accuracy of EBVs by 13% in the TP and 82% in the

VP while purebred population gained an increase in accuracy of EBVs by 7% in TP and

30% in VP, on average.

The high accuracies of GBVs observed when training in the unselected

indigenous population in low LD may be due to the method of ridge regression BLUP, as

used in this study, which fitted all markers simultaneously and thus captured more

genetic relationships resulting in higher accuracy. Habier et al. (2007) indicated that the

ridge regression method exploits genomic relationships among individuals and LD

between markers and QTL to improve accuracy of GBVs. In addition, the indigenous

population was not subjected to directional selection in the past so many of the markers

were still segregating and could capture more of the QTL effects. Note that the same

marker map used in the exotic population was applied in all other population including

the unselected indigenous population. Furthermore, the genetic variance for the traits in

the indigenous population was still very large to allow for higher accuracy of GBV.

The lower accuracy observed in the exotic population was because of the previous

selection that resulted in many of the markers not segregating and the loss of genetic

variation due to selection leading to a small amount of genetic variance being explained

by marker-QTL association. Luo et al. (1997) noted that the greater the LD between a

pair of loci like marker and QTL, the larger is the variance that is associated with the

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marker. However, there is a limitation to the amount of genetic variation that can be

explained. Another reason for the low accuracy of GBV in the selected exotic population

was that the major QTL alleles which normally will be in high frequency had their effects

estimated with difficulty due to low average MAF compared to the unselected indigenous

population where they segregate with higher frequency.

The accuracy of GBVs when the training and validation population differ in

structure and composition are presented in Table 4.3. Training in the same breed as the

validation population resulted in greater accuracy for majority of the cases. Accuracies

tended to be lower if the training and validation population differ in structure, but

reductions in accuracy were not large in some cases and depended on the breed

composition of the training individuals. Training in the selected exotic population

resulted in lower accuracy when validation was in the unselected indigenous population

but accuracy increased slightly when validation was in the crossbred. A similar trend was

observed when training in the indigenous population and validating in the exotic and

crossbred populations, respectively. The reason been that different QTLs were fixed or at

low MAF in the exotic and indigenous populations such that QTL that explain more

variation in the traits are different in the two populations. In the crossbred, QTL alleles

from both populations that control variations in trait are present resulting in higher

accuracy than in the purebreds.

For all traits studied, training in the crossbred population resulted in GBV

accuracy of 0.45 – 0.97 when validation was in the crossbred and a considerable range of

accuracy (0.12 – 0.36) when validation was in the purebreds (Table 4.3). All crossbred

individuals consisted of varying proportions of the purebred alleles resulting in greater

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accuracy of GBV when validation was in the purebred populations. By estimating allele

substitution effects based on crossbred phenotypes, the effects of purebred alleles will be

estimated against the genetic background in which they will be expressed. This might be

a potential practice of GS in the tropics in future, for example, marker allele effects can

be trained in a range of RPs made up of different breed compositions, admixtures and

nondescript breeds. The estimates of marker effects for a defined SNP panel can then be

used for genomic predictions of purebreds followed by selection within breed in the

nucleus.

The drop in accuracy when the training and validation population differed in

structure can also be attributed to the differences in the level of LD between the two

populations and differences in allele frequency. Whereas the exotic population in this

study, for example, were in moderate LD, the indigenous population had very low LD

with differing MAF of SNP markers. Thus, even though the same marker panel was

utilized for GS in both populations, accuracy was very low. The marker-QTL linkage

phase may be different in both populations. Although this phenomenon was not evaluated

in the present study, marker-QTL linkage phase will need to be consistent in both

populations for GS to work (Dekkers and Hospital, 2002; Goddard et al. 2006). As

distance in time since divergence between this two population is high (about 50

generations, Figure 4.1), there is a greater chance for recombination to break down the

LD that was present in the ancestral population and drift to create new LD within each

subpopulation which may change the marker-QTL phase (Hill and Robertson, 1968;

Goddard et al., 2006). The same explanation could apply to the case of training in

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purebreds and validating in crossbreds and vice versa. The use of denser marker panel for

GS implementation in future will ensure consistency in marker-QTL phase.

To further explain the differences in accuracy of GBVs between the purebred and

crossbred training populations described earlier, LD in the different training populations

was examined by comparing the average r2 between adjacent marker pairs for an average

distance of 0.05 cM (Table 4.4). There were significant differences in the amount of LD

between the training and validation populations. Figure 4.4 depicts how LD decayed with

distance in the founders of the training populations and showed differences in the rate of

breakdown of LD between the populations. Whereas LD in the purebred population was a

result of historic LD in the founders and drift (Sved, 1971; Du et al., 2007; Amaral et al.,

2008), LD in the crossbreds came from two sources (Lo et al., 1993). The first part is the

average LD that existed within the parental populations, referred to as old LD, and the

second part is the LD generated in the cross as a result of differences in gene frequencies

between the parental lines, referred to as new LD because it was created by a recent event

(Toosi et al., 2010). Old LD is usually confined to shorter distances due to accumulation

of historic recombination events while new LD extends over longer intervals. The LD at

short intervals is a function of Ne in the distant past, whereas LD at longer intervals

reflects Ne in the recent past (Hayes et al., 2003).

The phenomenon described above explains the higher accuracy of GBVs

observed in the crossbred population when compared to results from the purebreds.

Similar observations were documented by Toosi et al. (2010). In addition, alleles in a

crossbred line originated from purebred parental lines. If these purebred lines are not

closely related as in this study, the SNP allele effects will depend on their line of origin.

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Thus, a model that accounts for line of origin effects may be warranted to avoid

overestimation of allele substitution effects. Ibanez-Escriche et al. (2009) carried out a

simulation study to evaluate the effect of using a model with breed-specific SNP allele

effects versus classical genomic model with across-breed SNP genotype effects. They

concluded that in implementing GS in crossbred populations, models that fit breed-

specific SNP allele effects may not be necessary if high density markers are utilized to

trace ancestor alleles with precision ( Ibanez-Escriche et al., 2009). As a result a breed-

specific SNP model was not entertained in the present study.

In concluding this section, training the prediction model based on crossbred

performance in the field for genetic improvement of purebreds in the nucleus holds some

promise. Although the results from this study showed lower accuracy of GBVs for this

kind of scenario due to lower level of LD in one of the parental lines, if LD was greater

or if marker density can be increased, this will be a potential option in the future. Traits

with low heritability such as reproduction and disease resistance, which are a challenge to

improve with conventional methods, may show greater genetic progress using a genome-

wide selection scheme.

4.3.5 Persistency of accuracy of GBVs

The decay of accuracy of GBVs in different populations over ten generations of

random mating for all traits studied are presented in Figure 4.7. The results showed that

for the purebred populations, accuracy tended to decrease faster under random mating in

the early generations and stabilises in the later generations. For the synthetic line, there

was stability in accuracy across generations. The backcross population exhibited the same

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pattern as in the purebreds. From Figure 4.7, a trait with low heritability in all populations

studied can achieve accuracy greater than the heritability of the trait for at least five to

seven generations without re-estimation of marker effects. Also, for a trait with moderate

to high heritability, marker effects should be re-estimated after two generations if

accuracy greater than the trait heritability is desired.

Training the genomic prediction model in generations closer to the validation

dataset will lead to more accurate GBVs compared to training in more distant

generations. With increasing generations, the relationships between the training and

validation population will become weak due to recombination events and changes in

marker-QTL phase, and a reduction in the LD level between markers and QTLs. Muir

(2007) observed that several generations following the estimation of marker effects the

accuracy of GBVs was reduced and suggested that these effects should be re-estimated

after three to four generations of selection. For crossbred population, while

recombination breaks down existing LD between markers and QTL, recent crosses create

new LD which helps to stabilise accuracy. Traits with low heritability are known to

benefit from genome-wide selection by exploiting the Mendelian sampling effects

(Goddard and Hayes, 2007; Daetwyler et al., 2007).

Figure 4.8 presents the influence of trait variance on the persistency of GBV

accuracy in different populations over ten generation of random mating. In the exotic

population with a previous history of intense directional selection that could lead to loss

of genetic variability, accuracy for a low heritability trait like number born alive (NBA)

was stable over ten generations due to moderate LD between markers and QTL and more

genetic variance explained. For other traits in this population, accuracy decreased due to

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loss of genetic variance (Bulmer, 1976). For the unselected indigenous population,

accuracy decreased at a faster rate in each generation of random mating for all traits. This

decrease in accuracy is due to low initial LD in this population and recombination

between markers and QTL which breaks down LD. For the crossbred populations, there

was appreciable stability in accuracy over generations of random mating for all traits.

New LD and higher allele frequency created by recent crosses in the crossbred replaced

LD that was loss by recombination between markers and QTL, thus accuracy of GBV

was maintained.


The accuracy of GBVs can be very high for populations in moderate to low LD

regardless of their selection history. High accuracy requires high density markers that are

well distributed across the genome to allow for marker-QTL association. For best result,

a reference population with genotypic and phenotypic data for training the prediction

model should have a multigenerational data structure with greater than 2000 individuals.

Young animals with no phenotypic records can have GBVs early in life with high

accuracy for selection decision based on their genotypic data. Crossbred training

population performed better than purebred training set when validation was in

populations with similar structure as the training set.

In situations where infrastructures for swine genetic improvement are lacking, a

genome-wide selection program can circumvent the need to develop facilities for a

conventional selection scheme and will guarantee faster genetic progress especially for

traits with low heritability. In a system where purebreds in the nucleus are crossed to

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produce crossbred market pigs, the prediction model can be trained based on crossbred

records in the field for genetic improvement of purebreds in the nucleus.

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Parameters Values

Genome size per chromosome 150 cM

Number of chromosomes 5

Marker density per cM 22

Number of segregating QTL per chromosome 50

Mutation rate of Marker locus 2.5 x 10-3

Mutation rate of QTL locus 2.5 x 10-5

Minor allele frequency 0.05

Distribution of additive QTL effects Gamma (shape = 0.4)

Traits

Number born alive (NBA) = 0.08; = 7.73

Average daily gain (ADG) = 0.28; = 10361.20

Back fat Thickness (BFT) = 0.63; = 20.88

Number of animals with phenotypic or genotypic records, or both

Training 1275

Prediction/Validation 1000

Number of replicates per scenario 10

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Table 4.2 Correlations1 (r) between estimated breeding values ( ) and true breeding

values ( ) in the training (TP) and validation (VP) populations

Traits Training Methods r ( , ) r ( , ) Δ2(%)TP Δ(%)VP

NBA Exotic Pedigree 0.28 0.20

Genomics 0.31 0.31 10.71 55.00

Indigenous Pedigree 0.45 0.36

Genomics 0.45 0.41 0.00 13.89

Synthetic Pedigree 0.33 0.24

Genomics 0.37 0.34 12.12 41.67

Backcross1 Pedigree 0.56 0.23

Genomics 0.72 0.59 28.57 156.52


Genomics 0.69 0.52 23.21 147.62

ADG Exotic Pedigree 0.36 0.27

Genomics 0.40 0.35 11.11 29.63


Genomics 0.70 0.60 1.45 17.65


Genomics 0.61 0.62 17.31 51.22


Genomics 0.91 0.78 7.06 85.71


Genomics 0.91 0.76 8.33 80.95

BFT Exotic Pedigree 0.45 0.31

Genomics 0.51 0.45 13.33 45.16


Genomics 0.85 0.68 1.19 21.43


Genomics 0.69 0.70 16.95 59.09


Genomics 0.96 0.86 1.05 59.26


Genomics 0.97 0.84 2.11 55.56 1Correlations are least square means of 10 replicates per scenario (Standard error ≈ 0.020)

2Percentage change in accuracy due to change in selection method

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Table 4.3 Average accuracy1 of genomic breeding values when training and validation population differ in structure

Traits

Training

Validation population

Exotic Indigenous Synthetic Backcross1 Backcross2

NBA Exotic 0.31 (0.039) 0.07 (0.026) 0.20 (0.049) 0.22 (0.047) 0.22 (0.062)

Indigenous 0.08 (0.036) 0.45 (0.055) 0.13 (0.053) 0.13 (0.024) 0.16 (0.012)

Synthetic 0.21 (0.054) 0.12 (0.028) 0.37 (0.038) 0.45 (0.027) 0.47 (0.039)

Backcross1 0.30 (0.024) 0.13 (0.046) 0.72 (0.019) 0.72 (0.016) 0.77(0.015)

Backcross2 0.26 (0.050) 0.19 (0.034) 0.68 (0.029) 0.77 (0.025) 0.69 (0.014)

ADG Exotic 0.40 (0.062) 0.02 (0.022) 0.20 (0.057) 0.23 (0.055) 0.27 (0.056)

Indigenous 0.03 (0.062) 0.70 (0.014) 0.27 (0.038) 0.22 (0.068) 0.22 (0.061)

Synthetic 0.22 (0.058) 0.21 (0.025) 0.61 (0.015) 0.78 (0.012) 0.79 (0.019)

Backcross1 0.35 (0.024) 0.29 (0.046) 0.85 (0.007) 0.91 (0.009) 0.86 (0.012)

Backcross2 0.24 (0.049) 0.32 (0.020) 0.86 (0.012) 0.90 (0.008) 0.91 (0.009)

BFT Exotic 0.51 (0.029) 0.05 (0.019) 0.27 (0.066) 0.26 (0.046) 0.35 (0.069)

Indigenous 0.06 (0.019) 0.85 (0.014) 0.27 (0.022) 0.27 (0.043) 0.25 (0.021)

Synthetic 0.22 (0.020) 0.18 (0.031) 0.68 (0.012) 0.86 (0.006) 0.86 (0.007)

Backcross1 0.26 (0.019) 0.33 (0.028) 0.88 (0.012) 0.96 (0.002) 0.89 (0.004)

Backcross2 0.20 (0.030) 0.36 (0.014) 0.92 (0.005) 0.94 (0.002) 0.97 (0.001) 1Values are average correlations between estimated and true breeding values and their standard errors (s.e) over 10 replicates per scenario

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Table 4.4 Average linkage disequilibrium between adjacent marker pairs in training and

validation1 populations

Populations Exotic Indigenous Synthetic Backcross1 Backcross2

Training 0.30 0.05 0.09 0.13 0.06

Validation 0.30 0.06 0.10 0.18 0.05

1Validation population are the immediate progeny of the training population

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Selection strategy began from T1 - T10 and based on Ne = 91 for all populations simulated. Inter se

mating of backcross population took place from T6 - T10

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Figure 4.2 Distribution of true allelic effects simulated for the three traits studied

Distribution of allele effects for ADG

Allele effects

Fre

quency

0 5 10 15

0100

200

300

400

500

Distribution of allele effects for NBA

Allele effects

Fre

quency

0.00 0.05 0.10 0.15 0.20

0100

200

300

400

Distribution of allele effects for BFT

Allele effects

Fre

quency

0.0 0.2 0.4 0.6 0.8 1.0 1.2

0100

200

300

400

114

Figure 4.3 Distribution of average minor allele frequency (MAF) for the SNP markers in

the founder populations

115

Figure 4.4 Average linkage disequilibrium (LD) as measured by r2 against distance

(cM) between a pair of marker in different founder populations

Ne _167 and Ne _667 are LD expectations based on Sved (1971): E (r2) ≈ 1/ (1 + 4Nec),

where Ne = 167 or 667 are the effective population size for the exotic and indigenous

population, respectively, and c is the recombination rate calculated as 0.5(1 – exp(-2*map

distance)). The graph was based on average r2 over 10 replicates plotted against distance

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Figure 4.5 Effect of training population size and structure on accuracy and persistency

of accuracy following ten generations of random mating in selected exotic (top) and

unselected indigenous (bottom) populations and for NBA

Population structure includes founders (F_275), founders and progeny (F_P_775 and F_P_1275),

progeny (P_1000) or multigenerational dataset (M_G_2275). The straight line is the accuracy

level based on trait heritability

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Figure 4.6 Effect of training population size and structure on accuracy and persistency

of accuracy following ten generations of random mating in selected exotic (top) and

unselected indigenous (bottom) populations and for BFT

Population structure includes founders (F_275), founders and progeny (F_P_775 and F_P_1275),

progeny (P_1000) or multigenerational dataset (M_G_2275). The straight line is the accuracy

level based on trait heritability

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Figure 4.7 Decay of accuracy of GBVs in different populations and traits under random

mating for ten generations. The straight line is the accuracy level based on trait

heritability

119

Figure 4.8 Influence of trait heritability on persistency of accuracy of GBVs in different

populations under random mating

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CHAPTER 5

OPPORTUNITIES FOR GENOME-WIDE SELECTION FOR PIG

BREEDING IN DEVELOPING COUNTRIES


1, M. Sargolzaei

13, R. M. Friendship

2 and J. A. B.

Robinson1






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5.0 ABSTRACT

Genetic improvement of exotic and indigenous pigs in tropical developing

countries is desired. Implementations of traditional selection methods on tropical pig

populations are limited by lack of necessary infrastructure. Genome-wide selection (GS)

provides an approach for achieving faster genetic progress without developing a pedigree

recording system for this environment. The implications of GS on long term gain and

inbreeding should be studied prior to actual implementation especially where low linkage

disequilibrium (LD) is anticipated in the target population. A simulation case-study of

this option was carried out based on the available 60 K single nucleotide polymorphism

panel for porcine genome. Computer simulation was used to explore the effects of

various selection methods, trait heritability and different breeding programs when

applying GS. Genomic predictions were based on ridge regression method. Genome-wide

selection performed better than BLUP and phenotypic selection methods by increasing

genetic gain and maintaining genetic variation while lowering inbreeding especially for

traits with low heritability. Indigenous pig populations in low LD can be improved by

using GS if high density marker panel are available for use. Combining GS with repeated

backcrossing of a crossbred population to previously selected exotic pig population in

moderate LD promises to rapidly improve the genetic merit of the commercial

population. Application of this novel method on a real population will need to be carried

out to validate these results.

Keywords: Genome-wide selection, indigenous population, swine, developing countries

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5.1 INTRODUCTION

Genetic selection is the process that determines which individuals become

parents, how many offspring they may produce and how long they remain in the breeding

population (Bourdon, 1997). This method has been successful in making long-term

genetic change in animals and is widely accepted as the primary cause of adaptive

evolution within natural populations. For swine populations in many tropical developing

countries, the approach has been to improve by basic phenotypic selection (Pathiraja,

1986; Kahi et al., 2005). In phenotypic selection, a potential candidate is selected based

on their own performance, disregarding pedigree or the performance of sibs or progeny.

Theory shows that estimated breeding value (EBV) for an individual as a selection

criterion is equal to = h2 ( - ), where and are the individual‘s EBV and

performance record and is the population average for the trait. A problem with EBV

derived in this way is that the estimates are confounded by other extraneous factors that

need to be separated. Unless trait h2

is high, accuracy is generally low, leading to slower

genetic progress. Moreover, the reduction of genetic variance is minimal and rate of

inbreeding (ΔFG) is low for this method, because co-selection of relatives is unlikely

except for the case of high h2 (Daetwyler et al., 2007).

Henderson (1984) developed a pedigree-based method that uses information on

relatives to provide EBVs that are adjudged to be best linear unbiased predictions

(BLUP). When BLUP EBVs were used in ranking candidates and truncating the

distribution to choose those with the highest values, genetic change (ΔG) was increased,

but so was ΔFG (Belonsky and Kennedy, 1988) with a reduction in genetic variance

(Bulmer, 1976). BLUP leads to co-selection of sibs and higher rates of inbreeding

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especially when h2 is low

(Sonesson et al., 2005). Although, the BLUP method has been

successful for swine genetic improvement in developed countries (Belonsky and

Kennedy, 1988; Kennedy et al., 1996; Gibson et al., 2001), implementation in developing

countries is limited to small nucleus herds with pedigree recording (Smith, 1988; Kahi et

al., 2005). The breeding value of an individual ( ) estimated using BLUP consists of

three components as given by the equation = (0.5) + 0.5 ( ) + , where and

are the sire and dam EBVs, respectively, and is the Mendelian sampling term.

The accuracy of the BLUP EBV for an individual depends mainly on the accuracy of its

parent EBVs, number of own phenotypic observations and collateral relatives including

progeny (Daetwyler et al., 2007). The more information on the parents and relatives that

is included in the genetic model, the higher the accuracy of EBV for the individual,

especially for lowly heritable traits.

Today, swine breeders in developing countries are interested in selection methods

that are straight forward to implement at reasonable cost. The focus is on the

identification of quantitative trait loci (QTL) associated with variation in the traits of

interest. The infinitesimal model assumed by phenotypic and BLUP selection is expected

to give way to genomic models that will enable the dissection of phenotypic variation and

unravel the interplay between genes and environment (Visscher et al., 2008). Methods

that guarantee a sustainable increase in ΔG while maintaining genetic variation and

keeping the level of ΔFG low are preferred long-term by swine breeding industry. In

schemes that allow a young animal to be selected before phenotypic performance can be

measured, phenotypic selection will be difficult to implement and BLUP selection will

have low accuracy for an individual‘s EBV based only on parent average.

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One possible solution for selection in tropical pig production is genome-wide

selection (GS) based on genomic breeding values (GBV) predicted from dense single

nucleotide polymorphism (SNP) marker genotyping (Meuwissen et al., 2001; Muir,

2007). The ability of GS to use genetic markers to exploit Mendelian sampling effect is

promising (Daetwyler et al., 2007; Nielsen et al., 2010). Exploitation of Mendelian

sampling variation is the major source of increased accuracy which directly increases ΔG

and reduces the level of ΔFG for GS over conventional approaches.

Therefore, the objective of this study was to quantify how much gain is to be

expected in production traits using GS compared to conventional methods of selection for

pig breeding programs in developing countries. The impacts of various selection methods

on inbreeding, genetic variance, realized accuracy, linkage disequilibrium and QTL allele

frequency change were also investigated.


5.2.1 Simulation software

A modified version of the QMSim simulation software developed by Sargolzaei

and Schenkel (2009) was used for this study. This version of the software allows for the

marker effects to be estimated externally using any preferred method and GBVs of

selection candidates are calculated and provided to the program for selection purpose. In

addition, other methods of breeding value estimation such as BLUP and phenotypic

selection can be applied and various selection events can be imposed on the populations.

Table 5.1 gives a description of the parameters used for the simulation.

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5.2.2 Population structure

All pigs found in tropical developing countries were assumed to come from a

common base of unrelated individuals which served as ancestors to the exotic and

indigenous populations. The common base population was mated based on the union of

gametes randomly sampled from the male and female gametic pools for 1,000

generations with effective population size (Ne) of 5,000 breeding individuals (Figure 5.1).

This ancestral population was later contracted to Ne of 1,050 individuals consisting of

300 breeding males at the end of another 1,000 generations in order to generate initial

historic linkage disequilibrium (LD). This is typical of Ne in livestock populations which

have been declining due to domestication (Henson, 1992). The ancestral population was

split into two unequal sized purebred subpopulations (referred to as exotic and indigenous

populations) at generation 2001.

The exotic population was created by drawing the top ranking 50 males and top

ranking 250 females based on their phenotypes from the common base population

resulting in Ne of 167 and subjected to phenotypic selection for the trait under

consideration for 50 generations with an intensity of 10% for males and 50% for females.

Each female produced 4 offspring (2 males and 2 females) resulting in 1,000 breeding

animals in each generation from which selection was made. The remaining members of

the common base population consisting of 250 males and 500 females were allowed to

mate at random concurrently for 50 generations resulting in the indigenous population

with Ne of 667. The Ne assumed in this study was chosen to yield a simulated level of LD

observed in real life. The actual Ne might differ slightly.

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At generation 2049 in the simulation, randomly selected sires from the exotic

population were mated to randomly selected dams from the indigenous population to

produce F1 crossbreds and their reciprocals which were also generated by selecting sires

from the indigenous population and dams from the exotic population and randomly

mating them. Therefore, at generation 2050, four different populations namely: exotic,

exotic x indigenous, indigenous x exotic and indigenous were available to conduct the

selection experiment.

The resulting populations in generation 2050 were used in the different breeding

schemes in the ten subsequent generations (T1 – T10) that were produced (Figure 5.1).

Pure-breeding was carried out for the exotic and indigenous populations separately. The

synthetic population was produced by mating F1 sires to their reciprocal crossbred dams

followed by inter se mating. For the backcross breeding program, repeated backcrossing

was conducted for generations T1 - T5, followed by inter se mating of the backcross

population from generations T6 – T10. All lines in the breeding schemes were kept at a

constant size of 1,000 breeding candidates and 5% of males and 50% of females were

randomly selected in each line per generation. Throughout the selection experiment, all

sires and dams were replaced each generation, thus discrete generations were assumed.

The goal of the breeding structure described above was to mimic the selected exotic and

unselected indigenous pig populations and their crosses currently used for pork

production in tropical developing countries.

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5.2.3 Genome structure

The simulated genome consisted of 5 chromosomes of 150 cM each with 250

multi-allelic segregating QTL and a marker density of 22 bi-allelic loci per cM evenly

spaced across the genome (Table 5.1). This was chosen to make the simulation

computationally tractable and in such a way that the marker density represents

approximately 60 K SNP marker map currently available (Ramos et al., 2009). To

establish mutation drift equilibrium in the ancestral population after 2000 generations,

about 3 and 20 times as many markers and QTL, respectively, were simulated with equal

starting allele frequencies and a recurrent mutation rate of 2.5 x 10-3

for markers and

infinite-allele mutation rate of 2.5 x 10-5

for QTL.

The required number of QTL and markers were drawn at random from

segregating loci with a minor allele frequency (MAF) of ≥ 0.05 at generation 2000 before

creating the two recent purebred populations. No new mutation was simulated in the

recent populations, resulting in many of the markers and QTL not segregating in the

exotic population after 50 generations of phenotypic selection. Mutation rate per

generation in mammals is estimated to be about 2.5 x 10-9

(Kumar and Subramanian,

2002) and may not have resulted in a significant number of new mutants after 50

generations in real populations. The Haldane mapping function with no interference and a

Mendelian inheritance of all loci was assumed. The allelic effects of the QTL including

original and new mutations were sampled from a gamma distribution with a shape

parameter of 0.4 (Hayes and Goddard, 2001) and scaled such that the sum of the QTL

variances in the last historic generation equals the input QTL variance. The QTL was

randomly distributed across the genome.

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The magnitude of LD in the exotic and indigenous founders at generation 2050

were estimated as average correlations between adjacent pairs of markers (r2)

5.1

(Hill and Robertson, 1968), where DAB = DA1B1 = pA1B1 – pA1pB1, pA1 is the frequency of

allele A1 at locus A, pB1 is the frequency of allele B1 at the locus B and pA1B1 is the

frequency of haplotype A1B1 in the population. Only markers with a MAF ≥ 0.10 were

included in this analysis.

The level of genetic diversity present in the simulated populations was

investigated using Wright‘s fixation indices such as FIS which defines the inbreeding

coefficient of individuals relative to the subpopulations and FST which is a relative

measure of where the subpopulations are in allelic fixation (Wright, 1965). The Fstat

program developed by Goudet (2001) was used to calculate these statistics. Genotypes at

200 loci from a random sample of 300 individuals from each of the two simulated

purebred founder populations from generation 2050 were used to estimate fixation

indices.

5.2.4 Phenotype

To simulate data for each animal with a record at generation 2000, a finite

additive locus model was assumed such that the true breeding value of an individual was

equal to the sum of the 250 QTL effects scaled on the basis of the specified heritability

(h2) and phenotypic variance (σ

2p). Given the genetic variance, environmental effects

were generated from a normal distribution with a mean of zero and a variance set to give

the desired h2. The phenotype was assumed to be the sum of the genotypic and

129

environmental effects, i.e. no pleiotropy or genotype-environment interactions were

simulated. For the traits considered, h2 and σ

2p used in the simulation were obtained from

a previous meta-analysis study carried out for the tropical environment.

Three traits namely: number born alive (NBA), average daily gain (ADG) and

back fat thickness (BFT) were simulated independently assuming a normal distribution.

All individuals within the various genetic groups in the last 10 generations were

genotyped for the available markers. In addition, for training the prediction models,

individuals in the training dataset were assumed to be recorded for all traits considered.

5.2.5 Model for genetic evaluation

The phenotypic information from the different breeding populations in each

generation of selection was used to calculate the EBV from the traditional BLUP model

according to Henderson (1984). The model was

y = μ1n + Za + e 5.2

where y is data vector of size n, μ is the overall mean, 1n is a vector of n ones, a is a

vector of random additive genetic effects of each individual ~N (0, Aσ2

a), A is the

additive relationship matrix, Z is a design matrix for the additive genetic effect and e is a




2 where the true heritability at each generation was used.

In the GS scheme, marker effects were estimated using ridge regression model

described in Meuwissen et al. (2001) and extended by Fernando et al. (2007). The

statistical model was

y = μ1n + Wg + e 5.3

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The mixed model equation (MME) was:

5.4

where y is data vector, g is a vector of allele substitution effects due to the ith genotype

~N (0, I ), W is a design matrix that has a 0, 1 and 2 for the number of alleles of type

gi present in the jth animal, p is the base allele frequency at ith locus, q is (1-p), σ2

g is the

true genetic variance at each generation and m is the total number of markers. The other

parameters are as described previously.

For training the marker effects in all of the different breeding programs simulated,

only the segregating markers with MAF ≥ 0.10 and based on the exotic population

marker panel were used. The training set consisted of 1,000 breeding individuals and

marker effects were re-estimated in each of the last ten generations. Once estimates of

marker effects were obtained, the genomic breeding values of kth selection candidates

(GBVk) in each generation was computed as

GBVk = 5.5

where and are the recorded genotype and the estimated marker allele substitution

effect at the ith locus, respectively, and m is the total number of markers.

5.2.6 Selection strategies

For the various breeding programs investigated in this study, individuals were

selected for breeding on the basis of their phenotype, BLUP EBV, GBV and at random

for each trait. In each generation, 5% of males and 50% of females for the different

breeding programs were selected resulting in Ne of 91.

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Each combination of breeding scenario and selection method was replicated 50

times, each replicate beginning with the same base population up to generation 2050. The

average level of true breeding values for the traits and average level of inbreeding

calculated based on pedigree were produced for each replicate and generation. Genetic

gains for the traits were calculated as differences in true genetic level from generation T1

to T10 and standardized by dividing by the true genetic standard deviation in each

generation. Cumulative response per generation was obtained by summing up the annual

gains in each generation.

Comparisons between methods were based on cumulative response, inbreeding

level, adjacent marker LD, realised accuracies and QTL allele frequency change in each

generation of selection. Realised accuracy was calculated as

5.6

where G (t) is the mean genotypic value in generation t, σA (t) is the additive genetic

standard deviation in generation t and 1.40 is the average selection intensity for the

proportions of males and females selected in each generation (Falconer and Mackay,

1996). All analyses were coded in R statistical software (Ihaka and Gentleman, 1996).


5.3.1 LD and population differentiation

To show the differences between the exotic and indigenous populations which

served as founders for the various breeding programs in this study, some genetic

132

properties were examined at generation 2050. The mean heterozygosity in selected exotic

founders was 0.326 while that of the unselected indigenous founders was 0.468. The

average LD based on adjacent marker pairs (r2) was 0.286 in the exotic and 0.048 in the

indigenous for markers separated by 0.05 cM, on average. Wright‘s fixation indices were

used to quantify the amount of divergence between the simulated populations in

generation 2050. The estimated FST and FIS were 0.169 (SE = 0.014) and 0.004 (SE =

0.020), respectively, when populations were separated in generation 2000.

The mean heterozygosity in the exotic and indigenous populations implies that the

amount of genetic variation present in both populations differed significantly. Whereas

the exotic population has dissipated much of the genetic variability due to previous

selection, the unselected indigenous population still has their genetic variability intact. In

addition, due to differences in Ne for the exotic (167) and indigenous (667) populations,

the level of LD in the two populations differed markedly. Markers in LD with putative

QTL are valuable for the implementation of GS. Linkage disequilibrium levels of 0.2 and

above are considered appropriate for using genomic information in breeding programs

(Meuwissen et al., 2001; Du et al., 2007).

The estimate of FIS of 0.004 implies only a minor deficit of heterozygosity within

populations. This value corroborates with the estimate of average inbreeding level in the

indigenous population (0.030) based on pedigree information at generation 2050, but not

to the same extent as estimate in the exotic population (0.147) with a previous selection

history. The FST estimate of 0.169 shows that about 17% of the total genetic variability in

the whole population can be attributed to the differences among populations (e.g., Canon

et al. 2001), or that about 17% of shared allelic diversity was lost within each population

133

since they were separated (Toosi et al., 2010). Recently, Sollero et al. (2008) published

global FST values for indigenous and commercial breeds of pigs in Brazil based on 28

microsatellite loci. They found that 14% of the total genetic variation observed was due

to differences between populations. Therefore, the simulated founder populations in this

study had enough divergence to represent current exotic and indigenous pig populations.

5.3.2 Selection response

Average cumulative response in standard deviation units at generations 5 and 10

for different traits, breeding strategies and methods of selection are given in Table 5.2. As

expected, all breeding programs in which directional selection was practiced showed

response for all traits studied. In the pure breeding scenario, genetic progress was greater

with GS than with BLUP and differences were higher in generation 5 than in generation

10 especially for the exotic population in moderate LD. For crossbreeding program, GS

also performed better than BLUP except for backcrossing of crossbreds to the indigenous

population where BLUP showed higher response than GS.

At generation 5, response to GS averaged across population was greater than

BLUP by 41%, 31% and 21% for number born alive (NBA), average daily gain (ADG)

and back-fat thickness (BFT), respectively. The result at generation 10 shows that

response to GS was greater than BLUP by 49%, 33% and 23% for NBA, ADG and BFT,

respectively, on average. Among the purebreds, response to GS averaged across traits

was 120% and 64% higher than BLUP at generations 5 and 10, respectively, in the

previously selected exotic population while the unselected indigenous population was

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only 15% and 19% higher in selection response to GS than BLUP at generations 5 and

10, respectively.

One of the objectives of this study was to compare response to selection from

conventional BLUP and GS methods in breeding programs involving selected exotic and

unselected indigenous pig populations. Formulas usually used to predict selection

response such as ΔG = iρσA (Falconer and Mackay, 1996); where i is selection intensity,

ρ is accuracy of prediction method and σA is the additive genetic standard deviation, over-

estimate response because it does not account for reduction of genetic variability from

directional selection and inbreeding. Consequently, increases in ΔG anticipated from GS

methods due to higher accuracies of GBV that were measured in randomly mated

populations may not reflect expectations with selection because selection changes allele

frequencies and reduces genetic variance (Bulmer, 1976).

At all levels of trait heritability and in the pure breeding scenario, GS was clearly

superior to BLUP both in the short and long term. GS achieves this increase in selection

response over BLUP for two reasons: 1) GS selects directly on QTL controlling the traits

through its LD with molecular markers, 2) GS exploits Mendelian sampling effects to

improve accuracy of selection (Daetwyler et al., 2007). The BLUP method on the other

hand can increase accuracy of EBV for a selection candidate by capturing additional

information on ancestors and collateral relatives, however, when truncated selection is

practiced this leads to an increase in ΔFG. In a crossbreeding program, GS can improve

selection response through the creation of new LD in the crossbred population in addition

to the LD in the parental breeds.

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For traits with low heritability where BLUP methods need help, GS can improve

genetic gain by exploiting genetic variation within families through the exploitation of

Mendelian sampling effects (Daetwyler et al., 2007). This reduces the chances of

selecting sibs as well as inbreeding. Another important observation is the reduction in

selection response at generation 10 compared to generation 5 when using GS. This

reduction is a result of reduction in accuracy due to recombination breaking the

association between markers and QTL. Using denser marker panel for implementing GS

will ensure that marker-QTL association is sustained in later generation thus improving

accuracy of selection.

5.3.3 Inbreeding and genetic variance

Table 5.3 showed the average cumulated inbreeding and genetic variance at

generations 5 and 10 for different traits, breeding strategies and selection methods.

Inbreeding was higher with BLUP selection than GS at generations 5 and 10. The

inbreeding level at generation 10 across traits and populations was twice the value at

generation 5 for all selection methods. At generation 10, average inbreeding coefficients

for BLUP ranged from 0.04 to 0.27 across traits and populations while the range was 0.03

to 0.12 in the GS scheme. Inbreeding decreased with increasing trait heritability for both

selection methods. As expected, inbreeding was zero at generation 5 for most of the

crossbred populations due to crossbreeding which creates more heterozygosity in the

population. Genetic variances were considerably more reduced with BLUP selection

across traits and populations than with GS. The rate of reduction was slower for traits

with low heritability than for moderate to highly heritable traits as expected. Using GS in

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the pure breeding scenario reduced genetic variances faster in the unselected indigenous

population than in the selected exotic population.

In this study, inbreeding was calculated based on pedigree information and this is

an expectation assuming neutral loci, therefore, two alleles at the same neutral locus on

two homologous chromosomes have an equal chance of being selected. This ignores the

existence of linkage between marker allele and a QTL affecting a trait under selection.

With the availability of genomic information, inbreeding can be calculated such that the

expectation is adjusted with identity-by-state probabilities at the marker loci to yield

actual inbreeding at specific locations across the genome (Pong-Wong et al., 2001; Liu et

al., 2002). This suggestion could make a lot of difference in the inbreeding level reported

here. In addition, if GS can result in shorter generation interval in swine breeding then the

rate of inbreeding per unit of time might differ from what is expected.

The increase in inbreeding rate associated with BLUP selection is particularly

problematic for traits with low heritability. Under BLUP selection at low heritability

more weight is placed on relatives of the selection candidate and less weight is placed on

the individual‘s own record. This causes related candidates to have similar EBV and it is

more likely that relatives will be selected. Genome-wide selection can offset this

tendency through exploiting Mendelian sampling effects (Daetwyler et al., 2007). High

levels of inbreeding can result in inbreeding depression if non-additive genetic effects are

important. This study was simulated under a finite additive locus model and inbreeding

depression was not considered, however, it could be a real concern for some traits like

NBA in the nucleus population. Commercial pig production is based primarily on

crossbred sows and market pigs, and inbreeding depression in the parental lines, if it

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occurs, should be recovered through heterosis in the crosses (Visscher and Haley, 1995).

Increased inbreeding contributed to reduced genetic variation. Reductions in

genetic variances are expected with directional selection methods. This reduction is due

to gametic phase disequilibrium and inbreeding and both as a result of Ne and selection.

One advantage of using GS is the exploitation of new genetic variance through the

estimation of Mendelian sampling effects (Daetwyler et al., 2007). New variation is

expected to balance the loss of genetic variability due to gametic phase disequilibrium;

however, because the Mendelian sampling variance itself is also reduced by the loss of

alleles due to inbreeding, this equilibrium can never be reached in a dynamic finite

population.

5.3.4 Comparison of different selection methods

Figure 5.2 compared the various selection methods based on response, inbreeding,

LD and realized accuracy by generation in an unselected indigenous population for ADG.

Patterns of change were similar for the selected exotic populations. Genetic response

increased in each generation when directional selection was implemented on the

population, and was greater for GS, followed by BLUP and phenotypic selection (Figure

5.2A). As expected, there was no change in the randomly selected population.

No method of selection is free of inbreeding because inbreeding is a function of

Ne and can increase with selection (Figure 5.2B). In generations 1 and 2, the inbreeding

level was the same for all methods of selection, but increased much more quickly for

BLUP followed by GS. The rate of inbreeding per generation for phenotypic selection

was slightly higher than that of random selection. At the same level of inbreeding at

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generation 2, GS had slightly higher selection response than BLUP and phenotypic

selection (Figure 5.2A). The high inbreeding that occurs with BLUP selection is because

this method achieves high accuracy of EBV estimation through the use of additional

information on ancestors and collateral relatives resulting in sibs having similar EBV and

are therefore likely to be selected together leading to an increase in ΔFG. Thus, GS

provides a method for achieving both short-term goal of increased and sustained ΔG and

long-term needs for maintaining genetic variation while keeping inbreeding low.

LD is caused by gametic phase disequilibrium due to genetic drift and selection.

The results of average LD for the various selection methods showed that GS and BLUP

can move a population in approximate linkage equilibrium to a state of LD within a very

short time (Figure 5.2C). Similarly, phenotypic and random selection can create LD in

the population. Genome-wide selection can exploit the high LD between QTL and

molecular markers to increase genetic gain, but this is not true for BLUP and phenotypic

selection. Selection based on BLUP relies on accuracy of EBV which depends on trait

heritability and the amount of information available for EBV estimation, while

phenotypic selection depends solely on trait heritability in order to make genetic

progress. The presence of gametic phase disequilibrium created by BLUP can actually

slow down genetic progress (Bulmer, 1976), as evident in Figure 5.2A.

Accuracy of selection increased for all methods of selection in the first 2

generations but decreased thereafter (Figure 5.2D). The rate of reduction in accuracy after

generation 2 was faster for BLUP, but slower for GS and phenotypic selection. The slight

reduction in accuracy overtime for GS was attributed to the exploitation of Mendelian

sampling effects (Daetwyler et al., 2007) and LD. The reduction in accuracy for BLUP

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and phenotypic selection methods was due to a reduction in variances (Bulmer, 1976).

Accuracy was zero or near zero for random selection as expected.

5.3.5 Comparison of different breeding programs in a GS scheme

The implications of using GS in different breeding programs for improving ADG

are presented in Figure 5.3. Patterns of change are similar but differ in magnitude for

other traits. Genome-wide selection resulted in an increase in genetic response in pure

breeding and crossbreeding systems and in the short and long terms (Figure 5.3A).

Indigenous purebreds responded to GS better than the exotic purebreds and surpassed all

other populations at generation 10. The synthetic line had higher response to selection

than the purebreds in the short term. Backcrossing crossbreds to a previously selected

exotic population leads to higher genetic response while repeated backcrossing to the

inferior unselected indigenous population results in a negative response during the

backcrossing phase but a positive response during inter se mating with gains that are

comparable to the exotic purebreds obtained at generation 10.

Using GS in breeding programs aims to exploit markers in LD with QTL

affecting traits of interest. In an introgression scheme, favourable QTL in the donor line

are potentially selected and introgressed into a recipient population. After three

generations of repeated backcrossing, only about 6.25% (0.54) of the neutral alleles from

the donor line will be left in the backcross population. However, by using GS, any

favourable QTL alleles from the donor line that are rare or nonexistent in the recipient

line may be preserved at a frequency much higher than the expected value (Odegard et

al., 2009). Recently, Oseni (2005) compared different breeding schemes involving

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Nigerian indigenous pigs and exotic Large White pigs for litter size. The result showed

that backcrossing crossbreds to the exotic Large White breed gave higher performance

for litter size than purebreds.

The relative advantage of using crossbred lines depends on how far the parental

lines have diverged. With a longer time since divergence of lines, the larger the

advantage of crossbreeding in GS scheme (Toosi et al., 2010). Combining GS with

backcrossing of crossbreds to an unselected indigenous population is not expected to

yield appreciable response especially when the indigenous population is inferior for the

trait under selection and had very low LD for the markers. The higher genetic progress

observed in unselected indigenous population in this study was due to high

heterozygosity, genetic variance and trait heritability leading to increased accuracy of

selection and faster genetic gain than the selected exotic population (Figure 5.3A). The

use of dense markers as in this study will ensure that markers are in close association

with QTL. This finding suggests that as long as a high density marker panel is utilized

and the reference population size is large, faster genetic progress is anticipated for

indigenous pigs found in developing countries when implementing a genome-wide

selection scheme.

The rate of inbreeding by generation when GS was applied in different breeding

programs is presented in Figure 5.3B. Inbreeding rate was similar in the purebreds in the

first three generations, but increased faster in the indigenous purebreds at later

generation. The increased inbreeding in the indigenous population was associated with

the faster genetic progress achieved in this population by using GS. Genome-wide

selection can lead to fixation of favourable QTL and homozygosity. Inbreeding was zero

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in the crossbred lines undergoing repeated backcrossing and increased slightly in a linear

manner in synthetic lines.

Figure 5.3C presents the average LD between adjacent markers associated with

the different breeding programs when applying GS. Starting from a relatively low level of

LD at the initial crossbred generation, average LD for the backcross1 line was slightly

similar to LD for the exotic line from generations 3 upwards. The synthetic line also

showed increased LD, but did not exceed that of the exotic line. The purebred indigenous

and backcross2 line both had similar patterns of LD increases. The high LD level

observed in the backross1 line in the GS scheme explains the higher response to selection

observed in this population (Figure 5.3A). Odegard et al. (2009) studied the combination

of an introgression scheme with genomic selection via simulation. They concluded that a

backcross lines with similar or better performance than a purebred line can be obtained

within three to five generations of repeated backcrossing. The general increase in LD

level in the crossbreds arises from old LD in the parental lines and new LD created due to

crossing.

Realised accuracy over generations in a genome-wide selection program was

slightly stable in the purebreds but decreased rapidly in the synthetic and backcross

populations (Figure 5.3D). Genetic variation was slightly reduced in the GS scheme for

breeding programs simulated (data not shown). The slight reduction in genetic variance

was attributed to exploitation of Mendelian sampling effects which help to minimize

inbreeding and genetic variance (Daetwyler et al., 2007).

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5.3.6 Impact of trait heritability in a GS scheme

The influence of different trait heritability on cumulative response, inbreeding,

LD and realised accuracy when GS was applied in an indigenous population is given in

Figure 5.4. The traits studied included number born alive (NBA), average daily gain

(ADG) and back-fat thickness (BFT). These traits were chosen because they represent a

range of heritability estimates and also affect profitability of pig production in developing

countries. The genetic parameters used to simulate data for each trait are weighted

estimates based on a previous meta-analysis of genetic parameters for pig production

traits in tropical developing countries (Chapter 3). Selecting for low BFT based on GBVs

leads to higher genetic response and is followed by ADG and NBA (Figure 5.4A). The

higher response obtained for BFT is due to higher accuracy of phenotype used for

training the marker effects and higher heritability of the trait leading to more reliable

GBVs. In contrast, traits like NBA have faster rate of change when using GS due to

exploitation of Mendelian sampling variation at low heritability.

Inbreeding decreased slightly with increased trait heritability under a GS scheme

(Figure 5.4B). As in traditional BLUP selection where high emphasis is placed on sib

information for low heritability traits leading to co-selection of sibs and higher inbreeding

rate, traits with low heritability have higher inbreeding rate than moderate to highly

heritable traits in a GS scheme. Linkage disequilibrium in an indigenous population

starting at very low level increases slowly in a GS scheme and the trend was

comparatively similar for all traits (Figure 5.4C). Whereas recombination breaks down

LD, selection and genetic drift creates new gametic phase disequilibrium among marker

and QTL alleles. Realised accuracy was stable across generations and the trend was

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similar for all traits (Figure 5.4D). This suggests that genetic variation is maintained in a

GS scheme.

5.3.7 QTL minor allele frequency changes due to different factors

Figure 5.5A shows the effect of various selection methods on average QTL minor

allele frequency (MAF). A slight change in MAF when random selection was invoked is

due to genetic drift. For BLUP and phenotypic selection, small changes in QTL MAF

were observed. The BLUP and phenotypic selection methods are based on an

infinitesimal model which assumes that quantitative traits are controlled by large number

of genes each with a small effect on the trait (Fisher, 1918); consequently, changes in

allele frequency due to selection are expected to be small. A remarkable change in QTL

MAF was observed for GS. This is because GS acts directly on the QTL alleles in LD

with marker alleles that are utilised for selection and can cause more inbreeding at QTL

location compared to what can be captured by pedigree inbreeding analysis. When MAF

was similar for all directional selection methods at generations 1 and 2, the cumulative

response, inbreeding and LD levels in these generations were also similar (Figure 5.2).

This is because all of the population-wide variables studied depend on allele frequency

which can be changed significantly by any method that has a direct impact on QTL MAF.

The distribution of average MAF of QTLs for the different breeding programs

studied over 10 generations of genome-wide selection are presented in Figure 5.5B. The

change in MAF was relatively faster in the unselected indigenous purebreds than in the

previously selected exotic purebreds because at allele frequency close to 0.5, rate of

change is expected to be higher. Synthetic line showed a downward trend in MAF until

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generation 5 when MAF stabilised. This pattern of MAF distribution in synthetic lines

was due to genetic drift in this population. Average MAF was similar at generations 4

and 5 in the indigenous and backcross2 lines. This agrees with earlier findings that

generation 4 is an appropriate cut-off for a repeated backcrossing program (Odegard et

al., 2009), because QTL allele frequency is expected to be similar in the recipient and

backcross line at this time.

Changes in average MAF of QTLs are expected for various traits undergoing a

genome-wide selection program (Figure 5.5C). Selection on any trait can drive

favourable QTL alleles to fixation while reducing the frequency of the unfavourable

alleles.


This study has evaluated the application of GS in pig breeding programs

involving imported exotic and existing indigenous pig populations typical of tropical

developing countries. Given that the data recording and analysis infrastructure for

implementing the traditional BLUP selection method is generally lacking in developing

countries, GS provides an approach for achieving faster genetic progress without

developing a pedigree recording system. Genome-wide selection did better than

traditional BLUP and phenotypic selection methods by increasing genetic gain and

maintaining lower levels of inbreeding especially for traits with low heritability.

Genome-wide selection achieved this by exploiting LD and the Mendelian sampling

effects which provides opportunity for higher genetic progress while controlling rates of

inbreeding per generation.

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Indigenous pig populations in the tropics that have a low level of population-wide

LD can be improved genetically by using the available high density 60 K SNP panel

developed for the exotic populations to implement GS. This will ensure that marker-QTL

associations capture most of the genetic variability within a trait. Combining GS with

repeated backcrossing of crossbreds to the selected exotic population in moderate LD

promises to improve the genetic merit of the commercial population more rapidly than

the other methods considered in this study. This possibility will lead to the introgression

of favourable QTLs required for tropical pork production from the indigenous population

into the commercial line.

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Parameters Values

Genome size per chromosome 150 cM

Number of chromosomes 5

Marker density per cM 22

Number of segregating QTL per chromosome 50

Mutation rate of Marker locus 2.5 x 10-3

Mutation rate of QTL locus 2.5 x 10-5

Minor allele frequency 0.05

Distribution of additive QTL effects Gamma (shape = 0.4)

Traits

Number born alive (NBA) = 0.08; = 7.73

Average daily gain (ADG) = 0.28; = 10361.20

Back fat Thickness (BFT) = 0.63; = 20.88

Number of animals with phenotypic or genotypic records, or both

Training 1275

Prediction/Validation 1000

Number of replicates per scenario 50

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Table 5.2 Average cumulative responses (standard deviation units) after five and ten

generations of selection using a pedigree or genome-based methods for different traits

and breeding programs

Traits Populations Methods G5 G10 Δ1(%)G5 Δ(%)G10

NBA Exotic Pedigree 0.89 2.49

Genomics 2.02 4.20 126.97 68.67


Genomics 2.77 6.19 22.57 27.37


Genomics 3.64 6.50 45.02 30.52


Genomics 4.86 7.22 13.55 34.70

Backcross2 Pedigree -1.25 1.38

Genomics -1.19 2.54 -4.80 84.06

ADG Exotic Pedigree 1.05 3.02

Genomics 2.29 4.91 118.10 62.58


Genomics 3.89 8.48 14.08 17.61


Genomics 4.55 7.70 16.48 14.58


Genomics 5.38 8.03 11.39 33.83

Backcross2 Pedigree -1.10 3.21

Genomics -1.14 4.37 -3.64 36.14

BFT Exotic Pedigree -1.19 -3.39

Genomics -2.57 -5.44 115.97 60.47

Indigenous Pedigree -4.43 -9.39

Genomics -4.77 -10.39 7.68 10.65

Synthetic Pedigree -4.66 -7.89

Genomics -4.97 -8.47 6.65 7.35

Backcross1 Pedigree -5.11 -6.50

Genomics -5.53 -8.46 8.22 30.15

Backcross2 Pedigree 0.86 -4.97

Genomics 1.13 -5.38 -31.40 8.25 1Percentage change in genetic gain due to change in selection method

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Table 5.3 Average cumulated inbreeding and genetic variance after five and ten

generations of selection using a pedigree or genome-based methods for different traits

and breeding programs

Traits

Populations

Methods

Inbreeding Genetic variance

G5 G10 G5 G10

NBA Exotic Pedigree 0.14 0.27 0.37 0.30

Genomics 0.05 0.11 0.35 0.29

Indigenous Pedigree 0.08 0.19 0.64 0.55

Genomics 0.06 0.12 0.64 0.50

Synthetic Pedigree 0.07 0.19 0.73 0.43

Genomics 0.04 0.10 0.60 0.37

Backcross1 Pedigree 0.00 0.15 0.40 0.31

Genomics 0.00 0.04 0.40 0.31


Genomics 0.00 0.05 0.71 0.57

ADG Exotic Pedigree 0.14 0.24 15.64 12.82

Genomics 0.04 0.09 15.29 12.27


Genomics 0.05 0.10 41.63 31.52


Genomics 0.03 0.08 32.46 15.99


Genomics 0.00 0.04 17.75 12.73


Genomics 0.00 0.04 52.24 43.63

BFT Exotic Pedigree 0.12 0.22 0.65 0.50

Genomics 0.04 0.08 0.59 0.47


Genomics 0.04 0.09 2.67 1.96


Genomics 0.03 0.07 1.79 0.66


Genomics 0.00 0.03 0.73 0.51


Genomics 0.00 0.04 3.67 3.28

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Selection strategy began from T1 - T10 and based on Ne = 91 for all populations simulated. Inter se

mating of backcross population took place from T6 - T10

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Figure 5.2 Cumulative response (A), inbreeding (B), LD (C) and realised accuracy (D) for various selection methods in an unselected

indigenous population and for ADG

151

Figure 5.3 Cumulative response (A), inbreeding (B), LD (C) and realised accuracy (D) for different breeding programs using

genome-wide selection and for ADG

152

Figure 5.4 Cumulative responses (A), inbreeding (B), LD (C) and realised accuracy (D) for different traits in a genome-wide selection

scheme for unselected indigenous population

153

Figure 5.5 Average minor allele frequency (MAF) changes due to (A) different selection methods in an unselected indigenous

population for ADG, (B) different breeding programs in a genome-wide selection scheme and for ADG and (C) different traits in a

genome-wide selection scheme for unselected indigenous population

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CHAPTER 6

RELATIVE ECONOMIC RETURNS FROM SELECTION SCHEMES

FOR A NUCLEUS SWINE BREEDING PROGRAM


1, M. Sargolzaei

1, R. M. Friendship

2 and J. A. B.

Robinson1






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6.0 ABSTRACT

The objective of this study was: 1) to evaluate a two-step selection strategy

involving genome-wide selection (GS) versus phenotypic and pedigree-based selection

methods, and 2) to compare the differential returns from selection schemes in a nucleus

swine breeding program. Annual genetic gain and return per dollar invested were used as

evaluation criteria. Data for the study was generated by simulation of relevant target

populations typical of swine population structure in developing countries and assuming

that genetic improvement was concentrated on purebreds in a nucleus breeding program.

Selection of replacement candidates were based on selection indices constructed for all

traits in the breeding objectives for pig production in developing countries. Traits in the

selection index included number born alive, average daily gain, feed conversion ratio and

loin eye area. The result shows that accuracy of genomic indices for the pre-selection and

selection strategy ranged from 0.38 to 0.66 and were higher than the accuracy of

conventional selection indices (0.10 – 0.64). Genome-wide selection generated an

increase of about 38% to 172% in annual returns compared to other conventional

approaches for a population in moderate linkage disequilibrium. The expected return per

dollar invested in a GS scheme was comparable to conventional selection methods when

the cost of genotyping for 60 K single nucleotide polymorphism panel was $30 per pig.

This genotyping cost is realisable using genotype imputation techniques with lower

density, lower cost genotyping panels.

Keywords: Genome-wide selection, nucleus breeding program, differential returns,

simulation, developing countries

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6.1 INTRODUCTION

Nucleus breeding schemes (NBS) describe a breeding program in which selection

is carried out on a small number of elite breeding animals in a nucleus that produces

germ-plasm or breeding stock for use in a multiplier or commercial population. This

program has been proposed to overcome the technical constraints limiting the

implementation of genetic improvement of pigs for low-input production systems in

developing countries (Smith, 1988). The genetic superiority generated in the nucleus is

disseminated to the whole population. Genetic evaluation to select replacement stock is

implemented after a performance testing program has been completed for all possible

candidates (de Roo, 1987).

Performance testing is utilized to prevent biases in estimated breeding value

(EBV) caused by preferential treatment (Kuhn et al., 1994) and to avoid the impact of

heterogeneous variances in different environments (Garrick and van Vleck, 1987). The

performance testing of young boars and gilts is associated with a substantial cost, usually

an average of $55 per tested pig based on Canadian prices (B. Sullivan, personal

communication, 2011). This price is based on costs for animal registration, performance

testing, cost for marketing the non-selected intact males, costs for raising purebred

females and cost for genetic evaluation (B. Sullivan, personal communication, 2011).

Owing to the cost of performance testing, swine breeders often pre-select the animals to

be tested. In developing countries, pre-selection of candidates for performance testing is

based on knowledge of the phenotypic performance of their parents and when they have

attained certain age or body weight (Dzama, 2002). In addition, selection of replacement

157

stock from tested candidates is strictly on the pigs own performance excluding records of

its relatives (Aker and Kennedy, 1988: Dzama, 2002).

With the advent of genome-wide selection (GS) based on genomic breeding

values (GBV) predicted from dense single nucleotide polymorphism (SNP) marker

genotyping (Meuwissen et al., 2001), a genomic index (Dekkers 2007) of traits in the

breeding objective can be constructed for all possible candidates and used for pre-

selection of individuals entering the test station and for selection of replacement

candidates. Although, a variety of factors influence the outcome of a swine nucleus

breeding scheme (see for e.g. Figure 1 in de Roo (1987)), the objective of this simulation

study was not to consider all of these factors, but; 1) to evaluate a two-step selection

strategy involving genome-wide selection versus phenotypic and pedigree-based

selection methods, and 2) to compare the differential returns from selection schemes in a

nucleus swine breeding program. Two scenarios involving previously selected

populations in moderate linkage disequilibrium (LD) and unselected populations in low

LD were studied.


6.2.1 Simulation of populations

Data for this study were simulated following the approach described in section 5.2

of Chapter 5. The simulated genome consisted of 5 chromosomes of 150 cM each with

250 multi-allelic segregating quantitative trait loci (QTL) and a marker density of 22 bi-

allelic loci per cM evenly spaced across the genome to represent approximately 60 K

SNP marker panel that is currently available (Ramos et al., 2009). Two purebred lines

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consisting of a previously selected population in moderate LD between adjacent markers

(r2 = 0.29) and unselected population in low LD (r

2 = 0.05) served as the source of

foundation stock for the nucleus and for the two scenarios investigated. A 250 sow

operation that is capable of producing on average two litters per sow per year was

assumed. A total of 250 sows and 25 boars were randomly chosen from each of the

purebred lines to establish the foundation stock for the nucleus. These were randomly

mated to produce 20 piglets per sow per year resulting in a total of 5,000 piglets annually

assuming litter size and litter rate are already corrected for fertility and piglet survival.

Besides the nucleus population, another population was simulated for each trait

starting from the same base population as in the nucleus. This population was used as a

training set to estimate the marker effects to be used as priors for the application of GS in

the nucleus breeding scheme. This design eliminates the effect of close relationship

between the training set and the prediction set in the nucleus.

6.2.2 Traits in the model

The traits included in the breeding objective were number born alive (NBA),

average daily gain (ADG), feed conversion ratio (FCR) and loin eye area (LEA). The

genetic parameters used for simulating the data are presented in Table 6.1. Traits were

simulated independently assuming a finite additive locus model. No pleiotropy or

genotype-environment interaction was simulated. The genetic parameter estimates used

for simulation were from a meta-analysis study carried out for pig production traits in

tropical developing countries (Chapter 3). However, the economic values for each trait

159

were based on Canadian prices obtained from Canadian Centre for Swine Improvement

(CCSI) website. All cost figures are presented in Canadian dollars.

6.2.3 Model for genetic evaluation

In this era of genomics, it is reasonable to assume the availability of a repository

of marker effect estimates defined for specific locations on a given SNP panel and for a

given population. Using the same SNP panel, individuals can be genotyped at any time

and their GBVs obtained for selection decision based on prior estimates of marker effects

from a similar training population. The assumption is that marker density is high enough

to capture useful LD between markers and QTL leading to high accuracy of selection.

This is akin to the conventional method where genetic parameter estimates from other

sources are used as priors in genetic evaluation of a novel similar population in order to

obtain estimated breeding values (EBVs) for selection purposes.

The separate population used as training set to estimate the marker effects was

also evaluated using a pedigree-based best linear unbiased prediction (BLUP) model for

comparison. Comparisons were based on accuracy between true and estimated breeding

values for the training and validation populations. The training set consisted of 2,275

individuals and 15,100 segregating markers with minor allele frequency (MAF) ≥ 0.10,

while the validation set consisted of 5,000 breeding candidates produced annually in the

nucleus.

The pedigree-based BLUP model assumed was according to Henderson (1984).

The model used was

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y = μ1n + Za + e 6.1

where y is observed phenotype of size n, μ is the overall mean, 1n is a vector of n ones, a

is a vector of random additive genetic effects of each individual ~N (0, Aσ2

a), A is the

additive relationship matrix, Z is a design matrix for the additive genetic effects and e is a




2. The ASREML program (Gilmour et al., 2009) was applied for

estimation of genetic effects and variances.

For the genome-based model, the marker effects were estimated using ridge

regression described in Meuwissen et al. (2001) and extended by Fernando et al. (2007).

The statistical model was

y = μ1n + Wg + e 6.2

The mixed model equations (MME) were:

6.3

where y is observed phenotype of size n, g is a vector of SNP allele substitution effects

~N (0, I ), W is a design matrix that contains 0, 1 or 2 for the number of one of the

two alleles in the SNP genotypes, is the average base SNP heterozygosity, σ2

g is the

genetic variance estimated using ASREML program and m is the total number of

markers. The other parameters are as describe previously.

Once estimates of marker effects were obtained from the training data set, the

genomic breeding values of kth animal (GBVk) in the training and for the future

candidates in the nucleus (prediction set) was computed as

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GBVk = 6.4

where and are the recorded genotype (0, 1 or 2) and the estimated marker allele

substitution effect at the jth locus, respectively, and m is the total number of markers. For

predicting GBV of an individual in the two population scenarios, only about 15,100

segregating markers with MAF ≥ 0.10 in the selected population marker panel were used.

6.2.4 Pre-selection strategy

All individuals in the rearing piggery were pre-selected to enter into the test

station for performance testing (Figure 6.1). Three pre-selection strategies were applied

namely: 1) phenotypic selection which uses a phenotypic index (PI) constructed from

parent average phenotypic performance of all individual‘s in the rearing piggery as

follows:

PI = 6.5

where vl is the economic weight of trait l, Pl is an individual‘s parent average of

phenotypic performance for trait l, is the mean parent average of phenotypic

performance for trait l, is the trait heritability and t is the number of traits in the index,

2) pedigree selection based on selection index (SI) calculated for each individual as

follows:

SI = 6.6

where vl is the economic weight of trait l, PAl is the predicted parent average breeding

values of an individual for trait l obtained by using the BLUP model in equation 6.1 and t

is the number of traits in the index, and 3) genome-wide selection based on a genomic

index (GI) constructed for all individuals in the rearing piggery as follows:

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GI = 6.7

where vl is the economic weight of trait l, GBVl is the genomic breeding values of an

individual for trait l predicted using equation 6.4 assuming that estimates of marker

effects were available from a similar training population and t is the number of traits in

the index. All piglets in the rearing piggery were assumed to be genotyped for the same

SNPs as in the training population. A total of 1,000 top ranking individuals made up of

male/female candidates were pre-selected for performance testing based on the selection

indices described previously.

6.2.5 Selection criteria

In order to predict genetic gain per year for the whole operation to compare

between selection schemes implemented, records were assumed to be available for all

performance tested candidates at the end of the test and for all traits studied. Three

selection criteria were applied namely; 1) phenotypic index based on an individual‘s own

phenotype, 2) selection index based on BLUP evaluation of animals with observation and

3) genomic index based on marker effects re-estimated for all traits using phenotype from

performance testing. The accuracy of selection was determined as correlation between the

index and the aggregate genotype (H: the product of the economic value and true

breeding value for each trait summed across all traits in the breeding objective) for all

tested candidates.

In another scenario for the genome-wide selection scheme, accuracy of GI for

pre-selection strategy was applied to predict genetic gain per year. This allowed for the

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evaluation of a strategy that completely eliminates performance testing program

assuming that marker effects will continue to be available from other sources.


Assuming that 5% of the young boars and 50% of gilts were selected annually as

replacement stock from all tested candidates, expected genetic gain per pig per year was

predicted as follow:

6.9

where i is the selection intensity derived based on the proportion of each sex selected, rHI

is the correlation between the aggregate genotype and the selection index in males and

females, L is the generation interval which was assumed as 1.8 years based on Canadian

average and σH is the standard deviation of the aggregate genotype which was derived by

solving equation 6.9 using parameters from Table 6.1 and assuming that covariance

between traits is zero.

6.9

where v is the economic weight and is the genetic variance of traits in the aggregate

genotype. The expected standard deviation of the aggregate genotype (σH) worked out to

be $178.95. Return per year was calculated by multiplying by 5,000 assuming

that the nucleus produces 5,000 improved breeding stocks annually. Depending on the

scenario evaluated, total cost involved in the operation included cost of genotyping 5,000

breeding candidates for pre-selection and the cost of performance testing 1,000 pre-

selected candidates. The return on investment was obtained as total returns minus total

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cost while return per dollar invested was obtained by dividing return on investment by

total cost. All analyses were coded in R statistical software (Ihaka and Gentleman, 1996)

and each scenario was replicated ten times.


6.3.1 Accuracy of selection

The accuracy of EBVs in the training and validation sets when using the two

different selection methods (pedigree and genomics) and for traits in the breeding

objective is given in Table 6.2. Accuracy tended to increase with increasing heritability

of the traits across methods and populations. Genome-wide selection had higher accuracy

than pedigree-based method in the training and validation sets and for all traits in the

breeding objective. The validation result for the pedigree method was based on parent

average of individuals in the nucleus. Previously selected population in moderate LD had

lower accuracy than unselected population in low LD for different traits and selection

methods due to differences in selection pressure which reduced the genetic variation in

the selected population (Bulmer, 1976) and also due to difficulty in the estimation of

marker allele substitution effects at low MAF in the selected population. In addition, GS

method had higher accuracy than the pedigree method because GS acts directly on the

QTL in LD with markers to improve accuracy (Habier et al., 2007). The accuracies of

genetic evaluation presented in this study for different trait heritabilities were in

agreement with results from previous study (Chapter 4).

Table 6.3 presents the correlation (rHI) between aggregate genotype and various

selection indices applied for pre-selection and selection strategies. Accuracy of genomic

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index (GI) for pre-selection strategy was similar to the average accuracy based on a

single trait (Table 6.2). For a GS scheme, the accuracy of the GI is expected to be slightly

lower than the accuracy based on a single trait (Sitzenstock et al., 2010) because of the

weighting effect of the economic values on the trait breeding values. Similar trend was

observed when comparing accuracies of selection index to the average accuracy for a

single trait in the pedigree-based BLUP method. The accuracy of GI in the selected

population was slightly lower than the values observed for unselected population due to

differences in selection pressure. The accuracy of GI for the unselected population was

similar to the values reported by König and Swalve (2009) and Sitzenstock et al. (2010)

in simulation studies.

For pre-selecting candidates entering the test station, accuracy of GI used was

higher than the accuracy of conventional methods in the selected and unselected

populations (Table 6.3). The pre-selection of candidates using the conventional approach

had lower accuracy because they were constructed based on parent average information.

In real life, candidates for pre-selection have no records of their own to allow for

construction of selection or phenotypic indices. Swine breeders in developing countries

usually pre-select candidate for performance testing based on knowledge of their parents

performance (Dzama, 2002). For example, a breeder will pre-select a candidate for

performance test if it comes from a sow with greater than 10 piglets in a litter or gilt with

greater than 7 piglets in a litter. The availability of genomic information early in life of

selection candidates offers the opportunity to make selection decision that is accurately

informed on the basis of an individual‘s genetic merit.

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Furthermore, accuracy of genomic index for pre-selection strategy was greater

than the accuracy of phenotypic and BLUP selection indices when records became

available in the nucleus for genetic evaluation (Table 6.3). This is because GS uses dense

marker panel to exploit LD between marker and QTLs affecting traits in the index

leading to improved accuracy of selection. When marker allele substitution effects were

re-estimated for all traits using the record of performance of 1,000 breeding candidates

from the test station, accuracy of GI was higher than the accuracy of conventional

selection indices in the selected population but slightly lower than accuracy of BLUP

selection index in unselected population. This decrease in rHI value in the unselected

population when marker effects were re-estimated was due to the sample size used in the

re-estimation (1,000 vs. 2,275 individuals used previously for training) such that QTL

allele effects were likely estimated with lower accuracy due to lower average MAF.

Accuracy of GS is expected to increase when re-estimating marker allele effects from a

large reference dataset of greater than 2,000 individuals (Chapter 4).

The expected accuracy of phenotypic index currently employed in nucleus test

stations in developing countries was lower than the accuracy for conventional BLUP and

genome-wide selection methods and was greatly reduced in the selected population than

in the unselected population. For all methods of selection, the accuracy of selection

indices was similar in males and females because neither the young boars nor the gilts

have progeny with records. When parent records were included in the evaluation, males

had higher accuracies than females due to more information on the sire pathway (result

not shown). Under a GS scheme, genetic evaluation can proceed on selection candidates

based on their own genotype and phenotype information without requiring knowledge of

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their parent records (Meuwissen et al., 2001) which is not the case for a pedigree-based

method.

As in developed countries, non successful attempts have been made over the past

two decades to institute a pedigree recording system that will allow traditional selection

strategies for swine improvement in developing countries (Kahi et al., 2005), NBS was

proposed as an alternative by selecting within a nucleus using conventional approach

(Smith, 1988). Performance testing of individuals in the nucleus was an additional

strategy to compare the genetic merit of potential candidates for selection of replacement

stock. Under a performance testing program, the use of an individual's own performance

as a measure of its genetic merit is reliable for traits of high heritability like loin eye area

and average daily gain. An individual‘s estimated genetic merit can further be improved

by including the records of its relatives in the genetic evaluation program. However, it

becomes problematic when dissecting an animal genetic merit for a trait with low

heritability like number born alive. More records would need to be collected in order to

accurately estimate breeding value for such a trait. In addition, developing a recording

system for implementing a conventional BLUP method can be very costly and may be

impracticable for developing countries.

As shown in Tables 6.2 and 6.3, the availability of genetic markers will help to

improve the accuracy of selection for traits with low heritability and will provide GBV

early in life of selection candidates which offers an opportunity to reduce the cost of

performance testing. In real life more records will need to be collected from performance

tested candidates and added to the records from training set for re-estimation of marker

effects in order to improve accuracy of selection. As an alternative, GS can be

168

implemented based on accuracy of pre-selection strategy assuming that estimates of

marker effects will continue to be available from other sources which eliminates

completely the practice of performance testing by keeping only individuals with GI that

are above the threshold for selection. The selected candidates are then raised to

reproductive maturity and can be mated early in life, thus reducing both generation

interval and the cost of housing and performance testing. In addition, by preselecting

individuals that enter the test station, potential candidates can further be evaluated for

secondary traits such as conformation and health. However, will farmers accept genomic

indices as a selection criterion without having a prior knowledge of the performance of

selection candidates in a test station? This will be the crucial point for practical

implementation of GS in a nucleus swine breeding program in future.

6.3.2 Expected genetic change per pig per year

The expected genetic gain per pig per year in the nucleus is instrumental in

showing the differences between the various selection methods employed (Table 6.4).

For the selected population in moderate LD, using GS based on re-estimated marker

effects to select replacement stock will achieve about 132% and 17% greater returns per

year compared to current phenotypic selection method and a possible pedigree-based

approach, respectively. The expected increase in returns of 17% was less than the 37%

increase in returns reported by Simianer (2009) using a deterministic approach to

evaluate GS. When GS was used to replace phenotypic selection in an unselected

population in low LD, annual returns was increased by 43% but was reduced by 3% when

substituting for a pedigree BLUP method. This outcome supports the fact that having

169

sufficient level of LD in the population of interest is central to the functionality of GS.

For population with low LD, a higher marker density than 60 K SNP panel current

employed in this study may lead to more accurate GBV.

Under a nucleus breeding program, it is possible to select replacement stock

directly from the rearing piggery based on accuracy of GS used in the pre-selection

strategy. The expected change in genetic gain from such a GS approach will be 172% and

38% greater returns compared to phenotypic and pedigree BLUP method, respectively, in

selected population in moderate LD (Table 6.4). For unselected population in low LD, a

greater return of 50% and 2% compared to phenotypic and pedigree BLUP methods,

respectively, can be expected when GS is applied based on pre-selection accuracy. In

addition, the possibility of re-estimating marker allele substitution effects based only on

small data set from the test station will lead to an expected reduction in annual returns of

15% and 5% for selected and unselected populations, respectively. Because

recombination events due to exchange of genetic material during meiosis can change

marker-QTL phase configuration leading to a reduction in accuracy of GS and

consequently, a reduction in the expected annual returns, marker effects should be re-

estimated with large datasets (>2000 animals, Chapter 4) whenever possible and after two

generations of selection.

In the study by Schaeffer (2006), annual genetic gain was doubled because of a

substantial reduction of generation intervals in three of the four pathways of selection in

dairy cattle. Reduction of generation interval might be unfeasible with swine but by

increasing the accuracy of genomic indices through the use of denser marker panel,

annual genetic gain can be increased and this will offset the cost of genotyping animals.

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6.3.3 Differential returns from selection schemes in a nucleus breeding program

By taking into account the cost of genotyping service and performance testing, the

differential returns from various selection methods in a nucleus swine breeding program

were compared (Tables 6.5 and 6.6). Depending on the selection method used, the pre-

selection of 1,000 pigs that entered the test station out of 5,000 pigs produced annually in

the nucleus offered an opportunity to reduce the cost of performance testing. The

remaining 4,000 pigs were expected to be sold to multipliers as additional returns.

However, since accuracy of pre-selection differed with the method applied, it is possible

to choose different breeding candidates to be performance tested by these methods.

Consequently, good breeding candidates may be sent to the market rather than been fully

performance tested. This is why it is important to establish an accurate method for pre-

selection. Genome-wide selection offers an opportunity to have accurate GBV early in

life of selection candidates and this will have an impact on expected rate of annual

returns.

The total cost involved in the GS scheme when marker effects were re-estimated

consisted of the cost of genotyping 5,000 pigs in order to obtain GBV for pre-selection

and the cost of performance testing 1,000 pre-selected candidates. Starting at the current

cost of genotyping for 60 K SNP panel which is around $120 per pig at DNA Landmarks

(M. McNairnay, personal communication, 2011), several genotyping cost were evaluated

by dropping the price by $30 up to a minimum price of $15 per pig. This provides an

opportunity to find the optimum genotyping cost that will guarantee faster return per

dollar invested when using GS at the current marker density of 60 K SNP chip. Cost of

genotyping for thousands of marker is known to have reduced by a rate of 33% over a

171

three year period (Simianer, 2009). In this study prior estimates of marker effects were

assumed available at no extra cost but in reality there is a cost. In the scenario where GS

was applied based on accuracy of pre-selection strategy, cost of performance testing was

ignored.

The total cost involved in the phenotypic and pedigree-based methods was only

the cost of performance testing valued at about $55 per pig for 1,000 tested candidates

based on prices from a Canadian test station (B. Sullivan, personal communication,

2011). For the pedigree-based method, it was assumed that the infrastructure for

implementing this method already existed. In tropical developing countries, where the

infrastructure for swine genetic improvement using the conventional BLUP method are

lacking, instituting a pedigree recording system for this environment will be a source of

additional cost which can be very substantial.

The results presented in Table 6.5 suggests that if genotyping cost for 60 K SNP

chip currently at $120 per pig can be reduced to $15 per pig, GS scheme will generate

similar returns per dollar invested compared to phenotypic selection in a nucleus breeding

program that uses a previously selected population in moderate LD. For the unselected

population in low LD, GS method achieved a lower return than phenotypic selection at

the same genotyping cost of $15 per pig. This suggests the importance of having LD in

the population in order to apply GS. Also for all range of genotyping costs per pig, the

GS scheme had lower returns per dollar invested compared to the pedigree BLUP method

for the selected and unselected populations, respectively.

In the scenario where GS was applied based on accuracy of pre-selection strategy

(Table 6.6), reduction of genotyping cost to $15 per pig will lead to an increase of $4.32

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and 11 cents per dollar invested compared to phenotypic and pedigree BLUP methods,

respectively, in previously selected population. At a genotyping cost of $30 per pig, GS

can generate similar expected returns per dollar invested compared to currently practiced

phenotypic selection in previously selected population. For unselected population,

genotyping cost will need to be reduced to $15 per pig for the expected returns from GS

to be higher than phenotypic selection by $1.14. Presently, the cost of genotyping can be

reduced by genotype imputation (Howie et al., 2009; Sargolzaei et al., 2011). In a recent

study in dairy cattle, Sargolzaei et al. (2011) imputed 50 K genotypes from 6 K SNP

panel with accuracy of about 99%. The cost of genotyping an animal for the 6 K SNP

panel is around $40 at DNA Landmarks (M. Sargolzaei, personal communication, 2011).

This technology can be applied to swine breeding, thus reducing the genotyping cost.

A major concern while genome-wide selection is conjectured to be disadvantaged

compared to conventional approach is the cost of genotyping animals for millions of

marker loci. As genotyping cost decreases and marker density that is greater than 60 K

SNP panel assumed in the current study become available in future, QTL allele effects

are expected to be estimated with more precision due to higher LD and leading to higher

accuracy of selection and greater returns per dollar invested. The overall results from this

study indicates that, although the returns per dollar invested are greater for conventional

BLUP selection method ignoring cost of instituting a pedigree recording system for

developing countries, the marginal rates of returns to selection using GS are expected to

be greater as cost of genotyping for 60 K SNP panel become reduced to $30 per pig or as

next generation sequencing technology become feasible for swine breeding in future.

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Relative economic returns from the implementation of genome-wide selection in a

nucleus swine breeding program are possible. Benefits arise from an increase in accuracy

of genomic indices and the opportunity to eliminate performance testing costs.

Genotyping costs are critical to the return on investment in a GS scheme. Through the use

of lower density, lower cost of genotyping panels and imputation of genotypes equivalent

to a high density panel, genotyping costs can be controlled with no appreciable loss in

genetic response provided the population is in reasonable LD. For unselected populations

in low LD, denser marker panels will need to be used in order to increase accuracy of

selection. In addition, individuals entering a performance test station in a nucleus

breeding program can be pre-selected with higher accuracy by using a genomic index

compared to selection indices that are based on parent average information.

In a nucleus breeding scheme and in countries where a recording system for

implementing a conventional BLUP selection scheme are lacking, genome-wide selection

can supplant the development of this infrastructure. Increasing the accuracy of genomic

indices in the future through the use of denser marker panels and large training

population sizes will have a greater impact on the economic efficiency of GS and this

will offset the cost of genotyping animals. Selection of breeding candidates from the

rearing piggery based on genomic indices in a pre-selection strategy can possibly be

implemented. However, will farmers accept genomic indices as a selection criterion

without having a prior knowledge of the performance of selection candidates in a test

station? This will be the crucial point for practical implementation of GS in a nucleus

swine breeding program in future.

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Figure 6.1 Decision point in a nucleus swine breeding program

175

Table 6.1 Traits in the breeding objective and their parameters

Variables NBA ADG FCR LEA

Heritability 0.08 0.28 0.32 0.51

Standard deviation (SD) 2.78 101.79 0.35 3.85

Economic value, $ 18.07 3.02 -5.17 1.13

NBA = number born alive; ADG = average daily gain; FCR feed conversion ratio;

LEA = loin eye area

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Table 6.2 Accuracy1 of estimated breeding values of pigs in the training and validation

sets for traits in the selection index

Populations Traits Methods Training set Validation set2

Selected NBA Pedigree 0.36±0.011 0.10±0.024

Genomics 0.49±0.021 0.43±0.029

ADG Pedigree 0.41±0.023 0.15±0.018

Genomics 0.54±0.016 0.47±0.016

FCR Pedigree 0.40±0.019 0.13±0.019

Genomics 0.51±0.019 0.46±0.021

LEA Pedigree 0.45±0.022 0.19±0.023

Genomics 0.58±0.020 0.52±0.027

Unselected NBA Pedigree 0.47±0.018 0.19±0.018

Genomics 0.55±0.016 0.44±0.014

ADG Pedigree 0.69±0.012 0.40±0.024

Genomics 0.77±0.011 0.67±0.018

FCR Pedigree 0.70±0.007 0.42±0.024

Genomics 0.78±0.006 0.68±0.016

LEA Pedigree 0.80±0.005 0.50±0.021

Genomics 0.85±0.003 0.74±0.008 1Correlation between true and estimated breeding values averaged over 10 replicates

2Validation result for pedigree method is based on parent average of individuals in the nucleus

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Table 6.3 Accuracy of selection indices (rHI) for two steps selection strategies using

different methods and for different populations in a nucleus swine breeding program

Populations Methods Group Pre-selection Selection

Selected Phenotypic Males 0.10±0.006 0.17±0.014

Females 0.11±0.009 0.16±0.012

Pedigree Males 0.12±0.018 0.33±0.046

Females 0.13±0.020 0.33±0.042

Genomics Males 0.45±0.019 0.39±0.026

Females 0.47±0.019 0.38±0.028

Unselected Phenotypic Males 0.33±0.019 0.44±0.015

Females 0.32±0.013 0.42±0.034

Pedigree Males 0.36±0.024 0.64±0.020

Females 0.36±0.024 0.64±0.021

Genomics Males 0.65±0.019 0.62±0.016

Females 0.66±0.018 0.62±0.019

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Table 6.4 Expected genetic change per year for different selection schemes and

populations

Populations Methods ΔG/pig/year ($) 1 Return ($) Δ (%)

2 Δ (%)

3

Selected Phenotypic 47.54 237,705.25 131.58 172.48

Pedigree 93.83 469,147.25 17.33 38.06

Genomics 110.09 550,470.08 - -

PreGBV 129.54 647,699.58 - -

Unselected Phenotypic 123.52 617,576.33 42.72 50.27

Pedigree 181.97 909,861.33 -3.12 2.00

Genomics 176.29 881,428.17 - -

PreGBV 185.61 928,054.58 - - 15% of the males and 50% of the females were assumed to be selected annually

2Percent change in annual returns due to the use of GS based on marker effects re-estimation

(Genomics) 3Percent change in annual returns due to the use of GS based on pre-selection accuracy (PreGBV)

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Table 6.5 Differential returns from different selection methods and for a range of genotyping cost in a nucleus breeding program

Populations

Methods

Genotyping

cost/pig ($)

Total returns ($)

Total costs ($)1

Return on

investment ($)

Return per dollar

invested ($)

Selected Phenotypic 0 237,705.25 55,000.00 182,705.25 3.32

Pedigree 0 469,147.25 55,000.00 414,147.25 7.53

Genomics 15 550,470.08 130,000.00 420,470.08 3.23

Genomics 30 550,470.08 205,000.00 345,470.08 1.69

Genomics 60 550,470.08 355,000.00 195,470.08 0.55

Genomics 90 550,470.08 505,000.00 45,470.08 0.09

Genomics 120 550,470.08 655,000.00 -104,529.92 -0.16

Unselected Phenotypic 0 617,576.33 55,000.00 562,576.33 10.23

Pedigree 0 909,861.33 55,000.00 854,861.33 15.54

Genomics 15 881,428.17 130,000.00 751,428.17 5.78

Genomics 30 881,428.17 205,000.00 676,428.17 3.30

Genomics 60 881,428.17 355,000.00 526,428.17 1.48

Genomics 90 881,428.17 505,000.00 376,428.17 0.75

Genomics 120 881,428.17 655,000.00 226,428.17 0.35 1Total cost for GS scheme includes cost of performance testing 1000 candidates

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Table 6.6 Differential returns from selection when GS scheme is based on pre-selection accuracy in a nucleus breeding program

Populations

Methods

Genotyping

cost/pig ($)

Total returns ($)

Total costs ($)1

Return on

investment ($)

Return per dollar

invested ($)

Selected Phenotypic 0 237,705.25 55,000.00 182,705.25 3.32

Pedigree 0 469,147.25 55,000.00 414,147.25 7.53

PreGBV 15 647,699.58 75,000.00 572,699.58 7.64

PreGBV 30 647,699.58 150,000.00 497,699.58 3.32

PreGBV 60 647,699.58 300,000.00 347,699.58 1.16

PreGBV 90 647,699.58 450,000.00 197,699.58 0.49

PreGBV 120 647,699.58 600,000.00 47,699.58 0.08

Unselected Phenotypic 0 617,576.33 55,000.00 562,576.33 10.23

Pedigree 0 909,861.33 55,000.00 854,861.33 15.54

PreGBV 15 928,054.58 75,000.00 853,054.58 11.37

PreGBV 30 928,054.58 150,000.00 778,054.58 5.19

PreGBV 60 928,054.58 300,000.00 628,054.58 2.09

PreGBV 90 928,054.58 450,000.00 478,054.58 1.06

PreGBV 120 928,054.58 600,000.00 328,054.58 0.55 1Total cost for GS scheme excludes cost of performance testing

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CHAPTER 7

GENERAL DISCUSSION AND CONCLUSION

E. C. Akanno



182

7.1 SUMMARY

Genetically improving the productivity of indigenous swine populations in the

tropics is important. This improvement can ultimately contribute to poverty alleviation

and food security for the ever increasing human population in developing countries. A

lack of knowledge of genetic improvement strategies has remained a major limitation to

genetically improving indigenous swine populations by limiting the decision-making

capability of both national and regional improvement agencies. This thesis has focused

on the evaluation of genome-wide selection as a strategy to overcome such constraints

that limit the genetic improvement of indigenous pig populations in tropical developing

countries.

The development of swine genetic improvement programs for tropical developing

countries requires an understanding of the constraints on productivity and profitability.

Chapter 2 reviews the current challenges limiting the implementation of genetic

improvement programs for tropical swine populations. Given that the infrastructure for

implementing traditional BLUP selection methods is lacking in these regions, the

application of a genome-wide selection program (GS) based on the available 60 K single

nucleotide polymorphism (SNP) marker panel was proposed. Genome-wide selection can

be adapted to existing tropical pig breeding systems and has the potential to increase

genetic gain by increasing the accuracy of genomic breeding values (GBV) of potential

selection candidates.

In Chapter 3, genetic parameters for economically important traits of indigenous

and exotic pigs determined under tropical conditions were meta-analyzed. A random

effect model that incorporated ―between‖ and ―within‖ study variance components was

183

used to obtain weighted means and variances for all parameter estimates. The weighted

mean estimates of genetic parameters presented in this study are recommended for use

when reliable estimates are not available for a specific indigenous swine population.

Where estimates within a population are available, these could be combined with the

weighted literature average by simply weighting each estimate by the inverse of the

sampling variance. This would yield a population-specific estimate that incorporates prior

values based on much more data. Also, these results should allow researchers in

developing countries to extract genetic parameter estimates suitable for a wide range of

tropical conditions. The data base of published parameters from the tropics and the

accompanying references are available for extraction of specific estimates in portable

format from the author.

The application of a genome-wide selection program as opposed to the

development of infrastructure for implementing a traditional pedigree and performance-

based selection method for pig genetic improvement in developing countries was

evaluated in Chapters 4 and 5 using simulation.

The results of genomic predictions presented in Chapter 4 showed that

accuracies of GBVs in the range of 0.31 - 0.86 are expected for validation populations

with low linkage disequilibrium (LD) levels and a previous selection history as long as a

high density panel of markers was utilized. High accuracy in the indigenous population

will require a multi-generational reference population with a size of not less than 2,000

individuals and marker density of greater than 60 K SNP panel. However, if resources are

a limitation in choosing an appropriate reference population, then a population size of at

least 1,000 individuals is recommended, but this will require re-estimation of marker

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effects after two generations of selection. Genome-wide selection improved accuracy of

breeding value prediction over pedigree-based methods for traits with low heritability and

for young animals with no performance data. Crossbred training populations had higher

accuracy than purebred training populations. One way to implement genome-wide

selection in the tropics is to train the prediction model based on crossbred performance in

the field for genetic improvement of purebreds in the nucleus.

Chapter 5 presents an assessment of the opportunities for genome-wide selection

scheme in tropical pig breeding programs. Genome-wide selection performed better than

existing conventional methods by increasing genetic gain and maintaining genetic

variation while lowering inbreeding especially for traits with low heritability. Genome-

wide selection achieves this by exploiting LD and the Mendelian sampling effects. An

indigenous pig population with very low LD level can be improved by using GS if a high

density marker panel is available for use. Combining GS with repeated backcrossing of

crossbreds to the selected exotic population in moderate LD promises faster genetic

improvement of the commercial population.

A two-step selection strategy that involves the use of GS to pre-select candidates

that entered the performance test station and for selecting replacement candidates in a

nucleus swine breeding program was evaluated and compared to other conventional

approaches in Chapter 6. Benefits from a GS scheme arise from an increase in accuracy

of genomic indices. However, a dramatic reduction in the cost of genotyping for the 60 K

SNP panel and potential elimination of performance testing cost for a population in

moderate LD is required for a cost effective implementation of a GS scheme. Individuals

entering a performance test station in a nucleus breeding program can be pre-selected

185

with higher accuracy by using a genomic index compared to selection indices based on

parent average information. Genome-wide selection generated an increase of 38% to

172% in annual returns compared to other conventional approaches for previously

selected populations in moderate LD and about 2% to 50% increases in return for

unselected population in low LD. Selection of breeding candidates directly from the

rearing piggery based on genomic indices for a pre-selection strategy can be

implemented. However, will farmers accept genomic indices as a selection criterion

without having a prior knowledge of the performance of selection candidates in a test

station? This will be the crucial point for practical implementation of GS in a nucleus

swine breeding program in future.

7.2 RECOMMENDATIONS

From the foregoing, this thesis makes the following recommendations:

1. Despite the large number of genetic parameter estimates reported in the literature

for pig production traits in the tropics, some economically important traits like

carcass and meat quality traits have fewer parameters reported, and there is a need

for a complete set of genetic parameters, especially for the indigenous pig

populations.

2. There are good prospects for genome-wide selection in pig genetic improvement

in developing countries as breeding values of selection candidates can be

estimated with high accuracy without using pedigree information.

3. Genome-wide selection schemes can supplant the development of conventional

BLUP selection methods for genetic improvement of unselected indigenous pig

186

populations, but this will require a denser than 60 K SNP marker panel and a

multi-generational training set of at least 1,000 individuals.

4. Combining genome-wide selection with repeated backcrossing of the crossbred

population to the selected exotic pig promises rapid genetic improvement of the

commercial population.

5. In a nucleus breeding program, individuals entering a performance test station can

be pre-selected with higher accuracy using a genomic index compared to a

conventional selection index that is based on parent average information.

6. Potential breeding candidates can be selected directly from the rearing piggery

without undergoing performance testing but this assumes that there would be a

suitable training set for providing the estimates of marker effects for a given SNP

panel.

7.3 FUTURE DIRECTIONS

There are a number of challenges to the widespread implementation of GS in

developing countries. The feasibility of GS for genetic improvement of indigenous and

exotic pig populations found in tropical environments needs to be evaluated by

considering both the increase in genetic gain that is achievable with GS over

conventional selection methods and the extra cost involved in genotyping individuals for

a high density marker panel. The economic returns associated with genetic progress must

outweigh the cost of genotyping and phenotyping for GS to be profitable.

In a swine population, assuming selection intensity is constant, increases in

annual genetic gain from GS are expected to come from increases in accuracy of GBVs

187

used for selecting parents of future generation. The potential of GS to appreciably

decrease generation interval in swine is limited because generation interval is already

short. Therefore, in the application of GS on real populations, methods that will guarantee

higher accuracy of GBVs should be explored.

Predictive formulae have shown that a large multigenerational dataset of greater

than 2,000 individuals is required as a training set to get high GS accuracy in the

prediction population (Chapter 4 this thesis; Hayes et al., 2009). Usually only a limited

number of phenotypes and genotypes are available, particularly for traits of low

heritability like reproduction and health traits. However, it is exactly these traits for

which GS could create the greatest benefit. Increasing the sample size of the training set

requires consideration of the cost of both genotyping and phenotyping. In developing

countries where a coordinated system for data collection is lacking, it may be difficult to

collect a large number of accurate phenotypic records to achieve high GS accuracy. An

increased effort to obtain large numbers of accurate phenotypes in order to reach high GS

accuracy is desirable.

The accuracy of GS can be increased by using high density markers that are well

distributed across the genome to allow for marker-QTL association. While the cost of

genotyping for a high density marker panel is expected to decrease in future, it still may

be too costly to justify widespread genotyping in swine populations where annual genetic

gain is dependent on accuracy of GS alone. Lower cost solutions that will enable swine

breeders in developing countries to benefit from the expected extra gains from GS

method should be developed. One such solution is to use a low cost, low density panel

and then impute missing genotypes to recreate a higher density panel. Recent studies

188

showed that a high density panel can be imputed from a low density panel with 99%

accuracy (Howie et al., 2009; Sargolzaei et al., 2011). Genotyping cost would be reduced

because selection candidates would only need to be genotyped with a low cost, low

density panel. Therefore, genotype imputation could provide a lower cost solution to

increase the number of genotypes. However, successful imputation of genotypes requires

knowledge of the pedigree of genotyped individuals. In developing countries, absence of

coordinated pedigree recording system may limit this effort. In swine nucleus breeding,

the typical use of a relatively small number of founders would likely create a population

structure suitable for imputation (Daetwyler, 2009).

The model used in the simulation of traits for genomic evaluation assumes finite

additive locus effects. No dominance or epistatic effect at each locus or pair of loci was

considered. As is often the case, this simplified model was necessary to make the

simulation computationally tractable. In litter bearing species like swine, and with

crossbreeding programs, dominance effects are known to exist for traits with low and

moderate heritability (Oseni, 2005; Ilatsia et al., 2008). The inclusion of dominance

effects in models for the prediction of GBVs could increase the accuracy of genomic

predictions if the data structure allows it (Wellmann and Bennewitz, 2010). Moreover,

the predicted dominance or epistatic effects could be used to choose mating pairs to

maximise heterozygosity thereby recovering inbreeding depression and utilizing possible

overdominance (Wellmann and Bennewitz, 2010).

Based on results from the current study (Chapters 4 and 5), higher accuracy of

genomic predictions were associated with higher genetic variation in the training

populations. For indigenous pig populations in tropical developing countries that have

189

experienced mild selection pressure in the past, sufficient genetic variation exists to drive

selection (Chapter 3). Consequently, using the indigenous pig population as a reference

population will increase accuracy of GS if implemented in a population with similar

structure. In addition, crossbreeding of indigenous and exotic pig populations will lead to

an increase in heterozygosity in the crossbreds with an attendant increase in genetic

variation. Using such a crossbred population as a training set promises to increase

expected accuracy of GS.

In conclusion, genome-wide selection can increase genetic gain in swine

populations and there is great potential to reorganize the current swine breeding program

in tropical developing countries in order to exploit GS benefits. While the best

implementation strategy on a real swine population is only suggested by this work,

significant potential exists for GS to eliminate the need for a performance testing

program and to adapt itself to tropical pig breeding systems in developing countries.

190

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