Genetic analysis of kernel oil content in tropical maizewith design III and QTL mapping

Gustavo Vitti Moro • Mateus Figueiredo Santos •

Dyeme Antonio Vieira Bento • Aurelio

Mendes Aguiar • Claudio Lopes de Souza Jr.

Received: 12 July 2011 / Accepted: 17 December 2011 / Published online: 1 January 2012

� Springer Science+Business Media B.V. 2011

Abstract Oil content and grain yield in maize are

negatively correlated, and so far the development of

high-oil high-yielding hybrids has not been accom-

plished. Then a fully understand of the inheritance of

the kernel oil content is necessary to implement a

breeding program to improve both traits simulta-

neously. Conventional and molecular marker analyses

of the design III were carried out from a reference

population developed from two tropical inbred lines

divergent for kernel oil content. The results showed

that additive variance was quite larger than the

dominance variance, and the heritability coefficient

was very high. Sixteen QTL were mapped, they were

not evenly distributed along the chromosomes, and

accounted for 30.91% of the genetic variance. The

average level of dominance computed from both

conventional and QTL analysis was partial dominance.

The overall results indicated that the additive effects

were more important than the dominance effects, the

latter were not unidirectional and then heterosis could

not be exploited in crosses. Most of the favorable

alleles of the QTL were in the high-oil parental inbred,

which could be transferred to other inbreds via marker-

assisted backcross selection. Our results coupled with

reported information indicated that the development of

high-oil hybrids with acceptable yields could be

accomplished by using marker-assisted selection

involving oil content, grain yield and its components.

Finally, to exploit the xenia effect to increase even

more the oil content, these hybrids should be used in the

Top CrossTM

procedure.

Keywords Tropical maize � Oil � SSR markers �QTL � Design III � Genetic variances

G. V. Moro � M. F. Santos � D. A. V. Bento �A. M. Aguiar � C. L. de Souza Jr. (&)

Department of Genetics, University of Sao Paulo,

Agriculture College ‘‘Luiz de Queiroz’’, P.O. Box 83,

13.400-970 Piracicaba, Sao Paulo, Brazil

e-mail: clsouza@esalq.usp.br

G. V. Moro

e-mail: gvmoro@esalq.usp.br

M. F. Santos

e-mail: mateus@cnph.embrapa.br

D. A. V. Bento

e-mail: dyeme.bento@bayer.com

A. M. Aguiar

e-mail: aurelio.aguiar@fibria.com.br

Present Address:

M. F. Santos

Embrapa/Vegetables, P.O. Box 218, 70.359-970 Gama,

DF, Brazil

Present Address:

D. A. V. Bento

Nunhes do Brasil Comercio de Sementes Ltda,

P.O. Box 2081, 59.611-970 Mossoro, RN, Brazil

Present Address:

A. M. Aguiar

Fibria Celulose, Rodovia Gen. Euryale de Jesus Zerbini,

Km 84, 12.340-010 Jacareı, SP, Brazil

123

Euphytica (2012) 185:419–428

DOI 10.1007/s10681-011-0604-x

Introduction

The primary use of maize grain in several countries as

Brazil and USA is for feeding livestock as poultry and

swine (Goldman et al. 1994; Mangolin et al. 2004).

Several reports have shown that feed prepared with

high oil maize kernels increased the daily weight gain

in both species above cited, which was attributable to

the greater caloric content of oil than that of starch on a

weight basis (Lambert et al. 2004). Current maize

hybrids have about 4% of oil content in the kernels

(Laurie et al. 2004) and, although high-oil ([6%)

maize cultivars is in demand, the negative genetic

correlation between grain yield and oil content has

hampered the development of high-oil high-yielding

cultivars since the goal of the breeding programs is to

increase grain yield (Val et al. 2009).

Inheritance studies have shown that maize kernel oil

content is a quantitatively inherited trait that presents

high heritability, additive effects are more important

than non-additive effects (dominance and epistasis),

low magnitude or absence of genotype by environment

interaction and, also, presents xenia effect (Goldman

et al. 1994; Berke and Rocheford 1995; Lambert et al.

2004; Laurie et al. 2004; Mittelman et al. 2003).

Several studies have been conducted to map QTL for

kernel oil content in temperate maize germplasm, and

as summarized by Val et al. (2009) the number of QTL

mapped and reported from them varied from 7 to 70.

However, to the author’s knowledge only one study has

been conducted to map QTL for kernel oil content in

tropical maize germplasm (Mangolin et al. 2004),

which mapped 13 QTL. The number, position, mag-

nitudes of the QTL effects, and the magnitudes of the

phenotypic variances accounted for by the QTL in

these reports varied greatly, which could be due to the

differences in the genetic background of the popula-

tion, the map saturation, the design and the model used

to map QTL (Lima et al. 2006).

The design III was outlined to estimate the com-

ponents of the genetic variance as well as the average

level of dominance (Comstock and Robinson 1952),

and it has been used to study the inheritance of several

traits in maize (Hallauer and Miranda Filho 1988).

Recently, Lu et al. (2003) presented a procedure to use

the design III to map QTL, and coupled with the

classical analysis this procedure is a powerful frame-

work for inheritance studies. Despite the importance

of kernel oil content there is limited information on its

inheritance in tropical maize germplasm, which is

needed to design an effective breeding program to

develop high-oil genotypes with acceptable yields.

Thus, this research was aimed to study the inheritance

of the kernel oil content in a tropical maize population

using the design III coupled with molecular markers.

Materials and methods

Genetic material

Parental inbred lines L-08-05F (orange flint kernels)

and L-14-04B (yellow dent kernels) with 71.33 and

47.00 g kg-1 of kernel oil content, respectively, were

used to develop a reference F2 population. Both inbreds

were developed by the Maize Breeding Program at the

Department of Genetics, Agriculture College ‘‘Luiz de

Queiroz’’, University of Sao Paulo, Brazil. Inbred

L-08-05F was derived from IG-1, an early-flowering

with orange flint kernels population, developed from

crosses of Brazilian and Thai populations; and inbred

L-14-04B was derived from BR-106, an early-flower-

ing with yellow dent kernels population, developed

from crosses of Brazilian and Mexican populations

(Silva et al. 2004). These populations and their

respective inbreds are in different heterotic groups

and they are genetically divergent for several traits

besides kernel oil content, and both populations were

derived from tropical germplasm (Sabadin et al. 2008;

Moreira et al. 2009). The parental inbreds were crossed

and three F1 plants, previously tested against the

parental inbreds to check their genetical identity with

microsatellite markers, were selfed to develop the F2

population. Two-hundred and fifty plants were ran-

domly taken and selfed to develop F2:3 progenies,

which were then backcrossed to the parental inbreds in

isolation blocks. The F2:3 progenies were used as

females and the parental inbreds as males. The males

were sown at each set of four progenies and each plot

consisted of 30 plants. Each plot was harvested

separately and their seeds bulked. Thus, from each F2

plant two backcrossed progeny was developed, for a

total of 500 backcrossed progenies.

Experimental evaluation

The 500 backcrossed progenies and their parental

inbreds were evaluated at the Experimental Station

420 Euphytica (2012) 185:419–428

123

Department of Genetics in the 2004/2005 growing

season, near the city of Piracicaba, the state of Sao

Paulo, Brazil. The progenies were allocated in five

experiments following the 10 9 10 lattice design,

with 50 progenies backcrossed to each parental inbred

allocated in each experiment (lattice), with three

replications. Plots were one row 4.0 m long spaced

0.8 m between rows, and 0.2 m between plants within

rows; plots were overplanted and thinned to 20 plants

per plot (62,500 plants ha-1). Ten plants per plot were

selfed to prevent against the xenia effect, and after

harvest 10 kernels from the mid part of each ear of

each backcrossed progeny from each replication were

randomly taken and bulked. The three random samples

of the kernels from each backcrossed progeny were

used to record the kernel oil content in a nuclear

magnetic resonance device (NMR). The trait was

recorded in only one environment with three replica-

tions since kernel oil content presents higher narrow-

or broad-sense heritability coefficient ([70%)

(Hallauer and Miranda Filho 1988), absence (Berke

and Rocheford 1995; Mittelman et al. 2003) or presence

of non-crossover genotype by environment interaction

(Goldman et al. 1994), and even when genotype by

environment interactions were significant their magni-

tudes ranged from about 12 (Laurie et al. 2004) to 43

(Wasson et al. 2008b) times inferior than the genotype

variation indicating that likely they were non-crossover

interactions. Furthermore, the long-term breeding pro-

grams to increase kernel oil content have been

conducted with one cycle of phenotypic selection per

year in only one experimental station, and their

successful results (Dudley and Lambert 2004; Song

and Chen 2004) could be only explained because of its

high heritability and the absence or presence of non-

crossover genotype by environment interaction.

Analysis of variance

Analyses of variance were computed for each experi-

ment following the lattice design, and these analyses

were then pooled in a single analysis of variance

summing up their respective degrees of freedom and

sums of squares. All analyses were computed using

PROC GLM from SAS software (SAS Institute and Inc.

2001). From the pooled analysis of variance, estimates

of additive ðr2AÞ and dominance ðr2

DÞ variances, and

the average level of dominance ½ALD ¼ ð2r2D=r

2AÞ

0:5�

were computed following the design III procedure

(Comstock and Robinson 1952). Also, the genetic

variance ðr2G ¼ r2

A þ r2DÞ, the phenotypic variance on

a progeny mean-basis ðr2Ph�x ¼ r2

G þ r2=RÞ and on a

plant-basis ðr2Ph ¼ r2

G þ r2Þ, and the broad-sense on a

progeny mean-basis ðh2�x ¼ r2

G=r2Ph�xÞ and the narrow-

sense ðh2r ¼ r2

A=r2PhÞ heritability coefficients were

computed; r2and R stand for the error variance and

number of replications, respectively. Confidence

intervals at the 95% probability level for the estimates

of the genetic and phenotypic variances and of the

heritability coefficient were computed following the

Burdick and Graybill (1992) procedures.

Genetic map

The genetic map used, and the procedures used to

develop it, was previously described by Sibov et al.

(2003). Briefly, the F2 plants that gave rise to the F2:3

progenies were genotyped with microsatellite markers.

The genetic map was developed using the MAP-

MAKER/EXP version 3.0b (Lincoln et al. 1992) with

a LOD threshold of 3.0 and a maximum distance

between adjacent markers of 50 cM; i.e., 0.38 as the

maximum recombination frequency, to establish the

linkage groups, and the Kosambi’s (1944) mapping

function was used to convert recombination frequencies

into map distances. Sixty new microsatellite markers

were added to the map of Sibov et al. (2003), for a total

of 177 markers distributed along the 10 linkage groups.

The genetic map spanned 2,052 cM in length with an

average interval of 11.60 cM between adjacent markers.

QTL mapping

The composite interval mapping method (CIM)

extended to perform a joint analysis of multiple

environments (mCIM) was used to map QTL, since it

is more powerful than the CIM procedure (Jiang and

Zeng 1995). The least square means of kernel oil content

from each replication were used to perform the analysis,

using the sample of each replication as an environment.

The underlying mixture model for QTL mapping is:

Yjk ¼ b0k þ a�kx�j þ d�k z�j þXt

l

ðalkxjl þ dlkzjlÞ þ ejk

where Yjk is the phenotypic mean of the jth progeny at

the kth replication; b0k is the mean effect of the model

Euphytica (2012) 185:419–428 421

123

for replication k; a�k is the additive effect of the

putative QTL at replication k; x�j counts the number of

alleles from parental inbred L-08-05F at the putative

QTL taking values of 2, 1, and 0 for genotypes QQ,

Qq, and qq, respectively, with probabilities depending

on genotypes of the markers flanking the putative

QTL and the recombination frequencies between

the QTL and the markers; d�k is the dominance effect

of the putative QTL in replication k; z�j is an indicator

variable taking values of 1 for genotype Qq and 0 for

genotypes QQ and qq of the putative QTL, with

probabilities depending on genotypes of the markers

flanking the putative QTL and the recombination

frequencies between the QTL and the markers; xjl and

zjl are corresponding variables for marker l, with

t markers selected as cofactors for controlling residual

variation; alk and dlk are the partial regression

coefficients of Yjk on xjl and on zjl, respectively; and

ejk is the residual of the model. The cofactors were

selected using the stepwise regression procedure

(P B 0.05) for each replication and combined for the

joint QTL analysis.

The QTL mapping was performed for each set of

the progeny backcrossed to one of the parental inbreds,

and then two analyses were performed: one for the

progenies backcrossed to the parental inbred L-08-05F

(BC1) and the other one to the parental inbred L-14-

04B (BC2). The LR threshold used to declare a

presence of QTL was 13.83, which corresponds to a

LOD score of 3.0. This value was used since the LOD

score values used for other authors have ranged from

2.5 to 3.8 (Mangolin et al. 2004; Song et al. 2004;

Willmot et al. 2006; Zhang et al. 2008; Wasson et al.

2008a; Wasson et al. 2008b). Only the mapped QTL

stable across replications were considered for this

study. The QTL mapping was performed with the

software Windows QTL Cartographer, version 2.5,

JZmap procedure (Wang et al. 2005), using a window

size of 10 cM and walking speed of 1 cM.

Since the analyses were performed for each of the

backcrossed progenies the correct values of the additive

(a) and dominance (d) effects for the QTL mapped were

not provided by the mCIM analyses, which was

developed for progenies as F2:3, F3:4, etc. Lu et al.

(2003) showed that they can be computed from the

pseudo-additive values (‘‘a’’) computed from each

backcross analysis as follows: the ‘‘a’’ value for a

QTL mapped for the BC1 progenies is actually

‘‘a1’’ = (a - d)/2, and for a QTL mapped for the BC2

progenies ‘‘a2’’ = (a ? d)/2, and then the additive and

dominance effects for each mapped QTL could be

computed as: a = (‘‘a1’’ ? ‘‘a2’’) and d = (‘‘a2’’-

‘‘a1’’), respectively. In addition, Melchinger et al. (2008)

reported that the genetic effects of the ith QTL are H1 ¼ai � di þ ð1=2Þ

Pij aaij � ð1=2Þ

Pij daij and H2 ¼

ai þ di � ð1=2ÞP

ij aaij � ð1=2ÞP

ij daij for the BC1

and BC2 progenies, respectively. In these expressions

aaij and daij stand for the additive by additive and

dominance by additive epistatic effects, respectively.

Notice that ‘‘a1’’ = H1/2 and ‘‘a2’’ = H2/2 , and then

the additive effect is actually a�i ¼ ½ðH1 þ H2Þ=2� ¼ai � ð1=2Þ

Pij daij and the dominance effect is actu-

ally d�i ¼ ½ðH2 � H1Þ=2� ¼ di � ð1=2ÞP

ij aaij. The

a�i and d�i effects were termed as the augmented

additive and augmented dominance effects, which

contains the additive ðaiÞ and dominance ðdiÞ effects

of the ith QTL and half of the epistatic effects daij and

aaij of the QTL with other QTL underlying the trait;

i.e., with the genetic background of the population,

and they are related to the parental inbreds difference

PD ¼P

2a�i and to the mid-parent heterosis MPH ¼Pd�i (Melchinger et al. 2008). Thus, the additive and

dominance effects of the QTL reported by Lu et al.

(2003), and those ones estimated in this paper, actually

estimate the a�i and d�i effects, which contains the

additive ðaiÞ and dominance ðdiÞ effects of the ith QTL

confounded with their epistatic interactions daij and

aaij, respectively, with the genetic background of the

population.

The phenotypic variances explained by each QTL

and by the set of all mapped QTL ðR2PhÞ were also

computed using the Windows QTL Cartographer. All

QTL previously mapped using the mCIM procedure

were fitted in a single model with multiple QTL, and

then, the software uses the EM algorithm to evaluate

the likelihood of this mixture model with multiple

QTL. Once the model was fitted, the coefficients of

phenotypic determination of each QTL and of all QTL

were automatically computed by the software. The

genotypic coefficient of determination was computed

dividing the R2Ph by the coefficient of heritability; i.e.,

ðR2G ¼ R2

Ph=h2Þ.Parental inbred L-08-05F has higher oil content in

the kernels than L-14-04B and, therefore, the positive

additive effects of the mapped QTL indicated that the

422 Euphytica (2012) 185:419–428

123

favorable alleles (alleles that increase oil content)

were from the L-08-05F; whereas the negative signs

indicated that the favorable alleles were from L-14-

04B. Levels of dominance were computed as LD ¼d�j j= a�j j ratio for each QTL, and the average level of

dominance (ALD) was computed weighing each LD

ratio by its respective R2G value. Following several

reports (Sibov et al. 2003; Mangolin et al. 2004; Lima

et al. 2006; Moreira et al. 2009), gene action was

characterized as: additive (A) 0.00 B LD B 0.20;

partial dominance (PD) 0.21 B LD B 0.80; domi-

nance (D) 0.81 B LD B 1.20; and overdominance

LD C 1.21.

Results

Means and components of variance

Highly significant differences (P B 0.01) were detected

in the pooled analysis of variance of the Design III for

progenies and for progenies x parents, indicating that

both additive and dominance effects were present in the

variation for kernel oil content. The coefficient of

experimental variation was 11.45%, value similar to

other reports (Goldman et al. 1994; Berke and Roche-

ford 1995; Zhang et al. 2008) indicating that the

experimental data presented good precision. The overall

means of the parental inbreds differed significantly

(P \ 0.05), with values of 71.33 g kg-1 for the inbred

L-08-05F and 47.00 g kg-1 for the inbred L-14-04B.

The average of the 500 progenies (49.10 g kg-1)

differed significantly from the mean of the parental

inbreds (59.17 g kg-1), and the average of the progenies

backcrossed to L-08-05F (58.18 g kg-1) was signifi-

cantly higher than the average of those backcrossed to

L-14-04B (40.04 g kg-1). The means of the progenies

assessed ranged from 28.30 to 75.27 g kg-1, showing

the presence of transgressive progenies for both lower

and higher kernel oil content (Table 1).

The estimate of the genetic variance ðr2GÞ, as well as

its components additive ðr2AÞ and dominance variances

ðr2DÞ, the broad-sense on a progeny mean-basis ðh2

�xÞand the narrow-sense ðh2

r Þ heritability coefficients, and

the average level of dominance differed significantly

from zero. Also, additive variance was quite larger

than the dominance variance, and the level of dom-

inance was 0.58 (Table 2).

QTL mapping

Sixteen QTL were mapped for kernel oil content on

chromosomes 1–8; i.e.; QTL were not mapped only on

chromosomes 9 and 10. The number of QTL mapped

on each chromosome varied greatly, with one QTL

mapped on each one of the chromosomes 2, 3, 6, 7 and

8, three QTL mapped on each one of the chromosomes

1 and 5, and five QTL mapped on chromosome 4. The

augmented additive effects ða�i Þ of the QTL ranged

from -3.39 to 2.57 g kg-1, and the augmented domi-

nance effects ðd�i Þ ranged from -1.66 to 1.48 g kg-1.

The mapped QTL presented all types of gene action

Table 1 Mean values and confidence intervals (CI) of the

parental inbreds (L-08-05F and L-14-04D), of the backcrossed

progenies to the parental inbreds (BC1 and BC2) and the

average of the backcrosses, for kernel oil content in a tropical

maize population

Entriesa Means (g kg-1) CIb

L-08-05F 71.33 [62.32; 80.35]

L-14-04B 47.00 [37.99; 56.01]

BC1 (L-08-05F) 58.18 [49.17; 67.20]

BC2 (L-14-04B) 40.04 [31.03; 49.05]

Average (BC1 ? BC2)/2 49.10 [40.09; 58.12]

a L-08-05F: high oil parental inbred, L-14-04B: normal oil

parental inbred, BC1: backcross to parental inbred L-08-05F,

BC2: backcross to parental inbred L-14-04Bb CI Confidence intervals at the 95% probability level

Table 2 Estimates of the genetic variances, heritability coef-

ficients, average level of dominance and their confidence

intervals for kernel oil content in a tropical maize population

Parametersa Estimates C.I.b

r2A

75.05 [60.93; 96.10]

r2D

12.41 [9.30; 17.87]

r2G

87.46 [70.22; 113.96]

h2�x

0.89 [0.80; 0.99]

h2r

0.77 [0.68; 0.87]

d^ 0.58 [0.66; 1.47]

a r2A additive genetic variance, r2

D dominance genetic

variance, r2G ¼ r2

A þ r2D, r2

�Phphenotypic variance on a

progeny mean-basis,h2�x broad-sense heritability coefficient on

a mean-basis, h2r narrow-sense heritability coefficient, d

^

average level of dominanceb CI Confidence intervals at the 95% probability level

Euphytica (2012) 185:419–428 423

123

since seven QTL displayed additive gene action, six

partial dominance, one complete dominance, and two

QTL displayed overdominance; and the average level

of dominance (ALD) was 0.44 characterizing partial

dominance. From the 16 QTL mapped, 11 (68.75%)

with favorable alleles (alleles increasing oil content)

were from the parental inbred L-08-05F, and five

(31.25%) from the parental inbred L-14-04B. The

percentage of the phenotypic (genotypic) variance

accounted for by the QTL ranged from 0.17 (0.19) to

6.58% (7.37%), and collectively the set of the QTL

mapped accounted for by 27.57 and 30.91% of the pheno-

typic and genotypic variance, respectively (Table 3).

Discussion

Means and variances

The parental inbred line L-08-05F could be considered

as high oil genotype since it presented above 60 g kg-1

kernel oil content, but the other parental inbred L-14-

04B could not be considered as low oil genotype since

its kernel oil content is similar to the cultivars selected

for grain yield. Nevertheless, the difference between

their means was quite high 24.33 g kg-1 (51.76%),

which allowed the development of a population with

high genetic variability, which displayed transgressive

progenies for lower (28.30 g kg-1) and higher

(75.27 g kg-1) kernel oil content. The presence of a

few transgressive genotypes for higher kernel oil

content indicates that the inbred L-14-04B has a small

set of loci with favorable alleles for increasing kernel

oil content, and the presence of several transgressive

progenies for lower oil content indicates that the inbred

L-14-04B has most of the loci underlying kernel oil

content with alleles decreasing this trait. Then, as

expected the progenies backcrossed to the inbred L-08-

05F presented higher kernel oil content than the mean

of the progenies backcrossed to the inbred L-14-04B;

and the difference between their means was quite high;

i.e., 18.14 g kg-1 (45.30%). Mangolin et al. (2004) did

Table 3 Genomic position, genetic effects, levels of dominance, direction of the favorable alleles, and coefficients of determination

for the mapped QTL

QTLa Bin Marker interval P (cM)b LOD a* d* LDc DdR2

Ph R2G

Qoil1a 1.01 umc1177–bnlg1014 4 3.89 1.66 -1.46 0.88 (D) L-08-05 1.95 2.19

Qoil1b 1.01 bnlg1014–umc1106 14 4.01 2.06 -0.28 0.14 (A) L-08-05 2.18 2.45

Qoil1c 1.01–1.02 umc1106–bnlg1627 22 4.18 2.02 -0.53 0.27 (PD) L-08-05 2.15 2.41

Qoil2 2.08 umc1464–umc1633 201 3.66 0.39 -0.94 2.42 (OD) L-08-05 0.30 0.34

Qoil3 3.04 bnlg1452–bnlg602 52 3.07 0.57 0.08 0.13 (A) L-08-05 0.17 0.19

Qoil4a 4.06–4.07 bnlg2291–dupssr34 94 3.24 1.05 1.48 1.42 (OD) L-08-05 1.12 1.25

Qoil4b 4.07–4.08 dupssr34–bnlg2244 100 3.07 -0.74 0.10 0.14 (A) L-14-04 0.28 0.31

Qoil4c 4.08 bnlg2244–bnlg2162 114 3.11 -3.39 -1.66 0.49 (PD) L-14-04 6.58 7.37

Qoil4d 4.08 bnlg2162–umc1086 116 3.19 -2.49 -0.56 0.23 (PD) L-14-04 3.24 3.63

Qoil4e 4.08–4.09 umc1051–umc1989 126 3.36 -1.21 0.27 0.23 (PD) L-14-04 0.77 0.86

Qoil5a 5.04 bnlg1892–dupssr10 106 3.47 -2.36 -0.21 0.09 (A) L-14-04 2.86 3.20

Qoil5b 5.07–5.08 phi0128–bnlg1711 238 3.49 2.57 -0.42 0.16 (A) L-08-05 3.40 3.81

Qoil5c 5.08–6.00 bnlg1711–umc1023 256 3.26 1.85 -0.23 0.12 (A) L-08-05 1.75 1.96

Qoil6 6.00–6.01 phi0126–bnlg1371 17 3.01 1.87 0.99 0.53 (PD) L-08-05 2.04 2.29

Qoil7 7.05–8.01 umc1407–umc1139 194 3.27 1.74 -0.83 0.47 (PD) L-08-05 1.72 1.93

Qoil8 8.03 bnlg1863–phi0115 82 3.50 2.12 -0.20 0.10 (A) L-08-05 2.31 2.59

– – – – ALD 0.44 (PD) Total 27.57 30.91

a QTL are indicated as Qoil followed by a chromosome number and by a word for more than one QTL in the same chromosomeb P position of QTL refers to the distance in centimorgans (cM) from the first marker of the chromosome to the mapped QTLc LD level of dominance A additive, PD partial dominance, D dominance, OD overdominance, ALD average level of dominanced D direction of the favorable allele: negative and positive a* values indicates that favorable alleles were from L-14-04B and L-08-

05F, respectively

424 Euphytica (2012) 185:419–428

123

not report transgressive progenies in tropical germ-

plasm, may be because their parental inbreds had

greater difference in oil content (29.24 vs. 79.25

g kg-1) than the parental inbred lines used in this

study. Yet the range of their entry means (44.93–

72.63 g kg-1) was lower than the range of the inbred

parental means even using a number of genotypes

greater (408 F2 plants) than we used.

The magnitude of the additive variance was about

six times greater than the dominance variance and the

level of dominance was partial dominance. As is well-

known estimates of additive and dominance variances

are biased in populations with high levels of linkage

disequilibrium, with the dominance variance always

biased upward regardless the phase of linkage dis-

equilibrium and the additive variance biased upward

in association-phase and downward in repulsion-phase

linkage disequilibrium, respectively (Comstock and

Robinson 1952). Thus, the estimate of the level of

dominance is probably biased upward because the

population used was in high level of linkage disequi-

librium, evidencing that the additive effects are more

important than the dominance effects in the loci

underlying kernel oil content in maize (Val et al.

2009). The narrow- and broad-sense heritability

coefficients were quite high (0.77 and 0.89), which

agreed with the reported results (Hallauer and Miranda

Filho 1988; Mangolin et al. 2004; Wasson et al. 2008b;

Yang et al. 2010).

QTL mapping

The 16 mapped QTL were not evenly distributed in the

chromosomes; instead 11 QTL out of 16 were on only

three chromosomes (five on chromosome 4, and three

on chromosomes 1 and 3). The set of the mapped QTL

accounted for only 30.91% of the genetic variance,

with Qoil4c with the greatest contribution (7.37%) to

the genetic variance. The low magnitude of the genetic

variance accounted for by the mapped QTL has also

been reported for almost all studies for this trait

(Mangolin et al. 2004; Wasson et al. 2008b; Yang et al.

2010). These results indicate that several QTL were not

mapped which could be due to (1) failure of the model

to map QTL with very small effects, (2) the model

allows to map only one QTL per interval, and then sets

of QTL in the same interval will be mapped as only one

QTL, and (3) furthermore if the genetic effects of sets

of QTL within the same interval are different they

could have the total effect canceled or highly reduced

and not being mapped as a unique QTL with small

effect (Lima et al. 2006). From the QTL mapped, 11

displayed positive and five displayed negative aug-

mented additive effects ða�i Þ. Thus, there were favor-

able alleles, i.e., alleles that increase kernel oil content,

in both parental inbred lines. Although most of these

QTL were in the parental inbred L-08-05F (68.75%),

the presence of QTL with favorable alleles in the

parental inbred L-14-04B allowed the occurrence of

transgressive progenies as above mentioned. Aug-

mented dominance effects ðd�i Þ of the QTL displayed

positive and negative signs showing that this effect

was not unidirectional, and the average level of

dominance was partial dominance (0.44); notice that

this result was similar to that computed from the

estimates of variance components (0.58). Two QTL

Qoil2 and Qoil4a presented overdominance, but since

true overdominance is not expected, this might be due

to repulsion-phase linkages of sets of QTL with

dominance and/or partially dominance effects mim-

icking pseudo-overdominances (Lu et al. 2003). Most

of the reported results have shown that the average

level of dominance was in the range of additive to

partial dominance and that the dominance effects was

not unidirectional (Berke and Rocheford 1995;

Mangolin et al. 2004; Song et al. 2004; Zhang et al.

2008), which are consistent with the results from this

research. Therefore, the additive effects being more

important than the dominance effects, and the latter

being not unidirectional, likely the magnitude of

inbreeding depression and of heterosis for kernel oil

content will be of low magnitude.

The number of QTL mapped has varied greatly

across several studies; i.e., from 7 to 70. There are

several reasons for these differences as the populations,

genetic designs, number of markers, methods used for

mapping etc., but those that reported a high number of

QTL mapped were in studies involving populations

that have been subjected to a long-term divergent

selection for kernel oil content (Val et al. 2009). All

QTL mapped in this study have already been mapped in

the same bins in other studies (Goldman et al. 1994;

Berke and Rocheford 1995; Laurie et al. 2004;

Mangolin et al. 2004; Song et al. 2004; Willmot et al.

2006; Dudley et al. 2007; Wasson et al. 2008b; Yang

et al. 2010; Zhang et al. 2008). Although in the same

Euphytica (2012) 185:419–428 425

123

bins, they could not be considered as the same QTL.

However, the QTL Qoil3, Qoil5a, and Qoil8 were

mapped in at least five studies, and therefore, they were

stable across populations, environments and methods

of mapping.

Implications for selection

The results of this research and those already reported

in tropical (Mangolin et al. 2004) as well as in

temperate germplasm (Goldman et al. 1994; Berke and

Rocheford 1995; Song et al. 2004; Willmot et al. 2006;

Dudley et al. 2007; Zhang et al. 2008; Wasson et al.

2008a; Wasson et al. 2008b) showed that the kernel oil

content in maize presents high heritability, additive

effects are more important than non-additive effects,

and the dominance effects are not unidirectional.

Thus, the magnitude of heterosis is expected to be low

and could not be exploited in crosses. Also, the

magnitude of inbreeding depression should be low and

coupled with the high magnitude of the coefficient of

heritability simple selection procedures as individual

phenotypic selection could be used to develop inbred

lines and/or populations with high kernel oil content.

In fact, reported results of long-term phenotypic

recurrent selection have shown steady increases in

the kernel oil content (Dudley and Lambert 2004;

Song and Chen 2004). Notice, also, that a transgres-

sive progeny had 75.27 g kg-1 of kernel oil content,

indicating that we can develop inbred lines from this

genotype with high levels of kernel oil content.

We mapped 16 QTL underlying kernel oil content,

and three of them were already reported in other studies

with temperate germplasm, and then these genomic

regions should be considered for fine mapping and/or

cloning; also these mapped QTL could be used for

marker-assisted selection (MAS). Although MAS will

not be as effective as phenotypic selection (PS) on a per

cycle basis, PS could be coupled with MAS to design

more effective breeding programs since MAS could be

conducted under both growing and off-growing sea-

sons speeding-up breeding programs aimed to increase

kernel oil content in maize. For instance, the favorable

alleles of the set of QTL Qoil1b, Qoil1c, Qoil5b, and

Qoil8, whose additive effects are 2.06, 2.02, 2.57, and

2.12 g kg-1 of oil content are in the parental inbred

L-08-05F and could be transferred by marker-assisted

backcross selection to pyramid them in other inbreds,

as has been successfully reported for maize and other

species for several traits (Stuber and Sisco 1992;

Neereja et al. 2007; Garzon et al. 2008).

However, the major constraint to develop high oil

maize hybrids is the high negative genetic correlation

of kernel oil content and grain yield (Lambert 2001).

To overcome this drawback the Top CrossTM

procedure

has been proposed to exploit the xenia effect to allow

the production of high-oil maize while maintaining the

productivity (Berquist et al. 1998). This procedure

consists in sowing a mixture of seeds of a normal

kernel oil content high-yielding male-sterile single-

cross (&90%) and a high-oil (but low-yielding) male

pollinator (&10%). Lambert et al. (1998) reported that

hybrids pollinated with high oil and normal oil content

strains had comparable yield, but those hybrids

pollinated with high oil strains yielded 19 g kg-1

more oil than those pollinated with the normal oil

content strains. Also, Thomison et al. (2003) reported

that with the Top CrossTM

procedure the kernel oil

content was quite higher (72.12 g kg-1) than in the

normal single-crosses crop (41.25 g kg-1), but a

significant reduction on grain yield was reported

(Thomison et al. 2002). Although there were divergent

results on the use of the Top CrossTM

procedure, the

reduction on grain yield is expected since the yields of

the pollinator genotypes are too low and they occupy

about 10% of the field, and also because oil requires

2.25 times more energy than starch to be produced

(Val et al. 2009).

Thus, further researches with molecular markers

could be useful to design new approaches to develop

high oil genotypes without significant reduction on

grain yield. Sibov et al. (2003) evaluated F2:3 prog-

enies of the same population used in this study and

mapped QTL for grain yield, but none of them were

mapped in the same genomic regions of the QTL for

kernel oil content mapped in this study. Also, Wasson

et al. (2008b) mapped QTL for grain yield and kernel

oil content in different genomic regions. These results

indicate that the introgression of specific genomic

regions associated with kernel oil but not with yield

into inbred lines could allow the development of

cultivars with above-average oil content while main-

taining yield. Furthermore, Berke and Rocheford

(1995) mapped QTL for kernel weight and for kernel

oil content in different genomic regions. They empha-

sized that ‘‘three major QTL for oil concentration on

chromosomes 2, 6, and 9 were not associated with

changes in kernel weight, indicating that selection for

426 Euphytica (2012) 185:419–428

123

increased oil concentration without changing kernel

weight should be possible in this population’’. Kernel

weight is one of the grain yield components, along

with the number of ears per plant (prolificacy), kernels

per row and kernel-row number. Thus, it is necessary

to investigate the genetic architecture of these traits

jointly; i.e., to estimate the genetic correlations

between kernel oil content and grain yield compo-

nents, to map QTL for kernel oil content and for yield

components, and to investigate whether there are QTL

underlying kernel oil content and grain yield compo-

nents in different genomic regions.

One can speculate that if the findings of Berke and

Rocheford (1995) for kernel weight could be verified

for the others grain yield components we could be able

to increase kernel oil content without decreasing

significantly grain yield. Thus, selection to increase

grain yield and kernel oil content simultaneously could

be conducted by manipulating the QTL underlying

grain yield and its components and kernel oil content

that are in different genomic regions using a selection

index in a marker-assisted selection or in a genomic-

wide selection framework (Lorenzana and Bernardo

2009). This procedure could allow the development of

high-yielding above-average kernel oil content (say

60 g kg-1) elite single-crosses, and single-crosses

with high kernel oil content (say 90 g kg-1) with a

lower reduction in grain yield. Then, the high-yielding

above-average kernel oil content single-cross should

be used as female (male-sterile plants) and the high

kernel oil content single-cross as pollinator in the Top

CrossTM

procedure to allow the production of grains

with high oil content (say 80 g kg-1) with a minimum

loss in productivity.

Concluding, although this research did not include

grain yield components, it contributed to increase the

knowledge on the inheritance of the kernel oil content

in maize, mainly in tropical maize, and how to use this

information to implement an effective maize breeding

program aimed to increase this trait. Also, coupled with

reported information, we proposed further research

aimed to develop hybrids to be used in the Top CrossTM

procedure to exploit the xenia effect to increase even

more the oil content in the grains. Finally, the inbred

line L-08-05F is an important source of favorable

alleles (QTL) for oil content and these QTL could be

transferred to other inbreds to develop high kernel oil

content inbreds or populations.

Acknowledgements This research was supported by Conselho

Nacional de Desenvolvimento Cientıfico e Tecnologico (CNPq-

308499/2006-9), and by Department of Genetics at the

Agriculture College ‘‘Luiz de Queiroz’’/University of Sao

Paulo. C. L. Souza Jr. is recipient of a research fellowship

from CNPq. Authors are grateful to Dr. Anete Pereira de Souza,

from the University of Campinas, for the genetic mapping of the

population; to Dr. Luis Alberto Colnago, from Embrapa/

Instrumentacao, for the analysis of the kernel oil content; and

to A. J. Desiderio, A. S. Oliveira, A. O. Gil, C. R. Segatelli, and A.

Silva for their assistance with the field experiments. We are also

grateful to three anonymous reviewers for their constructive

suggestions.

References

Berke TG, Rocheford TR (1995) Quantitative trait loci for

flowering, plant and ear height, and kernel traits in maize.

Crop Sci 35:1542–1549

Berquist RR, Nubel DS, Thompson DL (1998) Production

method for corn with enhanced quality grain traits. Patent-

USA-5706603

Burdick RK, Graybill FA (1992) Confidence intervals on vari-

ance components. M. Dekker Inc., New York

Comstock RE, Robinson HF (1952) Estimation of average

dominance genes. In: Gowen JW (ed) Heterosis. Iowa State

College Press, Ames, pp 494–516

Dudley JW, Lambert RJ (2004) 100 generations of selection for

oil and protein in corn. In: Janick J (ed) Plant breeding

reviews: long-term selection-Maize, vol 24 (1). Wiley,

Oxford, pp 79–110

Dudley JW, Clark D, Rocheford TR, LeDeaux JR (2007)

Genetic analysis of corn kernel chemical composition in

the random mated 7 generation of the cross of generations

70 of IHP 9 ILP. Crop Sci 47:45–57

Garzon LN, Ligareto GA, Blair MW (2008) Molecular marker-

assisted backcrossing of anthracnose resistance into

Andean climbing beans. Crop Sci 48:562–570

Goldman IL, Rocheford TR, Dudley JW (1994) Molecular

marker associated with maize kernel oil concentration in an

Illinois high protein 9 Illinois low protein cross. Crop Sci

34:908–915

Hallauer AR, Miranda Filho JB (1988) Quantitative genetics in

maize breeding, 2nd edn. Iowa State University Press, Ames

Jiang C, Zeng ZB (1995) Multiple trait analysis of genetic map-

ping for quantitative trait loci. Genetics 140:1111–1127

Kosambi DD (1944) The estimation of map distances from

recombination values. Ann Eugenics 12:172–175

Lambert RJ (2001) High-oil corn hybrids. In: Hallauer AR (ed)

Specialty corns, 2nd edn. CRC Press, Boca Raton, pp 123–145

Lambert RJ, Alexander DE, Han ZJ (1998) A high oil pollinator

enhancement of kernel oil and effects on grain yields of

maize hybrids. Agron J 90:211–215

Lambert RJ, Alexander DE, Mejaya IJ (2004) Single kernel

selection for increased grain oil in maize synthetics and

high-oil hybrid development. In: Janick J (ed) Plant

breeding reviews: long-term selection-Maize, vol 24 (1).

Wiley, Oxford, pp 153–175

Euphytica (2012) 185:419–428 427

123

Laurie CC, Chasalow SD, LeDeaux JR, McCarroll R, Bush D,

Hauge B, Lai CQ, Clark D, Rocheford TR, Dudley JW

(2004) The genetic architecture of response to long-term

artificial selection for oil concentration in the maize kernel.

Genetics 168:2141–2155

Lima MLA, Souza CL Jr, Bento DAV, Souza AP, Garcia LC

(2006) Mapping QTL for grain yield and plant traits in a

tropical maize population. Mol Breed 17:227–239

Lincoln SE, Daly MJ, Lander ES (1992) Constructing genetic

maps with Mapmaker/Exp. 3.0, 3rd edn. Whitehead Insti-

tute for Biometrical Research, MAS

Lorenzana RE, Bernardo R (2009) Accuracy of genotypic value

predictions for marker-based selection in biparental plant

populations. Theor Appl Genet 120:151–161

Lu H, Romero-Severson J, Bernardo R (2003) Genetic basis of

heterosis explored by simple sequence repeat markers in a

random-mated maize population. Theor Appl Genet 107:

494–502

Mangolin CA, Souza CL Jr, Garcia AAF, Garcia AF, Sibov ST,

Souza AP (2004) Mapping QTLs for kernel oil content in a

tropical maize population. Euphytica 137:251–259

Melchinger AE, Utz HF, Schon CC (2008) Genetic expectations

of quantitative trait loci main and interaction effects

obtained with the triple testcross design and their relevance

for the analysis of heterosis. Genetics 178:2265–2274

Mittelman A, Miranda Filho JB, Lima GJMM, Hara-Klein C,

Tanaka RT (2003) Potential of the ESA23B maize popu-

lation for protein and oil content improvement. Scientia

Agricola 60:319–327

Moreira JUV, Bento DAV, Souza AP, Souza CL Jr (2009) QTL

mapping for reaction to Phaeosphaeria leaf spot in a trop-

ical maize population. Theor Appl Genet 119:1361–1369

Neereja CN, Maghirang-Rodriguez R, Pamplona A, Heuer S,

Collard BCY, Sptiningsih EM, Vergara G, Sanchez D, Xu

K, Ismail AM, Mackill DJ (2007) A marker-assisted

backcross approach for developing submergence-tolerant

rice cultivars. Theor Appl Genet 115:767–776

Sabadin PK, Souza CL Jr, Souza AP, Garcia AAF (2008) QTL

mapping for yield components in a tropical maize popula-

tion using microsatellite markers. Hereditas 145:194–203

SAS Institute Inc (2001) SAS/STAT User’s guide, v.6.03. SAS

Institute, Cary

Sibov ST, Souza CL Jr, Garcia AAF, Silva AR, Mangolin CA,

Benchimol LL, Souza AP (2003) Molecular mapping in

tropical maize using microsatellite markers. 2. Quantitative

trait loci (QTL) for grain yield, ear height, and grain

moisture. Hereditas 139:107–115

Silva AR, Souza CL Jr, Aguiar AM, Souza AP (2004) Estimates

of genetic variance and level of dominance in a tropical

maize population.I. Grain yield and plant traits. Maydica

49:65–71

Song TM, Chen SJ (2004) Long term selection for oil concen-

tration in five maize populations. Maydica 49:9–14

Song XF, Song TM, Daı JR, Rocheford TR, Li JS (2004) QTL

mapping of kernel oil concentration with high-oil maize by

SSR markers. Maydica 49:41–48

Stuber CW, Sisco P (1992) Marker-facilitated transfer of QTL

alleles between inbred lines and responses in hybrids. In:

Proceedings of 46th Ann Corn Sorghum Res. Conference.

ASTA, Washington pp 104–113

Thomison PR, Geyer AB, Lotz LD, Siegrist HJ, Dobbels TL

(2002) TopCross high-oil corn production: agronomic

performance. Agron J 94:290–299

Thomison PR, Geyer AB, Lotz LD, Siegrist HJ, Dobbels TL

(2003) TopCross high oil corn production: select grain

quality attributes. Agron J 95:147–154

Val DL, Schwartz SH, Kerns MR, Deikman J (2009) Devel-

opment of a high oil trait for maize. In: Kriz AL, Larkins

BA (eds) Biotechnology in agriculture and forestry, vol 63:

molecular genetic approaches to maize improvement.

Springer-Verlag Press, Berlin, pp 303–323

Wang S, Basten CJ, Zeng ZB (2005) Windows QTL Cartogra-

pher 2.5. Department of Statistics, North Caroline State

University, Raleigh. http://statgen.ncsu.edu/qtlcart/WQTL

Cart.htm. Accessed March 17, 2010

Wasson JJ, Mikkelineni V, Bohn MO, Rocheford TR (2008a)

QTL for fatty acid composition of maize kernel oil in

Illinois High Oil 9 B73 backcross-derived lines. Crop Sci

48:69–78

Wasson JJ, Wong JC, Martinez E, King JJ, DeBaene J, Hotch-

kiss JR, Mikkilineni V, Bohn MO, Rocheford TR (2008b)

QTL associated with maize kernel oil, protein, and starch

concentrations; kernel mass; and grain yield in Illinois high

oil 9 B73 backcross-derived lines. Crop Sci 48:243–252

Willmot DB, Dudley JW, Rocheford TR, Bari AL (2006) Effect

of random mating on marker QTL associations for grain

quality traits in the cross of Illinois high oil 9 Illinois low

oil. Maydica 51:187–199

Yang XH, Guo YQ, Yan JB, Zhang J, Song TM, Rocheford TR,

Li JS (2010) Major and minor QTL and epistasis contribute

to fatty acid compositions and oil concentration in high-oil

maize. Theor Appl Genet 20:665–678

Zhang J, Lu XQ, Song XF, Yan JB, Song TM, Dai JR, Roche-

ford TR, Li JS (2008) Mapping quantitative trait loci for

oil, starch, and protein concentrations in grain with high-oil

maize by SSR markers. Euphytica 162:335–344

428 Euphytica (2012) 185:419–428

123