Workshop on a Selection and Breeding Program for …ipsfad2.homestead.com/files/Documents/Reports/2009_-_NSERC_SWP... · 1 TABLE OF CONTENTS ... David Bevan AARS, ... Eric Boucher

Interprovincial Partnership for Sustainable Freshwater Aquaculture Development

Partenariat interprovincial pour le développement durable de l’aquaculture en eau douce

IPSFAD/PIDDAED

Pavillon Paul-Comtois, Université Laval, 2425 Rue de l'Agriculture, Québec (Qué) G1V 0A6

Workshop on a Selection and Breeding Program for Rainbow Trout Aquaculture in Canada

Organizing Committee:

Prof. Richard Moccia (University of Guelph) Dr Grant Vandenberg (Université Laval) Eric Boucher (IPSFAD) Michael Burke (University of Guelph) David Bevan (University of Guelph) Steve Naylor (Ontario Ministry of Agriculture, Food and Rural Affairs) Karen Tracey (Northern Ontario Aquaculture Association) Dan Stechey (Canadian Aquaculture Systems)

National Research Conseil national Council Canada de recherches Canada

Northern Ontario Aquaculture Association

ACRDP- PCRDA AMD-NASAPI

Interprovincial Partnership for Sustainable Freshwater Aquaculture Development

Partenariat interprovincial pour le développement durable de l’aquaculture en eau douce

IPSFAD/PIDDAED Pavillon Paul-Comtois, Université Laval, 2425 Rue de l'Agriculture, Québec (Qué) G1V 0A6

Workshop on a Selection and Breeding Program for Rainbow Trout in Canada

Workshop Summary Report

Sponsored by: NSERC Strategic Workshops Program

Ramada Hotel Guelph, Ontario

February 12-13, 2009

NSERC STRATEGIC WORKSHOPS PROGRAMWorkshop on Selection and Breeding Program for Rainbow Trout in Canada

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TABLE OF CONTENTS

TABLE OF CONTENTS..............................................................................................................1 SUMMARY OF WORKSHOP .....................................................................................................3 1. INTRODUCTION - RATIONALE FOR THE WORKSHOP .....................................................4 2. WORKSHOP OBJECTIVES...................................................................................................4

Agenda...............................................................................................................................5 List of Strategic Workshop Delegates ................................................................................7

3. INTER-PROVINCIAL PARTNERSHIP FOR SUSTAINABLE FRESHWATER

AQUACULTURE DEVELOPMENT Third Action Plan : Overview & Broodstock Management Dr. Grant Vandenberg, IPSFAD ........................................................................................8

4. A SNAPSHOT OF CANADIAN RAINBOW TROUT PRODUCTION

Prof. Richard Moccia and David Bevan, University of Guelph.........................................12 5. PRIMER COURSE IN QUANTITATIVE GENETICS & LESSONS FROM THE POULTRY

BREEDING PROGRAM DEVELOPMENT Dr. Ben Wood, Geneticist, Hybrid – Hendrix Genetics Company....................................15

6. RAINBOW TROUT BROOSTOCK PROGRAMS - A WORLD OVERVIEW

Dr Graham Gall, Emeritus Professor, Dept of Animal Science, UC Davis ......................22 7. DESIGNING COMPREHENSIVE FISH BREEDING PROGRAMS

Dr Graham Gall, Emeritus Professor, Dept of Animal Science, UC Davis ......................26 Part I: Step-by-Step Design .............................................................................................26

1. Assess-the Production System.............................................................................26 2. Formulate Breeding Objectives ............................................................................26 3. Review Performance of Available Stocks and Select Initial Stock(s) ...................27 4. Obtain Estimates of Genetic Parameters .............................................................27 5. Devise System of Animal Evaluation ....................................................................27 6. Determine Selection Method ................................................................................28 7. Develop Mating System for Selected Animals......................................................28 8. Develop Plans for Physical Facilities, Management and Staffing.........................29 9. System for Expansion and Dissemination ............................................................29 10. Continuous Evaluation and Modification of Program...........................................29


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Part II: Selection Method Examples.................................................................................30

Data Source...............................................................................................................30 The Selection Schemes.............................................................................................30 Characteristics of the 1998 Population ......................................................................31 Summary of Selection Effort ......................................................................................31 Results Based on Harvest Weight .............................................................................31 Summary of Selection................................................................................................32 Response to Selection...............................................................................................33 Effectiveness of Selection..........................................................................................34

Commonsense Animal Breeding..........................................................................................47 8. WRAP-UP OF THE WORKSHOP - RECAPITULATION OF BREEDING PLAN &

ELABORATION OF AN ACTION PLAN Prof. Richard Moccia, University of Guelph .....................................................................50

ACKNOWLEDGMENTS ..........................................................................................................52


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SUMMARY OF WORKSHOP The Canadian aquaculture industry is relatively young with much potential to expand; currently, annual growth exceeds 10% and injects one billion dollars into the Canadian economy each year. The main sectors of the Canadian aquaculture industry are marine finfish and shellfish production, with freshwater production being a minor contributor. However, Canada has significant expansion potential for the freshwater sector, in part because of the quantity and quality of its ground and surface waters. The Canadian freshwater aquaculture sector is predominantly that of rainbow trout, with increasing amount of steelhead trout from Saskatchewan and the Atlantic provinces. The production of rainbow and steelhead trout has become dependent upon eggs imported from the US west coast. This reliance and the genetic suitability of the supply have been identified as significant issues for the further development of the Canadian trout industry. As first step in resolving this issue, the Interprovincial Partnership for Sustainable Freshwater Aquaculture Development (IPSFAD) held a Workshop on a Selection and Breeding Program for Rainbow Trout Aquaculture in Canada in Guelph, Ontario, February 12-13, 2009. The workshop delegates included a diverse group of Canadian trout industry stakeholders and several invited experts in developing national breeding programs. The workshop began with a brief overview of the regional status of trout production, given by representatives from British Columbia, Alberta, Saskatchewan, Ontario, Quebec and the Atlantic provinces. This was followed by an introduction to quantitative genetics by Dr. Ben Wood, who described the experiences of the poultry industry as an example. The keynote speaker, Dr. Graham Gall, then took the delegates through an overview of rainbow trout broodstock programs in Norway, Chile and Finland as a preview to what can be achieved. The main component of the workshop, lead by Dr. Gall, directed the delegates through selected1 topics described in the document “Designing Comprehensive Fish Breeding Programs” written by Dr. Gall for the workshop. As part of this process, the delegates provided input and comments on:

1. Assess the production system 2. Formulate breeding objectives 3. Review performance of available stocks, choose initial stocks 5. Devise system for animal evaluation 8. Develop plans for facilities management and staffing 9. System for expansion and dissemination

The recorded input for each of these topics was discussed and obtained by consensus to provide a starting point for the further development of a Canadian focused rainbow trout breeding program.

1 Some topics were omitted because of time constraints. See main text for complete document and outcomes.


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INTRODUCTION - RATIONALE FOR THE WORKSHOP The continued sustainable development of the rainbow trout industry across Canada, from land-based model farms to cage grow-out operations, requires an increasing reliable supply of high quality fingerlings. Currently, more than half the rainbow trout grown in Central and Arctic Region are imported from the United States. Importation of eggs, reaching 4 million per year in Ontario alone, imposes undue risk on the sector in the event of border closures (e.g. disease issues). Secondarily, the genetic characteristics of the imported strains are not ideally suited to the grow-out conditions here in Canada. In the absence of any effort to enhance the quality and quantity of Canadian trout broodstock, long-term reliance on external suppliers is a threat to the rainbow trout sector. Furthermore, substantial economic gains could be attained through genetic selection to enhance the quality and performance of captive strains of rainbow trout produced in the Canadian aquaculture sector. Within the sector, it is recognized that there are two avenues to achieve genetic improvement: (1) short-term gains induced through the introduction of identified commercial strains from other jurisdictions; and (2) long-term gains attained from the introduction of a wider genetic base from feral and/or captive populations and implementation of a selective breeding program to target desirable traits, including growth, disease resistance, late maturation, yield, etc. A national broodstock program to develop enhanced performance in rainbow trout, specifically targeting improved fillet yield, enhanced growth rate and greater tolerance to warm-water conditions, is one of the six themes in IPSFAD’s current Industry Action Plan. A rainbow trout genetic selection program ranked second overall among industry’s priorities for targeted research and development in three (BC, ON, Atlantic Provinces) of the five regional stakeholder workshops. 1. WORKSHOP OBJECTIVES To assemble a team of experts in all aspects of Canadian rainbow trout aquaculture to

generate ideas and strategies regarding the scope and nature of a “Canadian Rainbow Trout Broodstock Program”

To review the current status of available technologies and practices regarding the selection

and breeding of fish, in an effort to target effective approaches for a “Canadian Rainbow Trout Broodstock Program”

To identify the research, development and commercialization components necessary to

establish a successful program.


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WORKSHOP ON SELECTION AND BREEDING PROGRAM FOR RAINBOW TROUT AQUACULTURE IN CANADA

Where: Ramada Hotel, Guelph, Ontario

When: February 12th & 13th, 2009

Organizers: Prof. Richard Moccia (U. of Guelph) Dr Grant Vandenberg (U. Laval) Eric Boucher (IPSFAD) David Bevan (U. of Guelph) Michael Burke (U. of Guelph) Karen Tracey (NOAA) Steve Naylor (OMAFRA) Dan Stechey (CAS)

Agenda Thursday, February 12th

7:30 Breakfast

8:00 Welcome and Objectives of the Meeting (Dr. Grant Vandenberg, President of IPSFAD)

Purpose of the meeting IPSFAD 3rd Industry Action Plan: overview, & broodstock management

Objectives of the meeting Review of Agenda

8:15 A Snapshot of Canadian Rainbow Trout Production (Prof. Rich Moccia)

8:25 Broodstock Management Challenges - Industry Perspectives

British Columbia - Larry Albright, FAABC Alberta – Lorne Loudon, AAA Prairies - Dean Foss, WWS Ontario - Gord Cole, NOAA Québec - Sylvain Lareau, AAQ Atlantic Prov. – Dale Jordison, Coldwater Fisheries Panel discussion (Dan Stechey, Canadian Aquaculture Systems)

9:00 Primer Course in Quantitative Genetics & Lessons from the Poultry Breeding Program Development (Dr. Ben Wood)

9:30 Rainbow Trout Broostock Programs – A World Overview (Dr. Graham Gall)

9:45 Q&A

10:00 Break & interactive session


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10:30 Designing Comprehensive Fish Breeding Programs (Dr. Graham Gall)

Overview of the document

11:00 i - Assess the Production System

ii - Formulate Breeding Objectives

12:00 Lunch 1:30 iii - Review Performance of Available Stocks and Select Initial Stock(s)

vi - Devise System of Animal Evaluation

3:00 Break

3:30 viii - Develop Plans for Physical Facilities, Management and Staffing

ix - System for Expansion and Dissemination

5:00 Q&A

5:30 Cocktails Friday, February 13th

9:00 Wrap-up of the Workshop

Recapitulation of Breeding Plan

Elaboration of an Action Plan Commitments and allocation of responsibilities Follow-up

Dialogue on Possible Research Projects 12:00 Lunch

13:00 Departure for the Alma Aquaculture Research Station, University of Guelph

15:00 Return to Ramada Hotel, Guelph


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List of Strategic Workshop Delegates Name Affiliation EmailLarry Albright Freshwater Aquaculture

Association of BC [email protected]

David Bevan AARS, U. of Guelph [email protected] Eric Boucher IPSFAD [email protected] Michael Burke AARS, U. of Guelph [email protected] Gord Cole Aqua-Cage Fisheries [email protected]

Guillaume Dagenais DFO-Aquaculture Management Directorate [email protected]

Roy Danzmann Integrative Biology, U. of Guelph [email protected]

Robert Devine Cold Water Fisheries Alvis Fogel Springhills Trout Farm [email protected] Dean Foss WildWest Steelhead [email protected]

Graham Gall Dept of Animal Science, UC Davis [email protected]

Amber Garber HMSC NB Cod Genome project [email protected]

Doug Geilling DFO-ACRDP [email protected] Tricia.Gheorghe DFO-Science Division [email protected]

Brian Glebe DFO, Aquaculture and Biological Interactions [email protected]

Stéphanie Houle SORDAC [email protected] Jordison Cold Water Fisheries [email protected] Sylvain Langlois NSERC [email protected]

Lorne Louden Alberta Aquaculture Association [email protected]

Richard Moccia Dept. of Animal & Poultry Science, U. of Guelph [email protected]

Richard Morin MAPAQ [email protected] Steve Naylor OMAFRA [email protected]

Luc Picard Centre de transfert et de sélection des salmonidés [email protected]

Sean Pressey Lyndon Fish Hatcheries [email protected] Lynn Rieck Lyndon Fish Hatcheries [email protected]

Bill Robertson Huntsman Marine Science Center [email protected]

Andy Robinson CGIL, U. of Guelph [email protected]

Dan Stechey Canadian Aquaculture Systems [email protected]

Bruce Swift Swift Aquaculture [email protected] Jim Taylor Cedar Crest Trout Farm [email protected] Grant Vandenberg IPSFAD [email protected] Ben Wood Hendrix-Genetics [email protected]


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2. INTER-PROVINCIAL PARTNERSHIP FOR SUSTAINABLE FRESHWATER AQUACULTURE DEVELOPMENT – Third Action Plan : Overview & Broodstock Management Dr. Grant Vandenberg, IPSFAD

In recognition of this challenge, in 2001, a joint effort was undertaken between the Society for Research and Development for Continental Aquaculture (SORDAC) Inc., the Quebec Aquaculture Network (RAQ), and Fisheries and Oceans’ Office of the Commissioner for Aquaculture Development (OCAD). Recognizing common interests and objectives, the Ontario industry also engaged in the exercise, followed by freshwater producers in Western Canada. This consortium solicited the views and participation of the major players in the Canadian freshwater aquaculture industry regarding challenges and constraints facing the sector and resulted in formation of the Inter-Provincial Collaborative R&D Initiative for Sustainable Freshwater Aquaculture (the Initiative). The Initiative established research, development and commercialization (RDC) partnerships among Canadian experts to carry out specific projects related to the issues voiced by industry stakeholders. This approach generated consensus on industry priorities, identified pertinent RDC expertise, sought out synergies between various players and reduced duplication of efforts. In 2003, the first national meeting was held in association with the Aquaculture Association of Canada (AAC) in Victoria, BC. At the 2004 annual meeting of the AAC in Québec City, the Initiative hosted a national symposium to address a wide range of issues regarding freshwater aquaculture development. In 2006, the Initiative changed its name to Interprovincial Partnership for Sustainable Freshwater Aquaculture Development (IPSFAD) The IPSFAD is national in scope and brings together several internationally-recognized experts into a collaborative framework of industry, academic and government interests that enriches and stimulates the overall Initiative. It is a unique opportunity to pool expertise and resources and to focus them around a primary cause; namely, fostering the sustainable development of freshwater aquaculture. The following general principles are fundamental to all aspects of the Initiative:

Recognizing regional differences and needs in planning and development; and

Promoting faster implementation of applied research, development and commercialization initiatives and delivery of results.

In keeping with these principles, efforts to advance sustainable development of freshwater aquaculture were targeted at four principal challenges: Nutrition, Waste Management, Farm Management and Assimilative Capacity. Third Industry Action Plan – 2007 The Third Industry Action Plan of the Inter-Provincial Partnership for Sustainable Freshwater Aquaculture Development (IPSFAD) is intended to focus applied research, development and commercialization (RDC) efforts on those issues that can best enhance productivity and prosperity within the sector. Pure research is not within the scope of IPSFAD’s activities.

In the autumn of 2006 and early winter of 2007, the IPSFAD coordinated five 1-day workshops with industry and government stakeholders to solicit input regarding those RDC initiatives deemed to be most pertinent to industry development and to once again update and prioritize sectoral challenges and opportunities. Meetings were held in Alberta, Québec, British Columbia


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and Ontario, and a pan-Atlantic meeting was held in New Brunswick. At each workshop, an identical process was followed, as outlined below:

Status of aquaculture development in the province / region Overview of IPSFAD and results of past projects Identification of fundamental challenges and opportunities Overview of potential RDC projects Prioritization of potential RDC projects

The workshops successfully identified stakeholder perspectives on the fundamental RDC issues in each region, including identification of specific project objectives. Consolidation of stakeholder input from these meetings is the foundation for a renewed three-year Industry Action Plan that will serve as a coordinating instrument for sustainable freshwater aquaculture development throughout Canada from 2007-2009. Unquestionably, the IPSFAD’s third Industry Action Plan is industry driven. It builds upon its predecessor, using similar approaches, and addresses a broadened range of themes related to sustainable freshwater aquaculture development in Canada.

To consolidate resources and effort in those areas where interests, challenges, needs and opportunities are similar, 32 of the 49 issues identified in the regional workshops have been re-classified into six thematic groups:

Fish Health Management Alternative Species and Practices

Nutrition Experimental Farm Initiative (Land-Based)

Broodstock Management Cage Culture

Based on stakeholder input, the RDC Themes outlined in the Action Plan are fundamental to industry development; however, within each theme, the issues are not necessarily static. It is recognized that the Action Plan must maintain a degree of flexibility to address emerging issues. Therefore, it is conceivable that the Board of Directors may, from time to time through the life of this Action Plan, recommend new initiatives based on the evolving requirements to advance sustainable freshwater aquaculture development.

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Grant Vandenberg, President of IPSFAD presenting IPSFAD’s Third Action Plan


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3. A SNAPSHOT OF CANADIAN RAINBOW TROUT PRODUCTION Prof. Richard Moccia and David Bevan, University of Guelph

Richard Moccia holds three senior management and research cross-appointments at the University of Guelph. He is the Associate Vice-President of Research (Agrifood and Partnerships) which includes responsibility for the negotiation and management of a $55 million per year research, education and laboratory services agreement with the province of Ontario. He is also the Director of the Aquaculture Centre and Alma Aquaculture Research Station, both centres of excellence dedicated to the near-commercial evaluation of aquaculture technologies. Richard Moccia also holds a faculty appointment as a full Professor of Aquatic Science in the Department of Animal and Poultry Sciences. Richard Moccia has been an integral member of the Canadian aquaculture sector for nearly 30 years. He was past President of the Ontario Aquaculture Association, as well as Research Director and Vice-president of International Aquaculture Developments Inc., an aquaculture technology and production company. He also established and ran Aquatic Pathology Services, a company dedicated to solving commercial development issues within the Canadian aquafood sector. Richard Moccia has gained significant international experience and has worked with numerous private sector clients in North America, Europe and Central America. Richard Moccia’s research has been largely directed at industrial and regulatory problem-solving in relation to commercial aquaculture, specializing in fish health, nutrition, water quality management and business development. Richard Moccia is a founding member of IPSFAD.

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Richard Moccia (left) in conversation with Graham Gall (right).


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4. PRIMER COURSE IN QUANTITATIVE GENETICS & LESSONS FROM THE POULTRY BREEDING PROGRAM DEVELOPMENT Dr. Ben Wood, Geneticist, Hybrid – Hendrix Genetics Company

Ben Wood grew up on a commercial duck and cattle farm in the lower Hunter Valley north of Newcastle, Australia. He trained as a veterinarian at the University of Queensland and practiced for a number of years in both Australia and the UK. After returning from the UK he completed a PhD in quantitative genetics in beef cattle, specifically on the economic feasibility of use of genetic and physiological markers in cattle breeding. For the past 3½ years he has been employed as a geneticist by Hybrid Turkeys, a primary turkey breeding company operating out of farms spread around Kitchener-Waterloo area. Additionally he has a position as adjunct professor within the Department of Animal and Poultry Science (University of Guelph) considering aspects of genetic improvement of the turkey. He has interests in the use of new technologies and there application in breeding programs and also in the epidemiology and control of pathogens in poultry systems.


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5. RAINBOW TROUT BROOSTOCK PROGRAMS A WORLD OVERVIEW Dr. Graham A.E. Gall, Emeritus Professor, Dept of Animal Science, UC Davis, Graham Gall retired from Animal Science in January 2003. He joined the department in September 1966 after completing degrees in Animal Science (B.Sc.) and Animal Breeding (M.Sc.) at the University of Alberta and in Animal Genetics (Ph.D.) at Purdue University. Early in his career, working in the area of biochemical genetics related to quantitative animal performance, Graham began his efforts on breed improvement problems with rainbow trout. This resulted in an association with the hatchery program of the California Department of Fish and Game that lasted for more than 20 years. In the mid-1970s, problems associated with the conservation of native trout species in California allowed Graham Gall to return to an old interest of applying biochemical methods to understanding the genetic relationship among natural populations. One of the highlights of his career was identifying and restoring native Golden Trout to parts of the Kern River Basin in California's Sierra Nevada Mountains. In addition, he published extensively on breed improvement of fish for farming, including original and landmark work on the genetics of rainbow trout reproduction. He became nationally and internationally respected as an authority on fish genetics and worked in many countries of the world for various governmental and international organizations dealing with improving fish species for farming and the conservation of native fish species. He participated in trout, salmon and tilapia research projects in Chile, the Philippines and Israel. He was a founding officer of the International Association for Genetics in Aquaculture. Graham Gall's teaching career included undergraduate courses in animal genetics, animal breeding, statistics and wildlife genetics, as well as graduate courses on the theory of quantitative genetics. For the last ten years of his career, he supervised the undergraduate teaching and student advising programs in Animal Science.

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Dr Graham Gall presenting an overview of rainbow trout broodstock programs existing worldwide.


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6. DESIGNING COMPREHENSIVE FISH BREEDING PROGRAMS Dr. Graham A.E. Gall, Emeritus Professor, Dept of Animal Science, UC Davis,

The process of designing and implementing a comprehensive breeding program can be complex. The first section of this document outlines an orderly process for the design of a breed improvement program. The process consists of nine ordered steps that can be used to evaluate a program. The second section discusses three examples of selection in an effort to demonstrate options and the effectiveness of breed improvement. Part I: Step-by-Step Design The often overwhelming number of details to consider and decisions to be made in designing a breed improvement program can be simplified by approaching the task an organized, step-by-step manner. The steps for designing such a program are listed below; each is discussed in the sections following:

1. Assess the production system 2. Formulate breeding objectives 3. Review performance of available stocks, choose select initial stocks 4. Obtain estimates of genetic parameters 5. Devise system for animal evaluation 6. Determine selection method 7. Develop mating system for selected animals 8. Develop plans for facilities, management and staffing 9. System for expansion and dissemination 10. Continuous evaluation and modification

1. Assess-the Production System

Breed improvement programs vary depending upon production system and product to be marketed. Thus, the initial step in the design of a breeding program is to describe the production system and define the product the program is intended to enhance through improved production efficiency. Initial decisions as to how the selection program will fit into the overall animal production system are made at this step (and to be re-examined in Step 9). For example, will the selection-program stocks be part of the production system or will these stocks be part of a separate nucleus breeding program. At this stage the breeder determines the level of sophistication necessary for the breeding program. 2. Formulate Breeding Objectives

Both long- and short-term goals should be identified, based on genetic feasibility and the production system. All traits considered beneficial to production system efficiency should be outlined and ranked. A minimum number of primary traits should be chosen from the top ranked traits, with others identified for future monitoring; including an excessive number of primary traits reduces the expected improvement for all traits. All objectives should be constrained to the capabilities of the production system. Because of the long-term nature of breeding programs, the goals identified should remain relevant for several generations. Future changes in priorities and economic conditions should be anticipated, if at all possible, in an attempt to minimize the frequency of modifications to the breed improvement program.


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3. Review Performance of Available Stocks and Select Initial Stock(s)

Starting a breeding program with a stock with performance characteristics that most nearly correspond to the defined objectives can potentially save years of breeding effort while enhancing initial production. It may be necessary for the breeder to physically evaluate different strains or lines under a given set of conditions to assess available stocks. Consideration should be given to initiating the breeding program with a new stock created by systematic crossing of the most desirable stocks available to ensure a high level of genetic variation in the foundation stock. 4. Obtain Estimates of Genetic Parameters

Estimates of genetic parameters are essential in deciding upon the appropriate method of selection and to identify the traits likely to maximize improvement in net value of production. The parameter estimates necessary include genetic and phenotypic variances and correlations among the desired traits. Such estimates can usually be found or extrapolated from current literature. Ideally, estimates of genetic parameters should be taken from studies utilizing stocks similar to those to be used for production. 5. Devise System of Animal Evaluation

Decisions must be made concerning how to evaluate the animals being considered for selection; this is known as defining the selection criteria. In addition, a determination must be made regarding how the pedigree of the population will be maintained. The simplest identification system is to identify only the stock or groups of animals according to parentage, for example groups of full-sibs. However, the only truly effective system is to identify every individual animal with some form of physical mark. The design of the animal evaluation system should include the following:

1. Age or size at which performance records are to be taken. 2. The traits to be measured at each point in time during the production cycle. 3. The point in the production cycle where selection should be carried out. 4. Length of time selected animals are to be used as breeding stock.

The performance of each animal should be determined by direct measurement and/or measurements made on close relatives. Compound traits or traits that are difficult or expensive to measure (for example, food conversion, food consumption, survival) should be excluded from the initial list of primary traits. Often improvement for many of these "secondary" traits can be achieved more easily as an indirect selection response resulting from genetic correlations with the primary traits. The target "market" for animals from the improved stock should be considered in designing the animal evaluation system. The target market can range from all fish going to production on a single farm to the sale of all eggs or juveniles to other producers. Performance testing should be designed to assess performance for the specific target market to ensure performance is evaluated under the target production systems (environments). The simplest case is the situation where the breed improvement program is designed for production only within the local farm. However, if the target market includes other farming locations or other farms, then performance should be evaluated over a larger environmental range than the local farm. This can be achieved through the use of "test stations" located in representative environments. Performance can be evaluated at test stations by marking and transferring a sample of fish from each family unit in the program to each test station.


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Test-station performance has a number of additional advantages. Since a sample of each family unit is evaluated at each test station, these performance records represent performance of close relatives of the individuals retained at the local farm. Also, it is expected that all individuals reared at test stations will be harvested facilitating the evaluation of carcass traits; all their relatives located at the local farm are potential breeding stock. Consequently, this system makes it is possible to evaluate live-animal performance traits at the local farm and to evaluate live-animal traits plus carcass traits with test station fish, greatly enhancing the potential scope of the breed improvement scheme. 6. Determine Selection Method

A variety of selection approaches are available to the breeder, each differing in relative merit depending upon the production system and breed improvement goals. The simplest methods include individual, family, and within-family selection based directly on measured performance. More efficient methods incorporate performance records from various types of relatives such as parents, full-sibs, and half-sibs, and possibly even progeny records. In addition, the method adopted must be compatible with the criteria (traits) to be used for selection. It is generally desirable to initiate a new selection program with only one or two traits while gaining knowledge and experience with the breeding program. The optimal procedure for more than one trait involves the construction of a weighted mathematical function (selection index). Selection index theory is highly developed and its application through the use of best linear unbiased prediction (BLUP) statistical techniques to estimate breeding values from animal-model analysis has proven to be an extremely efficient system. For multiple trait selection, selection indices can be designed to include records for a number of traits weighted by their relative economic values, most effectively based on estimated breeding values. 7. Develop Mating System for Selected Animals

In this step the breeder decides how to mate selected males and females to produce the next generation of animals for both performance testing and for production. The most common approach is to randomly mate each male to several females, although other systems are possible such as producing sets of "mating groups" by factorially mating several males to several females. A balance must be achieved between the objective of intense selection for improved performance and reduction of genetic variability within the population due to inbreeding. The balance can be achieved by considering the proportion of the breeding population to be selected (selection intensity) and the relative contribution of each family to the final selected group. Outcrossing systems can be used in later generations to reduce or eliminate losses in performance due to small population size within lines. This can be achieved by maintaining genetically separate "year-class" groups within the breeding program and then crossing the year-classes after a number of generations of selection. The relationship of the "breeding group" and the production system is an important factor. If all production animals are to be obtained as progeny of selected parents, then the mating system must be designed to produce the required number of production animals along with the progeny needed for the next cycle of performance testing. Conversely, if the improvement program is based on a "nucleus" breeding group with production animals coming from multiplier farms, then the mating system must include a scheme for expanding the nucleus group to provide production animals as determined under Step 9. Pedigree information on individuals becomes an extremely useful aid in designing the mating system. Without knowledge of ancestry, accuracy of selection is compromised and effective


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population size can be reduced unknowingly due to selected individuals coming from relatively few well performing families. Therefore, it is desirable to include an optimum number of families and to maintain optimum family sizes in the mating design and to develop and maintain an extensive pedigree-information system. For example, it is possible to greatly enhance effective population size by reducing variation in family size, that is, the number of individuals selected from each target family group. The availability on ancestry, and thus, the performance of relatives, also will result in an increase in the accuracy of selection which should more than offset any increase in production costs related to maintaining the pedigree. 8. Develop Plans for Physical Facilities, Management and Staffing

Special facilities must be designed to accommodate spawning, hatching, rearing and grow-out. The facilities also must provide the resources necessary for performance testing and recording measurement on large numbers of fish. Some examples include:

1. Holding facility of maturing adults; ability to identify individual males 2. Hatch and rear family units; single female spawn 3. Marking system to accommodate communal grow-out 4. Grow-out system for “on-station” and “on-farm” testing 5. Slaughter facilities, if carcass traits are important 6. Data management system and computer facilities 7. Expert staff; quantitative genetics and fish husbandry 8. Standardized feeding, rearing, and management.

The facilities and staff form the backbone of the whole program. The program must be consistent from year-to-year, must provide accurate and precise data, and must be managed so events occur in timely manner. It is never clear whether “top” management responsibility should reside with the expert on husbandry or the expert on selection but it is very clear that the two roles must be absolutely complementary. 9. System for Expansion and Dissemination

Several different forms of breeding-system organization are available for efficient dissemination of the genetic improvement into the production system. The choice of expansion strategy must consider both the number of improved animals required for production and the expected rate of genetic improvement. For instance, if the selection program occurs in only a small fraction of the total population involved in the production system, it may be necessary to use a multi-tier multiplication scheme to facilitate expansion (e.g., grandparent generation selected; parent generation multiplier, progeny generation to production). Such a system involves an "improvement lag" or delay in the transfer of the genetic gains made in the selection program to the production groups; it is desirable to minimize this lag. 10. Continuous Evaluation and Modification of Program

Future changes in the production system, fluctuations in economic and marketing conditions, advances in technology or experience gained during the initial years, will require re-evaluation of the breeding program on a regular basis. Examples of such modifications might include changes in performance testing protocols, changes in market demand, improvement of fish-culture technology, disease considerations (introduced disease, vaccine development, etc.), changes in consumer demand, or shifts in marketing or production strategy. Repeated re-evaluation of the design of a breed improvement program in the event of such changes should insure continued optimal efficiency of the breeding program.


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Part II: Selection Method Examples Data Source

Data were taken from nine years (1992 - 2000) of harvest performance of the Chilean CMG Coho Salmon breed improvement program. The dataset contained records on harvest weight taken at about 21 months of age for 12,834 fish. The 1998 year-class, consisting of 2380 performance records, was chosen to demonstrate the application of three selection methods. All selection methods define a procedure for estimating the breeding value of each fish available for selection. The three methods used for this example were mass selection, combined family and within family selection, and animal-model “estimated breeding value” selection. Under mass selection, the observed harvest weight of the fish is assumed to be a reliable estimate of the breeding value of the animal; the accuracy of this estimate is a function of heritability; it was about 0.23 for this dataset which yields an accuracy of about 0.48 (correlation between harvest weight and true breeding value). Combined family and within family selection utilizes an "index" calculated for each individual fish. An index is a mathematical function that "weights" information from difference sources based on the "reliability" of the information. For this example, a simple index was constructed to include the performance of the individual fish and the average performance of all full-sib family members of the fish. The parameters used were an intraclass correlation among full-sibs of t = 0.145 and the additive genetic relationship among full-sibs of r = 0.50. Estimated breeding value (EBV) selection was carries out by ranking all individual using an estimate of breeding value. The breeding values were estimated by applying a simple animal model to the complete dataset (1992-2000). So in this example the estimated breeding values for the 1998 year-class were from records on the progeny of the 1998 fish, records on the 1998 fish themselves, plus records for their parents, grandparents and great-grandparents. Fixed effects of year and sex were included in the model. After estimation, the breeding values for all fish in the 1998 year-class were extracted and used for comparisons of the three selection methods as applied to the 1998 year-class. The Selection Schemes

It was assume the breed improvement program used a nested mating system with a ratio of one male to three females during mating. Parental population size, that is, the number of breeding individuals selected, was varied to accommodate two selection intensities: selection of the top 20 % (20 % Selected) and selection of the top 10 % (10 Selected) based on harvest weight. The number of males and females to be selected under ideal conditions is summarized here, based on a total population of N = 2380 fish in the 1998 year-class: 20 % Selected [with 3 females for each male]. Total selected: 20 % of 2380 fish, or Nselected = 476 Males selected: 10 % of 1190 males, so Nmales = 119 Females selected: 30 % of 1190 females, so Nfemales = 357 10 % Selected [with 3 females for each male].

Total selected: 10 % of 2380 fish, or Nselected = 238, � round to 236 Males selected: 5 % of 1190, so Nmales = 59 Females selected: 15 % of 1190, so Nfemales = 177


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Characteristics of the 1998 Population

The average harvest weight for the 1998 year-class was 4069 g (Table 1). The population contained 1373 males and 1007 females so there were significantly more males than females. Males were lighter than females and were less variable (standard deviations: 533 g for males and 790 g for females). The smallest individuals in the population weighed 500 g while the largest male and female weighed 5600 g and 5900 g, respectively. The higher variability for females indicates that greater selection potential exists for females compared to males. However, the population contained fewer females than males, and more females than males are to be selected so selection opportunity among females is reduced relative to males. Summary of Selection Effort

Selecting by targeting a particular proportion of the available population determines the selection pressure applied to the population and can differ between the sexes if the number of males and females is not equal. Table 2 lists the selection pressure achieved for the 20 % selected and 10 % selected schemes applied to the data for this example. In terms of the proportion of fish of each sex selected, doubling selection pressure, that is, reducing the percentage selected by one-half, effectively reduces the proportion selected by one-half. Note that the lack of females in the population resulted in fewer males and more females being selected than expected if there had been equal numbers of each sex in the population. A more realistic measure of selection pressure is the parameter, "selection intensity" calculated as superiority of the selected group, in number of standard deviations, relative to the mean of the population. This represents an estimate of the expected superiority of selected individuals, in terms of average harvest weight. Based on the proportion selected, the selection intensity for males and females was 1.10 and 0.76 standard deviations under the 20 % selected scheme (Table 2). Increasing selection pressure from 20 % to 10 % resulted in an increase in selection intensity of only 0.30 standard deviations for males, from 1.10 to 1.40, and only 0.18 for females, from 0.76 to 0.94. Results Based on Harvest Weight Mass Selection

Table 3 lists a number of measures of harvest weight for selected fish when mass selection was applied at two levels of selection intensity. It is interesting to note that the minimum weight among selected fish exceeds the population average in all cases (Table 1). One measure of superiority is the “selection differential” defined as the difference between the average harvest weight of selected fish and the average for the population. For 20 % Selected, the mean of the 119 of males selected exceeded that of the male population by 865 g. The selection differential for the 357 females selected was only 733 g. The difference in selection differentials for the two sexes is the result of a balance between proportion selected (8.7 % for males verses 35.5 % for females) and the higher variability among females. The combined selection differential, calculated as the average of the two sexes, was 799 g which represents the superiority of selected individuals. Converted to selection intensity, the selection effort equals 1.28 standard deviations and is called the realized selection intensity; in this case it is larger than that predicted in table 1 (average 1.10 and 0.76 = 0.93). Selection at a level of 10 % rather than 20 % resulted in larger selection differentials for the two sexes: they were 995 g for 59 selected males and 938 g for 177 selected females. The combined selection differential was 967 g, about 20 % greater than it was with 20 % Selected. The selection intensity was 1.53 standard deviations which is higher than predicted.


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Note that reducing the proportion selected does not result in a doubling of selection intensity, demonstrating the effect of diminishing returns of increasing selection pressure. Also selection intensity has a direct effect on “effective population size,” the number of breeding individual (i.e., number of parents) for the next generation. It was considerably smaller under 10 % Selected (59 males and 177 females) than under 20 % Selected (119 males and 357 females)`, meaning rate of inbreeding will be more rapid under the 10 % Selected scheme. Combined Family and Within Family Index Selection

The combined-selection index utilized the average harvest weight of members of the individual's full-sib family in addition to the individuals own harvest weight to improve the accuracy of estimating each individual's breeding value. Consequently, an individual with low harvest weight could be selected if it was a member of a high performing family. Table 4 lists the results of applying this type of selection index to the 1998 year-class population. Under the 20 % selected scheme, the average harvest weight of selected males and females was 4564 g and 4793 g and the smallest individuals selected weighed 3600 g (male) and 3700 g (female). The selection differentials were 587 g for males and 599 g for females; the combined selection differential was 593 g, representing a selection intensity of 0.93 standard deviations. With only 10 % selection, the average harvest weight of selected males and females was 4724 g and 4935 g; the smallest individuals selected weighed 4100 g (male) and 3760 g (female). The selection differentials were 747 g for males and 740 g for females; the combined selection differential was 744 g, representing a selection intensity of 1.17 standard deviations. Thus, selection at a level of 10 % resulted in a 25 % increase in selection differential (and intensity) compared to 20 % Selected. Breeding Value Selection

Breeding values were estimated for all individuals using data for all five generations of fish in the dataset. Selection was carried out by selecting the appropriate number of males and females exhibiting the highest estimated breeding values (EBV). Table 5 summarizes phenotypic values (i.e. harvest weight) observed when selection was practiced at the two levels of selection. For 20 % Selected, the mean harvest weight of selected males exceeded that of the male population by 603 g (selection differential) and selected females were 619 g heavier than the female population. The combined selection differential, calculated as the average of the two sexes, was 611 g which represented a selection intensity of 0.96 standard deviations. With selection at a level of 10 %, there was a large difference between the selection differentials for the two sexes; 615 g for males and 779 g for females. The combined selection differential was 697 g, a selection intensity of 1.07 standard deviations. The realized selection intensities were similar to those expected under ideal conditions (0.93 males, 1.17 females; Table 2). Summary of Selection

Mean harvest weight and the selection differential realized for the three selection methods at the two levels of selection are summarized in Table 6. One obvious outcome was that the average harvest weight of selected males and females was lower for combined selection and EBV selection than it was for mass selection, regardless of level of selection. The selection differentials for males was always larger than that for females with mass selection, tended to be similar with combined index selection but were higher for females than males under EBV selection. The largest selection differentials were achieved for both sexes


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under mass selection. Selection using EBV resulted in a 25 to 30 % decrease in selection differentials relative to mass selection while combined index selection tends to give intermediate results. These differences were expected since only mass selection targets the actual phenotypic value of individual fish. Estimated breeding values are one-step removed from the phenotypic value of individuals since the EBV is derived from the phenotypic performance of the individual plus all relatives of the individual (grandparents, parents, half-sibs, full-sibs and progeny if any). The combined index selection represents an intermediate case in which ranking of individuals is modified by the average performance of the individual's full-sibs only. The “proof of the pudding” of course is the realized performance in the progeny generation. One factor affecting this outcome is the correlation between phenotypic value and breeding value for harvest weight for individual fish. This is a direct function of the heritability of harvest weight. The heritability of harvest weight in our dataset was about 0.23. Mass selection assumes that fish with the highest harvest weights will have the highest breeding values for harvest weight and the accuracy of mass selection is equal to the square root of the heritability. Thus, the correlation between phenotypic and breeding values should be about 0.48 for our examples. Indexes, including combined selection and EBV selection, are methods designed to increase the accuracy of selection. Consequently, the observed correlation between phenotypic and breeding values is replaced by a correlation between the index value and breeding value. One outcome of this approach is that fish selected under combined index selection or EBV selection can have low harvest weights if they are determined to have high breeding values. Let’s evaluate how these ideas show up in our demonstration results. Response to Selection

The effect of selection can be evaluated by comparing the estimated breeding values of selected fish to the average breeding values of all individuals in the 1998 year-class. It must be noted that the average breeding value of selected males and females is an unbiased estimate of the average harvest weight expected for all possible progeny produced by these males and females. Consequently, a comparison of EBV for the three methods of selection provides a direct guide to selection response. Selection response is defined as the difference between the average performance of the parental and progeny generations. The information listed in Tables 7 and 8 summarizes the response to selection expected from the three selection methods. Detailed results are listed in Appendix Tables 1, 2 and 3, for mass selection, combined selection, and EBV selection, respectively. The Selected Individuals

The first rows of Tables 7 and 8, under "Selected Individuals," characterize the differences between the average harvest weight (phenotypic value) and average estimated breeding value of selected fish. As expected due to the upward bias of upper end phenotypic values, the average estimated breeding values are less than the average phenotypic values (harvest weight). The largest differences were observed for mass selection (367 g and 488 g under 20 % and 10 % Selected, respectively) indicating, again as expected, that observed individual harvest weight is the poorest estimate of breeding value. The difference between average harvest weights and average estimated breeding values were smaller for combined index selection and EBV selection, being from one-half to one-quarter of the differences under mass selection. It is generally expected that EBV selection will be superior to combined index selection; this was most evident at 10 % Selected in these examples. The similarity of results for combined index selection and EBV selection may be a


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reflection of the large full-sib family sizes in the 1998 dataset improving the index predictions for combined index selection. Average family size for the 99 dams was 24 fish, with a range of 12 to 45 fish; about 50 % of the families contained more than 25 fish. The Selection Response

The estimated breeding value for the selected fish can be used as an unbiased estimate of the average phenotypic value (harvest weight) of their progeny. Thus, it is possible to estimate the selection response for the examples by comparing this estimate of the average harvest weight of progeny (progeny generation) to the average of the population of their parents (parental generation). An evaluation of performance in the two consecutive generations for the three selection methods is listed in Tables 7 and 8 in the rows under the heading "Selection Response." The differences ranged from 449 g to 518 g under 20 % Selected and from 495 g to 588 g under 10 % Selected. Selection response under 10 % Selected was about 9 % greater than with 20 % Selected demonstrating that doubling the selection intensity does not yield a doubling of selection response. The largest selection response was achieved with EBV selection (588 g) under 10 % Selected, exceeded mass selection by 93 g (19 %); response at 20 % Selected was 516 g exceeding mass selection by 67 g (15 %). With combined index selection the responses were 535 g under 10% Selected (8 % gain over mass selection) and 518 g under 20 % Selected (15 5 gain over mass selection). These results confirm that basing selection of a fully pedigreed population yield superior results (See Appendix Tables). Effectiveness of Selection

Selection response can be evaluated as the improvement per generation as a percent of the initial parental harvest weight; this provides a more direct measure of expected rate of improvement in performance. The selection response in absolute weight terms (also called genetic gain) provides a measure of potential economic gain from a selection program. Based in the use of the most accurate selection procedure, EBV selection, one generation of selection of coho salmon for increased weight at harvest could yield a response of 500 to 550 g or 12 % above the previous generation mean of 4069 g. This means that a coho producer could expect at least a 12 % increase in total weight of harvested fish. For a production of 100,000 fish averaging 4 kg at harvest, this would represent an additional 58,800 kg of harvested weight. Since genetic gain is cumulative over generations, production level would continue to improve at a rate at or near 12 % per generation. If the production cycle is three years (egg to egg), then the gain per year would one-third of these values, or about 4 % per year in economic terms. Rainbow trout populations express at least as great a potential for genetic improvement as coho salmon. Expected results in absolute weight gain would be scaled down from the examples here since rainbow trout are harvested at a lower weight. However, the percentage improvement could be very similar to those given here since the selection response in weight would scale in proportion to the harvest weight. It is not clear from experience how production at very low harvest weights of say 200 g to 300 g would respond. However, research assessing correlations between weight at various ages suggests response would be strong.


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Table 1. Harvest weight (g) characteristics of the 1998 population.

Statistic All Fish Males Females

Number 2380 1373 1007

Mean weight 4069 3977 4195

Standard Deviation 663 533 790

Minimum weight 500 501 500

Maximum weight 5900 5600 5900

Table2: Selection pressure achieved for each selection scheme

Parameter Males Females

Proportion selected (% of each sex)

Select 20 % 8.7 35.5

Select 10 % 4.3 17.6

Selection intensity (i) “standard deviation units”

Select 20 % 1.10 0.76

Select 10 % 1.40 0.94


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Table 3. Results of mass selection of males and females for harvest weight.

Select 20 % Select 10 %

Measure (for selected fish) Males Female Males Females

Number 119 357 59 177

Mean (g) 4842 4928 4972 5133

Standard Deviation (g) 168 265 145 228

Minimum (g) 4640 4600 4800 4900

Maximum (g) 5600 5900 5600 5900

Selection Differential (g) 865 733 995 938

Proportion Selected (%) 8.7 35.5 4.3 17.6

Selection Intensity (i) 1.62 0.93 1.87 1.19

Combined Mean (g) 4885 5052

Combined Selection Differential (g) 799 967

Combined Intensity (i) 1.28 1.53


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Table 4. Results of combined index selection of males and females for harvest weight.

Select 20 % Select 10

Measure (for selected fish) Males Females Males Females

Number 119 357 59 177

Mean (g) 4564 4793 4724 4935


Minimum (g) 3600 3700 4100 3760

Maximum (g) 5600 5900 5600 5900






Combined Intensity (i) 0.93 1.17


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Table 5. Results of breeding value selection of males and females for harvest weight.

Select 20 % Select 10

Measure (for selected fish) Males Females Males Females

Number 119 357 59 177

Mean (g) 4580 4813 4591 4974


Minimum (g) 3760 3620 3900 3800

Maximum (g) 5600 5750 5600 5700






Combined Intensity 0.96 1.07


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Table 6. Summary of selection for harvest weight achieved using three methods of selection.

Mass Combined EBV

Measure (selected fish) Males Females Males Females Males Females

20 % selected

Mean harvest weight (g) 4842 4928 4564 4793 4580 4813

Selection Differential (g) 865 733 587 599 603 619

10 % selected

Mean harvest weight (g) 4972 5133 4724 4935 4591 4974

Selection Differential (g) 995 938 747 740 615 779


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Table 7. Comparison of three selection methods with selection of 20 % Selected based on average harvest weight and estimated breeding values.

Selection method Parameter

Mass Combined EBV

Selected Individuals

Average harvest weight (g) 4885 4678 4696

Average EBV (g) 4518 4587 4585

Difference (g) 367 91 111

Selection Response

*Average progeny generation(g) 4518 4587 4585

Average parent's generation (g) 4069 4069 4069


Selection response (%) 11.0 12.7 12.6 * The expected average harvest weight of progeny is equal to the average EBV of selected parents; thus, EBV of parents was used to estimate progeny performance.


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Table 8. Comparison of three selection methods with selection of 10 % Selected based on average harvest weight and estimated breeding values.

Selection method Parameter

Mass Combined EBV

Selected Individuals

Average harvest weight (g) 5052 4830 4782

Average EBV (g) 4564 4604 4657


Selection Response

*Average progeny generation(g) 4564 4604 4657

Average parental generation (g) 4069 4069 4069


Selection response (%) 12.2 13.1 14.4 * The expected average harvest weight of progeny is equal to the average EBV of selected parents; thus, EBV of parents was used to estimate progeny performance.


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Appendix Tables Appendix Table 1. Results of mass selection for harvest weight based on Estimated Breeding Values.

Select 20 % Select 10 % Statistic

Males Females Males Females

Mean EBV (g) 4545 4490 4587 4542


Minimum EBV (g) 4245 4118 4319 4118

Maximum EBV (g) 4878 5029 4878 5029

Selection Differential [SD] (g) 229 174 272 226

Combined Mean EBV (g) 4518 4564

Combined SD (EBV; g) 202 249 Appendix Table 2. Results of combined index selection for harvest weight based on Estimated Breeding Values.



Mean EBV (g) 4584 4513 4629 4578


Minimum EBV (g) 4274 4118 4319 4118

Maximum EBV (g) 4878 5029 4878 5029



Combined SD (EBV; g) 233 288


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Appendix Table 3. Results of breeding value select ion for harvest weight based on Estimated Breeding Values.



Mean EBV (g) 4646 4524 4704 4610

Standard Deviation (g) 74 111 61 96.

Minimum EBV (g) 4559 4394 4623 4498

Maximum EBV (g) 4878 5029 4878 5029



Combined SD (EBV; g) 269 342


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Commonsense Animal Breeding1

The technical aspects of animal breeding can be overwhelming. It is important for breeders to be knowledgeable about quantitative genetics, genetic theory and statistical methodologies, but knowledge is not a guarantee of success. Success has more to do with common sense in the application of theories and technologies. In addition, knowledge and common sense in managing the facilities, animals and production are equally important. Breeding programs that have lasted for a long time and produced superior product tend to display the following attributes:

Understanding Good information Deliberation Consistency Simplicity Patience

Understanding: Being Knowledgeable Being knowledgeable about animal breeding means understanding a few important concepts, for example:

“Environment” in the broadest sense, as well as genotype-environment interactions. The randomness of inheritance and how it establishes opportunity and limitations. Inheritance of “polygenic” (quantitative) traits and how they differs from simple-

inherited traits. Components of the genetic model of a quantitative trait. Factors that determine or effect genetic change, and their trade-offs. Importance of male (sire) selection; relative value of maternal performance. Role of strains and the potential of hybrid vigor and crossbreeding.

These ideas are important not so much in terms of the details, these can always be looked up, but in understanding their significance and implications in a common sense manner. Each species has its favorable and not so favorable attributes; the animal breeder must accommodate these idiosyncrasies in the management of an effective breed improvement program.

Good Information The quality of decisions is always directly proportional to the quality of information used in making the decision; sound obvious but should not go unstated. In an animal breeding context this idea applies to the quality of information obtained and maintained on the population and individuals because all genetic improvement comes from accurate prediction of genetic worth. The “best” predictors will vary depending on the trait and production system. For example, carcass traits cannot be measured generally, on animals needed for reproduction so information for prediction of genetic value (breeding value) is likely to be obtained from performance records on siblings, while growth data can be obtained both for the individual and for its siblings. What are the appropriated uses of these various sources of information? What level of data collection and information

1 Taken from Richard M. Bourdon, “Understanding Animal Breeding,” 2000, Prentice Hall


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management can a given system support, and how should one decide what information to forgo? These questions demonstrate the importance of understanding information.

Deliberation – Take Time to Think The design phase of a breeding program to get things as right as possible. Determining selection goals that will yield long-term economic improvement for the industry. How can the “best” animal be characterized? What is the simplest, fastest, least expensive way to obtain accurate information on these characteristics? It also is important to be continually challenging methods and procedures as the program matures so the program learns from experience. Try to see the big picture. Be analytical

Be Consistent Lack of consistency is one of the greatest downfalls of many proffered breeding programs. For one thing, genetic improvement is a long-term effort and changing genetic goals frequently tends to destroy progress. Also, breeding programs that respond to industry trends that turn out to be fads end up putting a lot of effort into goals with no longstanding efficacy. Given the constant pressures on production and profit, industry consensus of what constitutes the best animal can shift from one industry gathering to the next, like the proverbial pendulum!

To be consistent, a breeding program must look into the future and determine the type of animal that will be the most useful in the long run. So, this requires thinking, setting goals, and sticking with the program. This is not to say that there are not time or legitimate reasons for modifying a breeding program. Amendments may be forced by economic factors or consumer preferences, new information may indicate greater progress could be achieved using an improved method of prediction or to change long held notions. However, the more consistent the goals of a breed improvement program the more defined and thus, the more reliable and marketable is the product.

Keep It Simple Successful breeding programs are largely simple, that is, simple in concept not in technology. Goals are specific, management follows common sense protocols not elaborate rules, and all concerned have faith in the future. It is not possible to “beat the odds” of Mendelian inheritance; random chance rules genetic transmission from generation to generation; all a breeding program can hope to achieve is a redirection of the randomness. It also is critical to include only those characteristics (traits) that have a true and known economic impact on net value (during production and at the market); complicating the program with traits of an aesthetic nature or characteristics that are primarily environmentally induced with place a major drag on success. Complex programs are difficult to maintain and increase costs; simplicity breeds consistency!

Be Patient Genetic progress is slow, much slower than most would like, and much slower than many contemporary activities. Changing a ration formulation can quickly yield a change in growth rate, finding a suitable treatment often ends a disease crisis, breeding for a similar response is slow and requires an article of faith. Because genetic segregation and the union of gametes is largely random, and minor environmental shifts can have significant implications, successful breeders patiently “play the averages” knowing that if the next generation is not markedly better, it is likely the following generation will show results. There always is the possibility the randomness of things will produce an outstanding generation; with a sound program this gift of nature will not be lost to future generations.


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Brainstorming session on designing a National Rainbow Trout Breeding and Genetics Program.

Pho

to: G

uilla

ume

Dag

enai

s, A

MD


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7. WRAP-UP OF THE WORKSHOP - RECAPITULATION OF BREEDING PLAN & ELABORATION OF AN ACTION PLAN Prof. Richard Moccia, University of Guelph


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Dear workshop participant,

The organizing committee has been honoured with your participation at the Workshop on aSelection and Breeding Program for Rainbow Trout in Canada and would like to thank you

for your contribution to a productive session on the development of a national trout broodstock

program.

We thought the 2 days went extremely well, and judging by the workshop evaluations, so did all

of the attendees. We regard this as a very successful workshop that met the needs of the

attendees.

Special thanks to you Graham, for an expert delivery. Nice to have you back in Canada again!

We would also like to send a very sincere "Thanks" to our financial supporters, since without

them, this event would not have happened. We would like to especially recognize the following

generous contributors to the session.

Northern OntarioAquaculture Association

ACRDP- PCRDA AMD-NASAPI

Without these agencies, we go no-where with R&D efforts to support the aquaculture industry.

Sincerely,

The Organizing Committee