Genomic Selection in Animals.pdf

Genomic Selection in Animals.pdf



The field of whole genome selection has quickly developed into the breeding methodology of the future. As efforts to map a wide variety of animal genomes have matured and full animal genomes are now available for many animal scientists and breeders are looking to apply these techniques to livestock production.

Providing a comprehensive, forward-looking review of animal genomics, Genomic Selection in Animals provides coverage of genomic selection in a variety of economically important species including cattle, swine, and poultry. The historical foundations of genomic selection are followed by chapters that review and assess current techniques. The final chapter looks toward the future and what lies ahead for field as application of genomic selection becomes more widespread.

A concise, useful summary of the field by one of the world’s leading researchers, Genomic Selection in Animals fills an important gap in the literature of animal breeding and genomics.


Preface: Welcome to the “promised land”

Chapter 1: Historical overview

1.1 Introduction

1.2 The Mendelian theory of genetics

1.3 The Mendelian basis of quantitative variation

1.4 Detection of QTL with morphological and biochemical markers

1.5 DNA-level markers, 1974-1994

1.6 DNA-level markers since 1995, SNPs and CNV

1.7 QTL detection prior to genomic selection

1.8 Marker-assisted selection prior to genomic selection

1.9 Summary

Chapter 2: Types of current genetic markers and genotyping methodologies

2.1 Introduction

2.2 From biochemical markers to DNA-level markers

2.3 DNA microsatellites

2.4 Single nucleotide polymorphisms

2.5 Copy Number variation

2.6 Summary

Chapter 3: Advanced animal breeding programs prior to genomic selection

3.1 Introduction

3.2 Within a breed selection, basic principles and equations

3.3 Traditional selection schemes for dairy cattle

3.4 Crossbreeding schemes, advantages and disadvantages

3.5 Summary

Chapter 4: Economic evaluation of genetic breeding programs

4.1 Introduction

4.2 National economy, vs. competition among breeders

4.3 Criteria for economic evaluation, profit horizon, interest rate, return on investment.

4.4 Summary

Chapter 5: Least squares, maximum likelihood and Bayesian parameter estimation

5.1 Introduction

5.2 Least squares parameter estimation

5.3 Maximum likelihood estimation for a single parameter

5.4 Maximum likelihood multi-parameter estimation

5.5 Confidence intervals and hypothesis testing for MLE

5.6 Methods to maximize likelihood functions

5.7 Bayesian estimation

5.8 Parameter estimation via the Gibbs sampler

5.9 Summary

Chapter 6: Trait-based genetic evaluation, the mixed model

6.1 Introduction

6.2 Principles of selection index

6.3 The mixed linear model

6.4 The mixed model equations

6.5 Solving the mixed model equations

6.6 Important properties of mixed model solutions

6.7 Multivariate mixed model analysis

6.8 The individual animal model

6.9 Yield deviations and daughter yield deviations

6.10 Analysis of DYD as the dependent variable

6.11 Summary

Chapter 7: Maximum likelihood and Bayesian estimation of QTL parameters with random effects included in the model

7.1 Introduction

7.2 Maximum likelihood estimation of QTL effects with random effects included in the model, the daughter design

7.3 The granddaughter design

7.4 Determination of prior distributions of the QTL parameters for the granddaughter design

7.5 Formula for Bayesian estimation and tests of significance of a segregating QTL in a granddaughter design

7.6 Summary

Chapter 8: Maximum likelihood, restricted maximum likelihoodand Bayesian estimation for mixed models

8.1 Introduction

8.2 Derivation of solutions to the mixed model equations by maximum likelihood

8.3 Estimation of the mixed model variance components

8.4 Maximum likelihood estimation of variance components

8.5 Restricted maximum likelihood estimation of variance components

8.6 Estimation of variance components via the Gibbs sampler

8.7 Summary

Chapter 9: Distribution of genetic effects, theory and results

9.1 Introduction

9.2 Modeling the polygenic variance

9.3 The effective number of QTL

9.4 The case of the missing heritability

9.5 Methods for determination of causative mutations for QTL in animals and humans

9.6 Determination of QTN in dairy cattle

9.7 Estimating the number of segregating QTL based on linkage mapping studies

9.8 Results of genome scans of dairy cattle by granddaughter designs

9.9 Results of genome-wise association studies (GWAS) in dairy cattle by SNP chips


Chapter 10: The multiple comparison problem

10.1 Introduction

10.2 Multiple markers and whole genome scans

10.3 QTL detection by permutation tests

10.4 A priori determination of the proportion of false positives

10.5 Biases with estimation of multiple QTL

10.6 Bayesian estimation of QTL from whole genome scans, theory

10.7 Bayes-A and Bayes-B models

10.8 Bayesian estimation of QTL from whole genome scans, simulation results

10.9 Summary

Chapter 11: Linkage mapping of QTL

11.1 Introduction

11.2 Interval mapping by nonlinear regression, the backcross design

11.3 Interval mapping for daughter and granddaughter designs

11.4 Computation of confidence intervals

11.5 Simulation studies of confidence intervals

11.6 Summary

Chapter 12: Linkage disequilibrium mapping of QTL

12.1 Introduction

12.2 Estimation of linkage disequilibrium in animal populations

12.3 Linkage disequilibrium mapping QTL mapping, basic principles

12.4 Joint linkage and linkage disequilibrium mapping

12.5 Multi-trait and multiple QTL LD mapping

12.6 Summary

Chapter 13: Marker assisted selection, basic strategies

13.1 Introduction

13.2 Situations in which selection index is inefficient

13.3 Potential contribution of MAS for selection within a breed - general considerations

13.4 Phenotypic selection vs. MAS for individual selection

13.5 MAS for sex-limited traits

13.6 MAS including marker and phenotypic information on relatives

13.7 Maximum selection efficiency of MAS with all QTL known, relative to trait-based selection, and the reduction in RSE due to sampling variance

13.8 Marker information in segregating populations

13.9 Inclusion of marker information in “animal model” genetic evaluations

13.10 Predicted genetic gains with genomic evaluations, results of simulation studies

13.11 Summary

Chapter 14: Genetic evaluation based on dense marker maps, basic strategies

14.1 Introduction

14.2 The basic steps in genomic evaluation

14.3 Evaluation of genomic estimated breeding values

14.4 Sources of bias in genomic evaluation

14.5 Marker effects fixed or random?

14.6 Individual markers vs. haplotypes

14.7 Total markers vs. usable markers

14.8 Deviation of genotype frequencies from their expectations

14.9 Inclusion of all markers vs. selection of markers with significant effects

14.10 The genomic relationship matrix

14.11 Summary

Chapter 15: Genetic evaluation based on analysis of genetic evaluations or daughter-yield evaluations

15.1 Introduction

15.2 Comparison of single-stage and multi-stage models

15.3 Derivation and properties of daughter yields and DYD

15.4 Computation of "deregressed" genetic evaluations

15.5 Analysis of DYD as the dependent variable with all markers included as random effects

15.6 Computation of reliabilities for genomic estimated breeding values

15.7 Bayesian weighting of marker effects

15.8 Additional Bayesian methods for genomic evaluation

15.9 Summary

Chapter 16: Genomic evaluation based on analysis of production records

16.1 Introduction

16.2 Single-stage methodologies, the basic strategy

16.3 Computation of the modified relationship matrix when only a fraction of the animals are genotyped, the problem

16.4 Criteria for valid genetic relationship matrices

16.5 Computation of the modified relationship matrix when only a fraction of the animals are genotyped, the solution

16.6 Solving the mixed model equations without inverting H

16.7 Inverting the genomic relationship matrix

16.8 Estimation of reliabilities for genomic breeding values derived by single-stage methodologies

16.9 Single-stage computation of genomic evaluations with unequally weighted marker effects

16.10 Summary

Chapter 17: Validation of methods for genomic estimated breeding values

17.1 Introduction

17.2 Criteria for evaluation of estimated genetic values

17.3 Methods used to validate genomic genetic evaluations

17.4 Evaluation of multi-step methodology based on simulated dairy cattle data

17.5 Evaluation of multi-step methodology based on actual dairy cattle data

17.6 Evaluation of single-step methodologies based on actual dairy cattle data

17.7 Evaluation of single- and multi-step methodologies based on actual poultry data

17.8 Evaluation of single- and multi-step methodologies based on actual swine data

17.9 Evaluation of GEBV for plants based on actual data

17.10 Summary

Chapter 18: Byproducts of genomic analysis: pedigree validation and determination

18.1 Introduction

18.2 The effects of incorrect parentage identification on breeding programs

18.3 Principles of parentage verification and identification with genetic markers

18.4 Paternity validation prior to high density SNP chips

18.5 Paternity validation and determination with SNP chips

18.6 Validation of more distant relationships

18.7 Pedigree reconstruction with high density genetic markers

18.8 Summary

Chapter 19: Imputation of missing genotypes: methodologies, accuracies, and effects on genomic evaluations

19.1 Introduction

19.2 Determination of haplotypes for imputation

19.3 Imputation in humans vs. imputation in farm animals

19.4 Algorithms proposed for imputation in human and animal populations

19.5 Comparisons of accuracy and speed of imputation methods

19.6 Effect of imputation on genomic genetic evaluations

19.7 Summary

Chapter 20: Detection and validation of quantitative trait nucleotides (QTN)

20.1 Introduction

20.2 Genome-wide association studies (GWAS) for economic traits in commercial animals

20.3 Detection of quantitative trait nucleotides (QTN), is it worth the effort?

20.4 QTN determination in farm animals, what constitutes proof?

20.5 Concordance between DNA-level genotypes and QTL status

20.6 Determination of concordance by the “a posteriori granddaughter design” (APGD)

20.7 Determination of phase for grandsires heterozygous for the QTL

20.8 Determination of recessive lethal genes by GWAS and effects associated with heterozygotes

20.9 Verification of QTN by statistical and biological methods

20.10 Summary

Chapter 21: Future directions and conclusions

21.1 Introduction

21.2 More markers vs. more individuals with genotypes

21.3 Computation of genomic evaluations for cow and female calves

21.4 Improvement of genomic evaluation methods

21.5 Long-term considerations

21.6 Weighting evaluations of old vs. young bulls

21.7 Direct genetic manipulation in farm animals

21.8 Velogenetics - the synergistic use of MAS and germ-line manipulation

21.9 Summary


Author Index

Subject Index


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