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.
Table of Contents
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
9.10Summary
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
References
Author Index
Subject Index
