Woolliams, John - The Roslin Institute - University of Edinburgh

Prospects & Progress: 

Challenges for the Future 

John Woolliams 

The Roslin Institute & R(D)SVS 

University of Edinburgh, U.K.

Introduction 

What may technology offer

One Vision of the Future 

• Clone-tested on 5 farm types 

» most suited to farm type ‘C’ 

• Carries udder health transgene 

• Naturally resistant to bTB 

• Category ‘A’ environmental impact 

» very low methane per kg milk 

• Information provided 

» optimum feeding profile 

» predicted sensitivities to suboptimal 

conditions 

» genetic & breeding merit for 30 

trait with accuracy > 0.9 

• Chris Warkup 

• Nextgen Daisy

Issues 

• Cloning 

• Transgenics 

• Disease Resistance 

• Genotype x Environment Interaction 

• Detailed Breeding Information 

• Complex Breeding Objectives

Issues 

• Cloning 

• Transgenics 

 

 

• Disease Resistance 

 

• Genotype x Environment Interaction 

• Detailed Breeding Information 

• Complex Breeding Objectives

Precision Animal Breeding

Precision Animal Breeding 

• Relevant to 

» breeding for products and services 

» medical & scientific research 

» conservation 

» leisure and recreation

Precision Breeding Goals 

• To increase the scope and precision of predictions of the 

outcomes of breeding 

• To avoid the introduction and advance of characteristics 

deleterious to animal well-being or, more generally, the wellbeing 

of the species 

• To manage genetic resources and diversity between and 

within populations in accordance with the principles set out in 

the Convention on Biological Diversity 

Flint & Woolliams, 2008, Proc.Roy.Soc.B

Translation to Poultry 

• Better precision of traits in the broad breeding goal 

» improving evaluation methods 

» addressing disease traits 

» proactively predicting genetic consequences (correlations) 

» genotype by environment interactions 

» breeding for different (global) environments

Genomic Evaluation & Selection

Dairy Progress 

• Going well! 

• Accuracy of a 

newborn > 0.8 

» Milk Net Merit 

» 2 years ago! 

Source: 

USDA

Dairy Progress 

• However 

» easy to identify 

genotyping costs 

» low risk testing 

» good phenotypes 

» low Ne! 

Many thousands of tested & 

genotyped bulls 

i.e. 

high accuracy phenotypes 

Source: 

USDA

Factors for Accuracy 

Phenotypes 

Genome Data 

Methodology 

Accuracy


Phenotypes 

Genetic 

Architecture 

Genome Data 

Heritability 

Methodology 

Number 

Records 

Accuracy


Phenotypes 

Genetic 

Architecture 

Population 

Genome 

Structure 

Genome Data 

Heritability 

Number 

Markers 

Methodology 

Number 

Records 

Marker 

Selection 

Accuracy

Capturing Variance by SNP 

• Max accuracy of bovine 50k 

chip ~ 0.9 for milk yield net merit 

» confidence interval up to 0.93 

Daetwyler (2009)

Poultry Phenotypes 

Phenotypes 

• Phenotypes – poultry well-placed (with commitment) 

» number records 

» heritability 

» genetic architecture 

 

 

 

» breeding company structure promotes relevant recording in good & 

accumulating numbers

Poultry Genotypes 

Genotypes 

• Genotypes 

» marker choice 

» SNP polymorphism must be within populations 

» layers and broilers e.g. between – breed predictors still poor


Genotypes 

• Genotypes 


» number of markers 

» larger Ne than dairy cattle in some sectors 

» marker density scaled by Ne, 50k chip in dairy ~ 100k in broilers


Genotypes 

• Genotypes 



» population genome structure 

» impact of the micro-chromosomes


Genotypes 

• Genotypes – problems being overcome for chickens 



» population genome structure


Genotypes 

• Genotypes – problems being overcome for chickens 



» population genome structure 

 

 

» current status of turkey & duck genomes and SNP discovery well 

behind chickens!

Genomic Evaluation 

• However may look to accuracy → 0.9+ for new born animals 

over time for routinely recorded traits 

• Precision in breeding for what we routinely measure 

» less conflict between ‘desired’/’most profitable’ direction of gain and 

achieved gain 

Desired Gain 

Egg Number 

Egg Number 

Achieved Gain 

Growth 

Growth

Genetic Epidemiology

Precision in Goals 

• Disease resistance is an important part of the 

complex goal 

• How well do we address disease traits

Example from Dairy Cattle 

• Analysis of bovine TB 

» large scale field data 

» disease occurs in ‘herd size’ epidemics 

» record which animals culled for bTB in each herd before 

epidemic halted 

» phenotype ‘1’ if culled, 0 if survive 

» evidence of genetic variation in bTB susceptibility 

» h 2 = 0.15 on ‘underlying complementary log-log scale’

Gen’ Epi’ Problems (1) 

• Bias in estimates 

» exposure, sensitivity & specificity of diagnosis 

• Theory to quantify degree of bias 

» all factors lead to underestimate of h 2 

» consistently underestimated importance of genetics 

» e.g. correction for bTB gives h 2 ~ 0.20 to 0.25 

Bishop & Woolliams (2010) 

• More theory to be done 

• Better analytical models to correct biases


• Incomplete disease models for genetic variance 

» ignore infectivity i.e. ‘shedders’ 

» impact of variation in infectivity potentially as large as 

variation in susceptibility 

» no easy way to capture this variance in genetic models


• What does h 2 ~ 0.25 on ‘underlying complementary 

log-log scale’ mean to an epidemiologist 

» epidemiologists work on ‘SIR’ models 

» time-dependent models with very different 

parameterisation 

• Need to reconcile models

Role of Genomics 

• Why debate genetic epidemiology in a session on 

next generation technology & ‘omics 

• Currently, genetics of disease relies upon 

» repeated challenge testing by breeding companies 

» cost 

» biosecurity 

» welfare of birds 

» pedigree analysis of epidemics 

» relies on quality commercial data

Role of Genomics 

• Genomics releases these 

constraints 

» can generate progress in 

absence of epidemic or 

routine testing 

» retain ‘genomic memory’ of 

desirable disease resistance 

Potential 

Breeding Pyramid 

Progress 

Infrequent 

challenge test 

Dissemination 

Genomic 

information

Predicting Genetic Correlations

Genotype to Phenotype 

Genome 

Gene 1 

Gene 3 

Gene 2 

Gene 4 

Gene 6 

Gene 5 

Gene 7 

Protein A 

Protein B 

Protein C 

Protein D 

Complex D-E 

Protein E 

Protein F 

Protein G 

Environment 

Metabolite 1 

Metabolite 2 

Metabolite 3 

Metabolite 4 

Metabolite 5 

Cell 

Division 

Growth rate 

Cell 

Growth


Genome 

Gene 1 

Gene 3 

Gene 2 

Gene 4 

Gene 6 

Gene 5 

Gene 7 

Protein A 

Protein B 

Protein C 

Protein D 

Complex D-E 

Protein E 

Protein F 

Protein G 

Environment 

Physiology 

Metabolite 1 

Metabolite 2 

Metabolite 3 

Metabolite 4 

Metabolite 5 

Cell 

Division 

Growth rate 

Cell 

Growth


Genome 

Gene 1 

Gene 2 

Gene 6 

Gene 3 

Gene 4 

Gene 5 

Gene 7 

Protein A 

Protein B 

Protein C 

Protein D 

Complex D-E 

Protein E 

Protein F 

Protein G 

Environment 

Physiology 

Quantitative Genetics 

Metabolite 1 

Metabolite 2 

Metabolite 3 

Metabolite 4 

Metabolite 5 

Cell 

Division 

Growth rate 

Cell 

Growth


Genome 

Gene 1 

Gene 3 

Gene 2 

Gene 4 

Gene 6 

Gene 5 

Gene 7 

Protein A 

Protein B 

Protein C 

Protein D 

Complex D-E 

Protein E 

Protein F 

Protein G 

Environment 

Metabolite 1 

Metabolite 2 

Metabolite 3 

Metabolite 4 

Metabolite 5 

Cell 

Division 

Growth rate 

Cell 

Growth


• Gene expression is where physiology, systems 

biology and genetics ‘join up’ & communicate 

directly

Variation in Expression 

• eQTL 

» genetic variants affecting 

mRNA abundance 

» genetical genomics 

» Gibbs et al., PLoS Genetics 6 

(5): e1000952 

» study of human brain 

» many eQTL


• eQTL 

» genetic variants affecting 

mRNA abundance 

» genetical genomics 


(5): e1000952 


» many eQTL 

cis 

trans


• Epigenetic factors regulate expression 

» such as methylation (DNA, histone marks) 

» ‘switches’ of DNA transcription & expression 

• Methylation is a very dynamic process 

» regulation of heat shock in chickens 

• Opened up by next generation sequencing


• mQTL exist! 

» and in abundance 

» some loci affect the 

methylation state of other loci 


(5): e1000952 


» genetical epigenetics 

cis 

trans

Precision Breeding 

• Bioinformatics analysis of eQTL & mQTL can begin 

to predict genetic correlations 

» network building 

» pathway analysis

Precision Breeding 

• Bioinformatics analysis of eQTL & mQTL can begin 

to predict genetic correlations 

» network building 

» pathway analysis 

• Specific hypothesis that mQTL may be important to 

understand and overcome G x E 

» poor regulation of ‘production’ loci under challenge 

» disease or nutritional challenges

Summary

Summary 

• Challenges to deliver Precision Breeding 

1. Delivery of genomic evaluation 

• < 2015 

• made feasible by next generation technology 

2. Genetic epidemiology, theory and application 

• < 2020 

• beneficial exploitation requires genomic evaluation + 

3. Predicting genetic correlations, including GxE 

• < 2025 

• requires next generation sequencing

Woolliams, John - The Roslin Institute - University of Edinburgh

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