Woolliams, John - The Roslin Institute - University of Edinburgh

roslin.ed.ac.uk

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


Factors for Accuracy

Phenotypes

Genetic

Architecture

Genome Data

Heritability

Methodology

Number

Records

Accuracy


Factors for 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


Poultry Genotypes

Genotypes

• Genotypes

» marker choice

» number of markers

» larger Ne than dairy cattle in some sectors

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


Poultry Genotypes

Genotypes

• Genotypes

» marker choice

» number of markers

» population genome structure

» impact of the micro-chromosomes


Poultry Genotypes

Genotypes

• Genotypes – problems being overcome for chickens

» marker choice

» number of markers

» population genome structure


Poultry Genotypes

Genotypes

• Genotypes – problems being overcome for chickens

» marker choice

» number of markers

» 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


Gen’ Epi’ Problems (2)

• 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


Gen’ Epi’ Problems (3)

• 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


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

Physiology

Metabolite 1

Metabolite 2

Metabolite 3

Metabolite 4

Metabolite 5

Cell

Division

Growth rate

Cell

Growth


Genotype to Phenotype

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


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


Genotype to Phenotype

• 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


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

cis

trans


Variation in Expression

• 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


Variation in Expression

• mQTL exist!

» and in abundance

» some loci affect the

methylation state of other loci

» Gibbs et al., PLoS Genetics 6

(5): e1000952

» study of human brain

» 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

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