Optimum Selection for Identified Major Genes - Department of ...

Animal Genomics 

and 

Genomic Selection 

Jack Dekkers 

Animal Breeding & Genetics 

Department of Animal Science 

Iowa State University

Outline 

• Limitations of past and current selection strategies 

• Including Marker-assisted selection 

• A revolution in genomic technology 

Genomic or Whole-Genome Selection 

• Opportunities to enhance breeding programs 

• Redesign of breeding programs 

• Dairy cattle example 

• Layer chicken example 

• Selection for crossbred performance in field 

• Genetic improvement in developing countries 

• Conclusions

Past and Current 

Selection Strategies 

selection 

Black box of 

Genes 

Quantitative genetics 

h 2 

Phenotype 

Estimated 

Breeding 

Value 

Environment 

Phenotype 

of relatives

‘70 – ‘00: 

= effect of # G alleles 

Promise of 

Mean 

weight (kg) 

Effect ‘G’ allele = +5 

105 

100 

Molecular Genetics 

95 

G 

G 

G 

A 

A 

A 

Q 

Q 

Q 

q 

q 

q 

Find major genes 

or 

markers linked to QTL 

and use these for 

Marker-Assisted Selection

Use of MAS to enhance Selection 

Molec. 

Genes 

genetics 

Major genes 

Markers 

QTL 

Phenotypic 

data 

Molecular 

data 

Marker-Assisted 

Selection 

• Expressed in both sexes 

• Expressed at early age 

• Doesn’t require phenotypes on 

animal itself or close relatives

Estrogen Receptor Gene 

(Rothschild et al. 1991, Short et al. 1997) 

Effect on Number Born Alive 

ESR First parity Later parities 

genotype n=4,262 n=4,753 

AA 9.4 10.0 

AB 9.9 10.5 

BB 10.2 10.7

a 

Allele 1 

homozygote 

sequence 

Allele 2 

homozygote 

sequence 

293 295 297 299 300 

C N S I I D P L I Y 

C N S I I N P L I Y 

MC4R mutation 

and Test 

(Kim et al., Mam. Gen. 2000) 

H 2 

Transmembrane 

domains 

I II III IV V VI VII 

1/1 2/2 1/2 

COO 

H 

11 vs 22 genotype 

in 2 commercial types 

Backfat 

(mm) 

Loin 

depth 

(mm) 

Daily 

Gain 

(g/d) 

Daily 

Feed 

Intake 

(kg/d) 

542 

466 

-1.3 +1.4 -26.0 -0.15 

P

Many markers and 

QTL have been 

reported but few 

have been utilized 

Examples of gene 

tests in 

commercial 

breeding 

D = dairy cattle 

B = beef cattle 

C = poultry 

P = pigs 

S = sheep 

Dekkers, 2004, J.Anim.Sci 

Trait Direct markers LD markers LE LE markers 

Congenital BLAD (D) 

defects 

Citrulinaemia (D,B) 

DUMPS (D) 

CVM (D) 

Maple syrup urine (D,B) 

Mannosidosis (D,B) 

RYR (P) 

RYR (P) 

Appearance CKIT (P) Polled (B) 

MC1R/MSHR (P,B,D) 

MGF (B) 

Milk quality 

-Casein (D) 

-lactoglobulin (D) 

FMO3 (D) 

Meat quality RYR (P) RYR (P) 

RN/PRKAG3 (P) 

RN/PRKAG3 (P) 

A-FABP/FABP4 (P) 

H-FABP/FABP3 (P) 

CAST (P, B) 

>15 PICmarq TM (P) 

THYR (B) 

Leptin (B) 

Feed intake 

MC4R (P) 

Disease Prp (S) B blood group (C) 

F18 (P) 

K88 (P) 

Reproduction Booroola (S) Booroola (S) 

Inverdale(S) 

ESR (P) 

Hanna (S) 

PRLR (P) 

RBP4 (P) 

Growth & 

composition 

Milk yield & 

composition 

MC4R (P) CAST (P) QTL (P) 

IGF-2 (P) 

IGF-2 (P) 

Myostatin (B) 

QTL (B) 

Callipyge (S) 

Carwell (S) 

DGAT (D) PRL (D) QTL (D) 

GRH (D) 

-Casein (D)

Reasons for limited 

use of Genetic Markers 

• Available markers only explain a limited % of 

genetic variance 

• High genotyping costs 

• Marker effects are not always consistent 

across populations

Since 2000: A Revolution in Molecular Technology 

2.8 million SNPs 

Nature 2004 

Single 

Nucleotide 

Polymorphisms 

High-through-put 

SNP genotyping 

International Swine Genome 

Sequencing Consortium 

AAGCCTTGATAATT 

maternal 

paternal 

AAGCCTTGCTAATT 

NOW AVAILABLE: 

Illumina Bovine 50k Beadchip 

+ discovery of many 

Single 

Nucleotide 

Polymorphisms 

SNPs 

50,000 DNA tests for

How to use these new tools 

Genotype large # of 

Individuals 

for large numbers of SNPs 

+ collect their phenotypes 

Statistical Analysis 

to estimate SNP effects

Repeat for all 50,000 SNPs 

Estimation of 

SNP effects 

AAGCCTTGATAATT 

Progeny tested bulls grouped by SNP genotype 

A 

A 

A 

C 

C 

C 

SNP 

Genotype 

AAGCCTTGCTAATT 

Average EBV 

protein yield 

AA +20 

AC +15 

CC +10 

SNP effect estimate = +5 for A

Use of high-density SNPs 

Genotype large # of 

Individuals 

for large numbers of SNPs 

+ collect their phenotypes 

Statistical Analysis 

to estimate effect of each SNP 

Use only 

significant SNPs 

for MAS 

Use all 

SNPs 

for MAS 

Genomic 

selection 

(Meuwissen et al. ‘01)


Breeding Value Estimation using 

high-density SNPs 

Meuwissen et al. 2001 Genetics 

• All SNPs are analyzed simultaneously, i.e. 50,000 vs. 1 at a time 

y i = m + S k g ik 

SNP k 

+ e i 

Estimates of SNP effects k 

Implemented using Bayesian methods 

Or by using genomic vs. pedigree 

relationships in animal model BLUP (GBLUP) 

^ 

Use to estimate 

breeding value of new 

animals based on SNP 

genotypes alone 

^ 

Genomic EBV = S k g ik

Animal 

Example Genomic EBV 

with 3 SNPs 

with estimated effects ( for # A alleles) of: 

SNP 1 SNP 2 SNP 3 

Genotype Value Genotype Value Genotype Value 

GEBV 

1 AA +10 AA +5 AA -10 +5 

2 AA +10 AA +5 BB +10 +25 

3 AB 0 BB -5 AB 0 -5 

4 AB 0 BB -5 AA -10 -15 

5 BB -10 AA +5 AB 0 -5 

^ 

+10 for SNP 1 

+ 5 for SNP 2 

–10 for SNP 3 

Genomic EBV = S^ k g ik


Meuwissen et al. 2001 

Selection based on high-density SNP genotyping 

Select at young age 

Phenotype 

Genotype 

for >30,000 

SNPs 

Genotype 


SNPs 

Genotype 


SNPs 

Training data 

Estimate 

effect of 

each SNP 

Predict BV 

from marker 

genotypes at 

early age 

Predict BV 

from marker 

genotypes at 

early age

Genomic EBV increase accuracy 

for young animals 

E.g. for Young Holstein Bulls 

(VanRaden and Tooker, 2009 USDA-AIPL) 

ftp://aipl.arsusda.gov/pub/outgoing/GenomicReliability0608.doc 

Trait 

Net merit + 23 

Milk yield + 32 

Fat yield + 36 

Protein yield + 28 

Productive life + 33 

Dtr. Pregancy rate + 20 

Gain over parent average 

reliability (~39%)

The Promise of Genomic Selection 

(based on simulation) 

• Ability to increase accuracy of EBV 

at a young age 

• Reduce generation intervals 

• Reduce rates of inbreeding 

• Reduce requirement to obtain 

phenotypes on selection candidates 

and / or on close relatives

Outline 







• Dairy cattle example 




• Conclusions

ow could Genomic Selection change dairy breeding 

Illumina Bovine 50k Beadchip 

Superior progenytested 

bull 

X 

Embryo 

Transfer 

Superior genometested 

young bull 

5 yrs 

& 

$$$$$$ 

later 

Semen 

samples 

Which is best 

DNA 

samples 

< 6 mo 

& 

$$ 

later

Implementation of Genomic 

Selection in Layer Chickens 

Jack Dekkers 1 , Chris Stricker 2 

Rohan Fernando 1 , Dorian Garrick 1 , Susan Lamont 1 

Neil O’Sullivan 3 , Janet Fulton 3 , Jesus Arango 3 , Petek Settar 3 

Andreas Kranis 4 , Jim McKay 5 , Kellie Watson 4 , 

Alfons Koerhuis 4 , Rudi Preisinger 6 

1 

Iowa State University 

2 

applied genetics network, 

3 

Hy-Line Int 

4 

Aviagen Ltd., 

5 

EW Group 

6 

Lohmann Tierzucht

Implementation of Genomic 

Selection in Layer Chickens 

Research Objective 

Evaluate and demonstrate 

the advantages and pitfalls of Genomic Selection 

in a commercial breeding population 

Research Questions / Goals 

In layer chickens, Genomic Selection can: 

• increase response by halving the generation interval 

• without increasing the rate of inbreeding per year 

• in a breeding program comprising fewer individuals

Implementation 

A Commercial Breeding Line was split in two 

Traditional and Genomic Selectrion compared side-by-side 

Selection strategy Traditional Genomic 

Selection parameters ♂♂ ♀♀ ♂♂ ♀♀ 

# candidates/gener. 1,000 3,000 300 300 

# phenotyped a 3,000 300 

# selected 60 360 50 b 50 b 

Generation interval 12 mo c 12 mo c 6 mo d 6 mo d 

a Complete phenotypes available at ~10 months of age 

b 

Equal selection of ♂ and ♀ maximizes response for given F 

c Traditional selection is after ♀♀ are phenotyped 12 mo. 

Traditional selection is limited by cost to rear and phenotype 

Male traditional selection is on sib data low accuracy high F

Expected Response and Inbreeding 

Based on simulation

Outline 







• dairy cattle example 




• Conclusions

Current Pyramid Selection Programs 

Limitations: - limited selection for performance in the field 

- no selection for traits not recorded in nucleus 

- disease traits 

Sire 

line 

NUCLEUS 

herds 

Dam 

line 

High health 

environment 

Multiplier 

Multiplier 

r g < 1 

Crossbred animals in 

Production herds 

Field 

environment

Field data on relatives 

Selection for Performance in Field 

‘Traditional’ Breeding Solution: 

Collect phenotypes on relatives in field 

Combined Crossbred-Purebred Selection 

Purebred 

data 

Sire 

line 

Multiplier 



 

 

G field 

F 

Bijma & van Arendonk, ‘98 

Requirements/limitations: 

- Costly logistics - Pedigree-based 

phenotyping in field 

- Longer generation intervals 

- Higher rates of inbreeding 

- family data vs. own phenotype

Selection for Performance in Field 

Genomic Selection Solution 

Genomic 

Selection 

(Dekkers 2007 JAS) 

Genomic selection using SNP 

effects estimated in field data 

Genomic 

EBV 

^ 

^ 

S k g ik 

g ik 

Genotype 

Sire 

line 

Multiplier 

Advantages: 

- Potential to increase response 

and reduce inbreeding 

- Removes requirement for 

pedigree-based phenotypes 

- Opportunities to select for 

traits not observed in nucleus 

SNP effect 

estimates 

Genotype 

Phenotype 



(Ibanez-Escriche, Fernando, Toosi, Dekkers, GSE, 2009)

Genetic Improvement in Developing Countries 

Limitations 

• G x E interactions 

• Limited acceptance 

by local farmers 

• Lack of pedigree 

at field level 

Nucleus 

Breeding 

Program 

Bulls 

or 

Semen 

Field 

population

Genotype 

Phenotype 

Genetic Improvement in Developing Countries 

Potential Genomic Selection Solution 

Bulls or 

Semen 

Identify best 

genetics in the 

field using 

Genomic EBV 

S^ k g ik 

Genotype 

g ik 

Field 

population 

^ 

SNP effect 

estimates

Outline 







• dairy cattle example 




• Conclusion

College of Agriculture and Life Sciences 

Genomic Selection has potential to 

revolutionize Animal Breeding 

by removing limitations on 

• when 

• where 

• and on which animals 

phenotypes are recorded 

• With opportunities to 

• reduce generation intervals 

• reduce inbreeding 

• improve field performance 

• reduce phenotyping costs

Optimum Selection for Identified Major Genes - Department of ...

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