QTL Mapping and
Marker-Assisted Selection
Ans653A lecture 2010
Jack Dekkers
Past and Current
Selection Strategies
selection
Black box of
Genes
h2
Phenotype
Quantitative genetics
Environment
Estimated
Breeding
Value
Phenotype
of relatives
Molecular Genetics
“In Search of the Holy Grail”
M Q
M Q
M Q
m q
m q
m q
Major genes
Quantitative
Trait
Loci (QTL)
= position (locus) on
genome associated
with genetic
differences for a
quantitative trait
Use of Molecular Data in Selection
Unknown
genes
Molec.
Phenotypic
data
?
genetics
Genes
QTL
Phenotype
EBV
Molecular
data
Selection
strategy
Markerbased EBV
Advantage of Molecular
Genetic data for selection
Genes
Molecular
genetics
• Heritability of genotypes = 1
QTL
• Expressed in both sexes
• Expressed at early age
• Requires less phenotypic data
From Molecular Genetics
to
Marker-Assisted Selection
Four Key Areas of R&D:
1) Molecular genetics
- molecular markers
- genetic maps
2) QTL or major gene detection
3) Use of gene / marker data
in breeding value estimation
4) Use of gene / marker data in selection
Outline
• Strategies and designs for QTL mapping
• QTL mapping in breed crosses
• Within-breed QTL mapping
• Half-sib designs
• Linkage disequilibrium mapping
• candidate genes
• high-density SNP panels
• Marker-assisted genetic evaluation
• Use of markers in selection
• MAS
• Marker-assisted composite line development
• Genomic selection
• MAS for commercial crossbred performance
Suggested Reading
• USDA-NSIF QTL mapping and MAS conference
2003 NSIF conference proceedings:
http://www.nsif.com/Conferences/2003/contents.html
• in particular the chapter on MAS
• Dekkers and van der Werf (2007) Chapter 10 at
http://www.fao.org/docrep/010/a1120e/a1120e00.htm
• Dekkers (2010) Use of high-density marker genotyping for
genetic improvement. CAB Reviews.
http://www.cabi.org/cabreviews/ShowPDF.aspx?PAN=20103261356
• Goddard and Hayes. 2009 Nature Reviews: Genetics 10: 381
Principle of QTL detection
Mean
weight
105
(kg)
M Q
M Q
100
M Q
m q
95
m q
m q
1. Genotype pigs for
marker(s)
2. Test for association
between marker
genotype and
phenotype
Presence of association
requires not only
linkage but also
linkage disequilibrium
between marker and
QTL
Processes that create LD
Mutation
M Q
M Q
M MQ q
Selection
Crossing
M Q
M QQ
M
M Q
Inbreeding/drift
m q
m q
X
m q
m q
m q
m q
M Q
m q
M Q
m q
M Q
m q
M Q
M Q
M Q
m q
M Q
m q
m q
m q
Measure of LD
r = correlation
betw. Alleles
11
00
01
10
00
11
01
Example LD in outbred population
1
1
c=.001
c=.01
0.9
0.8
0.7
0.6
0.5
0.9
0.4
c=.2
0.3
c=.05
c=.1
0.2
0.1
0
0.8
0
5
10
15
Generation
0.7
r-squared
0.6
0.5
0.4
0.3
0.2
0.1
0
0
5000
10000
15000
20000
25000
30000
Distance (kb)
35000
40000
45000
50000
20
25
But LD always exists within families
Half-sib family QTL mapping design
r = 0.2
M
Q
Sire
m
Half-sib
Progeny
M
1/
M
Q
2(1-r)=
Freq.
m
meiosis
M
0.4
q
1/
2r
q
=0.1
m
1/
2(1-r)=
m
q
0.4
Freq.
Q
1/
2r=
Prob(Q|M received from sire) = 0.8
 Marker and QTL are in LD among progeny
0.1
Strategies for QTL mapping in livestock
Outbred population
Breed cross
c =.001
c =.01
LD
c =.05
c =.2
0
c =.1
5
10
15
20
25
Generation
LD used
Recomb.
LD extent
Marker map
Coverage
Map resol.
Linkage analysis
LD markers
Linkage analysis
LE markers
F2 / BC
HS/FS
AIL
families
LD mapping
LD markers
Cand.
pedigree genes
Ext.
High
density
QTL detection in F2 breed cross
Genome Scan for Pork Quality
Massoud Malek
Hauke Thomsen
Jong-Joo Kim
Hong-hua Zhao
Max Rothschild
Rohan Fernando
Jack Dekkers
Berkshire
x
F2 cross
Yorkshire
Cross creates extensive
Linkage Disequilibrium
M Q
M Q
M Q
M QQ
M
M MQ q
m q
M Q
m q
M Q
m q
r=.001
r=.01
r=.05
r=.1
r=.2
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0
5
10
15
Generation
20
25
m q
m q
m q
M Q
m q
M Q
M Q
M Q
0.1
0
m q
M Q
m q
1
X
m q
m q
M Q
m q
m q
10-20 cM
marker
distance
sufficient
F0 2 Berkshire sires
M1 N1
BB
9 Yorkshire dams
x
YY M2 N2
M1 N1
F1
F2
M2 N2
8 sires
BY
x
BY 26 dams
M1 N1
M1 N1
M2 N2
M2 N2
525
Breed origin
probabilities
BB
BY
YB
YY
M1 N1
M1 N1
M2 N2
M1 N1
M2 N2
M2 N2
PBB
PBY
PYB
PYY
derived for a given position
SSC1 MARBLING
4.5
-logP
4.0
3.5
http://www.animalgenome.org/QTLdb/pig.html
Line-Cross
BB = - 0.13
BY = +0.19
YY = +0.13
1% Chr.w
Malek et al. 2001
3.0
2.5
5% Chr.w
2.0
1.5
1.0
0.5
Detect
QTL
that
differ
in
0.0
cM frequency
0 10 20 30 40 between
50 60 70 80 breeds
90 100 110 120 130
Breed
cross
QTL
scan
F0 2 Berkshire sires
BB
9 Yorkshire dams
x
YY
F1
x
F2
8 sires
525 BB
BY
BY
BY 26 dams
YB YY
QTL that differ
 in frequency
between breeds
 Wide QTL region
(20-50 cM)
 LD-markers for - introgression
- composite development
(see later)
BUT: Within-breed MAS requires QTL that
segregate within breeds
Follow-up within-breed research in QTL region
- QTL mapping
- candidate genes
Within-breed QTL mapping
Half-sib family design
r = 0.2
M
Q
Sire
m
Half-sib
Progeny
M
1/
M
Q
2(1-r)=
Freq.
m
meiosis
M
0.4
q
1/
2r
q
=0.1
m
1/
2(1-r)=
m
q
0.4
Freq.
Q
1/
2r=
Prob(Q|M received from sire) = 0.8
 Marker and QTL are in LD among progeny
0.1
Halfsib design for QTL detection and MAS
Mm
M
M
M
M
M
M
M
M
M
m
m
m
m
m
m
m
m
m
Compare production
Problem: within-family LD is not consistent
across families
Sire 1
Sire 2
Sire 3
Sire 4
M
Q
M
q
M
Q
M
q
m
q
m
Q
m
Q
m
q
 Analysis must allow for
different marker-QTL linkage phases within each family
Linkage phase = assortment of alleles into haplotypes
Sire 1 has genotypes Mm and Qq;
haplotypes MQ and mq
Within breed QTL detection
 Half-sib analysis confirm w/in breed segregation of QTL
Evans et al.
(2003 Genetics:621)
- confirmed QTL in 10 commercial lines
- Does not narrow QTL region
 LD mapping or association analysis
– find markers close enough to the QTL,
such that associations are consistent
across the population (pop-wide LD)
 Candidate gene approach
 High density SNP panels  GWAS
Example LD in outbred population
1
1
c=.001
c=.01
0.9
0.8
0.7
0.6
0.5
0.9
0.4
c=.2
0.3
c=.05
c=.1
0.2
0.1
0
0.8
0
5
10
15
Generation
0.7
r-squared
0.6
0.5
0.4
0.3
0.2
0.1
0
0
5000
10000
15000
20000
25000
30000
Distance (kb)
35000
40000
45000
50000
20
25
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
NOW AVAILABLE:
AAGCCTTGATAATT Illumina Porcine 60k Beadchip
maternal
paternal
AAGCCTTGCTAATT
+ discovery of many
Single
Nucleotide
SNPs
Polymorphisms
60,000 DNA tests for <$250
Genomic Selection
How to use high-density SNP data?
Genotype large # of
Individuals
for large numbers of SNPs
+ collect their phenotypes
Statistical Analysis to detect QTL /
estimate SNP effects (GWAS)
Use only
significant SNPs
for MAS
Allows detecting more LD markers
but still suffers from only using
significant markers
• Small effects are missed
• Beavis effect
1
r=.001
r=.01
0.9
0.8
0.7
0.6
Summary of QTL mapping strategies
r=.05
r=.1
r=.2
0.5
0.4
0.3
0.2
Outbred population
Breed cross
Linkage
analysis
Assoc.analysis
LD mapping
0.1
0
0
5
10
15
20
Generation
25
F2 / BC
AIL
HS/FS
families
Cand.
pedigree genes
Ext.
LD used
Population wide
in cross
Recomb.
1 rnd
>1 rnd
1 rnd
>1 rnd
LD extent
Long
Smaller
Long
Smaller
Denser
Sparse
Denser
Marker map Sparse
Coverage
Map resol.
Genome wide
Poor
Better
Population wide
Within family
Genome wide
Poor
Better
High
density
>>> 1 round
Small
Few loci
Dense
Local
Genome
High
LD-LA analysis
Summary QTL mapping
 Breed cross QTL scan
 QTL that differ between breeds
 Wide QTL regions
 LD-markers within cross - use for introgression,
composite development
 Within-breed QTL scan
 QTL that segregate within breeds
 Wide QTL regions
 LE-markers - difficult for MAS
 Candidate gene approach
 LD-markers for within-breed MAS
 High-density genotyping LD mapping (GWAS)
 LD-markers for within-breed MAS
So you‟ve found a QTL
........
Now what ??
Use of Molecular Genetics in
Breeding Programs
Jack Dekkers
Department of Animal Science
Center for Integrated Animal Genomics
QTL detection  3 types of selectable loci
Direct markers
LD-markers
Functional mutations
- known genes
q
In pop.-wide Linkage Disequilibrium
with mutation
Linkage phase
~consistent
across population
LE-markers
Q
MQ
MQ
mq
MQ
mq
mq
In pop.-wide Linkage Equilibrium
with mutation
Linkage phase NOT consistent across families
Sire 2
Sire 1
Sire 3
Sire 4
M
Q
M
q
M
Q
M
q
m
q
m
Q
m
Q
m
q
Outline
• Strategies and designs for QTL mapping
• QTL mapping in breed crosses
• Within-breed QTL mapping
• Half-sib designs
• Linkage disequilibrium mapping
• candidate genes
• high-density SNP panels
• Marker-assisted genetic evaluation
• Use of markers in selection
• MAS
• Marker-assisted composite line development
• Genomic selection
• MAS for commercial crossbred performance
Genes and LD-Markers
Linkage phase tends to be consistent
across families and generations
MQ
MQ
mq
MQ
mq
mq
Include marker genotype(s) as fixed effect in
animal model
y = marker genotype + a + e

Estimate effects in population under selection

Must be re-estimate on a regular basis
LE-markers
Linkage phase not consistent between
sires
Sire 1
Sire 2
Sire 3
Sire 4
M
Q
M
q
M
Q
M
q
m
q
m
Q
m
Q
m
q
QTL effect must be estimated for each
individual/family
•
Based on family information
•
•
marker genotypes
phenotypes
LE-markers
Marker-assisted BLUP
(Fernando and Grossman, 1989, GSE)
Sire
Dam
M s Q sp
Md Qdp
M s Q sm
Md Qdm
Progeny
Ms Qip
M d Q im
^ m + u^
Total EBV = v^ip + v
i
yi = m + vip + vim + u + e
Paternal / Maternal PolyQTL allele effect
genic
Var(u) = Asu2
Var(v) = Gsv2
G = gametic relationship matrix
for QTL effects
Computed from
- marker genotypes
- marker-QTL rec. rate
Examples of
gene tests in
commercial
breeding
Trait
Congenital
defects
Appearance
Milk quality
D = dairy cattle
B = beef cattle
C = poultry
P = pigs
S = sheep
Dekkers, 2004, JAS
“MAS has seen
limited
application”
Meat quality
Direct marker
BLAD (D)
Citrulinaemia (D,B)
DUMPS (D)
CVM (D)
Maple syrup urine (D,B)
Mannosidosis (D,B)
RYR (P)
CKIT (P)
MC1R/MSHR (P,B,D)
MGF (B)
-Casein (D)
-lactoglobulin (D)
FMO3 (D)
RYR (P)
RN/PRKAG3 (P)
LD marker
LE marker
RYR (P)
Polled (B)
RYR (P)
RN/PRKAG3 (P)
A-FABP/FABP4 (P)
H-FABP/FABP3 (P)
CAST (P, B)
>15 PICmarqTM (P)
THYR (B)
Leptin (B)
Feed intake
Disease
Reproduction
Growth &
composition
Milk yield &
composition
MC4R (P)
Prp (S)
F18 (P)
Booroola (S)
Inverdale(S)
Hanna (S)
MC4R (P)
IGF-2 (P)
Myostatin (B)
Callipyge (S)
DGAT (D)
GRH (D)
-Casein (D)
B blood group (C)
K88 (P)
Booroola (S)
ESR (P)
PRLR (P)
RBP4 (P)
CAST (P)
IGF-2 (P)
QTL (P)
QTL (B)
Carwell (S)
PRL (D)
QTL (D)
Reasons for limited use of MAS
in livestock
•
•
•
•
# markers available is limited
Markers only explain limited % of genetic variance
•
Only QTL with moderate – large effects detected
Genotyping costs
Marker/QTL effects are not consistent /
not transferable to commercial breeding populations
•
•
•
•
„Beavis‟ effect – effects of „significant‟ markers
tend to be overestimated
Marker effects were estimated within families
or in experimental crosses
Interactions of marker/QTL effects with genetic
background and / or environment
Inconsistent marker-QTL LD across populations
Outline
• Strategies and designs for QTL mapping
• QTL mapping in breed crosses
• Within-breed QTL mapping
• Half-sib designs
• Linkage disequilibrium mapping
• candidate genes
• high-density SNP panels
• Marker-assisted genetic evaluation
• Use of markers in selection
• MAS
• Marker-assisted composite line development
• Genomic selection
• MAS for commercial crossbred performance
Use of Molecular Data in Selection
Unknown
genes
Molec.
Phenotypic
data
?
genetics
Genes
QTL
Phenotype
EBV
Molecular
data
Selection
strategy
Markerbased EBV
Example Marker-based EBV based on 3 SNPs
^ for # A alleles) of:
with estimated effects (
+10 for SNP 1
+ 5 for SNP 2
–10 for SNP 3
MarkerSNP 1
SNP 2
SNP 3
based
Individual Genotype Value Genotype Value Genotype Value
EBV
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
Possible Selection strategies
• Select on Marker-based EBV alone
• Tandem selection
• Index selection
• Pre-selection
1) MAS (index) at young age
2) Select on EBV at later age
E.g. Pre-selection for Entry
into Testing Program
Testing
program
Pre-selection for Entry
into Testing Program
Q
q
q Q q
q
Q
q
Q q q
Q Q
Testing
program
QTL Pre-selection
Testing
program
QTL variance = 20%
Merit of boars entered
2.0
1.8
1.6
1.4
No pre-selection
1.2
1.0
0.8
0.6
0.4
0.2
0.0
100
No preselection
50
33
25
% pre-selected
based on QTL
QTL Pre-selection
Testing
program
QTL variance = 20%
Merit of boars entered
2.0
With pre-selection
1.8
1.6
1.4
No pre-selection
1.2
1.0
0.8
0.6
0.4
0.2
0.0
100
No preselection
50
33
25
% pre-selected
based on QTL
Potential gains from MAS in livestock
Meuwissen & Goddard, 1996 (GSE)
Selection on MA-BLUP EBV (= index)
QTL with 1/3 of genetic variance haplotype-marked
h2=.27
64
70
MAS is most
beneficial for
„difficult‟ traits
62
55
60
50
38
38
40
39
37
31
30
30
25
21
20
15
10
9
5
0
1
4
2
2
Generation
3
5
Trait
characteristic
Carcass trait
Sex-limited trait
Phenotyped after selection
Phenotyped before selection
Gains from LE-MAS
Effect of Heritability (Meuwissen & Goddard 1996)
Single marked QTL with 1/3 of genetic variance
70
60
45
50
36
38
34
40
30
23
25
30
21
15
17
20
13
9
10
6
5
4
0
1
2
2
Generation
3
5
h2=.11
h2=.27 Phenotyping after
h2=.11
Phenotyping before
h2=.27
Gains from MAS
Effect of QTL effect (Meuwissen & Goddard 1996)
Phenotyping after selection. Heritability=32%
70
60
47
40
50
33
40
29
25
23
30
19
13
20
12
12
10
10
5
0
1
7
5
4
2
3
Generation
4
5
46.7
26.7
13.3
6.7
MAS for meat quality
Meuwissen & Goddard, 1996 (GSE)
QTL with 1/3 of genetic variance haplotype-marked
h2=.27
Two scenarios
1. Random 2/4 from each litter slaughtered for meat quality.
Remaining littermates are selected on the basis of a MAEBV for meat quality, once data on their sibs is recorded.
2. Animals are selected on the basis a MA-EBV and nonselected animals are slaughtered to provide data for the
next generation of selection
70
64
62
Extra response from MAS (%)
60
55
50
40
39
30
24
23
20
25
22
10
0
Strategy 2
1
Strategy 1
2
Generation
3
5
Fat allele
higher BF Lean allele
IGF-2
lower LEA
Only paternal allele is expressed (imprinting)
Tactical use in cross breeding
FF
LL
X
LL
FL
X
Terminal progeny
LF LL
All Lean
Fat  sufficient
reserves
Outline
• Strategies and designs for QTL mapping
• QTL mapping in breed crosses
• Within-breed QTL mapping
• Half-sib designs
• Linkage disequilibrium mapping
• candidate genes
• high-density SNP panels
• Marker-assisted genetic evaluation
• Use of markers in selection
• MAS
• Genomic selection
• MAS for commercial crossbred performance
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
NOW AVAILABLE:
AAGCCTTGATAATT Illumina Porcine 60k Beadchip
maternal
paternal
AAGCCTTGCTAATT
+ discovery of many
Single
Nucleotide
SNPs
Polymorphisms
60,000 DNA tests for <$250
Genomic Selection
Genomic Selection in outbred population
Meuwissen et al. (2001)
Genotype
Many SNPs
Genotype
for SNPs
Genotype
for SNPs
MODEL
to predict
phenotype/BV
from SNP
genotypes
Predict
BV or
phenotype
Accuracy
Phenotype
1.0
0.8
0.6
3
4
5
6
Generation
7
8
How to use high-density SNP data?
Genotype large # of
Individuals
for large numbers of SNPs
+ collect their phenotypes
Statistical Analysis
to detect QTL / estimate SNP effects
Use only
significant SNPs
for MAS
Allows detecting more LD markers
but still suffers from only using
significant markers
• Small effects are missed
• Beavis effect
Solution: Genomic selection
Meuwissen et al. 2001 Genetics
Genetic Evaluation using high-density SNPs
• All SNPs are fitted simultaneously, i.e. 50,000 vs. 1 at a time
• SNP effects are fitted as random vs. fixed effects
• enables all SNPs to be fitted simultaneously
• shrinks SNP effect estimates to 0 depending on evidence from data
yi = m +
S
SNP k
k gik
+ ei
^
Estimates of SNP effects 
k
Implemented using a variety of
Bayesian methods (Bayes-A, -B, -C)
Or by using genomic vs. pedigree
relationships in animal model BLUP (GBLUP)
Use to estimate
breeding value of new
animals based on
genotypes alone
Genomic EBV =
S ^ g
k
ik
1000
Predictor
Predictee
>3,500 progeny-tested bulls
800
Young
600
400
200
2008
2006
2004
2002
2000
1998
1996
1994
1992
1990
1970
0
1950
Number of Animals
Data used to develop
Genomic predictions in Holsteins
Year of Birth
National Swine Improvement Federation Symposium, Dec. 2008 (55)
Paul VanRaden
2008
Genomic EBV have greater reliability
for young bulls and heifers
than Parent Average EBV
E.g. for Young Holstein Bulls
(VanRaden and Tooker, 2009 USDA-AIPL)
ftp://aipl.arsusda.gov/pub/outgoing/GenomicReliability0608.doc
Trait
Net merit
Milk yield
Fat yield
Protein yield
Productive life
Dtr. Pregancy rate
Gain over parent average
reliability (~39%)
+ 23
+ 32
+ 36
+ 28
+ 33
+ 20
Genomic Selection (GS)
•GS provides unique opportunities to enhance
breeding programs by:
• increasing / maintaining response to selection
• reducing rates of inbreeding
• reducing loss of favorable alleles
• reducing generation intervals
• limiting need for pedigree-based phenotypes
GS requires large training populations
Goddard and Hayes. 2009 Nature Reviews: Genetics 10: 381
Sample size depends on the extent of
LD in the population, which depends
(historical) effective population size (Ne)
and on trait heritability
Outline
• Strategies and designs for QTL mapping
• QTL mapping in breed crosses
• Within-breed QTL mapping
• Half-sib designs
• Linkage disequilibrium mapping
• candidate genes
• high-density SNP panels
• Marker-assisted genetic evaluation
• Use of markers in selection
• MAS
• Genomic selection
• MAS for commercial crossbred performance
Current Pyramid Selection Programs
Limitations: - limited selection for performance in the field
- no selection for traits not recorded in nucleus
- disease traits
NUCLEUS
herds
Sire
line
Multiplier
Dam
line
Multiplier
Production herds
High health
environment
rg < 1
Field
environment
Selection for Performance in Field
„Traditional‟ Breeding Solution:
Field data on relatives
Collect phenotypes on relatives in field
 Combined Crossbred-Purebred Selection


Purebred
data
Sire
line
DGfield
DF
Bijma & van Arendonk, „98
Requirements/limitations:
- Costly logistics - Pedigree-based
phenotyping in field
Multiplier
- Longer generation intervals
- Higher rates of inbreeding
- family data vs. own phenotype
Production herds
Selection for Performance in Field
Possible Molecular Genetic Solution:
Markerassisted
EBV
Identify markers with effects on
performance in the field  MAS/GS
MAS
Advantages:
Markerbased
EBV
Sire
line
Genotype
Current limitations:
Multiplier
SNP effect
estimates
- No pedigree-based phenotypes
- Reduce generation intervals
- Select for low heritable traits
Genotype
Phenotype
- Few useful markers available
- Most detected in purebreds
- Effects not consistent across
breeds and environments
Production herds
Genomic Selection for Field Performance
Potential benefits
Markerassisted
EBV
Genomic
selection
EBV
MAS/GS
Genetic correlation
(purebred – commercial)
= 0.7
Sire
line
Genotype
Multiplier
SNP effect
estimates
Genotype
Phenotype
(Dekkers 2007 JAS)
Production herds
Accuracy of selection
Selection for commercial performance
for Accuracy
field performance
of selection
r
=0.7
Accuracy
Dekkers
JAS, 2007
pb,cb
by Crossbred Genomic Selection
(GSXb)
0.8
0.7
0.6
0.5
PPb+ PXbred
0.4
PPurebred
0.3
0.2
0.1
0.2
0.3
0.4
0.5
0.6
Accuracy of GS-EBV
0.7
0.8
Accuracy of selection
Selection for commercial performance
for Accuracy
field performance
of selection
r
=0.7
Accuracy
Dekkers
JAS, 2007
pb,cb
by Crossbred Genomic Selection
(GSXb)
0.8
0.7
0.6
0.5
PPb+ PXbred
0.4
GSPb
PPurebred
0.3
0.2
0.1
0.2
0.3
0.4
0.5
0.6
Accuracy of GS-EBV
0.7
0.8
Accuracy of selection
Selection for commercial performance
for Accuracy
field performance
of selection
r
=0.7
Accuracy
Dekkers
JAS, 2007
pb,cb
by Crossbred Genomic Selection
(GSXb)
0.8
0.7
GSXb
0.6
0.5
PPb+ PXbred
0.4
GSPb
PPurebred
0.3
0.2
0.1
0.2
0.3
0.4
0.5
0.6
Accuracy of GS-EBV
0.7
0.8
Accuracy of selection
Selection for commercial performance
for
field ofperformance
Accuracy
selection
r pb,cb=0.7
Accuracy
Dekkers
JAS, 2007
by Crossbred Genomic Selection
PPb+GSXb
(GSXb)
0.8
GSXb
0.7
0.6
0.5
PPb+ PXbred
0.4
GSPb
PPurebred
0.3
0.2
0.1
0.2
0.3
0.4
0.5
0.6
Accuracy of GS-EBV
0.7
0.8
Impact on Inbreeding
DF (%)
Dekkers
JAS, 2007
Crossbred Genomic Selection
(GSXb)
3.5
PPb+ PXbred
3
2.5
PPurebred
2
1.5
1
0.5
0.2
0.3
0.4
0.5
0.6
Accuracy of GS-EBV
0.7
0.8
Impact on Inbreeding
DF (%)
Dekkers
JAS, 2007
Crossbred Genomic Selection
(GSXb)
3.5
PPb+ PXbred
3
2.5
PPurebred
2
1.5
1
GSXb
0.5
0.2
0.3
0.4
0.5
0.6
Accuracy of GS-EBV
0.7
0.8
Impact on Inbreeding
DF (%)
Dekkers
JAS, 2007
Crossbred Genomic Selection
(GSXb)
3.5
PPb+ PXbred
3
2.5
PPurebred
2
1.5
PPb+GSXb
1
GSXb
0.5
0.2
0.3
0.4
0.5
0.6
Accuracy of GS-EBV
0.7
0.8
8 Partial LD
Estimation
q
6
4
of
marker
q
Q
q
2
effects in
Q
q
q
Q
Q
Q
crosses
GG GA AA Sire
Dam GG GA AA
10 Complete LD
6
GQ
Aq
line
line
Multiplier
Multiplier
GQ
Aq
Production herds
9
q
Marker effects
differ between
purebreds vs.
crossbreds
6.5
Q
5.5
q
Reasons:
3
Q
Q Q
Q q
q Q
q q
GG GA AG AA
Dominance
Epistasis
GxEnvironment
Marker-QTL LD
Genomic selection
in crossbred
populations
Methods
Founders
1 M chromosome
500 -6000 SNPs
100 QTL
h2=0.3
Bayes-B
1000 generations
Ne = 500
Ali Toosi, Noelia Ibañez,
Rohan Fernando,
Jack Dekkers
Breed A
Breed B
50 generations
Ne = 100
50 generations
Ne = 100
Training data sets
N=1000
Breed B
Breed A
Mix A+B
NRI Award
2007-35205-17862
Breed C
8 generations
Ne = 100
F1 AxB
Breed B
validation
C(AB)
F2 AxB
Accuracy of GS based on
admixed training populations
0.9
#
markers
on 1 M
chrom.
0.8
0.6
500
0.5
0.4
0.3
0.2
0.1
Training populations
)
)(C
D
(A
B
)C
(A
B
X
AM
)
)(A
B
(A
B
AB
A+
B
A
0
B
Accuracy
0.7
74
Accuracy of GS based on
admixed training populations
0.9
0.8
#
markers
on 1 M
chrom.
0.6
0.5
500
2000
0.4
0.3
0.2
0.1
Training populations
)
)(C
D
(A
B
)C
(A
B
X
AM
)
)(A
B
(A
B
AB
A+
B
A
0
B
Accuracy
0.7
75
Summary / Conclusions
HD SNP genotyping offers unique opportunities
to enhance animal breeding programs
by removing limitations on when, where, and on whom
phenotypes are recorded
• Genomic selection of purebreds for field performance
of crossbreds
• With opportunities to
• reduce generation intervals
• reduce inbreeding
• Detect QTL for animal health
• Select for animal health
Simulation results look very promising
Empirical results are becoming available
Concluding remarks
Recent advances in genotyping technology and
reduced costs have increased opportunities for MAS
• Use of LD- instead of LE-markers
• Use all vs only significant markers (?)
• genomic selection
• MAS on commercial performance
• Increases response
• Reduces rate of inbreeding
• Requires continued emphasis on phenotypic recording
• Requires careful economic analysis
Integration in breeding
& business goals
Phenotype
LE markers
LD markers
Monogenic
traits
Genes
Costs Risks
Polygenic
traits
BLUP
EBV
Breeding
Business
goal
Selection
strategy
Genotype
(prob)
Genetic gain
Market share
Differentiation
Implementation of MAS requires comprehensive
approach Business
R&D
objectives
Farms
DNA collection
Phenotyping
Pedigree
Genotyping
Genotypic
Database
Phenotypic
Database
Analytical
tools
Selection