This document provides information about Nazir A Ganai's SABRE training at the Department of Genetics and Genomics at Edinburgh University in Scotland from November 17th to December 31st, 2008. It discusses using molecular data like genes and quantitative trait loci (QTL) in animal selection and breeding. Specifically, it covers techniques for QTL mapping like single marker analysis in backcross and F2 populations, interval mapping to estimate QTL position and effects, and using molecular data for marker-assisted selection within and across breeds.

1. SABRE Training
Candidate:
Nazir A Ganai
SK University of Agricultural Sciences and Technology Kashmir India
Host Institute:
Department of Genetics and Genomics
Roslin Biocenter, Edinburgh University Scotland
Advisor:
Dr DJ de Koning
Period:
17-11-2008 to 31-12-2008

2. Use of Molecular Data in SelectionUse of Molecular Data in Selection
UnknownUnknown
genesgenes
IdentifyIdentify
Major genesMajor genes //
QTLsQTLs
PhenotypicPhenotypic
datadata
EBVEBV
GenotypicGenotypic
datadata
SelectionSelection
strategystrategy
Molec. geneticsMolec. genetics
??
MolecularMolecular
score (MS)score (MS)

3. Finding Genes
for Quantitative Traits
• QTL mapping
– high probability of success
– hard to use
• Candidates genes
– low probability of success
– easy to use

4. QTL mapping
• QTL : a region of the genome that is associated with an
effect on a quantitative trait.
– a QTL can be a single gene, or it may be a cluster of linked genes
– the aim of QTL mapping is primarily to:
• detect which regions (QTL) of the genome affect the trait,
• describe the effect of the QTL on the trait
– how much of the variation for the trait is caused by a QTL
– what is the gene action associated with the QTL - additive /domininat
effect?
– which marker allele is associated with the favorable effect?
– Use of QTLs in MAS:
• Assign breeding values to lines or families based on their genotypes at
one or more QTLs. In this way we can use information obtained in QTL
mapping experiments for applied marker-assisted breeding strategies.

5. GenesGenes
Advantage of MolecularAdvantage of Molecular
Genetic data for selectionGenetic data for selection
Molecular geneticsMolecular genetics
QTLQTL
• Heritability of genotypes = 1Heritability of genotypes = 1
• Expressed in both sexesExpressed in both sexes
• Expressed at early ageExpressed at early age
• Requires less phenotypic dataRequires less phenotypic data
Candidate
genes

6. QTL Analysis
• Detection of QTLs depends on five main factors:
– How tightly linked the QTL is to a marker
• Linkage Mapping – exploits LD within families
• LD Mapping – exploits LD across the families in a population
– The size of effect
• Small QTL effect – Low power of detection
• Large QTL effect – high power of detection
– Experimental design:
• Inbred / Line bred cross progeny
– Backcross
– F2
– Recombinant Inbred Lines
• Segregating populations
– Full sib families
– Half sib families
– Three generation families (Grand Daughter Design)
– Selective genotyping
– Selective DNA pooling (Bulk segregant analysis)
– The size of the population scored
– The heritability of the involved trait

7. Techniques of QTL mapping
• Single marker analysis
– each marker - trait association test is performed independently.
– Only detects linkage between marker and a QTL
– Does not estimate position and effect of QTL.
• Flanking / interval marker mapping –
– a separate analysis is performed for each pair of adjacent marker loci.
– produces a slight increase in the power of detection, compared to single
marker,
– much greater precision in estimating QTL effects and position.
• Composite multipoint mapping –
– considers all the linked markers on a chromosome simultaneously.
– reduce the bias that is present using interval mapping approaches when
two or more QTL are linked to the markers.

8. BC progeny Genotype
frequency
Genotypic
value of QTL
Total Ave. of marker group
(sum / freq)
M Q / m q ½ (1-r) d ½ d(1-r) ½ d(1-r) - ½ a r
½ (1-r) + ½ r
µ Mm =
(1-r)d – raM q / m q ½ r -a - ½ a r
m q / m q ½ (1-r) -a - ½ a (1-r) -½ a (1-r) + ½ r d
½ (1-r) + ½ r
µ mm =
rd -a(1-r)m Q / m q ½ r d ½ r d
Single Marker Analysis- Back Cross
Parent inbred lines: M Q / M Q X m q / m q
F1 M Q / m q
Back Cross: M Q / m q X m q / m q
Contrast : µMm - µmm = (a +d)(1-2r) …. (i)
•In equation (i) QTL effect (a + d) and recombination freq (r) are entangled.
•We cannot distinguish a QTL with large effect but loosely linked ( high r) with the one
having small effect but tightly linked (low r).
•A zero contrast : no evidence of a QTL

9. Genotypic values:
QQ = a, Qq = d, qq = -a
Freq.
¼(1-r)2
¼r.(1-r)
¼(1-r)r
¼ r2
Freq.
¼ (1-r).r
¼ r2
¼ (1-r)2
¼ r.(1-r)
Freq.
¼(1-r).r
¼ (1- r)2
¼ r2
¼r.(1-r)
Freq.
¼ r2
¼(1-r).r
¼(1-r).r
¼(1-r)2
Value
a
d
d
-a
Value
a
d
d
-a
Value
a
d
d
-a
Value
a
d
d
-a
µ = freq . Value / total freq
µMM = ¼[(1-r)2.
a + 2rd.(1-r) –a r2
] / ¼
µMm = ½[r2.
d + (1-r)2
.d] / ½
µmm = ¼[a.r2
+ 2rd.(1-r) –a(1-r)2
] / ¼
Marker Contrast (µMM-µmm) = 2a(1-2r)
Single Marker Analysis-
F2 cross
Marker Contrast

10. Single Marker Analysis- F2 cross
Gamete probabilities
½ (1-r) ½ (1-r) ½ r ½ r
Gamete M Q
½ (1-r)
M q
½ (r )
m Q
½ (r )
m q
½ (1-r)
M Q ½ (1-r) ¼ (1-r)2
¼ r. (1-r) ¼ r. (1-r) ¼ (1-r)2
M q ½ (r ) ¼ r.(1-r) ¼ r2 ¼ r2
¼ r.(1-r)
m Q ½ (r ) ¼ r. (1-r) ¼ r2
¼ r2
¼ r. (1-r)
m q ½ (1-r) ¼ (1-r)2
¼ r.(1-r) ¼ r.(1-r) ¼ (1-r)2
Joint Probability of Marker and QTL genotype
……Next slide

11. F2 design : Genotype prob. & QT expectations
Marker
Genoty.
Marker
Genotyp
Prob.
F2
QTL
Genotype
Marker +
QTL Prob
Conditional
Probability of
QTL given
Marker
Genotype*
Genotypic
value of
QTL
Marker
Genotype-
QTL
expectation
M M 1/4 QQ ¼(1-r)2
(1-r)2
a a(1-2r) +
2dr(1-r)
Qq ¼[2r(1-r)] 2r.(1-r) d
qq ¼(r2
) r2
-a
M m ½ QQ ½.r(1-r)] r.(1-r) a d(1-2r+2r2)
Qq ½[r2
+(1-r)2
1-2r + 2r2
d
qq ½[r.(1-r)] r.(1-r) -a
m m ¼ QQ ¼(r2
) r2
a -a(1-2r) +
2dr(1-r)
Qq ¼[2r(1-r)] 2r.(1-r) d
qq ¼(1-r)2
(1-r)2
-a
* Prob (QTL/Marker Genotype) = Pr(QTL and marker) / Pr (marker)
Contrast: MM – mm = 2a(1-2r)

12. Estimates of Additive & Dominant effects
Additive effect:
[µMM - µmm] / 2 = a(1-2r)
Dominant effect estimate:
µMm – (µMM +µmm)/2 = d(1-2r)
(µMM -µmm)/2
Significance Test:
T-Test: This method can only be used in selected populations where there are
only two different marker genotypes (e.g backcross progeny with Mm and mm
groups only)
T = Marker Contrast / SE of MC.
ANOVA: Significance Test:
It is used in experimental designs which have more than two genotypes, such as
F2 or double backcross. The test allows directional distinction between marker
groups that are being tested. F is calculated as a ration of marker mean-square to
error MS.
Model : Yijk= U + Mi + eijk, where Yijk is the trait value of kth individual of ith genotye,

13. Single Marker Analysis
Crosses between Outbred Lines
Lines are not fixed for alternate QTL alleles
Breed A X Breed B
Frequency of Q pA pB
Backcross: µMM - µmm = (pA – pB) (1-2r)(a+d)
F2 Cross:: µMM - µmm = (pA – pB) 2(1-2r) a
•Choose the breeds that differ in the trait of interest (divergent)
•Choose markers that differ in frequency between lines and F1
parents that are heterozygous for markers (such that marker
alleles can be traced back to F2 progeny)

14. Limitations of Single Marker approaches
QTL position and effect are confounded
(1-2r)a
Need to use more than one marker simultaneously
- Interval mapping

15. Interval / Flanking Marker Mapping
– Lander & Botstein (1989)- introduced the concept of
Interval Mapping
– a separate analysis is performed for each pair of
adjacent marker loci.
– much greater precision in estimating QTL effects and
position
– Requirement: Genetic Map
• with a number of markers on each chromosome
• Distances between adjacent markers is assumed known
– Flanking markers- help find recombination event b/w
them, which gives a better idea of the QTL genotype
of the animal

16. Use of flanking markers
To estimate QTL position and effect separately
Contrast:
Backcross: µMm - µmm = (1-2r1)(a +d)
F2 Cross:: µNn - µnn = (1-2r2)(a +d)

17. Possible Gametes Gamete Prob.
½(1-r1)(1-r2)
½ r1.r2.
½(1-r1).r2
½ r1.(1-r2)
½.r1.(1-r2)
½(1-r1).r2
½ r1.r2
½(1-r1)(1-r2)
BC genotypes
MQN / mqn
MqN / mqn
MQn / mqn
Mqn / mqn
mQN / mqn
mqN / mqn
mQn / mqn
mqn / mqn
QTL Value
µ + d
µ - a
µ + d
µ - a
µ + d
µ - a
µ + d
µ - a
Marker Contrast
µMm - µmm =
(1-2r1)(a+d)
µNn - µnn =
(1-2r2)(a+d)
Note: in BC gamete &
genotype prob. Is same

18. Probabilities of having inherited the paternal Q allele of
different marker haplotypes.
Marker
Haplotype
Prob. Marker
Hap.
Marker-QTL
Haplotype
Marker + QTL
probability
QTL | Marker
M1M2 ½ (1-Ө) M1QM2 ½(1-r1)(1-r2) ½(1-r1)(1-r2)
½ (1-Ө)
M1m2 ½. Ө M1Qm2 ½(1-r1).r2 ½(1-r1).r2
½. Ө
m1M2 ½. Ө m1QM2 ½ r1.(1-r2) ½ r1.(1-r2)
½. Ө
m1m2 ½ (1-Ө) m1Qm2 ½ r1.r2 ½ r1.r2
½ (1-Ө)

19. Regression Interval Mapping
To estimate QTL position & effect separately Haley & Knott 1992
Yi = µ + βQ XQ,i + ei
XQ,I = Prob (Q | marker genotype, QTL position
E(βQ) = a + d
•Fit Model for various positions of QTL (1 cM steps).
•Position with lowest F ratio gives best estimate of position
and effect
Backcross

20. Interval / Flanking Marker Mapping
F2 Cross between Inbred Lines
Parents: MQN / MQN X mqn / mqn
F1 : MQN / mqn
Markers Pr (QQ|markers) Pr (Qq| markers) Pr (qq|markers) Additive Coef
(X a)
Pr (QQ) – Pr (qq)
Domin. Coef
(X d)
Pr (Qq)
MM NN f (r1,r2, Ө) f (r1,r2, Ө) f (r1,r2, Ө) f (r1,r2, Ө) f (r1,r2, Ө)
MM Nn
MM nn
Mm NN
Mm Nn
Mm nn
mm NN
mm Nn
mm nn
Yi = µ + βa Xadd,i + βd Xdom,i + ei
E(βa) = a
E(βd) = d
Genome Scan:
Fit Regression Model at every
position along the chromosome

21. Number of QTL in Cattle by Trait Types
Milk Fat 90
Milk Protein 130
Milk Yield 62
Mastitis 68
Meat Quality 59
Carcase Characteris 20
Disease Resistance 10
Fertility 44
General 145
Growth 190
Life History Traits 16
Lifetime Production 10
http://www.animalgenome.org
Total QTLs 846
Publications 55
Traits 112

24. How big are the gene effects?
0
2
4
6
8
10
12
14
16
18
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1.1 1.2
Effect (phenotypic standard deviations)
Frequency
Reported gene effects in cows
• To have a panel of
major candidate
genes with an effect
of mean ± 2 SD, we
need to grow by 15 to
20 times more in the
information on such
genes.

25. QTL in SelectionQTL in Selection
• Use of QTL detected in breed crossesUse of QTL detected in breed crosses
• Marker-assisted introgressionMarker-assisted introgression
• Marker-assisted selection in crossesMarker-assisted selection in crosses
• Marker-assisted selection within breedsMarker-assisted selection within breeds
• Gene Assisted Selection (candidate genes)Gene Assisted Selection (candidate genes)

26. • Direct use of a QTL effect for selection across families is not possible.
• Statistical estimation errors: causing both false positive and false negative effects,
particularly when the effect of the QTL is small.
• Lack of consistency of the effect of the same QTL between studies, caused by
QTL by genetic background (epistasis) and by environment interactions.
• Advantage from within-family selection for a QTL over BLUP or phenotypic
selection alone is frequently low and the methodology to exploit this information for
selection is complex and relatively inefficient.
• Net economic effect of the QTL may be lower than the effect on single traits,
because unfavourable effects on other traits.
• Selection using QTL is more complex than phenotypic selection alone. QTLs add
to the list of traits used as selection criteria. Reduced selection intensity and
relative emphasis given to each trait, make optimal selection more difficult.
• Short-term gains due to MAS may be at the expense of medium to long-term
polygenic responses for important traits.
Problems related to use of QTLs in genetic improvement programs