Presentation on the distribution and location of genetic effects for dairy traits made to the Genex emerging markets conference in 2009.

1. 200
7
Paul VanRaden, George Wiggans, Jeff O’Connell, John Cole,Paul VanRaden, George Wiggans, Jeff O’Connell, John Cole,
Animal Improvement Programs Laboratory
Tad Sonstegard, and Curt Van Tassell
Bovine Functional Genomics Laboratory
USDA Agricultural Research Service, Beltsville, MD, USA
Paul.VanRaden@ars.usda.gov
200
9
Distribution and Location ofDistribution and Location of
Genetic Effects for Dairy TraitsGenetic Effects for Dairy Traits

2. Genex emerging markets conference, March 2009 (2) John B. Cole
200
9
Questions of InterestQuestions of Interest
 What model best fits our data?
 Have we found any genes of large effect?
 Can we use marker effects to locate
autosomal recessives?
 How do we handle the X chromosome?
 How can we use marker effects to make
better breeding decisions?

3. Genex emerging markets conference, March 2009 (3) John B. Cole
200
9
Experimental DesignExperimental Design
 Predict April 2008 daughter
deviations from August 2003 PTA
• Similar to Interbull trend test 3
• 3576 older Holstein bulls
• 1759 younger bulls (total = 5335)
 Results computed for 27 traits: 5
yield, 5 health, 16 conformation,
and Net Merit (NM$)

4. Genex emerging markets conference, March 2009 (4) John B. Cole
200
9
Linear and Nonlinear PredictionsLinear and Nonlinear Predictions
 Linear model
• Infinitesimal alleles model in which
all loci have non-zero effects
 Nonlinear models
• Model A: infinitesimal alleles with a
heavy-tailed prior
• Model B: finite locus model with
normally-distributed marker effects
• Model AB: finite locus model with a
heavy-tailed prior

5. Genex emerging markets conference, March 2009 (5) John B. Cole
200
9
Regressions for marker allele effectsRegressions for marker allele effects

6. Genex emerging markets conference, March 2009 (6) John B. Cole
200
9
R-square values comparing linear toR-square values comparing linear to
nonlinear genomic predictionsnonlinear genomic predictions
Model
Trait Linear A B AB
Net Merit 28.2 28.4 27.6 27.6
Milk 47.2 48.5 46.7 47.3
Fat 41.8 44.2 41.5 43.6
Protein 47.5 47.0 46.8 46.6
Fat % 55.3 63.3 57.5 63.9
Protein % 51.4 57.7 51.4 56.6
Longevity 25.6 27.4 25.4 26.4
Somatic cell 37.3 38.3 37.3 37.6

7. Genex emerging markets conference, March 2009 (7) John B. Cole
200
9
Largest EffectsLargest Effects
 Fat %: largest effect on BTA 14
flanking the DGAT1 gene, with
lesser effects on milk and fat yield
 Protein %: large effects on BTA 6
flanking the ABCG2 gene
 Net Merit: a marker on BTA 18 had
the largest effect on NM$, in a
region previously identified as
having a large effect on fertility

8. Genex emerging markets conference, March 2009 (8) John B. Cole
200
9
Distribution of Marker Effects (Distribution of Marker Effects (Net MeritNet Merit))

9. Genex emerging markets conference, March 2009 (9) John B. Cole
200
9
Distribution of Marker Effects (Distribution of Marker Effects (DPRDPR))

10. Genex emerging markets conference, March 2009 (10) John B. Cole
200
9
Dystocia ComplexDystocia Complex
 Markers on BTA 18 had the largest
effects for several traits:
• Dystocia and stillbirth: Sire and
daughter calving ease and sire
stillbirth
• Conformation: rump width, stature,
strength, and body depth
• Efficiency: longevity and net merit
 Large calves contribute to shorter
PL and decreased NM$

11. Genex emerging markets conference, March 2009 (11) John B. Cole
200
9
Marker Effects for Dystocia ComplexMarker Effects for Dystocia Complex

12. Genex emerging markets conference, March 2009 (12) John B. Cole
200
9
Biology of the Dystocia ComplexBiology of the Dystocia Complex
 The key marker is ss86324977 at
57,125,868 Mb on BTA 18
 Located in a cluster of CD33-
related Siglec genes
• Many Siglecs are involved in the
leptin signaling system
 Preliminary results also indicate
an effect on gestation length

13. Genex emerging markets conference, March 2009 (13) John B. Cole
200
9
Locating Causative MutationsLocating Causative Mutations
 Genomics may allow for faster
identification of causative mutations
 Identifies SNP in strong linkage
disequilibrium with recessive loci
 Tested using BLAD, CVM, and RED
 Only a few dozen genotyped carriers
are needed

14. Genex emerging markets conference, March 2009 (14) John B. Cole
200
9
Marker Effects for Autosomal RecessivesMarker Effects for Autosomal Recessives

15. Genex emerging markets conference, March 2009 (15) John B. Cole
200
9
SNP on X ChromosomeSNP on X Chromosome
 Each animal has two evaluations
• Expected genetic merit of daughters
• Expected genetic merit of sons
• Difference is sum of effects on X
• SD = 0.1 σG, smaller than expected
 Correlation with sire’s daughter
vs. son PTA difference was
significant (P < 0.0001), regression
close to 1.0

16. Genex emerging markets conference, March 2009 (16) John B. Cole
200
9
X,X, YY,, Pseudo-autosomalPseudo-autosomal SNPSNP
487 SNP
35 SNP
0 SNP
35 SNP

17. Genex emerging markets conference, March 2009 (17) John B. Cole
200
9
Chromosomal EBVChromosomal EBV
 Sum of marker effects for
individual chromosomes
• Individual chromosomal EBV sum to
an animal’s genomic EBV
 Chromosomal EBV are normally
distributed in the absence of QTL
 QTL can change the mean and SD
of chromosomal EBV

18. Genex emerging markets conference, March 2009 (18) John B. Cole
200
9
Distribution of Chromosomal EBVDistribution of Chromosomal EBV
fat percent on BTA 14 (fat percent on BTA 14 (DGATDGAT))

19. Genex emerging markets conference, March 2009 (19) John B. Cole
200
9
Distribution of Chromosomal EBVDistribution of Chromosomal EBV
sire calving ease on BTA 14 (sire calving ease on BTA 14 (no QTLno QTL))

20. Genex emerging markets conference, March 2009 (20) John B. Cole
200
9
Positive or Negative TraitsPositive or Negative Traits

21. Genex emerging markets conference, March 2009 (21) John B. Cole
200
9
Net Merit by ChromosomeNet Merit by Chromosome
FreddieFreddie - highest Net Merit bull- highest Net Merit bull
-40
-20
0
20
40
60
80
100
X 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30
Chromosome
NM$
NM$

22. Genex emerging markets conference, March 2009 (22) John B. Cole
200
9
Net Merit by ChromosomeNet Merit by Chromosome
O ManO Man – Sire of Freddie– Sire of Freddie
-40
-20
0
20
40
60
80
100
X 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30
Chromosome
NM$
NM$

23. Genex emerging markets conference, March 2009 (23) John B. Cole
200
9
Net Merit by ChromosomeNet Merit by Chromosome
Die-HardDie-Hard - maternal grandsire- maternal grandsire
-40
-20
0
20
40
60
80
100
X 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30
Chromosome
NM$
NM$

24. Genex emerging markets conference, March 2009 (24) John B. Cole
200
9
Net Merit by ChromosomeNet Merit by Chromosome
PlanetPlanet – high Net Merit bull– high Net Merit bull
-40
-20
0
20
40
60
80
100
X 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30
Chromosome
NM$
NM$

25. Genex emerging markets conference, March 2009 (25) John B. Cole
200
9
ConclusionsConclusions
 A heavy-tailed model fits the data better
than linear or finite loci models
 Markers on BTA 18 had large effects on
net merit, longevity, calving traits, and
conformation
 Marker effects may be useful for locating
causative mutations for recessive alleles
 Results validate quantitative genetic
theory, notably the infinitessimal model

26. Genex emerging markets conference, March 2009 (26) John B. Cole
200
9
AcknowledgmentsAcknowledgments
 Genotyping and DNA extraction:
• USDA Bovine Functional Genomics Lab, U.
Missouri, U. Alberta, GeneSeek, Genetics & IVF
Institute, Genetic Visions, and Illumina
 Computing:
• AIPL staff (Mel Tooker, Leigh Walton, Jay
Megonigal)
 Funding:
• National Research Initiative grants
– 2006-35205-16888, 2006-35205-167012006-35205-16888, 2006-35205-16701
• Agriculture Research Service
• Holstein and Jersey breed associations
• Contributors to Cooperative Dairy DNA Repository
(CDDR)