1. Distribution and Location of Genetic Effects for Dairy Traits

2. Questions 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. Experimental 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. Linear 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. Regressions for marker allele effects

6. R-square values comparing linear to nonlinear genomic predictions
Model
Trait
Linear A B AB
Net Merit
28.2
28.4
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
56.6
Longevity
25.6
27.4
25.4
26.4
Somatic cell
37.3
38.3
37.6

7. Largest 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. Distribution of Marker Effects (Net Merit)

9. Distribution of Marker Effects (DPR)

10. Marker Effects on Website

11. Marker Effects on Website

12. Marker Effects on Website

13. Dystocia 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$

14. Marker Effects for Dystocia Complex

15. Biology of the Dystocia Complex
The key marker is ss 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

16. From whom did the bad allele come
From whom did the bad allele come? Round Oak Rag Apple Elevation (7HO00058)

17. Locating 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

18. Marker Effects for Autosomal Recessives

19. SNP 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 < ), regression close to 1.0

20. X, Y, Pseudo-autosomal SNP

21. Chromosomal 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

22. Distribution of Chromosomal EBV fat percent on BTA 14 (DGAT)

23. Distribution of Chromosomal EBV sire calving ease on BTA 14 (no QTL)

24. Positive or Negative Traits

25. Net Merit by Chromosome Freddie (1HO08784) - highest Net Merit bull

26. Net Merit by Chromosome O Man (7HO06417) – Sire of Freddie

27. Net Merit by Chromosome Die-Hard (29HO08538) - maternal grandsire

28. Net Merit by Chromosome Planet (7HO08081) – high Net Merit bull

29. New Chromosomal PTA Query

30. Chromosomal PTA Query Example

31. Genotype Parents and Grandparents
Manfred
O-Man
Jezebel
O-Style
Teamster
Deva
Dima
We are all familiar with a traditional pedigree chart. Animal is expected to be an average of his parents.

32. Expected Relationship Matrix1 1HO9167 O-Style
PGS
PGD
MGS
MGD
Sire
Dam
Bull
Manfred
1.0 .0 .5
.25
Jezebel
Teamster
. 0
Dima
O-Man
Deva
O-Style
1Calculated assuming that all grandparents are unrelated

33. Pedigree Relationship Matrix 1HO9167 O-Style
PGS
PGD
MGS
MGD
Sire
Dam
Bull
Manfred
1.053
.090
.105
.571
.098
.334
Jezebel
1.037
.051
.099
.563
.075
.319
Teamster
1.035
.120
.071
.578
.324
Dima
1.042
.102
.581
.342
O-Man
1.045
.086
.566
Deva
1.060
.573
O-Style
1.043

34. Genomic Relationship Matrix 1HO9167 O-Style
PGS
PGD
MGS
MGD
Sire
Dam
Bull
Manfred
1.201
.058
.050
.093
.609
.054
.344
Jezebel
1.131
.008
.135
.618
.079
.357
Teamster
1.110
.100
.014
.613
.292
Dima
1.139
.131
.610
.401
O-Man
1.166
.080
.626
Deva
1.148
O-Style
1.157

35. Difference (Genomic – Pedigree) 1HO9167 O-Style
PGS
PGD
MGS
MGD
Sire
Dam
Bull
Manfred
.149
-.032
-.040
-.012
.038
-.043
.010
Jezebel
.095
.036
.055
.004
Teamster
.075
-.021
-.057
.035
Dima
.097
.029
.059
O-Man
.121
-.006
.060
Deva
.087
.040
O-Style
.114

36. O-Style’s Chromosomal PTA

37. Conclusions
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

38. Acknowledgments Genotyping and DNA extraction: Computing: Funding:
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 ,
Agriculture Research Service
Holstein, Jersey & Brown Swiss breed associations
Contributors to Cooperative Dairy DNA Repository (CDDR)