Distribution and Location of Genetic Effects for Dairy Traits - PowerPoint PPT Presentation

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Distribution and Location of Genetic Effects for Dairy Traits

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Distribution and Location of Genetic Effects for Dairy Traits
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Distribution and Location of Genetic Effects for Dairy Traits

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  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

  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 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

  16. 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 < 0.0001), regression close to 1.0

  20. X, Y, Pseudo-autosomal SNP 35 SNP 35 SNP 0 SNP 487 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 EBVfat percent on BTA 14 (DGAT)

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

  24. Positive or Negative Traits

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

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

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

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

  29. New Chromosomal PTA Query

  30. Chromosomal PTA Query Example

  31. Genotype Parents and Grandparents

  32. Expected Relationship Matrix11HO9167 O-Style 1Calculated assuming that all grandparents are unrelated

  33. Pedigree Relationship Matrix1HO9167 O-Style

  34. Genomic Relationship Matrix 1HO9167 O-Style

  35. Difference (Genomic – Pedigree) 1HO9167 O-Style

  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: • 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, 2006-35205-16701 • Agriculture Research Service • Holstein, Jersey & Brown Swiss breed associations • Contributors to Cooperative Dairy DNA Repository (CDDR)