Distribution and Location of Genetic Effects for Dairy Traits

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

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Distribution and Location of Genetic Effects for Dairy Traits

  1. 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. 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. 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$)
  4. 4. Genex emerging markets conference, March 2009 (4) John B. Cole 200 9 Linear and Nonlinear PredictionsLinear and Nonlinear Predictions  Linear model • Infinitesimal alleles model in which all loci have non-zero effects  Nonlinear models • Model A: infinitesimal alleles with a heavy-tailed prior • Model B: finite locus model with normally-distributed marker effects • Model AB: finite locus model with a heavy-tailed prior
  5. 5. Genex emerging markets conference, March 2009 (5) John B. Cole 200 9 Regressions for marker allele effectsRegressions for marker allele effects
  6. 6. Genex emerging markets conference, March 2009 (6) John B. Cole 200 9 R-square values comparing linear toR-square values comparing linear to nonlinear genomic predictionsnonlinear genomic predictions Model Trait Linear A B AB Net Merit 28.2 28.4 27.6 27.6 Milk 47.2 48.5 46.7 47.3 Fat 41.8 44.2 41.5 43.6 Protein 47.5 47.0 46.8 46.6 Fat % 55.3 63.3 57.5 63.9 Protein % 51.4 57.7 51.4 56.6 Longevity 25.6 27.4 25.4 26.4 Somatic cell 37.3 38.3 37.3 37.6
  7. 7. Genex emerging markets conference, March 2009 (7) John B. Cole 200 9 Largest EffectsLargest Effects  Fat %: largest effect on BTA 14 flanking the DGAT1 gene, with lesser effects on milk and fat yield  Protein %: large effects on BTA 6 flanking the ABCG2 gene  Net Merit: a marker on BTA 18 had the largest effect on NM$, in a region previously identified as having a large effect on fertility
  8. 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. 9. Genex emerging markets conference, March 2009 (9) John B. Cole 200 9 Distribution of Marker Effects (Distribution of Marker Effects (DPRDPR))
  10. 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. 11. Genex emerging markets conference, March 2009 (11) John B. Cole 200 9 Marker Effects for Dystocia ComplexMarker Effects for Dystocia Complex
  12. 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. 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. 14. Genex emerging markets conference, March 2009 (14) John B. Cole 200 9 Marker Effects for Autosomal RecessivesMarker Effects for Autosomal Recessives
  15. 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. 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. 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. 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. 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. 20. Genex emerging markets conference, March 2009 (20) John B. Cole 200 9 Positive or Negative TraitsPositive or Negative Traits
  21. 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. 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. 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. 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. 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. 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)

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