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Applications of haplotypes in dairy farm management

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Presentation describing how haplotypes can be used to make on-farm management decisions made at the 2012 European Association for Animal Production meeting in Bratislava.

Presentation describing how haplotypes can be used to make on-farm management decisions made at the 2012 European Association for Animal Production meeting in Bratislava.

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  • 1. John B. Cole Animal Improvement Programs Laboratory Agricultural Research Service, USDA Beltsville, MD 20705-2350, USA john.cole@ars.usda.gov Applications of haplotypes in dairy farm management
  • 2. 63rd EAAP Meeting, Bratislava, Slovakia, 29 August 2012 (2) Cole Introduction  Genomic selection increases selection response by reducing generation interval  Bulls were genotyped first due to cost  Now we have genotypes for many cows  What can we do with those data that we couldn’t do before?
  • 3. 63rd EAAP Meeting, Bratislava, Slovakia, 29 August 2012 (3) Cole Genetic merit of Jersey bulls 0 100 200 300 400 500 600 2006 2007 2008 2009 2010 Active Genotyped Breeding Year NetMerit($)
  • 4. 63rd EAAP Meeting, Bratislava, Slovakia, 29 August 2012 (4) Cole Many cows have been genotyped 0 30000 60000 90000 120000 150000 180000 1004 1008 1012 1104 1108 1112 1204 1208 Bulls Cows Evaluation Date (YYMM) Genotypes
  • 5. 63rd EAAP Meeting, Bratislava, Slovakia, 29 August 2012 (5) Cole Haplotypes for farm management  Many uses other than genetic evaluation  Mating strategies  Culling decisions  Identification of new recessive defects  Phenotypic prediction ARS Image Number K7964-1
  • 6. 63rd EAAP Meeting, Bratislava, Slovakia, 29 August 2012 (6) Cole Input costs are rising quickly 0 0.5 1 1.5 2 2.5 3 1 2 3 4 5 6 7 8 9 10 11 12 2010 2011 2012 M:FP = price of a kg of milk / price of a kg of a 16% protein ration Month Milk:FeedPriceRatio July 2012 Grain Costs Soybeans: $15.60/bu (€0.46/kg) Corn: $ 7.36/bu (€0.23/kg)
  • 7. 63rd EAAP Meeting, Bratislava, Slovakia, 29 August 2012 (7) Cole Optimal culling decisions  Low density genotypes on females can be used to guide early culling decisions  Sexed semen increases heifer population from which to select  What animals should be genotyped?
  • 8. 63rd EAAP Meeting, Bratislava, Slovakia, 29 August 2012 (8) Cole Bottom line economics No sexed 2x sexed No sexed 2x sexed Pre-ranking calf reliability 0% 0% 35% 35% Genomic testing policy1 20-100 0-100 70-90 50-90 Statistics (€/cow/year): Profit without heifer calf value 381 378 381 378 Heifer calves sold 14 31 14 31 NPV calves before pre-ranking 99 101 99 101 NPV calves due to pre-ranking 0 0 30 51 Added NPV from genomic testing 38 71 7 16 Cost of genomic testing 7 23 4 9 Heifer calf value 146 180 148 191 Profit with heifer calf value 527 558 529 569 17K SNP genomic test (€36.50, 65% reliability)
  • 9. 63rd EAAP Meeting, Bratislava, Slovakia, 29 August 2012 (9) Cole O-Style Haplotypes Chromosome 15
  • 10. 63rd EAAP Meeting, Bratislava, Slovakia, 29 August 2012 (10) Cole Farmers want new genomic tools
  • 11. 63rd EAAP Meeting, Bratislava, Slovakia, 29 August 2012 (11) Cole New haplotype query Cole, J.B., and Null, D.J. 2012. AIPL Research Report GENOMIC2: Use of chromosomal predicted transmitting abilities. Available: http://aipl.arsusda.gov/reference/chromosomal_pta_query.html.
  • 12. 63rd EAAP Meeting, Bratislava, Slovakia, 29 August 2012 (12) Cole Simulated matings  Mated all genotyped Jersey bulls and cows in a fully cross-classified design  5 877 bulls and 15 553 cows − 91 404 981 matings  Crossovers, independent assortment  100 replicates per mate pair  Mean, variance, skewness, and kurtosis
  • 13. 63rd EAAP Meeting, Bratislava, Slovakia, 29 August 2012 (13) Cole Distribution of progeny DGV Distribution of 6,000,000 randomly sampled simulated matings.
  • 14. 63rd EAAP Meeting, Bratislava, Slovakia, 29 August 2012 (14) Cole Most extreme groups for progeny DGV
  • 15. 63rd EAAP Meeting, Bratislava, Slovakia, 29 August 2012 (15) Cole Most extreme groups for DGV variance
  • 16. 63rd EAAP Meeting, Bratislava, Slovakia, 29 August 2012 (16) Cole Most- and least-skewed progeny groups
  • 17. 63rd EAAP Meeting, Bratislava, Slovakia, 29 August 2012 (17) Cole Most- and least-kurtotic progeny groups
  • 18. 63rd EAAP Meeting, Bratislava, Slovakia, 29 August 2012 (18) Cole Within-herd analysis  Selected 3 Jersey herds  Ranked by number of genotyped animals and percentage of 50K genotypes  Compared actual with possible matings  Could the herd manager have selected better mate pairs?
  • 19. 63rd EAAP Meeting, Bratislava, Slovakia, 29 August 2012 (19) Cole Comparison to actual matings  Simulated matings were compared to 220 actual matings from 142 mate pairs  Three strategies tested in simulation  Mating plans using traditional and genomic PTA as in Pryce et al. (2012)  Alternatively, selection of mate pairs with greatest mean DGV
  • 20. 63rd EAAP Meeting, Bratislava, Slovakia, 29 August 2012 (20) Cole Sire portfolios Bullsusedinherd Cows in herd Genotyped calves Consider each bull as a mate for each cow using different strategies.
  • 21. 63rd EAAP Meeting, Bratislava, Slovakia, 29 August 2012 (21) Cole Actual DGV and inbreeding Similar distribution of DGV Different distribution of relation- ships – different sire portfolios
  • 22. 63rd EAAP Meeting, Bratislava, Slovakia, 29 August 2012 (22) Cole Herd 1 results Actual matings1 Best PTA2 Best gPTA2 Best DGV2 PTA 416 308 446 452 Difference − -108 +28 +36 SE PTA 12 7 11 12 Inbreeding 0.053 0.075 0.083 0.070 Min 0.010 0.005 0.027 <0.001 Max 0.110 0.274 0.145 0.112 Correlation − 0.443 0.218 0.247 1Results from 94 genotyped offspring of 62 cows. 2Results from simulated matings of 62 cows to a portfolio of 54 bulls (n=3348 combinations).
  • 23. 63rd EAAP Meeting, Bratislava, Slovakia, 29 August 2012 (23) Cole Herd 2 results Actual matings1 Best PTA2 Best gPTA2 Best DGV2 PTA 468 396 534 538 Difference − -72 66 70 SE PTA 23 14 13 13 Inbreeding 0.068 0.051 0.077 0.077 Min 0.025 0.001 0.021 0.021 Max 0.090 0.120 0.124 0.106 Correlation − 0.577 0.735 0.745 1Results from 31 genotyped offspring of 19 cows. 2Results from simulated matings of 19 cows to a portfolio of 31 bulls (n=589 combinations).
  • 24. 63rd EAAP Meeting, Bratislava, Slovakia, 29 August 2012 (24) Cole Herd 3 results Actual matings1 Best PTA2 Best gPTA2 Best DGV2 PTA 480 342 505 501 Difference − -138 25 21 SE PTA 19 8 12 10 Inbreeding 0.068 0.076 0.093 0.068 Min 0.015 0.000 0.045 0.015 Max 0.125 0.183 0.178 0.106 Correlation − 0.665 0.682 0.495 1Results from 95 genotyped offspring of 38 cows. 2Results from simulated matings of 38 cows to a portfolio of 25 bulls (n=950 combinations).
  • 25. 63rd EAAP Meeting, Bratislava, Slovakia, 29 August 2012 (25) Cole Inbreeding effects  Are inbreeding effects distributed uniformly across the genome?  Where are the recessives and the over- and under-dominant loci?  Inbreeding changes transcription levels and gene expression profiles in D. melanogaster (Kristensen et al., 2005)
  • 26. 63rd EAAP Meeting, Bratislava, Slovakia, 29 August 2012 (26) Cole Precision inbreeding  Runs of homozygosity may indicate genomic regions where inbreeding is acceptable  Can we target those regions by selecting among haplotypes?  Is it worth it? Dominance RecessivesNo dominance/under- dominance
  • 27. 63rd EAAP Meeting, Bratislava, Slovakia, 29 August 2012 (27) Cole Phenotypic prediction  Can we predict unknown phenotypes or accurately forecast performance?  Models with GxE are better predictors (Bryant et al., 2005)  Models with A+D better than records from relatives (Lee et al., 2008)  Disease risk can be predicted even if mechanisms unknown (Wray et al., 2005)
  • 28. 63rd EAAP Meeting, Bratislava, Slovakia, 29 August 2012 (28) Cole Unknown phenotypes  Susceptibility to disease  e.g., Johne’s is difficult to diagnose,  Differential response to management  Conversion efficiency of different rations  Response to superovulation  Resistance to heat stress
  • 29. 63rd EAAP Meeting, Bratislava, Slovakia, 29 August 2012 (29) Cole Novel haplotypes affecting fertility Name Chrom- osome Loca- tion Carrier Freq Earliest Known Ancestors BTA Mbase % HH1 5 62-68 4.5 Pawnee Farm Arlinda Chief HH2 1 93-98 4.6 Willowholme Mark Anthony HH3 8 92-97 4.7 Glendell Arlinda Chief, Gray View Skyliner JH1 15 11-16 23.4 Observer Chocolate Soldier BH1 7 42-47 14.0 West Lawn Stretch Improver VanRaden et al. (2011)
  • 30. 63rd EAAP Meeting, Bratislava, Slovakia, 29 August 2012 (30) Cole Loss-of-function mutations  At least 100 LoF per human genome surveyed (MacArthur et al., 2010)  Of those genes ~20 are completely inactivated  Previously uncharacterized LoF variants likely to have phenotypic effects  How can mating programs deal with this?
  • 31. 63rd EAAP Meeting, Bratislava, Slovakia, 29 August 2012 (31) Cole Precision mating  Eliminate undesirable haplotypes  Detection at low allele frequencies  Avoid carrier-to-carrier matings  Easy with few recessives, difficult with many recessives  Include in selection indices  Requires many inputs
  • 32. 63rd EAAP Meeting, Bratislava, Slovakia, 29 August 2012 (32) Cole Threats to continued progress Provisional US patent filed on 20 NOV 2010 after the 9WCGALP in Leipzig – no disclosure at that time! This MS with similar ideas was submitted 22 SEP 2010 and published on 12 APR 2011. Why share?
  • 33. 63rd EAAP Meeting, Bratislava, Slovakia, 29 August 2012 (33) Cole Conclusions  …
  • 34. 63rd EAAP Meeting, Bratislava, Slovakia, 29 August 2012 (34) Cole Acknowledgments Dan Null and Tabatha Cooper Animal Improvement Programs Laboratory, ARS, USDA Beltsville, MD Albert De Vries Department of Animal Sciences University of Florida, Gainesville, FL David Galligan School of Veterinary Medicine University of Pennsylvania Kennett Square, PA
  • 35. 63rd EAAP Meeting, Bratislava, Slovakia, 29 August 2012 (35) Cole References Bryant, J., et al. 2005. Simulation modelling of dairy cattle performance based on knowledge of genotype, environment and genotype by environment interactions: current status. Agric. Sys. 86:121–143. Kristensen, T.N. 2005. Genome-wide analysis on inbreeding effects on gene expression in Drosophila melanogaster. Genetics 171:157-167. doi:10.1534/genetics.104.039610. Lee, S.H., et al. 2012. Predicting unobserved phenotypes for complex traits from whole- genome SNP data. PLoS Genet. 4:e1000231. doi:10.1371/journal.pgen.1000231. MacArthur, D.G., et al. 2012. A systematic survey of loss-of-function variants in human Protein-coding genes. Science 335:823-828. doi:10.1126/science.1215040. Pryce, J.E., et al. 2012. Novel strategies to minimize progeny inbreeding while maximizing genetic gain using genomic information. J. Dairy Sci. 95:377–388. VanRaden, P.M., et al. 2011. Harmful recessive effects on fertility detected by absence of homozygous haplotypes. J. Dairy Sci. 94:6153–6161. Varona, L., and I. Misztal. 1999. Prediction of parental dominance combinations for planned matings, methodology, and simulation results. J. Dairy Sci. 82:2186–2191. Wray, N.L., et al. 2008. Prediction of individual genetic risk of complex disease. Curr. Opin.