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Genomic Selection in Dairy Cattle

Genomic Selection in Dairy Cattle. Simulated Results World Bull Population. 15,197 older and 5,987 younger bulls in Interbull file 40,000 SNPs and 10,000 QTLs Provided timing, memory test Reliability vs parent average REL REL = corr 2 (EBV, true BV) 80% vs 34% expected for young bulls

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Genomic Selection in Dairy Cattle

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  1. Genomic Selection in Dairy Cattle

  2. Simulated ResultsWorld Bull Population • 15,197 older and 5,987 younger bulls in Interbull file • 40,000 SNPs and 10,000 QTLs • Provided timing, memory test • Reliability vs parent average REL • REL = corr2 (EBV, true BV) • 80% vs 34% expected for young bulls • 72% vs 30% observed in simulation

  3. Current Genotyped Animals (n=6005)

  4. Genomic Methods • Direct genomic evaluation • Inversion for linear prediction, REL • Iteration for nonlinear prediction • Combined genomic evaluation • Traditional PA or PTA, subset PA or PTA, and direct genomic combined by REL in 3 x 3 selection index • Nonlinear genomic predictions used

  5. Nonlinear and Linear Regressions for marker allele effects

  6. Experimental DesignActual North American Data • August 2003 PTAs for 3,576 older bulls to predict January 2008 daughter deviations for 1,759 younger bulls (total = 5,335 bulls) • Results computed for 27 traits: 5 yield, 5 health, 16 conformation, and Net Merit • Nonlinear A used, B didn’t work

  7. Marker P-Values for Net Merit

  8. Marker Effects for Net Merit

  9. Marker Effects for Milk

  10. R2 and Reliabilities comparing traditional to genomic predictions

  11. R2 and Reliabilities comparing traditional to genomic predictions

  12. SNP Density Comparison

  13. X, Y, Pseudo-autosomal SNPs 35 SNPs 35 SNPs 0 SNPs 487 SNPs

  14. Conclusions • Genomic predictions significantly better than parent average (P < .0001) for all 26 traits tested • Gains in reliability equivalent on average to 11 daughters with records • Analysis used 3,576 historical bulls • Current data includes 5,285 proven bulls • Larger populations require more SNPs

  15. Acknowledgments • Funding: • NRI grants 2006-35205-16888, 16701 • CDDR Contributors (NAAB, Semex) • Genotyping and DNA extraction: • BFGL, U. Missouri, U. Alberta, GeneSeek, GIFV, and Illumina • Computing from AIPL staff

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