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Predicting Genetic Merit Using Genomic Data

Predicting Genetic Merit Using Genomic Data. How Related are Relatives?. Example: Full sibs are expected to share 50% of their DNA on average may actually share 40% to 60% of their DNA because each inherits a different mixture of chromosome segments from the two parents.

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Predicting Genetic Merit Using Genomic Data

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  1. Predicting Genetic Merit Using Genomic Data

  2. How Related are Relatives? • Example: Full sibs • are expected to share 50% of their DNA on average • may actually share 40% to 60% of their DNA because each inherits a different mixture of chromosome segments from the two parents. • Exact DNA sharing computed from genotypes instead of pedigrees

  3. Traditional Pedigreeanimal, parents, grandparents

  4. DNA Pedigree1 chromosome

  5. Genomic Pedigree1 chromosome atagatcgatcg ctgtagcgatcg ctgtagcttagg agatctagatcg agggcgcgcagt cgatctagatcg ctgtctagatcg atgtcgcgcagt cggtagatcagt agagatcgcagt agagatcgatct atgtcgctcacg atggcgcgaacg ctatcgctcagg

  6. Genotype Data for ElevationChromosome 1 1000111220020012111011112111101111001121100020122002220111 1202101200211122110021112001111001011011010220011002201101 1200201101020222121122102010011100011220221222112021120120 2010020220200002110001120201122111211102201111000021220200 0221012020002211220111012100111211102112110020102100022000 2201000201100002202211022112101121110122220012112122200200 0200202020122211002222222002212111121002111120011011101120 0202220001112011010211121211102022100211201211001111102111 2110211122000101101110202200221110102011121111011202102102 1211011022122001211011211012022011002220021002110001110021 1021101110002220020221212110002220102002222121221121112002 0110202001222222112212021211210110012110110200220002001002 0001111011001211021212111201010121202210101011111021102112 2111111212111210110120011111021111011111220121012121101022 202021211222120222002121210121210201100111222121101

  7. Gene Flow • Descendants of 1 bull (Elevation) • Born in 1965 • 80,000 daughters • 2.3 million granddaughters • 6.5 million great-granddaughters • 15.2% of genes in US Holsteins • Appears in >80% of Holstein pedigrees in every country studied

  8. Sequencing and Genotyping • Cattle genome sequenced in 2004 • 30 chromosome pairs (including X,Y) • 3 billion letters from each parent • Illumina BovineSNP50 BeadChip • 58,000 genetic markers in 2007 • 38,416 used in genomic predictions • Current cost <$250 per animal

  9. Experimental Design • Compute genomic evaluations and parent averages from 2003 data • 3576 older Holstein bulls born 1952-1998 • Compare ability to predict daughter performance in 2008 data • 1759 younger bulls born 1999-2002 • Test results for 27 traits: 5 yield, 5 health, 16 conformation, and Net Merit

  10. Marker Effects for Net Merit

  11. Value of Genotyping More SNP9,604 (10K), 19,208 (20K), and 38,416 (40K) SNP REL of PA = reliability of parent average

  12. Value of Genotyping More Bulls

  13. Top Net Merit Bull:O-Man • Semen sales 198,000 units / year • Semen price $40 / unit • Income ~$35 million to date • 25,429 daughters already milking • 19,848 in United States • 1,348 in France, 1,108 in Italy, 946 in Denmark, 527 in the Netherlands, etc.

  14. Justice Daughters vs. Average Cows

  15. Net Merit by Chromosome for O-ManTop bull for Net Merit

  16. Genetic ProgressHolsteins • Assume 60% REL for net merit • Sires mostly 2 instead of 6 years old • Dams of sons mostly genotyped heifers instead of cows with phenotype and genotype • Progress could increase by >50% • 0.37 vs. 0.23 genetic SD per year • Reduce generation interval more than accuracy

  17. 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 • High reliability requires many genotypes and phenotypes • 5,335 genotyped bulls had >10 million daughters with phenotypic (milk yield) data

  18. Acknowledgments • Genotyping and DNA extraction: • BFGL, U. Missouri, U. Alberta, GeneSeek, GIFV, and Illumina • Computing: • AIPL staff (George Wiggans, Mel Tooker, Leigh Walton, etc.) • Funding: • National Research Initiative grants • 2006-35205-16888, 2006-35205-16701 • Agriculture Research Service • Contributors to Cooperative Dairy DNA Repository (CDDR)

  19. Citizens Fed / AcreData from year 2000, 1 acre = .435 hectare

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