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Genomic History and New Genetic Principles

Genomic History and New Genetic Principles. Genetic Markers: Changing Goals Past and Future. Determine if major genes exist (few) Estimate sparse marker effects Only within family analysis Find causative mutations (DGAT1, ABCG2) Estimate dense effects across families

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Genomic History and New Genetic Principles

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  1. Genomic History and New Genetic Principles

  2. Genetic Markers: Changing GoalsPast andFuture • Determine if major genes exist (few) • Estimate sparse marker effects • Only within family analysis • Find causative mutations (DGAT1, ABCG2) • Estimate dense effects across families • Implement routine predictions • Increase REL with more genotypes • Decrease cost with a selected SNP subset

  3. Genomic History at Beltsville • 174 markers, 1068 bulls, 8 sires • Illinois, Israel, and AIPL • 1991-1999 • 367 markers, 1415 bulls, 10 sires • GEML, AIPL, Illinois, and Israel • 1995-2004 • 38,416 markers, 16,646 animals • BFGL, AIPL, Missouri, Canada, and Illumina • Oct 2007- Oct 2008

  4. Experimental DesignHolstein, Jersey, and Brown Swiss breeds Data from 2003 used to predict independent data from 2008

  5. Reliability Gain1 by BreedYield traits and NM$ 1Gain above parent average reliability ~35%

  6. Reliability Gain by BreedHealth and type traits

  7. Genetic Progress • Assume 60% REL for net merit • Sires mostly 2 instead of 6 years old • Dams of sons mostly heifers with 60% REL instead of cows with phenotype and genotype (66% REL) • Progress could increase by >50% • 0.37 vs. 0.23 genetic SD per year • Reduce generation interval more than accuracy

  8. Genetic Terms • Predicted transmitting ability and parent average • PTA required progeny or own records • PA included only parent data • Genomics blurs the distinction • Reliability • REL of PA could not exceed 50% • Genomics can predict the other 50% • REL limit for calves theoretically 99%

  9. Positive or Negative Traits

  10. Net Merit by ChromosomePlanet - high Net Merit bull

  11. New Genetic Terms • Actual vs. expected genetic similarity • Genomic relationships and inbreeding • Genomic future inbreeding (GFI) vs. EFI • Daughter merit vs. son merit • Haplotyping and imputation • Which allele is from sire vs. dam? • Which alleles are linked together? • Can missing genotypes be predicted?

  12. Genomic vs. PedigreeInbreeding Correlation = .68

  13. Genomic vs. Expected Future Inbreeding

  14. Conclusions • 100X more markers allows MAS across rather than within families • 5X more bulls allows estimation of much smaller QTL effects (HO) • Reliability increases by tracing actual genes inherited instead of expected average from parents

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

  16. CDDR Contributors • National Association of Animal Breeders (NAAB, Columbia, MO) • ABS Global (DeForest, WI) • Accelerated Genetics (Baraboo, WI) • Alta (Balzac, AB) • Genex (Shawano, WI) • New Generation Genetics (Fort Atkinson, WI) • Select Sires (Plain City, OH) • Semex Alliance (Guelph, ON) • Taurus-Service (Mehoopany, PA)

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