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Using Genomic Data to Improve Dairy Cattle Genetic Evaluations

Using Genomic Data to Improve Dairy Cattle Genetic Evaluations. Acknowledgments. 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)

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Using Genomic Data to Improve Dairy Cattle Genetic Evaluations

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  1. Using Genomic Data to Improve Dairy Cattle Genetic Evaluations

  2. Acknowledgments • 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) • Funding: • National Research Initiative grants • 2006-35205-16888, 2006-35205-16701 • Agriculture Research Service • Holstein and Jersey breed associations • Contributors to Cooperative Dairy DNA Repository (CDDR)

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

  4. 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

  5. Old 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% because of Mendelian sampling • Genomics can predict the other 50% • REL limit at birth theoretically 99%

  6. New Genetic Terms • Genomic relationships and inbreeding • Actual genes in common (G) vs. expected genes in common (A) • Wright’s correlation matrix or Henderson’s numerator relationship (covariance) matrix • Average relationship to population • Expected future inbreeding (EFI) from A • Genomic future inbreeding (GFI) from G • Daughter merit vs. son merit (X vs. Y)

  7. Genomic Studies 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, 19,105 animals • BFGL, AIPL, Missouri, Canada, and Illumina • Oct 2007- Dec 2008

  8. SNP Edits & Counts • SNP available(Illumina SNP50 BeadChip) 58,336 Insufficient average number of beads 1,389 • Unscorable SNP 4,360 • Monomorphic in Holsteins 5,734 • Minor allele frequency (MAF) of <5% 6,145 • Not in Hardy-Weinberg equilibrium 282 • Highly correlated 2,010 • Used for genomic prediction 38,416

  9. Animal Genotype Edits • Require 90% call rate of SNP / animal • Check parent-progeny pair for conflicting homozygotes • If many conflicts or if parent not genotyped, check all genotyped animals for possible parent • Check maternal grandsire (MGS) for expected relationship • Check heterozygous SNP on X (only females)

  10. Repeatability of Genotypes • 2 laboratories genotyped the same 46 bulls • SNP scored the same by both labs • About 1% missing genotypes per lab • Mean of 37,624 out of 38,416 SNP (98% same) • Range across animals of 20 to 2,244 SNP missing • SNP conflict (<0.003%, or 99.997% concordance) • Mean of 0.9 SNP error per 38,416 • Range of 0 to 7 SNP

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

  12. Genotype Data from Inbred BullChromosome 24 of Megastar 1021222101021021011102110112112211211002202000222020002020220 0000220020222202202000020020222222000020222200000220200002002 2002000000222200022220000000000020222022002000222020222220002 2022222222200002002202022202000200022000000002202220000002200 2020002222002020020020202220222222220222020002022022022220202 2202020202200022002220220022200000220200002002002000200222220 0022220202002220022202000020200000022222020200002002002222000 2022022220022000222202200222202020002202202222002220022000200 2202000002200220222000022000022000222202002222000220020020202 2020002220002220022202202200000220220020020020220002000222202 2002220020220200222202220000020220002020020202000220022000002 2022200202220200022002000200022002002000200220222220022022000 2000020002000020220020220200200002220000222002000200222000022 0220020022002202202020202020200022202000220200202202220220000 2020200002020200022222200222200020022022220000020220020200202 022022020200002000200220220002200

  13. Close Inbreeding (F=14.7%): Double Grandson of Aerostar Aerostar Megastar Aerostar Chromosome 24

  14. Differences in G and AG = genomic and A = traditional relationships • Detected clones, identical twins, and duplicate samples • Detected incorrect DNA samples • Detected incorrect pedigrees • Identified correct source of DNA by genomic relationships with other animals

  15. 3 Formulas to Compute G • Sum products of genotypes (g) adjusted for allele frequency (p) • G1jk = ∑ (gij-pi) (gik-pi) / [2 ∑ pi(1-pi)] • Or individually weighted by p • G2jk = ∑ (gij-pi) (gik-pi) / 2pi(1-pi) • Or scaled by intercept (b0) and regression (b1) on A, using p = 0.5 • G3jk = [∑ (gij - 0.5) (gik - 0.5) – b0] / b1

  16. Compare A with 3 formulas for GActual Holstein Data 1Diagonal = 1 + Inbreeding

  17. Summary of G Formulasfor Genomic Inbreeding • Correlations ranked G3 > G1 > G2 in simulation vs. G2 > G1 > G3 with real data (opposite) • G2 and G1 biased down, G3 up • G1 and G2 can be adjusted toward A using b0 and b1, similar to G3 formula • After adjusting, mean G1 = 1.08 and G2 = 1.09 compared to G3 = 1.13 and A = 1.05 • G1 was unbiased in simulation using true rather than estimated frequencies

  18. Genomic vs. PedigreeInbreeding Correlation = .68

  19. Genomic vs. Expected Future Inbreeding

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

  21. Genotyped Holsteins (n=6005)As of April 2008

  22. Genomic Methods • Direct genomic evaluation • Evaluate genotyped animals by summing effects of 38,416 genetic markers (SNPs) • Combined genomic evaluation • Include phenotypes of non-genotyped ancestors by selection index • Transferred genomic evaluation • Propagate info from genotyped animals to non-genotyped relatives by selection index

  23. Reliability Gain1 by BreedYield traits and NM$ of young bulls 1Gain above parent average reliability ~35%

  24. Reliability Gain by BreedHealth and type traits of young bulls

  25. Reliability Gains for Proven Bulls • Proven bulls included in test had: • >10 daughters in August 2003 • >10% increase in reliability by 2008 • Numbers of bulls in test ranged from 104 to 735 across traits • Predicted the change in evaluation • Significant increase in R2 (P < .001) for 26 of 27 traits

  26. Value of Genotyping More Bulls

  27. Value of Genotyping More SNP9,604 (10K), 19,208 (20K), and 38,416 (40K) SNP

  28. Simulated ResultsWorld Holstein 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

  29. Marker Effects for Milk

  30. Marker Effects for Net Merit

  31. Major Gene on Chromosome 18Net Merit, Productive Life, Calving Ease, Stature, Strength, Rump Width

  32. Net Merit by ChromosomePlanet - high Net Merit bull

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

  34. SNPs 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 = .1 σG, smaller than expected • Correlation with sire’s daughter vs. son PTA difference was significant (P<.0001), regression close to 1.0

  35. Linear and Nonlinear Predictions • Linear model • Infinitesimal alleles model: all SNP have normally distributed effects • Nonlinear models • Model A: all SNP have effects, but with a heavy-tailed prior distribution • Model B: some SNP have no effects, the rest are normally distributed • ModelAB: some SNP have no effect, the rest have a heavy-tailed prior

  36. Regressions for marker allele effects

  37. R2 of Linear and Nonlinear Genomic Predictions

  38. 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

  39. Low Density SNP Chip • Choose 384 marker subset • SNP that best predict net merit • Parentage markers to be shared • Use for initial screening of cows • 40% benefit of full set for 10% cost • Could get larger benefits using haplotyping (Habier et al., 2008)

  40. Conclusions • 100X more markers allows MAS across rather than within families • 10X more bulls allows estimation of much smaller QTL effects (HO) • Reliability increases by tracing actual genes inherited instead of expected average from parents

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