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Using Genomic Data to Improve Dairy Cattle Genetic Evaluations Paul Van. Raden, George Wiggans, Using Genomic Data to Improve Dairy Cattle Genetic Evaluations Paul Van. Raden, George Wiggans, Jeff O’Connell, John Cole, Animal Improvement Programs Laboratory Tad Sonstegard, and Curt Van Tassell Bovine Functional Genomics Laboratory USDA Agricultural Research Service, Beltsville, MD, USA Paul. Van. [email protected] usda. gov 2008 2007

Acknowledgments Ø Genotyping and DNA extraction: • Ø Computing: • Ø USDA Bovine Functional Acknowledgments Ø Genotyping and DNA extraction: • Ø Computing: • Ø USDA Bovine Functional Genomics Lab, U. Missouri, U. Alberta, Gene. Seek, Genetics & IVF Institute, Genetic Visions, and Illumina 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) National Swine Improvement Federation Symposium, Dec. 2008 (2) Paul Van. Raden 2008

CDDR Contributors Ø National Association of Animal Breeders (NAAB, Columbia, MO) • ABS Global CDDR Contributors Ø National Association of Animal Breeders (NAAB, Columbia, MO) • ABS Global (De. Forest, 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) • • National Swine Improvement Federation Symposium, Dec. 2008 (3) Paul Van. Raden 2008

Genetic Markers: Changing Goals Past and Future Ø Determine if major genes exist (few) Genetic Markers: Changing Goals Past and Future Ø Determine if major genes exist (few) Ø Estimate sparse marker effects • Only within family analysis Ø Find causative mutations (DGAT 1, ABCG 2) Ø Estimate dense effects across families Ø Implement routine predictions • • Increase REL with more genotypes Decrease cost with a selected SNP subset National Swine Improvement Federation Symposium, Dec. 2008 (4) Paul Van. Raden 2008

Old Genetic Terms Ø Predicted transmitting ability and parent average • • • Ø Old Genetic Terms Ø Predicted transmitting ability and parent average • • • Ø 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% National Swine Improvement Federation Symposium, Dec. 2008 (5) Paul Van. Raden 2008

New Genetic Terms Ø Genomic relationships and inbreeding • • Ø Average relationship to New Genetic Terms Ø Genomic relationships and inbreeding • • Ø Average relationship to population • • Ø Actual genes in common (G) vs. expected genes in common (A) Wright’s correlation matrix or Henderson’s numerator relationship (covariance) matrix Expected future inbreeding (EFI) from A Genomic future inbreeding (GFI) from G Daughter merit vs. son merit (X vs. Y) National Swine Improvement Federation Symposium, Dec. 2008 (6) Paul Van. Raden 2008

Genomic Studies at Beltsville Ø 174 markers, 1068 bulls, 8 sires • • Ø Genomic Studies at Beltsville Ø 174 markers, 1068 bulls, 8 sires • • Ø 367 markers, 1415 bulls, 10 sires • • Ø Illinois, Israel, and AIPL 1991 -1999 GEML, AIPL, Illinois, and Israel 1995 -2004 38, 416 markers, 19, 105 animals • • BFGL, AIPL, Missouri, Canada, and Illumina Oct 2007 - Dec 2008 National Swine Improvement Federation Symposium, Dec. 2008 (7) Paul Van. Raden 2008

National Swine Improvement Federation Symposium, Dec. 2008 (8) Paul Van. Raden 2008 National Swine Improvement Federation Symposium, Dec. 2008 (8) Paul Van. Raden 2008

SNP Edits & Counts Ø SNP available (Illumina SNP 50 Bead. Chip) 58, 336 SNP Edits & Counts Ø SNP available (Illumina SNP 50 Bead. Chip) 58, 336 Insufficient average number of beads Ø Unscorable SNP Ø Monomorphic in Holsteins 5, 734 Ø Minor allele frequency (MAF) of <5% Ø Not in Hardy-Weinberg equilibrium 282 Ø Highly correlated Ø Used for genomic prediction 1, 389 4, 360 6, 145 2, 010 National Swine Improvement Federation Symposium, Dec. 2008 (9) 38, 416 Paul Van. Raden 2008

Animal Genotype Edits Ø Ø Ø Require 90% call rate of SNP / animal Animal Genotype Edits Ø Ø Ø 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) National Swine Improvement Federation Symposium, Dec. 2008 (10) Paul Van. Raden 2008

Repeatability of Genotypes Ø 2 laboratories genotyped the same 46 bulls Ø SNP scored Repeatability of Genotypes Ø 2 laboratories genotyped the same 46 bulls Ø SNP scored the same by both labs • • Mean of 37, 624 out of 38, 416 SNP (98% same) • Ø About 1% missing genotypes per lab 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 National Swine Improvement Federation Symposium, Dec. 2008 (11) Paul Van. Raden 2008

Genotype Data for Elevation Chromosome 1 10001112200200121110111121111001121100020122002220111 1202101200211122110021112001111001011011010220011002201101 12002011010202221211221020100111000112202212221120120 201002022020000211000112020112211102201111000021220200 0221012020002211220111012100111211102112110020102100022000 2201000201100002202211210112111012222001211212220020020202012221100222222200221211112100211112001101120 0202220001112011010211121211102022100211201211001111102111 2110211122000101101110202200221110102011121111011202102102 Genotype Data for Elevation Chromosome 1 10001112200200121110111121111001121100020122002220111 1202101200211122110021112001111001011011010220011002201101 12002011010202221211221020100111000112202212221120120 201002022020000211000112020112211102201111000021220200 0221012020002211220111012100111211102112110020102100022000 2201000201100002202211210112111012222001211212220020020202012221100222222200221211112100211112001101120 0202220001112011010211121211102022100211201211001111102111 2110211122000101101110202200221110102011121111011202102102 121101102212200121101202201100222002110001110021 1021101110002220020221212110002220102002222121221121112002 0110202001222222112212021211210110012110110200220002001002 000111101100121102121211120101012120221010101111102112 211111121211121011012001111102111101111122012121101022 202021211222120222002121210201100111222121101 National Swine Improvement Federation Symposium, Dec. 2008 (12) Paul Van. Raden 2008

Genotype Data from Inbred Bull Chromosome 24 of Megastar 1021222101021021011102110112112110022020002020220 000022002022220220200002002022222200002022220000022020000200200000022220000002022002000222020222220002 2022222000020022020222020002200002202220000002200 2020002222002002020222222220200020220220222202022000222022002220000022020000200200200222220 0022220202002220200000022222020200002002002222000 Genotype Data from Inbred Bull Chromosome 24 of Megastar 1021222101021021011102110112112110022020002020220 000022002022220220200002002022222200002022220000022020000200200000022220000002022002000222020222220002 2022222000020022020222020002200002202220000002200 2020002222002002020222222220200020220220222202022000222022002220000022020000200200200222220 0022220202002220200000022222020200002002002222000 2022022220002222022002222020200022022022220022000200 220200000220222000022000222202002222000220020020202 202000222002220220220000022022002002002022000222202 200222002022020022220000020220002020200022000002 2022200202220200022002002000200220222220022022000020000202202002000022200200222000022 02200200220220202020002220200022020020220220000 20202000020202000222222000200220222200000202200202 02202202020002002202200 National Swine Improvement Federation Symposium, Dec. 2008 (13) Paul Van. Raden 2008

Close Inbreeding (F=14. 7%): Double Grandson of Aerostar Megastar Aerostar Chromosome 24 National Swine Close Inbreeding (F=14. 7%): Double Grandson of Aerostar Megastar Aerostar Chromosome 24 National Swine Improvement Federation Symposium, Dec. 2008 (14) Paul Van. Raden 2008

Differences in G and A G = genomic and A = traditional relationships Ø Differences in G and A G = genomic and A = traditional relationships Ø Detected clones, identical twins, and duplicate samples Ø Detected incorrect DNA samples Ø Detected incorrect pedigrees Ø Identified correct source of DNA by genomic relationships with other animals National Swine Improvement Federation Symposium, Dec. 2008 (15) Paul Van. Raden 2008

3 Formulas to Compute G Ø Sum products of genotypes (g) adjusted for allele 3 Formulas to Compute G Ø Sum products of genotypes (g) adjusted for allele frequency (p) • Ø Or individually weighted by p • Ø G 1 jk = ∑ (gij-pi) (gik-pi) / [2 ∑ pi(1 -pi)] G 2 jk = ∑ (gij-pi) (gik-pi) / 2 pi(1 -pi) Or scaled by intercept (b 0) and regression (b 1) on A, using p = 0. 5 • G 3 jk = [∑ (gij - 0. 5) (gik - 0. 5) – b 0] / b 1 National Swine Improvement Federation Symposium, Dec. 2008 (16) Paul Van. Raden 2008

Compare A with 3 formulas for G Actual Holstein Data Diagonals 1 of G Compare A with 3 formulas for G Actual Holstein Data Diagonals 1 of G Formula Mean SD Corr. with A A 1. 05 . 023 1. 00 G 1 1. 00 . 031 0. 68 G 2 0. 98 . 031 0. 69 G 3 1. 13 . 035 0. 63 1 Diagonal = 1 + Inbreeding National Swine Improvement Federation Symposium, Dec. 2008 (17) Paul Van. Raden 2008

Summary of G Formulas for Genomic Inbreeding Ø Ø Correlations ranked G 3 > Summary of G Formulas for Genomic Inbreeding Ø Ø Correlations ranked G 3 > G 1 > G 2 in simulation vs. G 2 > G 1 > G 3 with real data (opposite) G 2 and G 1 biased down, G 3 up • • • G 1 and G 2 can be adjusted toward A using b 0 and b 1, similar to G 3 formula After adjusting, mean G 1 = 1. 08 and G 2 = 1. 09 compared to G 3 = 1. 13 and A = 1. 05 G 1 was unbiased in simulation using true rather than estimated frequencies National Swine Improvement Federation Symposium, Dec. 2008 (18) Paul Van. Raden 2008

Genomic vs. Pedigree Inbreeding Bull O Man Ramos Shottle Planet Earnit Nifty Pedigree F Genomic vs. Pedigree Inbreeding Bull O Man Ramos Shottle Planet Earnit Nifty Pedigree F 4. 5 2. 3 5. 6 6. 7 6. 2 3. 1 Genomic F 15. 8 11. 5 11. 9 18. 8 12. 8 11. 7 Correlation =. 68 National Swine Improvement Federation Symposium, Dec. 2008 (19) Paul Van. Raden 2008

Genomic vs. Expected Future Inbreeding Bull Blackstar Elevation Chief Emory RC Matt Juror EFI Genomic vs. Expected Future Inbreeding Bull Blackstar Elevation Chief Emory RC Matt Juror EFI 7. 9 7. 6 7. 1 7. 0 National Swine Improvement Federation Symposium, Dec. 2008 (20) GFI 7. 9 7. 4 6. 8 6. 9 6. 7 Paul Van. Raden 2008

Experimental Design Holstein, Jersey, and Brown Swiss breeds HO Predictor: Bulls born <1999 Cows Experimental Design Holstein, Jersey, and Brown Swiss breeds HO Predictor: Bulls born <1999 Cows with data Predicted: Bulls born >1999 JE BS 3, 576 743 202 225 1, 759 425 118 Data from 2003 used to predict independent data from 2008 National Swine Improvement Federation Symposium, Dec. 2008 (21) Paul Van. Raden 2008

Genotyped Holsteins (n=6005) As of April 2008 National Swine Improvement Federation Symposium, Dec. 2008 Genotyped Holsteins (n=6005) As of April 2008 National Swine Improvement Federation Symposium, Dec. 2008 (22) Paul Van. Raden 2008

Genomic Methods Ø Direct genomic evaluation • Ø Combined genomic evaluation • Ø Evaluate Genomic Methods Ø Direct genomic evaluation • Ø Combined genomic evaluation • Ø Evaluate genotyped animals by summing effects of 38, 416 genetic markers (SNPs) Include phenotypes of non-genotyped ancestors by selection index Transferred genomic evaluation • Propagate info from genotyped animals to non-genotyped relatives by selection index National Swine Improvement Federation Symposium, Dec. 2008 (23) Paul Van. Raden 2008

Reliability Gain 1 by Breed Yield traits and NM$ of young bulls Trait Net Reliability Gain 1 by Breed Yield traits and NM$ of young bulls Trait Net merit Milk Fat Protein Fat % Protein % 1 Gain HO 23 23 33 22 43 34 JE 9 11 15 4 41 29 BS 3 0 5 1 10 5 above parent average reliability ~35% National Swine Improvement Federation Symposium, Dec. 2008 (24) Paul Van. Raden 2008

Reliability Gain by Breed Health and type traits of young bulls Trait Productive life Reliability Gain by Breed Health and type traits of young bulls Trait Productive life Somatic cell score Dtr pregnancy rate Final score Udder depth Foot angle Stature HO 18 21 16 18 35 14 26 National Swine Improvement Federation Symposium, Dec. 2008 (25) JE 12 1 5 6 13 10 9 BS 2 16 3 3 Paul Van. Raden 2008

Reliability Gains for Proven Bulls Ø Proven bulls included in test had: • • Reliability Gains for Proven Bulls Ø Proven bulls included in test had: • • Ø >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 R 2 (P <. 001) for 26 of 27 traits National Swine Improvement Federation Symposium, Dec. 2008 (26) Paul Van. Raden 2008

Value of Genotyping More Bulls R 2 for Net Merit Predictor Predicted PA Genomic Value of Genotyping More Bulls R 2 for Net Merit Predictor Predicted PA Genomic Gain 1151 251 8 12 4 2130 261 8 17 9 2609 510 8 21 13 3576 1759 11 28 17 National Swine Improvement Federation Symposium, Dec. 2008 (27) Paul Van. Raden 2008

Value of Genotyping More SNP 9, 604 (10 K), 19, 208 (20 K), and Value of Genotyping More SNP 9, 604 (10 K), 19, 208 (20 K), and 38, 416 (40 K) SNP Trait Net Merit $ Milk yield Fat yield Protein yield Productive Life SCS (mastitis) Dtr Preg Rate REL Genomic REL of PA 10 K 20 K 40 K 30 48 50 53 35 53 56 58 35 64 66 68 35 54 56 57 27 38 41 45 30 45 47 51 25 37 39 41 National Swine Improvement Federation Symposium, Dec. 2008 (28) Paul Van. Raden 2008

Simulated Results World Holstein Population Ø 15, 197 older and 5, 987 younger bulls Simulated Results World Holstein Population Ø 15, 197 older and 5, 987 younger bulls in Interbull file Ø 40, 000 SNPs and 10, 000 QTLs Ø Provided timing, memory test Ø Reliability vs parent average REL • • • REL = corr 2 (EBV, true BV) 80% vs 34% expected for young bulls 72% vs 30% observed in simulation National Swine Improvement Federation Symposium, Dec. 2008 (29) Paul Van. Raden 2008

Marker Effects for Milk National Swine Improvement Federation Symposium, Dec. 2008 (30) Paul Van. Marker Effects for Milk National Swine Improvement Federation Symposium, Dec. 2008 (30) Paul Van. Raden 2008

Marker Effects for Net Merit National Swine Improvement Federation Symposium, Dec. 2008 (31) Paul Marker Effects for Net Merit National Swine Improvement Federation Symposium, Dec. 2008 (31) Paul Van. Raden 2008

Major Gene on Chromosome 18 Net Merit, Productive Life, Calving Ease, Stature, Strength, Rump Major Gene on Chromosome 18 Net Merit, Productive Life, Calving Ease, Stature, Strength, Rump Width National Swine Improvement Federation Symposium, Dec. 2008 (32) Paul Van. Raden 2008

Net Merit by Chromosome Planet - high Net Merit bull National Swine Improvement Federation Net Merit by Chromosome Planet - high Net Merit bull National Swine Improvement Federation Symposium, Dec. 2008 (33) Paul Van. Raden 2008

X, Y, Pseudo-autosomal SNPs 35 SNPs 487 SNPs National Swine Improvement Federation Symposium, Dec. X, Y, Pseudo-autosomal SNPs 35 SNPs 487 SNPs National Swine Improvement Federation Symposium, Dec. 2008 (34) 35 SNPs 0 SNPs Paul Van. Raden 2008

SNPs on X Chromosome Ø Each animal has two evaluations: • • Ø Expected SNPs on X Chromosome Ø Each animal has two evaluations: • • Ø 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 National Swine Improvement Federation Symposium, Dec. 2008 (35) Paul Van. Raden 2008

Linear and Nonlinear Predictions Ø Linear model • Ø Infinitesimal alleles model: all SNP Linear and Nonlinear Predictions Ø Linear model • Ø 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 Model AB: some SNP have no effect, the rest have a heavy-tailed prior National Swine Improvement Federation Symposium, Dec. 2008 (36) Paul Van. Raden 2008

Regressions for marker allele effects National Swine Improvement Federation Symposium, Dec. 2008 (37) Paul Regressions for marker allele effects National Swine Improvement Federation Symposium, Dec. 2008 (37) Paul Van. Raden 2008

R 2 of Linear and Nonlinear Genomic Predictions Trait Net merit Milk Fat Protein R 2 of Linear and Nonlinear Genomic Predictions Trait Net merit Milk Fat Protein Fat % Longevity Mastitis Linear 28. 2 47. 2 41. 8 47. 5 55. 3 25. 6 37. 3 Model A B 28. 4 27. 6 48. 5 46. 7 44. 2 41. 5 47. 0 46. 8 63. 3 57. 5 27. 4 25. 4 38. 3 37. 3 National Swine Improvement Federation Symposium, Dec. 2008 (38) AB 27. 6 47. 3 43. 6 46. 6 63. 9 26. 4 37. 6 Paul Van. Raden 2008

Genetic Progress Ø Assume 60% REL for net merit • • Ø Sires mostly Genetic Progress Ø Assume 60% REL for net merit • • Ø 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 National Swine Improvement Federation Symposium, Dec. 2008 (39) Paul Van. Raden 2008

Low Density SNP Chip Ø Choose 384 marker subset • • Ø SNP that Low Density SNP Chip Ø Choose 384 marker subset • • Ø 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) National Swine Improvement Federation Symposium, Dec. 2008 (40) Paul Van. Raden 2008

Conclusions Ø Ø Ø 100 X more markers allows MAS across rather than within Conclusions Ø Ø Ø 100 X more markers allows MAS across rather than within families 10 X more bulls allows estimation of much smaller QTL effects (HO) Reliability increases by tracing actual genes inherited instead of expected average from parents National Swine Improvement Federation Symposium, Dec. 2008 (41) Paul Van. Raden 2008