2007 Paul VanRaden, 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 Using Genomic Data to Improve Dairy Cattle Genetic Evaluations

National Swine Improvement Federation Symposium, Dec (2)Paul VanRaden 2008 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 – , Agriculture Research Service Holstein and Jersey breed associations Contributors to Cooperative Dairy DNA Repository (CDDR)

National Swine Improvement Federation Symposium, Dec (3)Paul VanRaden 2008 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)

National Swine Improvement Federation Symposium, Dec (4)Paul VanRaden 2008 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  Implement routine predictions Increase REL with more genotypes Decrease cost with a selected SNP subset

National Swine Improvement Federation Symposium, Dec (5)Paul VanRaden 2008 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%

National Swine Improvement Federation Symposium, Dec (6)Paul VanRaden 2008 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)

National Swine Improvement Federation Symposium, Dec (7)Paul VanRaden 2008 Genomic Studies at Beltsville  174 markers, 1068 bulls, 8 sires Illinois, Israel, and AIPL  367 markers, 1415 bulls, 10 sires GEML, AIPL, Illinois, and Israel  38,416 markers, 19,105 animals BFGL, AIPL, Missouri, Canada, and Illumina Oct Dec 2008

National Swine Improvement Federation Symposium, Dec (8)Paul VanRaden 2008

National Swine Improvement Federation Symposium, Dec (9)Paul VanRaden 2008 SNP Edits & Counts  SNP available (Illumina SNP50 BeadChip) 58,336 Insufficient average number of beads1,389  Unscorable SNP4,360  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

National Swine Improvement Federation Symposium, Dec (10)Paul VanRaden 2008 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)

National Swine Improvement Federation Symposium, Dec (11)Paul VanRaden 2008 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 % concordance) Mean of 0.9 SNP error per 38,416 Range of 0 to 7 SNP

National Swine Improvement Federation Symposium, Dec (12)Paul VanRaden 2008 Genotype Data for Elevation Chromosome

National Swine Improvement Federation Symposium, Dec (13)Paul VanRaden 2008 Genotype Data from Inbred Bull Chromosome 24 of Megastar

National Swine Improvement Federation Symposium, Dec (14)Paul VanRaden 2008 Close Inbreeding (F=14.7%): Double Grandson of Aerostar Aerostar Megastar Chromosome 24

National Swine Improvement Federation Symposium, Dec (15)Paul VanRaden 2008 Differences in G and A G = 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

National Swine Improvement Federation Symposium, Dec (16)Paul VanRaden Formulas to Compute G  Sum products of genotypes (g) adjusted for allele frequency (p) G1 jk = ∑ (g ij -p i ) (g ik -p i ) / [2 ∑ p i (1-p i )]  Or individually weighted by p G2 jk = ∑ (g ij -p i ) (g ik -p i ) / 2p i (1-p i )  Or scaled by intercept (b 0 ) and regression (b 1 ) on A, using p = 0.5 G3 jk = [∑ (g ij - 0.5) (g ik - 0.5) – b 0 ] / b 1

National Swine Improvement Federation Symposium, Dec (17)Paul VanRaden 2008 Compare A with 3 formulas for G Actual Holstein Data Diagonals 1 of G FormulaMeanSDCorr. with A A G G G Diagonal = 1 + Inbreeding

National Swine Improvement Federation Symposium, Dec (18)Paul VanRaden 2008 Summary of G Formulas for 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 b 0 and b 1, 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

National Swine Improvement Federation Symposium, Dec (19)Paul VanRaden 2008 Genomic vs. Pedigree Inbreeding BullPedigree FGenomic F O Man Ramos Shottle Planet Earnit Nifty Correlation =.68

National Swine Improvement Federation Symposium, Dec (20)Paul VanRaden 2008 Genomic vs. Expected Future Inbreeding BullEFIGFI Blackstar7.9 Elevation Chief Emory RC Matt Juror7.06.7

National Swine Improvement Federation Symposium, Dec (21)Paul VanRaden 2008 Experimental Design Holstein, Jersey, and Brown Swiss breeds HOJEBS Predictor: Bulls born <19993, Cows with data202 Predicted: Bulls born >19991, Data from 2003 used to predict independent data from 2008

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

National Swine Improvement Federation Symposium, Dec (23)Paul VanRaden 2008 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

National Swine Improvement Federation Symposium, Dec (24)Paul VanRaden 2008 Reliability Gain 1 by Breed Yield traits and NM$ of young bulls TraitHOJEBS Net merit2393 Milk23110 Fat33155 Protein2241 Fat % Protein % Gain above parent average reliability ~35%

National Swine Improvement Federation Symposium, Dec (25)Paul VanRaden 2008 Reliability Gain by Breed Health and type traits of young bulls TraitHOJEBS Productive life18122 Somatic cell score21116 Dtr pregnancy rate165- Final score186- Udder depth35133 Foot angle1410- Stature2693

National Swine Improvement Federation Symposium, Dec (26)Paul VanRaden 2008 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 R 2 (P <.001) for 26 of 27 traits

National Swine Improvement Federation Symposium, Dec (27)Paul VanRaden 2008 Value of Genotyping More Bulls BullsR 2 for Net Merit PredictorPredictedPAGenomicGain

National Swine Improvement Federation Symposium, Dec (28)Paul VanRaden 2008 Value of Genotyping More SNP 9,604 (10K), 19,208 (20K), and 38,416 (40K) SNP REL of PA Genomic REL Trait10K20K40K Net Merit $ Milk yield Fat yield Protein yield Productive Life SCS (mastitis) Dtr Preg Rate

National Swine Improvement Federation Symposium, Dec (29)Paul VanRaden 2008 Simulated Results World 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 = corr 2 (EBV, true BV) 80% vs 34% expected for young bulls 72% vs 30% observed in simulation

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

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

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

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

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

National Swine Improvement Federation Symposium, Dec (35)Paul VanRaden 2008 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 σ G, 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 (36)Paul VanRaden 2008 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 Model AB: some SNP have no effect, the rest have a heavy-tailed prior

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

National Swine Improvement Federation Symposium, Dec (38)Paul VanRaden 2008 R of Linear and Nonlinear Genomic Predictions R 2 of Linear and Nonlinear Genomic Predictions Model TraitLinearABABAB Net merit Milk Fat Protein Fat % Longevity Mastitis

National Swine Improvement Federation Symposium, Dec (39)Paul VanRaden 2008 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 genetic SD per year Reduce generation interval more than accuracy

National Swine Improvement Federation Symposium, Dec (40)Paul VanRaden 2008 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)

National Swine Improvement Federation Symposium, Dec (41)Paul VanRaden 2008 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