1. Added value of whole-genome sequence data to genomic predictions in dairy cattle Rianne van Binsbergen 1,2, Mario Calus 1, Chris Schrooten 3, Fred van Eeuwijk 2, Roel Veerkamp 1, Marco Bink 2 1 Animal Breeding & Genetics Centre, Wageningen UR (NL) 2 Biometris, Wageningen UR (NL) 3 CRV (cattle breeding company), Arnhem (NL)

2. Genomic Prediction in agricultural species Goddard & Hayes (2009) Nature Reviews Genetics 10:381 Reference population: 1)Estimate effects for each SNP (w) 2)Generate a prediction equation that combines all the marker genotypes with their effects to predict the breeding value of each individual Each SNP represented by a variable (x), which takes the values 0 [A A] 1 [A B] 2 [B B] Apply prediction equation to a group of individuals that have genotypes but not phenotypes  Estimated genomic breeding values  Select the best individuals for breeding Advantages: Select at early age (before phenotypes available) Save costs to phenotype candidates Increase accuracy of predicted Breeding Values

3. One seminal paper on Genomic Prediction Dense marker maps SNP markers at 1cM density Prediction Accuracy Least Squares method: 0.32 Genomic BLUP method: 0.73 Bayesian methods(A,B):0.85 Conclusion: “selection on genetic values predicted from markers could substantially increase the rate of genetic gain in animals and plants, especially if combined with reproductive techniques to shorten the generation interval” Simulation Study

4. Another (seminal) paper on Genomic Prediction “Only few SNPs were useful for predicting the trait [because they were in linkage disequilibrium (LD) with mutations causing variation in the trait] while many SNPs were not useful.” Prediction of Total Genetic Value Using Genome-Wide Dense Marker Maps T. H. E. Meuwissen,* B. J. Hayes† and M. E. Goddard†,‡ Higher accuracy in genomic predictions since causal mutation is included (assumption)  No dependency on LD  Persistency across generations  Genomic prediction across breeds “In the case of whole-genome sequence data, the polymorphisms that are causing the genetic differences between the individuals are among those being analyzed.”

5. Genomic predictions from whole-genome sequence data  Tremendous increase in number of SNPs (more noise)  Large (sequence) data are required Solution  Sequence core set of individuals (e.g. founders)  Impute whole-genome sequence genotypes of other individuals Accuracy of imputation to whole-genome sequence data was generally high for imputation from 777K SNP panel Van Binsbergen, et al. Genet Sel Evol 2014 (in press) This presentation: First results of genomic prediction with imputed whole-genome sequence data for 5503 bulls with accurate phenotypes

6. Dataset: SNP genotypes & trait phenotypes 1000 bull genomes project 28M SNP genotypes De-regressed progeny based proofs (DRP 1 ) and associated effective daughter contributions (EDC 2 )  Somatic cell score (SCS)  Interval fist and last insemination (IFL)  Protein yield (PY) 1 VanRaden et al. 2009 (J Dairy Sci) VanRaden et al. 2009 (J Dairy Sci) 2 VanRaden and Wiggans 1991 (J Dairy Sci) VanRaden and Wiggans 1991 (J Dairy Sci) 5503 Holstein Friesian bulls 777K SNP genotypes (Illumina BovineHD BeadChip) 5503 Holstein Friesian bulls 12M SNP genotypes MAF > 0.005 Imputation accuracy > 0.05 Imputation - Beagle v4 software 429 bulls (multiple breeds)

7. Prediction reliability Validation population  Youngest bulls with EDC  0  Mainly sons of bulls in training population  Mimics breeding practice = squared correlation between original phenotype (DRP) and estimated genetic values (GEBV) 5503 Holstein Friesian bulls 777K SNP genotypes (Illumina BovineHD BeadChip) 5503 Holstein Friesian bulls 12M SNP genotypes MAF > 0.005 Imputation accuracy > 0.05 training population validation population 4322 old bulls 1181 young bulls training population validation population 4322 old bulls 1181 young bulls differences?

8. Genomic prediction – 2 methods GBLUP  Genome-enabled best linear unbiased prediction  Distribution QTL effects to be close to infinitesimal model (all SNPs equally small effect)  Build a genomic relationship matrix to model variance- covariance structure BSSVS  Bayes stochastic search variable selection  Large number of SNPs with tiny (close to zero) and a few SNPs with moderate effects (=mixture of two Normal distributions) Implementation via Markov chain Monte Carlo (MCMC) simulation algorithms (computer intensive) Calus M (2014). Right-hand-side updating for fast computing of genomic breeding values. Genetics Selection Evolution 46(1): 24. 3 chains of 60,000 cycles (10,000 cycles burn-in)

11. Computation GBLUP ● HPC – 1 node ● ~ 3 hours ● ~ 32 GB RAM ● HPC – 12 nodes ● ~ 6 hours ● ~ 600 GB RAM BSSVS (per MCMC chain) ● Windows – 1 CPU ● ~ 5 days ● ~ 1.6 GB RAM ● HPC – 1 node ● ~ 50 days ● ~ 32 GB RAM 777K SNP 12M SNP Windows 7 Enterprise desktop pc: 32 CPU – 8 GB RAM/CPU (clock speed 2.60 GHz) HPC Linux cluster: Normal nodes – 64 GB/node (2.60 GHz); 2 fat nodes – 1 TB RAM/node (2.20 GHz) 3 chains of 60,000 cycles (10,000 cycles burn-in)

12. Results: Prediction Reliability * Based on 45,000 cycles BSSVS: Average over 3 chains of 60,000 cycles (10,000 cycles burn-in)

13. Results: Prediction Reliability * Based on 45,000 cycles

14. BSSVS: Convergence & SNP effects Sequence: 45,000 cycles 3 chains of 60,000 cycles (10,000 cycles burn-in) Trace of variance of SNP effects Bayes Factor for SNP effects 777K SNP 12M SNP

15. Suitability of BSSVS model?  Large number of SNPs with tiny and a few SNPs with moderate effects ● Sequence data: Really large number of SNPs with tiny effects  Captures too much signal?  Another Bayesian Prediction Model: Bayes-C ● Large number of SNPs with NO effect and a few SNPs with moderate effects

16. Concentrate on single chromosome (BTA 6) 777K SNP 12M SNP BSSSVS Bayes-C MCMC convergence

17. Concentrate on single chromosome (BTA 6) 777K SNP 12M SNP Reliability estimates BSSSVS Bayes-C BSSVSBayesC BovineHD0.328 Sequence 0.3240.325 Signal of QTL effects

18. Conclusions  Genomic prediction using sequence data becomes reality ● However, sequence data requires intensive computation  Need for faster algorithms  Use of Sequence Data did not improve Prediction reliability ● Convergence issues with BSSVS  Longer chains may yield better results  BSSVS slightly better compared to GBLUP  Preliminary results BTA6 hint that Bayes-C method may work better (than BSSVS) for sequence data Next Steps: Did we bet on the wrong horse - named BSSVS?  Review choice of priors in BSSVS model.  Apply Bayes-C model to whole genome sequence data

19. Thanks! 1000 bull genomes project (www.1000bullgenomes.com)www.1000bullgenomes.com Acknowledgments

20. De-regressed proofs (DRP) Effective daughter contribution (EDC) Published reliability of EBV VanRaden and Wiggans 1991 (J Dairy Sci)VanRaden et al. 2009 (J Dairy Sci) Parent average Estimated breeding value Effective Daughter Contribution