1. Sequencing Brown Swiss Cattle: GWAS and Genomic Prediction based on sequence data Mirjam Frischknecht 08.04.2016 World Brown Swiss Congress, Mende Von Michael Ströck (mstroeck) - Created by Michael Ströck.Copied to Commons from en.wikipedia.org., CC BY-SA 3.0, https://commons.wikimedia.org/w/index.php?curid=694302

2. Swiss Low Input Genetics (SLIG) Aim: „Sequence-optimised“ genomic breeding values especially for functional traits

3. SNP chip vs. Sequencing http://www.geneticgenealogysig.org/research-by-topic/snp/ Modified from Marchini and Howie Nat Rev Genet 2010 Von Michael Ströck (mstroeck) - Created by Michael Ströck.Copied to Commons from en.wikipedia.org., CC BY-SA 3.0, https://commons.wikimedia.org/w/index.php?curid=694302

4. Procedure ① Sequence key ancestors (i.e. Elegant, Improver, Pete-Rose-ET...) ② Impute genotyped Individuals to sequence data ③ Perform GWAS ④ Estimate SNP effects and predict direct genomic breeding values

5. ①X ②X ①GWAS: Calculated for 41 traits based on deregressed breeding values and dosage genotypes in BV/BS; calculated for 45 traits in OB ④Genomic prediction: Evaluating different algorithms based on the trait non-return rate in heifer after 56 days Current status PopulationFMTotal BV41791563919818 OB6427721414 ROB76- BS2416641688 Total BSW49211807522996 ①Sequences of 123 BV/BS/OB (33 from SLIG) in 1000 Bull genomes project ②Sequence imputed genotypes (16‘184‘800 Variants):

6. Material and methods Trait: Non-return rate in heifer 2,258 Genotyped individuals for GWAS 892 additional individuals for genomic prediction GWAS with EMMAX Genomic prediction with gbcpp

7. GWAS Results

8. Genomic prediction based on sequence data BayesC algorithm Accuracy: Correlation deregressed breeding value – genomic breeding value of 892 validation individuals Different densities: 1. 50k (n=38,190) Modified from Marchini and Howie Nat Rev Genet 2010

9. Genomic prediction based on sequence data 2. Top (n=50,000)

10. Genomic prediction based on sequence data 3. Missense (n=33,817) https://en.wikipedia.org/wiki/LMNA#/media/File:LMNA_protein_%281ifr%29_mutation_R527L_PMID_22549407_surface_and_cartoon.png

11. Genomic prediction based on sequence data 4. LD pruned sequence (n=5,786,846) Modified from Marchini and Howie Nat Rev Genet 2010

12. Genomic prediction based on sequence data 4. LD pruned sequence (n=5,786,846) Modified from Marchini and Howie Nat Rev Genet 2010

13. Genomic prediction based on sequence data 5. Sequence (n=13,037,654) Modified from Marchini and Howie Nat Rev Genet 2010

14. Results: Accuracy 0.252 0.292 0.301 0.318 0.315

15. Conclusions Region influencing NRH detected by GWAS Genomic prediction: - Higher accuracies with denser SNP data - LD pruning leads to slightly higher accuracy than direct sequence data Outlook: Test different programs, algorithms and traits

16. Acknowledgements B. Bapst, F.R. Seefried, B. Gredler, J. Moll, U. Schnyder C. Flury, H. Signer-Hasler D. Garrick C. Stricker T.H.E. Meuwissen F. Schmitz-Hsu Project Partners and Financial support

17. Acknowledgements Data providers Arbeitsgemeinschaft der Österreichischen Braunviehzüchter Arbeitsgemeinschaft Deutsches Braunvieh Associazione nazionale allevatori bovini della razza Bruna, Italy Brown Swiss Association of the US Brune Genetique Services, France Braunvieh Schweiz Zveza rejcev govedi rjave pasme Slovenije TUM München (R. Fries) Tierzuchtforschung e.V. (I. Russ) BOKU Wien (J. Sölkner) Uni Milano (A. Bagnato) FiBL (A. Bieber)

18. Thank you for your attention