2. Why this paper Causal genetic variants at loci contributing to complex phenotypes unknown Rat/mice model organisms in physiology and diseases Relevant to our work – Integration of GWAS of different traits – Interpretation of human GWAS

3. Advantages of genetic mapping using heterogeneous stocks Accuracy of QTL mapping to Mb resolution WGS imputation from progenitor genomes Haplotypes well defined – Single SNP vs haplotype (spatial) association – Difficult in humans, large #of rare/unknown haplotypes

4. Design Sequencing Reconstruction of rat genomes as mosaic of founder haplotypes based on 265,551 SNPs (“sequence imputation”)

5. Genotypes 1,407 phenotyped NIH-HS animals 198 parents (~14.2 litter size) RATDIV genotyping array (13 inbred strains) – 803,485 SNPs – 560,000 segregating in NIG-HS – 265,551 used for haplotype reconstruction Sequencing of founder samples – Number ? – 22x coverage

6. Phenotypes 160 measurements

7. Sequencing 7.2M SNP 633,000 indels 44,000 structural variants

8. Sequencing False Positives 2.7% SNP 2.2% indels 16.7% structural variants False Negatives 17.2% SNPs 41.4% indels 65% structural variants

9. Nucleotide diversity in NIH-HS progenitors Similar diversity between strains

10. Nucleotide diversity in NIH-HS progenitors Similar diversity between strains 29% SNP private to particular strain – Unique haplotypes relatively common Regions of low diversity are small (~400 kb)

11. Genotyping

12. QTL mapping Reconstruction of rat genomes as mosaics of founder haplotypes – R HAPPY

13. Svenson K L et al. Genetics 2012;190:437-447 Copyright © 2012 by the Genetics Society of America

14. QTL mapping Reconstruction of rat genomes as mosaics of founder haplotypes – R HAPPY. – Mixed Linear Model (EMMA, normal phenotypes) – Resample model averaging (BAGPHENOTYPE,non-normal) Non-parametric bootstrap aggregation (bagging) Haplotype from strain s at locus l random effect Expected number of haplotypes

15. Haplotype Strain ABC ------------------------------ y1=200 y2=020 y3=011 QTL mapping

16. QTL results 355 QTLs for 122 phenotypes (avg. 2.9)

17. QTL results

19. Haplotype (1) Strain ABC ------------------------------ y1=200 y2=020 y3=011 Sequence variants ABC Strain CCCCTT ------------------------------ SDP001 Merge analyses Strain distribution pattern (SDP) ABCABCABCABC = 0 0 1 = 1 0 0

20. Haplotype (1) Strain ABC ------------------------------ y1=200 y2=020 y3=011 Sequence variants Strain CCCCTT ------------------------------ y1=200 y2=020 y3=011 Merge model (2) Strain CT ------------------------------ y1=20 y2=20 y3=11 (2) Sub model (1) if QTL == single variant R 2 (2)~R 2 (1) [logP merge – logP haplotype ] > 0 Merge analyses

21. 343 QTLs – 131 (38%) at least 1 candidate variant Increased resolution – 90% of variants ruled out, d <0 – Candidates in coding regions affecting protein structure more likely to be causal – Eliminates candidate genes that are distant from candidate variant

22. Merge analyses (examples) 3 QTL for patelet aggregation

23. Merge analyses (examples) Candidate variant in single gene

24. Merge analyses (examples) Candidate variant in coding region

25. Merge analysis Single variants rarely account for QTL effects – 212 (68%) QTL had no candidate variant Possible reasons – Causative variants missed in sequencing – QTL mapping biased towards QTL without candidate variants – Merge underestimates statistical significance – Multiple causal variants

26. Merge analysis – Causative variants missed in sequencing Simulation of all possible SDPs for di-tri-allelic SNPs and merge analysis 168 (49%) would still have no causative variant – Simulation different QTL architectures Single variants Multiple variants within gene, multiple variants linked loci Haplotype effects/ no individual variants

27. Merge analysis – Simulation of causal variants

28. Merge analysis Haplotype mapping overestimates QTL without causative variant (?) Merge analysis underestimates number of QTL without causative variant (?) – Multiple causative variants

30. Concordance between species 38 measures common between NIG-HS and mice HS Orthologous rarely contribute to the same phenotype

31. Concordance between species 38 measures common between NIG-HS and mice HS Orthologous rarely contribute to the same phenotype KEGG pathways for QTL associated genes in rat in mice only significantly enriched for “proportion of B cells”)

32. Discussion Combining sequence with mapping data can identify candidate loci 50% of QTL can not be attributed to single causal variant – Multiple causal variants, more complex models required – Rat QTL similar to Trans eQTL Not possible to accurately asses overlap between species – limited power of pathway analysis – limited power from comparing phenotypes (within species?) – Variants in orthologous genes rarely contribute to same phenotype