1. The Ashkenazi Genome Project Shai Carmi Pe’er lab, Columbia University and The Ashkenazi Genome Consortium (TAGC) Personal Genomes & Medical Genomics Cold Spring Harbor, NY November 2012

2. Recent History of Ashkenazi Jews Mediterranean origin (?) Ca. 1000: Small communities in N. France, Rhineland Migration east Expansion ~10M today, mostly in US and Israel Relative isolation

3. Ashkenazi Jewish Genetics Behar et al., Nature 2010. Bray et al., PNAS 2010. Guha et al., Genome Biology 2012. 300 Jewish individuals; SNP arrays Recently, AJ shown to be a genetically distinct group Close to Middle-Eastern & South-European populations Price et al., PLoS Genetics 2008. Olshen et al., BMC Genetics 2008. Need et al., Genome Biology 2009. Kopelman et al., BMC Genetics, 2009. AJ Atzmon et al., AJHG 2010 Jewish non-AJ Middle- Eastern Europeans

4. Recent Demography & IBD In small populations, common ancestors are likely recent. A B

5. Recent Demography & IBD In small populations, common ancestors are likely recent. A B A B A shared segment IBD is highly informative on recent history! IBD common in AJ. (Gusev et al., MBE 2011)

6. AJ Genetic History Expansion rate ≈34% per generation 2,300 N t Effective size 45,000 270 4,300,000 Years ago 800 Present Palamara et al., AJHG 2012 High potential for genetic studies! AJ UK Power of imputation by IBD

7. The Ashkenazi Genome Consortium Phase I: 58 AJ personal genomes (86 under way) ~60yo, healthy controls Unrelated, PCA-validated AJ Technology: Complete Genomics Goal: Sequence to high coverage hundreds of healthy AJ o Use as a reference panel for association studies, imputation, and clinical interpretation o Understand population history and functional genetic variation in AJ

8. Quality Control PropertyGenome (exome) Coverage~55x Fraction called96.5±0.003% (98%) Fraction with coverage > 20x92.4±0.018% (94.9%) Concordance with SNP array99.87±0.1% Ti/Tv ratio2.14±0.003 (3.05) Ti/Tv

9. Variant Statistics & Comparison to Europeans TAGC 14 Flemish genomes (Belgium) (M) (k) Similar results in 13 CG European public genomes.

10. Comparison to Europeans Allele frequency spectrum: – No excess singletons. – Slight excess of doubletons. More novel SNPs in AJ (3.8% vs. 3.1%). singletons doubletons

11. Quality Control (2) False positive rate assessment by runs of homozygosity: Assume hets in high confidence roh are FP. hets Paternal Maternal

12. Applicability to Clinical Genomics Variants of unknown significance – Technical false positives – True variants without health impact Novel variants per sample Not in TAGC

13. Demographic Inference Use allele frequency spectrum and coalescent simulations. Assume the demographic model previously mentioned. Parameters qualitatively similar to those inferred from IBD Bottleneck 35gbp of size 500; Pre-bottleneck size 90,000 100 10 1 0.1 %sites

14. Summary IBD reveals AJ population bottleneck and expansion and potential for genetics studies. High quality genomes sequenced by TAGC indicate utility in clinical setting. Confirm demography and demonstrate subtle differences from Europeans. Ongoing analysis: – Imputation power using TAGC vs. 1kG as ref panels – Local ancestry inference – Functional variants; AJ disease genes – Mobile element insertions

15. Thank you! TAGC consortium members: Columbia University Computer Science: Itsik Pe’er, Pier Francesco Palamara Undergrads: Fillan Grady, Ethan Kochav, James Xue IT: Shlomo Hershkop Long-Island Jewish Medical Center: Todd Lencz, Semanti Mukherjee, Saurav Guha Columbia University Medical Center: Lorraine Clark, Xinmin Liu Albert Einstein College of Medicine: Gil Atzmon, Harry Ostrer Mount Sinai School of Medicine: Inga Peter, Laurie Ozelius Memorial Sloan Kettering Cancer Center: Ken Offit, Vijai Joseph Yale School of Medicine: Judy Cho, Ken Hui, Monica Bowen The Hebrew University of Jerusalem: Ariel Darvasi Funding: Human Frontiers Science program. VIB, Gent, Belgium Herwig Van Marck, Stephane Plaisance Complete Genomics Jason Laramie

16. Formal Inference Using IBD A B A B A shared segment Palamara et al., AJHG 2012

17. Data processing CGA tools VCF generator: called sites only. Correct multi-nucleotide substitution bug. Compress, index, and distribute. Generate high-quality genotypes set for population genetic analyses. – Remove indels and multi-nucleotide substitutions. – Remove low-quality SNPs. – Remove multi-alleic SNPs. – Remove half-calls. – Remove SNPs with high no-call rate. – Remove SNPs not in Hardy-Weinberg equilibrium. – Remove monomorphic reference SNPs. – Remove an inbred individual. – Format as Plink file.

18. Variant statistics StatisticPer genome (exome) Total SNPs3.4M (22k) Novel SNPs3.7% (4%) Het/hom ratio1.64 (1.67) Insertions count223k (246) Deletions count237k (218) Substitutions count83k (374) Synonymous SNPs10525 Non-synonymous SNPs9695 Nonsense SNPs71 Other disrupting241 CNV count336 SV count1486 MEI count3475