The Ashkenazi Genome Project

1. The Ashkenazi Genome Project Shai Carmi Pe’er lab, Columbia University Joint Group Meeting November 2012

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

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

4. Recent Demography & IBD In small populations, common ancestors are likely recent. Generation 1 2 3 A B

5. Recent Demography & IBD In small populations, common ancestors are likely recent. Generation • For g-generation ancestor, chances of IBD , but length (M). • IBD is highly informative on recent history! A B A B Many long haplotypes identical-by-descent A shared segment

6. Formal Inference Using IBD • Assume a population of historical size . • Total shared segments of length : • Detect IBD in sample Infer history . Palamara et al., AJHG 2012 B A IBD sharing abundant in AJ Atzmonet al., AJHG 2012 Gusev et al., MBE 2011 A B A shared segment

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

8. The Ashkenazi Genome Consortium 10 labs from NY area and Israel. Goals: • Sequence to high coverage hundreds of healthy AJ • Use as a reference panel for • Association studies • Imputation • Clinical interpretation • Understand AJ population history • Understand AJ functional genetic variation (negative/positive selection)

9. The Ashkenazi Genome Consortium • Phase I: • 144 AJ personal genomes • ~60yo, healthy controls • Unrelated, PCA-validated AJ • Selected to maximize sharing with rest of cohort • Technology: Complete Genomics • Sequenced so far: ~100 genomes • Data presented: 58 genomes • Phase II: • Hundreds of genomes (2013?) • More collaborators

10. Quality Measures Ti/Tv

11. Variant statistics

12. Comparison to Europeans Extrapolated to 100% genome (M) Similar results in 13 CG European public genomes. Flemish (k) TAGC

13. Het/HomRatio • Significant in comparison to both Flemish and HapMap EU. • Was observed in SNP arrays (Need et al., Genome Biology 2009). • Did I not just say that AJ have more IBD?

14. Het/Hom Ratio Years ago t AJ EU IBD observed Present

15. Data Flow Pipeline Backup 3x CGA tools testVariants VCF Fix Plink/Seq QC Compress, index Plink Phase Distribute

16. Quality Control • False positive rate assessment by runs of homozygosity: • Assume hetsin high confidence roh are FP. hets Paternal Maternal • High confidence rohs only (>7.5MB, no gaps). • 7 segments in 7 individuals (total 72MB). • Count het SNPs in original files. • Genome wide extrapolation: ~20,000 per genome. • ~3-5% FP rate for indels.

17. Quality Control Remove: FP after QC: ~5,000 per genome. Indels and MNPs Low-quality SNPs Multi-allelic SNPs Half-calls SNPs with high no-call rate SNPs not in HWE Monomorphic reference SNPs Inbred individual

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

19. Phasing • Sequencing is in mate-pairs • Haplotype information available for ~30-35% of hets. • BEAGLE error rate: 3-4%. • Seqphase: new phasing tool • Based on SHAPEIT • Incorporates reads • 18 hours on chromosome 1. Frequency 100 300 500 Distance between phased hets

20. Variant Discovery • Number of non-reference variants. • Extrapolation using Gravel et al., PNAS 2011.

21. Variant Discovery • Number of segregating sites Sn(t), heterozygosityH(t). • Zivkovic and Stephan, Theor. Pop. Biol. 2011. • N(t): # diploids at time t; N=N(t=0); ρ(t)=N(t)/N; n: # diploid samples • t: #generations/2N; θ=4Nμ;μ: mutation rate per generation • Use double expansion model of Palamara et al., AJHG 2012. • Define t=0 at the start of the first expansion. • Match H(t).

22. Variant Discovery ?

23. Allele Frequency Spectrum Fractions Counts All Pop.-specific

24. Demographic Inference • Folded allele frequency spectrum+ coalescent simulations. • Double expansion model + ancient AJ foundation bottleneck. • Find maximum likelihood solution (Gutenkunst et al., PLoS Genet. 2009) • Average over simulations to obtain expected spectrum. • Assume mutation frequency is drawn according to expected spectrum. • Multinomial probability approximated as Poisson. 100 10 %sites 1 0.1

25. Demographic Inference t • Similar to Palamara et al., with somewhat larger population sizes. • To do: Gene flow from EU; better inference tools. Years ago 3,000 5000 90,000 500 875 Present 7,500,000 N Effective size

26. Ongoing Analysis • Exome analysis • Genes w/ AJ-specific high mutation load • Mobile elements insertion • Common insertions frequencies correlated with 1KG • AJ disease genes (Ostrer & Skorecki, Human Genetics 2012) • Some carriers detected • 276 non-synonymous mutations,>65 known • 60 loss-of-function

27. Summary • AJ bottleneck and expansion reveal potential for genetics studies. • High quality genomes sequenced by TAGC indicate utility in clinical setting. • Complete variant discovery improves demographic inference; subtle differences from Europeans. • Future directions: • Imputation power using TAGC vs. 1000Genomes • Local ancestry inference • Effect of natural selection

28. Thank you! TAGC consortium members: Columbia University Computer Science: ItsikPe’er, Pier Francesco PalamaraUndergrads:Fillan Grady, Ethan Kochav, James XueIT:ShlomoHershkop Long-Island Jewish Medical Center: Todd Lencz, Semanti Mukherjee, SauravGuha 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 VIB, Gent, Belgium Herwig Van Marck, StephanePlaisance Complete GenomicsJason Laramie Funding: Human Frontiers Science program.