1. IBD sharing: Theory and applications in the Ashkenazi Jewish population Shai Carmi Pe’er lab, Columbia University Mt. Sinai, NY March 2014

2. About Me 2006-2008: Empirical network analysis (computational) 2007-2010: Diffusion/navigation in random networks (theory) 2010-2011: Anomalous diffusion (theory) 2008-2011: RNA splicing and editing (computational/experimental) 2012-2014: Population genetics, with Itsik Pe’er

3. Outline IBD Sharing: Introduction Ashkenazi Jewish Genetics Demographic inference Imputation Future Directions & Summary

4. Outline IBD Sharing: Introduction Ashkenazi Jewish Genetics Demographic inference Imputation Future Directions & Summary

5. Identical-by-Descent (IBD) Sharing A B A B A shared segment g Definition: A segment is shared IBD if it is inherited from a single recent common ancestor.

6. What’s “recent”? A B A B A shared segment g Definition: A segment is shared IBD if it is inherited from a single recent common ancestor. Textbook/Pedigrees: MRCA more recent than a given time (Thompson, Genetics, 2013) In practice: o A segment is IBD if it is longer than a cutoff o Allow small differences o Present methods can detect segments > ≈1cM

7. When is the Common Ancestor “recent”? N=10 g=7 Present Time (generations)

8. Why is IBD Useful? A B A B A shared segment g

9. Applications A B A B A shared segment g A segment indicates recent co- ancestry: o Disease mapping o Pedigree reconstruction o Detecting natural selection o Demographic (historical) inference Identical sequence across individuals: o Phasing o Imputation o Estimating heritability o Estimating genotyping error rate Browning and Browning, Annu. Rev. Genet., 2012

10. IBD Sharing Theory Model: o A population with constant effective size N o A minimal segment length m o Two chromosomes of length L The fraction of the chromosome in shared segments? The number of shared segments?

11. The IBD Process along the Chromosome ℓ1ℓ1 0L Coordinate ℓ2ℓ2 ℓ3ℓ3 ℓ4ℓ4 ℓ5ℓ5 ℓ6ℓ6 ℓ7ℓ7 ℓ8ℓ8 ℓ9ℓ9 ℓ 10 t1t1 t2t2 t3t3 t4t4 t5t5 t6t6 t7t7 t8t8 t9t9 t 10 cutoff m

12. Sample Results Palamara et al., AJHG, 2012; Carmi et. al., Genetics, 2013; Carmi and Pe’er, arXiv, 2014

13. Outline IBD Sharing: Introduction Ashkenazi Jewish Genetics Demographic inference Imputation Future Directions & Summary

14. Founder Populations Time Founder population Non-founder population Disease alleles B Population size

15. Founder Populations Recent successes: Greece (Tachmazidou et al., Nat. Comm. 2013) Finland (Kurki et al. PLoS Genet., 2014) Iceland (deCODE) (many papers; most recently Steinthorsdottir et al., Nat. Genet. 2014; Grarup, PLoS Genet., 2013)

16. A Brief History of Ashkenazi Jews Unclear origin Ca. 1000: Small communities in Northern France, Rhineland Migration east Expansion Migration to US and Israel ≈10M today Relative isolation

17. Ashkenazi Jewish (AJ) Genetics Behar et al., Nature, 2010 Bray et al., PNAS, 2010 Guha et al., Genome Biol, 2012 Behar et al., Hum. Biol., 2014 Price et al., PLoS Genet., 2008 Olshen et al., BMC Genet, 2008 Need et al., Genome Biol, 2009 Kopelman et al., BMC Genet, 2009 Atzmon et al., AJHG, 2010 AJ Jewish, non-AJ Middle- East Europ e

18. AJ Genetics: Interim Summary Current large population (≈10M) IBD analysis: bottleneck of effective size ≈300 (later) Mendelian disorders, high frequency risk alleles Insight on both European & Middle-Eastern past No genealogies

19. The Ashkenazi Genome Consortium NY area labs interested in specific diseases Quantify utility in medical genetics Learn about population history Phase I: 128 whole genomes (CG; completed) Phase II: ≈300 whole genomes (NYGC; under way) Large genotype d cohorts Impute

20. Sequencing Statistics StatisticPer genome (exome) SNVs3.4M (22k) Novel SNVs3.8% (4.1%) Het/hom ratio1.65 (1.67) Insertions220k (242) Deletions235k (223) Multi-nucleotide variants83k (374) Synonymous SNVs10,536 Non-synonymous SNVs9706 Nonsense SNVs72 Other disrupting255 CNVs302 SVs1480 MEIS4090

21. Results Highlights Low false positive rate at ≈5,000 per genome 50% more novel variants per genome in AJ (compared to non-Jewish Europeans) More genetic diversity in AJ (θ), but less projected for large samples More AJ-specific variants compared to EU-specific variants A model for EU-Middle-East-AJ ancient history A model for AJ recent history The panel is necessary for screening clinical AJ genomes Catalog of mutations in known AJ disease genes Slightly higher mutation burden in AJ The panel is useful for imputation S. C. et al., submitted

22. A Model for Ancient History

23. Outline IBD Sharing: Introduction Ashkenazi Jewish Genetics Demographic inference Imputation Future Directions & Summary

24. A Simple Approach Palamara et al., AJHG, 2012; Carmi et. al., Genetics, 2013

25. A Simple Approach

26. A Maximum Likelihood Approach Carmi and Pe’er, arXiv, 2014

27. A Practical Approach Palamara et al., AJHG, 2012 Method: Detect IBD in sample Plot the empirical P(ℓ) Using Eq. (1), find the history N(t) that fits best Segment length ℓ P(ℓ)

28. IBD Sharing in AJ Atzmon et al., AJHG, 2010 Bray et al., PNAS, 2010 Gusev et al., MBE, 2012 ≈50cM per pair in segments >3cM

29. An AJ Bottleneck S. C. et al., submitted Time (years)

30. Caveats Phasing and genotyping errors; IBD detection errors Reasonable power only for 10-50 generations ago Model specification (e.g. prolonged bottleneck, admixture) Fitting ParameterAncestra l size Bottlene ck size Growth rate (per gen) Bottleneck time (gen) 95% confidence interval 3654- 5856 249-41916-53%25-32

31. Outline IBD Sharing: Introduction Ashkenazi Jewish Genetics Demographic inference Imputation Future Directions & Summary

32. Imputation Impute2 Cost-effective association study design: o Fully sequence a small reference panel o Impute many sparsely genotyped individuals

33. AJ Panel Performance Fraction of non-ref variants with maf ≤1% wrongly imputed: 13% for AJ, 35% for CEU

34. Imputation by IBD Sequence A Gusev at al., Genetics, 2012

35. Imputation by IBD Sequence A How to select individuals for sequencing? Is there enough IBD sharing? How to impute effectively? Palin et al., Genet. Epidemiol., 2011; Kong et al., Nat. Genet., 2008

36. Selection for Sequencing Improve performance by selecting top-sharing samples Gusev et al., Genetics, 2012: INFOSTIP Theory for coverage in a population model Carmi et al., Genetics, 2013 Not terribly important

37. Coverage by IBD TAGC (sequencing; n=128)SZ study (genotyping; n=2500)

38. Coverage by IBD: Theory Time (gen) Present g g+1 B 1-α

39. Outline IBD Sharing: Introduction Ashkenazi Jewish Genetics Demographic inference Imputation Future Directions & Summary

40. Future Directions N-way IBD sharing o Derived P(ℓ 1 <ℓ<ℓ 2 ) for three chromosomes o Important for demographic inference, disease mapping, detecting natural selection Dating mutations using IBD Phasing/imputation using IBD o A fast approach needed

41. Dating f 2 mutations x x

42. Summary IBD is useful in genetics We characterized IBD in population models IBD abundant in AJ and can be used for historical inference and imputation Many interesting future applications

43. Acknowledgements Funding: Human Frontiers Science program Itsik Pe’er’s lab: James Xue, Ethan Kochav, Yunzhi Ye TAGC consortium members: Todd Lencz, Semanti Mukherjee (LIJMC) Lorraine Clark, Xinmin Liu (CUMC) Gil Atzmon, Harry Ostrer, Danny Ben-Avraham (AECOM) Inga Peter, Judy Cho (MSSM) Joseph Vijai (MSKCC) Ken Hui (Yale) Thank you for your attention!