1. High-density admixture mapping to find genes for complex disease David Reich Harvard Medical School Department of Genetics Broad Institute July 13, 2004 (work with Nick Patterson)

2. Why do we want to find disease-causing variants? Identify new targets for rational drug design and treatment Clinical genetic testing Identify new biological pathways

3. Linkage mapping doesn’t work well for common diseases Heart attack Stroke High cholesterol Diabetes Obesity High blood pressure Breast cancer Manic depression Multiple sclerosis Turn to association methods instead

4. Association Mapping Direct association between mutations and disease ACTGAACATTTAGACA ACTGATCATTTAGACA ACTGAACATTTAGACA ACTGATCATTTAGACA ACTGAACATTTAGACA ACTGATCATTTAGACA ACTGAACATTTAGACA Patients with disease Healthy controls Association more powerful but requires looking at more places (Risch and Merikangas 1996)

5. Admixture mapping. In favorable circumstances, the most economical method for a whole-genome scan 2) Methods 1)The idea of admixture mapping 3) A practical whole-genome map 4) Two real studies

6. Admixture Mapping (type of association mapping) Can be as powerful as haplotype association but requires 100- to 500-times fewer SNPs Populations like African and Hispanic Americans Most promising for diseases with different population risks : multiple sclerosis, prostate cancer, …

7. 1 generation ago 2 generations ago 3 generations ago 4 generations ago Admixture creates a mosaic Two African chromosomes Two European chromosomes One African, one European chromosome Today

8. How does admixture mapping work? African chromosome European chromosome Disease locus These samples will be enriched in European ancestry at the disease locus Cases with disease

9. The Signal of Admixture Association Controls are not necessary! 100% 50% 0% 20cM40cM60cM80cM100cM120cM140cM Position on chromosome (centimorgans) Percent European Ancestry The perfect control is the rest of peoples’ genome ~2,000 SNPs for genome-wide mapping

10. EXPERIMENTALLY how do you distinguish African and European ancestry? The most informative ~1% of SNPs provide powerful information about ancestry

11. How does one identify European or African segments despite similar gene frequencies? 100 kb West Africans African American Strong evidence of European ancestry Strong evidence of African ancestry No evidence of one ancestry or another

12. New Methods The Markov Chain Monte Carlo (to deal with uncertainties in the HMM parameters that can produce false-positives in analysis) The Hidden Markov Model (for combining information from closely linked, partially informative markers to make inferences about ancestry)

13. How to track regions of European & African ancestry along the genome? M i = % European ancestry in individual’s ancestors >40 generations ago i = Number of generations since mixture Key parameters for the HMM

14. Position (cM) on chrom. 22 based on data from 44 SNPs in real patients Genome of an African American is a mosaic of European and African ancestry Hidden Markov Model (HMM) to combine information from neighboring markers

16. Scoring for disease genes The ‘locus- genome’ statistic  i,0 = M i 2  i,1 = 2M i (1-M i )  i,2 = (1-M i ) 2  i 1,  i 2 = increased risks due to 1, 2 European alleles 100% 50% 0% 20cM 40cM60cM 80cM 100cM120cM140cM Position on chromosome (centimorgans) Percent European Ancestry

17. Can detect regions of increased European ancestry in a data set of 756 SNPs and 442 samples Section 3

18. Problem with the HMM p j European and p j African are assumed known In fact, they are unknown due to… sampling error when genotyping the parental populations modern populations aren’t the true parental populations This can cause false-positives!

19. Markov Chain Monte Carlo to account for this uncertainty (MCMC) Frequency estimates p j European and p j African affect the inferences across ALL samples, so we no longer treat individuals independently to estimate  i and i In a study of 2,500 markers, 2,500 samples, there would be about ~10,000 unknown parameters, so we use an MCMC to average over them

20. How many burn-in and follow-on iterations for the MCMC? 100 burn-in iterations OK 200 follow-on iterations are recommended as whole- genome score is 97% correlated to 2,000 follow-ons

21. >2,000 simulations to assess power to detect disease genes show the method is robust with current maps

22. European frequency (at 80% power) European frequency Genotypes required for whole-genome scans with admixture, linkage and haplotype mapping Risk = 1.3Risk = 1.5 Risk = 2.0 5% allele in Africans 50% allele in Africans

23. Making admixture mapping work 1)>2,000 samples required for a powerful study (far more than the ~300 previously recommended) (no controls strictly necessary – cases from one study are controls for another) 3)New resources · High density 2,154 marker map, 50x more powerful than before · Powerful, conservative methods (ANCESTRYMAP program) 2)Diseases to study Hypertension, End-stage renal disease, prostate cancer, Multiple sclerosis, ovarian cancer, Alzheimer’s disease, Type II diabetes (Hispanic Americans) Note: 10-30% more samples to study diseases prevalent in Africans

24. The first practical admixture map # of SNPsSource ~450,000Non-redundant snps in our dbase 3,583Experimentally revalidated 3,378Genotyped in at least 20 Eur and Afr 3,250Hardy Weinberg p > 0.005 3,095Information content SIC > 0.035 3,045No significant population differentiation (P >0.002) 2,504SNP spacing of >= 50kb 2,138No LD in West Africans or Europeans

26. Power of the map for discerning ancestry

27. Our first two large-scans Prostate cancer 2-3 fold more prevalent in African Americans 650 cases, 698 controls already in lab Multiple sclerosis 1.5-2 fold more prevalent in European Americans 502 cases, 175 controls already in lab

28. Initial screen of 39% of the genome focusing on linkage peaks in 442 MS patients Nothing compelling yet

29. Currently planning to increase power Current multiple sclerosis data ‘theoretically’ 50% power loss due to current map Targeted power

30. Conclusions Must do SEVERAL large-scale studies to assess whether admixture mapping works The imperative now is on finding something with this new method

31. Acknowledgements Methods Nick Patterson Neil Hattangadi New map Mike Smith Steve O’Brien Dennis Gilbert Francisco de la Vega Trevor Woodage Charles Scafe Nick Patterson Gavin McDonald Alicja Walizewska David Altshuler Multiple sclerosis David Hafler Nick Patterson Gavin McDonald Alicja Waliszewska Phil de Jager Jorge Oksenberg Stephen Hauser Amy Swerdlin Bruce Cree Robin Lincoln Cari de Loa Prostate caner Matt Freedman David Altshuler Chris Haiman Brian Henderson