The HapMap Project and Haploview

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Presentation transcript:

The HapMap Project and Haploview David Evans Ben Neale University of Oxford Wellcome Trust Centre for Human Genetics

Human Haplotype Map General Idea: Characterize the distribution of Linkage Disequilibrium across the genome. Why?: Infeasible to type every polymorphism in the human genome => Because of LD, type a subset of variants that captures most of common variation in genome Output: -- Raw genotype data freely available (monthly release) -- www.hapmap.org Deliverables: Sets of haplotype tagging SNPs

Human Haplotype Map - Funding - Total US $120 million Japanese Ministry of Education, Culture, Sports, Science and Technology (MEXT), Tokyo National Institutes of Health, US The Wellcome Trust, UK Genome Canada in Ottawa and Genome Quebec, Montreal Chinese Academy of Sciences, Chinese Ministry of Science and Technology, Natural Science Foundation of China, Beijing The SNP Consortium (TSC), US

Human Haplotype Map - Participants - Genotyping 25% RIKEN/Univ Tokyo (Nakamura) 24% Sanger Institute (Bentley) 16% Illumina (Chee) 10% Genome Quebec (Hudson) 10% Beijing/Shanghai/Hong Kong (Yang, Zeng, Huang, Tsui) 9% Whitehead Institute (Altshuler) 4% Baylor Coll Medicine, US (Gibbs) 2% Univ Calif San Francisco (Kwok) Ethical, Legal, Social Issues Japan (Matsuda) China (Zhang, Zeng) US (Leppert) Nigeria (Rotimi) Samples Nigeria (Yoruba; Ibadan) 30 trios  90 individuals US (CEPH) China (Han) 45 unrelateds Japan (Tokyo) Data Analysis Whitehead (Altshuler, Daly) Johns Hopkins Univ (Chakravarti, Cutler) Oxford Statistics (Donnelly, McVean) Oxford Genetics (Cardon, Weir, Abecasis) Data Coordination Cold Spring Harbor (Stein)

Human Haplotype Map Status March 2005 “Phase I” complete ~1 million SNPs typed in 270 individuals at an average spacing of 1 SNP per 5 KB Study of data accuracy across centres (1,500 markers) revealed concordance, internal consistency > 99.8% For several centres, accuracy > 99.9% “Phase II” underway Type an additional 2.25 million SNPs in the same samples (~1 SNP per 1 KB)

Encode Regions Resequence ten ~500KB regions in 16 CEPH, 16 Yoruba, 8 Japanese and 8 Chinese Genotype all dbSNPs and “new” SNPs in all 270 individuals ENCODE Regions Genotype Information Region name Chromosome band Genomic interval (NCBI ) Available SNPs Genotyped SNPs Genotyping group dbSNP New SNPs CEU HCB JPT YRI rs# no rs# ENr112 2p16.3 Chr2:51633239..52133238 1,624 1,720 1,064 937 867 900 868 879 922 McGill-GQIC, Perlegen ENr131 2q37.1 Chr2:234778639..235278638 1,787 1,233 1,179 719 923 690 925 932 704 ENr113 4q26 Chr4:118705475..119205474 1,516 1,819 1,017 1,614 878 1,589 1,597 Broad, Perlegen ENm010 7p15.2 Chr7:26699793..27199792 1,274 1,857 757 459 291 500 284 456 UCSF-WU, Perlegen ENm013 7q21.13 Chr7:89395718..89895717 1,545 1,713 927 1,382 740 1,393 748 1,391 ENm014 7q31.33 Chr7:126135436..126632577 1,354 1,562 963 1,428 794 1,417 800 1,419 ENr321 8q24.11 Chr8:118769628..119269627 1,468 1,682 936 905 726 907 713 903 Illumina, Perlegen ENr232 9q34.11 Chr9:127061347..127561346 1,494 1,646 694 707 508 702 517 689 ENr123 12q12 Chr12:38626477..39126476 1,904 1,551 859 80 78 74 BCM, Perlegen ENr213 18q12.1 Chr18:23717221..24217220 1,465 809 820 643 816 817 644 819 Total 15,357 16,248 9,205 8,971 6,450 8,914 6,451 8,915 6,470 8,900

US Caucasian vs UK Caucasian Population Recombination Rate Population

HapMap website: Haploview website: www.hapmap.org www.broad.mit.edu/mpg/haploview/index.php

Haploview .ped file 1 1 0 0 1 2 1 2 0 0 1 2 0 0 2 2 1 2 3 3 1 3 0 0 1 2 1 1 1 1 1 4 1 2 2 2 1 2 0 0 1 5 3 4 2 2 1 1 1 1 1 6 3 4 1 2 2 2 1 3 .info file rs1474567 38362947 rs2179083 38364233

Exercise F:\davide\Boulder2005\hapmap af1.dat African dataset af1.ped cauc1.dat cauc1.ped Caucasian dataset How many “blocks” are there in the Caucasian dataset? Do the number and position of blocks vary according to whether the Gabriel et al or four gamete block definition is employed? Choose a set of tagging SNPs for the Caucasian dataset to summarize the genotype data efficiently. Do LD patterns vary between the Caucasian and African datasets? Why?