Presentation on theme: "The International HapMap Project: A Rich Resource of Genetic Information Julia Krushkal 04/15/2010 Lecture in Bioinformatics."— Presentation transcript:
The International HapMap Project: A Rich Resource of Genetic Information Julia Krushkal 04/15/2010 Lecture in Bioinformatics
The International HapMap Project Population-specific sequence variation Allele frequencies Linkage disequilibrium patterns Haplotype information Tag SNPs Structural genome variation Better understanding of human population dynamics and of the history of human populations Cell lines available from Coriell Inst. for Medical Research A rich resource for biomedical genetic analysis “…Determine the common patterns of DNA sequence variation in the human genome, by characterizing sequence variants, their frequencies, and correlations between them, in DNA samples from populations with ancestry from parts of Africa, Asia and Europe.” Nature (2003)
HapMap Population Samples Project launched in 2002 to provide a public resource for accelerating medical genetic research 270 Individuals from 4 Geographically Diverse Populations YRI: 90 Yorubans from Ibadan, Nigeria 30 parent-offspring trios CEU: 90 northern and western European-descent living in Utah, USA from the Centre d’Etude du Polymorphisme Humain (CEPH) collection 30 parent-offspring trios CHB: 45 unrelated Han Chinese from Beijing,China JPT: 45 unrelated Japanese from Tokyo, Japan http://www.hapmap.org/ HapMap http://www.genome.gov/page.cfm?pageID=10001688 NHGRI Combined in many analyses
International HapMap Project Papers The Int. HapMap Consortium. A second generation human haplotype map of over 3.1 million SNPs. Nature 449, 851-861. 2007 The Int. HapMap Consortium. A Haplotype Map of the Human Genome. Nature 437:1299-1320.2005 The Int. HapMap Consortium. The International HapMap Project. Nature 426, 789-796.. 2003 The Int. HapMap Consortium. Integrating Ethics and Science in the International HapMap Project. Nature Reviews Genet 5, 467 -475. 2004 Thorisson et al. The International HapMap Project Web site. Genome Res 15:1591-1593. 2005 HapMap-related papers Sabeti et al. Genome-wide detection and characterization of positive selection in human populations. Nature 449, 913-918. 2007. Clark et al. Ascertainment bias in studies of human genome-wide polymorphism. Genome Res, 15:1496-1502. 2005 Clayton et al. Population structure, differential bias and genomic control in a large-scale, case-control association study. Nature Genet 37(11):1243-1246. 2005 de Bakker et al. Efficiency and power in genetic association studies. Nature Genet 37:1217- 1223. 2005 Goldstein, Cavalleri. Genomics: Understanding human diversity. Nature 437:1241-1242. 2005. Hinds et al. Whole genome patterns of common DNA variation in three human populations. Science 307:1072-1079. 2005. Myers et al. A fine-scale map of recombination rates and hotspots across the human genome. Science, 310:321-324. 2005 Nielsen et al. Genomic scans for selective sweeps using SNP data.Genome Res 15:1566-1575. 2005 Smith et al. Sequence features in regions of weak and strong linkage disequilibrium. Genome Res 15: 1519-1534. 2005 Weir et al. Measures of human population structure show heterogeneity among genomic regions. Genome Res 15: 1468-1476. 2005.
Human Chromosomes Contain DNA 22 pairs of autosomes + sex-chromosomes (X and Y) + mitochondrial genome Contain functional units (genes) and other DNA Human genome sequence is available as a reference, as a result of the Human Genome Project A significant amount of inter-individual variation exists
Chromosomes are sets of continuously linked genetic loci Example: Integrated map of chromosome 5 from the International HapMap Project, http://www.hapmap.org
Genetic Variation Some DNA loci vary among individuals Linked genetic loci are inherited non-independently Loci may change with time (mutation, selection, genetic drift) Some DNA changes lead to quantitative changes in RNA expression and to quantitative or qualitative changes in protein production Some genetic changes, even small, may lead to disease A large amount of natural variation occurs in healthy individuals, i.e., many changes are neutral Loci genetically linked to the disease-causing locus can be used as genetic markers to search for the disease locus SNP1SNP2 Sequence variation AAAC/TGGCTA There are many types of DNA variation, e.g. Microsatellite repeats …AATG AATG AATG AATG…
Polymorphic Site A locus with common DNA variation 2 alleles in a population Shows difference in DNA sequence among individuals In most definitions: the most common allele with frequency < 99%, or minor allele frequency (MAF) 1%, or MAF 2%, or at least two alleles have frequencies 1%. A rare allele that occurs in <1% of the population is usually non considered a polymorphic site. 90%of sequence variation among individuals is due to common variation (MAF 1%, ); 10% are rare variants Not all disease-predisposing variants are common
SNP=Single Nucleotide Polymorphism A and C are alleles at SNP locus rs6870660 SNP locus rs6870660 CAAATTCCATG[A or C]AGAAGGAAATACAT http://www.ensembl.org A SNP locus on the distal end of the long arm of human chromosome 5 (data from Ensembl)
A SNP locus on the distal end of the long arm of chromosome 5 SNP locus rs6870660 http://www.hapmap.org
2 alleles, A and B frequencies p and q p+q =1 The allele frequencies remain constant through time. F1 Hardy-Weinberg Equilibrium Egg Sperm P AA =p 2 P AB =2pqP BB =q 2 Under Hardy-Weinberg equilibrium, the relative genotype frequencies are: F1: (p+q ) 2 Departures can be characterized by disequilibrium In autosomal genes, and in absence of disturbing influences, this proportion is maintained through all subsequent generations.
Linkage Disequilibrium D=p A1B1 -p A1 p B1 D = Linkage disequilibrium coefficient Coefficient of association Locus A Locus B A1 B1 A2 B2 Associations among alleles at different loci D’=D/|D| max |D| max = | min(p A1 p B2, p A2 p B1 )| -1 D’ 1 r 2 =D 2 /(p A1 p A2 p B1 p B2 ) Normalized disequilibrium coefficient Squared Correlation coefficient Also ranges from 0 to 1 1 – absolute or perfect linkage; 0.8 is the cutoff often used Extended to multiallelic markers
Regulatory Interactions: The ENCODE Project 2003-Pilot project launched (1% of the genome) 2007- Pilot project completed; production phase launched on the entire genome <> Production Scale Effort Pilot Scale Effort Data Coordination Center Technology Development Effort High-through-put experimental and computational approaches to studies of DNA regulatory sites, regulatory interactions, and DNA modification
Genome SNP Variation Size of human genome 3.2 10 9 bp 99.9% identical 9-10 mln SNPs may have MAF 5% 30,000 genes Phase I (published in 2005) 931,340 SNPs passed quality control 1 SNP / 3000 bp 11,500 nsSNP 10 ENCODE regions, 500 kb each 17,944 SNPs 1 SNP / 279 bp Phase II (published in 2007) Consensus data set: 3,107,620 SNPs, QC+ in all panels, polymorphic in 1 panel 1 SNP / 875bp 25-30% of all SNPs with MAF 5% HapMap SNP Density Coverage The cumulative # of non-redundant SNPs is shown as a solid line, the # of SNPs validated by genotyping as a dotted line, and double-hit status as a dashed line.
21,177 SNPs from Phase I that had ambiguous position or other low reliability feature were not included in Phase II Chimpanzee, rhesus macaque used for comparisons and to infer ancestral states of SNPs 3,107,620 SNPs, QC+ in all panels, polymorphic in 1 panel 1 SNP / 875bp 1.14 SNP/kb 25-30% of all SNPs with MAF 5% 98.6% of the genome is within 5 kb of the nearest polymorphic SNP Better representation of rare variation/ SNPs with MAF 1% Phase II marker data capture overwhelming majority of genome SNP variation, mean r 2 of 0.9-0.96 for different populations
SNP Differences among Individuals Far Exceed Differences among Populations Phase 1: Autosomes: Across the 1 million SNPs genotyped, only 11 have fixed differences between CEU and YRI, 21 between CEU and CHB/JPT, and 5 between YRI and CHB/JPT. X chromosome 123 SNPs were completely differentiated between YRI and CHB/JPT, but only 2 between CEU and YRI and 1 between CEU and CHB/JPT.
Importance of Understanding Patterns of Human Genetic Variation Without knowing the patterns of correlation, one would need to analyze millions of SNPs and other polymorphisms in the genome Alleles at nearby loci occur non-independently Knowledge about correlations among polymorphisms allows to us significantly reduce the number of genetic tests, while surveying extensively for variation patterns Patterns of correlation are complex Need to know local patters of genetic variation rather than simply use SNPs at regular intervals
Patil et al. 2001, Blocks of Limited Haplotype Diversity Revealed by High- Resolution Scanning of Human Chromosome 21. Science 294(5547):1719-23 Genome regions decomposed into discrete haplotype blocks, which capture similarity in haplotype organization Haplotype Maps of the Human Genome
Haplotype Maps Generated by The International HapMap Project 3 steps of the HapMap construction (a) SNPs are identified in DNA samples from multiple individuals. (b) Adjacent SNPs that are inherited together are compiled into haplotypes. (c)"Tag" SNPs are identified within haplotypes that uniquely describe those haplotypes. Source: The International HapMap Project
Haplotype Maps of the Human Genome Haplotypes were inferred for the HapMap project from trios data and from unrelated individuals using Phase (Stephens 01; Stephens and Donnely 03) Helmuth 2001, Science 293:583-585 Find correlations among groups of SNPs
Haplotype Block Partition Results for Three Populations Population Blocks Average size, kb * Required SNPs African-American 235,663 8.8 570,886 European-American 109,913 20.7 275,960 Han Chinese 89,994 25.2 220,809 * Average distance spanned by segregating sites in each block. Minimum number of SNPs required to distinguish common haplotype patterns with frequencies of 5% or higher. Hinds et al. 2005 Science 1,586,383 (SNPs) genotyped in 71 Americans of European, African, and Asian ancestry
Hinds et al 2005 Extended LD bin and haplotype block structure around the CFTR gene. LD bins, where each bin has at least one SNP with r 2 > 0.8 with every other SNP, are depicted as light horizontal bars, with the positions of constituent SNPs indicated by vertical tick marks as well as the extreme ends of the bars. Isolated SNPs are indicated by plain tick marks. Haplotype blocks, within which at least 80% of observed haplotypes could be grouped into common patterns with frequencies of at least 5%, are depicted as dark horizontal bars. Unlike haplotype blocks that are by design sequential and nonoverlapping, SNPs in one LD bin can be interdigitated with SNPs in multiple other overlapping bins Population differences in local bin structure Differences in allele and haplotype frequencies “Although analysis panels are characterized both by different haplotype frequencies and, to some extent, different combinations of alleles, both common and rare haplotypes are often shared across populations” ( The Int. HapMap Project, Nature, 2005)
Amount of Captured Sequence Variation in HapMap Phase II For common variants (MAF 0.05) the mean maximum r 2 of any SNP to a typed one is 0.90 in YRI, 0.96 in CEU and 0.95 in CHB/JPT. 1.09 million SNPs capture all common Phase II SNPs with r 2 0.8 in YRI. Very common SNPs with MAF 0.25 are captured extremely well (mean maximum r 2 of 0.93 in YRI to 0.97 in CEU) Rarer SNPs with MAF<0.05 are less well covered (mean maximum r 2 of 0.74 in CHB/JPT to 0.76 in YRI).
Amount of Captured Sequence Variation in HapMap Phase II Additional tag SNPs are unlikely to capture large groups of additional SNPs Can use to phase new data using HapMap haplotype information, missing data imputation
DNA Chips and Resequencing: High-through-put Analysis of Sequence Variation An easy way to access genome-wide variation Both Affymetrix and Illumina DNA chips contain representative SNP and CNV probes Affymetrix GeneChip 6.0: 1.8 million markers for genetic variation, including 906,000 SNPs and 946,000 copy number probes. Illumina 1M Bead Chip and 1M-duo Bead Chip: ~950,000 genome-spanning tag SNPs; ~100,000 additional non-HapMap SNPs, >565,000 SNPs in and near coding regions such as nsSNPs, promoter regions, 3’ and 5’ UTRs; dense coverage in ADME and MHC regions. ~260,000 markers located in novel and reported copy number polymorphic regions. Sequenom mass arrays (based on Maldi-TOF)
Common Ancestry and Segmental Sharing Relatedness High Med Low Homozygocity
Recombination and tagSNPs Recombination hotspots are frequently insufficient to break down allelic associations Common haplotypes often span recombination hotspots 0.5-1% of SNPs are untaggable: no SNPs with r 2 0.2 within 100 kb Untaggable SNPs are not in segmental duplications They often are in recombination hotspots; some may be due to genealogical structure, mutation hotspots, or gene conversion
Demographic History of Human Populations Genealogical History and Allelic Associations The genealogy for the 13 haplotypes observed in a 40-kb region of Chromosome 1 (between SNPs rs12085605 and rs932087) where there is no evidence for recombination. Location of polymorphic mutations is indicated by circles. Relative frequency of each haplotype in the sample from each of the three panels (with white indicating 0% and black indicating 100%). The dotted line in the genealogy indicates a branch of the tree that is not present in the CEU sample and whose removal results in perfect association between SNPs rs12085824 and rs11205476. Can track genealogical history McVean et al., 2005. PLoS Genetics 1:e54 Complex patterns of stochastic mutation, recombination, selection, genetic drift in evolutionary history shape the patterns of genome variation
Mirrors at Sanger Center and Baylor College of Medicine FUNDING AGENCIES National Institutes of Health – National Human Genome Research Institute (NHGRI) Wellcome Trust HapMap 3
Hardy-Weinberg p>0.000001 (per population) missingness <0.05 (per population) <3 Mendel errors (per population; only applies to YRI, CEU, ASW, MEX, MKK) SNP must have a rsID and map to a unique genomic location The "consensus" data set contains data for 1115 individuals (558 males, 557 females; 924 founders and 191 non-founders), only keeping SNPs that passed QC in all populations (overall call rate is 0.998). The "consensus|polymorphic" data set has 35023 monomorphic SNPs (across the entire data set) removed. QC in HapMap 3
PCR RESEQUENCING DATA “The sequence-based variant calls were generated by tiling with PCR primer sets spaced approximately 800 bases apart across the ENCODE 3 regions. Following filtering low-quality reads the data were analyzed with SNP Detector version 3, for polymorphic site discovery and individual genotype calling. Various QC filters were then applied. Specifically, we filtered out PCR amplicons with too many SNPs, and SNPs with discordant allele calls in mutliple amplicons. “ Also filtered out were SNPs with low completeness in samples, or with too many conflicting genotype calls in two different strands. “In the QC+ data set, …filtered out samples which had low completeness, and filtered out SNPs with low call rate in each population (<80%) and not in HWE (p<0.001). In the QC+ data set, the overall false positive rate is ~3.2%, based on a limited number of validation assays.” http://www.broadinstitute.org/~debakker/p3.html
PCR RESEQUENCING DATA labelnumber of samples ASW55 CEU119 CHB90 CHD30 GIH60 JPT91 LWK60 MEX27 MKK0 TSI60 YRI120 total712 Data Content
HapMap Project is a Unique Resource for Genome-Wide Association Studies Resource for selection of representative tag SNPs from low diversity haplotype blocks or from highly correlated SNPs Tag SNPs with r 2 0.8 chosen for popular SNP chips Resource for selecting custom SNPs for dense genotyping in candidate regions, determined from genetic pathways of the 1 st stage of multistage GWAS LD and haplotype information utilized for missing SNP imputation for genotypic problems or in meta-analyses
http://www.genome.gov/26525384 As of 04/15/10, this table includes 543 publications and 2658 SNPs.
Published Genome-Wide Associations through 6/2009, 439 published GWA at p < 5 x 10 -8 NHGRI GWA Catalog www.genome.gov/GWAStudies
Software from Jonathan Marchini’s group http://www.stats.ox.ac.uk/~marchini/software/gwas/gwas.html
Imputation Common HapMap panel & tagSNPs Use of HapMap Resources in Meta-Analyses
Structural Genome Variation Large number of copy number variants (CNVs) and other genome rearrangements found among individuals Some variation is assumed normal, other may cause disease Genome databases, e.g. Database of Genomics Variants at the TCAG of the Toronto Hospital of Sick Children, the Copy Number Variation Project Map at the Sanger Center HapMap samples are also used as a resource for CNV analysis
Segmental duplications are recombination hotspots, causing global genome rearrangements
Ethical, Legal, and Social Implications Issues Patterns of variation can be compared among individuals and populations, e.g. Genetic profiling Racial profiling Population history studies Extensive genetic information on donors publicly available Generalization of biomedical results/stigmatization/ genetic determinism Limitation of population identifiers Loose populations, self-described Limited number of representatives, e.g., Individuals samples from residential community in Bejing Normal University do not represent all 56 officially recognized ethnicities in China Future use of cell line samples from same donors, they will not be able to withdraw their samples Nature Reviews Genet. 2004 5:467-475
Ethical, Legal, and Social Implications Approaches Ethical considerations incorporated from the inception of the HapMap project Choice of several world populations New samples obtained with appropriate consent, rather than use of previously stored samples No personal identifiers included (CEPH samples have links to individuals, strictly confidential) No medical information included Population and gender information included Community engagement, taking into account international and local ethical guidelines Community Advisory Groups established/can withdraw community samples Sensitivity to cultural issues Nature Reviews Genet. 2004 5:467-475
Beyond the HapMap 1000 Genomes An international research consortium formed to create the most detailed and medically useful picture to date of human genetic variation. The project involves sequencing the genomes of approximately 1200 people from around the world and receives major support from the Wellcome Trust Sanger Institute in Hinxton, England, the Beijing Genomics Institute Shenzhen in China and the National Human Genome Research Institute (NHGRI), part of the National Institutes of Health (NIH). Wellcome Trust Sanger InstituteBeijing Genomics Institute ShenzhenNational Human Genome Research Institute National Institutes of Health
1000 Genomes An international research consortium formed to create the most detailed and medically useful picture to date of human genetic variation. The project involves sequencing the genomes of approximately 1200 people from around the world and receives major support from the Wellcome Trust Sanger Institute in Hinxton, England, the Beijing Genomics Institute Shenzhen in China and the National Human Genome Research Institute (NHGRI), part of the National Institutes of Health (NIH).Wellcome Trust Sanger InstituteBeijing Genomics Institute ShenzhenNational Human Genome Research InstituteNational Institutes of Health
www.1000genomes.org The goal of the 1000 Genomes Project is to find most genetic variants that have frequencies of at least 1% in the populations studied
Both Affymetrix and Illumina’s newest genotyping platforms include variants discovered in the 1000 genomes project.
Illumina HumanOmni1-Quad BeadChip Kits 4-sample Contains aggressively selected SNP and CNV probes Markers derived from the 1,000 Genomes Project, all three HapMap phases, and recently published studies. >1 Mln available assays per sample, containing carefully selected content that delivers dense coverage of the human genome and targets regions known to play a role in human disease. SNP selection optimized to maintain comprehensive genomic coverage, while reducing tag SNP redundancy. This has enabled the inclusion of additional content carefully chosen to target high-value regions of the genome and new coding variants identified by the 1000 Genomes Project including: ABO blood typing SNPs, cSNPs, disease-associated SNPs, eSNPs, SNPs in mRNA splice sites, Absorption, Distribution, Metabolism and Excretion (ADME genes), Ancestry-Informative Markers (AIMs), HLA complexes, indels, introns, MHC regions, miRNA binding sites, mitochondrial DNA, pseudoautosomal region (PAR), promoter regions, and Y-chromosome
Illumina HumanOmni1-Quad BeadChip Kits Includes 10,000 SNPs targeting four 1Mb regions known to be associated with three or more human diseases > 31,000 SNPs predicted to be non-synonymous; 40,000 SNPs covering an additional 100 intervals surrounding published peak markers from the NHGRI GWAS database; and the remaining top single-marker associated SNPs from the GWAS database. High density markers with a median spacing of 1.2 kb ensure the highest level of resolution for CNV identification in the industry A complete optimization of the BeadChip design increases the available complexity, while reducing the amount of required DNA to 200 ng