Julia Krushkal 4/11/2017 The International HapMap Project: A Rich Resource of Genetic Information Julia Krushkal Lecture in Bioinformatics 04/15/2010.

Julia Krushkal 4/11/2017 The International HapMap Project: A Rich Resource of Genetic Information Julia Krushkal Lecture in Bioinformatics 04/15/2010

The International HapMap Project
Julia Krushkal 4/11/2017 “…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) 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

270 Individuals from 4 Geographically Diverse Populations
HapMap Population Samples Julia Krushkal 4/11/2017 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 CHB: 45 unrelated Han Chinese from Beijing,China JPT: 45 unrelated Japanese from Tokyo, Japan Combined in many analyses HapMap NHGRI

International HapMap Project Papers
The Int. HapMap Consortium. A second generation human haplotype map of over 3.1 million SNPs. Nature 449, The Int. HapMap Consortium.A Haplotype Map of the Human Genome. Nature 437: The Int. HapMap Consortium. The International HapMap Project. Nature 426, The Int. HapMap Consortium. Integrating Ethics and Science in the International HapMap Project. Nature Reviews Genet 5, Thorisson et al. The International HapMap Project Web site. Genome Res 15: HapMap-related papers Sabeti et al. Genome-wide detection and characterization of positive selection in human populations. Nature 449, Clark et al. Ascertainment bias in studies of human genome-wide polymorphism. Genome Res, 15: Clayton et al. Population structure, differential bias and genomic control in a large-scale, case-control association study. Nature Genet 37(11): de Bakker et al. Efficiency and power in genetic association studies. Nature Genet 37: Goldstein, Cavalleri. Genomics: Understanding human diversity. Nature 437: Hinds et al. Whole genome patterns of common DNA variation in three human populations. Science 307: Myers et al. A fine-scale map of recombination rates and hotspots across the human genome. Science, 310: Nielsen et al. Genomic scans for selective sweeps using SNP data.Genome Res 15: Smith et al. Sequence features in regions of weak and strong linkage disequilibrium. Genome Res 15: Weir et al. Measures of human population structure show heterogeneity among genomic regions. Genome Res 15: Julia Krushkal 4/11/2017

Julia Krushkal 4/11/2017 Nature (2003)

Human Chromosomes Contain DNA 22 pairs of autosomes +
Julia Krushkal 4/11/2017 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,

There are many types of DNA
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 SNP1 SNP2 There are many types of DNA variation, e.g. Sequence variation AAAC/TGGCTA 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 SNP locus on the distal end of the long arm of human chromosome 5 (data from Ensembl) SNP locus rs CAAATTCCATG[A or C]AGAAGGAAATACAT A and C are alleles at SNP locus rs

A SNP locus on the distal end of the long arm of chromosome 5
SNP locus rs

Hardy-Weinberg Equilibrium
Julia Krushkal 4/11/2017 Hardy-Weinberg Equilibrium 2 alleles, A and B frequencies p and q p+q =1 The allele frequencies remain constant through time. Sperm Egg F1 Under Hardy-Weinberg equilibrium, the relative genotype frequencies are: PAA=p2 PAB=2pq PBB =q2 F1: (p+q )2 In autosomal genes, and in absence of disturbing influences, this proportion is maintained through all subsequent generations. Departures can be characterized by disequilibrium

Linkage Disequilibrium
Associations among alleles at different loci A B1 D = Linkage disequilibrium coefficient Coefficient of association A B2 D=pA1B1-pA1pB1 Locus A Locus B Normalized disequilibrium coefficient Squared Correlation coefficient Also ranges from 0 to 1 1 – absolute or perfect linkage; 0.8 is the cutoff often used D’=D/|D|max |D| max = | min(pA1pB2, pA2pB1)| -1  D’  1 r2 =D2/(pA1pA2pB1pB2) Extended to multiallelic markers

Regulatory Interactions: The ENCODE Project
Julia Krushkal <> 4/11/2017 2003-Pilot project launched (1% of the genome) 2007- Pilot project completed; production phase launched on the entire genome High-through-put experimental and computational approaches to studies of DNA regulatory sites, regulatory interactions, and DNA modification Production Scale Effort Pilot Scale Effort Data Coordination Center Technology Development Effort

Julia Krushkal 4/11/2017

HapMap SNP Density Coverage
Genome SNP Variation Julia Krushkal 4/11/2017 Size of human genome  3.2  109 bp 99.9% identical 9-10 mln SNPs may have MAF 5%  30,000 genes HapMap SNP Density Coverage 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% 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.

HapMap Phase II Julia Krushkal 4/11/2017

Julia Krushkal HapMap Phase II 4/11/2017 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 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 r2 of for different populations

Julia Krushkal 4/11/2017 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
Julia Krushkal 4/11/2017 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

Haplotype Maps of the Human Genome
Julia Krushkal Haplotype Maps of the Human Genome 4/11/2017 Genome regions decomposed into discrete haplotype blocks, which capture similarity in haplotype organization Patil et al. 2001, Blocks of Limited Haplotype Diversity Revealed by High-Resolution Scanning of Human Chromosome 21. Science 294(5547):

Haplotype Maps Generated by The International HapMap Project
Julia Krushkal 4/11/2017 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
Julia Krushkal 4/11/2017 Haplotype Maps of the Human Genome Helmuth 2001, Science 293: Find correlations among groups of SNPs Haplotypes were inferred for the HapMap project from trios data and from unrelated individuals using Phase (Stephens 01; Stephens and Donnely 03)

Julia Krushkal 4/11/2017 Haplotype Block Partition Results for Three Populations 1,586,383 (SNPs) genotyped in 71 Americans of European, African, and Asian ancestry 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 Science

Population differences in local bin structure
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 r2 > 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
Julia Krushkal 4/11/2017 Amount of Captured Sequence Variation in HapMap Phase II For common variants (MAF  0.05) the mean maximum r2 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 r2  0.8 in YRI. Very common SNPs with MAF  0.25 are captured extremely well (mean maximum r2 of 0.93 in YRI to 0.97 in CEU) Rarer SNPs with MAF<0.05 are less well covered (mean maximum r2 of 0.74 in CHB/JPT to 0.76 in YRI).

Amount of Captured Sequence Variation in HapMap Phase II
Julia Krushkal 4/11/2017 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:
Julia Krushkal 4/11/2017 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
Julia Krushkal 4/11/2017 Common Ancestry and Segmental Sharing Relatedness High Med Low Homozygocity

Recombination Hotspots
Julia Krushkal 4/11/2017 Recombination Hotspots 32,996 recombination hotspots 60% of genome recombination, 6% sequence

Recombination and tagSNPs
Julia Krushkal 4/11/2017 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 r2  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
Julia Krushkal 4/11/2017 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 rs 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 rs and rs Can track genealogical history Complex patterns of stochastic mutation, recombination, selection, genetic drift in evolutionary history shape the patterns of genome variation McVean et al., PLoS Genetics 1:e54

The Int. HapMap Consortium, Nature, 2005
Julia Krushkal 4/11/2017 The Int. HapMap Consortium, Nature, 2005

HapMap 3 Mirrors at Sanger Center and Baylor College of Medicine
FUNDING AGENCIES National Institutes of Health – National Human Genome Research Institute (NHGRI) Wellcome Trust Mirrors at Sanger Center and Baylor College of Medicine

QC in 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 monomorphic SNPs (across the entire data set) removed.

HapMap 3 samples

Data Content SNP GENOTYPE DATA ASW 71 1632186 1536247
label # samples # QC+ SNPs # polymorphic QC+ SNPs ASW CEU CHB CHD GIH JPT LWK MEX MKK TSI YRI consensus

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.”

Data Content PCR RESEQUENCING DATA label number of samples ASW 55
CEU 119 CHB 90 CHD 30 GIH 60 JPT 91 LWK 60 MEX 27 MKK 0 TSI 60 YRI 120 total 712

Julia Krushkal 4/11/2017 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 r2  0.8 chosen for popular SNP chips Resource for selecting custom SNPs for dense genotyping in candidate regions, determined from genetic pathways of the 1st stage of multistage GWAS LD and haplotype information utilized for missing SNP imputation for genotypic problems or in meta-analyses

Julia Krushkal 4/11/2017 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

Julia Krushkal 4/11/2017 Microarray analysis

Genotype Imputation Using HapMap Information

Genotype Imputation Using HapMap Information
Software from Jonathan Marchini’s group Julia Krushkal 4/11/2017

Use of HapMap Resources in Meta-Analyses
Julia Krushkal 4/11/2017 Use of HapMap Resources in Meta-Analyses Imputation Common HapMap panel & tagSNPs

Structural Genome Variation
Julia Krushkal 4/11/2017 HapMap samples are also used as a resource for CNV analysis 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

Julia Krushkal 4/11/2017 Segmental duplications are recombination hotspots, causing global genome rearrangements

Ethical, Legal, and Social Implications
Julia Krushkal 4/11/2017 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 :

Ethical, Legal, and Social Implications
Julia Krushkal 4/11/2017 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 :

HapMap Genome Browser

HapMap Genome Browser Julia Krushkal 4/11/2017

UCSC Genome Browser Julia Krushkal 4/11/2017

Beyond the HapMap 1000 Genomes
Julia Krushkal Beyond the HapMap 4/11/2017 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).

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).

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

Julia Krushkal 4/11/2017 The International HapMap Project: A Rich Resource of Genetic Information Julia Krushkal Lecture in Bioinformatics 04/15/2010.

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Julia Krushkal 4/11/2017 The International HapMap Project: A Rich Resource of Genetic Information Julia Krushkal Lecture in Bioinformatics 04/15/2010.

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