Genome-wide detection and characterization of positive selection in human populations

Abstract

With the advent of dense maps of human genetic variation, it is now possible to detect positive natural selection across the human genome. Here we report an analysis of over 3 million polymorphisms from the International HapMap Project Phase 2 (HapMap2)¹. We used ‘long-range haplotype’ methods, which were developed to identify alleles segregating in a population that have undergone recent selection², and we also developed new methods that are based on cross-population comparisons to discover alleles that have swept to near-fixation within a population. The analysis reveals more than 300 strong candidate regions. Focusing on the strongest 22 regions, we develop a heuristic for scrutinizing these regions to identify candidate targets of selection. In a complementary analysis, we identify 26 non-synonymous, coding, single nucleotide polymorphisms showing regional evidence of positive selection. Examination of these candidates highlights three cases in which two genes in a common biological process have apparently undergone positive selection in the same population: LARGE and DMD, both related to infection by the Lassa virus³, in West Africa; SLC24A5 and SLC45A2, both involved in skin pigmentation^4,5, in Europe; and EDAR and EDA2R, both involved in development of hair follicles⁶, in Asia.

An increasing amount of information about genetic variation, together with new analytical methods, is making it possible to explore the recent evolutionary history of the human population. The first phase of the International Haplotype Map, including ~1 million single nucleotide polymorphisms (SNPs)⁷, allowed preliminary examination of natural selection in humans. Now, with the publication of the Phase 2 map (HapMap2)¹ in a companion paper, over 3 million SNPs have been genotyped in 420 chromosomes from three continents (120 European (CEU), 120 African (YRI) and 180 Asian from Japan and China (JPT + CHB)).

In our analysis of HapMap2, we first implemented two widely used tests that detect recent positive selection by finding common alleles carried on unusually long haplotypes². The two, the Long-Range Haplotype (LRH)⁸ and the integrated Haplotype Score (iHS)⁹ tests, rely on the principle that, under positive selection, an allele may rise to high frequency rapidly enough that long-range association with nearby polymorphisms—the long-range haplotype⁸—will not have time to be eliminated by recombination. These tests control for local variation in recombination rates by comparing long haplotypes to other alleles at the same locus. As a result, they lose power as selected alleles approach fixation (100% frequency), because there are then few alternative alleles in the population (Supplementary Fig. 2 and Supplementary Tables 1–2).

We next developed, evaluated and applied a new test, Cross Population Extended Haplotype Homozogysity (XP-EHH), to detect selective sweeps in which the selected allele has approached or achieved fixation in one population but remains polymorphic in the human population as a whole (Methods, and Supplementary Fig. 2 and Supplementary Tables 3–6). Related methods have recently also been described^10-12.

Our analysis of recent positive selection, using the three methods, reveals more than 300 candidate regions¹(Supplementary Fig. 3 and Supplementary Table 7), 22 of which are above a threshold such that no similar events were found in 10 Gb of simulated neutrally evolving sequence (Methods). We focused on these 22 strongest signals (Table 1), which include two well-established cases, SLC24A5 and LCT^2,5,13, and 20 other regions with signals of similar strength.

Table 1.

The twenty-two strongest candidates for natural selection

Region	Chr:position (MB, HG17)	Selected population	Long Haplotype Test	Size (Mb)	Total SNPs with Long Haplotype Signal	Subset of SNPs that fulfil criteria 1	Subset of SNPs that fulfil criteria 1 and 2	Subset of SNPs that fulfil criteria 1, 2 and 3	Genes at or near SNPs that fulfil all three criteria
1	chr1:166	CHB+JPT	LRH, iHS	0.4	92	39	30	2	BLZF1, SLC19A2
2	chr2:72.6	CHB+JPT	XP-EHH	0.8	732	250	0	0
3	chr2:108.7	CHB+JPT	LRH, iHS, XP-EHH	1.0	972	265	7	1	EDAR
4	chr2:136.1	CEU	LRH, iHS, XP-EHH	2.4	1,213	282	24	3	RAB3GAP1, R3HDM1, LCT
5	chr2:177.9	CEU,CHB+JPT	LRH, iHS, XP-EHH	1.2	1,388	399	79	9	PDE11A
6	chr4:33.9	CEU,YRI, CHB+JPT	LRH, iHS	1.7	413	161	33	0
7	chr4:42	CHB+JPT	LRH, iHS, XP-EHH	0.3	249	94	65	6	SLC30A9
8	chr4:159	CHB+JPT	LRH, iHS, XP-EHH	0.3	233	67	34	1
9	chr10:3	CEU	LRH, iHS, XP-EHH	0.3	179	63	16	1
10	chr10:22.7	CEU, CHB+JPT	XP-EHH	0.3	254	93	0	0
11	chr10:55.7	CHB+JPT	LRH, iHS, XP-EHH	0.4	735	221	5	2	PCDH15
12	chr12:78.3	YRI	LRH, iHS	0.8	151	91	25	0
13	chr15:46.4	CEU	XP-EHH	0.6	867	233	5	1	SLC24A5
14	chr15:61.8	CHB+JPT	XP-EHH	0.2	252	73	40	6	HERC1
15	chr16:64.3	CHB+JPT	XP-EHH	0.4	484	137	2	0
16	chr16:74.3	CHB+JPT, YRI	LRH, iHS	0.6	55	35	28	3	CHST5, ADAT1, KARS
17	chr17:53.3	CHB+JPT	XP-EHH	0.2	143	41	0	0
18	chr17:56.4	CEU	XP-EHH	0.4	290	98	26	3	BCAS3
19	chr19:43.5	YRI	LRH, iHS, XP-EHH	0.3	83	30	0	0
20	chr22:32.5	YRI	LRH	0.4	318	188	35	3	LARGE
21	chr23:35.1	YRI	LRH, iHS	0.6	50	35	25	0
22	chr23:63.5	YRI	LRH, iHS	3.5	13	3	1	0
		Total SNPs		16.74	9,166	2,898	480	41

Twenty-two regions were identified at a high threshold for significance (Methods), based on the LRH, iHS and/or XP-EHH test. Within these regions, we examined SNPs with the best evidence of being the target of selection on the basis of having a long haplotype signal, and by fulfilling three criteria: (1) being a high-frequency derived allele; (2) being differentiated between populations and common only in the selected population; and (3) being identified as functional by current annotation. Several candidate polymorphisms arise from the analysis including well-known LCT and SLC24A5 (ref. ²), as well as intriguing new candidates.

The challenge is to sift through genetic variation in the candidate regions to identify the variants that were the targets of selection. Our candidate regions are large (mean length, 815 kb; maximum length, 3.5 Mb) and often contain multiple genes (median, 4; maximum, 15). A typical region harbours ~400–4,000 common SNPs (minor allele frequency >5%), of which roughly three-quarters are represented in current SNP databases and half were genotyped as part of HapMap2 (Supplementary Table 8).

We developed three criteria to help highlight potential targets of selection (Supplementary Fig. 1): (1) selected alleles detectable by our tests are likely to be derived (newly arisen), because long-haplotype tests have little power to detect selection on standing (pre-existing) variation¹⁴; we therefore focused on derived alleles, as identified by comparison to primate outgroups; (2) selected alleles are likely to be highly differentiated between populations, because recent selection is probably a local environmental adaptation²; we thus looked for alleles common in only the population(s) under selection; (3) selected alleles must have biological effects. On the basis of current knowledge, we therefore focused on non-synonymous coding SNPs and SNPs in evolutionarily conserved sequences. These criteria are intended as heuristics, not absolute requirements. Some targets of selection may not satisfy them, and some will not be in current SNP databases. Nonetheless, with ~50% of common SNPs in these populations genotyped in HapMap2, a search for causal variants is timely.

We applied the criteria to the regions containing SLC24A5 and LCT, each of which already has a strong candidate gene, mutation and trait. At SLC24A5, the 600 kb region contains 914 genotyped SNPs. Applying filters progressively (Table 1 and Fig. 1a–d), we found that 867 SNPs are associated with the long-haplotype signal, of which 233 are high-frequency derived alleles, of which 12 are highly differentiated between populations, and of which only 5 are common in Europe and rare in Asia and Africa. Among these five SNPs, there is only one implicated as functional by current knowledge; it has the strongest signal of positive selection and encodes the A111T polymorphism associated with pigment differences in humans and thought to be the target of positive selection⁵. Our criteria thus uniquely identify the expected allele.

Figure 1. Localizing SLC24A5 and EDAR signals of selection.

a–d, SLC24A5. a, Strong evidence for positive selection in CEU samples at a chromosome 15 locus: XP-EHH between CEU and JPT + CHB (blue), CEU and YRI (red), and YRI and JPT + CHB (grey). SNPs are classified as having low probability (bordered diamonds) and high probability (filled diamonds) potential for function. SNPs were filtered to identify likely targets of selection on the basis of the frequency of derived alleles (b), differences between populations (c) and differences between populations for high-frequency derived alleles (less than 20% in non-selected populations) (d). The number of SNPs that passed each filter is given in the top left corner in red. The threonine to alanine candidate polymorphism in SLC24A5 is the clear outlier. e–h, EDAR. e, Similar evidence for positive selection in JPT + CHB at a chromosome 2 locus: XP-EHH between CEU and JPT + CHB (blue), between YRI and JPT + CHB (red), and between CEU and YRI (grey); iHS in JPT + CHB (green). A valine to alanine polymorphism in EDAR passes all filters: the frequency of derived alleles (f), differences between populations (g) and differences between populations for high-frequency derived alleles (less than 20% in non-selected populations) (h). Three other functional changes, a D→E change in SULT1C2 and two SNPs associated with RANBP2 expression (Methods), have also become common in the selected population.

At the LCT locus, we found similar degrees of filtration. Within the 2.4 Mb selective sweep, 24 polymorphisms fulfil the first two criteria (Table 1, and Supplementary Fig. 4), with the polymorphism thought to confer adult persistence of lactase among them. However, this SNP was only identified as functional after extensive study of the LCT gene¹⁵. Thus LCT shows both the utility and the limits of the heuristics.

Given the encouraging results for SLC24A5 and LCT, we performed a similar analysis on all 22 candidate regions (Table 1). Filtering the 9,166 SNPs associated with the long-haplotype signal, we found that 480 satisfied the first two criteria. We identified 41 out of the 480 SNPs (0.2% of all SNPs genotyped in the regions) as possibly functional on the basis of a newly compiled database of polymorphisms in known coding elements, evolutionarily conserved elements and regulatory elements (Methods; B.F., unpublished), together containing ~ 5.5% of all known SNPs.

Eight of the forty-one SNPs encode non-synonymous changes (Table 1 and Supplementary Table 9). Apart from the well-known case of SLC24A5, they are found in EDAR, PCDH15, ADAT1, KARS, HERC1, SLC30A9 and BLFZ1. The remaining 33 potentially functional SNPs lie within conserved transcription factor motifs, introns, UTRs and other non-coding regions.

To identify additional candidates, we reversed the process by taking non-synonymous coding SNPs with highly differentiated high-frequency derived alleles; these SNPs comprise a tiny fraction of all SNPs and have a higher a priori probability of being targets of selection. Of the 15,816 non-synonymous SNPs in HapMap2, 281 (Supplementary Table 10) have both a high derived-allele frequency (frequency >50%) and clear differentiation between populations (F_ST is in the top 0.5 percentile). We examined these 281 SNPs to identify those embedded within long-range haplotypes¹⁶, and identified 26 putative cases of positive selection. These include the eight non-synonymous SNPs identified in the genome-wide analysis above.

Interestingly, analysis of the top regions and the non-synonymous SNPs together revealed three cases of two genes in the same pathway both having strong evidence of selection in a single population.

In the European sample, there is strong evidence for two genes already shown to be associated with skin pigment differences among humans. The first is SLC24A5, described above. We further examined the global distribution (Fig. 2) and the predicted effect on protein activity of the SLC24A5 A111T polymorphism (Supplementary Fig. 5, 6). The second, SLC45A2, has an important role in pigmentation in zebrafish, mouse and horse⁴. An L374F substitution in SLC45A2 is at 100% frequency in the European sample, but absent in the Asian and African samples. A recent association study has shown that the Phe-encoding allele is correlated with fair skin and non-black hair in Europeans⁴. Together, the data support SLC45A2 as a target of positive selection in Europe^10,17.

Figure 2. Global distribution of SLC24A5 A111T and EDAR V370A.

Worldwide allele-frequency distributions for candidate polymorphisms with the strongest evidence for selection²⁰. a, SLC24A5 A111T is common in Europe, Northern Africa and Pakistan, but rare or absent elsewhere. b, EDAR V370A is common in Asia and the Americas, but absent in Europe and Africa.

In the African sample (Yoruba in Ibadan, Nigeria), there is evidence of selection for two genes with well-documented biological links to the Lassa fever virus. The strongest signal in the genome, on the basis of the LRH test, resides within a 400 kb region that lies entirely within the gene LARGE. The LARGE protein is a glycosylase that post-translationally modifies α-dystroglycan, the cellular receptor for Lassa fever virus (as well as other arenaviruses), and the modification has been shown to be critical for virus binding³. The virus name is derived from Lassa, Nigeria, where the disease is endemic, with 21% of the population showing signs of exposure¹⁸. We also noted that the DMD locus is on our larger candidate list of regions, with the signal of selection again in the Yoruba sample. DMD encodes a cytosolic adaptor protein that binds to α-dystroglycan and is critical for its function. We hypothesize that Lassa fever created selective pressure at LARGE and DMD¹². This hypothesis can be tested by correlating the geographical distribution of the selected haplotype with endemicity of the Lassa virus, studying infection of genotyped cells in vitro, and searching for an association between the selected haplotype and clinical outcomes in infected patients.

In the Asian samples, we found evidence of selection for non-synonymous polymorphisms in two genes in the ectodysplasin (EDA) pathway, which is involved in development of hair, teeth and exocrine glands⁶. The genes are EDAR and EDA2R, which encode the key receptors for the ligands EDA A1 and EDA A2, respectively. Notably, the EDA signalling pathway has been shown to be under positive selection for loss of scales in multiple distinct populations of freshwater stickleback fish¹⁹. A mutation encoding a V370A substitution in EDAR is near fixation in Asia and absent in Europe and Africa (Fig. 1e–h). An R57K substitution in EDA2R has derived-allele frequencies of 100% in Asia, 70% in Europe and 0% in Africa.

The EDAR polymorphism is notable because it is highly differentiated between the Asian and other continental populations (the 3rd most differentiated among 15,816 non-synonymous SNPs), and also within Asian populations (in the top 1% of SNPs differentiated between the Japanese and Chinese HapMap samples). Genotyping of the EDAR polymorphism in the CEPH (Centre d'Etudie du Polymorphisme Humain) global diversity panel²⁰ shows that it is at high but varying frequency throughout Asia and the Americas (for example, 100% in Pima Indians and in parts of China, and 73% in Japan) (Fig. 2, and Supplementary Fig. 7). Studying populations like the Japanese, in which the allele is still segregating, may provide clues to its biological significance.

EDAR has a central role in generation of the primary hair follicle pattern, and mutations in EDAR cause hypohidrotic ectodermal dysplasia (HED) in humans and mice, characterized by defects in the development of hair, teeth and exocrine glands⁶. The V370A polymorphism, proposed to be the target of selection, lies within EDAR's highly conserved death domain (Supplementary Fig. 8), the location of the majority of EDAR polymorphisms causing HED²¹. Our structural modelling predicts that the polymorphism lies within the binding site of the domain (Fig. 3).

Figure 3. Structural model of the EDAR death domain.

Ribbon representation of a homology model of the EDAR death domain (DD), based on the alignment of the EDAR DD amino acid sequence (EDAR residues 356–431), with multiple known DD structures. The helices are labelled H1 to H6. Residues in blue (the H1–H2 and H5–H6 loops, residues 370–376 and 419–425, respectively) correspond to the homologous residues in Tube that interact with Pelle in the Tube-DD–Pelle-DD structure²⁴. These EDAR-DD residues therefore form a potential region of interaction with a DD-containing EDAR-interacting protein, such as EDARADD. The V370A polymorphic residue (red) is located prominently within this potential binding region in the H1–H2 loop. Seven of the thirteen known mis-sense mutations in EDAR that lead to hypohidrotic ectodermal dysplasia (HED) in humans are located in the EDAR-DD: the only four mutations in EDAR that lead to the dominant transmission of HED (green) and three recessive mutations (yellow)²¹. Four of these mutations, R375H, L377F, R420Q and I418T are located in the vicinity of the predicted interaction interface.

Our analysis only scratches the surface of the recent selective history of the human genome. The results indicate that individual candidates may coalesce into pathways that reveal traits under selection, analogous to the alleles of multiple genes (for example, HBB, G6PD and DARC) that arose and spread in Africa and other tropical populations as a result of the partial protection they confer against malaria^2,12. Such endeavours will be enhanced by continuing development of analytical methods to localize signals in candidate regions, generation of expanded data sets, advances in comparative genomics to define coding and regulatory regions, and biological follow-up of promising candidates. True understanding of the role of adaptive evolution will require collaboration across multiple disciplines, including molecular and structural biology, medical and population genetics, and history and anthropology.

METHODS SUMMARY

Genotyping data

Phase 2 of the International Haplotype Map (HapMap2) (www.hapmap.org) contains 3.1 million SNPs genotyped in 420 chromosomes in 3 continental populations (120 European (CEU), 120 African (YRI) and 180 Asian (JPT+CHB))¹. We further genotyped our top HapMap2 functional candidates in the HGDR-CEPH Human Genome Diversity Cell Line Panel²⁰.

LRH, iHS and XP-EHH tests

The Long-Range Haplotype (LRH), integrated Haplotype Score (iHS) and Cross Population EHH (XP-EHH) tests detect alleles that have risen to high frequency rapidly enough that long-range association with nearby polymorphisms—the long-range haplotype—has not been eroded by recombination; haplotype length is measured by the EHH^8,9. The first two tests detect partial selective sweeps, whereas XP-EHH detects selected alleles that have risen to near fixation in one but not all populations. To evaluate the tests, we simulated genomic data for each HapMap population in a range of demographic scenarios—under neutral evolution and twenty scenarios of positive selection—developing the program Sweep (www.broad.mit.edu/mpg/sweep) for analysis. For our top candidates by the three tests, we tested for haplotype-specific recombination rates and copy-number polymorphisms, possible confounders.

Localization

We calculated F_ST and derived-allele frequency for all SNPs within the top candidate regions. We developed a database for those regions to annotate all potentially functional DNA changes (B.F., unpublished), including non-synonymous variants, variants disrupting predicted functional motifs, variants within regions of conservation in mammals and variants previously associated with human phenotypic differences, as well as synonymous, intronic and untranslated region variants.

Structural model

We generated a homology model of the EDAR death domain (DD) from available DD structures using Modeller 9v1 (ref. ²²). The distribution of conserved residues, built using ConSurf²³ with an EDAR sequence alignment from 22 species, shows a bias to the protein core in helices H1, H2 and H5, supporting our model.

METHODS

Genotyping data

The chromosomes examined in HapMap 2 were phased by the consortium using PHASE²⁵.

The HGDR-CEPH Human Genome Diversity Cell Line Panel²⁰ consists of 1,051 individuals from 51 populations across the world. We obtained DNA for the panel from the Foundation Jean Dausset (CEPH) and genotyped our top functional candidates for selection in the panel.

LRH, iHS, and XP-EHH tests

The Long-Range Haplotype (LRH) and the integrated Haplotype Score (iHS) tests have been previously described^8,9 and our methods are given in Supplementary Methods.

EHH between two SNPs, A and B, is defined as the probability that two randomly chosen chromosomes are homozygous at all SNPs between A and B, inclusive⁸; it is usually calculated using a sample of chromosomes from a single population. Explicitly, if the N chromosomes in a sample form G homozygous groups, with each group i having n_i elements, EHH is defined as

$$\mathrm{EHH}=\frac{{\displaystyle \underset{i=1}{\overset{G}{\Sigma }}}\left(\begin{array}{c}\hfill n_i\hfill \\ \hfill 2\hfill \end{array}\right)}{\left(\begin{array}{c}\hfill N\hfill \\ \hfill 2\hfill \end{array}\right)}$$

The XP-EHH test detects selective sweeps in which the selected allele has risen to high frequency or fixation in one population, but remains polymorphic in the human population as a whole; for this purpose it is more powerful than either iHS or LRH (Supplementary Fig. 2 and Supplementary Tables 3-6). XP-EHH uses cross-population comparison of haplotype lengths to control for local variation in recombination rates. Such cross-population comparison is complicated by the fact that haplotype lengths also depend on population history, such as bottlenecks and expansions²⁶. The XP-EHH test normalizes for genome-wide differences in haplotype length between populations.

We define the XP-EHH test with respect to two populations, A and B, a given core SNP and a given direction (centromere distal or proximal). EHH is calculated for all SNPs in population A between the core SNP and X, and the value integrated with respect to genetic distance, with the result defined as I_A. I_B is defined analogously for population B. The statistic ln(I_A/I_B) is then calculated; an unusually positive value suggests selection in population A, a negative value selection in B. For identifying outliers, the log-ratio is normalized to have zero mean and unit variance. Details are given in Supplementary Methods.

We developed a computer program, Sweep, to implement these tests (LRH, iHS and XP-EHH) for positive selection, (Supplementary Methods; www.broad.mit.edu/mpg/sweep). In identifying the 22 strongest candidate regions, we considered regions with signals in at least two of five tests (LRH, iHS and XP-EHH in the three pairwise comparisons among the three populations), as well as those that had the strongest signal for each individual test. With this threshold we found no events in 10 Gb of simulated neutrally evolving sequence. For the top candidates by the three tests, we have taken additional steps to rule out the effects of recombination rate variation and copy number polymorphisms (Supplementary Methods).

Simulations and power calculations

We simulated the evolution of 1 MB sections of 120 chromosomes from each of the three continental HapMap populations, using a previously validated demographic model²⁷, under neutrality and under twenty scenarios of positive selection. We studied the effects of demo-graphy by further simulating recent bottlenecks with a range of intensity. Details of simulations and power calculations are given in Supplementary Methods.

Functional annotation

We developed an annotation database for our candidate regions to identify all DNA changes with potential functional consequence (B.F., unpublished). We first examined candidates most likely to be functional, including non-synonymous mutations, variants that disrupt predicted functional motifs (transcription factor motifs in conserved regions up to 10-kb 5′ of known genes and miRNA binding-site motifs in conserved 3′ untranslated regions of known genes), and variations reported to be associated with human phenotypic differences. For the last category, we identified variations associated with a clinical state (for example, malaria resistance) by a review of the published literature and those associated with changes to gene expression in lymphoblastoid cell lines from the HapMap individuals. The annotation included insertion/deletion mutations of all sizes. We also examined candidates with lower probability of being functional, including synonymous, intronic and untranslated variations and those that occur within regions of conservation in mammalian species. These methods are described in greater detail in Supplementary Methods.

Structural model of EDAR's death domain

We generated a homology model for EDAR's death domain (DD) using six solved DD structures: p75 NGFR-DD, RAIDD-DD, Pelle-DD, FADD-DD, Fas-DD and IRAK4-DD^24,28-32. We aligned the corresponding protein sequences using SALIGN³³. We then added the amino acid sequence of EDAR's DD (residues 356-431) to this structural alignment using Modeller 9v1 (ref. ²²). The resulting alignment was used as the input to Modeller 9v1 to build ten EDAR-DD structure models, and the best model was selected based on the Objective Function Score. Owing to the high DOPE scores in the H1-H2 loop we performed a loop refinement using Modeller9v1, significantly reducing the energy of this region. We further evaluated the model by examining the distribution of conserved residues using ConSurf²³ with an alignment of EDAR-DD sequences from 22 species. We observed a bias of conserved residues to the protein core in H1, H2 and H5, which supports our EDAR-DD model. To identify potential binding regions of EDAR-DD, we used LSQMAN³⁴ to superimpose the model to the Tube-DD-Pelle-DD complex structure²⁴. The H1-H2 and H5-H6 loops of the EDAR-DD correspond to Tube residues interacting with Pelle, and H2-H3 and H4-H5 loops to Pelle residues interacting with Tube. We focused our analysis on the residues corresponding to the interacting region in Tube because our EDAR-DD model is most similar to Tube. Figures were generated with PyMOL³⁵.

Other analysis

Description of methods for calculating F_ST, derived-allele frequency, alignment of the SLC24 amino acids, species alignments, conservation graphs, and estimation of the fraction of SNPs genotyped in HapMap2 and identified in dbSNP, are given in Supplementary Methods.

The International HapMap Consortium

The International HapMap Consortium (Participants are arranged by institution and then alphabetically within institutions except for Principal Investigators and Project Leaders, as indicated.)

Genotyping centres: Perlegen Sciences Kelly A. Frazer (Principal Investigator)¹, Dennis G. Ballinger², David R. Cox², David A. Hinds², Laura L. Stuve²; Baylor College of Medicine and ParAllele BioScience Richard A. Gibbs (Principal Investigator)³, John W. Belmont³, Andrew Boudreau⁴, Paul Hardenbol⁵, Suzanne M. Leal³, Shiran Pasternak⁶, David A. Wheeler³, Thomas D. Willis⁴, Fuli Yu⁷; Beijing Genomics Institute Huanming Yang (Principal Investigator)⁸, Changqing Zeng (Principal Investigator)⁸, Yang Gao⁸, Haoran Hu⁸, Weitao Hu⁸, Chaohua Li⁸, Wei Lin⁸, Siqi Liu⁸, Hao Pan⁸, Xiaoli Tang⁸, Jian Wang⁸, Wei Wang⁸, Jun Yu⁸, Bo Zhang⁸, Qingrun Zhang⁸, Hongbin Zhao⁸, Hui Zhao⁸, Jun Zhou⁸; Broad Institute of Harvard and Massachusetts Institute of Technology Stacey B. Gabriel (Project Leader)⁷, Rachel Barry⁷, Brendan Blumenstiel⁷, Amy Camargo⁷, Matthew Defelice⁷, Maura Faggart⁷, Mary Goyette⁷, Supriya Gupta⁷, Jamie Moore⁷, Huy Nguyen⁷, Robert C. Onofrio⁷, Melissa Parkin⁷, Jessica Roy⁷, Erich Stahl⁷, Ellen Winchester⁷, Liuda Ziaugra⁷, David Altshuler (Principal Investigator)^7,9; Chinese National Human Genome Center at Beijing Yan Shen (Principal Investigator)¹⁰, Zhijian Yao¹⁰; Chinese National Human Genome Center at Shanghai Wei Huang (Principal Investigator)¹¹, Xun Chu¹¹, Yungang He¹¹, Li Jin¹², Yangfan Liu¹¹, Yayun Shen¹¹, Weiwei Sun¹¹, Haifeng Wang¹¹, Yi Wang¹¹, Ying Wang¹¹, Xiaoyan Xiong¹¹, Liang Xu¹¹; Chinese University of Hong Kong Mary M. Y. Waye (Principal Investigator)¹³, Stephen K. W. Tsui¹³; Hong Kong University of Science and Technology Hong Xue (Principal Investigator)¹⁴, J. Tze-Fei Wong¹⁴; Illumina Luana M. Galver (Project Leader)¹⁵, Jian-Bing Fan¹⁵, Kevin Gunderson¹⁵, Sarah S. Murray¹, Arnold R. Oliphant¹⁶, Mark S. Chee (Principal Investigator)¹⁷; McGill University and Génome Québec Innovation Centre Alexandre Montpetit (Project Leader)¹⁸, Fanny Chagnon¹⁸, Vincent Ferretti¹⁸, Martin Leboeuf¹⁸, Jean-Franc¸ois Olivier⁴, Michael S. Phillips¹⁸, Stéphanie Roumy¹⁵, Clémentine Sallée¹⁹, Andrei Verner¹⁸, Thomas J. Hudson (Principal Investigator)²⁰; University of California at San Francisco and Washington University Pui-Yan Kwok (Principal Investigator)²¹, Dongmei Cai²¹, Daniel C. Koboldt²², Raymond D. Miller²², Ludmila Pawlikowska²¹, Patricia Taillon-Miller²², Ming Xiao²¹; University of Hong Kong Lap-Chee Tsui (Principal Investigator)²³, William Mak²³, You Qiang Song²³, Paul K. H. Tam²³; University of Tokyo and RIKEN Yusuke Nakamura (Principal Investigator)^24,25, Takahisa Kawaguchi²⁵, Takuya Kitamoto²⁵, Takashi Morizono²⁵, Atsushi Nagashima²⁵, Yozo Ohnishi²⁵, Akihiro Sekine²⁵, Toshihiro Tanaka²⁵, Tatsuhiko Tsunoda²⁵; Wellcome Trust Sanger Institute Panos Deloukas (Project Leader)²⁶, Christine P. Bird²⁶, Marcos Delgado²⁶, Emmanouil T. Dermitzakis²⁶, Rhian Gwilliam²⁶, Sarah Hunt²⁶, Jonathan Morrison²⁷, Don Powell²⁶, Barbara E. Stranger²⁶, Pamela Whittaker²⁶, David R. Bentley (Principal Investigator)²⁸

Analysis groups: Broad Institute Mark J. Daly (Project Leader)^7,9, Paul I. W. de Bakker^7,9, Jeff Barrett^7,9, Yves R. Chretien⁷, Julian Maller^7,9, Steve McCarroll^7,9, Nick Patterson⁷, Itsik Pe'er²⁹, Alkes Price⁷, Shaun Purcell⁹, Daniel J. Richter⁷, Pardis Sabeti⁷, Richa Saxena^7,9, Stephen F. Schaffner⁷, Pak C. Sham²³, Patrick Varilly⁷, David Altshuler (Principal Investigator)^7,9; Cold Spring Harbor Laboratory Lincoln D. Stein (Principal Investigator)⁶, Lalitha Krishnan⁶, Albert Vernon Smith⁶, Marcela K. Tello-Ruiz⁶, Gudmundur A. Thorisson³⁰; Johns Hopkins University School of Medicine Aravinda Chakravarti (Principal Investigator)³¹, Peter E. Chen³¹, David J. Cutler³¹, Carl S. Kashuk³¹, Shin Lin³¹; University of Michigan Gonc¸alo R. Abecasis (Principal Investigator)³², Weihua Guan³², Yun Li³², Heather M. Munro³³, Zhaohui Steve Qin³², Daryl J. Thomas³⁴; University of Oxford Gilean McVean (Project Leader)³⁵, Adam Auton³⁵, Leonardo Bottolo³⁵, Niall Cardin³⁵, Susana Eyheramendy³⁵, Colin Freeman³⁵, Jonathan Marchini³⁵, Simon Myers³⁵, Chris Spencer⁷, Matthew Stephens³⁶, Peter Donnelly (Principal Investigator)³⁵; University of Oxford, Wellcome Trust Centre for Human Genetics Lon R. Cardon (Principal Investigator)³⁷, Geraldine Clarke³⁸, David M. Evans³⁸, Andrew P. Morris³⁸, Bruce S. Weir³⁹; RIKEN Tatsuhiko Tsunoda (Principal Investigator)²⁵, Todd A. Johnson²⁵; US National Institutes of Health James C. Mullikin⁴⁰; US National Institutes of Health National Center for Biotechnology Information Stephen T. Sherry⁴¹, Michael Feolo⁴¹, Andrew Skol⁴²

Community engagement/public consultation and sample collection groups: Beijing Normal University and Beijing Genomics Institute Houcan Zhang⁴³, Changqing Zeng⁸, Hui Zhao⁸; Health Sciences University of Hokkaido, Eubios Ethics Institute, and Shinshu University Ichiro Matsuda (Principal Investigator)⁴⁴, Yoshimitsu Fukushima⁴⁵, Darryl R. Macer⁴⁶, Eiko Suda⁴⁷; Howard University and University of Ibadan Charles N. Rotimi (Principal Investigator)⁴⁸, Clement A. Adebamowo⁴⁹, Ike Ajayi⁴⁹, Toyin Aniagwu⁴⁹, Patricia A. Marshall⁵⁰, Chibuzor Nkwodimmah⁴⁹, Charmaine D. M. Royal⁴⁸; University of Utah Mark F. Leppert (Principal Investigator)⁵¹, Missy Dixon⁵¹, Andy Peiffer⁵¹

Ethical, legal and social issues: Chinese Academy of Social Sciences Renzong Qiu⁵²; Genetic Interest Group Alastair Kent⁵³; Kyoto University Kazuto Kato⁵⁴; Nagasaki University Norio Niikawa⁵⁵; University of Ibadan School of Medicine Isaac F. Adewole⁴⁹; University of Montréal Bartha M. Knoppers¹⁹; University of Oklahoma Morris W. Foster⁵⁶; Vanderbilt University Ellen Wright Clayton⁵⁷; Wellcome Trust Jessica Watkin⁵⁸

SNP discovery: Baylor College of Medicine Richard A. Gibbs (Principal Investigator)³, John W. Belmont³, Donna Muzny³, Lynne Nazareth³, Erica Sodergren³, George M. Weinstock³, David A. Wheeler³, Imtaz Yakub³; Broad Institute of Harvard and Massachusetts Institute of Technology Stacey B. Gabriel (Project Leader)⁷, Robert C. Onofrio⁷, Daniel J. Richter⁷, Liuda Ziaugra⁷, Bruce W. Birren⁷, Mark J. Daly^7,9, David Altshuler (Principal Investigator)^7,9; Washington University Richard K. Wilson (Principal Investigator)⁵⁹, Lucinda L. Fulton⁵⁹; Wellcome Trust Sanger Institute Jane Rogers (Principal Investigator)²⁶, John Burton²⁶, Nigel P. Carter²⁶, Christopher M. Clee²⁶, Mark Griffiths²⁶, Matthew C. Jones²⁶, Kirsten McLay²⁶, Robert W. Plumb²⁶, Mark T. Ross²⁶, Sarah K. Sims²⁶, David L. Willey²⁶

Scientific management: Chinese Academy of Sciences Zhu Chen⁶⁰, Hua Han⁶⁰, Le Kang⁶⁰; Genome Canada Martin Godbout⁶¹, John C. Wallenburg⁶²; Génome Québec Paul L'Archevêque⁶³, Guy Bellemare⁶³; Japanese Ministry of Education, Culture, Sports, Science and Technology Koji Saeki⁶⁴; Ministry of Science and Technology of the People's Republic of China Hongguang Wang⁶⁵, Daochang An⁶⁵, Hongbo Fu⁶⁵, Qing Li⁶⁵, Zhen Wang⁶⁵; The Human Genetic Resource Administration of China Renwu Wang⁶⁶; The SNP Consortium Arthur L. Holden¹⁵; US National Institutes of Health Lisa D. Brooks⁶⁷, Jean E. McEwen⁶⁷, Mark S. Guyer⁶⁷, Vivian Ota Wang^67,68, Jane L. Peterson⁶⁷, Michael Shi⁶⁹, Jack Spiegel⁷⁰, Lawrence M. Sung⁷¹, Lynn F. Zacharia⁶⁷, Francis S. Collins⁷²; Wellcome Trust Karen Kennedy⁶¹, Ruth Jamieson⁵⁸, John Stewart⁵⁸