Transferability of tag SNPs in genetic association studies in multiple populations

A general question for linkage disequilibrium-based association studies is how power to detect an association is compromised when tag SNPs are chosen from data in one population sample and then deployed in another sample. Specifically, it is important to know how well tags picked from the HapMap DNA samples capture the variation in other samples. To address this, we collected dense data uniformly across the four HapMap population samples and eleven other population samples. We picked tag SNPs using genotype data we collected in the HapMap samples and then evaluated the effective coverage of these tags in comparison to the entire set of common variants observed in the other samples. We simulated case-control association studies in the non-HapMap samples under a disease model of modest risk, and we observed little loss in power. These results demonstrate that the HapMap DNA samples can be used to select tags for genome-wide association studies in many samples around the world.     

Evaluating and improving power in whole-genome association studies using fixed marker sets

Emerging technologies make it possible for the first time to genotype hundreds of thousands of SNPs simultaneously, enabling whole-genome association studies. Using empirical genotype data from the International HapMap Project, we evaluate the extent to which the sets of SNPs contained on three whole-genome genotyping arrays capture common SNPs across the genome, and we find that the majority of common SNPs are well captured by these products either directly or through linkage disequilibrium. We explore analytical strategies that use HapMap data to improve power of association studies conducted with these fixed sets of markers and show that limited inclusion of specific haplotype tests in association analysis can increase the fraction of common variants captured by 25-100%. Finally, we introduce a Bayesian approach to association analysis by weighting the likelihood of each statistical test to reflect the number of putative causal alleles to which it is correlated.     

Evaluating potential for whole-genome studies in Kosrae, an isolated population in Micronesia

Whole-genome association studies are predicted to be especially powerful in isolated populations owing to increased linkage disequilibrium (LD) and decreased allelic diversity, but this possibility has not been empirically tested. We compared genome-wide data on 113,240 SNPs typed on 30 trios from the Pacific island of Kosrae to the same markers typed in the 270 samples from the International HapMap Project. The extent of LD is longer and haplotype diversity is lower in Kosrae than in the HapMap populations. More than 98% of Kosraen haplotypes are present in HapMap populations, indicating that HapMap will be useful for genetic studies on Kosrae. The long-range LD around common alleles and limited diversity result in improved efficiency in genetic studies in this population and augments the power to detect association of 'hidden SNPs'.     

Efficiency and power in genetic association studies

We investigated selection and analysis of tag SNPs for genome-wide association studies by specifically examining the relationship between investment in genotyping and statistical power. Do pairwise or multimarker methods maximize efficiency and power? To what extent is power compromised when tags are selected from an incomplete resource such as HapMap? We addressed these questions using genotype data from the HapMap ENCODE project, association studies simulated under a realistic disease model, and empirical correction for multiple hypothesis testing. We demonstrate a haplotype-based tagging method that uniformly outperforms single-marker tests and methods for prioritization that markedly increase tagging efficiency. Examining all observed haplotypes for association, rather than just those that are proxies for known SNPs, increases power to detect rare causal alleles, at the cost of reduced power to detect common causal alleles. Power is robust to the completeness of the reference panel from which tags are selected. These findings have implications for prioritizing tag SNPs and interpreting association studies.     

Extremely low-coverage sequencing and imputation increases power for genome-wide association studies

Genome-wide association studies (GWAS) have proven to be a powerful method to identify common genetic variants contributing to susceptibility to common diseases. Here, we show that extremely low-coverage sequencing (0.1-0.5×) captures almost as much of the common (>5%) and low-frequency (1-5%) variation across the genome as SNP arrays. As an empirical demonstration, we show that genome-wide SNP genotypes can be inferred at a mean r(2) of 0.71 using off-target data (0.24× average coverage) in a whole-exome study of 909 samples. Using both simulated and real exome-sequencing data sets, we show that association statistics obtained using extremely low-coverage sequencing data attain similar P values at known associated variants as data from genotyping arrays, without an excess of false positives. Within the context of reductions in sample preparation and sequencing costs, funds invested in extremely low-coverage sequencing can yield several times the effective sample size of GWAS based on SNP array data and a commensurate increase in statistical power.     

Population genomics of human gene expression

Genetic variation influences gene expression, and this variation in gene expression can be efficiently mapped to specific genomic regions and variants. Here we have used gene expression profiling of Epstein-Barr virus-transformed lymphoblastoid cell lines of all 270 individuals genotyped in the HapMap Consortium to elucidate the detailed features of genetic variation underlying gene expression variation. We find that gene expression is heritable and that differentiation between populations is in agreement with earlier small-scale studies. A detailed association analysis of over 2.2 million common SNPs per population (5% frequency in HapMap) with gene expression identified at least 1,348 genes with association signals in cis and at least 180 in trans. Replication in at least one independent population was achieved for 37% of cis signals and 15% of trans signals, respectively. Our results strongly support an abundance of cis-regulatory variation in the human genome. Detection of trans effects is limited but suggests that regulatory variation may be the key primary effect contributing to phenotypic variation in humans. We also explore several methodologies that improve the current state of analysis of gene expression variation.     

Mapping autism risk loci using genetic linkage and chromosomal rearrangements

Autism spectrum disorders (ASDs) are common, heritable neurodevelopmental conditions. The genetic architecture of ASDs is complex, requiring large samples to overcome heterogeneity. Here we broaden coverage and sample size relative to other studies of ASDs by using Affymetrix 10K SNP arrays and 1,181 [corrected] families with at least two affected individuals, performing the largest linkage scan to date while also analyzing copy number variation in these families. Linkage and copy number variation analyses implicate chromosome 11p12-p13 and neurexins, respectively, among other candidate loci. Neurexins team with previously implicated neuroligins for glutamatergic synaptogenesis, highlighting glutamate-related genes as promising candidates for contributing to ASDs.     

Systematic assessment of copy number variant detection via genome-wide SNP genotyping

SNP genotyping has emerged as a technology to incorporate copy number variants (CNVs) into genetic analyses of human traits. However, the extent to which SNP platforms accurately capture CNVs remains unclear. Using independent, sequence-based CNV maps, we find that commonly used SNP platforms have limited or no probe coverage for a large fraction of CNVs. Despite this, in 9 samples we inferred 368 CNVs using Illumina SNP genotyping data and experimentally validated over two-thirds of these. We also developed a method (SNP-Conditional Mixture Modeling, SCIMM) to robustly genotype deletions using as few as two SNP probes. We find that HapMap SNPs are strongly correlated with 82% of common deletions, but the newest SNP platforms effectively tag about 50%. We conclude that currently available genome-wide SNP assays can capture CNVs accurately, but improvements in array designs, particularly in duplicated sequences, are necessary to facilitate more comprehensive analyses of genomic variation.     

Measurement of the human allele frequency spectrum demonstrates greater genetic drift in East Asians than in Europeans

Large data sets on human genetic variation have been collected recently, but their usefulness for learning about history and natural selection has been limited by biases in the ways polymorphisms were chosen. We report large subsets of SNPs from the International HapMap Project that allow us to overcome these biases and to provide accurate measurement of a quantity of crucial importance for understanding genetic variation: the allele frequency spectrum. Our analysis shows that East Asian and northern European ancestors shared the same population bottleneck expanding out of Africa but that both also experienced more recent genetic drift, which was greater in East Asians.     

Genome-wide association study of quantitative resistance to southern leaf blight in the maize nested association mapping population

Nested association mapping (NAM) offers power to resolve complex, quantitative traits to their causal loci. The maize NAM population, consisting of 5,000 recombinant inbred lines (RILs) from 25 families representing the global diversity of maize, was evaluated for resistance to southern leaf blight (SLB) disease. Joint-linkage analysis identified 32 quantitative trait loci (QTLs) with predominantly small, additive effects on SLB resistance. Genome-wide association tests of maize HapMap SNPs were conducted by imputing founder SNP genotypes onto the NAM RILs. SNPs both within and outside of QTL intervals were associated with variation for SLB resistance. Many of these SNPs were within or near sequences homologous to genes previously shown to be involved in plant disease resistance. Limited linkage disequilibrium was observed around some SNPs associated with SLB resistance, indicating that the maize NAM population enables high-resolution mapping of some genome regions.     

A worldwide survey of haplotype variation and linkage disequilibrium in the human genome

Recent genomic surveys have produced high-resolution haplotype information, but only in a small number of human populations. We report haplotype structure across 12 Mb of DNA sequence in 927 individuals representing 52 populations. The geographic distribution of haplotypes reflects human history, with a loss of haplotype diversity as distance increases from Africa. Although the extent of linkage disequilibrium (LD) varies markedly across populations, considerable sharing of haplotype structure exists, and inferred recombination hotspot locations generally match across groups. The four samples in the International HapMap Project contain the majority of common haplotypes found in most populations: averaging across populations, 83% of common 20-kb haplotypes in a population are also common in the most similar HapMap sample. Consequently, although the portability of tag SNPs based on the HapMap is reduced in low-LD Africans, the HapMap will be helpful for the design of genome-wide association mapping studies in nearly all human populations.     

Natural selection has driven population differentiation in modern humans

The considerable range of observed phenotypic variation in human populations may reflect, in part, distinctive processes of natural selection and adaptation to variable environmental conditions. Although recent genome-wide studies have identified candidate regions under selection, it is not yet clear how natural selection has shaped population differentiation. Here, we have analyzed the degree of population differentiation at 2.8 million Phase II HapMap single-nucleotide polymorphisms. We find that negative selection has globally reduced population differentiation at amino acid-altering mutations, particularly in disease-related genes. Conversely, positive selection has ensured the regional adaptation of human populations by increasing population differentiation in gene regions, primarily at nonsynonymous and 5'-UTR variants. Our analyses identify a fraction of loci that have contributed, and probably still contribute, to the morphological and disease-related phenotypic diversity of current human populations.     

An evaluation of HapMap sample size and tagging SNP performance in large-scale empirical and simulated data sets

A substantial investment has been made in the generation of large public resources designed to enable the identification of tag SNP sets, but data establishing the adequacy of the sample sizes used are limited. Using large-scale empirical and simulated data sets, we found that the sample sizes used in the HapMap project are sufficient to capture common variation, but that performance declines substantially for variants with minor allele frequencies of <5%.     

Conserved noncoding sequences are selectively constrained and not mutation cold spots

Noncoding genetic variants are likely to influence human biology and disease, but recognizing functional noncoding variants is difficult. Approximately 3% of noncoding sequence is conserved among distantly related mammals, suggesting that these evolutionarily conserved noncoding regions (CNCs) are selectively constrained and contain functional variation. However, CNCs could also merely represent regions with lower local mutation rates. Here we address this issue and show that CNCs are selectively constrained in humans by analyzing HapMap genotype data. Specifically, new (derived) alleles of SNPs within CNCs are rarer than new alleles in nonconserved regions (P = 3 x 10(-18)), indicating that evolutionary pressure has suppressed CNC-derived allele frequencies. Intronic CNCs and CNCs near genes show greater allele frequency shifts, with magnitudes comparable to those for missense variants. Thus, conserved noncoding variants are more likely to be functional. Allele frequency distributions highlight selectively constrained genomic regions that should be intensively surveyed for functionally important variation.     

Evaluating coverage of genome-wide association studies

Genome-wide association studies involving hundreds of thousands of SNPs in thousands of cases and controls are now underway. The first of many analytical challenges in these studies involves the choice of SNPs to genotype. It is not practical to construct a different panel of tag SNPs for each study, so the first generation of genome-wide scans will use predefined, commercially available marker panels, which will in part dictate their success or failure. We compare different approaches in use today, and show that although many of them provide substantial coverage of common variation in non-African populations, the precise extent is strongly dependent on the frequencies of alleles of interest and on specific considerations of study design. Overall, despite substantial differences in genotyping technologies, marker selection strategies and number of markers assayed, the first-generation high-throughput platforms all offer similar levels of genome coverage.     

Comparing strategies to fine-map the association of common SNPs at chromosome 9p21 with type 2 diabetes and myocardial infarction

Noncoding variants at human chromosome 9p21 near CDKN2A and CDKN2B are associated with type 2 diabetes, myocardial infarction, aneurysm, vertical cup disc ratio and at least five cancers. Here we compare approaches to more comprehensively assess genetic variation in the region. We carried out targeted sequencing at high coverage in 47 individuals and compared the results to pilot data from the 1000 Genomes Project. We imputed variants into type 2 diabetes and myocardial infarction cohorts directly from targeted sequencing, from a genotyped reference panel derived from sequencing and from 1000 Genomes Project low-coverage data. Polymorphisms with frequency >5% were captured well by all strategies. Imputation of intermediate-frequency polymorphisms required a higher density of tag SNPs in disease samples than is available on first-generation genome-wide association study (GWAS) arrays. Our association analyses identified more comprehensive sets of variants showing equivalent statistical association with type 2 diabetes or myocardial infarction, but did not identify stronger associations than the original GWAS signals.     

A high-resolution survey of deletion polymorphism in the human genome

Recent work has shown that copy number polymorphism is an important class of genetic variation in human genomes. Here we report a new method that uses SNP genotype data from parent-offspring trios to identify polymorphic deletions. We applied this method to data from the International HapMap Project to produce the first high-resolution population surveys of deletion polymorphism. Approximately 100 of these deletions have been experimentally validated using comparative genome hybridization on tiling-resolution oligonucleotide microarrays. Our analysis identifies a total of 586 distinct regions that harbor deletion polymorphisms in one or more of the families. Notably, we estimate that typical individuals are hemizygous for roughly 30-50 deletions larger than 5 kb, totaling around 550-750 kb of euchromatic sequence across their genomes. The detected deletions span a total of 267 known and predicted genes. Overall, however, the deleted regions are relatively gene-poor, consistent with the action of purifying selection against deletions. Deletion polymorphisms may well have an important role in the genetics of complex traits; however, they are not directly observed in most current gene mapping studies. Our new method will permit the identification of deletion polymorphisms in high-density SNP surveys of trio or other family data.     

Resequencing of 31 wild and cultivated soybean genomes identifies patterns of genetic diversity and selection

We report a large-scale analysis of the patterns of genome-wide genetic variation in soybeans. We re-sequenced a total of 17 wild and 14 cultivated soybean genomes to an average of approximately ×5 depth and >90% coverage using the Illumina Genome Analyzer II platform. We compared the patterns of genetic variation between wild and cultivated soybeans and identified higher allelic diversity in wild soybeans. We identified a high level of linkage disequilibrium in the soybean genome, suggesting that marker-assisted breeding of soybean will be less challenging than map-based cloning. We report linkage disequilibrium block location and distribution, and we identified a set of 205,614 tag SNPs that may be useful for QTL mapping and association studies. The data here provide a valuable resource for the analysis of wild soybeans and to facilitate future breeding and quantitative trait analysis.