Juxtaposed regions of extensive and minimal linkage disequilibrium in human Xq25 and Xq28

Linkage disequilibrium (LD), or the non-random association of alleles, is poorly understood in the human genome. Population genetic theory suggests that LD is determined by the age of the markers, population history, recombination rate, selection and genetic drift. Despite the uncertainties in determining the relative contributions of these factors, some groups have argued that LD is a simple function of distance between markers. Disease-gene mapping studies and a simulation study gave differing predictions on the degree of LD in isolated and general populations. In view of the discrepancies between theory and experimental observations, we constructed a high-density SNP map of the Xq25-Xq28 region and analysed the male genotypes and haplotypes across this region for LD in three populations. The populations included an outbred European sample (CEPH males) and isolated population samples from Finland and Sardinia. We found two extended regions of strong LD bracketed by regions with no evidence for LD in all three samples. Haplotype analysis showed a paucity of haplotypes in regions of strong LD. Our results suggest that, in this region of the X chromosome, LD is not a monotonic function of the distance between markers, but is more a property of the particular location in the human genome.     

Recombination and linkage disequilibrium in Arabidopsis thaliana

Linkage disequilibrium (LD) is a major aspect of the organization of genetic variation in natural populations. Here we describe the genome-wide pattern of LD in a sample of 19 Arabidopsis thaliana accessions using 341,602 non-singleton SNPs. LD decays within 10 kb on average, considerably faster than previously estimated. Tag SNP selection algorithms and 'hide-the-SNP' simulations suggest that genome-wide association mapping will require only 40%-50% of the observed SNPs, a reduction similar to estimates in a sample of African Americans. An Affymetrix genotyping array containing 250,000 SNPs has been designed based on these results; we demonstrate that it should have more than adequate coverage for genome-wide association mapping. The extent of LD is highly variable, and we find clear evidence of recombination hotspots, which seem to occur preferentially in intergenic regions. LD also reflects the action of selection, and it is more extensive between nonsynonymous polymorphisms than between synonymous polymorphisms.     

The genetically isolated populations of Finland and sardinia may not be a panacea for linkage disequilibrium mapping of common disease genes

The choice of which population to study in the mapping of common disease genes may be critical. Isolated founder populations, such as that found in Finland, have already proved extremely useful for mapping the genes for specific rare monogenic disorders and are being used in attempts to map the genes underlying common, complex diseases. But simulation results suggest that, under the common disease-common variant hypothesis, most isolated populations will prove no more useful for linkage disequilibrium (LD) mapping of common disease genes than large outbred populations. There is very little empirical data to either support or refute this conclusion at present. Therefore, we evaluated LD between 21 common microsatellite polymorphisms on chromosome 18q21 in 2 genetic isolates (Finland and Sardinia) and compared the results with those observed in two mixed populations (United Kingdom and United States of America). Mean levels of LD were similar across all four populations. Our results provide empirical support for the expectation that genetic isolates like Finland and Sardinia will not prove significantly more valuable than general populations for LD mapping of common variants underlying complex disease.     

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'.     

The extent of linkage disequilibrium in Arabidopsis thaliana.

Linkage disequilibrium (LD), the nonrandom occurrence of alleles in haplotypes, has long been of interest to population geneticists. Recently, the rapidly increasing availability of genomic polymorphism data has fueled interest in LD as a tool for fine-scale mapping, in particular for human disease loci. The chromosomal extent of LD is crucial in this context, because it determines how dense a map must be for associations to be detected and, conversely, limits how finely loci may be mapped. Arabidopsis thaliana is expected to harbor unusually extensive LD because of its high degree of selfing. Several polymorphism studies have found very strong LD within individual loci, but also evidence of some recombination. Here we investigate the pattern of LD on a genomic scale and show that in global samples, LD decays within approximately 1 cM, or 250 kb. We also show that LD in local populations may be much stronger than that of global populations, presumably as a result of founder events. The combination of a relatively high level of polymorphism and extensive haplotype structure bodes well for developing a genome-wide LD map in A. thaliana.     

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.     

Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits

We present an approximate conditional and joint association analysis that can use summary-level statistics from a meta-analysis of genome-wide association studies (GWAS) and estimated linkage disequilibrium (LD) from a reference sample with individual-level genotype data. Using this method, we analyzed meta-analysis summary data from the GIANT Consortium for height and body mass index (BMI), with the LD structure estimated from genotype data in two independent cohorts. We identified 36 loci with multiple associated variants for height (38 leading and 49 additional SNPs, 87 in total) via a genome-wide SNP selection procedure. The 49 new SNPs explain approximately 1.3% of variance, nearly doubling the heritability explained at the 36 loci. We did not find any locus showing multiple associated SNPs for BMI. The method we present is computationally fast and is also applicable to case-control data, which we demonstrate in an example from meta-analysis of type 2 diabetes by the DIAGRAM Consortium.     

SNPs in KCNQ1 are associated with susceptibility to type 2 diabetes in East Asian and European populations

We conducted a genome-wide association study using 207,097 SNP markers in Japanese individuals with type 2 diabetes and unrelated controls, and identified KCNQ1 (potassium voltage-gated channel, KQT-like subfamily, member 1) to be a strong candidate for conferring susceptibility to type 2 diabetes. We detected consistent association of a SNP in KCNQ1 (rs2283228) with the disease in several independent case-control studies (additive model P = 3.1 x 10(-12); OR = 1.26, 95% CI = 1.18-1.34). Several other SNPs in the same linkage disequilibrium (LD) block were strongly associated with type 2 diabetes (additive model: rs2237895, P = 7.3 x 10(-9); OR = 1.32, 95% CI = 1.20-1.45, rs2237897, P = 6.8 x 10(-13); OR = 1.41, 95% CI = 1.29-1.55). The association of these SNPs with type 2 diabetes was replicated in samples from Singaporean (additive model: rs2237895, P = 8.5 x 10(-3); OR = 1.14, rs2237897, P = 2.4 x 10(-4); OR = 1.22) and Danish populations (additive model: rs2237895, P = 3.7 x 10(-11); OR = 1.24, rs2237897, P = 1.2 x 10(-4); OR = 1.36).     

Genome-wide association study of prostate cancer in men of African ancestry identifies a susceptibility locus at 17q21

In search of common risk alleles for prostate cancer that could contribute to high rates of the disease in men of African ancestry, we conducted a genome-wide association study, with 1,047,986 SNP markers examined in 3,425 African-Americans with prostate cancer (cases) and 3,290 African-American male controls. We followed up the most significant 17 new associations from stage 1 in 1,844 cases and 3,269 controls of African ancestry. We identified a new risk variant on chromosome 17q21 (rs7210100, odds ratio per allele = 1.51, P = 3.4 × 10(-13)). The frequency of the risk allele is ～5% in men of African descent, whereas it is rare in other populations (<1%). Further studies are needed to investigate the biological contribution of this allele to prostate cancer risk. These findings emphasize the importance of conducting genome-wide association studies in diverse populations.     

Prospects for whole-genome linkage disequilibrium mapping of common disease genes.

Recently, attention has focused on the use of whole-genome linkage disequilibrium (LD) studies to map common disease genes. Such studies would employ a dense map of single nucleotide polymorphisms (SNPs) to detect association between a marker and disease. Construction of SNP maps is currently underway. An essential issue yet to be settled is the required marker density of such maps. Here, I use population simulations to estimate the extent of LD surrounding common gene variants in the general human population as well as in isolated populations. Two main conclusions emerge from these investigations. First, a useful level of LD is unlikely to extend beyond an average distance of roughly 3 kb in the general population, which implies that approximately 500,000 SNPs will be required for whole-genome studies. Second, the extent of LD is similar in isolated populations unless the founding bottleneck is very narrow or the frequency of the variant is low (<5%).     

Genome-wide association study identifies susceptibility loci for dengue shock syndrome at MICB and PLCE1

Hypovolemic shock (dengue shock syndrome (DSS)) is the most common life-threatening complication of dengue. We conducted a genome-wide association study of 2,008 pediatric cases treated for DSS and 2,018 controls from Vietnam. Replication of the most significantly associated markers was carried out in an independent Vietnamese sample of 1,737 cases and 2,934 controls. SNPs at two loci showed genome-wide significant association with DSS. We identified a susceptibility locus at MICB (major histocompatibility complex (MHC) class I polypeptide-related sequence B), which was within the broad MHC region on chromosome 6 but outside the class I and class II HLA loci (rs3132468, P(meta) = 4.41 × 10(-11), per-allele odds ratio (OR) = 1.34 (95% confidence interval: 1.23-1.46)). We identified associated variants within PLCE1 (phospholipase C, epsilon 1) on chromosome 10 (rs3765524, P(meta) = 3.08 × 10(-10), per-allele OR = 0.80 (95% confidence interval: 0.75-0.86)). We identify two loci associated with susceptibility to DSS in people with dengue, suggesting possible mechanisms for this severe complication of dengue.     

A new multipoint method for genome-wide association studies by imputation of genotypes

Genome-wide association studies are set to become the method of choice for uncovering the genetic basis of human diseases. A central challenge in this area is the development of powerful multipoint methods that can detect causal variants that have not been directly genotyped. We propose a coherent analysis framework that treats the problem as one involving missing or uncertain genotypes. Central to our approach is a model-based imputation method for inferring genotypes at observed or unobserved SNPs, leading to improved power over existing methods for multipoint association mapping. Using real genome-wide association study data, we show that our approach (i) is accurate and well calibrated, (ii) provides detailed views of associated regions that facilitate follow-up studies and (iii) can be used to validate and correct data at genotyped markers. A notable future use of our method will be to boost power by combining data from genome-wide scans that use different SNP sets.     

Adaptive evolution of the tumour suppressor BRCA1 in humans and chimpanzees. Australian Breast Cancer Family Study

Mutations in BRCA1 (ref. 1) confer an increased risk of female breast cancer. In a genome-wide scan of linkage disequilibrium (LD), a high level of LD was detected among microsatellite markers flanking BRCA1 (ref. 3), raising the prospect that positive natural selection may have acted on this gene. We have used the predictions of evolutionary genetic theory to investigate this further. Using phylogeny-based maximum likelihood analysis of the BRCA1 sequences from primates and other mammals, we found that the ratios of replacement to silent nucleotide substitutions on the human and chimpanzee lineages were not different from one another (P=0.8), were different from those of other primate lineages (P=0.004) and were greater than 1 (P=0.04). This is consistent with the historic occurrence of positive darwinian selection pressure on the BRCA1 protein in the human and chimpanzee lineages. Analysis of genetic variation in a sample of female Australians of Northern European origin showed evidence for Hardy-Weinberg (HW) disequilibrium at polymorphic sites in BRCA1, consistent with the possibility that natural selection is affecting genotype frequencies in modern Europeans. The clustering of between-species variation in the region of the gene encoding the RAD51-interaction domain of BRCA1 suggests the maintenance of genomic integrity as a possible target of selection.     

Joint analysis is more efficient than replication-based analysis for two-stage genome-wide association studies

Genome-wide association is a promising approach to identify common genetic variants that predispose to human disease. Because of the high cost of genotyping hundreds of thousands of markers on thousands of subjects, genome-wide association studies often follow a staged design in which a proportion (pi(samples)) of the available samples are genotyped on a large number of markers in stage 1, and a proportion (pi(samples)) of these markers are later followed up by genotyping them on the remaining samples in stage 2. The standard strategy for analyzing such two-stage data is to view stage 2 as a replication study and focus on findings that reach statistical significance when stage 2 data are considered alone. We demonstrate that the alternative strategy of jointly analyzing the data from both stages almost always results in increased power to detect genetic association, despite the need to use more stringent significance levels, even when effect sizes differ between the two stages. We recommend joint analysis for all two-stage genome-wide association studies, especially when a relatively large proportion of the samples are genotyped in stage 1 (pi(samples) >or= 0.30), and a relatively large proportion of markers are selected for follow-up in stage 2 (pi(markers) >or= 0.01).     

Bayesian inference of epistatic interactions in case-control studies

Epistatic interactions among multiple genetic variants in the human genome may be important in determining individual susceptibility to common diseases. Although some existing computational methods for identifying genetic interactions have been effective for small-scale studies, we here propose a method, denoted 'bayesian epistasis association mapping' (BEAM), for genome-wide case-control studies. BEAM treats the disease-associated markers and their interactions via a bayesian partitioning model and computes, via Markov chain Monte Carlo, the posterior probability that each marker set is associated with the disease. Testing this on an age-related macular degeneration genome-wide association data set, we demonstrate that the method is significantly more powerful than existing approaches and that genome-wide case-control epistasis mapping with many thousands of markers is both computationally and statistically feasible.     

Genome-wide association study identifies five loci associated with susceptibility to pancreatic cancer in Chinese populations

Pancreatic cancer has the lowest survival rate among human cancers, and there are no effective markers for its screening and early diagnosis. To identify genetic susceptibility markers for this cancer, we carried out a genome-wide association study on 981 individuals with pancreatic cancer (cases) and 1,991 cancer-free controls of Chinese descent using 666,141 autosomal SNPs. Promising associations were replicated in an additional 2,603 pancreatic cancer cases and 2,877 controls recruited from 25 hospitals in 16 provinces or cities in China. We identified five new susceptibility loci at chromosomes 21q21.3, 5p13.1, 21q22.3, 22q13.32 and 10q26.11 (P = 2.24 × 10(-13) to P = 4.18 × 10(-10)) in addition to 13q22.1 previously reported in populations of European ancestry. These results advance our understanding of the development of pancreatic cancer and highlight potential targets for the prevention or treatment of this cancer.     

Genome-wide strategies for detecting multiple loci that influence complex diseases

After nearly 10 years of intense academic and commercial research effort, large genome-wide association studies for common complex diseases are now imminent. Although these conditions involve a complex relationship between genotype and phenotype, including interactions between unlinked loci, the prevailing strategies for analysis of such studies focus on the locus-by-locus paradigm. Here we consider analytical methods that explicitly look for statistical interactions between loci. We show first that they are computationally feasible, even for studies of hundreds of thousands of loci, and second that even with a conservative correction for multiple testing, they can be more powerful than traditional analyses under a range of models for interlocus interactions. We also show that plausible variations across populations in allele frequencies among interacting loci can markedly affect the power to detect their marginal effects, which may account in part for the well-known difficulties in replicating association results. These results suggest that searching for interactions among genetic loci can be fruitfully incorporated into analysis strategies for genome-wide association studies.     

Genome-wide efficient mixed-model analysis for association studies

Linear mixed models have attracted considerable attention recently as a powerful and effective tool for accounting for population stratification and relatedness in genetic association tests. However, existing methods for exact computation of standard test statistics are computationally impractical for even moderate-sized genome-wide association studies. To address this issue, several approximate methods have been proposed. Here, we present an efficient exact method, which we refer to as genome-wide efficient mixed-model association (GEMMA), that makes approximations unnecessary in many contexts. This method is approximately n times faster than the widely used exact method known as efficient mixed-model association (EMMA), where n is the sample size, making exact genome-wide association analysis computationally practical for large numbers of individuals.     

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.     

Differential confounding of rare and common variants in spatially structured populations

Well-powered genome-wide association studies, now made possible through advances in technology and large-scale collaborative projects, promise to characterize the contribution of rare variants to complex traits and disease. However, while population structure is a known confounder of association studies, it remains unknown whether methods developed to control stratification are equally effective for rare variants. Here, we demonstrate that rare variants can show a stratification that is systematically different from, and typically stronger than, common variants, and this is not necessarily corrected by existing methods. We show that the same process leads to inflation for load-based tests and can obscure signals at truly associated variants. Furthermore, we show that populations can display spatial structure in rare variants, even when Wright's fixation index F(ST) is low, but that allele frequency-dependent metrics of allele sharing can reveal localized stratification. These results underscore the importance of collecting and integrating spatial information in the genetic analysis of complex traits.