Genome-wide association study of flowering time and grain yield traits in a worldwide collection of rice germplasm

A high-density haplotype map recently enabled a genome-wide association study (GWAS) in a population of indica subspecies of Chinese rice landraces. Here we extend this methodology to a larger and more diverse sample of 950 worldwide rice varieties, including the Oryza sativa indica and Oryza sativa japonica subspecies, to perform an additional GWAS. We identified a total of 32 new loci associated with flowering time and with ten grain-related traits, indicating that the larger sample increased the power to detect trait-associated variants using GWAS. To characterize various alleles and complex genetic variation, we developed an analytical framework for haplotype-based de novo assembly of the low-coverage sequencing data in rice. We identified candidate genes for 18 associated loci through detailed annotation. This study shows that the integrated approach of sequence-based GWAS and functional genome annotation has the potential to match complex traits to their causal polymorphisms in rice.     

Genome-wide association studies of 14 agronomic traits in rice landraces

Uncovering the genetic basis of agronomic traits in crop landraces that have adapted to various agro-climatic conditions is important to world food security. Here we have identified ～ 3.6 million SNPs by sequencing 517 rice landraces and constructed a high-density haplotype map of the rice genome using a novel data-imputation method. We performed genome-wide association studies (GWAS) for 14 agronomic traits in the population of Oryza sativa indica subspecies. The loci identified through GWAS explained ～ 36% of the phenotypic variance, on average. The peak signals at six loci were tied closely to previously identified genes. This study provides a fundamental resource for rice genetics research and breeding, and demonstrates that an approach integrating second-generation genome sequencing and GWAS can be used as a powerful complementary strategy to classical biparental cross-mapping for dissecting complex traits in rice.     

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.     

Genome-wide association study identifies 12 new susceptibility loci for primary biliary cirrhosis

In addition to the HLA locus, six genetic risk factors for primary biliary cirrhosis (PBC) have been identified in recent genome-wide association studies (GWAS). To identify additional loci, we carried out a GWAS using 1,840 cases from the UK PBC Consortium and 5,163 UK population controls as part of the Wellcome Trust Case Control Consortium 3 (WTCCC3). We followed up 28 loci in an additional UK cohort of 620 PBC cases and 2,514 population controls. We identified 12 new susceptibility loci (at a genome-wide significance level of P < 5 × 10??) and replicated all previously associated loci. We identified three further new loci in a meta-analysis of data from our study and previously published GWAS results. New candidate genes include STAT4, DENND1B, CD80, IL7R, CXCR5, TNFRSF1A, CLEC16A and NFKB1. This study has considerably expanded our knowledge of the genetic architecture of PBC.     

Efficient mapping of mendelian traits in dogs through genome-wide association

With several hundred genetic diseases and an advantageous genome structure, dogs are ideal for mapping genes that cause disease. Here we report the development of a genotyping array with approximately 27,000 SNPs and show that genome-wide association mapping of mendelian traits in dog breeds can be achieved with only approximately 20 dogs. Specifically, we map two traits with mendelian inheritance: the major white spotting (S) locus and the hair ridge in Rhodesian ridgebacks. For both traits, we map the loci to discrete regions of <1 Mb. Fine-mapping of the S locus in two breeds refines the localization to a region of approximately 100 kb contained within the pigmentation-related gene MITF. Complete sequencing of the white and solid haplotypes identifies candidate regulatory mutations in the melanocyte-specific promoter of MITF. Our results show that genome-wide association mapping within dog breeds, followed by fine-mapping across multiple breeds, will be highly efficient and generally applicable to trait mapping, providing insights into canine and human health.     

A mixed-model approach for genome-wide association studies of correlated traits in structured populations

Genome-wide association studies (GWAS) are a standard approach for studying the genetics of natural variation. A major concern in GWAS is the need to account for the complicated dependence structure of the data, both between loci as well as between individuals. Mixed models have emerged as a general and flexible approach for correcting for population structure in GWAS. Here, we extend this linear mixed-model approach to carry out GWAS of correlated phenotypes, deriving a fully parameterized multi-trait mixed model (MTMM) that considers both the within-trait and between-trait variance components simultaneously for multiple traits. We apply this to data from a human cohort for correlated blood lipid traits from the Northern Finland Birth Cohort 1966 and show greatly increased power to detect pleiotropic loci that affect more than one blood lipid trait. We also apply this approach to an Arabidopsis thaliana data set for flowering measurements in two different locations, identifying loci whose effect depends on the environment.     

Genome-wide genetic association of complex traits in heterogeneous stock mice

Difficulties in fine-mapping quantitative trait loci (QTLs) are a major impediment to progress in the molecular dissection of complex traits in mice. Here we show that genome-wide high-resolution mapping of multiple phenotypes can be achieved using a stock of genetically heterogeneous mice. We developed a conservative and robust bootstrap analysis to map 843 QTLs with an average 95% confidence interval of 2.8 Mb. The QTLs contribute to variation in 97 traits, including models of human disease (asthma, type 2 diabetes mellitus, obesity and anxiety) as well as immunological, biochemical and hematological phenotypes. The genetic architecture of almost all phenotypes was complex, with many loci each contributing a small proportion to the total variance. Our data set, freely available at http://gscan.well.ox.ac.uk, provides an entry point to the functional characterization of genes involved in many complex traits.     

Genome-wide association study identifies eight new risk loci for polycystic ovary syndrome

Following a previous genome-wide association study (GWAS 1) including 744 cases and 895 controls, we analyzed genome-wide association data from a new cohort of Han Chinese (GWAS 2) with 1,510 polycystic ovary syndrome (PCOS) cases and 2,016 controls. We followed up significantly associated signals identified in the combined results of GWAS 1 and 2 in a total of 8,226 cases and 7,578 controls. In addition to confirming the three loci we previously reported, we identify eight new PCOS association signals at P < 5 × 10(-8): 9q22.32, 11q22.1, 12q13.2, 12q14.3, 16q12.1, 19p13.3, 20q13.2 and a second independent signal at 2p16.3 (the FSHR gene). These PCOS association signals show evidence of enrichment for candidate genes related to insulin signaling, sexual hormone function and type 2 diabetes (T2D). Other candidate genes were related to calcium signaling and endocytosis. Our findings provide new insight and direction for discovering the biological mechanisms of PCOS.     

From HLA association to function

A new study refines the association signals for rheumatoid arthritis susceptibility in the major histocompatibility complex (MHC) region to five amino-acid positions encoded in three HLA genes, all within peptide-binding grooves. By adapting statistical methods from genome-wide association studies (GWAS) and using imputation from a large reference panel, they demonstrate the potential for this approach to identify functional variants in associated regions.     

Bayesian inference analyses of the polygenic architecture of rheumatoid arthritis

The genetic architectures of common, complex diseases are largely uncharacterized. We modeled the genetic architecture underlying genome-wide association study (GWAS) data for rheumatoid arthritis and developed a new method using polygenic risk-score analyses to infer the total liability-scale variance explained by associated GWAS SNPs. Using this method, we estimated that, together, thousands of SNPs from rheumatoid arthritis GWAS explain an additional 20% of disease risk (excluding known associated loci). We further tested this method on datasets for three additional diseases and obtained comparable estimates for celiac disease (43% excluding the major histocompatibility complex), myocardial infarction and coronary artery disease (48%) and type 2 diabetes (49%). Our results are consistent with simulated genetic models in which hundreds of associated loci harbor common causal variants and a smaller number of loci harbor multiple rare causal variants. These analyses suggest that GWAS will continue to be highly productive for the discovery of additional susceptibility loci for common diseases.     

Estimating the proportion of variation in susceptibility to schizophrenia captured by common SNPs

Schizophrenia is a complex disorder caused by both genetic and environmental factors. Using 9,087 affected individuals, 12,171 controls and 915,354 imputed SNPs from the Schizophrenia Psychiatric Genome-Wide Association Study (GWAS) Consortium (PGC-SCZ), we estimate that 23% (s.e. = 1%) of variation in liability to schizophrenia is captured by SNPs. We show that a substantial proportion of this variation must be the result of common causal variants, that the variance explained by each chromosome is linearly related to its length (r = 0.89, P = 2.6 × 10(-8)), that the genetic basis of schizophrenia is the same in males and females, and that a disproportionate proportion of variation is attributable to a set of 2,725 genes expressed in the central nervous system (CNS; P = 7.6 × 10(-8)). These results are consistent with a polygenic genetic architecture and imply more individual SNP associations will be detected for this disease as sample size increases.     

Genome-wide association analysis identifies 20 loci that influence adult height

Adult height is a model polygenic trait, but there has been limited success in identifying the genes underlying its normal variation. To identify genetic variants influencing adult human height, we used genome-wide association data from 13,665 individuals and genotyped 39 variants in an additional 16,482 samples. We identified 20 variants associated with adult height (P < 5 x 10(-7), with 10 reaching P < 1 x 10(-10)). Combined, the 20 SNPs explain approximately 3% of height variation, with a approximately 5 cm difference between the 6.2% of people with 17 or fewer 'tall' alleles compared to the 5.5% with 27 or more 'tall' alleles. The loci we identified implicate genes in Hedgehog signaling (IHH, HHIP, PTCH1), extracellular matrix (EFEMP1, ADAMTSL3, ACAN) and cancer (CDK6, HMGA2, DLEU7) pathways, and provide new insights into human growth and developmental processes. Finally, our results provide insights into the genetic architecture of a classic quantitative trait.     

Genome-wide association identifies a susceptibility locus for coronary artery disease in the Chinese Han population

Coronary artery disease (CAD) causes more than 700,000 deaths each year in China. Previous genome-wide association studies (GWAS) in populations of European ancestry identified several genetic loci for CAD, but no such study has yet been reported in the Chinese population. Here we report a three-stage GWAS in the Chinese Han population. We identified a new association between rs6903956 in a putative gene denoted as C6orf105 on chromosome 6p24.1 and CAD (P = 5.00 × 10?3, stage 2 validation; P = 3.00 × 10?3, P = 1.19 × 10?? and P = 4.00 × 10?3 in three independent stage 3 replication populations; P = 4.87 × 10?12, odds ratio = 1.51 in the combined population). The minor risk allele A of rs6903956 is associated with decreased C6orf105 mRNA expression. We report the first GWAS for CAD in the Chinese Han population and identify a SNP, rs6903956, in C6orf105 associated with susceptibility to CAD in this population.     

A unified mixed-model method for association mapping that accounts for multiple levels of relatedness

As population structure can result in spurious associations, it has constrained the use of association studies in human and plant genetics. Association mapping, however, holds great promise if true signals of functional association can be separated from the vast number of false signals generated by population structure. We have developed a unified mixed-model approach to account for multiple levels of relatedness simultaneously as detected by random genetic markers. We applied this new approach to two samples: a family-based sample of 14 human families, for quantitative gene expression dissection, and a sample of 277 diverse maize inbred lines with complex familial relationships and population structure, for quantitative trait dissection. Our method demonstrates improved control of both type I and type II error rates over other methods. As this new method crosses the boundary between family-based and structured association samples, it provides a powerful complement to currently available methods for association mapping.     

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.     

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.     

The sex-specific genetic architecture of quantitative traits in humans

Mapping genetically complex traits remains one of the greatest challenges in human genetics today. In particular, gene-environment and gene-gene interactions, genetic heterogeneity and incomplete penetrance make thorough genetic dissection of complex traits difficult, if not impossible. Sex could be considered an environmental factor that can modify both penetrance and expressivity of a wide variety of traits. Sex is easily determined and has measurable effects on recognizable morphology; neurobiological circuits; susceptibility to autoimmune disease, diabetes, asthma, cardiovascular and psychiatric disease; and quantitative traits like blood pressure, obesity and lipid levels, among others. In this study, we evaluated sex-specific heritability and genome-wide linkages for 17 quantitative traits in the Hutterites. The results of this study could have important implications for mapping complex trait genes.