An efficient multi-locus mixed-model approach for genome-wide association studies in structured populations

Population structure causes genome-wide linkage disequilibrium between unlinked loci, leading to statistical confounding in genome-wide association studies. Mixed models have been shown to handle the confounding effects of a diffuse background of large numbers of loci of small effect well, but they do not always account for loci of larger effect. Here we propose a multi-locus mixed model as a general method for mapping complex traits in structured populations. Simulations suggest that our method outperforms existing methods in terms of power as well as false discovery rate. We apply our method to human and Arabidopsis thaliana data, identifying new associations and evidence for allelic heterogeneity. We also show how a priori knowledge from an A. thaliana linkage mapping study can be integrated into our method using a Bayesian approach. Our implementation is computationally efficient, making the analysis of large data sets (n > 10,000) practicable.     

A genome-wide approach accounting for body mass index identifies genetic variants influencing fasting glycemic traits and insulin resistance

Recent genome-wide association studies have described many loci implicated in type 2 diabetes (T2D) pathophysiology and β-cell dysfunction but have contributed little to the understanding of the genetic basis of insulin resistance. We hypothesized that genes implicated in insulin resistance pathways might be uncovered by accounting for differences in body mass index (BMI) and potential interactions between BMI and genetic variants. We applied a joint meta-analysis approach to test associations with fasting insulin and glucose on a genome-wide scale. We present six previously unknown loci associated with fasting insulin at P < 5 × 10(-8) in combined discovery and follow-up analyses of 52 studies comprising up to 96,496 non-diabetic individuals. Risk variants were associated with higher triglyceride and lower high-density lipoprotein (HDL) cholesterol levels, suggesting a role for these loci in insulin resistance pathways. The discovery of these loci will aid further characterization of the role of insulin resistance in T2D pathophysiology.     

Genomic screening and replication using the same data set in family-based association testing

The Human Genome Project and its spin-offs are making it increasingly feasible to determine the genetic basis of complex traits using genome-wide association studies. The statistical challenge of analyzing such studies stems from the severe multiple-comparison problem resulting from the analysis of thousands of SNPs. Our methodology for genome-wide family-based association studies, using single SNPs or haplotypes, can identify associations that achieve genome-wide significance. In relation to developing guidelines for our screening tools, we determined lower bounds for the estimated power to detect the gene underlying the disease-susceptibility locus, which hold regardless of the linkage disequilibrium structure present in the data. We also assessed the power of our approach in the presence of multiple disease-susceptibility loci. Our screening tools accommodate genomic control and use the concept of haplotype-tagging SNPs. Our methods use the entire sample and do not require separate screening and validation samples to establish genome-wide significance, as population-based designs do.     

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.     

Cell type-specific eQTLs in the human immune system

A new study reports the mapping of gene expression in primary immune cell subsets, showing the presence of cell type-specific cis and trans expression quantitative trait loci (eQTLs). The identification of cell type-specific trans-regulated networks can inform functional studies of susceptibility loci identified from genome-wide association studies for human complex diseases.     

Genome-wide association study identifies susceptibility loci for IgA nephropathy

We carried out a genome-wide association study of IgA nephropathy, a major cause of kidney failure worldwide. We studied 1,194 cases and 902 controls of Chinese Han ancestry, with targeted follow up in Chinese and European cohorts comprising 1,950 cases and 1,920 controls. We identified three independent loci in the major histocompatibility complex, as well as a common deletion of CFHR1 and CFHR3 at chromosome 1q32 and a locus at chromosome 22q12 that each surpassed genome-wide significance (P values for association between 1.59 × 10?2? and 4.84 × 10?? and minor allele odds ratios of 0.63-0.80). These five loci explain 4-7% of the disease variance and up to a tenfold variation in interindividual risk. Many of the alleles that protect against IgA nephropathy impart increased risk for other autoimmune or infectious diseases, and IgA nephropathy risk allele frequencies closely parallel the variation in disease prevalence among Asian, European and African populations, suggesting complex selective pressures.     

The effects of human population structure on large genetic association studies

Large-scale association studies hold substantial promise for unraveling the genetic basis of common human diseases. A well-known problem with such studies is the presence of undetected population structure, which can lead to both false positive results and failures to detect genuine associations. Here we examine approximately 15,000 genome-wide single-nucleotide polymorphisms typed in three population groups to assess the consequences of population structure on the coming generation of association studies. The consequences of population structure on association outcomes increase markedly with sample size. For the size of study needed to detect typical genetic effects in common diseases, even the modest levels of population structure within population groups cannot safely be ignored. We also examine one method for correcting for population structure (Genomic Control). Although it often performs well, it may not correct for structure if too few loci are used and may overcorrect in other settings, leading to substantial loss of power. The results of our analysis can guide the design of large-scale association studies.     

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.     

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.     

Genome-wide meta-analysis increases to 71 the number of confirmed Crohn's disease susceptibility loci

We undertook a meta-analysis of six Crohn's disease genome-wide association studies (GWAS) comprising 6,333 affected individuals (cases) and 15,056 controls and followed up the top association signals in 15,694 cases, 14,026 controls and 414 parent-offspring trios. We identified 30 new susceptibility loci meeting genome-wide significance (P < 5 × 10??). A series of in silico analyses highlighted particular genes within these loci and, together with manual curation, implicated functionally interesting candidate genes including SMAD3, ERAP2, IL10, IL2RA, TYK2, FUT2, DNMT3A, DENND1B, BACH2 and TAGAP. Combined with previously confirmed loci, these results identify 71 distinct loci with genome-wide significant evidence for association with Crohn's disease.     

Genome-wide association study identifies three new susceptibility loci for adult asthma in the Japanese population

Bronchial asthma is a common inflammatory disease caused by the interaction of genetic and environmental factors. Through a genome-wide association study and a replication study consisting of a total of 7,171 individuals with adult asthma (cases) and 27,912 controls in the Japanese population, we identified five loci associated with susceptibility to adult asthma. In addition to the major histocompatibility complex and TSLP-WDR36 loci previously reported, we identified three additional loci: a USP38-GAB1 locus on chromosome 4q31 (combined P = 1.87 × 10(-12)), a locus on chromosome 10p14 (P = 1.79 × 10(-15)) and a gene-rich region on chromosome 12q13 (P = 2.33 × 10(-13)). We observed the most significant association with adult asthma at rs404860 in the major histocompatiblity complex region (P = 4.07 × 10(-23)), which is close to rs2070600, a SNP previously reported for association with FEV(1)/FVC in genome-wide association studies for lung function. Our findings offer a better understanding of the genetic contribution to asthma susceptibility.     

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.     

Common deletions and SNPs are in linkage disequilibrium in the human genome

Humans show great variation in phenotypic traits such as height, eye color and susceptibility to disease. Genomic DNA sequence differences among individuals are responsible for the inherited components of these complex traits. Reports suggest that intermediate and large-scale DNA copy number and structural variations are prevalent enough to be an important source of genetic variation between individuals. Because association studies to identify genomic loci associated with particular phenotypic traits have focused primarily on genotyping SNPs, it is important to determine whether common structural polymorphisms are in linkage disequilibrium with common SNPs, and thus can be assessed indirectly in SNP-based studies. Here we examine 100 deletion polymorphisms ranging from 70 bp to 7 kb. We show that common deletions and SNPs ascertained with similar criteria have essentially the same distribution of linkage disequilibrium with surrounding SNPs, indicating that these polymorphisms may share evolutionary history and that most deletion polymorphisms are effectively assayed by proxy in SNP-based association studies.     

Meta-analysis of genome-wide association studies of asthma in ethnically diverse North American populations

Asthma is a common disease with a complex risk architecture including both genetic and environmental factors. We performed a meta-analysis of North American genome-wide association studies of asthma in 5,416 individuals with asthma (cases) including individuals of European American, African American or African Caribbean, and Latino ancestry, with replication in an additional 12,649 individuals from the same ethnic groups. We identified five susceptibility loci. Four were at previously reported loci on 17q21, near IL1RL1, TSLP and IL33, but we report for the first time, to our knowledge, that these loci are associated with asthma risk in three ethnic groups. In addition, we identified a new asthma susceptibility locus at PYHIN1, with the association being specific to individuals of African descent (P = 3.9 × 10(-9)). These results suggest that some asthma susceptibility loci are robust to differences in ancestry when sufficiently large samples sizes are investigated, and that ancestry-specific associations also contribute to the complex genetic architecture of asthma.     

Large-scale association analyses identify new loci influencing glycemic traits and provide insight into the underlying biological pathways

Through genome-wide association meta-analyses of up to 133,010 individuals of European ancestry without diabetes, including individuals newly genotyped using the Metabochip, we have increased the number of confirmed loci influencing glycemic traits to 53, of which 33 also increase type 2 diabetes risk (q < 0.05). Loci influencing fasting insulin concentration showed association with lipid levels and fat distribution, suggesting impact on insulin resistance. Gene-based analyses identified further biologically plausible loci, suggesting that additional loci beyond those reaching genome-wide significance are likely to represent real associations. This conclusion is supported by an excess of directionally consistent and nominally significant signals between discovery and follow-up studies. Functional analysis of these newly discovered loci will further improve our understanding of glycemic control.     

Meta-analysis of genome-wide association studies identifies eight new loci for type 2 diabetes in east Asians

We conducted a three-stage genetic study to identify susceptibility loci for type 2 diabetes (T2D) in east Asian populations. We followed our stage 1 meta-analysis of eight T2D genome-wide association studies (6,952 cases with T2D and 11,865 controls) with a stage 2 in silico replication analysis (5,843 cases and 4,574 controls) and a stage 3 de novo replication analysis (12,284 cases and 13,172 controls). The combined analysis identified eight new T2D loci reaching genome-wide significance, which mapped in or near GLIS3, PEPD, FITM2-R3HDML-HNF4A, KCNK16, MAEA, GCC1-PAX4, PSMD6 and ZFAND3. GLIS3, which is involved in pancreatic beta cell development and insulin gene expression, is known for its association with fasting glucose levels. The evidence of an association with T2D for PEPD and HNF4A has been shown in previous studies. KCNK16 may regulate glucose-dependent insulin secretion in the pancreas. These findings, derived from an east Asian population, provide new perspectives on the etiology of T2D.     

Meta-analysis identifies 29 additional ulcerative colitis risk loci, increasing the number of confirmed associations to 47

Genome-wide association studies and candidate gene studies in ulcerative colitis have identified 18 susceptibility loci. We conducted a meta-analysis of six ulcerative colitis genome-wide association study datasets, comprising 6,687 cases and 19,718 controls, and followed up the top association signals in 9,628 cases and 12,917 controls. We identified 29 additional risk loci (P < 5 × 10(-8)), increasing the number of ulcerative colitis-associated loci to 47. After annotating associated regions using GRAIL, expression quantitative trait loci data and correlations with non-synonymous SNPs, we identified many candidate genes that provide potentially important insights into disease pathogenesis, including IL1R2, IL8RA-IL8RB, IL7R, IL12B, DAP, PRDM1, JAK2, IRF5, GNA12 and LSP1. The total number of confirmed inflammatory bowel disease risk loci is now 99, including a minimum of 28 shared association signals between Crohn's disease and ulcerative colitis.     

Meta-analysis identifies common variants associated with body mass index in east Asians

Multiple genetic loci associated with obesity or body mass index (BMI) have been identified through genome-wide association studies conducted predominantly in populations of European ancestry. We performed a meta-analysis of associations between BMI and approximately 2.4 million SNPs in 27,715 east Asians, which was followed by in silico and de novo replication studies in 37,691 and 17,642 additional east Asians, respectively. We identified ten BMI-associated loci at genome-wide significance (P < 5.0 × 10(-8)), including seven previously identified loci (FTO, SEC16B, MC4R, GIPR-QPCTL, ADCY3-DNAJC27, BDNF and MAP2K5) and three novel loci in or near the CDKAL1, PCSK1 and GP2 genes. Three additional loci nearly reached the genome-wide significance threshold, including two previously identified loci in the GNPDA2 and TFAP2B genes and a newly identified signal near PAX6, all of which were associated with BMI with P < 5.0 × 10(-7). Findings from this study may shed light on new pathways involved in obesity and demonstrate the value of conducting genetic studies in non-European populations.     

Genome-wide association study in Han Chinese identifies four new susceptibility loci for coronary artery disease

We performed a meta-analysis of 2 genome-wide association studies of coronary artery disease comprising 1,515 cases and 5,019 controls followed by replication studies in 15,460 cases and 11,472 controls, all of Chinese Han ancestry. We identify four new loci for coronary artery disease that reached the threshold of genome-wide significance (P < 5 × 10(-8)). These loci mapped in or near TTC32-WDR35, GUCY1A3, C6orf10-BTNL2 and ATP2B1. We also replicated four loci previously identified in European populations (in or near PHACTR1, TCF21, CDKN2A-CDKN2B and C12orf51). These findings provide new insights into pathways contributing to the susceptibility for coronary artery disease in the Chinese Han population.     

Genome-wide association study and mouse model identify interaction between RET and EDNRB pathways in Hirschsprung disease

Genetic studies of Hirschsprung disease, a common congenital malformation, have identified eight genes with mutations that can be associated with this condition. Mutations at individual loci are, however, neither necessary nor sufficient to cause clinical disease. We conducted a genome-wide association study in 43 Mennonite family trios using 2,083 microsatellites and single-nucleotide polymorphisms and a new multipoint linkage disequilibrium method that searches for association arising from common ancestry. We identified susceptibility loci at 10q11, 13q22 and 16q23; the gene at 13q22 is EDNRB, encoding a G protein-coupled receptor (GPCR) and the gene at 10q11 is RET, encoding a receptor tyrosine kinase (RTK). Statistically significant joint transmission of RET and EDNRB alleles in affected individuals and non-complementation of aganglionosis in mouse intercrosses between Ret null and the Ednrb hypomorphic piebald allele are suggestive of epistasis between EDNRB and RET. Thus, genetic interaction between mutations in RET and EDNRB is an underlying mechanism for this complex disorder.