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A likelihood ratio test for genome-wide association under genetic heterogeneity.

ABSTRACT: Most existing association tests for genome-wide association studies (GWASs) fail to account for genetic heterogeneity. Zhou and Pan proposed a binomial-mixture-model-based association test to account for the possible genetic heterogeneity in case-control studies. The idea is elegant, however, the proposed test requires an expectation-maximization (EM)-type iterative algorithm to identify the penalised maximum likelihood estimates and a permutation method to assess p-values. The intensive computational burden induced by the EM-algorithm and the permutation becomes prohibitive for direct applications to GWASs. This paper develops a likelihood ratio test (LRT) for GWASs under genetic heterogeneity based on a more general alternative mixture model. In particular, a closed-form formula for the LRT statistic is derived to avoid the EM-type iterative numerical evaluation. Moreover, an explicit asymptotic null distribution is also obtained, which avoids using the permutation to obtain p-values. Thus, the proposed LRT is easy to implement for GWASs. Furthermore, numerical studies demonstrate that the LRT has power advantages over the commonly used Armitage trend test and other existing association tests under genetic heterogeneity. A breast cancer GWAS dataset is used to illustrate the newly proposed LRT.

SUBMITTER: Qian M

PROVIDER: S-EPMC3910100 | biostudies-literature | 2013 Mar

REPOSITORIES: biostudies-literature

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A likelihood ratio test for genome-wide association under genetic heterogeneity.

Qian Meng M Shao Yongzhao Y

Annals of human genetics 20130131 2

Most existing association tests for genome-wide association studies (GWASs) fail to account for genetic heterogeneity. Zhou and Pan proposed a binomial-mixture-model-based association test to account for the possible genetic heterogeneity in case-control studies. The idea is elegant, however, the proposed test requires an expectation-maximization (EM)-type iterative algorithm to identify the penalised maximum likelihood estimates and a permutation method to assess p-values. The intensive computa ...[more]

PMID: 23362943

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Project description:Genetic and environmental covariances between pairs of complex traits are important quantitative measurements that characterize their shared genetic and environmental architectures. Accurate estimation of genetic and environmental covariances in genome-wide association studies (GWASs) can help us identify common genetic and environmental factors associated with both traits and facilitate the investigation of their causal relationship. Genetic and environmental covariances are often modeled through multivariate linear mixed models. Existing algorithms for covariance estimation include the traditional restricted maximum likelihood (REML) method and the recent method of moments (MoM). Compared to REML, MoM approaches are computationally efficient and require only GWAS summary statistics. However, MoM approaches can be statistically inefficient, often yielding inaccurate covariance estimates. In addition, existing MoM approaches have so far focused on estimating genetic covariance and have largely ignored environmental covariance estimation. Here we introduce a new computational method, GECKO, for estimating both genetic and environmental covariances, that improves the estimation accuracy of MoM while keeping computation in check. GECKO is based on composite likelihood, relies on only summary statistics for scalable computation, provides accurate genetic and environmental covariance estimates across a range of scenarios, and can accommodate SNP annotation stratified covariance estimation. We illustrate the benefits of GECKO through simulations and applications on analyzing 22 traits from five large-scale GWASs. In the real data applications, GECKO identified 50 significant genetic covariances among analyzed trait pairs, resulting in a twofold power gain compared to the previous MoM method LDSC. In addition, GECKO identified 20 significant environmental covariances. The ability of GECKO to estimate environmental covariance in addition to genetic covariance helps us reveal strong positive correlation between the genetic and environmental covariance estimates across trait pairs, suggesting that common pathways may underlie the shared genetic and environmental architectures between traits.

Project description:This article presents the ability of an omnibus permutation test on ensembles of two-locus analyses (2LOmb) to detect pure epistasis in the presence of genetic heterogeneity. The performance of 2LOmb is evaluated in various simulation scenarios covering two independent causes of complex disease where each cause is governed by a purely epistatic interaction. Different scenarios are set up by varying the number of available single nucleotide polymorphisms (SNPs) in data, number of causative SNPs and ratio of case samples from two affected groups. The simulation results indicate that 2LOmb outperforms multifactor dimensionality reduction (MDR) and random forest (RF) techniques in terms of a low number of output SNPs and a high number of correctly-identified causative SNPs. Moreover, 2LOmb is capable of identifying the number of independent interactions in tractable computational time and can be used in genome-wide association studies. 2LOmb is subsequently applied to a type 1 diabetes mellitus (T1D) data set, which is collected from a UK population by the Wellcome Trust Case Control Consortium (WTCCC). After screening for SNPs that locate within or near genes and exhibit no marginal single-locus effects, the T1D data set is reduced to 95,991 SNPs from 12,146 genes. The 2LOmb search in the reduced T1D data set reveals that 12 SNPs, which can be divided into two independent sets, are associated with the disease. The first SNP set consists of three SNPs from MUC21 (mucin 21, cell surface associated), three SNPs from MUC22 (mucin 22), two SNPs from PSORS1C1 (psoriasis susceptibility 1 candidate 1) and one SNP from TCF19 (transcription factor 19). A four-locus interaction between these four genes is also detected. The second SNP set consists of three SNPs from ATAD1 (ATPase family, AAA domain containing 1). Overall, the findings indicate the detection of pure epistasis in the presence of genetic heterogeneity and provide an alternative explanation for the aetiology of T1D in the UK population.

Project description:ObjectiveThe Sjögren's International Collaborative Clinical Alliance (SICCA) is an international data registry and biorepository derived from a multisite observational study of participants in whom genotyping was performed on the Omni2.5M platform and who had undergone deep phenotyping using common protocol-directed methods. The aim of this study was to examine the genetic etiology of Sjögren's syndrome (SS) across ancestry and disease subsets.MethodsWe performed genome-wide association study analyses using SICCA subjects and external controls obtained from dbGaP data sets, one using all participants (1,405 cases, 1,622 SICCA controls, and 3,125 external controls), one using European participants (585, 966, and 580, respectively), and one using Asian participants (460, 224, and 901, respectively) with ancestry adjustments via principal components analyses. We also investigated whether subphenotype distributions differ by ethnicity, and whether this contributes to the heterogeneity of genetic associations.ResultsWe observed significant associations in established regions of the major histocompatibility complex (MHC), IRF5, and STAT4 (P = 3 × 10-42 , P = 3 × 10-14 , and P = 9 × 10-10 , respectively), and several novel suggestive regions (those with 2 or more associations at P < 1 × 10-5 ). Two regions have been previously implicated in autoimmune disease: KLRG1 (P = 6 × 10-7 [Asian cluster]) and SH2D2A (P = 2 × 10-6 [all participants]). We observed striking differences between the associations in Europeans and Asians, with high heterogeneity especially in the MHC; representative single-nucleotide polymorphisms from established and suggestive regions had highly significant differences in the allele frequencies in the study populations. We showed that SSA/SSB autoantibody production and the labial salivary gland focus score criteria were associated with the first worldwide principal component, indicative of higher non-European ancestry (P = 4 × 10-15 and P = 4 × 10-5 , respectively), but that subphenotype differences did not explain most of the ancestry differences in genetic associations.ConclusionGenetic associations with SS differ markedly according to ancestry; however, this is not explained by differences in subphenotypes.

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A likelihood ratio test for genome-wide association under genetic heterogeneity.

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A likelihood ratio test for genome-wide association under genetic heterogeneity.

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