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A method for analyzing multiple continuous phenotypes in rare variant association studies allowing for flexible correlations in variant effects.

ABSTRACT: For region-based sequencing data, power to detect genetic associations can be improved through analysis of multiple related phenotypes. With this motivation, we propose a novel test to detect association simultaneously between a set of rare variants, such as those obtained by sequencing in a small genomic region, and multiple continuous phenotypes. We allow arbitrary correlations among the phenotypes and build on a linear mixed model by assuming the effects of the variants follow a multivariate normal distribution with a zero mean and a specific covariance matrix structure. In order to account for the unknown correlation parameter in the covariance matrix of the variant effects, a data-adaptive variance component test based on score-type statistics is derived. As our approach can calculate the P-value analytically, the proposed test procedure is computationally efficient. Broad simulations and an application to the UK10K project show that our proposed multivariate test is generally more powerful than univariate tests, especially when there are pleiotropic effects or highly correlated phenotypes.

SUBMITTER: Sun J

PROVIDER: S-EPMC4989219 | biostudies-literature | 2016 Aug

REPOSITORIES: biostudies-literature

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A method for analyzing multiple continuous phenotypes in rare variant association studies allowing for flexible correlations in variant effects.

Sun Jianping J Oualkacha Karim K Forgetta Vincenzo V Zheng Hou-Feng HF Brent Richards J J Ciampi Antonio A Greenwood Celia Mt CM

European journal of human genetics : EJHG 20160210 9

For region-based sequencing data, power to detect genetic associations can be improved through analysis of multiple related phenotypes. With this motivation, we propose a novel test to detect association simultaneously between a set of rare variants, such as those obtained by sequencing in a small genomic region, and multiple continuous phenotypes. We allow arbitrary correlations among the phenotypes and build on a linear mixed model by assuming the effects of the variants follow a multivariate ...[more]

PMID: 26860061

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Project description:PURPOSE:A genetic correlation is the proportion of phenotypic variance between traits that is shared on a genetic basis. Here we explore genetic correlations between diabetes- and glaucoma-related traits. DESIGN:Cross-sectional study. METHODS:We assembled genome-wide association study summary statistics from European-derived participants regarding diabetes-related traits like fasting blood sugar (FBS) and type 2 diabetes (T2D) and glaucoma-related traits (intraocular pressure [IOP], central corneal thickness [CCT], corneal hysteresis [CH], corneal resistance factor [CRF], cup-to-disc ratio [CDR], and primary open-angle glaucoma [POAG]). We included data from the National Eye Institute Glaucoma Human Genetics Collaboration Heritable Overall Operational Database, the UK Biobank, and the International Glaucoma Genetics Consortium. We calculated genetic correlation (rg) between traits using linkage disequilibrium score regression. We also calculated genetic correlations between IOP, CCT, and select diabetes-related traits based on individual level phenotype data in 2 Northern European population-based samples using pedigree information and Sequential Oligogenic Linkage Analysis Routines. RESULTS:Overall, there was little rg between diabetes- and glaucoma-related traits. Specifically, we found a nonsignificant negative correlation between T2D and POAG (rg = -0.14; P = .16). Using Sequential Oligogenic Linkage Analysis Routines, the genetic correlations between measured IOP, CCT, FBS, fasting insulin, and hemoglobin A1c were null. In contrast, genetic correlations between IOP and POAG (rg ? 0.45; P ? 3.0 × 10-4) and between CDR and POAG were high (rg = 0.57; P = 2.8 × 10-10). However, genetic correlations between corneal properties (CCT, CRF, and CH) and POAG were low (rg range -0.18 to 0.11) and nonsignificant (P ? .07). CONCLUSION:These analyses suggest that there is limited genetic correlation between diabetes- and glaucoma-related traits.

Dataset Information

A method for analyzing multiple continuous phenotypes in rare variant association studies allowing for flexible correlations in variant effects.

Publications

A method for analyzing multiple continuous phenotypes in rare variant association studies allowing for flexible correlations in variant effects.

Similar Datasets

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