Dataset Information

Re-analysis and meta-analysis of summary statistics from gene-environment interaction studies.

ABSTRACT:

Motivation

statistics from genome-wide association studies enable many valuable downstream analyses that are more efficient than individual-level data analysis while also reducing privacy concerns. As growing sample sizes enable better-powered analysis of gene-environment interactions, there is a need for gene-environment interaction-specific methods that manipulate and use summary statistics.

Results

We introduce two tools to facilitate such analysis, with a focus on statistical models containing multiple gene-exposure and/or gene-covariate interaction terms. REGEM (RE-analysis of GEM summary statistics) uses summary statistics from a single, multi-exposure genome-wide interaction study to derive analogous sets of summary statistics with arbitrary sets of exposures and interaction covariate adjustments. METAGEM (META-analysis of GEM summary statistics) extends current fixed-effects meta-analysis models to incorporate multiple exposures from multiple studies. We demonstrate the value and efficiency of these tools by exploring alternative methods of accounting for ancestry-related population stratification in genome-wide interaction study in the UK Biobank as well as by conducting a multi-exposure genome-wide interaction study meta-analysis in cohorts from the diabetes-focused ProDiGY consortium. These programs help to maximize the value of summary statistics from diverse and complex gene-environment interaction studies.

Availability and implementation

REGEM and METAGEM are open-source projects freely available at https://github.com/large-scale-gxe-methods/REGEM and https://github.com/large-scale-gxe-methods/METAGEM.

SUBMITTER: Pham DT

PROVIDER: S-EPMC10724851 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Re-analysis and meta-analysis of summary statistics from gene-environment interaction studies.

Pham Duy T DT Westerman Kenneth E KE Pan Cong C Chen Ling L Srinivasan Shylaja S Isganaitis Elvira E Vajravelu Mary Ellen ME Bacha Fida F Chernausek Steve S Gubitosi-Klug Rose R Divers Jasmin J Pihoker Catherine C Marcovina Santica M SM Manning Alisa K AK Chen Han H

Bioinformatics (Oxford, England) 20231201 12

<h4>Motivation</h4>statistics from genome-wide association studies enable many valuable downstream analyses that are more efficient than individual-level data analysis while also reducing privacy concerns. As growing sample sizes enable better-powered analysis of gene-environment interactions, there is a need for gene-environment interaction-specific methods that manipulate and use summary statistics.<h4>Results</h4>We introduce two tools to facilitate such analysis, with a focus on statistical ...[more]

PMID: 38039147

Similar Datasets

Project description:Genome-wide association studies (GWAS) for complex diseases have focused primarily on single-trait analyses for disease status and disease-related quantitative traits. For example, GWAS on risk factors for coronary artery disease analyze genetic associations of plasma lipids such as total cholesterol, LDL-cholesterol, HDL-cholesterol, and triglycerides (TGs) separately. However, traits are often correlated and a joint analysis may yield increased statistical power for association over multiple univariate analyses. Recently several multivariate methods have been proposed that require individual-level data. Here, we develop metaUSAT (where USAT is unified score-based association test), a novel unified association test of a single genetic variant with multiple traits that uses only summary statistics from existing GWAS. Although the existing methods either perform well when most correlated traits are affected by the genetic variant in the same direction or are powerful when only a few of the correlated traits are associated, metaUSAT is designed to be robust to the association structure of correlated traits. metaUSAT does not require individual-level data and can test genetic associations of categorical and/or continuous traits. One can also use metaUSAT to analyze a single trait over multiple studies, appropriately accounting for overlapping samples, if any. metaUSAT provides an approximate asymptotic P-value for association and is computationally efficient for implementation at a genome-wide level. Simulation experiments show that metaUSAT maintains proper type-I error at low error levels. It has similar and sometimes greater power to detect association across a wide array of scenarios compared to existing methods, which are usually powerful for some specific association scenarios only. When applied to plasma lipids summary data from the METSIM and the T2D-GENES studies, metaUSAT detected genome-wide significant loci beyond the ones identified by univariate analyses. Evidence from larger studies suggest that the variants additionally detected by our test are, indeed, associated with lipid levels in humans. In summary, metaUSAT can provide novel insights into the genetic architecture of a common disease or traits.

Project description:The two-phase sampling design is a cost-efficient way of collecting expensive covariate information on a judiciously selected sub-sample. It is natural to apply such a strategy for collecting genetic data in a sub-sample enriched for exposure to environmental factors for gene-environment interaction (G × E) analysis. In this paper, we consider two-phase studies of G × E interaction where phase I data are available on exposure, covariates and disease status. Stratified sampling is done to prioritize individuals for genotyping at phase II conditional on disease and exposure. We consider a Bayesian analysis based on the joint retrospective likelihood of phase I and phase II data. We address several important statistical issues: (i) we consider a model with multiple genes, environmental factors and their pairwise interactions. We employ a Bayesian variable selection algorithm to reduce the dimensionality of this potentially high-dimensional model; (ii) we use the assumption of gene-gene and gene-environment independence to trade-off between bias and efficiency for estimating the interaction parameters through use of hierarchical priors reflecting this assumption; (iii) we posit a flexible model for the joint distribution of the phase I categorical variables using the non-parametric Bayes construction of Dunson and Xing (2009). We carry out a small-scale simulation study to compare the proposed Bayesian method with weighted likelihood and pseudo likelihood methods that are standard choices for analyzing two-phase data. The motivating example originates from an ongoing case-control study of colorectal cancer, where the goal is to explore the interaction between the use of statins (a drug used for lowering lipid levels) and 294 genetic markers in the lipid metabolism/cholesterol synthesis pathway. The sub-sample of cases and controls on which these genetic markers were measured is enriched in terms of statin users. The example and simulation results illustrate that the proposed Bayesian approach has a number of advantages for characterizing joint effects of genotype and exposure over existing alternatives and makes efficient use of all available data in both phases.

Dataset Information

Re-analysis and meta-analysis of summary statistics from gene-environment interaction studies.

Motivation

Results

Availability and implementation

Publications

Re-analysis and meta-analysis of summary statistics from gene-environment interaction studies.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets