Dataset Information

GEInfo: an R package for gene-environment interaction analysis incorporating prior information.

ABSTRACT:

Summary

Gene-environment (G-E) interactions have important implications for many complex diseases. With higher dimensionality and weaker signals, G-E interaction analysis is more challenged than the analysis of main G (and E) effects. The accumulation of published literature makes it possible to borrow strength from prior information and improve analysis. In a recent study, a 'quasi-likelihood + penalization' approach was developed to effectively incorporate prior information. Here, we first extend it to linear, logistic and Poisson regressions. Such models are much more popular in practice. More importantly, we develop the R package GEInfo, which realizes this approach in a user-friendly manner. To facilitate direct comparison and routine data analysis, the package also includes functions for alternative methods and visualization.

Availability and implementation

The package is available at https://CRAN.R-project.org/package=GEInfo.

Supplementary information

Supplementary materials are available at Bioinformatics online.

SUBMITTER: Wang X

PROVIDER: S-EPMC9154264 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

GEInfo: an R package for gene-environment interaction analysis incorporating prior information.

Wang Xiaoyan X Liu Hongduo H Ma Shuangge S

Bioinformatics (Oxford, England) 20220501 11

<h4>Summary</h4>Gene-environment (G-E) interactions have important implications for many complex diseases. With higher dimensionality and weaker signals, G-E interaction analysis is more challenged than the analysis of main G (and E) effects. The accumulation of published literature makes it possible to borrow strength from prior information and improve analysis. In a recent study, a 'quasi-likelihood + penalization' approach was developed to effectively incorporate prior information. Here, we f ...[more]

PMID: 35485739

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Project description:BackgroundCharacterizing aggregate genetic risk for alcohol misuse and identifying variants involved in gene-by-environment (G × E) interaction effects has so far been a major challenge. We hypothesized that functional genomic information could be used to enhance detection of polygenic signal underlying alcohol misuse and to prioritize identification of single nucleotide polymorphisms (SNPs) most likely to exhibit G × E effects.MethodsWe examined these questions in the young adult FinnTwin12 sample (n = 1,170). We used genomewide association estimates from an independent sample to derive 2 types of polygenic scores for alcohol problems in FinnTwin12. Genomewide polygenic scores included all SNPs surpassing a designated p-value threshold. DNase polygenic scores were a subset of the genomewide polygenic scores including only variants in DNase I hypersensitive sites (DHSs), which are open chromatin marks likely to index regions with a regulatory function. We conducted parallel analyses using height as a nonpsychiatric model phenotype to evaluate the consistency of effects. For the G × E analyses, we examined whether SNPs in DHSs were overrepresented among SNPs demonstrating significant G × E effects in an interaction between romantic relationship status and intoxication frequency.ResultsContrary to our expectations, we found that DNase polygenic scores were not more strongly predictive of alcohol problems than conventional polygenic scores. However, variants in DNase polygenic scores had per-SNP effects that were up to 1.4 times larger than variants in conventional polygenic scores. This same pattern of effects was also observed in supplementary analyses with height. In G × E models, SNPs in DHSs were modestly overrepresented among SNPs with significant interaction effects for intoxication frequency.ConclusionsThese findings highlight the potential utility of integrating functional genomic annotation information to increase the signal-to-noise ratio in polygenic scores and identify genetic variants that may be most susceptible to environmental modification.

Dataset Information

GEInfo: an R package for gene-environment interaction analysis incorporating prior information.

Summary

Availability and implementation

Supplementary information

Publications

GEInfo: an R package for gene-environment interaction analysis incorporating prior information.

Similar Datasets

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