Wtest: an integrated R package for genetic epistasis testing

Rui Sun,Maggie Haitian Wang,Ka Chun Chong,Xiaoxuan Xia,William Ka Kei Wu,Benny Chung-Ying Zee

doi:10.1186/s12920-019-0638-9

Rui Sun, Maggie Haitian Wang + Show 4 more

Open Access

https://doi.org/10.1186/s12920-019-0638-9

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Abstract

BackgroundWith the increasing amount of high-throughput genomic sequencing data, there is a growing demand for a robust and flexible tool to perform interaction analysis. The identification of SNP-SNP, SNP-CpG, and higher order interactions helps explain the genetic etiology of human diseases, yet genome-wide analysis for interactions has been very challenging, due to the computational burden and a lack of statistical power in most datasets.ResultsThe wtest R package performs association testing for main effects, pairwise and high order interactions in genome-wide association study data, and cis-regulation of SNP and CpG sites in genome-wide and epigenome-wide data. The software includes a number of post-test diagnostic and analysis functions and offers an integrated toolset for genetic epistasis testing.ConclusionsThe wtest is an efficient and powerful statistical tool for integrated genetic epistasis testing. The package is available in CRAN: https://CRAN.R-project.org/package=wtest.

Highlights

With the increasing amount of high-throughput genomic sequencing data, there is a growing demand for a robust and flexible tool to perform interaction analysis
We demonstrate its usage by two data sets: a genotypic dataset for bipolar disorder from the Genetic Association Information Network (GAIN) project, and a gene-methylation data for the lipid control treatment
Genetic epistasis testing is important to fathom the massive genomic data, and it provides a way to explore the relationship between diseases and various types of biomarkers

Summary

Introduction

With the increasing amount of high-throughput genomic sequencing data, there is a growing demand for a robust and flexible tool to perform interaction analysis. The identification of SNP-SNP, SNP-CpG, and higher order interactions helps explain the genetic etiology of human diseases, yet genome-wide analysis for interactions has been very challenging, due to the computational burden and a lack of statistical power in most datasets. The etiology of complex disorder involves an interplay of polygenic biomarkers, lifestyle and environmental factors [1]. Robust and efficient statistical tools are needed to perform interaction analysis in high volume genome data. Besides SNP-SNP interactions, the analysis of interactions of SNPs and cytosine-phosphate-guanine (CpG) sites might provide novel insight into the regulatory mechanism DNA methylation and gene expression underlying complex diseases.

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