SNPxE: SNP-environment interaction pattern identifier

• Published in: BMC bioinformatics Vol. 22; no. 1; pp. 1 - 425
• Main Authors: Lin, Hui-Yi, Huang, Po-Yu, Tseng, Tung-Sung, Park, Jong Y
• Format: Journal Article
• Language: English
• Published: London BioMed Central Ltd 07.09.2021; BioMed Central; BMC
• Subjects: Bayesian analysis; Cancer; Datasets; Disease; Environmental factors; Genetic research; Genotype & phenotype; Heredity; Heritability; Interaction models; Interactomes; Medical genetics; Nucleotides; Pathogenesis; Phenotypes; R (Programming language); Single nucleotide polymorphisms; Single-nucleotide polymorphism; Software; Statistical analysis; Statistical methods; Statistical software; Statistics; United States
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-021-04326-x

Interactions of single nucleotide polymorphisms (SNPs) and environmental factors play an important role in understanding complex diseases' pathogenesis. A growing number of SNP-environment studies have been conducted in the past decade; however, the statistical methods for evaluating SNP-environment interactions are still underdeveloped. The conventional statistical approach with a full interaction model with an additive SNP mode tests one specific interaction type, so the full interaction model approach tends to lead to false-negative findings. To increase detection accuracy, developing a statistical tool to effectively detect various SNP-environment interaction patterns is necessary. SNPxE, a SNP-environment interaction pattern identifier, tests multiple interaction patterns associated with a phenotype for each SNP-environment pair. SNPxE evaluates 27 interaction patterns for an ordinal environment factor and 18 patterns for a categorical environment factor. For detecting SNP-environment interactions, SNPxE considers three major components: (1) model structure, (2) SNP's inheritance mode, and (3) risk direction. Among the multiple testing patterns, the best interaction pattern will be identified based on the Bayesian information criterion or the smallest p-value of the interaction. Furthermore, the risk sub-groups based on the SNPs and environmental factors can be identified. SNPxE can be applied to both numeric and binary phenotypes. For better results interpretation, a heat-table of the outcome proportions can be generated for the sub-groups of a SNP-environment pair. SNPxE is a valuable tool for intensively evaluate SNP-environment interactions, and the SNPxE findings can provide insights for solving the missing heritability issue. The R function of SNPxE is freely available for download at GitHub (https://github.com/LinHuiyi/SIPI).