Region-based interaction detection in genome-wide case-control studies

Abstract

BackgroundIn genome-wide association study (GWAS), conventional interaction detection methods such as BOOST are mostly based on SNP-SNP interactions. Although single nucleotides are the building blocks of human genome, single nucleotide polymorphisms (SNPs) are not necessarily the smallest functional unit for complex phenotypes. Region-based strategies have been proved to be successful in studies aiming at marginal effects.MethodsWe propose a novel region-region interaction detection method named RRIntCC (region-region interaction detection for case-control studies). RRIntCC uses the correlations between individual SNP-SNP interactions based on linkage disequilibrium (LD) contrast test.ResultsSimulation experiments showed that our method can achieve a higher power than conventional SNP-based methods with similar type-I-error rates. When applied to two real datasets, RRIntCC was able to find several significant regions, while BOOST failed to identify any significant results. The source code and the sample data of RRIntCC are available at http://bioinformatics.ust.hk/RRIntCC.html.ConclusionIn this paper, a new region-based interaction detection method with better performance than SNP-based interaction detection methods has been proposed.

Assessing the effect of interaction between gut microbiome and inflammatory bowel disease on the risks of depression

“…However, limited efforts were made to explore the effect of the interaction between the gut microbiome and IBD on the risk of depression through the application of PRS analysis. SNPs are the major genetic variants in GWAS, and most GWAS analyses follow a single-locus test procedure for SNP marginal effects ( Zhang et al, 2019a ). SNP-SNP interactions are very important in biological systems ( Wang et al, 2019a ), several studies conducted using SNP-SNP interactions to determine the genetics of diseases including atherosclerotic ischemic stroke ( Shen et al, 2021 ), schizophrenia ( Lee et al, 2020a ) and CD ( Dinu et al, 2012 ), whereas some SNPs with weak marginal effects but strong interaction effects cannot be found by marginal effect detection ( Zhang et al, 2019a ).…”

“…SNPs are the major genetic variants in GWAS, and most GWAS analyses follow a single-locus test procedure for SNP marginal effects ( Zhang et al, 2019a ). SNP-SNP interactions are very important in biological systems ( Wang et al, 2019a ), several studies conducted using SNP-SNP interactions to determine the genetics of diseases including atherosclerotic ischemic stroke ( Shen et al, 2021 ), schizophrenia ( Lee et al, 2020a ) and CD ( Dinu et al, 2012 ), whereas some SNPs with weak marginal effects but strong interaction effects cannot be found by marginal effect detection ( Zhang et al, 2019a ). PLINK software performs a series of basic, large-scale analyses in a computationally efficient manner and is well able to assess SNP interaction effects ( Purcell et al, 2007 ).…”

Gene-Based Testing of Interactions Using XGBoost in Genome-Wide Association Studies

“…Genetic interaction was first studied at the SNP level, and SNP–SNP interactions (i.e., epistasis) were detected by applying several methods ( Li et al, 2015a ; Ritchie and Van Steen, 2018 ), such as statistics based on entropy ( Dong et al, 2008 ), logistic regression ( Lin et al, 2016 ), and odds ratio ( Emily, 2012 ); other techniques include multifactor dimensionality reduction (MDR) ( Ritchie et al, 2003 ), BOOST ( Wan et al, 2010 ), RRIntCC ( Zhang et al, 2019 ), GenEpi ( Chang et al, 2020 ), and some accelerate method ( Nobre et al, 2021 ). One of the general challenges encountered by these SNP-based approaches is the statistical weakness of the higher-order or pairwise tests that result from massive multiple testing corrections over all the groups or pairs of SNPs.…”