SNP-SNP Interactions Discovered by Logic Regression Explain Crohn's Disease Genetics

Irina Dinu,Yutaka Yasui,Shahab Jabbari,Surakameth Mahasirimongkol,Hideki Yanai,Noha Sharaf Eldin,Xuan Wu,Katsushi Tokunaga,Erin Kreiter,Qi Liu

doi:10.1371/journal.pone.0043035

Abstract

In genome-wide association studies (GWAS), the association between each single nucleotide polymorphism (SNP) and a phenotype is assessed statistically. To further explore genetic associations in GWAS, we considered two specific forms of biologically plausible SNP-SNP interactions, ‘SNP intersection’ and ‘SNP union,’ and analyzed the Crohn's Disease (CD) GWAS data of the Wellcome Trust Case Control Consortium for these interactions using a limited form of logic regression. We found strong evidence of CD-association for 195 genes, identifying novel susceptibility genes (e.g., ISX, SLCO6A1, TMEM183A) as well as confirming many previously identified susceptibility genes in CD GWAS (e.g., IL23R, NOD2, CYLD, NKX2-3, IL12RB2, ATG16L1). Notably, 37 of the 59 chromosomal locations indicated for CD-association by a meta-analysis of CD GWAS, involving over 22,000 cases and 29,000 controls, were represented in the 195 genes, as well as some chromosomal locations previously indicated only in linkage studies, but not in GWAS. We repeated the analysis with two smaller GWASs from the Database of Genotype and Phenotype (dbGaP): in spite of differences of populations and study power across the three datasets, we observed some consistencies across the three datasets. Notable examples included TMEM183A and SLCO6A1 which exhibited strong evidence consistently in our WTCCC and both of the dbGaP SNP-SNP interaction analyses. Examining these specific forms of SNP interactions could identify additional genetic associations from GWAS. R codes, data examples, and a ReadMe file are available for download from our website: http://www.ualberta.ca/~yyasui/homepage.html.

Highlights

Analysis of genome-wide association studies (GWAS) often focuses on identifying individual single nucleotide polymorphisms (SNPs) that modify the risk of a phenotype, assuming the underlying association of an individual SNP without considering the involvement of any other SNPs
Under a single or combination of SNP intersections and SNP unions, assessing the independent marginal effect of each individual SNP without considering these interaction forms will either fail to discover, or observe only weak, association between the individual SNP and the phenotype of interest. To incorporate these specific forms of SNP-SNP interactions in GWAS data analysis, we propose using logic regression to search for sets of SNPs that are jointly associated with the phenotype of interest in the form of a single SNP intersection or union, or in combinations of thereof [2]
Thirty-seven (63%) of the 59 chromosomal locations, that were previously identified by a meta-analysis of single-SNP studies that involved over 22,000 cases and 29,000 controls [1], were included in the 195 genes

Summary

Introduction

Analysis of genome-wide association studies (GWAS) often focuses on identifying individual single nucleotide polymorphisms (SNPs) that modify the risk of a phenotype, assuming the underlying association of an individual SNP without considering the involvement of any other SNPs. If individual SNPs (or the regions tagged by them) are independently critical in the CD-risk-altering biological functions, this approach would be effective This may be the case for the association between rs11209026 of the IL23R gene and CD risk, where the marginal association is quantified as an estimated odds ratio of 2.661. CD risk may be elevated through multiple independent ways, each of which may be a SNPintersection or an individual SNP (i.e., (SNP-A and SNP-B) or (SNP-C) each taking its respective high-risk genotype. This form of SNP-SNP interaction is referred to as a SNP union, derived from the set theory terminology. Under a single or combination of SNP intersections and SNP unions, assessing the independent marginal effect of each individual SNP without considering these interaction forms will either fail to discover, or observe only weak, association between the individual SNP and the phenotype of interest

Methods

Results

Conclusion