The Role of SNP Interactions when Determining Independence of Novel Signals in Genetic Association Studies-An Application to ARG1 and Bronchodilator Response.

Ryan Walsh,Merry-Lynn Mcdonald,Sharon M Lutz,Joanne E Sordillo,Christoph Lange,Kirsten Voorhies,Ann Chen Wu,Michael Mcgeachie

doi:10.3390/jpm11020145

Abstract

Genome-wide association studies (GWAS) play a critical role in identifying many loci for common diseases and traits. There has been a rapid increase in the number of GWAS over the past decade. As additional GWAS are being conducted, it is unclear whether a novel signal associated with the trait of interest is independent of single nucleotide polymorphisms (SNPs) in the same region that has been previously associated with the trait of interest. The general approach to determining whether the novel association is independent of previous signals is to examine the association of the novel SNP with the trait of interest conditional on the previously identified SNP and/or calculate linkage disequilibrium (LD) between the two SNPs. However, the role of epistasis and SNP by SNP interactions are rarely considered. Through simulation studies, we examined the role of SNP by SNP interactions when determining the independence of two genetic association signals. We have created an R package on Github called gxgRC to generate these simulation studies based on user input. In genetic association studies of asthma, we considered the role of SNP by SNP interactions when determining independence of signals for SNPs in the ARG1 gene and bronchodilator response.

Highlights

Introduction published maps and institutional affilGenome-wide association studies (GWAS) play a critical role in identifying many loci for common diseases [1] as well as complex traits [2]
We examined the impact of single nucleotide polymorphisms (SNPs) by SNP interactions when determining whether two signals are independent in a GWAS
To illustrate the effect of SNP by SNP interactions when determining conditional independence of genetic signals, we considered SNPs in chromosome 6 [ARG1], which has previously been associated with bronchodilator response (BDR) in asthma [11]

Summary

Materials and Methods

In the following simulation scenarios, we examined the impact of SNP by SNP interactions when determining the independence of two SNPs by regressing the trait of interest. After the data were simulated using Equations (1) and (2), we fit 3 algorithms and tested the following null hypotheses for each algorithm: Algorithm 0: Fitting E[Y ] = δ0 + δ1 X1 , we tested H0 : δ1 = 0 to determine if the SNP X1 is associated with the trait of interest Y. Algorithm 1: Fitting E[Y ] = α0 + α1 X1 + α2 X2 , we tested H0 : α1 = 0 to determine if the SNP X1 is associated with the trait of interest Y conditional on the SNP X2.

Results

H0 : φbetween simulations show that

Data Analysis

Discussion