Detecting SNPs with interactive effects on a quantitative trait

TL;DR

This paper introduces a novel statistical test to detect SNP effects on quantitative traits by focusing on individual SNP involvement in interactions, reducing multiple testing issues and capturing both main and interactive effects.

Contribution

It proposes a mixture test that simplifies interaction detection by testing each SNP's involvement, enabling analysis of unobserved interactions and improving power.

Findings

Test maintains type I error rate.

Detects meaningful SNP signals.

Effective in simulated and real data.

Abstract

Here we propose a test to detect effects of single nucleotide polymorphisms (SNPs) on a quantitative trait. Significant SNP-SNP interactions are more difficult to detect than significant SNPs, partly due to the massive amount of SNP-SNP combinations. We propose to move away from testing interaction terms, and move towards testing whether an individual SNP is involved in any interaction. This reduces the multiple testing burden to one test per SNP, and allows for interactions with unobserved factors. Analysing one SNP at a time, we split the individuals into two groups, based on the number of minor alleles. If the quantitative trait differs in mean between the two groups, the SNP has a main effect. If the quantitative trait differs in distribution between some individuals in one group and all other individuals, it possibly has an interactive effect. We propose a mixture test to detect both types of effects. Implicitly, the membership probabilities may suggest potential interacting variables. Analysing simulated and experimental data, we show that the proposed test is statistically powerful, maintains the type I error rate, and detects meaningful signals. The R package semisup is available from Bioconductor.