A simple genome-wide association study algorithm

TL;DR

This paper introduces a computationally simple GWAS algorithm that estimates main and epistatic effects by analyzing pairs of individuals, reducing dependence on the large number of SNPs.

Contribution

It proposes a novel GWAS method based on individual pairs, which is less affected by the high dimensionality of SNP data compared to traditional approaches.

Findings

Algorithm performs well on real datasets

Reduces computational complexity for large SNP datasets

Effective in estimating genetic effects

Abstract

A computationally simple genome-wide association study (GWAS) algorithm for estimating the main and epistatic effects of markers or single nucleotide polymorphisms (SNPs) is proposed. It is based on the intuitive assumption that changes of alleles corresponding to important SNPs in a pair of individuals lead to large difference of phenotype values of these individuals. The algorithm is based on considering pairs of individuals instead of SNPs or pairs of SNPs. The main advantage of the algorithm is that it weakly depends on the number of SNPs in a genotype matrix. It mainly depends on the number of individuals, which is typically very small in comparison with the number of SNPs. Numerical experiments with real data sets illustrate the proposed algorithm.