High-throughput Genome-wide Association Analysis for Single and Multiple Phenotypes

TL;DR

This paper introduces two high-throughput algorithms, CLAK-CHOL and CLAK-EIG, that dramatically improve the efficiency of genome-wide association studies for single and multiple phenotypes, enabling analysis of millions of polymorphisms in days.

Contribution

The paper presents novel algorithms that leverage problem-specific knowledge to significantly accelerate GWAS for multiple traits, outperforming existing tools.

Findings

Algorithms reduce computational time for GWAS

Able to analyze thousands of traits in days

Outperform current state-of-the-art methods

Abstract

The variance component tests used in genomewide association studies of thousands of individuals become computationally exhaustive when multiple traits are analysed in the context of omics studies. We introduce two high-throughput algorithms -- CLAK-CHOL and CLAK-EIG -- for single and multiple phenotype genome-wide association studies (GWAS). The algorithms, generated with the help of an expert system, reduce the computational complexity to the point that thousands of traits can be analyzed for association with millions of polymorphisms in a course of days on a standard workstation. By taking advantage of problem specific knowledge, CLAK-CHOL and CLAK-EIG significantly outperform the current state-of-the-art tools in both single and multiple trait analysis.