Accurate Estimation of Quantitative Trait Locus Effects with Epistatic by Improved Variational Linear Regression

TL;DR

This paper introduces an improved variational linear regression method for QTL mapping that enhances accuracy in high-dimensional settings with many variables, by dynamically reducing components with known effects.

Contribution

The paper proposes a novel approach to improve variational linear regression accuracy in high-dimensional QTL mapping by dynamically reducing known-effect components.

Findings

Significant accuracy improvement over existing variational methods.

Effective in high-dimensional variable selection scenarios.

Minimal additional computational cost.

Abstract

Bayesian approaches to variable selection have been widely used for quantitative trait locus (QTL) mapping. The Markov chain Monte Carlo (MCMC) algorithms for that aim are often difficult to be implemented for high-dimensional variable selection problems, such as the ones arising in epistatic analysis. Variational approximation is an alternative to MCMC, and variational linear regression (VLR) is an effective solution for the variable selection problems, but lacks accuracy in some QTL mapping problems where there are many more variables than samples. In this paper, we propose an effective method with aim to improve the accuracy of VLR in the case of above by dynamically reducing components (variable or markers) with known effects (zero or fixed). We show that the proposed method can greatly improve the accuracy of VLR with little increase in computational cost. The method is compared with several other variational methods used for QTL mapping, and simulation results show that its performance is higher than those methods when applied in high-dimensional cases.