Regression Based Robust QTL Analysis for F₂ Population

Abstract

This Quantitative trait locus (QTL) analysis is a widely used statistical approach for the detection of important genes in the chromosomes. Maximum likelihood (ML) based interval mapping (IM) is one of the most popular approaches for QTL analysis. However, it is relatively complex and computationally slower than regression based IM. Haley-Knott (HK) and extended Haley-Knott (eHK) regression based IM save computation time and produce similar results as ML-IM. However, these approaches are not robust against phenotypic outliers. In this research, we have developed a robust regression based IM approach by maximizing beta-likelihood function for intercross (F₂) population. The proposed method reduces to the HK-IM method when beta ? 0. The tuning parameter beta controls the performance of the proposed method. The simulation results show that the proposed method improves performance over the existing IM approaches in the case of data contaminations; otherwise, it shows almost the same results as the classical IM approaches.