A robust multiple-locus method for quantitative trait locus analysis of non-normally distributed multiple traits.

ABSTRACT: Linear regression-based quantitative trait loci/association mapping methods such as least squares commonly assume normality of residuals. In genetics studies of plants or animals, some quantitative traits may not follow normal distribution because the data include outlying observations or data that are collected from multiple sources, and in such cases the normal regression methods may lose some statistical power to detect quantitative trait loci. In this work, we propose a robust multiple-locus regression approach for analyzing multiple quantitative traits without normality assumption. In our method, the objective function is least absolute deviation (LAD), which corresponds to the assumption of multivariate Laplace distributed residual errors. This distribution has heavier tails than the

SUBMITTER: Li Z 

PROVIDER: S-EPMC4806903 | biostudies-literature | 2015 Dec

REPOSITORIES: biostudies-literature