Functional Mapping of Dynamic Traits with Robust t-Distribution
Year: 2011
Type: article
Abstract: Functional mapping has been a powerful tool in mapping quantitative trait loci (QTL) underlying dynamic traits of agricultural or biomedical interest. In functional mapping, multivariate normality is often assumed for the underlying data distribution, partially due to the ease of parameter estimation. The normality assumption however could be easily violated in real applications due to various reasons such as heavy tails or extreme observations. Departure from normality has negative effect on testing power and inference for QTL identification. In this work, we relax the normality assumption and propose a robust multivariate t-distribution mapping framework for QTL identification in functional mapping. Simulation studies show increased mapping power and precision with the t distribution than that of a normal distribution. The utility of the method is demonstrated through a real data analysis.
Source: PLoS ONE
Authors: Cen Wu, Gengxin Li, Jun Zhu, Yuehua Cui
Institutions: Michigan State University, Zhejiang University
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $2,382
Funder: National Science Foundation
OpenAlex ID: W2035725901