On the Generalized Poisson Regression Mixture Model for Mapping Quantitative Trait Loci With Count Data

Genetics - Tập 174 Số 4 - Trang 2159-2172 - 2006
Yuehua Cui1, Dong‐Yun Kim1, Jun Zhu2
1Department of Statistics and Probability, Michigan State University, East Lansing, Michigan 48824 and
2College of Agricultural and Biotechnology, Zhejiang University, Hangzhou, Zhejiang 310029, People's Republic of China

Tóm tắt

Abstract Statistical methods for mapping quantitative trait loci (QTL) have been extensively studied. While most existing methods assume normal distribution of the phenotype, the normality assumption could be easily violated when phenotypes are measured in counts. One natural choice to deal with count traits is to apply the classical Poisson regression model. However, conditional on covariates, the Poisson assumption of mean–variance equality may not be valid when data are potentially under- or overdispersed. In this article, we propose an interval-mapping approach for phenotypes measured in counts. We model the effects of QTL through a generalized Poisson regression model and develop efficient likelihood-based inference procedures. This approach, implemented with the EM algorithm, allows for a genomewide scan for the existence of QTL throughout the entire genome. The performance of the proposed method is evaluated through extensive simulation studies along with comparisons with existing approaches such as the Poisson regression and the generalized estimating equation approach. An application to a rice tiller number data set is given. Our approach provides a standard procedure for mapping QTL involved in the genetic control of complex traits measured in counts.

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Thông tin xuất bản

Genetics

Tập 174 Số 4

2159-2172

Thông tin tác giả