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Bayesian interval mapping of count trait loci based on zero‐inflated generalized Poisson regression model
Author(s) -
Chi Jinling,
Zhou Ying,
Chen Lili,
Zhou Yajing
Publication year - 2020
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.201900274
Subject(s) - quantitative trait locus , statistics , count data , mathematics , poisson distribution , bayesian probability , poisson regression , markov chain monte carlo , data set , algorithm , regression , statistical hypothesis testing , chromosome , biology , genetics , population , demography , sociology , gene
Count phenotypes with excessive zeros are often observed in the biological world. Researchers have studied many statistical methods for mapping the quantitative trait loci (QTLs) of zero‐inflated count phenotypes. However, most of the existing methods consist of finding the approximate positions of the QTLs on the chromosome by genome‐wide scanning. Additionally, most of the existing methods use the EM algorithm for parameter estimation. In this paper, we propose a Bayesian interval mapping scheme of QTLs for zero‐inflated count data. The method takes advantage of a zero‐inflated generalized Poisson (ZIGP) regression model to study the influence of QTLs on the zero‐inflated count phenotype. The MCMC algorithm is used to estimate the effects and position parameters of QTLs. We use the Haldane map function to realize the conversion between recombination rate and map distance. Monte Carlo simulations are conducted to test the applicability and advantage of the proposed method. The effects of QTLs on the formation of mouse cholesterol gallstones were demonstrated by analyzing an F 2 mouse data set.