Two-part zero-inflated negative binomial regression model for quantitative trait loci mapping with count trait.
Poisson regression models provide a standard framework for quantitative trait locus (QTL) mapping of count traits. In practice, however, count traits are often over-dispersed relative to the Poisson distribution. In these situations, the zero-inflated Poisson (ZIP), zero-inflated generalized Poisson (ZIGP) and zero-inflated negative binomial (ZINB) regression may be useful for QTL mapping of count traits. Added genetic variables to the negative binomial part equation, may also affect extra zero data. In this study, to overcome these challenges, I apply two-part ZINB model. The EM algorithm with Newton-Raphson method in the M-step uses for estimating parameters. An application of the two-part ZINB model for QTL mapping is considered to detect associations between the formation of gallstone and the genotype of markers.
Modeling of Noncommunicable Disease Research Canter, Department of Biostatistics and Epidemiology, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran. Electronic address: moghimb@yahoo.com.
Abbas
A
Moghimbeigi
D000465:Algorithms; D000818:Animals; D002784:Cholesterol / Q000737:chemistry; D042882:Gallstones / Q000737:chemistry; D014644:Genetic Variation; D016678:Genome; D051379:Mice; D008957:Models, Genetic; D015233:Models, Statistical; D009010:Monte Carlo Method; D010641:Phenotype; D016012:Poisson Distribution; D040641:Quantitative Trait Loci; D012044:Regression Analysis
D002784:Cholesterol
Cholesterol gallstone; Extra zero; Negative binomial regression model; QTL mapping