Interval Mapping Of Zero-inflated Count-type Trait Loci Based On The MCMC Method

Count phenotypes with excessive zeros are often observed in the biological world.Researchers have studied many statistical methods for mapping the quan-titative 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 meth-ods 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 e?ects 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 e?ects of QTLs on the formation of mouse cholesterol gallstones were demonstrated by analyzing an F₂ mouse data set.