Count Traits Positional Parameters And Effect Parameters Of Statistical Inference

The variation of many quantitative traits in human, plants, or animals can beattributed to genetic efects. Quantitative traits locus (QTL) mapping, which mapsloci in the genome that afect a quantitative trait, is of important scientific andeconomic value. The method of interval mapping is widely used for the geneticmapping of QTL, and statistical methods have been extensively studied in mappingQTL. However, some biological trait is not controlled by one gene, not by manygenes, so multiple-interval mapping (MIM) will be used in mapping QTLs. Whengrown in diferent environments, an organism may show a range of phenotypes.Thegenotype of an organism, its environment, and the interaction between genotypeand environment determine the phenotype displayed. Phenotypic plasticity is theability of a single genotype to produce multiple phenotypes in response to diferentenvironments.While some biological traits are not continuous, but are discrete, for example,the number of branches. In this article, we consider the estimation problem of QTLparameters that control the phenotypic plasticity in diferent environments. Wedevelop a statistical methods that focus on count trait, while many of statisticalmethods are implemented to continuous data. The model is derived with in amultivariate Poisson mixture model on the basis of multivariate Poisson distribution.The EM algorithm is applied to obtain the maximum-likelihood estimates (MLE)of both QTLs position and the Poisson parameter simultaneous. Then, we also usecomputer simulation to study the statistical model, then the simulation results showthat our method has a certain practicality.