Premium
Generalized Character Process Models: Estimating the Genetic Basis of Traits That Cannot Be Observed and That Change with Age or Environmental Conditions
Author(s) -
Fletcher Scott D.,
Jaffrezic Florence
Publication year - 2002
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.0006-341x.2002.00157.x
Subject(s) - markov chain , computer science , markov chain monte carlo , monte carlo method , character (mathematics) , function (biology) , variable (mathematics) , statistics , econometrics , mathematics , machine learning , biology , evolutionary biology , mathematical analysis , geometry
Summary. The genetic analysis of characters that change as a function of some independent and continuous variable has received increasing attention in the biological and statistical literature. Previous work in this area has focused on the analysis of normally distributed characters that are directly observed. We propose a framework for the development and specification of models for a quantitative genetic analysis of function‐valued characters that are not directly observed, such as genetic variation in age‐specific mortality rates or complex threshold characters. We employ a hybrid Markov chain Monte Carlo algorithm involving a Monte Carlo EM algorithm coupled with a Markov chain approximation to the likelihood, which is quite robust and provides accurate estimates of the parameters in our models. The methods are investigated using simulated data and are applied to a large data set measuring mortality rates in the fruit fly, Drosophila melanogaster.