title: Semiparametric estimation of major gene effects for age of onset.
abstract: Analysis of age of onset is a key factor in the segregation and linkage analysis of some complex genetic traits. Previous work in the genetics literature has used parametric distributional assumptions on age of onset. In this paper, a Cox model with latent major gene effects is used: a semiparametric model with unspecified baseline hazard. A Monte Carlo EM procedure is used to obtain maximum likelihood estimates. Markov chain Monte Carlo is used to realize genotypic configurations from the posterior distribution given the current model and the observed data, and these genotypic configurations are used to estimate the expectations in the EM algorithm. Simulated data sets indicate that the parameters can be estimated well, and one real data set shows the practical applicability of the proposed method.
authors.affiliation: Section of Biostatistics, Mayo Clinic/Foundation, Rochester, Minnesota, USA.
authors.forename: H
authors.initials: H
authors.lastname: Li
authors.forename: E A
authors.initials: EA
authors.lastname: Thompson
authors.forename: E M
authors.initials: EM
authors.lastname: Wijsman
mesh_terms: D017677:Age Distribution; D017668:Age of Onset; D000544:Alzheimer Disease / Q000235:genetics; D003198:Computer Simulation; D016001:Confidence Intervals; D005260:Female; D030342:Genetic Diseases, Inborn / Q000235:genetics*; D008040:Genetic Linkage; D006801:Humans; D016013:Likelihood Functions; D008297:Male; D008540:Meiosis / Q000235:genetics*; D008957:Models, Genetic; D015233:Models, Statistical; D009010:Monte Carlo Method; D010375:Pedigree; D015203:Reproducibility of Results