首页 | 本学科首页   官方微博 | 高级检索  
   检索      


A note on the accuracy of PAC-likelihood inference with microsatellite data
Authors:Cornuet J M  Beaumont M A
Institution:Centre de Biologie et de Gestion des Populations, Institut National de la Recherche Agronomique, Campus International de Baillarguet, CS 30016 Montferrier-sur-Lez, 34988 Saint-Gély-du-Fesc Cedex, France. jmcornuet@ensam.inra.fr
Abstract:Stephens and Donnelly have introduced a simple yet powerful importance sampling scheme for computing the likelihood in population genetic models. Fundamental to the method is an approximation to the conditional probability of the allelic type of an additional gene, given those currently in the sample. As noted by Li and Stephens, the product of these conditional probabilities for a sequence of draws that gives the frequency of allelic types in a sample is an approximation to the likelihood, and can be used directly in inference. The aim of this note is to demonstrate the high level of accuracy of "product of approximate conditionals" (PAC) likelihood when used with microsatellite data. Results obtained on simulated microsatellite data show that this strategy leads to a negligible bias over a wide range of the scaled mutation parameter theta. Furthermore, the sampling variance of likelihood estimates as well as the computation time are lower than that obtained with importance sampling on the whole range of theta. It follows that this approach represents an efficient substitute to IS algorithms in computer intensive (e.g. MCMC) inference methods in population genetics.
Keywords:Microsatellite  Mutation model  Importance sampling  PAC-likelihood
本文献已被 ScienceDirect PubMed 等数据库收录!
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号