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An EM algorithm for obtaining maximum likelihood estimates in the multi‐phenotype variance components linkage model
Author(s) -
ITURRIA S. J.,
BLANGERO J.
Publication year - 2000
Publication title -
annals of human genetics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.537
H-Index - 77
eISSN - 1469-1809
pISSN - 0003-4800
DOI - 10.1046/j.1469-1809.2000.6440349.x
Subject(s) - linkage (software) , variance (accounting) , multivariate statistics , maximum likelihood , expectation–maximization algorithm , multivariate normal distribution , statistics , restricted maximum likelihood , algorithm , computer science , estimation theory , multivariate analysis , mathematics , biology , genetics , gene , accounting , business
In recent years variance components models have been developed for localising genes that contribute to human quantitative variation. In typical applications one assumes a multivariate normal model for phenotypes and estimates model parameters by maximum likelihood. For the joint analysis of several correlated phenotypes, however, finding the maximum likelihood estimates for an appropriate multivariate normal model can be a difficult computational task due to complex constraints among the model parameters. We propose an algorithm for computing maximum likelihood estimates in a multi‐phenotype variance components linkage model that readily accommodates these parameter constraints. Data simulated for Genetic Analysis Workshop 10 are used to demonstrate the potential increase in power to detect linkage that can be obtained if correlated phenotypes are analysed jointly rather than individually.