title: Least squares estimation of variance components for linkage.
abstract: We develop least squares (LS) procedures for variance components estimation in genetic linkage studies. The LS procedure is expressed by simple expressions, and does not require inversion of large matrices. Simulations comparing LS with maximum likelihood (ML) procedures for normal data show that both yield unbiased estimators, but the efficiency of the LS procedure was less than 50% of the ML procedure. For bivariate normal data, the efficiency of the LS procedure relative to the ML method was better, generally over 60%. For skewed data, the LS method was markedly more efficient than ML for parameter estimation. The LS method was computationally rapid, over 4,000 times faster than ML estimation for bivariate data. Because ML estimation is time consuming, LS methods are suggested for initial interval mapping with multivariate data.
authors.affiliation: Department of Epidemiology, University of Texas - M.D. Anderson Cancer Center, Houston 77030, USA. camos@mdanderson.org
authors.forename: C I
authors.initials: CI
authors.lastname: Amos
authors.forename: X
authors.initials: X
authors.lastname: Gu
authors.forename: J
authors.initials: J
authors.lastname: Chen
authors.forename: B R
authors.initials: BR
authors.lastname: Davis
mesh_terms: D003198:Computer Simulation; D008040:Genetic Linkage; D014644:Genetic Variation; D016018:Least-Squares Analysis; D008957:Models, Genetic