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Least squares estimation of variance components for linkage
Author(s) -
Amos Christopher I.,
Gu Xiangjun,
Chen Jianfang,
Davis Barry R.
Publication year - 2000
Publication title -
genetic epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.301
H-Index - 98
eISSN - 1098-2272
pISSN - 0741-0395
DOI - 10.1002/1098-2272(2000)19:1+<::aid-gepi1>3.0.co;2-0
Subject(s) - bivariate analysis , statistics , mathematics , estimator , efficiency , least squares function approximation , variance (accounting) , multivariate normal distribution , multivariate statistics , generalized least squares , variance components , accounting , business
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 procedures. 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. Genet. Epidemiol. 19(Suppl 1):S1–S7, 2000. © 2000 Wiley‐Liss, Inc.