Methods to Estimate Genetic Components of Variance for
Quantitative Traits in Family Studies
de Andrade M, Amos CI, Thiel TJ
Department of Epidemiology, The University of Texas M.D. Anderson Cancer Center,
Houston 77030, USA. mandrade@request.mdacc.tmc.edu
Genetic Epidemiology, 17(1):64-76 (1999)
Abstract
The aim of this paper was to compare several methods of estimating the genetic
components of a quantitative trait in familial data. The Expectation and Maximization
(E-M) algorithm, the Newton-Raphson method, and the scoring method were
compared for estimating polygenic and environmental effects on nuclear families. We
also compared scoring and quasilikelihood (QL) methods when a linked genetic marker
was available to estimate effects from a major gene. Generally, all procedures performed
similarly in estimating polygenic and environmental variance components. The E-M
algorithm yielded more precise estimators when heritability was low. The scoring
method was much faster than the other methods and yielded slightly more precise
estimates of mean effects but slightly less precise estimates of the variance components.
Estimates of major gene effects were not affected by the number of alleles at the trait
locus. For these relatively large sample sizes, QL and scoring had similar precision, but
QL took 32 times longer than scoring. Finally, we compared the results of applying
these methods to data from the Bogalusa Heart Study. Results showed larger
imprecision when the QL method was applied, consistent with earlier studies that
showed decreased precision of quasilikelihood compared with maximum likelihood in
moderately small sample sizes.
Variance component linkage analysis