Monte Carlo Estimation of Variance Component Models
for Large Complex Pedigrees
Guo SW, Thompson EA
Department of Biostatistics, University of Washington, Seattle 98195.
IMA Journal of Mathematics Applied
in Medicine and Biology , 8(3):171-189 (1991).
Abstract
Variance component models are widely used in animal and plant breeding. In human
genetics, they can be used to identify, among other traits associated with the definition
of disease, those that have a significant genetic component in their aetiology. In
addition, they can be used in genetic counselling. Most of the methods currently
proposed for estimating variance component models often involve repeated inversion of
large matrices, resulting in intensive computations, large storage requirements, and
numerical instability. Consequently, these methods are restricted to data on nuclear
families, to small pedigrees, or to designed pedigrees of simple form. In this paper, the
authors propose a method for estimating variance component models for large complex
pedigrees using jointly the EM algorithm and the Gibbs sampler. The method can
handle variance component models with multiple variance components, without the
need for repeated inversion of large matrices even on large complex pedigrees. The
method is conceptually simple, numerically stable, and easy to implement.
Variance component linkage analysis