Estimating polygenic models for multivariate data on large pedigrees

E. A. Thompson, R. G. Shaw

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

We have developed algorithms for the likelihood estimation of additive genetic models for quantitative traits on large pedigrees. The approach uses the expectation L-maximization (EM) algorithm, but avoids intensive computation. In this paper, we focus on extensions of previous work to the case of multivariate data. We exemplify the approach by analyses of bivariate data on a four-generation, 949-member pedigree of the snail Lymnaea elodes, and on a three-generation pedigree of the guppy Poecilia reticulata containing about 400 individuals.

Original languageEnglish (US)
Pages (from-to)971-978
Number of pages8
JournalGenetics
Volume131
Issue number4
StatePublished - Jan 1 1992

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