Estimating polygenic models for multivariate data on large pedigrees

E. A. Thompson, R. G. Shaw

Research output: Contribution to journalArticlepeer-review

13 Scopus citations


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
Issue number4
StatePublished - Jan 1 1992


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