Estimating Polygenic Models for Multivariate Data on Large Pedigrees
AUTOR(ES)
Thompson, E. A.
RESUMO
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.
ACESSO AO ARTIGO
http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=1205106Documentos Relacionados
- Skew normal mixed models in microarray data generated from complex pedigrees
- Models of speciation by sexual selection on polygenic traits
- Estimating Coarse Gene Network Structure from Large-Scale Gene Perturbation Data
- Group selection for a polygenic behavioral trait: estimating the degree of population subdivision.
- Multivariate regression models for the simultaneous quantitative analysis of calcium and magnesium carbonates and magnesium oxide through drifts data