Extended pedigree patterned covariance matrix mixed models for quantitative phenotype analysis

Overt computational constraints in the formation of mixed models for the analysis of large extended-pedigree quantitative trait data which allow one to reliably characterize and partition sources of variation resulting from a variety sources have proven difficult to overcome. The present paper suggests that by combining a restricted patterned covariance matrix approach to modeling and partitioning the variation arising from polygenic and environmental forces with an Elston-Stewart like algorithmic approach to modeling variation resulting from a single genetic locus with large phenotypic effects one can produce a model that is at once intuitively appealing, efficient computationally, and reliable numerically. Extensions and variations of this approach are also discussed, as are some simulation and timing studies carried out in an effort to validate the accuracy and computational efficiency of the proposed methodology.