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A multivariate regression model for continuous genetic traits
Author(s) -
Yuan Ao,
Bonney George E.
Publication year - 2006
Publication title -
journal of the royal statistical society: series c (applied statistics)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.205
H-Index - 72
eISSN - 1467-9876
pISSN - 0035-9254
DOI - 10.1111/j.1467-9876.2006.00552.x
Subject(s) - multivariate statistics , univariate , covariate , covariance , multivariate analysis , statistics , econometrics , computer science , mathematics
Summary. In many commonly used models for multivariate traits, the likelihood is specified as a mixture of nested sums of products over the unobserved genotypes of all the family members, in which the familial covariance matrices vary in size and structure for different families, and their sizes can be immense for large family units. These issues pose computational difficulties in many applications. Bonney's compound regressive model for univariate traits simplifies the familial covariance structure and reduces the mixture of nested sums only to the parent–offspring level, thus enhancing computation significantly. This model has been extended to the multivariate case in the absence of unobserved genotypes. Here, we further extend this model to incorporate major genes, covariates and multiple loci. As is typical in practice, this causes new computational difficulties. We study the computational issues and explore the behaviour of this extended model.