Genetic variance components analysis for binary phenotypes using generalized linear mixed models (GLMMs) and Gibbs sampling

✍ Scribed by Paul R. Burton; Katrina J. Tiller; Lyle C. Gurrin; William O.C.M. Cookson; A. William Musk; Lyle J. Palmer

• Publisher: John Wiley and Sons
• Year: 1999
• Tongue: English
• Weight: 94 KB
• Volume: 17
• Category: Article
• ISSN: 0741-0395
• DOI: 10.1002/(sici)1098-2272(1999)17:2<118::aid-gepi3>3.0.co;2-v

✦ Synopsis

The common complex diseases such as asthma are an important focus of genetic research, and studies based on large numbers of simple pedigrees ascertained from population-based sampling frames are becoming commonplace. Many of the genetic and environmental factors causing these diseases are unknown and there is often a strong residual covariance between relatives even after all known determinants are taken into account. This must be modelled correctly whether scientific interest is focused on fixed effects, as in an association analysis, or on the covariances themselves. Analysis is straightforward for multivariate Normal phenotypes, but difficulties arise with other types of trait. Generalized linear mixed models (GLMMs) offer a potentially unifying approach to analysis for many classes of phenotype including multivariate Normal traits, binary traits,