title: Matched ascertainment of informative families for complex genetic modelling.
abstract: Family data are used extensively in quantitative genetic studies to disentangle the genetic and environmental contributions to various diseases. Many family studies based their analysis on population-based registers containing a large number of individuals composed of small family units. For binary trait analyses, exact marginal likelihood is a common approach, but, due to the computational demand of the enormous data sets, it allows only a limited number of effects in the model. This makes it particularly difficult to perform joint estimation of variance components for a binary trait and the potential confounders. We have developed a data-reduction method of ascertaining informative families from population-based family registers. We propose a scheme where the ascertained families match the full cohort with respect to some relevant statistics, such as the risk to relatives of an affected individual. The ascertainment-adjusted analysis, which we implement using a pseudo-likelihood approach, is shown to be efficient relative to the analysis of the whole cohort and robust to mis-specification of the random effect distribution.
authors.affiliation: Department of Psychiatry, University of Hong Kong, Hong Kong, China.
authors.forename: Benjamin H
authors.initials: BH
authors.lastname: Yip
authors.forename: Marie
authors.initials: M
authors.lastname: Reilly
authors.forename: Sven
authors.initials: S
authors.lastname: Cnattingius
authors.forename: Yudi
authors.initials: Y
authors.lastname: Pawitan
mesh_terms: D000465:Algorithms; D015331:Cohort Studies; D003198:Computer Simulation; D004777:Environment; D005192:Family Health; D055106:Genome-Wide Association Study; D006801:Humans; D016013:Likelihood Functions; D008957:Models, Genetic; D015233:Models, Statistical; D009010:Monte Carlo Method; D016017:Odds Ratio; D012044:Regression Analysis; D015203:Reproducibility of Results; D012107:Research Design