Rights Creative Commons: Attribution 3.0 Hong Kong License ORIGINAL RESEARCH Matched Ascertainment of Informative Families for Complex Genetic Modelling

Abstract Family data are used extensively in quantitative genetic studies to disentangle the genetic and environ-mental 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 compu-tational 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 confound-ers. We have developed a data-reduction method of ascertaining informative families from population-based family registers. We propose a scheme where the ascer-tained 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