When Less Is More: A Multi-locus Strategy for Family-based Association Studies

Abstract: In genetic studies it is critical to develop methods that can efficiently analyzing multiple genetic markers, as complex diseases are caused by multiple risk factors. For family-based association studies, we address this challenge by evaluating two pseudosibship methods: the 1:1 matching, which matches each affected offspring to the pseudo sibling formed by the alleles not transmitted to the affected offspring; the exhaustive matching, which matches each affected offspring to the pseudo siblings formed by all the other possible combinations of parental alleles. We prove that the two matching strategies use exactly and approximately the same amount of information from data under additive and multiplicative genetic models, respectively. This result paves the way for many existing multi-locus analysis methods developed for the case-control (or matched case-control) design to be applied to case-parents data with minor modifications. As an example, we apply an L1 regularized regression to a Crohn’s disease dataset using the 1:1 matching.