Department of Statistics, The Ohio State University
Statistics and Biostatistics Colloquium Series

Many complex diseases are usually considered as dichotomous traits but are also associated with quantitative biological markers or risk factors. For such dicotomous traits, if the associated quantitative trait is also linked to the chromosomal regions for the dichotomous trait, then joint analysis of dichotomous and quantitative trait should be more efficient than considering them separately. Previous studies have focused on the situation when a dichotomous trait can be modeled by a threshold process acting on a single liability distribution. However, for many complex disorders, diagnosis is generally based on a set of binary or discrete criteria. These traits cannot be modeled based on a threshold process acting on an underlying continuous trait. We propose a likelihood-based method that combines such a discrete trait and an associated quantitative trait in the analysis using affected sib-pair data. Our simulation studies suggest that joint analysis increases the power to detect linkage of dichotomous traits. We also illustratthe new method by applying it to an asthma genome scan data set and incorporate the total serum immunoglobulin E (IgE) level in the analysis.