Title: A powerful non-parametric statistical framework for family-based association analyses

The Framingham Heart Study dataset used for the analyses was obtained from the National Center for Biotechnology Information database of genotypes and phenotypes (NCBI dbGaP) through accession number phs000128.v3.p3. ABSTRACT Family-based study design is commonly used in genetic research. It has many ideal features, including being robust to population stratification (PS). With the advance of high-throughput technologies and ever-decreasing genotyping cost, it has become common for family studies to examine a large number of variants for their associations with disease phenotypes. The yield from the analysis of these family-based genetic data can be enhanced by adopting computationally efficient and powerful statistical methods. We propose a general framework of family-based U-statistic, referred to as family-U, for family-based association studies. Unlike existing parametric-based methods, the proposed method makes no assumption of the underlying disease models, and can be applied to various phenotypes (e.g. binary and quantitative phenotypes) and pedigree structures (e.g., nuclear families and extended pedigrees). By only using within-family information, it can offer robust protection against PS. In the absence of PS, it can also utilize additional information (i.e., between-family information) for power improvement. Through simulations, we demonstrated that family-U attained higher power over a commonly used method, FBAT, under various disease scenarios. We further illustrated the new method with an application to a large-scale family data from Framingham Heart Study. By utilizing additional information (i.e., between-family information), family-U confirmed a previous association of CHRNA5 with nicotine dependence.