A powerful nonparametric statistical framework for family-based association analyses.
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 a 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 using only 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, family-based association tests, under various disease scenarios. We further illustrated the new method with an application to large-scale family data from the Framingham Heart Study. By utilizing additional information (i.e., between-family information), family-U confirmed a previous association of CHRNA5 with nicotine dependence.
Division of Biostatistics, Department of Pediatrics, University of Arkansas for Medical Sciences, Little Rock, Arkansas 72202.
Ming
M
Li
Department of Biostatistics, University of Michigan, Ann Arbor, Michigan 48109.
Zihuai
Z
He
Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota 55905.
Daniel J
DJ
Schaid
Division of Biostatistics, Department of Pediatrics, University of Arkansas for Medical Sciences, Little Rock, Arkansas 72202.
Mario A
MA
Cleves
Division of Biostatistics, Department of Pediatrics, University of Arkansas for Medical Sciences, Little Rock, Arkansas 72202.
Todd G
TG
Nick
Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, Michigan 48824 qlu@epi.msu.edu.
Qing
http://orcid.org/0000-0002-7943-966X
Q
Lu
D056726:Genetic Association Studies / Q000379:methods*; D006801:Humans; D008957:Models, Genetic; D010375:Pedigree; D018709:Statistics, Nonparametric
between-family information; nicotine dependence; pedigree structure; population stratification; within-family information