A powerful nonparametric statistical framework for family-based association analyses

Ming Li, Zihuai He, Daniel J. Schaid, Mario A. Cleves, Todd G. Nick, Qing Lu

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


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 parametricbased 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 withinfamily information, it can offer robust protection against PS. In the absence of PS, it can also utilize additional information (i.e., betweenfamily 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.

Original languageEnglish (US)
Pages (from-to)69-78
Number of pages10
Issue number1
StatePublished - Jan 1 2015


  • Between-family information
  • Nicotine dependence
  • Pedigree structure
  • Population stratification
  • Within-family information

ASJC Scopus subject areas

  • Genetics


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