Ordered-subset analysis (OSA) for family-based association mapping of complex traits.

Journal Article

Association analysis provides a powerful tool for complex disease gene mapping. However, in the presence of genetic heterogeneity, the power for association analysis can be low since only a fraction of the collected families may carry a specific disease susceptibility allele. Ordered-subset analysis (OSA) is a linkage test that can be powerful in the presence of genetic heterogeneity. OSA uses trait-related covariates to identify a subset of families that provide the most evidence for linkage. A similar strategy applied to genetic association analysis would likely result in increased power to detect association. Association in the presence of linkage (APL) is a family-based association test (FBAT) for nuclear families with multiple affected siblings that properly infers missing parental genotypes when linkage is present. We propose here APL-OSA, which applies the OSA method to the APL statistic to identify a subset of families that provide the most evidence for association. A permutation procedure is used to approximate the distribution of the APL-OSA statistic under the null hypothesis that there is no relationship between the family-specific covariate and the family-specific evidence for allelic association. We performed a comprehensive simulation study to verify that APL-OSA has the correct type I error rate under the null hypothesis. This simulation study also showed that APL-OSA can increase power relative to other commonly used association tests (APL, FBAT and FBAT with covariate adjustment) in the presence of genetic heterogeneity. Finally, we applied APL-OSA to a family study of age-related macular degeneration, where cigarette smoking was used as a covariate.

Full Text

Duke Authors

Cited Authors

  • Chung, R-H; Schmidt, S; Martin, ER; Hauser, ER

Published Date

  • November 2008

Published In

Volume / Issue

  • 32 / 7

Start / End Page

  • 627 - 637

PubMed ID

  • 18473393

Electronic International Standard Serial Number (EISSN)

  • 1098-2272

Digital Object Identifier (DOI)

  • 10.1002/gepi.20340

Language

  • eng

Conference Location

  • United States