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

✍ Scribed by Ren-Hua Chung; Silke Schmidt; Eden R. Martin; Elizabeth R. Hauser

• Publisher: John Wiley and Sons
• Year: 2008
• Tongue: English
• Weight: 212 KB
• Volume: 32
• Category: Article
• ISSN: 0741-0395
• DOI: 10.1002/gepi.20340

✦ Synopsis

Abstract

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. Genet. Epidemiol. 2008. © 2008 Wiley‐Liss, Inc.