Ordered subset analysis in genetic linkage mapping of complex traits
Version of Record online: 8 APR 2004
© 2004 Wiley-Liss, Inc.
Volume 27, Issue 1, pages 53–63, July 2004
How to Cite
Hauser, E. R., Watanabe, R. M., Duren, W. L., Bass, M. P., Langefeld, C. D. and Boehnke, M. (2004), Ordered subset analysis in genetic linkage mapping of complex traits. Genet. Epidemiol., 27: 53–63. doi: 10.1002/gepi.20000
- Issue online: 2 JUN 2004
- Version of Record online: 8 APR 2004
- Manuscript Accepted: 8 SEP 2003
- Manuscript Received: 27 AUG 2002
- NIH. Grant Numbers: MH59528, DK09525, HG00040, HG00376
- linkage analysis;
- genetic heterogeneity;
- complex traits
Etiologic heterogeneity is a fundamental feature of complex disease etiology; genetic linkage analysis methods to map genes for complex traits that acknowledge the presence of genetic heterogeneity are likely to have greater power to identify subtle changes in complex biologic systems. We investigate the use of trait-related covariates to examine evidence for linkage in the presence of heterogeneity. Ordered-subset analysis (OSA) identifies subsets of families defined by the level of a trait-related covariate that provide maximal evidence for linkage, without requiring a priori specification of the subset. We propose that examining evidence for linkage in the subset directly may result in a more etiologically homogeneous sample. In turn, the reduced impact of heterogeneity will result in increased overall evidence for linkage to a specific region and a more distinct lod score peak. In addition, identification of a subset defined by a specific trait-related covariate showing increased evidence for linkage may help refine the list of candidate genes in a given region and suggest a useful sample in which to begin searching for trait-associated polymorphisms. This method provides a means to begin to bridge the gap between initial identification of linkage and identification of the disease predisposing variant(s) within a region when mapping genes for complex diseases. We illustrate this method by analyzing data on breast cancer age of onset and chromosome 17q [Hall et al., 1990, Science 250:1684–1689]. We evaluate OSA using simulation studies under a variety of genetic models. © 2004 Wiley-Liss, Inc.