Effect of linkage disequilibrium on the identification of functional variants
Article first published online: 29 NOV 2011
© 2011 Wiley Periodicals, Inc.
Supplement: Genetic Analysis Workshop 17: Approaches to Analysis of Next-Generation Sequencing Data
Volume 35, Issue Supplement 1, pages S115–S119, 2011
How to Cite
Thomas, A., Abel, H. J., Di, Y., Faye, L. L., Jin, J., Liu, J., Wu, Z. and Paterson, A. D. (2011), Effect of linkage disequilibrium on the identification of functional variants. Genet. Epidemiol., 35: S115–S119. doi: 10.1002/gepi.20660
- Issue published online: 29 NOV 2011
- Article first published online: 29 NOV 2011
- score tests;
- two-stage study designs;
- robust regression;
- higher criticism;
- principal components analysis;
- graphical modeling
We summarize the contributions of Group 9 of Genetic Analysis Workshop 17. This group addressed the problems of linkage disequilibrium and other longer range forms of allelic association when evaluating the effects of genotypes on phenotypes. Issues raised by long-range associations, whether a result of selection, stratification, possible technical errors, or chance, were less expected but proved to be important. Most contributors focused on regression methods of various types to illustrate problematic issues or to develop adaptations for dealing with high-density genotype assays. Study design was also considered, as was graphical modeling. Although no method emerged as uniformly successful, most succeeded in reducing false-positive results either by considering clusters of loci within genes or by applying smoothing metrics that required results from adjacent loci to be similar. Two unexpected results that questioned our assumptions of what is required to model linkage disequilibrium were observed. The first was that correlations between loci separated by large genetic distances can greatly inflate single-locus test statistics, and, whether the result of selection, stratification, possible technical errors, or chance, these correlations seem overabundant. The second unexpected result was that applying principal components analysis to genome-wide genotype data can apparently control not only for population structure but also for linkage disequilibrium. Genet. Epidemiol. 35:S115–S119, 2011. © 2011 Wiley Periodicals, Inc.