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- Title
A generalized least-squares framework for rare-variant analysis in family data.
- Authors
Dalin Li; Rotter, Jerome I.; Xiuqing Guo
- Abstract
Rare variants may, in part, explain some of the hereditability missing in current genome-wide association studies. Many gene-based rare-variant analysis approaches proposed in recent years are aimed at population-based samples, although analysis strategies for family-based samples are clearly warranted since the family-based design has the potential to enhance our ability to enrich for rare causal variants. We have recently developed the generalized least squares, sequence kernel association test, or GLS-SKAT, approach for the rare-variant analyses in family samples, in which the kinship matrix that was computed from the high dimension genetic data was used to decorrelate the family structure. We then applied the SKAT-O approach for gene-/region-based inference in the decorrelated data. In this study, we applied this GLS-SKAT method to the systolic blood pressure data in the simulated family sample distributed by the Genetic Analysis Workshop 18. We compared the GLS-SKAT approach to the rare-variant analysis approach implemented in family-based association test-v1 and demonstrated that the GLS-SKAT approach provides superior power and good control of type I error rate.
- Subjects
MATHEMATICAL models; FAMILIES; STATISTICAL methods in genetics; LEAST squares; ANALYSIS of variance; POPULATION genetics
- Publication
BMC Proceedings, 2014, Vol 9, Issue Supplement 5, p1
- ISSN
1753-6561
- Publication type
Article
- DOI
10.1186/1753-6561-8-S1-S28