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BMC Proc. 2014 Jun 17;8(Suppl 1):S36. doi: 10.1186/1753-6561-8-S1-S36. eCollection 2014.

Evaluation of the power and type I error of recently proposed family-based tests of association for rare variants.

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Department of Statistics, Baylor University, 1311 S 5th St., Waco, TX 76798, USA.
Department of Biostatistics, Florida International University, 11200 SW 8th St., Miami, FL 33199, USA.
Divison of Biostatistics, University of California, Berkeley, 101 Sproul Hall, Berkeley, CA 94720, USA.
Department of Mathematics and Statistics, Grinnell College, 733 Broad St., Grinnell, IA 50112, USA.
Department of Mathematics, Loyola University Chicago, 1032 W. Sheridan Rd, Chicago, IL 60660, USA.
Department of Mathematics, Statistics and Computer Science, 498 4th Ave. NE, Dordt College, Sioux Center, IA 51250, USA.
Contributed equally


Until very recently, few methods existed to analyze rare-variant association with binary phenotypes in complex pedigrees. We consider a set of recently proposed methods applied to the simulated and real hypertension phenotype as part of the Genetic Analysis Workshop 18. Minimal power of the methods is observed for genes containing variants with weak effects on the phenotype. Application of the methods to the real hypertension phenotype yielded no genes meeting a strict Bonferroni cutoff of significance. Some prior literature connects 3 of the 5 most associated genes (p <1 × 10(-4)) to hypertension or related phenotypes. Further methodological development is needed to extend these methods to handle covariates, and to explore more powerful test alternatives.

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