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

BMC Proceedings

Proceedings

Carolina Alvarez¹  Brian Greco²  Andrew Beck³  Nathan L Tintle⁴  Allison Hainline⁵  Alexander Luedtke⁶ 

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

关键词: Rare Variant;    Kinship Matrix;    Rare Genetic Variation;    GAW18 Data;    Simulated Phenotype;   

DOI  :  10.1186/1753-6561-8-S1-S36

来源: Springer

【 摘 要 】

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.

【 授权许可】

Unknown   
© Hainline et al.; licensee BioMed Central Ltd. 2014. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.