title: Testing rare variants for hypertension using family-based tests with different weighting schemes.
abstract: Next-generation sequencing technology makes directly testing rare variants possible. However, existing statistical methods to detect common variants may not be optimal for testing rare variants because of allelic heterogeneity as well as the extreme rarity of individual variants. Recently, several statistical methods to detect associations of rare variants were developed, including population-based and family-based methods. Compared with population-based methods, family-based methods have more power and can prevent bias induced by population substructure. Both population-based and family-based methods for rare variant association studies are essentially testing the effect of a weighted combination of variants or its function. How to model the weights is critical for the testing power because the number of observations for any given rare variant is small and the multiple-test correction is more stringent for rare variants. We propose 4 weighting schemes for the family-based rare variants test (FBAT-v) to test for the effects of both rare and common variants across the genome. Applying FBAT-v with the proposed weighting schemes on the Genetic Analysis Workshop 19 family data indicates that the power of FBAT-v can be comparatively enhanced in most circumstances.
authors.affiliation: Department of Mathematics, University of North Texas, Denton, TX 76203 USA.
authors.forename: Xuexia
authors.initials: X
authors.lastname: Wang
authors.affiliation: Joseph J. Zilber School of Public Health, University of Wisconsin-Milwaukee, Milwaukee, WI 53205 USA.
authors.forename: Xingwang
authors.initials: X
authors.lastname: Zhao
authors.affiliation: Division of Epidemiology and Biostatistics of Mel and Enid Zuckerman College of Public Health, University of Arizona, 1295 N. Martin Ave., Campus PO Box: 245211, Drachman Hall A242, Tucson, AZ 85724 USA.
authors.forename: Jin
authors.initials: J
authors.lastname: Zhou