Abstract

Genome-wide association studies are very powerful in determining the genetic variants affecting complex diseases. Most of the available methods are very useful in detecting association between common variants and complex diseases. Recently, methods to detect rare variants in association with complex diseases have been developed with the increasingly available sequencing data from next-generation sequencing. In this paper, we evaluate and compare several of these recent methods for performing statistical association using whole genome sequencing data in pedigrees. Specifically, functional principal component analysis (FPCA), extended combined multivariate and collapsing (CMC) method for families, a generalized T2 method, and chi-square minimum approach were compared by analyzing all the genetic variants, common and rare, of both the real data set and the simulated data set provided as part of Genetic Analysis Workshop 18.

Department(s)

Mathematics

Document Type

Conference Proceeding

DOI

https://doi.org/10.1186/1753-6561-8-S1-S48

Rights Information

© 2014 The authors; licensee 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.

Publication Date

2014

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