Comparison of several sequence-based association methods in pedigrees

BMC Proceedings

Proceedings

Hongyan Xu¹  Varghese George¹  George Mathew² 

[1] Department of Biostatistics & Epidemiology, Georgia Regents University, 1469 Laney Walker Boulevard, 30912-4900, Augusta, Georgia, USA;Department of Mathematics, Missouri State University, 901 South National Avenue, 65897, Springfield, Missouri, USA;

关键词: Significant Gene;    Rare Variant;    Pedigree Data;    Genetic Analysis Workshop;    Real Data Analysis;   

DOI  :  10.1186/1753-6561-8-S1-S48

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY   
© Mathew et al.; licensee BioMed Central Ltd. 2014