Year: 2014
Type: article
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 T(2) 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.
Source: BMC Proceedings
Authors: George Mathew, Varghese George, Hongyan Xu
Institutions: Missouri State University, Augusta University
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $2,290
Awards: Multiethnic Study of Type 2 Diabetes Genes, Identifying T2D Variants by DNA Sequencing in Multiethnic Samples, G2053005029, Identification and Replication of Type 2 Diabetes Genes in Mexican Americans, Discovery of Functional Variants in Type 2 Diabetes Genes in Mexican Americans, Identifying variants causal for Type 2 Diabetes in Major human populations, NIDDM Susceptibility Genes in Mexican Americans, Genetics of Gallbladder Disease in Mexican Americans, G725404114, Genetic Analysis of Common Diseases: An Evaluation
OpenAlex ID: W2018535772