NDLI: SNP Set Association Analysis for Genome-Wide Association Studies

Content Provider	PubMed Central
Author	Cai, Min Dai, Hui Qiu, Yongyong Zhao, Yang Zhang, Ruyang Chu, Minjie Dai, Juncheng Hu, Zhibin Shen, Hongbing Chen, Feng
Editor	Chen, Lin
Copyright Year	2013
Abstract	Genome-wide association study (GWAS) is a promising approach for identifying common genetic variants of the diseases on the basis of millions of single nucleotide polymorphisms (SNPs). In order to avoid low power caused by overmuch correction for multiple comparisons in single locus association study, some methods have been proposed by grouping SNPs together into a SNP set based on genomic features, then testing the joint effect of the SNP set. We compare the performances of principal component analysis (PCA), supervised principal component analysis (SPCA), kernel principal component analysis (KPCA), and sliced inverse regression (SIR). Simulated SNP sets are generated under scenarios of 0, 1 and ≥2 causal SNPs model. Our simulation results show that all of these methods can control the type I error at the nominal significance level. SPCA is always more powerful than the other methods at different settings of linkage disequilibrium structures and minor allele frequency of the simulated datasets. We also apply these four methods to a real GWAS of non-small cell lung cancer (NSCLC) in Han Chinese population
Related Links	http://dx.doi.org/10.1371/journal.pone.0062495
Starting Page	62495
File Format	PDF
ISSN	19326203
e-ISSN	19326203
Journal	PLoS ONE
Issue Number	5
Volume Number	8
Language	English
Publisher	Public Library of Science
Publisher Date	2013-05-01
Access Restriction	Open
Rights Holder	Public Library of Science
Subject Keyword	Biochemistry, Genetics and Molecular Biology(all) Agricultural and Biological Sciences(all) Medicine(all) Research in Higher Education
Content Type	Text
Resource Type	Article
Subject	Multidisciplinary

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