SNP set association analysis for genome-wide association studies.
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 stud...
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2013-01-01
|
| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC3643925?pdf=render |
| _version_ | 1818504239223144448 |
|---|---|
| author | Min Cai Hui Dai Yongyong Qiu Yang Zhao Ruyang Zhang Minjie Chu Juncheng Dai Zhibin Hu Hongbing Shen Feng Chen |
| author_facet | Min Cai Hui Dai Yongyong Qiu Yang Zhao Ruyang Zhang Minjie Chu Juncheng Dai Zhibin Hu Hongbing Shen Feng Chen |
| author_sort | Min Cai |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-10T21:34:29Z |
| format | Article |
| id | doaj.art-c3f682fea4904c1298b87fa826043c74 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-10T21:34:29Z |
| publishDate | 2013-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-c3f682fea4904c1298b87fa826043c742022-12-22T01:32:41ZengPublic Library of Science (PLoS)PLoS ONE1932-62032013-01-0185e6249510.1371/journal.pone.0062495SNP set association analysis for genome-wide association studies.Min CaiHui DaiYongyong QiuYang ZhaoRuyang ZhangMinjie ChuJuncheng DaiZhibin HuHongbing ShenFeng ChenGenome-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.http://europepmc.org/articles/PMC3643925?pdf=render |
| spellingShingle | Min Cai Hui Dai Yongyong Qiu Yang Zhao Ruyang Zhang Minjie Chu Juncheng Dai Zhibin Hu Hongbing Shen Feng Chen SNP set association analysis for genome-wide association studies. PLoS ONE |
| title | SNP set association analysis for genome-wide association studies. |
| title_full | SNP set association analysis for genome-wide association studies. |
| title_fullStr | SNP set association analysis for genome-wide association studies. |
| title_full_unstemmed | SNP set association analysis for genome-wide association studies. |
| title_short | SNP set association analysis for genome-wide association studies. |
| title_sort | snp set association analysis for genome wide association studies |
| url | http://europepmc.org/articles/PMC3643925?pdf=render |
| work_keys_str_mv | AT mincai snpsetassociationanalysisforgenomewideassociationstudies AT huidai snpsetassociationanalysisforgenomewideassociationstudies AT yongyongqiu snpsetassociationanalysisforgenomewideassociationstudies AT yangzhao snpsetassociationanalysisforgenomewideassociationstudies AT ruyangzhang snpsetassociationanalysisforgenomewideassociationstudies AT minjiechu snpsetassociationanalysisforgenomewideassociationstudies AT junchengdai snpsetassociationanalysisforgenomewideassociationstudies AT zhibinhu snpsetassociationanalysisforgenomewideassociationstudies AT hongbingshen snpsetassociationanalysisforgenomewideassociationstudies AT fengchen snpsetassociationanalysisforgenomewideassociationstudies |