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Model-based clustering for identifying disease-associated SNPs in case-control genome-wide association studies
Genome-wide association studies (GWASs) aim to detect genetic risk factors for complex human diseases by identifying disease-associated single-nucleotide polymorphisms (SNPs). The traditional SNP-wise approach along with multiple testing adjustment is over-conservative and lack of power in many GWAS...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6757104/ https://www.ncbi.nlm.nih.gov/pubmed/31548641 http://dx.doi.org/10.1038/s41598-019-50229-6 |
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author | Xu, Yan Xing, Li Su, Jessica Zhang, Xuekui Qiu, Weiliang |
author_facet | Xu, Yan Xing, Li Su, Jessica Zhang, Xuekui Qiu, Weiliang |
author_sort | Xu, Yan |
collection | PubMed |
description | Genome-wide association studies (GWASs) aim to detect genetic risk factors for complex human diseases by identifying disease-associated single-nucleotide polymorphisms (SNPs). The traditional SNP-wise approach along with multiple testing adjustment is over-conservative and lack of power in many GWASs. In this article, we proposed a model-based clustering method that transforms the challenging high-dimension-small-sample-size problem to low-dimension-large-sample-size problem and borrows information across SNPs by grouping SNPs into three clusters. We pre-specify the patterns of clusters by minor allele frequencies of SNPs between cases and controls, and enforce the patterns with prior distributions. In the simulation studies our proposed novel model outperforms traditional SNP-wise approach by showing better controls of false discovery rate (FDR) and higher sensitivity. We re-analyzed two real studies to identifying SNPs associated with severe bortezomib-induced peripheral neuropathy (BiPN) in patients with multiple myeloma (MM). The original analysis in the literature failed to identify SNPs after FDR adjustment. Our proposed method not only detected the reported SNPs after FDR adjustment but also discovered a novel BiPN-associated SNP rs4351714 that has been reported to be related to MM in another study. |
format | Online Article Text |
id | pubmed-6757104 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-67571042019-10-02 Model-based clustering for identifying disease-associated SNPs in case-control genome-wide association studies Xu, Yan Xing, Li Su, Jessica Zhang, Xuekui Qiu, Weiliang Sci Rep Article Genome-wide association studies (GWASs) aim to detect genetic risk factors for complex human diseases by identifying disease-associated single-nucleotide polymorphisms (SNPs). The traditional SNP-wise approach along with multiple testing adjustment is over-conservative and lack of power in many GWASs. In this article, we proposed a model-based clustering method that transforms the challenging high-dimension-small-sample-size problem to low-dimension-large-sample-size problem and borrows information across SNPs by grouping SNPs into three clusters. We pre-specify the patterns of clusters by minor allele frequencies of SNPs between cases and controls, and enforce the patterns with prior distributions. In the simulation studies our proposed novel model outperforms traditional SNP-wise approach by showing better controls of false discovery rate (FDR) and higher sensitivity. We re-analyzed two real studies to identifying SNPs associated with severe bortezomib-induced peripheral neuropathy (BiPN) in patients with multiple myeloma (MM). The original analysis in the literature failed to identify SNPs after FDR adjustment. Our proposed method not only detected the reported SNPs after FDR adjustment but also discovered a novel BiPN-associated SNP rs4351714 that has been reported to be related to MM in another study. Nature Publishing Group UK 2019-09-23 /pmc/articles/PMC6757104/ /pubmed/31548641 http://dx.doi.org/10.1038/s41598-019-50229-6 Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Xu, Yan Xing, Li Su, Jessica Zhang, Xuekui Qiu, Weiliang Model-based clustering for identifying disease-associated SNPs in case-control genome-wide association studies |
title | Model-based clustering for identifying disease-associated SNPs in case-control genome-wide association studies |
title_full | Model-based clustering for identifying disease-associated SNPs in case-control genome-wide association studies |
title_fullStr | Model-based clustering for identifying disease-associated SNPs in case-control genome-wide association studies |
title_full_unstemmed | Model-based clustering for identifying disease-associated SNPs in case-control genome-wide association studies |
title_short | Model-based clustering for identifying disease-associated SNPs in case-control genome-wide association studies |
title_sort | model-based clustering for identifying disease-associated snps in case-control genome-wide association studies |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6757104/ https://www.ncbi.nlm.nih.gov/pubmed/31548641 http://dx.doi.org/10.1038/s41598-019-50229-6 |
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