Identification of genes for complex diseases using integrated analysis of multiple types of genomic data.

Various types of genomic data (e.g., SNPs and mRNA transcripts) have been employed to identify risk genes for complex diseases. However, the analysis of these data has largely been performed in isolation. Combining these multiple data for integrative analysis can take advantage of complementary info...

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Autores principales:	Hongbao Cao, Shufeng Lei, Hong-Wen Deng, Yu-Ping Wang
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2012
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/a0c4983cd43347b9acc36946ec2c57b9
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spelling	oai:doaj.org-article:a0c4983cd43347b9acc36946ec2c57b92021-11-18T07:06:37ZIdentification of genes for complex diseases using integrated analysis of multiple types of genomic data.1932-620310.1371/journal.pone.0042755https://doaj.org/article/a0c4983cd43347b9acc36946ec2c57b92012-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22957024/?tool=EBIhttps://doaj.org/toc/1932-6203Various types of genomic data (e.g., SNPs and mRNA transcripts) have been employed to identify risk genes for complex diseases. However, the analysis of these data has largely been performed in isolation. Combining these multiple data for integrative analysis can take advantage of complementary information and thus can have higher power to identify genes (and/or their functions) that would otherwise be impossible with individual data analysis. Due to the different nature, structure, and format of diverse sets of genomic data, multiple genomic data integration is challenging. Here we address the problem by developing a sparse representation based clustering (SRC) method for integrative data analysis. As an example, we applied the SRC method to the integrative analysis of 376821 SNPs in 200 subjects (100 cases and 100 controls) and expression data for 22283 genes in 80 subjects (40 cases and 40 controls) to identify significant genes for osteoporosis (OP). Comparing our results with previous studies, we identified some genes known related to OP risk (e.g., 'THSD4', 'CRHR1', 'HSD11B1', 'THSD7A', 'BMPR1B' 'ADCY10', 'PRL', 'CA8','ESRRA', 'CALM1', 'CALM1', 'SPARC', and 'LRP1'). Moreover, we uncovered novel osteoporosis susceptible genes ('DICER1', 'PTMA', etc.) that were not found previously but play functionally important roles in osteoporosis etiology from existing studies. In addition, the SRC method identified genes can lead to higher accuracy for the diagnosis/classification of osteoporosis subjects when compared with the traditional T-test and Fisher-exact test, which further validates the proposed SRC approach for integrative analysis.Hongbao CaoShufeng LeiHong-Wen DengYu-Ping WangPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 7, Iss 9, p e42755 (2012)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Hongbao Cao Shufeng Lei Hong-Wen Deng Yu-Ping Wang Identification of genes for complex diseases using integrated analysis of multiple types of genomic data.
description	Various types of genomic data (e.g., SNPs and mRNA transcripts) have been employed to identify risk genes for complex diseases. However, the analysis of these data has largely been performed in isolation. Combining these multiple data for integrative analysis can take advantage of complementary information and thus can have higher power to identify genes (and/or their functions) that would otherwise be impossible with individual data analysis. Due to the different nature, structure, and format of diverse sets of genomic data, multiple genomic data integration is challenging. Here we address the problem by developing a sparse representation based clustering (SRC) method for integrative data analysis. As an example, we applied the SRC method to the integrative analysis of 376821 SNPs in 200 subjects (100 cases and 100 controls) and expression data for 22283 genes in 80 subjects (40 cases and 40 controls) to identify significant genes for osteoporosis (OP). Comparing our results with previous studies, we identified some genes known related to OP risk (e.g., 'THSD4', 'CRHR1', 'HSD11B1', 'THSD7A', 'BMPR1B' 'ADCY10', 'PRL', 'CA8','ESRRA', 'CALM1', 'CALM1', 'SPARC', and 'LRP1'). Moreover, we uncovered novel osteoporosis susceptible genes ('DICER1', 'PTMA', etc.) that were not found previously but play functionally important roles in osteoporosis etiology from existing studies. In addition, the SRC method identified genes can lead to higher accuracy for the diagnosis/classification of osteoporosis subjects when compared with the traditional T-test and Fisher-exact test, which further validates the proposed SRC approach for integrative analysis.
format	article
author	Hongbao Cao Shufeng Lei Hong-Wen Deng Yu-Ping Wang
author_facet	Hongbao Cao Shufeng Lei Hong-Wen Deng Yu-Ping Wang
author_sort	Hongbao Cao
title	Identification of genes for complex diseases using integrated analysis of multiple types of genomic data.
title_short	Identification of genes for complex diseases using integrated analysis of multiple types of genomic data.
title_full	Identification of genes for complex diseases using integrated analysis of multiple types of genomic data.
title_fullStr	Identification of genes for complex diseases using integrated analysis of multiple types of genomic data.
title_full_unstemmed	Identification of genes for complex diseases using integrated analysis of multiple types of genomic data.
title_sort	identification of genes for complex diseases using integrated analysis of multiple types of genomic data.
publisher	Public Library of Science (PLoS)
publishDate	2012
url	https://doaj.org/article/a0c4983cd43347b9acc36946ec2c57b9
work_keys_str_mv	AT hongbaocao identificationofgenesforcomplexdiseasesusingintegratedanalysisofmultipletypesofgenomicdata AT shufenglei identificationofgenesforcomplexdiseasesusingintegratedanalysisofmultipletypesofgenomicdata AT hongwendeng identificationofgenesforcomplexdiseasesusingintegratedanalysisofmultipletypesofgenomicdata AT yupingwang identificationofgenesforcomplexdiseasesusingintegratedanalysisofmultipletypesofgenomicdata
_version_	1718423931821490176

Identification of genes for complex diseases using integrated analysis of multiple types of genomic data.

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