An Integrative Computational Approach to Evaluate Genetic Markers for Bipolar Disorder

Abstract Studies to date have reported hundreds of genes connected to bipolar disorder (BP). However, many studies identifying candidate genes have lacked replication, and their results have, at times, been inconsistent with one another. This paper, therefore, offers a computational workflow that ca...

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Autores principales:	Yong Xu, Jun Wang, Shuquan Rao, McKenzie Ritter, Lydia C. Manor, Robert Backer, Hongbao Cao, Zaohuo Cheng, Sha Liu, Yansong Liu, Lin Tian, Kunlun Dong, Yin Yao Shugart, Guoqiang Wang, Fuquan Zhang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/f7aa9df1e4b148e58759b32fe1800041
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spelling	oai:doaj.org-article:f7aa9df1e4b148e58759b32fe18000412021-12-02T12:32:12ZAn Integrative Computational Approach to Evaluate Genetic Markers for Bipolar Disorder10.1038/s41598-017-05846-42045-2322https://doaj.org/article/f7aa9df1e4b148e58759b32fe18000412017-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-05846-4https://doaj.org/toc/2045-2322Abstract Studies to date have reported hundreds of genes connected to bipolar disorder (BP). However, many studies identifying candidate genes have lacked replication, and their results have, at times, been inconsistent with one another. This paper, therefore, offers a computational workflow that can curate and evaluate BP-related genetic data. Our method integrated large-scale literature data and gene expression data that were acquired from both postmortem human brain regions (BP case/control: 45/50) and peripheral blood mononuclear cells (BP case/control: 193/593). To assess the pathogenic profiles of candidate genes, we conducted Pathway Enrichment, Sub-Network Enrichment, and Gene-Gene Interaction analyses, with 4 metrics proposed and validated for each gene. Our approach developed a scalable BP genetic database (BP_GD), including BP related genes, drugs, pathways, diseases and supporting references. The 4 metrics successfully identified frequently-studied BP genes (e.g. GRIN2A, DRD1, DRD2, HTR2A, CACNA1C, TH, BDNF, SLC6A3, P2RX7, DRD3, and DRD4) and also highlighted several recently reported BP genes (e.g. GRIK5, GRM1 and CACNA1A). The computational biology approach and the BP database developed in this study could contribute to a better understanding of the current stage of BP genetic research and assist further studies in the field.Yong XuJun WangShuquan RaoMcKenzie RitterLydia C. ManorRobert BackerHongbao CaoZaohuo ChengSha LiuYansong LiuLin TianKunlun DongYin Yao ShugartGuoqiang WangFuquan ZhangNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-9 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yong Xu Jun Wang Shuquan Rao McKenzie Ritter Lydia C. Manor Robert Backer Hongbao Cao Zaohuo Cheng Sha Liu Yansong Liu Lin Tian Kunlun Dong Yin Yao Shugart Guoqiang Wang Fuquan Zhang An Integrative Computational Approach to Evaluate Genetic Markers for Bipolar Disorder
description	Abstract Studies to date have reported hundreds of genes connected to bipolar disorder (BP). However, many studies identifying candidate genes have lacked replication, and their results have, at times, been inconsistent with one another. This paper, therefore, offers a computational workflow that can curate and evaluate BP-related genetic data. Our method integrated large-scale literature data and gene expression data that were acquired from both postmortem human brain regions (BP case/control: 45/50) and peripheral blood mononuclear cells (BP case/control: 193/593). To assess the pathogenic profiles of candidate genes, we conducted Pathway Enrichment, Sub-Network Enrichment, and Gene-Gene Interaction analyses, with 4 metrics proposed and validated for each gene. Our approach developed a scalable BP genetic database (BP_GD), including BP related genes, drugs, pathways, diseases and supporting references. The 4 metrics successfully identified frequently-studied BP genes (e.g. GRIN2A, DRD1, DRD2, HTR2A, CACNA1C, TH, BDNF, SLC6A3, P2RX7, DRD3, and DRD4) and also highlighted several recently reported BP genes (e.g. GRIK5, GRM1 and CACNA1A). The computational biology approach and the BP database developed in this study could contribute to a better understanding of the current stage of BP genetic research and assist further studies in the field.
format	article
author	Yong Xu Jun Wang Shuquan Rao McKenzie Ritter Lydia C. Manor Robert Backer Hongbao Cao Zaohuo Cheng Sha Liu Yansong Liu Lin Tian Kunlun Dong Yin Yao Shugart Guoqiang Wang Fuquan Zhang
author_facet	Yong Xu Jun Wang Shuquan Rao McKenzie Ritter Lydia C. Manor Robert Backer Hongbao Cao Zaohuo Cheng Sha Liu Yansong Liu Lin Tian Kunlun Dong Yin Yao Shugart Guoqiang Wang Fuquan Zhang
author_sort	Yong Xu
title	An Integrative Computational Approach to Evaluate Genetic Markers for Bipolar Disorder
title_short	An Integrative Computational Approach to Evaluate Genetic Markers for Bipolar Disorder
title_full	An Integrative Computational Approach to Evaluate Genetic Markers for Bipolar Disorder
title_fullStr	An Integrative Computational Approach to Evaluate Genetic Markers for Bipolar Disorder
title_full_unstemmed	An Integrative Computational Approach to Evaluate Genetic Markers for Bipolar Disorder
title_sort	integrative computational approach to evaluate genetic markers for bipolar disorder
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/f7aa9df1e4b148e58759b32fe1800041
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An Integrative Computational Approach to Evaluate Genetic Markers for Bipolar Disorder

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