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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doaj-f7aa9df1e4b148e58759b32fe18000412020-12-08T01:03:17ZengNature Publishing GroupScientific Reports2045-23222017-07-01711910.1038/s41598-017-05846-4An Integrative Computational Approach to Evaluate Genetic Markers for Bipolar DisorderYong Xu0Jun Wang1Shuquan Rao2McKenzie Ritter3Lydia C. Manor4Robert Backer5Hongbao Cao6Zaohuo Cheng7Sha Liu8Yansong Liu9Lin Tian10Kunlun Dong11Yin Yao Shugart12Guoqiang Wang13Fuquan Zhang14Department of Psychiatry, First Clinical Medical College/First Hospital of Shanxi Medical UniversityWuxi Mental Health Center, Nanjing Medical UniversitySchool of Life Science and Engineering, Southwest Jiaotong UniversityUnit on Statistical Genomics, National Institute of Mental Health, National Institutes of HealthAmerican Informatics Consultant LLCDepartment of Psychological & Brain Sciences, University of DelawareUnit on Statistical Genomics, National Institute of Mental Health, National Institutes of HealthWuxi Mental Health Center, Nanjing Medical UniversityDepartment of Psychiatry, First Clinical Medical College/First Hospital of Shanxi Medical UniversityWuxi Mental Health Center, Nanjing Medical UniversityWuxi Mental Health Center, Nanjing Medical UniversityWuxi Mental Health Center, Nanjing Medical UniversityUnit on Statistical Genomics, National Institute of Mental Health, National Institutes of HealthWuxi Mental Health Center, Nanjing Medical UniversityWuxi Mental Health Center, Nanjing Medical UniversityAbstract 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.https://doi.org/10.1038/s41598-017-05846-4 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
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 |
spellingShingle |
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 Scientific Reports |
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 Publishing Group |
series |
Scientific Reports |
issn |
2045-2322 |
publishDate |
2017-07-01 |
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. |
url |
https://doi.org/10.1038/s41598-017-05846-4 |
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