Multivariate genome wide association and network analysis of subcortical imaging phenotypes in Alzheimer's disease
Genome-wide association studies (GWAS) have identified many individual genes associated with brain imaging quantitative traits (QTs) in Alzheimer's disease (AD). However single marker level association discovery may not be able to address the underlying biological interactions with disease mech...
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| Veröffentlicht in: | BMC genomics 2020-12, Vol.21 (Suppl 11), p.896-896, Article 896 |
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| creator | Meng, Xianglian Li, Jin Zhang, Qiushi Chen, Feng Bian, Chenyuan Yao, Xiaohui Yan, Jingwen Xu, Zhe Risacher, Shannon L Saykin, Andrew J Liang, Hong Shen, Li |
| description | Genome-wide association studies (GWAS) have identified many individual genes associated with brain imaging quantitative traits (QTs) in Alzheimer's disease (AD). However single marker level association discovery may not be able to address the underlying biological interactions with disease mechanism.
In this paper, we used the MGAS (Multivariate Gene-based Association test by extended Simes procedure) tool to perform multivariate GWAS on eight AD-relevant subcortical imaging measures. We conducted multiple iPINBPA (integrative Protein-Interaction-Network-Based Pathway Analysis) network analyses on MGAS findings using protein-protein interaction (PPI) data, and identified five Consensus Modules (CMs) from the PPI network. Functional annotation and network analysis were performed on the identified CMs. The MGAS yielded significant hits within APOE, TOMM40 and APOC1 genes, which were known AD risk factors, as well as a few new genes such as LAMA1, XYLB, HSD17B7P2, and NPEPL1. The identified five CMs were enriched by biological processes related to disorders such as Alzheimer's disease, Legionellosis, Pertussis, and Serotonergic synapse.
The statistical power of coupling MGAS with iPINBPA was higher than traditional GWAS method, and yielded new findings that were missed by GWAS. This study provides novel insights into the molecular mechanism of Alzheimer's Disease and will be of value to novel gene discovery and functional genomic studies. |
| doi_str_mv | 10.1186/s12864-020-07282-7 |
| format | Article |
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In this paper, we used the MGAS (Multivariate Gene-based Association test by extended Simes procedure) tool to perform multivariate GWAS on eight AD-relevant subcortical imaging measures. We conducted multiple iPINBPA (integrative Protein-Interaction-Network-Based Pathway Analysis) network analyses on MGAS findings using protein-protein interaction (PPI) data, and identified five Consensus Modules (CMs) from the PPI network. Functional annotation and network analysis were performed on the identified CMs. The MGAS yielded significant hits within APOE, TOMM40 and APOC1 genes, which were known AD risk factors, as well as a few new genes such as LAMA1, XYLB, HSD17B7P2, and NPEPL1. The identified five CMs were enriched by biological processes related to disorders such as Alzheimer's disease, Legionellosis, Pertussis, and Serotonergic synapse.
The statistical power of coupling MGAS with iPINBPA was higher than traditional GWAS method, and yielded new findings that were missed by GWAS. This study provides novel insights into the molecular mechanism of Alzheimer's Disease and will be of value to novel gene discovery and functional genomic studies.</description><identifier>ISSN: 1471-2164</identifier><identifier>EISSN: 1471-2164</identifier><identifier>DOI: 10.1186/s12864-020-07282-7</identifier><identifier>PMID: 33372590</identifier><language>eng</language><publisher>England: BioMed Central Ltd</publisher><subject>Alzheimer Disease - diagnostic imaging ; Alzheimer Disease - genetics ; Alzheimer's disease ; Analysis ; Annotations ; Apolipoprotein E ; Biological activity ; Biomarkers ; Brain - diagnostic imaging ; Brain imaging ; Consensus modules ; Coupling (molecular) ; Demographic aspects ; Diagnosis ; Disease ; Genes ; Genetic aspects ; Genetic markers ; Genetic Predisposition to Disease ; Genome-wide association studies ; Genome-Wide Association Study ; Genomes ; Genomics ; Humans ; iPINBPA network analysis ; Medical imaging ; Multivariate analysis ; Multivariate gene-based genome-wide analysis ; Network analysis ; Neurodegenerative diseases ; Neuroimaging ; Pertussis ; Phenotype ; Phenotypes ; Polymorphism, Single Nucleotide ; Protein interaction ; Protein Interaction Maps ; Proteins ; Risk analysis ; Risk factors ; Synapses</subject><ispartof>BMC genomics, 2020-12, Vol.21 (Suppl 11), p.896-896, Article 896</ispartof><rights>COPYRIGHT 2020 BioMed Central Ltd.</rights><rights>2020. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>The Author(s) 2020</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c597t-28c4e1153ab147afa6b0403e338d6809a8cc70212a2b3608dd8ff878ba31f5953</citedby><cites>FETCH-LOGICAL-c597t-28c4e1153ab147afa6b0403e338d6809a8cc70212a2b3608dd8ff878ba31f5953</cites><orcidid>0000-0002-2590-7210</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7771059/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7771059/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,724,777,781,861,882,2096,27905,27906,53772,53774</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/33372590$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Meng, Xianglian</creatorcontrib><creatorcontrib>Li, Jin</creatorcontrib><creatorcontrib>Zhang, Qiushi</creatorcontrib><creatorcontrib>Chen, Feng</creatorcontrib><creatorcontrib>Bian, Chenyuan</creatorcontrib><creatorcontrib>Yao, Xiaohui</creatorcontrib><creatorcontrib>Yan, Jingwen</creatorcontrib><creatorcontrib>Xu, Zhe</creatorcontrib><creatorcontrib>Risacher, Shannon L</creatorcontrib><creatorcontrib>Saykin, Andrew J</creatorcontrib><creatorcontrib>Liang, Hong</creatorcontrib><creatorcontrib>Shen, Li</creatorcontrib><creatorcontrib>Alzheimer’s Disease Neuroimaging Initiative</creatorcontrib><creatorcontrib>for the Alzheimer’s Disease Neuroimaging Initiative</creatorcontrib><title>Multivariate genome wide association and network analysis of subcortical imaging phenotypes in Alzheimer's disease</title><title>BMC genomics</title><addtitle>BMC Genomics</addtitle><description>Genome-wide association studies (GWAS) have identified many individual genes associated with brain imaging quantitative traits (QTs) in Alzheimer's disease (AD). However single marker level association discovery may not be able to address the underlying biological interactions with disease mechanism.
In this paper, we used the MGAS (Multivariate Gene-based Association test by extended Simes procedure) tool to perform multivariate GWAS on eight AD-relevant subcortical imaging measures. We conducted multiple iPINBPA (integrative Protein-Interaction-Network-Based Pathway Analysis) network analyses on MGAS findings using protein-protein interaction (PPI) data, and identified five Consensus Modules (CMs) from the PPI network. Functional annotation and network analysis were performed on the identified CMs. The MGAS yielded significant hits within APOE, TOMM40 and APOC1 genes, which were known AD risk factors, as well as a few new genes such as LAMA1, XYLB, HSD17B7P2, and NPEPL1. The identified five CMs were enriched by biological processes related to disorders such as Alzheimer's disease, Legionellosis, Pertussis, and Serotonergic synapse.
The statistical power of coupling MGAS with iPINBPA was higher than traditional GWAS method, and yielded new findings that were missed by GWAS. This study provides novel insights into the molecular mechanism of Alzheimer's Disease and will be of value to novel gene discovery and functional genomic studies.</description><subject>Alzheimer Disease - diagnostic imaging</subject><subject>Alzheimer Disease - genetics</subject><subject>Alzheimer's disease</subject><subject>Analysis</subject><subject>Annotations</subject><subject>Apolipoprotein E</subject><subject>Biological activity</subject><subject>Biomarkers</subject><subject>Brain - diagnostic imaging</subject><subject>Brain imaging</subject><subject>Consensus modules</subject><subject>Coupling (molecular)</subject><subject>Demographic aspects</subject><subject>Diagnosis</subject><subject>Disease</subject><subject>Genes</subject><subject>Genetic aspects</subject><subject>Genetic markers</subject><subject>Genetic Predisposition to Disease</subject><subject>Genome-wide association studies</subject><subject>Genome-Wide Association Study</subject><subject>Genomes</subject><subject>Genomics</subject><subject>Humans</subject><subject>iPINBPA network analysis</subject><subject>Medical imaging</subject><subject>Multivariate analysis</subject><subject>Multivariate gene-based genome-wide analysis</subject><subject>Network analysis</subject><subject>Neurodegenerative diseases</subject><subject>Neuroimaging</subject><subject>Pertussis</subject><subject>Phenotype</subject><subject>Phenotypes</subject><subject>Polymorphism, Single Nucleotide</subject><subject>Protein interaction</subject><subject>Protein Interaction Maps</subject><subject>Proteins</subject><subject>Risk analysis</subject><subject>Risk factors</subject><subject>Synapses</subject><issn>1471-2164</issn><issn>1471-2164</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>DOA</sourceid><recordid>eNptkstu1DAUhiMEoqXwAixQJBbAIsWXxHY2SKOKy0hFSFzW1olznPGQxIOdtAxPj2emlA5CXtg6_s5_Lvqz7Ckl55Qq8TpSpkRZEEYKIplihbyXndJS0oJRUd6_8z7JHsW4JoRKxaqH2QnnXLKqJqdZ-Dj3k7uC4GDCvMPRD5hfuxZziNGbFHV-zGFs8xGnax--pzf02-hi7m0e58b4MDkDfe4G6NzY5ZtVEpm2G4y5G_NF_2uFbsDwIuatiwgRH2cPLPQRn9zcZ9m3d2-_XnwoLj-9X14sLgtT1XIqmDIlUlpxaNIcYEE0pCQcOVetUKQGZYwkjDJgDRdEta2yVknVAKe2qit-li0Puq2Htd6E1GDYag9O7wM-dBp2vfeoDUEja45NI0Rp0QJwaEVTW1mlzdo2ab05aG3mZsDW4DgF6I9Ej39Gt9Kdv9JSSkqqOgm8vBEI_seMcdKDiwb7Hkb0c9SslDwVE4Ql9Pk_6NrPIW19TykpJKHVX6qDNIAbrU91zU5UL0RFysSRHXX-HyqdFgdn_IjWpfhRwqujhMRM-HPqYI5RL798PmbZgTXBxxjQ3u6DEr1zqD44VCeH6r1DtUxJz-5u8jbljyX5b6hI4Z4</recordid><startdate>20201229</startdate><enddate>20201229</enddate><creator>Meng, Xianglian</creator><creator>Li, Jin</creator><creator>Zhang, Qiushi</creator><creator>Chen, Feng</creator><creator>Bian, Chenyuan</creator><creator>Yao, Xiaohui</creator><creator>Yan, Jingwen</creator><creator>Xu, Zhe</creator><creator>Risacher, Shannon L</creator><creator>Saykin, Andrew J</creator><creator>Liang, Hong</creator><creator>Shen, Li</creator><general>BioMed Central Ltd</general><general>BioMed Central</general><general>BMC</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>ISR</scope><scope>3V.</scope><scope>7QP</scope><scope>7QR</scope><scope>7SS</scope><scope>7TK</scope><scope>7U7</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M7P</scope><scope>P64</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0002-2590-7210</orcidid></search><sort><creationdate>20201229</creationdate><title>Multivariate genome wide association and network analysis of subcortical imaging phenotypes in Alzheimer's disease</title><author>Meng, Xianglian ; Li, Jin ; Zhang, Qiushi ; Chen, Feng ; Bian, Chenyuan ; Yao, Xiaohui ; Yan, Jingwen ; Xu, Zhe ; Risacher, Shannon L ; Saykin, Andrew J ; Liang, Hong ; Shen, Li</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c597t-28c4e1153ab147afa6b0403e338d6809a8cc70212a2b3608dd8ff878ba31f5953</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Alzheimer Disease - diagnostic imaging</topic><topic>Alzheimer Disease - genetics</topic><topic>Alzheimer's disease</topic><topic>Analysis</topic><topic>Annotations</topic><topic>Apolipoprotein E</topic><topic>Biological activity</topic><topic>Biomarkers</topic><topic>Brain - diagnostic imaging</topic><topic>Brain imaging</topic><topic>Consensus modules</topic><topic>Coupling (molecular)</topic><topic>Demographic aspects</topic><topic>Diagnosis</topic><topic>Disease</topic><topic>Genes</topic><topic>Genetic aspects</topic><topic>Genetic markers</topic><topic>Genetic Predisposition to Disease</topic><topic>Genome-wide association studies</topic><topic>Genome-Wide Association Study</topic><topic>Genomes</topic><topic>Genomics</topic><topic>Humans</topic><topic>iPINBPA network analysis</topic><topic>Medical imaging</topic><topic>Multivariate analysis</topic><topic>Multivariate gene-based genome-wide analysis</topic><topic>Network analysis</topic><topic>Neurodegenerative diseases</topic><topic>Neuroimaging</topic><topic>Pertussis</topic><topic>Phenotype</topic><topic>Phenotypes</topic><topic>Polymorphism, Single Nucleotide</topic><topic>Protein interaction</topic><topic>Protein Interaction Maps</topic><topic>Proteins</topic><topic>Risk analysis</topic><topic>Risk factors</topic><topic>Synapses</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Meng, Xianglian</creatorcontrib><creatorcontrib>Li, Jin</creatorcontrib><creatorcontrib>Zhang, Qiushi</creatorcontrib><creatorcontrib>Chen, Feng</creatorcontrib><creatorcontrib>Bian, Chenyuan</creatorcontrib><creatorcontrib>Yao, Xiaohui</creatorcontrib><creatorcontrib>Yan, Jingwen</creatorcontrib><creatorcontrib>Xu, Zhe</creatorcontrib><creatorcontrib>Risacher, Shannon L</creatorcontrib><creatorcontrib>Saykin, Andrew J</creatorcontrib><creatorcontrib>Liang, Hong</creatorcontrib><creatorcontrib>Shen, Li</creatorcontrib><creatorcontrib>Alzheimer’s Disease Neuroimaging Initiative</creatorcontrib><creatorcontrib>for the Alzheimer’s Disease Neuroimaging Initiative</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Science</collection><collection>ProQuest Central (Corporate)</collection><collection>Calcium & Calcified Tissue Abstracts</collection><collection>Chemoreception Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Neurosciences Abstracts</collection><collection>Toxicology Abstracts</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>ProQuest Biological Science Collection</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Biological Science Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>BMC genomics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Meng, Xianglian</au><au>Li, Jin</au><au>Zhang, Qiushi</au><au>Chen, Feng</au><au>Bian, Chenyuan</au><au>Yao, Xiaohui</au><au>Yan, Jingwen</au><au>Xu, Zhe</au><au>Risacher, Shannon L</au><au>Saykin, Andrew J</au><au>Liang, Hong</au><au>Shen, Li</au><aucorp>Alzheimer’s Disease Neuroimaging Initiative</aucorp><aucorp>for the Alzheimer’s Disease Neuroimaging Initiative</aucorp><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Multivariate genome wide association and network analysis of subcortical imaging phenotypes in Alzheimer's disease</atitle><jtitle>BMC genomics</jtitle><addtitle>BMC Genomics</addtitle><date>2020-12-29</date><risdate>2020</risdate><volume>21</volume><issue>Suppl 11</issue><spage>896</spage><epage>896</epage><pages>896-896</pages><artnum>896</artnum><issn>1471-2164</issn><eissn>1471-2164</eissn><abstract>Genome-wide association studies (GWAS) have identified many individual genes associated with brain imaging quantitative traits (QTs) in Alzheimer's disease (AD). However single marker level association discovery may not be able to address the underlying biological interactions with disease mechanism.
In this paper, we used the MGAS (Multivariate Gene-based Association test by extended Simes procedure) tool to perform multivariate GWAS on eight AD-relevant subcortical imaging measures. We conducted multiple iPINBPA (integrative Protein-Interaction-Network-Based Pathway Analysis) network analyses on MGAS findings using protein-protein interaction (PPI) data, and identified five Consensus Modules (CMs) from the PPI network. Functional annotation and network analysis were performed on the identified CMs. The MGAS yielded significant hits within APOE, TOMM40 and APOC1 genes, which were known AD risk factors, as well as a few new genes such as LAMA1, XYLB, HSD17B7P2, and NPEPL1. The identified five CMs were enriched by biological processes related to disorders such as Alzheimer's disease, Legionellosis, Pertussis, and Serotonergic synapse.
The statistical power of coupling MGAS with iPINBPA was higher than traditional GWAS method, and yielded new findings that were missed by GWAS. This study provides novel insights into the molecular mechanism of Alzheimer's Disease and will be of value to novel gene discovery and functional genomic studies.</abstract><cop>England</cop><pub>BioMed Central Ltd</pub><pmid>33372590</pmid><doi>10.1186/s12864-020-07282-7</doi><tpages>1</tpages><orcidid>https://orcid.org/0000-0002-2590-7210</orcidid><oa>free_for_read</oa></addata></record> |
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| source | MEDLINE; DOAJ Directory of Open Access Journals; SpringerLink Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; PubMed Central Open Access; Springer Nature OA Free Journals; PubMed Central |
| subjects | Alzheimer Disease - diagnostic imaging Alzheimer Disease - genetics Alzheimer's disease Analysis Annotations Apolipoprotein E Biological activity Biomarkers Brain - diagnostic imaging Brain imaging Consensus modules Coupling (molecular) Demographic aspects Diagnosis Disease Genes Genetic aspects Genetic markers Genetic Predisposition to Disease Genome-wide association studies Genome-Wide Association Study Genomes Genomics Humans iPINBPA network analysis Medical imaging Multivariate analysis Multivariate gene-based genome-wide analysis Network analysis Neurodegenerative diseases Neuroimaging Pertussis Phenotype Phenotypes Polymorphism, Single Nucleotide Protein interaction Protein Interaction Maps Proteins Risk analysis Risk factors Synapses |
| title | Multivariate genome wide association and network analysis of subcortical imaging phenotypes in Alzheimer's disease |
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