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The Alzheimer’s comorbidity phenome: mining from a large patient database and phenome-driven genetics prediction

OBJECTIVE: Alzheimer’s disease (AD) is a severe neurodegenerative disorder and has become a global public health problem. Intensive research has been conducted for AD. But the pathophysiology of AD is still not elucidated. Disease comorbidity often associates diseases with overlapping patterns of ge...

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Autores principales: Zheng, Chunlei, Xu, Rong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6434979/
https://www.ncbi.nlm.nih.gov/pubmed/30944912
http://dx.doi.org/10.1093/jamiaopen/ooy050
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author Zheng, Chunlei
Xu, Rong
author_facet Zheng, Chunlei
Xu, Rong
author_sort Zheng, Chunlei
collection PubMed
description OBJECTIVE: Alzheimer’s disease (AD) is a severe neurodegenerative disorder and has become a global public health problem. Intensive research has been conducted for AD. But the pathophysiology of AD is still not elucidated. Disease comorbidity often associates diseases with overlapping patterns of genetic markers. This may inform a common etiology and suggest essential protein targets. US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) collects large-scale postmarketing surveillance data that provide a unique opportunity to investigate disease co-occurrence pattern. We aim to construct a heterogeneous network that integrates disease comorbidity network (DCN) from FAERS with protein–protein interaction (PPI) to prioritize the AD risk genes using network-based ranking algorithm. MATERIALS AND METHODS: We built a DCN based on indication data from FAERS using association rule mining. DCN was further integrated with PPI network. We used random walk with restart ranking algorithm to prioritize AD risk genes. RESULTS: We evaluated the performance of our approach using AD risk genes curated from genetic association studies. Our approach achieved an area under a receiver operating characteristic curve of 0.770. Top 500 ranked genes achieved 5.53-fold enrichment for known AD risk genes as compared to random expectation. Pathway enrichment analysis using top-ranked genes revealed that two novel pathways, ERBB and coagulation pathways, might be involved in AD pathogenesis. CONCLUSION: We innovatively leveraged FAERS, a comprehensive data resource for FDA postmarket drug safety surveillance, for large-scale AD comorbidity mining. This exploratory study demonstrated the potential of disease-comorbidities mining from FAERS in AD genetics discovery.
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spelling pubmed-64349792019-04-01 The Alzheimer’s comorbidity phenome: mining from a large patient database and phenome-driven genetics prediction Zheng, Chunlei Xu, Rong JAMIA Open Research and Applications OBJECTIVE: Alzheimer’s disease (AD) is a severe neurodegenerative disorder and has become a global public health problem. Intensive research has been conducted for AD. But the pathophysiology of AD is still not elucidated. Disease comorbidity often associates diseases with overlapping patterns of genetic markers. This may inform a common etiology and suggest essential protein targets. US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) collects large-scale postmarketing surveillance data that provide a unique opportunity to investigate disease co-occurrence pattern. We aim to construct a heterogeneous network that integrates disease comorbidity network (DCN) from FAERS with protein–protein interaction (PPI) to prioritize the AD risk genes using network-based ranking algorithm. MATERIALS AND METHODS: We built a DCN based on indication data from FAERS using association rule mining. DCN was further integrated with PPI network. We used random walk with restart ranking algorithm to prioritize AD risk genes. RESULTS: We evaluated the performance of our approach using AD risk genes curated from genetic association studies. Our approach achieved an area under a receiver operating characteristic curve of 0.770. Top 500 ranked genes achieved 5.53-fold enrichment for known AD risk genes as compared to random expectation. Pathway enrichment analysis using top-ranked genes revealed that two novel pathways, ERBB and coagulation pathways, might be involved in AD pathogenesis. CONCLUSION: We innovatively leveraged FAERS, a comprehensive data resource for FDA postmarket drug safety surveillance, for large-scale AD comorbidity mining. This exploratory study demonstrated the potential of disease-comorbidities mining from FAERS in AD genetics discovery. Oxford University Press 2018-12-19 /pmc/articles/PMC6434979/ /pubmed/30944912 http://dx.doi.org/10.1093/jamiaopen/ooy050 Text en © The Author(s) 2018. Published by Oxford University Press on behalf of the American Medical Informatics Association. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Research and Applications
Zheng, Chunlei
Xu, Rong
The Alzheimer’s comorbidity phenome: mining from a large patient database and phenome-driven genetics prediction
title The Alzheimer’s comorbidity phenome: mining from a large patient database and phenome-driven genetics prediction
title_full The Alzheimer’s comorbidity phenome: mining from a large patient database and phenome-driven genetics prediction
title_fullStr The Alzheimer’s comorbidity phenome: mining from a large patient database and phenome-driven genetics prediction
title_full_unstemmed The Alzheimer’s comorbidity phenome: mining from a large patient database and phenome-driven genetics prediction
title_short The Alzheimer’s comorbidity phenome: mining from a large patient database and phenome-driven genetics prediction
title_sort alzheimer’s comorbidity phenome: mining from a large patient database and phenome-driven genetics prediction
topic Research and Applications
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6434979/
https://www.ncbi.nlm.nih.gov/pubmed/30944912
http://dx.doi.org/10.1093/jamiaopen/ooy050
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