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Critical Issues in BDNF Val66Met Genetic Studies of Neuropsychiatric Disorders
Neurotrophins have been implicated in the pathophysiology of many neuropsychiatric diseases. Brain-derived neurotrophic factor (BDNF) is the most abundant and widely distributed neurotrophin in the brain. Its Val66Met polymorphism (refSNP Cluster Report: rs6265) is a common and functional single-nuc...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2018
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5962780/ https://www.ncbi.nlm.nih.gov/pubmed/29867348 http://dx.doi.org/10.3389/fnmol.2018.00156 |
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author | Tsai, Shih-Jen |
author_facet | Tsai, Shih-Jen |
author_sort | Tsai, Shih-Jen |
collection | PubMed |
description | Neurotrophins have been implicated in the pathophysiology of many neuropsychiatric diseases. Brain-derived neurotrophic factor (BDNF) is the most abundant and widely distributed neurotrophin in the brain. Its Val66Met polymorphism (refSNP Cluster Report: rs6265) is a common and functional single-nucleotide polymorphism (SNP) affecting the activity-dependent release of BDNF. BDNF Val66Met transgenic mice have been generated, which may provide further insight into the functional impact of this polymorphism in the brain. Considering the important role of BDNF in brain function, more than 1,100 genetic studies have investigated this polymorphism in the past 15 years. Although these studies have reported some encouraging positive findings initially, most of the findings cannot be replicated in following studies. These inconsistencies in BDNF Val66Met genetic studies may be attributed to many factors such as age, sex, environmental factors, ethnicity, genetic model used for analysis, and gene–gene interaction, which are discussed in this review. We also discuss the results of recent studies that have reported the novel functions of this polymorphism. Because many BDNF polymorphisms and non-genetic factors have been implicated in the complex traits of neuropsychiatric diseases, the conventional genetic association-based method is limited to address these complex interactions. Future studies should apply data mining and machine learning techniques to determine the genetic role of BDNF in neuropsychiatric diseases. |
format | Online Article Text |
id | pubmed-5962780 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-59627802018-06-04 Critical Issues in BDNF Val66Met Genetic Studies of Neuropsychiatric Disorders Tsai, Shih-Jen Front Mol Neurosci Neuroscience Neurotrophins have been implicated in the pathophysiology of many neuropsychiatric diseases. Brain-derived neurotrophic factor (BDNF) is the most abundant and widely distributed neurotrophin in the brain. Its Val66Met polymorphism (refSNP Cluster Report: rs6265) is a common and functional single-nucleotide polymorphism (SNP) affecting the activity-dependent release of BDNF. BDNF Val66Met transgenic mice have been generated, which may provide further insight into the functional impact of this polymorphism in the brain. Considering the important role of BDNF in brain function, more than 1,100 genetic studies have investigated this polymorphism in the past 15 years. Although these studies have reported some encouraging positive findings initially, most of the findings cannot be replicated in following studies. These inconsistencies in BDNF Val66Met genetic studies may be attributed to many factors such as age, sex, environmental factors, ethnicity, genetic model used for analysis, and gene–gene interaction, which are discussed in this review. We also discuss the results of recent studies that have reported the novel functions of this polymorphism. Because many BDNF polymorphisms and non-genetic factors have been implicated in the complex traits of neuropsychiatric diseases, the conventional genetic association-based method is limited to address these complex interactions. Future studies should apply data mining and machine learning techniques to determine the genetic role of BDNF in neuropsychiatric diseases. Frontiers Media S.A. 2018-05-15 /pmc/articles/PMC5962780/ /pubmed/29867348 http://dx.doi.org/10.3389/fnmol.2018.00156 Text en Copyright © 2018 Tsai. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Tsai, Shih-Jen Critical Issues in BDNF Val66Met Genetic Studies of Neuropsychiatric Disorders |
title | Critical Issues in BDNF Val66Met Genetic Studies of Neuropsychiatric Disorders |
title_full | Critical Issues in BDNF Val66Met Genetic Studies of Neuropsychiatric Disorders |
title_fullStr | Critical Issues in BDNF Val66Met Genetic Studies of Neuropsychiatric Disorders |
title_full_unstemmed | Critical Issues in BDNF Val66Met Genetic Studies of Neuropsychiatric Disorders |
title_short | Critical Issues in BDNF Val66Met Genetic Studies of Neuropsychiatric Disorders |
title_sort | critical issues in bdnf val66met genetic studies of neuropsychiatric disorders |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5962780/ https://www.ncbi.nlm.nih.gov/pubmed/29867348 http://dx.doi.org/10.3389/fnmol.2018.00156 |
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