Literature DB >> 25916525

Genome-wide association studies in pharmacogenomics of antidepressants.

Eugene Lin1, Hsien-Yuan Lane.   

Abstract

Major depressive disorder (MDD) is one of the most common psychiatric disorders worldwide. Doctors must prescribe antidepressants based on educated guesses due to the fact that it is unmanageable to predict the effectiveness of any particular antidepressant in an individual patient. With the recent advent of scientific research, the genome-wide association study (GWAS) is extensively employed to analyze hundreds of thousands of single nucleotide polymorphisms by high-throughput genotyping technologies. In addition to the candidate-gene approach, the GWAS approach has recently been utilized to investigate the determinants of antidepressant response to therapy. In this study, we reviewed GWAS studies, their limitations and future directions with respect to the pharmacogenomics of antidepressants in MDD.

Entities:  

Keywords:  antidepressants; genome-wide association study; major depressive disorder; pharmacogenomics; single nucleotide polymorphisms

Mesh:

Substances:

Year:  2015        PMID: 25916525     DOI: 10.2217/pgs.15.5

Source DB:  PubMed          Journal:  Pharmacogenomics        ISSN: 1462-2416            Impact factor:   2.533


  13 in total

Review 1.  Pharmacogenetics and Imaging-Pharmacogenetics of Antidepressant Response: Towards Translational Strategies.

Authors:  Tristram A Lett; Henrik Walter; Eva J Brandl
Journal:  CNS Drugs       Date:  2016-12       Impact factor: 5.749

2.  Prediction of functional outcomes of schizophrenia with genetic biomarkers using a bagging ensemble machine learning method with feature selection.

Authors:  Eugene Lin; Chieh-Hsin Lin; Hsien-Yuan Lane
Journal:  Sci Rep       Date:  2021-05-13       Impact factor: 4.379

Review 3.  Precision medicine: from pharmacogenomics to pharmacoproteomics.

Authors:  Allison B Chambliss; Daniel W Chan
Journal:  Clin Proteomics       Date:  2016-09-26       Impact factor: 3.988

Review 4.  Genetic Biomarkers on Age-Related Cognitive Decline.

Authors:  Chieh-Hsin Lin; Eugene Lin; Hsien-Yuan Lane
Journal:  Front Psychiatry       Date:  2017-11-21       Impact factor: 4.157

Review 5.  Machine learning and systems genomics approaches for multi-omics data.

Authors:  Eugene Lin; Hsien-Yuan Lane
Journal:  Biomark Res       Date:  2017-01-20

6.  Epigenetics and Depression: An Update.

Authors:  Eugene Lin; Shih-Jen Tsai
Journal:  Psychiatry Investig       Date:  2019-08-29       Impact factor: 2.505

7.  The functional variant rs334558 of GSK3B is associated with remission in patients with depressive disorders.

Authors:  Anastasia Levchenko; Innokentiy S Losenkov; Natalia M Vyalova; German G Simutkin; Nikolay A Bokhan; Bob Wilffert; Anton Jm Loonen; Svetlana A Ivanova
Journal:  Pharmgenomics Pers Med       Date:  2018-07-20

8.  A Deep Learning Approach for Predicting Antidepressant Response in Major Depression Using Clinical and Genetic Biomarkers.

Authors:  Eugene Lin; Po-Hsiu Kuo; Yu-Li Liu; Younger W-Y Yu; Albert C Yang; Shih-Jen Tsai
Journal:  Front Psychiatry       Date:  2018-07-06       Impact factor: 4.157

Review 9.  Current challenges and possible future developments in personalized psychiatry with an emphasis on psychotic disorders.

Authors:  Anastasia Levchenko; Timur Nurgaliev; Alexander Kanapin; Anastasia Samsonova; Raul R Gainetdinov
Journal:  Heliyon       Date:  2020-05-20

10.  Targeted exome sequencing identifies five novel loci at genome-wide significance for modulating antidepressant response in patients with major depressive disorder.

Authors:  Zhi Xu; Chunming Xie; Lu Xia; Yonggui Yuan; Hong Zhu; Xiaofa Huang; Caihua Li; Yu Tao; Xiaoxiao Qu; Fengyu Zhang; Zhijun Zhang
Journal:  Transl Psychiatry       Date:  2020-01-23       Impact factor: 6.222

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