Evidence map›Paper›PMID 27248517›Full record

ReviewPharmacogenomics2016

Identifying genetic loci affecting antidepressant drug response in depression using drug-gene interaction models.

Raymond Noordam, Christy L Avery, Loes E Visser, Bruno H Stricker

Abstract readReview
In one paragraph

Review in Pharmacogenomics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Raymond NoordamDepartment of Gerontology and Geriatrics, Leiden University Medical Center, PO Box 9600, 2300 RC Leiden, The Netherlands.
Christy L AveryDepartment of Epidemiology, University of North Carolina Gillings School of Global Public Health, Chapel Hill, NC, USA.
Loes E VisserDepartment of Epidemiology, Erasmus MC - University Medical Center Rotterdam, Rotterdam, The Netherlands.
Bruno H StrickerDepartment of Epidemiology, Erasmus MC - University Medical Center Rotterdam, Rotterdam, The Netherlands.

Funding

Research Services CoreP2CHD050924 · NICHD · UNIV OF NORTH CAROLINA CHAPEL HILL · PI KAREN B GUZZO · 2015 to 2026
$9.6M
CHARGE Consortium: Omics Discovery for CVD and Aging PhenotypesR01HL105756 · NHLBI · UNIVERSITY OF WASHINGTON · PI Bruce M Psaty, NICHOLAS L SMITH · 2011 to 2026
$9.5M
Prospective meta-analyses of drug-gene interactions: CHARGE GWAS consortiumR01HL103612 · NHLBI · UNIVERSITY OF WASHINGTON · PI PSATY, BRUCE M · 2011 to 2014
$5.4M
NHLBI NIH HHS R01 HL103612NHLBI NIH HHS R01 HL105756NICHD NIH HHS P2C HD050924
6 · The paper itself

Abstract

Antidepressants are often only moderately successful in decreasing the severity of depressive symptoms. In part, antidepressant treatment response in patients with depression is genetically determined. However, although a large number of studies have been conducted aiming to identify genetic variants associated with antidepressant drug response in depression, only a few variants have been repeatedly identified. Within the present review, we will discuss the methodological challenges and limitations of the studies that have been conducted on this topic to date (e.g., 'treated-only design', statistical power) and we will discuss how specifically drug-gene interaction models can be used to be better able to identify genetic variants associated with antidepressant drug response in depression.

Indexed as

Antidepressive AgentsDepressionGenetic LociHumansMajor Depressive DisorderPharmacogeneticsRandomized Controlled Trials as TopicAntidepressive Agentsantidepressive agentsdrug–gene interaction modelspharmacogenomics

Identifiers

PMID27248517
PMCPMC4996316

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.