Evidence map›Paper›PMID 28148926›Full record

ArticleJournal of human genetics2017

Single-nucleotide variant proportion in genes: a new concept to explore major depression based on DNA sequencing data.

Chenglong Yu, Bernhard T Baune, Julio Licinio, Ma-Li Wong

Abstract read
In one paragraph

Article in Journal of human genetics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

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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.

Chenglong YuMind and Brain Theme, South Australian Health and Medical Research Institute, North Terrace, Adelaide, SA, Australia.
Bernhard T BauneDiscipline of Psychiatry, School of Medicine, University of Adelaide, Adelaide, SA, Australia.
Julio LicinioMind and Brain Theme, South Australian Health and Medical Research Institute, North Terrace, Adelaide, SA, Australia.
Ma-Li WongMind and Brain Theme, South Australian Health and Medical Research Institute, North Terrace, Adelaide, SA, Australia.

Funding

UCLA PHARMACOGENETICS &PHARMACOGENOMICS RESEARCH GROUPU01GM061394 · NIGMS · UNIVERSITY OF MIAMI SCHOOL OF MEDICINE · PI LICINIO, JULIO · 2000 to 2005
$4.7M
NIGMS NIH HHS U01 GM061394
6 · The paper itself

Abstract

Major depressive disorder (MDD) is a common psychiatric illness with significant medical and socioeconomic impact. Genetic factors are likely to play important roles in the development of this condition. DNA sequencing technology has the ability to identify all private genetic mutations and provides new channels for studying the biology of MDD. In this proof-of-concept study we proposed a novel concept, single-nucleotide variant proportion (SNVP), to investigate MDD based on whole-genome sequencing (WGS) data. Our SNVP-based approach can be used to test newly found candidate genes as a complement to genome-wide genotyping analysis. Furthermore, we performed cluster analysis for MDD patients and ethnically matched healthy controls, and found that clusters based on SNVP may predict MDD diagnosis. Our results suggest that SNVP may be used as a potential biomarker associated with major depression. Our methodology could be a valuable predictive/diagnostic tool as one can test whether a new subject falls within or close to an existing MDD cluster. Advances in this study design have the potential to personalized treatments and could include the ability to diagnose patients based on their full or part DNA sequencing data.

Indexed as

Genetic Predisposition to DiseaseHigh-Throughput Nucleotide SequencingHumansMajor Depressive DisorderPolymorphism, Single Nucleotide

Identifiers

PMID28148926
PMCPMC12519448

What OpenQuestion holds

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Registered trials

None linked

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.