ArticleJournal of human genetics2017
Single-nucleotide variant proportion in genes: a new concept to explore major depression based on DNA sequencing data.
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.
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Who cites it
6 citing papers in PubMed.
- Host Genome Variation is Associated with Neurocognitive Outcome in Survivors of Pediatric Medulloblastoma.Translational oncology · 2019Article
- Genetic effects on white matter integrity in drug-naive patients with major depressive disorder: a diffusion tensor imaging study of 17 genetic loci associated with depressive symptoms.Neuropsychiatric disease and treatment · 2019Article
- A single-nucleotide polymorphism influences brain morphology in drug-naïve patients with major depressive disorder.Neuropsychiatric disease and treatment · 2019Article
- Low-frequency and rare variants may contribute to elucidate the genetics of major depressive disorder.Translational psychiatry · 2018Article
- Whole-genome single nucleotide variant distribution on genomic regions and its relationship to major depression.Psychiatry research · 2017Article
- To aggregate or not, that is the question. A commentary on single-nucleotide variant proportion in genes: a new concept to explore major depression based on DNA sequencing data.Journal of human genetics · 2017Article
Corrections and comments
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Authors and funding
4 authors.
Funding
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.
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