ArticleScientific reports2017
A novel strategy for clustering major depression individuals using whole-genome sequencing variant data.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Novel Network Method Major Minor Variation Clustering Enables Identification of Poliovirus Clusters with High-Resolution Linkages.Journal of computational biology : a journal of computational molecular cell biology · 2023Article
- Transcriptomics of the depressed and PTSD brain.Neurobiology of stress · 2021Article
- Analysis of Differentially Expressed Genes in the Dentate Gyrus and Anterior Cingulate Cortex in a Mouse Model of Depression.BioMed research international · 2021Article
- Genotyping coronavirus SARS-CoV-2: methods and implications.Genomics · 2020Article
- Variants in the Upstream Region of the Insulin Receptor Substrate-1 Gene Is Associated with Major Depressive Disorder in the Han Chinese Population.Neuropsychiatric disease and treatment · 2020Article
- Single-cell RNA-seq variant analysis for exploration of genetic heterogeneity in cancer.Scientific reports · 2019Article
- A single-nucleotide polymorphism influences brain morphology in drug-naïve patients with major depressive disorder.Neuropsychiatric disease and treatment · 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
- Low-frequency and rare variants may contribute to elucidate the genetics of major depressive disorder.Translational psychiatry · 2018Article
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Authors and funding
4 authors.
Funding
Abstract
Major depressive disorder (MDD) is highly prevalent, resulting in an exceedingly high disease burden. The identification of generic risk factors could lead to advance prevention and therapeutics. Current approaches examine genotyping data to identify specific variations between cases and controls. Compared to genotyping, whole-genome sequencing (WGS) allows for the detection of private mutations. In this proof-of-concept study, we establish a conceptually novel computational approach that clusters subjects based on the entirety of their WGS. Those clusters predicted MDD diagnosis. This strategy yielded encouraging results, showing that depressed Mexican-American participants were grouped closer; in contrast ethnically-matched controls grouped away from MDD patients. This implies that within the same ancestry, the WGS data of an individual can be used to check whether this individual is within or closer to MDD subjects or to controls. We propose a novel strategy to apply WGS data to clinical medicine by facilitating diagnosis through genetic clustering. Further studies utilising our method should examine larger WGS datasets on other ethnical groups.
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