A novel strategy for clustering major depression individuals using whole-genome sequencing variant data

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

Research output: Contribution to journalArticleResearchpeer-review

12 Citations (Scopus)

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.

Original languageEnglish
Article number44389
Number of pages7
JournalScientific Reports
Volume7
DOIs
Publication statusPublished - 13 Mar 2017
Externally publishedYes

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