Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/105224
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Type: Journal article
Title: A novel strategy for clustering major depression individuals using whole-genome sequencing variant data
Author: Yu, C.
Baune, B.
Licinio, J.
Wong, M.
Citation: Scientific Reports, 2017; 7(1):44389-1-44389-7
Publisher: Nature Publishing Group
Issue Date: 2017
ISSN: 2045-2322
2045-2322
Statement of
Responsibility: 
Chenglong Yu, Bernhard T. Baune, Julio Licinio and Ma-Li Wong
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.
Keywords: Humans; Cluster Analysis; Risk Factors; Case-Control Studies; Depressive Disorder, Major; Genotype; Polymorphism, Single Nucleotide; Genome, Human; Adolescent; Adult; Aged; Middle Aged; European Continental Ancestry Group; Mexican Americans; Female; INDEL Mutation; Genome-Wide Association Study; High-Throughput Nucleotide Sequencing; Whole Genome Sequencing
Rights: © The Author(s) 2017. This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
RMID: 0030066735
DOI: 10.1038/srep44389
Grant ID: http://purl.org/au-research/grants/nhmrc/1051931
http://purl.org/au-research/grants/nhmrc/1070935
http://purl.org/au-research/grants/nhmrc/1060524
Appears in Collections:Medicine publications

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