Please use this identifier to cite or link to this item:
https://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 Mexican Americans Female INDEL Mutation Genome-Wide Association Study High-Throughput Nucleotide Sequencing Whole Genome Sequencing White People |
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/ |
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 |
Published version: | http://dx.doi.org/10.1038/srep44389 |
Appears in Collections: | Aurora harvest 3 Medicine publications |
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hdl_105224.pdf | Published Version | 674.43 kB | Adobe PDF | View/Open |
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