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

• Published in: Scientific reports Vol. 7; no. 1; p. 44389
• Main Authors: Yu, Chenglong, Baune, Bernhard T., Licinio, Julio, Wong, Ma-Li
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 13.03.2017; Nature Publishing Group
• Subjects: 45/43; 631/114/2397; 692/699/476/1414; Adolescent; Adult; Aged; Case-Control Studies; Cluster Analysis; Computer applications; Depressive Disorder, Major - diagnosis; Depressive Disorder, Major - ethnology; Depressive Disorder, Major - genetics; Depressive Disorder, Major - physiopathology; Female; Genome, Human; Genome-Wide Association Study; Genomes; Genotype; Genotyping; High-Throughput Nucleotide Sequencing; Humanities and Social Sciences; Humans; INDEL Mutation; Mental depression; Mexican Americans; Middle Aged; multidisciplinary; Polymorphism, Single Nucleotide; Risk Factors; Science; White People; Whole Genome Sequencing
• DOI: 10.1038/srep44389
• ISSN: 2045-2322

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.