Genetic clustering of depressed patients and normal controls based on single-nucleotide variant proportion
Baune, Bernard T
Wong, Ma Li
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Background Genetic components play important roles in the susceptibility to major depressive disorder (MDD). The rapid development of sequencing technologies is allowing scientists to contribute new ideas for personalized medicine; thus, it is essential to design non-invasive genetic tests on sequencing data, which can help physicians diagnose and differentiate depressed patients and healthy individuals. Methods We have recently proposed a genetic concept involving single-nucleotide variant proportion (SNVP) in genes to study MDD. Using this approach, we investigated combinations of distance metrics and hierarchical clustering criteria for genetic clustering of depressed patients and ethnically matched controls. Results We analysed clustering results of 25 human subjects based on their SNVPs in 46 newly discovered candidate genes. Conclusions According to our findings, we recommend Canberra metric with Ward's method to be used in hierarchical clustering of depressed and normal individuals. Futures studies are needed to advance this line of research validating our approach in larger datasets, those may also be allow the investigation of MDD subtypes. Limitations High quality sequencing costs limited our ability to obtain larger datasets.