Chenglong Yu1, Bernhard T Baune2, Ke-Ang Fu3, Ma-Li Wong4, Julio Licinio5. 1. Robinson Research Institute, Adelaide Medical School, University of Adelaide, Adelaide, SA, Australia; Mind and Brain Theme, South Australian Health and Medical Research Institute, Adelaide, SA, Australia; College of Medicine and Public Health, Flinders University, Bedford Park, SA, Australia. Electronic address: chenglong.yu@adelaide.edu.au. 2. Discipline of Psychiatry, Adelaide Medical School, University of Adelaide, Adelaide, SA, Australia. 3. School of Statistics and Mathematics, Zhejiang Gongshang University, Hangzhou, Zhejiang, China. 4. Mind and Brain Theme, South Australian Health and Medical Research Institute, Adelaide, SA, Australia; College of Medicine and Public Health, Flinders University, Bedford Park, SA, Australia. 5. College of Medicine, Departments of Psychiatry, Pharmacology and Medicine, State University of New York, Upstate Medical University, Syracuse, NY, USA.
Abstract
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.
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 depressedpatients 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 depressedpatients 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.