In the US, 46% are afflicted with a mental illness at some point of life. The management and treatment of mental disorders pose significant economical and social burden to the society. Many rare and common genetic variants, including SNPs and CNVs, are reported to be associated with mental disorders, yet more remain to be discovered. However, despite the large amount of high-throughput genomics data, there is a lack of integrative methods to systematically prioritize variants that confer susceptibility to mental disorders; additionally, despite the presence of many candidate variants for mental disorders, their disease-contributory mechanism remains elusive, so appropriate functional follow-up experiments cannot be designed. Altogether, these problems resulted in a large gap between the copious amount of data about genetic variation and our understanding of their functional impacts on mental diseases, which ultimately delays the development of targeted therapeutic approaches. To address these problems, we propose to develop a suite of novel bioinformatics approaches: (1) NeuroComplex, an information integration approach that leverages phenotype information and multiple sources of prior biological knowledge to rank genes by their likelihood of contributing to specific phenotypic presentations. (2) integrated MEntal-disorder GEnome Score (iMEGES), which leverages a two-layer strategy to predict the impacts of variants in personal genomes on mental disorders. The first layer uses machine learning algorithm to build variant deleteriousness scores for coding, non-coding and structural variants; the second layer integrates variant scores and NeuroComplex to assign pathogenicity scores. (3) a hierarchical model called CombOmics that incorporates cross-omics information to characterizes how variants confers disease risk, in order to help formulate hypothesis of pathogenicity and facilitate the design of functional follow-up experiments. (4) we will develop user-friendly software tools and web applications that implement all the aforementioned bioinformatics approaches, and continuously release and maintain these tools to ensure the maximum benefits to the scientific community.
We will develop novel informatics approaches to understand how genetic variation impacts various mental disorders, such as schizophrenia and autism. These tools help clarify the genetic cause of mental diseases, characterize individual disease risks and develop individualized treatment strategies.
|Khan, Atlas; Liu, Qian; Wang, Kai (2018) iMEGES: integrated mental-disorder GEnome score by deep neural network for prioritizing the susceptibility genes for mental disorders in personal genomes. BMC Bioinformatics 19:501|
|Son, Jung Hoon; Xie, Gangcai; Yuan, Chi et al. (2018) Deep Phenotyping on Electronic Health Records Facilitates Genetic Diagnosis by Clinical Exomes. Am J Hum Genet 103:58-73|
|Doostparast Torshizi, Abolfazl; Duan, Jubao; Wang, Kai (2018) Transcriptional network analysis on brains reveals a potential regulatory role of PPP1R3F in autism spectrum disorders. BMC Res Notes 11:489|
|Doostparast Torshizi, Abolfazl; Wang, Kai (2017) Deconvolution of Transcriptional Networks in Post-Traumatic Stress Disorder Uncovers Master Regulators Driving Innate Immune System Function. Sci Rep 7:14486|
|Li, Quan; Wang, Kai (2017) InterVar: Clinical Interpretation of Genetic Variants by the 2015 ACMG-AMP Guidelines. Am J Hum Genet 100:267-280|
|Li, Jing; Zhang, Wangshu; Yang, Hui et al. (2017) Spatiotemporal profile of postsynaptic interactomes integrates components of complex brain disorders. Nat Neurosci 20:1150-1161|
|Dong, Chengliang; Guo, Yunfei; Yang, Hui et al. (2016) iCAGES: integrated CAncer GEnome Score for comprehensively prioritizing driver genes in personal cancer genomes. Genome Med 8:135|
|Shi, Lingling; Guo, Yunfei; Dong, Chengliang et al. (2016) Long-read sequencing and de novo assembly of a Chinese genome. Nat Commun 7:12065|