Genome-wide association studies (GWAS) and RNA sequencing (RNA-Seq) are two major approaches for studying the effects of genetic variations on complex diseases at the genomic and transcriptomic levels, respectively. Specifically for RNA-Seq, it is rapidly emerging as a powerful tool for identifying differentially expressed genes in diseases;however, many challenges remain because of the complexity in gene regulations. In this proposal, we combine statistics, bioinformatics, and genetics to develop novel analytical strategies that maximally leverage information from both GWAS and RNA-Seq studies in order to understand the genetic architecture underlying complex diseases, especially schizophrenia. Our proposal will be the first methodology development for a systems approach that integrates GWAS and RNA-Seq data. We propose the following four major aims: (1) To develop novel analytical strategies to identify genes and pathways with enriched association signals in GWAS by leveraging functional information measured by RNA sequencing. We define this approach as RNA-Seq assisted GWAS analysis. (2) To develop novel analytical strategies to identify genes and pathways with enriched association signals in RNA-Seq data by leveraging information from genetics of gene expression studies. We define this approach as RNA-Seq oriented analysis. (3) To apply the methods in Aims 1 and 2 to schizophrenia, which we have generated RNA-Seq data from 82 brain samples collected from the Stanley Medical Research Institute and gained access to four major GWAS datasets for schizophrenia (ISC, GAIN, nonGAIN, and CATIE: a total of more than 6000 cases and 6000 controls). This application will also help us refine the strategies in Aims 1 and 2. (4) To develop computational tools for detecting disease genes, pathways that lead to complex diseases. These tools will become a useful resource for the public community and can be applied to any complex diseases with available RNA-Seq and GWAS datasets. The successful completions of Aims 1 and 2 will provide us with important methods for integrative genomic analysis of GWAS and RNA-Seq datasets. The successful completion of Aim 3 will provide us with a list of prioritized candidate genes and pathways for future validation on schizophrenia. The successful completion of Aim 4 will provide computational tools and a user-friendly online system for investigators who study complex diseases using GWAS and RNA-Seq.
Rapid technology advances have helped biomedical investigators generate huge amount of biological data, among which genome-wide association studies (GWAS) and RNA sequencing (RNA-Seq) are two major sources. To meet the great challenges on analyzing such large and heterogeneous datasets, in this proposal we combine statistics, bioinformatics, and genetics to develop novel analytical strategies that maximally leverage information from both GWAS and RNA-Seq studies to understand the genetic architecture underlying schizophrenia and other complex diseases.
|Zhao, Junfei; Cheng, Feixiong; Jia, Peilin et al. (2018) An integrative functional genomics framework for effective identification of novel regulatory variants in genome-phenome studies. Genome Med 10:7|
|Kim, Pora; Jia, Peilin; Zhao, Zhongming (2018) Kinase impact assessment in the landscape of fusion genes that retain kinase domains: a pan-cancer study. Brief Bioinform 19:450-460|
|Jia, Peilin; Chen, Xiangning; Fanous, Ayman H et al. (2018) Convergent roles of de novo mutations and common variants in schizophrenia in tissue-specific and spatiotemporal co-expression network. Transl Psychiatry 8:105|
|Cao, Yuan; Zhu, Junjie; Jia, Peilin et al. (2017) scRNASeqDB: A Database for RNA-Seq Based Gene Expression Profiles in Human Single Cells. Genes (Basel) 8:|
|Jia, Peilin; Zhao, Zhongming; Hulgan, Todd et al. (2017) Genome-wide association study of HIV-associated neurocognitive disorder (HAND): A CHARTER group study. Am J Med Genet B Neuropsychiatr Genet 174:413-426|
|Zhao, Junfei; Cheng, Feixiong; Zhao, Zhongming (2017) Tissue-Specific Signaling Networks Rewired by Major Somatic Mutations in Human Cancer Revealed by Proteome-Wide Discovery. Cancer Res 77:2810-2821|
|Fang, J; Cai, C; Wang, Q et al. (2017) Systems Pharmacology-Based Discovery of Natural Products for Precision Oncology Through Targeting Cancer Mutated Genes. CPT Pharmacometrics Syst Pharmacol 6:177-187|
|Jia, Peilin; Zhao, Zhongming (2017) Impacts of somatic mutations on gene expression: an association perspective. Brief Bioinform 18:413-425|
|Shen, Qiancheng; Cheng, Feixiong; Song, Huili et al. (2017) Proteome-Scale Investigation of Protein Allosteric Regulation Perturbed by Somatic Mutations in 7,000 Cancer Genomes. Am J Hum Genet 100:5-20|
|Cheng, Feixiong; Liu, Chuang; Shen, Bairong et al. (2016) Investigating cellular network heterogeneity and modularity in cancer: a network entropy and unbalanced motif approach. BMC Syst Biol 10 Suppl 3:65|
Showing the most recent 10 out of 75 publications