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.

Public Health Relevance

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.

Agency
National Institute of Health (NIH)
Institute
National Library of Medicine (NLM)
Type
Research Project (R01)
Project #
5R01LM011177-02
Application #
8461680
Study Section
Biomedical Library and Informatics Review Committee (BLR)
Program Officer
Ye, Jane
Project Start
2012-05-01
Project End
2015-04-30
Budget Start
2013-05-01
Budget End
2014-04-30
Support Year
2
Fiscal Year
2013
Total Cost
$315,458
Indirect Cost
$103,111
Name
Vanderbilt University Medical Center
Department
Internal Medicine/Medicine
Type
Schools of Medicine
DUNS #
004413456
City
Nashville
State
TN
Country
United States
Zip Code
37212
Zhao, Min; Zhao, Zhongming (2016) Concordance of copy number loss and down-regulation of tumor suppressor genes: a pan-cancer study. BMC Genomics 17 Suppl 7:532
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
Liu, Shuang; Mitra, Ramkrishna; Zhao, Ming-Ming et al. (2016) The Potential Roles of Long Noncoding RNAs (lncRNA) in Glioblastoma Development. Mol Cancer Ther 15:2977-2986
Kim, Pora; Cheng, Feixiong; Zhao, Junfei et al. (2016) ccmGDB: a database for cancer cell metabolism genes. Nucleic Acids Res 44:D959-68
Wang, Yuanyuan; Guo, Xingyi; Bray, Michael J et al. (2016) An integrative genomics approach for identifying novel functional consequences of PBRM1 truncated mutations in clear cell renal cell carcinoma (ccRCC). BMC Genomics 17 Suppl 7:515
Zhao, Min; Kim, Pora; Mitra, Ramkrishna et al. (2016) TSGene 2.0: an updated literature-based knowledgebase for tumor suppressor genes. Nucleic Acids Res 44:D1023-31
Hart, Thomas; Dider, Shihab; Han, Weiwei et al. (2016) Toward Repurposing Metformin as a Precision Anti-Cancer Therapy Using Structural Systems Pharmacology. Sci Rep 6:20441
Zhao, Junfei; Cheng, Feixiong; Wang, Yuanyuan et al. (2016) Systematic Prioritization of Druggable Mutations in ∼5000 Genomes Across 16 Cancer Types Using a Structural Genomics-based Approach. Mol Cell Proteomics 15:642-56
Tenenbaum, Jessica D; Avillach, Paul; Benham-Hutchins, Marge et al. (2016) An informatics research agenda to support precision medicine: seven key areas. J Am Med Inform Assoc 23:791-5
Jiang, Wei; Jia, Peilin; Hutchinson, Katherine E et al. (2015) Clinically relevant genes and regulatory pathways associated with NRASQ61 mutations in melanoma through an integrative genomics approach. Oncotarget 6:2496-508

Showing the most recent 10 out of 62 publications