Principal Investigator/Program Director (Last, first, middle): Zhao, ZhongmingProject SummaryGenome-wide association studies (GWAS) and RNA sequencing (RNA-Seq) are two major approaches forstudying 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 differentiallyexpressed genes in diseases; however, many challenges remain because of the complexity in generegulations. In this proposal, we combine statistics, bioinformatics, and genetics to develop novel analyticalstrategies that maximally leverage information from both GWAS and RNA-Seq studies in order to understandthe genetic architecture underlying complex diseases, especially schizophrenia. Our proposal will be the firstmethodology development for a systems approach that integrates GWAS and RNA-Seq data. We propose thefollowing four major aims: (1) To develop novel analytical strategies to identify genes and pathways withenriched association signals in GWAS by leveraging functional information measured by RNA sequencing. Wedefine this approach as RNA-Seq assisted GWAS analysis. (2) To develop novel analytical strategies toidentify genes and pathways with enriched association signals in RNA-Seq data by leveraging information fromgenetics of gene expression studies. We define this approach as RNA-Seq oriented analysis. (3) To apply themethods in Aims 1 and 2 to schizophrenia, which we have generated RNA-Seq data from 82 brain samplescollected from the Stanley Medical Research Institute and gained access to four major GWAS datasets forschizophrenia (ISC, GAIN, nonGAIN, and CATIE: a total of more than 6000 cases and 6000 controls). Thisapplication will also help us refine the strategies in Aims 1 and 2. (4) To develop computational tools fordetecting disease genes, pathways that lead to complex diseases. These tools will become a useful resourcefor the public community and can be applied to any complex diseases with available RNA-Seq and GWASdatasets. The successful completions of Aims 1 and 2 will provide us with important methods for integrativegenomic analysis of GWAS and RNA-Seq datasets. The successful completion of Aim 3 will provide us with alist of prioritized candidate genes and pathways for future validation on schizophrenia. The successfulcompletion of Aim 4 will provide computational tools and a user-friendly online system for investigators whostudy complex diseases using GWAS and RNA-Seq.Project Description Page 6

Public Health Relevance

; first; middle): Zhao; ZhongmingProject NarrativeRapid 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 majorsources. To meet the great challenges on analyzing such large and heterogeneous datasets; in this proposalwe combine statistics; bioinformatics; and genetics to develop novel analytical strategies that maximallyleverage information from both GWAS and RNA-Seq studies to understand the genetic architecture underlyingschizophrenia and other complex diseases.Public Health Relevance Statement Page 7

National Institute of Health (NIH)
National Library of Medicine (NLM)
Research Project (R01)
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Biomedical Library and Informatics Review Committee (BLR)
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Ye, Jane
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University of Texas Health Science Center Houston
Schools of Allied Health Profes
United States
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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
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
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
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

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