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
; 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
Showing the most recent 10 out of 75 publications
