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, Z; Xu, J; Chen, J et al. (2015) Transcriptome sequencing and genome-wide association analyses reveal lysosomal function and actin cytoskeleton remodeling in schizophrenia and bipolar disorder. Mol Psychiatry 20:563-72|
|Meador, Catherine B; Micheel, Christine M; Levy, Mia A et al. (2014) Beyond histology: translating tumor genotypes into clinically effective targeted therapies. Clin Cancer Res 20:2264-75|
|Jiang, Junfeng; Jia, Peilin; Shen, Bairong et al. (2014) Top associated SNPs in prostate cancer are significantly enriched in cis-expression quantitative trait loci and at transcription factor binding sites. Oncotarget 5:6168-77|
|Jiang, Junfeng; Jia, Peilin; Zhao, Zhongming et al. (2014) Key regulators in prostate cancer identified by co-expression module analysis. BMC Genomics 15:1015|
|Lovly, Christine M; McDonald, Nerina T; Chen, Heidi et al. (2014) Rationale for co-targeting IGF-1R and ALK in ALK fusion-positive lung cancer. Nat Med 20:1027-34|
|Xia, Junfeng; Jia, Peilin; Hutchinson, Katherine E et al. (2014) A meta-analysis of somatic mutations from next generation sequencing of 241 melanomas: a road map for the study of genes with potential clinical relevance. Mol Cancer Ther 13:1918-28|
|Haq, Rizwan; Fisher, David E; Widlund, Hans R (2014) Molecular pathways: BRAF induces bioenergetic adaptation by attenuating oxidative phosphorylation. Clin Cancer Res 20:2257-63|
|Mitra, Ramkrishna; Edmonds, Mick D; Sun, Jingchun et al. (2014) Reproducible combinatorial regulatory networks elucidate novel oncogenic microRNAs in non-small cell lung cancer. RNA 20:1356-68|
|Cheng, Feixiong; Jia, Peilin; Wang, Quan et al. (2014) Studying tumorigenesis through network evolution and somatic mutational perturbations in the cancer interactome. Mol Biol Evol 31:2156-69|
|Lin, Chen-Ching; Mitra, Ramkrishna; Zhao, Zhongming (2014) A tri-component conservation strategy reveals highly confident microRNA-mRNA interactions and evolution of microRNA regulatory networks. PLoS One 9:e103142|
Showing the most recent 10 out of 22 publications