We propose to develop and apply innovative bioinformatic analysis tools for studying the genomics of vision disorders, focusing on lens- and retina-related diseases. We hypothesize that integrative analysis of existing functional genomic datasets, such as profiles of gene expression, histone modification, transcription factor binding, and perturbation experiments, can greatly facilitate interpretation of sequence and copy number variants identified in genome-wide association studies (GWAS) or whole-genome/exome sequencing studies.
In Aim 1, we will use available genomic datasets to construct a gene regulatory network for both the lens and the retina.
In Aim 2, we will perform meta-analysis of GWAS datasets to search for copy number variants that are correlated with the phenotype, using improved methods for cross-platform analysis.
In Aim 3, we will develop copy number variation detection algorithm for exome data and develop a framework for prioritization of variants using epigenomic data available from the Encyclopedia of DNA Elements (ENCODE) project.
In Aim 4, we propose to integrate the variants we find with information from existing resources in an eye-disease specific variant database and exploration platform, using a customized workflow system we have already developed. Many of the proposed analyses take advantage of the tools that were developed originally for cancer genome analysis. The gene regulatory networks, improved algorithms, and integrated workflows developed in this proposal are likely to be a widely applicable resource to the eye research community.
Discovery and interpretation of genes associated with rare or complex genetic eye diseases pose a significant computational challenge. To fully realize the potential of personalized genomic medicine, one must integrate genotypic variation-including sequence and copy number variations-with tissue type-specific regulatory network. This proposal will expand our knowledge of how genome variations are related to eye diseases.
|Nam, Jae-Yong; Kim, Nayoung K D; Kim, Sang Cheol et al. (2016) Evaluation of somatic copy number estimation tools for whole-exome sequencing data. Brief Bioinform 17:185-92|