A Genome-Wide Analysis Tool to Identify Functional Regulatory Single Nucleotide Polymorphisms (rSNPs) Impacting Disease

Overview

Objectives/Goals My research aims to develop a tool to enable the first ever genome-wide analysis of genetic variants, specifically single nucleotide polymorphisms (SNPs), in transcription factor binding sites. Since the vast majority (88%) of disease associated SNPs lie in non-coding regions such as transcription factor binding sites, characterizing these variants could elucidate their mechanism of action and thus lead to improved diagnostic and treatment strategies. Methods/Materials My project has three phases: (1) development of an intragenomic screening tool, (2) development of a methylation analysis module, and (3) applying the tool to identify new relationships between transcription factors and disease. In Phase 1, I proposed and developed a novel intragenomic screening tool, which seamlessly integrates three types of next generation sequencing data: sequence (PWM), transcription factor binding (chIP-seq), and open chromatin (DNase-seq). In Phase 2, I developed the methylation analysis module. In Phase 3, I successfully applied the tool to analyze a compiled list of 55,000+ disease associated SNPs to identify new relationships between transcription factors and disease. Results SNP Effect Matrix (SEM) scores, generated through the developed tool, were shown to reflect known transcription factor binding patterns and mirror transcription factor structure. Validation performed using published results from a high-throughput reporter assay show increased correlation (R^2 = 0.350) between SEM scores and normalized expression compared to the current standard for transcription factor binding (R^2 = 0.232). The methylation extension supports the ability of the tool to predict the impact of DNA methylation on transcription factor binding. The tool was successfully applied to identify new, statistically significant relationships between transcription factors and disease. Conclusions/Discussion My work represents the first tool that can predict the impact of a SNP on transcription factor binding at the whole genome level. The tool has been prepared as an open-source software package using GIT source code management and will be released as an addendum to an upcoming publication. By revolutionizing the framework of analyzing genetic variants that overlap with non-coding, regulatory regions, the tool promises to advance the development of disease diagnostics and treatments.

Awards (1)

• Fourth Place

Competition history

• CSEF 2014 Biochemistry/ Molecular Biology · Entry S0518

Resources

• CSEF project summary ↗

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