Annotation of functional variation in personal genomes using RegulomeDB

Area of Science:

• Genomics
• Bioinformatics
• Molecular Biology

Background:

• Genome sequencing is increasing, necessitating better interpretation of genetic variations.
• Most human genetic variation occurs outside protein-coding genes, complicating functional analysis.
• Current methods often overlook non-coding variants, missing key regulatory insights.

Purpose of the Study:

• To develop a novel database, RegulomeDB, for interpreting regulatory variants in the human genome.
• To integrate diverse datasets for accurate identification of functional non-coding variants.
• To provide testable hypotheses for the function of identified regulatory variants.

Main Methods:

• Integrated high-throughput experimental data (e.g., ENCODE) with computational predictions and manual annotations.
• Developed a scoring system to prioritize functional variants from large datasets.
• Applied RegulomeDB to annotate variants from multiple sequenced genomes and genome-wide association studies (GWAS).

Main Results:

• Demonstrated the utility of RegulomeDB in annotating non-coding variants from 69 genomes and a personal genome.
• Identified thousands of functionally associated variants using the database.
• Successfully pinpointed a known GWAS functional variant and proposed a functional hypothesis.

Conclusions:

• RegulomeDB is a valuable resource for annotating human genome sequences, particularly non-coding regions.
• The database aids in distinguishing functional variants from non-functional ones.
• This approach enhances the interpretation of genetic variation for understanding phenotypes and disease.