Annotation of functional variation in personal genomes using RegulomeDB

Abstract

As the sequencing of healthy and disease genomes becomes more commonplace, detailed annotation provides interpretation for individual variation responsible for normal and disease phenotypes. Current approaches focus on direct changes in protein coding genes, particularly nonsynonymous mutations that directly affect the gene product. However, most individual variation occurs outside of genes and, indeed, most markers generated from genome-wide association studies (GWAS) identify variants outside of coding segments. Identification of potential regulatory changes that perturb these sites will lead to a better localization of truly functional variants and interpretation of their effects. We have developed a novel approach and database, RegulomeDB, which guides interpretation of regulatory variants in the human genome. RegulomeDB includes high-throughput, experimental data sets from ENCODE and other sources, as well as computational predictions and manual annotations to identify putative regulatory potential and identify functional variants. These data sources are combined into a powerful tool that scores variants to help separate functional variants from a large pool and provides a small set of putative sites with testable hypotheses as to their function. We demonstrate the applicability of this tool to the annotation of noncoding variants from 69 full sequenced genomes as well as that of a personal genome, where thousands of functionally associated variants were identified. Moreover, we demonstrate a GWAS where the database is able to quickly identify the known associated functional variant and provide a hypothesis as to its function. Overall, we expect this approach and resource to be valuable for the annotation of human genome sequences. [Supplemental material is available for this article.]The increasing number of sequenced human genomes is providing a catalog of the large number of individual variations present in the human genome (The International HapMap Consortium 2005; The 1000 Genomes Project Consortium 2010). Many of these variants are expected to be responsible for normal and disease phenotypes. Similarly, large, genome-wide association studies (GWAS) continue to map diseases to associated genomic regions from large cohorts of individuals (Hindorff et al. 2012). Initial interpretation of results generated by both of these approaches has been limited to DNA regions that cause disruption of gene function through coding sequence changes typically identified using an application such as PolyPhen-2 (Adzhubei et al. 2010). However, ;95% of known variants within sequenced genomes and 88% of those variants from GWAS studies fall outside of coding regions and have been difficult to interpret ).Both large consortia and individual labs are generating a significant amount of regulatory information that is providing a better interpretation of the noncoding portions of the genome. The ENCODE Project, in particular, has mapped open chromatin and protein binding regions for large numbers of factors across many cell type...

Genomewide meta‐analysis identifies loci associated with IGF ‐I and IGFBP ‐3 levels with impact on age‐related traits

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“…To evaluate the potential functional effect of the identified loci, we identified all proxy SNPs in moderate to high LD ( R 2 ≥0.5) to the index SNP within ±500‐kb regions. We used dbSNP's predicted30 functional effect of variants and considered SNPs with RegulomeDB31 score <4. RegulomeDB31 integrates the RoadMap Epigenomics and ENCODE projects to identify variants which have potential or demonstrated regulatory function, and predicts potential mechanisms of functional involvement.…”

“…We used dbSNP's predicted30 functional effect of variants and considered SNPs with RegulomeDB31 score <4. RegulomeDB31 integrates the RoadMap Epigenomics and ENCODE projects to identify variants which have potential or demonstrated regulatory function, and predicts potential mechanisms of functional involvement. We used a GWAVA32 score to identify loci with likely functional non‐coding variants.…”

ATP2C2andDYX1C1are putative modulators of dyslexia‐related MMR

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