Unknown

Dataset Information

0

GREEN-DB: a framework for the annotation and prioritization of non-coding regulatory variants from whole-genome sequencing data.


ABSTRACT: Non-coding variants have long been recognized as important contributors to common disease risks, but with the expansion of clinical whole genome sequencing, examples of rare, high-impact non-coding variants are also accumulating. Despite recent advances in the study of regulatory elements and the availability of specialized data collections, the systematic annotation of non-coding variants from genome sequencing remains challenging. Here, we propose a new framework for the prioritization of non-coding regulatory variants that integrates information about regulatory regions with prediction scores and HPO-based prioritization. Firstly, we created a comprehensive collection of annotations for regulatory regions including a database of 2.4 million regulatory elements (GREEN-DB) annotated with controlled gene(s), tissue(s) and associated phenotype(s) where available. Secondly, we calculated a variation constraint metric and showed that constrained regulatory regions associate with disease-associated genes and essential genes from mouse knock-outs. Thirdly, we compared 19 non-coding impact prediction scores providing suggestions for variant prioritization. Finally, we developed a VCF annotation tool (GREEN-VARAN) that can integrate all these elements to annotate variants for their potential regulatory impact. In our evaluation, we show that GREEN-DB can capture previously published disease-associated non-coding variants as well as identify additional candidate disease genes in trio analyses.

SUBMITTER: Giacopuzzi E 

PROVIDER: S-EPMC8934622 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature

altmetric image

Publications

GREEN-DB: a framework for the annotation and prioritization of non-coding regulatory variants from whole-genome sequencing data.

Giacopuzzi Edoardo E   Popitsch Niko N   Taylor Jenny C JC  

Nucleic acids research 20220301 5


Non-coding variants have long been recognized as important contributors to common disease risks, but with the expansion of clinical whole genome sequencing, examples of rare, high-impact non-coding variants are also accumulating. Despite recent advances in the study of regulatory elements and the availability of specialized data collections, the systematic annotation of non-coding variants from genome sequencing remains challenging. Here, we propose a new framework for the prioritization of non-  ...[more]

Similar Datasets

| S-EPMC10626172 | biostudies-literature
| S-EPMC5892192 | biostudies-literature
| S-EPMC8754628 | biostudies-literature
| S-EPMC4201154 | biostudies-literature
| S-EPMC4828881 | biostudies-literature
| S-EPMC11436875 | biostudies-literature
| S-EPMC10793524 | biostudies-literature
| S-EPMC6400659 | biostudies-literature
| S-EPMC8752980 | biostudies-literature
| S-EPMC6978412 | biostudies-literature