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

View ORCID ProfileE Giacopuzzi, N Popitsch, JC Taylor
doi: https://doi.org/10.1101/2020.09.17.301960
E Giacopuzzi
1Wellcome Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, UK
2National Institute for Health Research Oxford Biomedical Research Centre, Oxford, OX4 2PG, UK
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  • ORCID record for E Giacopuzzi
N Popitsch
1Wellcome Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, UK
3Institute of Molecular Biotechnology of the Austrian Academy of Sciences (IMBA), Dr. Bohr-Gasse 3, VBC, 1030, Vienna, Austria
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JC Taylor
1Wellcome Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, UK
2National Institute for Health Research Oxford Biomedical Research Centre, Oxford, OX4 2PG, UK
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  • For correspondence: jenny.taylor@well.ox.ac.uk
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Abstract

Background Non-coding variants have emerged as important contributors to the pathogenesis of human diseases, not only as common susceptibility alleles but also as rare high-impact variants. 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.

Results We integrated 24 data sources to develop a standardized collection of 2.4 million regulatory elements in the human genome, transcription factor binding sites, DNase peaks, ultra-conserved non-coding elements, and super-enhancers. Information on controlled gene(s), tissue(s) and associated phenotype(s) are provided for regulatory elements when possible. We also calculated a variation constraint metric for regulatory regions and showed that genes controlled by constrained regions are more likely to be disease-associated genes and essential genes from mouse knock-out screenings. Finally, we evaluated 16 non-coding impact prediction scores providing suggestions for variant prioritization. The companion tool allows for annotation of VCF files with information about the regulatory regions as well as non-coding prediction scores to inform variant prioritization. The proposed annotation framework was able to capture previously published disease-associated non-coding variants and its integration in a routine prioritization pipeline increased the number of candidate genes, including genes potentially correlated with patient phenotype, and established clinically relevant genes.

Conclusion We have developed a resource for the annotation and prioritization of regulatory variants in WGS analysis to support the discovery of candidate disease-associated variants in the non-coding genome.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • edoardo.giacopuzzi{at}well.ox.ac.uk, niko.popitsch{at}well.ox.ac.uk, jenny.taylor{at}well.ox.ac.uk

  • https://zenodo.org/record/3981033

  • Abbreviations

    OR
    Odds-ratio
    TPR
    True positive rate (sensitivity)
    TNR
    True negative rate (specificity)
    FDR
    False discovery rate
    ACC
    Accuracy
    HPO
    Human Phenotype Ontology
    TFBS
    Transcription factor binding site
    UCNE
    Ultra-conserved non-coding element
    AUC
    Area under the curve
    OPM
    Overall Performance Measure
  • Copyright 
    The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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    Posted September 19, 2020.
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    GREEN-DB: a framework for the annotation and prioritization of non-coding regulatory variants in whole-genome sequencing
    E Giacopuzzi, N Popitsch, JC Taylor
    bioRxiv 2020.09.17.301960; doi: https://doi.org/10.1101/2020.09.17.301960
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    GREEN-DB: a framework for the annotation and prioritization of non-coding regulatory variants in whole-genome sequencing
    E Giacopuzzi, N Popitsch, JC Taylor
    bioRxiv 2020.09.17.301960; doi: https://doi.org/10.1101/2020.09.17.301960

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