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Functional annotation of noncoding sequence variants

Nature Methods volume 11, pages 294–296 (2014)Cite this article

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Abstract

Identifying functionally relevant variants against the background of ubiquitous genetic variation is a major challenge in human genetics. For variants in protein-coding regions, our understanding of the genetic code and splicing allows us to identify likely candidates, but interpreting variants outside genic regions is more difficult. Here we present genome-wide annotation of variants (GWAVA), a tool that supports prioritization of noncoding variants by integrating various genomic and epigenomic annotations.

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Figure 1: Mean ROC curves for tenfold cross-validation experiments on each of the three training sets.
Figure 2: Classifier scores for recurrent versus nonrecurrent noncoding somatic mutations from the COSMIC database.

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Acknowledgements

G.R.S.R. is supported by European Molecular Biology Laboratory and the Sanger Institute via an EBI-Sanger Postdoctoral Fellowship. This work was funded by the Wellcome Trust (098051 and 095908) and by the European Molecular Biology Laboratory. The research leading to these results has received funding from the EU Seventh Framework Programme (FP7/2007-2013) under grant agreement (282510–BLUEPRINT).

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Authors and Affiliations

  1. European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridge, UK.,

    Graham R S Ritchie, Ian Dunham & Paul Flicek

  2. Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK.,

    Graham R S Ritchie, Eleftheria Zeggini & Paul Flicek

Authors
  1. Graham R S Ritchie
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  2. Ian Dunham
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  3. Eleftheria Zeggini
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  4. Paul Flicek
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Contributions

G.R.S.R. implemented the method, performed all analyses and drafted the manuscript. I.D. assisted with access to ENCODE data and suggested how to construct the control sets. E.Z. and P.F. contributed to the development of the method, manuscript writing and jointly directed the work.

Corresponding authors

Correspondence to Eleftheria Zeggini or Paul Flicek.

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Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Results and Supplementary Figures 1–8 (PDF 1163 kb)

Supplementary Table 1

Summary annotation results for the SORT1 locus. (XLS 23 kb)

Supplementary Table 2

Summary annotation results for the TNFAIP3 locus. (XLS 25 kb)

Supplementary Table 3

Summary annotation results for the TCF7L2 locus. (XLS 18 kb)

Supplementary Table 4

Results from the gene-by-gene analysis of variants from a single individual showing the rank of the spike-in variant for each gene, and the number of background variants, using the classifier trained on variants matched by distance to the nearest TSS. (XLS 18 kb)

Supplementary Table 5

Statistics for enrichment/depletion for all annotations analyzed; methods are described in Online Methods. (XLS 47 kb)

Supplementary Software

Python software implementing the annotation pipeline and classifier, and the variant training sets used to train the classifier (ZIP 2119 kb)

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Ritchie, G., Dunham, I., Zeggini, E. et al. Functional annotation of noncoding sequence variants. Nat Methods 11, 294–296 (2014). https://doi.org/10.1038/nmeth.2832

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