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GARFIELD classifies disease-relevant genomic features through integration of functional annotations with association signals

Nature Genetics volume 51, pages 343–353 (2019)Cite this article

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Abstract

Loci discovered by genome-wide association studies predominantly map outside protein-coding genes. The interpretation of the functional consequences of non-coding variants can be greatly enhanced by catalogs of regulatory genomic regions in cell lines and primary tissues. However, robust and readily applicable methods are still lacking by which to systematically evaluate the contribution of these regions to genetic variation implicated in diseases or quantitative traits. Here we propose a novel approach that leverages genome-wide association studies’ findings with regulatory or functional annotations to classify features relevant to a phenotype of interest. Within our framework, we account for major sources of confounding not offered by current methods. We further assess enrichment of genome-wide association studies for 19 traits within Encyclopedia of DNA Elements- and Roadmap-derived regulatory regions. We characterize unique enrichment patterns for traits and annotations driving novel biological insights. The method is implemented in standalone software and an R package, to facilitate its application by the research community.

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Fig. 1: Outline of the GARFIELD method.
Fig. 2: Method assessment.
Fig. 3: Enrichment of GWA analysis P values in DHS (hotspots).
Fig. 4: Method comparison for 21 GWAS datasets in DHS (hotspots) and histone modifications (H3K27ac and H3K4me3) at the T < 10-8 GWAS significance threshold.
Fig. 5: Enrichment levels (log OR) and extent of sharing between traits for 25-state chromatin segmentations of the National Institutes of Health Roadmap and ENCODE projects at the T < 10−5 GWAS significance threshold.

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Code availability

Custom codes can be found at http://www.ebi.ac.uk/birney-srv/GARFIELD/.

Data availability

Web links for publicly available GWAS datasets and regulatory information databases are included in the URLs section. Restriction of availability applies to blood cell indices GWAS from van der Harst et al.33 and Gieger et al.34, which have been obtained through the manuscripts’ authors. Any other data that support the findings of this study are available from the corresponding authors upon reasonable request.

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Acknowledgements

This study made use of data generated by the UK10K Consortium. A full list of the investigators who contributed to the generation of the data is available from http://www.UK10K.org/. Funding for UK10K was provided by the Wellcome Trust under award no. WT091310. Research by N.S. is supported by the Wellcome Trust (grants WT098051 and WT091310). N.J.T. is supported as a Wellcome Trust Investigator (no. 202802/Z/16/Z), is supported as the principal investigator of the Avon Longitudinal Study of Parents and Children (no. MRC & WT 102215/2/13/2), is supported by the University of Bristol NIHR Biomedical Research Centre (no. BRC-1215-20011) and the MRC Integrative Epidemiology Unit (no. MC_UU_12013/3), and works within the CRUK Integrative Cancer Epidemiology Programme (no. C18281/A19169). G.R.S.R. and V.I. are supported by European Molecular Biology Laboratory-Wellcome Trust Sanger Institute postdoctoral fellowships.

Author information

Author notes

  1. A full list of members is available in the Supplementary Note.

Authors and Affiliations

  1. Human Genetics, Wellcome Sanger Institute, Hinxton, UK

    Valentina Iotchkova, Graham R. S. Ritchie, Matthias Geihs, Klaudia Walter & Nicole Soranzo

  2. European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK

    Valentina Iotchkova, Graham R. S. Ritchie, Sandro Morganella, Ian Dunham & Ewan Birney

  3. MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK

    Josine L. Min & Nicholas John Timpson

  4. Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK

    Josine L. Min & Nicholas John Timpson

  5. Department of Haematology, University of Cambridge, Cambridge, UK

    Nicole Soranzo

  6. The National Institute for Health Research Blood and Transplant Unit (NIHR BTRU) in Donor Health and Genomics, University of Cambridge, Cambridge, UK

    Nicole Soranzo

Authors
  1. Valentina Iotchkova
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UK10K Consortium

Contributions

G.R.S.R., J.L.M., K.W., N.J.T., I.D. and N.S. contributed data or materials. E.B., G.R.S.R., I.D., J.L.M., N.S. and V.I. developed the method. V.I. analyzed the data. E.B., I.D., N.J.T., N.S. and V.I. provided critical interpretation of the results. M.G. and S.M. designed the tools. E.B., N.S. and V.I. wrote the manuscript. E.B., G.R.S.R., I.D., J.L.M., K.W., M.G., N.J.T., N.S., S.M. and V.I. evaluated the manuscript. E.B. and N.S. designed and managed the project.

Corresponding authors

Correspondence to Ewan Birney or Nicole Soranzo.

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Integrated supplementary information

Supplementary Fig. 1 GARFIELD method assessment.

a, GARFIELD-estimated false-positive rate (FPR) from 29 real data GWAS and n = 1,000 independent simulated annotations. The black horizontal line denotes the 5% FPR threshold. Traits are shown on the x axis. Colored bars denote the GARFIELD threshold of T < 10–8 (red) and the GARFIELD threshold of T < 10–5 (gray). Error bars denote standard errors. b,c, Effect of feature correction for each of 29 GWAS at the T < 10–8 significance threshold when analyzing 424 DHS cell types. The figure shows the proportion of significant annotations with respect to feature correction, where N denotes the number of LD proxies and T distance to the nearest TSS. The y axis shows the corresponding values when no feature correction is employed. d, Difference in proportion of significant enrichments between a model not accounting for any feature to a model accounting for any combination of the features, respectively, applied to 424 DHS cell types.

Supplementary Fig. 2 GARFIELD enrichment wheel plots.

Enrichment of genome-wide association analysis P-values in DNase I–hypersensitive sites (hotspots) for 27 disease/quantitative traits. Radial lines show OR values at eight GWAS –log10 P-value thresholds (T) for n = 424 ENCODE and Roadmap Epigenomics DHS cell lines, sorted by tissue on the outer circle. Dots on the outer side of the circle denote significant enrichment (if present) at T < 10–5 (outermost) to T < 10–8 (innermost).

Supplementary Fig. 3 Comparison between real data results for 29 real GWAS and 424 open chromatin annotations at the T < 10–8 and T < 10–5 GWAS P-value thresholds.

Left, −log10 P-value comparison for all trait annotation pairs (n = 29 × 424 points). Horizontal and vertical lines denote the threshold for detecting enrichment after multiple-testing correction. The numbers in each corner denote the number of points in it. Right, odds ratio (OR) comparison for trait annotation pairs with significant enrichment in both the T < 10–8 and T < 10–5 GWAS P-value thresholds.

Supplementary Fig. 4 Multiple-annotation enrichment of genome-wide association analysis P-values in DNase I–hypersensitive sites (hotspots) for 15 disease or quantitative GWAS traits.

Cell types/tissues remaining after a heuristic multiple-annotation approach are shown on the y axis for each trait. Odds ratios (on log scale) are represented as dots and 95% CI with lines. The multiple-annotation model estimates are represented in red and the marginal effects of analysis of each annotation on its own are represented in black. Only phenotypes with at least a single detected enrichment are shown.

Supplementary Fig. 5 Enrichment levels (log OR) and extent of sharing between traits for 25-state chromatin segmentations of the NIH Roadmap and ENCODE projects at the T < 10–8 GWAS significance threshold.

a, Distribution of significant OR values across the 29 traits considered, split by segmentation state and colored to highlight predicted functional elements by Roadmap Epigenomics (see Supplementary Table 9). Number of points n is shown on the x axis below each category. b, Distribution of the pairwise difference between ORs from all enhancer, promoter and transcriptional enhancers and transcriptional regulatory states tested (‘state 1’) to ORs from transcription states for significant enrichments only (‘state 2’; for example, measuring ORc,tEnhA1 − ORc,tTx for all cell types c and traits t for which P-valuec,tEnhA1 and P-valuec,tTx are both significant). Number of points n is shown on the x axis below each category. c, Sharing of significantly enriched/depleted annotations across 27 phenotypes (excluding CD and UC). The bar plot displays the number of cell types where an annotation is uniquely enriched in a trait or shared among multiple traits.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–5 and Supplementary Note

Reporting Summary

Supplementary Table 1

Summary of available enrichment analysis approaches

Supplementary Table 2

Summary of overlap of UK10K sequence variants with DNase I–hypersensitive sites

Supplementary Table 3

Summary of the number of variants per disease/quantitative trait

Supplementary Table 4

GARFIELD enrichment of 29 publicly available GWAS studies in DNase I–hypersensitive sites from 424 ENCODE and Roadmap Epigenomics cell types

Supplementary Table 5

Enrichment P values (–log10) from five methods of 21 publicly available GWAS studies in DNase I–hypersensitive sites from 424 ENCODE and Roadmap Epigenomics cell types; in H3K27ac peaks from 127 ENCODE and Roadmap Epigenomics cell types; and in H3K4me3 peaks from 127 ENCODE and Roadmap Epigenomics cell types

Supplementary Table 6

Method running time and memory usage for 21 traits and 424 DNase I–hypersensitive site annotations

Supplementary Table 7

Roadmap epigenomics and ENCODE cell lines used for segmentations

Supplementary Table 8

GARFIELD enrichment of 29 publicly available GWAS studies in 25 state genome segmentations in 127 cell types

Supplementary Table 9

Segmentation state summary

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Iotchkova, V., Ritchie, G.R.S., Geihs, M. et al. GARFIELD classifies disease-relevant genomic features through integration of functional annotations with association signals. Nat Genet 51, 343–353 (2019). https://doi.org/10.1038/s41588-018-0322-6

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