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Large-scale identification of sequence variants influencing human transcription factor occupancy in vivo

Nature Genetics volume 47, pages 1393–1401 (2015)Cite this article

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An Erratum to this article was published on 01 January 2016

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Abstract

The function of human regulatory regions depends exquisitely on their local genomic environment and on cellular context, complicating experimental analysis of common disease- and trait-associated variants that localize within regulatory DNA. We use allelically resolved genomic DNase I footprinting data encompassing 166 individuals and 114 cell types to identify >60,000 common variants that directly influence transcription factor occupancy and regulatory DNA accessibility in vivo. The unprecedented scale of these data enables systematic analysis of the impact of sequence variation on transcription factor occupancy in vivo. We leverage this analysis to develop accurate models of variation affecting the recognition sites for diverse transcription factors and apply these models to discriminate nearly 500,000 common regulatory variants likely to affect transcription factor occupancy across the human genome. The approach and results provide a new foundation for the analysis and interpretation of noncoding variation in complete human genomes and for systems-level investigation of disease-associated variants.

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Figure 1: Identification of regulatory variants influencing DNA accessibility.
Figure 2: Effect of sampling depth on the detection of imbalance.
Figure 3: Cross–cell type analysis of imbalance.
Figure 4: Imbalance in CTCF occupancy and H3K4me3.
Figure 5: Profiles of transcription factor sensitivity to sequence variation.
Figure 6: Buffering of regulatory variation.
Figure 7: Recognition of variation affecting transcription factor occupancy across the genome.

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Change history

  • 17 November 2015

    In the version of this article initially published online, the Online Methods incorrectly abbreviated mapping quality as MAQ rather than MAPQ. Also in the Online Methods, the procedure for downsampling allele counts for cross–cell type analysis of imbalance was incorrectly written as "we subsampled each site to three cell types and further downsampled the allele counts to mapping quality for the lowest of the three cell types." The sentence should read "we subsampled each site to three cell types and further downsampled to the allele counts to match the lowest of the three cell types." The errors have been corrected for the print, PDF and HTML versions of this article.

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Acknowledgements

This work was supported by US National Institutes of Health grants U54HG004592, U54HG007010, U01ES01156, 1S10RR026770 and 1S10OD017999 to J.A.S. and National Institute of Mental Health fellowship F31MH094073 to M.T.M. J.V. was supported by a National Science Foundation Graduate Research Fellowship under grant DGE-071824.

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  1. Matthew T Maurano

    Present address: Present address: Institute for Systems Genetics, New York University Langone Medical Center, New York, New York, USA.,

Authors and Affiliations

  1. Department of Genome Sciences, University of Washington, Seattle, Washington, USA

    Matthew T Maurano, Eric Haugen, Richard Sandstrom, Jeff Vierstra, Anthony Shafer, Rajinder Kaul & John A Stamatoyannopoulos

  2. Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, Washington, USA

    Rajinder Kaul

  3. Division of Oncology, Department of Medicine, University of Washington, Seattle, Washington, USA

    John A Stamatoyannopoulos

  4. Altius Institute for Biomedical Sciences, Seattle, Washington, USA

    John A Stamatoyannopoulos

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Contributions

M.T.M., E.H. and J.A.S. conceived and designed the experiments. M.T.M. and E.H. analyzed the data. J.V. and M.T.M. performed transcription factor cluster analysis. R.S. provided bioinformatics support. A.S. generated targeted footprinting data. R.K. assisted with data collection. M.T.M. and J.A.S. wrote the manuscript. M.T.M. and J.A.S. jointly supervised research.

Corresponding authors

Correspondence to Matthew T Maurano or John A Stamatoyannopoulos.

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Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–13 and Supplementary Tables 3–13 and 15–17. (PDF 11308 kb)

Supplementary Table 1. Overview of the DNase I data used in this study.

DNase I mapping of 116 cell types and tissues used in the study, including the shorthand name for the tissue. Signal portion of tags (SPOT) scores are a measure of enrichment and refer to the proportion of reads mapping within a DHS. Read counts include reads mapped uniquely with ≤2 mismatches to an autosomal chromosome; paired-end reads were required to both properly map to the same chromosome. Read counts are in millions. *, FL_E was excluded from the primary analysis and used for independent validation of the predictions in Figure 7. Previously published data sets are labeled by publication (refs. 2,3,24,27,64–67). (TXT 35 kb)

Supplementary Table 2. Overview of the ChIP-seq data used in this study.

ChIP-seq mapping of CTCF and H3K4me3 in 77 cell types and tissues used in the study, Signal portion of tags (SPOT) scores are a measure of enrichment and refer to the proportion of reads mapping within a DHS. Read counts include reads mapped uniquely with ≤2 mismatches to an autosomal chromosome; paired-end reads were required to both properly map to the same chromosome. Read counts are in millions. Previously published data sets are labeled by publication (refs. 2,17,44,68). (TXT 12 kb)

Supplementary Table 14. Clustering of motifs into TF families.

Clustering of motifs from the JASPAR, UniProbe, TRANSFAC and Jolma et al.35 databases. Each TF cluster is listed along with the names of constituent motifs. (TXT 34 kb)

Supplementary Data Set 1. SNPs tested for imbalance in DNA accessibility.

SNPs are listed by their hg19 coordinates. The rsID is used for SNPs in dbSNP 138. SNPs are classified as imbalanced as in Figure 1c. PctRef refers to the proportion of reads mapping to the reference allele (Fig.1d). (TXT 27676 kb)

Supplementary Data Set 2. TF clusters of similar motifs.

Motif weblogos from the JASPAR, UniPROBE and Jolma et al.35 databases grouped by TF cluster. Motifs from TRANSFAC are listed by name without showing a weblogo. (PDF 23365 kb)

Supplementary Data Set 3. SNVs predicted to affect DNA accessibility.

List of SNVs from dbSNP 138 overlapping a TF recognition sequence in a DHS hotspot predicted to affect accessibility with a score greater than 0.10. The file is in extended bed format using hg19 coordinates and includes a header line. Each row contains the SNP coordinates and dbSNP ID, a score scaled as the probability of imbalance, the PWM name and strand, the position of the SNP relative to the PWM match and the two alleles of the SNP. (ZIP 9362 kb)

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Maurano, M., Haugen, E., Sandstrom, R. et al. Large-scale identification of sequence variants influencing human transcription factor occupancy in vivo. Nat Genet 47, 1393–1401 (2015). https://doi.org/10.1038/ng.3432

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