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Modeling disease risk through analysis of physical interactions between genetic variants within chromatin regulatory circuitry

Nature Genetics volume 48, pages 1313–1320 (2016)Cite this article

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Abstract

SNPs associated with disease susceptibility often reside in enhancer clusters, or super-enhancers. Constituents of these enhancer clusters cooperate to regulate target genes and often extend beyond the linkage disequilibrium (LD) blocks containing risk SNPs identified in genome-wide association studies (GWAS). We identified 'outside variants', defined as SNPs in weak LD with GWAS risk SNPs that physically interact with risk SNPs as part of a target gene's regulatory circuitry. These outside variants further explain variation in target gene expression beyond that explained by GWAS-associated SNPs. Additionally, the clinical risk associated with GWAS SNPs is considerably modified by the genotype of outside variants. Collectively, these findings suggest a potential model in which outside variants and GWAS SNPs that physically interact in 3D chromatin collude to influence target transcript levels as well as clinical risk. This model offers an additional hypothesis for the source of missing heritability for complex traits.

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Figure 1: The regulatory circuitry of GWAS loci frequently extends beyond the boundaries of haplotype blocks.
Figure 2: Physical interactions between outside variants and GWAS-associated SNPs influence target gene expression.
Figure 3: Functional outside variants share signature features of enhancer elements.
Figure 4: Outside variants alter clinical risk.

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Acknowledgements

We thank P. Tesar, T. LaFramboise, A. Lager and J. Morrow for helpful comments and discussion. This research was supported by NIH grants R01CA160356, R01CA143237, 1R01CA193677 and P50CA150964 (P.C.S.) and T32GM008056 (O.C.).

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Authors and Affiliations

  1. Department of Genetics and Genome Sciences, Case Western Reserve University, Cleveland, Ohio, USA

    Olivia Corradin, Andrea J Cohen, Jennifer M Luppino, Ian M Bayles & Peter C Scacheri

  2. Whitehead Institute for Biomedical Research, Cambridge, Massachusetts, USA

    Olivia Corradin

  3. Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, Ohio, USA

    Fredrick R Schumacher

  4. Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, Ohio, USA

    Peter C Scacheri

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  1. Olivia Corradin
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Contributions

O.C. and P.C.S. designed the study, performed the analyses and interpreted the results. A.J.C. and F.R.S. aided statistical analyses and discussion. O.C., J.M.L. and I.M.B. performed cell culture and luciferase experiments.

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Correspondence to Peter C Scacheri.

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Integrated supplementary information

Supplementary Figure 1 Prevalence of outside variants.

(a) Schematic depicting the outside variant model. Gray represents enhancers that regulate a common gene (blue arrow). SNPs in LD within a GWAS-associated locus are defined as ‘linked variants’ (blue). Additional variants located in putative enhancer elements that regulate the same gene but are not in LD with the linked SNPs are defined as ‘outside variants’ (green). (b,c) The proportion of GWAS loci that contain outside variants is defined by each D′ (b) and r2 (c) threshold.

Supplementary Figure 2 Definition of outside variants used in transcriptome analysis.

(a) Methodology used to define the outside variants that were evaluated in transcriptome analysis. (b) Number of lead SNPs, gene associations, outside variants and GWAS SNP–outside variant pairs that resulted from the methodology described in a. (b–e) For all outside variant–GWAS SNP pairs evaluated in Figure 2, the distribution of r2 (c), D′ (d) and LOD (e) is shown. (f) The minor allele frequency of each outside variant in the 373-individual panel used for transcriptome analysis. (g) Proportion of GWAS loci (total n = 186) that harbor outside variants determined to alter the impact of the GWAS allele on target transcript levels for multiple methods of multiple-test correction.

Supplementary Figure 3 Functional outside variants as compared to published eQTL results.

Proportion of outside variants that have previously been reported as eQTLs (red).

Supplementary Figure 4 Outside variant loci evaluated in luciferase reporter assays.

Sanger sequencing of outside variant genotype for the reporter constructs used in Figure 3g.

Supplementary Figure 5 Heritability explained by local controls as compared to outside variants.

Heritability estimates for ulcerative colitis (left) and multiple sclerosis (right) as compared to 1,000 sets of randomly selected local control variants, variants within 200 kb of a GWAS SNP that do not lie in the regulatory circuitry of the gene target (gray box plot, 5th- to 95th-percentile whiskers; two-sample z test: UC, P = 0.004; MS, P < 0.001).

Supplementary Figure 6 Evaluation of potential third variants for genes with functional outside variants (FDR q < 0.05).

Analytical design and results of the impact of ‘third’ variants on gene expression and clinical risk for all genes with functional outside variants defined at FDR q < 0.05. Dot plots provide examples of outside variant and GWAS alleles that explain additional variation in gene expression.

Supplementary Figure 7 Evaluation of potential third variants for genes with functional variants (FDR q < 0.1 or permutation P < 0.01).

Same as Supplementary Figure 6 for all genes with functional outside variants.

Supplementary Figure 8 Chromatin state of potential third variants.

Comparison of third SNPs that potentially explain the effect of outside variants on expression (see third arrow in Supplementary Fig. 7) to chromatin state. Proportion of these third SNPs that is significantly enriched for markers of regulatory function (H3K4me1, H3K27ac and DNase hypersensitivity) in B lymphoblasts (GM12878).

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Supplementary Figures 1–8 and Supplementary Tables 1–3. (PDF 6186 kb)

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Corradin, O., Cohen, A., Luppino, J. et al. Modeling disease risk through analysis of physical interactions between genetic variants within chromatin regulatory circuitry. Nat Genet 48, 1313–1320 (2016). https://doi.org/10.1038/ng.3674

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