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A method to predict the impact of regulatory variants from DNA sequence

Abstract

Most variants implicated in common human disease by genome-wide association studies (GWAS) lie in noncoding sequence intervals. Despite the suggestion that regulatory element disruption represents a common theme, identifying causal risk variants within implicated genomic regions remains a major challenge. Here we present a new sequence-based computational method to predict the effect of regulatory variation, using a classifier (gkm-SVM) that encodes cell type–specific regulatory sequence vocabularies. The induced change in the gkm-SVM score, deltaSVM, quantifies the effect of variants. We show that deltaSVM accurately predicts the impact of SNPs on DNase I sensitivity in their native genomic contexts and accurately predicts the results of dense mutagenesis of several enhancers in reporter assays. Previously validated GWAS SNPs yield large deltaSVM scores, and we predict new risk-conferring SNPs for several autoimmune diseases. Thus, deltaSVM provides a powerful computational approach to systematically identify functional regulatory variants.

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Figure 1: Overview of our deltaSVM method.
Figure 2: deltaSVM can accurately predict SNPs associated with DNase I hypersensitivity.
Figure 3: deltaSVM correlations with dsQTL and eQTL effect sizes.
Figure 4: deltaSVM accurately predicts change in luciferase expression in targeted mutagenesis of Tyr and Tyrp1 mouse melanocyte enhancers.
Figure 5: deltaSVM accurately predicts change of expression in massively parallel reporter assays.
Figure 6: deltaSVM only identifies validated causal SNPs when trained on the appropriate cell type.

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Acknowledgements

This research was supported in part by US National Institutes of Health grant R01 NS62972 to A.S.M. and by grant R01 HG007348 to M.A.B.

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Authors and Affiliations

Authors

Contributions

M.A.B., A.S.M., D.L. and D.U.G. designed the study and wrote the manuscript. D.U.G. and M.B. performed the experiments and analyzed the data. D.L. and M.A.B. developed the computational algorithms and analyzed the data. B.J.S. and A.L.A. contributed computational analysis. D.L. and D.U.G. contributed equally to this work.

Corresponding authors

Correspondence to Andrew S McCallion or Michael A Beer.

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The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Correlation of deltaSVM and dsQTL effect size drops with increasing distance between dsQTL SNPs and the center of the associated DNase I–sensitive regions.

The original set of dsQTLs was defined as SNPs within ±1,000 bp of covarying hypersensitive regions13. We find that deltaSVM is only consistent with dsQTL effect size (beta) when we constrain the set of dsQTLs to be within 200 bp of the modulated DHS region: (a) 0~50 bp (red), (b) 50~200 bp (green), (c) 200–500 bp and (d) 500–1,000 bp. Thus, our analysis is consistent with a local mechanism of action for dsQTLs.

Supplementary Figure 2 Bases predicted to reduce the activity of functional regions are evolutionarily constrained.

We calculated the average deltaSVM scores of all three possible mutations at each base within LCL GM12878 DHSs and compared the conservation (phyloP) for bases causing (a) negative (red), (b) neutral (gray) and (c) positive (blue) deltaSVM-predicted impact (the top 1% of bases with negative deltaSVM, 1% of bases with deltaSVM near 0 and the top 1% of bases with positive deltaSVM; n = 63,123). (d) Differential distributions relative to bases with neutral deltaSVM. Bases with negative or positive deltaSVM are more conserved than those with neutral deltaSVM; P < 1 × 10−300 (under machine precision) and P < 1 × 10−14 (Kolmogorov-Smirnov test), respectively. Also, bases with negative deltaSVM are much more conserved than those with positive deltaSVM (average phyloP of 1.00 versus 0.20, P < 1 × 10−300).

Supplementary Figure 3 Correlations of deltaSVM and in vivo mutation effect size in the ALDOB enhancer using an aggregate model.

We averaged the deltaSVM scores of all three possible mutations at each base and compared the expression changes from the univariate model reported by Patwardhan et al.22.

Supplementary Figure 4 High-confidence predicted causal SNPs in loci associated with autoimmune disease.

The significance of the maximum of Abs(deltaSVM) depends on the number of flanking candidate causal SNPs. Sampling of random SNPs scored with the TH1 gkm-SVM yielded the solid curves for the top 2% of all loci and the mean, with standard deviation shown (dashed line). Seventeen of the 413 immune-associated loci exceed the 2% threshold, whereas 8 would be expected by chance.

Supplementary Figure 5 Precision of deltaSVM prediction of dsQTLs as a function of gkm-SVM feature length.

As in Figure 2e, with varying (l, k) values (where l is the total k-mer length and k is the number of ungapped positions). Precision improves as k is increased, but gapped k-mer performance is always better than that of ungapped k-mers (where l = k). For this large training set (44,768 sequences), (11, 7) is a bit better than the default (10, 6), but for smaller training sets our default feature set (10,6) is recommended.

Supplementary Figure 6 Constraining distance to a TSS in the negative set does not affect the precision of deltaSVM prediction of dsQTLs.

In Figure 2, the gkm-SVM was trained on a negative sequence set matched for GC distribution and repeat fraction, but distance to a TSS was unconstrained. We generated an additional negative set that matched the GC distribution in the GM12878 positive set (a) but also matched the distribution of distance to a TSS of the positive set (b). As shown in c and d, using a gkm-SVM trained on this TSS-matched negative set does not affect performance in predicting dsQTLs.

Supplementary Figure 7 Constraining distance to a TSS or LD for negative dsQTL control SNPs does not affect the precision of deltaSVM prediction of dsQTLs.

In Figure 2, deltaSVM predictions were tested on the positive dsQTLs and a 50 times larger set of negative dsQTL control SNPs selected at random from the genome. Here we constrain the distance to a TSS for negative dsQTL control SNPs to match the distribution of distance to a TSS for the positive dsQTLs. This set is already matched to the positive dsQTL set in terms of the number of SNPs in strong LD (a). Further constraining distance to a TSS (b) does not affect performance in predicting dsQTLs relative to either negative set (c,d).

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–7. (PDF 303 kb)

Supplementary Table 1

All predictions of the 574 dsQTL SNPs and the 27,735 control SNPs. (XLSX 3759 kb)

Supplementary Table 2

deltaSVM predictions of all possible point mutations in the Tyr and Tyrp1 enhancers. (XLSX 58 kb)

Supplementary Table 3

Experimental validation results of randomly selected deltaSVM predictions from the Tyr and Typr1 enhancers. (XLSX 11 kb)

Supplementary Table 4

deltaSVM predictions of all possible single point mutations in the ALDOB enhancer and the corresponding in vivo effect size. (XLSX 48 kb)

Supplementary Table 5

deltaSVM predictions of mutations in K562 and HepG2 enhancers. (XLSX 23 kb)

Supplementary Table 6

deltaSVM predictions of all 3,113 autoimmune disease–associated SNPs. (XLSX 155 kb)

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Lee, D., Gorkin, D., Baker, M. et al. A method to predict the impact of regulatory variants from DNA sequence. Nat Genet 47, 955–961 (2015). https://doi.org/10.1038/ng.3331

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