Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps

Abstract

We expanded GWAS discovery for type 2 diabetes (T2D) by combining data from 898,130 European-descent individuals (9% cases), after imputation to high-density reference panels. With these data, we (i) extend the inventory of T2D-risk variants (243 loci, 135 newly implicated in T2D predisposition, comprising 403 distinct association signals); (ii) enrich discovery of lower-frequency risk alleles (80 index variants with minor allele frequency <5%, 14 with estimated allelic odds ratio >2); (iii) substantially improve fine-mapping of causal variants (at 51 signals, one variant accounted for >80% posterior probability of association (PPA)); (iv) extend fine-mapping through integration of tissue-specific epigenomic information (islet regulatory annotations extend the number of variants with PPA >80% to 73); (v) highlight validated therapeutic targets (18 genes with associations attributable to coding variants); and (vi) demonstrate enhanced potential for clinical translation (genome-wide chip heritability explains 18% of T2D risk; individuals in the extremes of a T2D polygenic risk score differ more than ninefold in prevalence).

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Fig. 1: Manhattan plots of the sex-combined BMI-unadjusted and BMI-adjusted meta-analysis for T2D.
Fig. 2: Comparison of estimated T2D effect sizes between BMI-adjusted and unadjusted models.
Fig. 3: Summary of fine-mapped associations.
Fig. 4: Comparison of fine-mapping resolution at 83 distinct signals.
Fig. 5: The relationship between effect size and MAF.
Fig. 6: Comparison of PPA for each variant with and without incorporation enrichment information.

Data availability

Summary-level data are available at the DIAGRAM consortium website http://diagram-consortium.org/ and Accelerating Medicines Partnership T2D portal http://www.type2diabetesgenetics.org/.

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Acknowledgements

This work was supported primarily by the NIDDK as part of the Accelerating Medicines Partnership-T2D, funded by U01DK105535 (M.I.M.), U01DK062370 (M.B.), and U01DK078616 (J.M.) grants. Part of this work was conducted using the UK Biobank resource under application number 9161. A full list of acknowledgements appears in the Supplementary Note.

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Contributions

Project coordination: A. Mahajan, A.P.M., M.B., and M.I.M. Writing: A. Mahajan, D.T., A.P.M., M.B., and M.I.M. Core analyses: A. Mahajan, D.T., M.T., J.M.T., A.J.P., A.P.M., M.B., and M.I.M. DIAMANTE analysis group: A. Mahajan, J.E.B., D.W.B., J.C.C., Y.J.K., M.C.Y.N., L.E.P., X.S., W.Z., A.P.M., M.B., and M.I.M. Statistical analysis in individual studies: A. Mahajan, D.T., N.R.R., N.W.R., V.S., R.A.S., N.G., J.P.C., E.M.S., M.W., C. Sarnowski, J.N., S.T., C. Lecoeur, M.H.P., B.P.P., X.G., L.F.B., J.B.-J., M.C., K.L., C.-T.L., A.E.L., J’a.L., C. Schurmann, L.Y., G.T., and A.P.M. Genotyping and phenotyping: A. Mahajan, R.A.S., R.M., C.G., S.T., K.-U.E., K.F., S.L.R.K., F.K., I.N., C.M.B., C. Schurmann, E.P.B., I.B., C.C., G.D., I.F., V.G., M.I., M.E.J., S.L., A.L., V.L., V.M., A.D.M., G.N., N.S., A.S., D.R.W., S.S., E.P.B., S.H., C.H., J. Kriebel, T.M., A.P., B.T., A.D., A.K., G.R.A., C. Langenberg, N.J.W., A.P.M., M.B., and M.I.M. Islet annotations: M.T., J.M.T., A.J.B., V.N., A.L.G., and M.I.M. Individual study design and principal investigators: E.P.B., J.C.F., O.H.F., T.M.F., A.T.H., M.A.I., T.J., J. Kuusisto, C.M.L., K.L.M., J.S.P., K. Strauch, K.D.T., U.T., J.T., J.D., P.A.P., E.Z., R.J.F.L., P.F., E.I., L.L., L.G., M.L., F.S.C., J.W.J., C.N.A.P., H.G., A. Metspalu, A.D., A.K., G.R.A., J.B.M., J.I.R., J.M., O.P., T.H., C. Langenberg, N.J.W., K. Stefansson, A.P.M., M.B., and M.I.M.

Corresponding authors

Correspondence to Anubha Mahajan or Mark I. McCarthy.

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Competing interests

J.C.F. has received consulting honoraria from Merck and from Boehringer-Ingelheim. O.H.F. works at ErasmusAGE, a center for aging research across the course of life, funded by Nestlé Nutrition (Nestec Ltd.), Metagenics Inc., and AXA. E.I. is a scientific advisor for Precision Wellness and Olink Proteomics for work unrelated to the present project. A.D. has received consultancy fees and research support from Metagenics Inc. (outside the scope of the present work). T.M.F. has consulted for Boeringer Ingelheim and Sanofi-Aventis on the genetics of diabetes and has an MRC CASE studentship with GSK. G.R.A. is a consultant for 23andMe, Regeneron, Merck, and Helix. R.A.S. is an employee of and shareholder in GlaxoSmithKline. N.S. is working with Boehringer-Ingelheim on a genetics project but has received no remuneration. M.I.M. has served on advisory panels for NovoNordisk and Pfizer, and has received honoraria from NovoNordisk, Pfizer, Sanofi-Aventis, and Eli Lilly. The companies named above had no role in the design or conduct of this study; collection, management, analysis, and interpretation of the data, or in the preparation, review, or approval of the manuscript. Authors affiliated with deCODE (V.S., G.T., U.T. and K.S.) are employed by deCODE Genetics/Amgen, Inc.

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Integrated supplementary information

Supplementary Figure 1 Sex-differentiated analyses.

(a) Manhattan plot (top panel) of genome-wide association results for T2D (without BMI adjustment) from female-specific meta-analysis of up to 30,053 cases and 434,336 controls. The association p-value (on -log10 scale) for each SNP (y-axis) is plotted against the genomic position (NCBI Build 37; x-axis). Association signals that reached genome-wide significance (p < 5×10−8) in sex-combined analysis are shown in purple or yellow, if novel. (b) Manhattan plot (bottom panel) of genome-wide association results for T2D without BMI adjustment from male-specific meta-analysis of up to 41,846 cases and 383,767 controls. (c) Z-score for each of the 403 distinct signals from male-specific analysis (y-axis) is plotted against the z-score from the female-specific analysis (y-axis). Colour of each point varies with –log10 gender heterogeneity p-value and diameter of the circle is proportional to sex-combined -log10 p-value.

Supplementary Figure 2 Distributions of the allele frequency, imputation score, and posterior probability of association.

Distribution of the risk allele frequencies for all variants having >1% posterior probability of association in genetic credible set (x-axis) plotted against average imputation quality (y-axis). Diameter varies with the posterior probability of association assigned to each variant.

Supplementary Figure 3 Islet annotation overlap of the variant with the highest probability in genetic credible sets.

Number of variants with posterior probability of association >1% (x-axis) plotted against the highest posterior probability of association (y-axis) assigned to a variant in the credible set. Points are colour coded according to (a) islet epigenome states and (b) overlap with transcription factor binding sites.

Supplementary Figure 4 Enrichment of cross-tissue epigenetic states in T2D GWAS data.

fGWAS log2 fold enrichment (based on joint model for each tissue) including 95% confidence intervals (x-axis) of all chromatin states (y-axis) genome-wide. Analyses are based on the Varshney et al.1 data which combined standard epigenomic annotations for the four principal tissues of interest. These analyses performed separately for each tissue show some enrichment for enhancers and/or promoters in all tissues with strongest and most consistent enrichment observed in islets. The universally enriched “transcript” category refers to coding sequence which is by definition represented by the same sequence in each “tissue-specific” analysis. 1Varshney, A. et al. Genetic regulatory signatures underlying islet gene expression and type 2 diabetes. Proc Natl Acad Sci U S A 114, 2301-2306 (2017).

Supplementary Figure 5 Enrichment of islet epigenetic states in T2D GWAS data.

fGWAS log2 fold enrichment including 95% confidence intervals (x-axis) of all chromatin states (y-axis) genome-wide.

Supplementary Figure 6 Epigenome landscape of the ST6GAL1 locus.

For variants included in 99% credible set (PPA>1%) of each distinct signal at ST6GAL1 locus, following information is shown: genomic position of each variant (colour coded for each distinct signal; variant with highest PPA in bold); whole genome bisulphite methylation data (black), 4 human islet ATAC-seq tracks (green, middle), islet chromatin states (from Thurner et al.1, Pasquali et al.2, and Varshney et al.3); and adipose, liver and skeletal muscle chromatin states from Varshney et al.3. 1 Thurner, M. et al. Integration of human pancreatic islet genomic data refines regulatory mechanisms at Type 2 Diabetes susceptibility loci. Elife 7(2018). 2 Pasquali, L. et al. Pancreatic islet enhancer clusters enriched in type 2 diabetes risk-associated variants. Nat Genet 46, 136-143 (2014). 3 Varshney, A. et al. Genetic regulatory signatures underlying islet gene expression and type 2 diabetes. Proc Natl Acad Sci U S A 114, 2301-2306 (2017).

Supplementary Figure 7 Epigenome landscape of the ANK1 locus.

For variants included in 99% credible set (PPA>1%) of each distinct signal at ANK1 locus, following information is shown: genomic position of each variant (colour coded for each distinct signal; variant with highest PPA in bold); whole genome bisulphite methylation data (black), 4 human islet ATAC-seq tracks (green, middle), islet chromatin states (from Thurner et al.1, Pasquali et al.2, and Varshney et al.3); and adipose, liver and skeletal muscle chromatin states from Varshney et al.3. 1 Thurner, M. et al. Integration of human pancreatic islet genomic data refines regulatory mechanisms at Type 2 Diabetes susceptibility loci. Elife 7(2018). 2 Pasquali, L. et al. Pancreatic islet enhancer clusters enriched in type 2 diabetes risk-associated variants. Nat Genet 46, 136-143 (2014).3 Varshney, A. et al. Genetic regulatory signatures underlying islet gene expression and type 2 diabetes. Proc Natl Acad Sci U S A 114, 2301-2306 (2017).

Supplementary Figure 8 Epigenome landscape of the TCF7L2 locus.

For variants included in 99% credible set (PPA>1%) of each distinct signal at TCF7L2 locus, following information is shown: genomic position of each variant (colour coded for each distinct signal; variant with highest PPA in bold); whole genome bisulphite methylation data (black), 4 human islet ATAC-seq tracks (green, middle), islet chromatin states (from Thurner et al.1, Pasquali et al.2, and Varshney et al.3); and adipose, liver and skeletal muscle chromatin states from Varshney et al.3. 1 Thurner, M. et al. Integration of human pancreatic islet genomic data refines regulatory mechanisms at Type 2 Diabetes susceptibility loci. Elife 7(2018). 2 Pasquali, L. et al. Pancreatic islet enhancer clusters enriched in type 2 diabetes risk-associated variants. Nat Genet 46, 136-143 (2014). 3 Varshney, A. et al. Genetic regulatory signatures underlying islet gene expression and type 2 diabetes. Proc Natl Acad Sci U S A 114, 2301-2306 (2017).

Supplementary Figure 9 Heritability estimates.

Chip heritability estimates for T2D (on the liability scale) at different empirical estimates of population- and sample-level T2D prevalence.

Supplementary Figure 10 Polygenic risk scores.

Genome-wide polygenic risk score (PRS) identifies individuals with significantly increased risk of T2D. a) PRS in UK Biobank individuals is normally distributed with a shift towards right, observed for T2D cases. PRS is plotted on the x-axis, with values scaled to a mean of 0 and standard deviation of 1. b) Individuals were binned into 40 groups based on PRS, with each grouping representing 2.5% of population. c) BMI distribution in T2D cases, within each PRS bin.

Supplementary Figure 11 Genetic correlations between T2D and biomedically relevant traits, estimated by LD-score regression implemented in LDHub.

Genetic correlations (z-score) between T2D (y-axis) and range of metabolic and anthropometric traits (x-axis) as estimated using LD Score regression. The genetic correlation estimates are colour coded according to phenotypic area. Allelic direction of effect is aligned to increased T2D risk. Size of the circle denotes the significance level for the correlation.

Supplementary Figure 12 Effect of BMI adjustment on genetic correlation estimates between various traits and T2D.

Genetic correlations (z-score) between range of metabolic and anthropometric traits and T2D without BMI adjustment (x-axis) and T2D with BMI adjustment (y-axis) as estimated using LD Score regression. The genetic correlation estimates are colour coded according to phenotypic area. Allelic direction of effect is aligned to increased T2D risk. Size of the circle denotes the significance level for the correlation.

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Mahajan, A., Taliun, D., Thurner, M. et al. Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps. Nat Genet 50, 1505–1513 (2018). https://doi.org/10.1038/s41588-018-0241-6

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