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Multi-ancestry polygenic mechanisms of type 2 diabetes

Nature Medicine volume 30, pages 1065–1074 (2024)Cite this article

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Abstract

Type 2 diabetes (T2D) is a multifactorial disease with substantial genetic risk, for which the underlying biological mechanisms are not fully understood. In this study, we identified multi-ancestry T2D genetic clusters by analyzing genetic data from diverse populations in 37 published T2D genome-wide association studies representing more than 1.4 million individuals. We implemented soft clustering with 650 T2D-associated genetic variants and 110 T2D-related traits, capturing known and novel T2D clusters with distinct cardiometabolic trait associations across two independent biobanks representing diverse genetic ancestral populations (African, n = 21,906; Admixed American, n = 14,410; East Asian, n =2,422; European, n = 90,093; and South Asian, n = 1,262). The 12 genetic clusters were enriched for specific single-cell regulatory regions. Several of the polygenic scores derived from the clusters differed in distribution among ancestry groups, including a significantly higher proportion of lipodystrophy-related polygenic risk in East Asian ancestry. T2D risk was equivalent at a body mass index (BMI) of 30 kg m−2 in the European subpopulation and 24.2 (22.9–25.5) kg m−2 in the East Asian subpopulation; after adjusting for cluster-specific genetic risk, the equivalent BMI threshold increased to 28.5 (27.1–30.0) kg m−2 in the East Asian group. Thus, these multi-ancestry T2D genetic clusters encompass a broader range of biological mechanisms and provide preliminary insights to explain ancestry-associated differences in T2D risk profiles.

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Fig. 1: Key loci and traits of multi-ancestry T2D genetic clusters.
Fig. 2: Multi-ancestry T2D genetic cluster associations with continuous traits and clinical phenotypes.
Fig. 3: Enrichment for cell-type-specific enhancers in multi-ancestry T2D clusters.
Fig. 4: Ancestry-specific relationship among T2D genetic clusters, BMI and T2D risk.

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Data availability

All referenced GWAS summary statistics are publicly available and are cited in Supplementary Tables 1, 3 and 9. Researchers can apply to access individual-level data in the All of Us program (https://researchallofus.org/). Individual-level data in the MGB Biobank are available only with approval from the MGB IRB. Databases of epigenomic activity are available online for CATLAS (https://catlas.org) and Roadmap (https://egg2.wustl.edu/roadmap/).

Code availability

Code for variant pre-processing, bNMF clustering and basic visualizations is available at https://github.com/gwas-partitioning/bnmf-clustering.

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Acknowledgements

A.J.D. is supported by National Institues of Health (NIH)/National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) T32 DK007028 and NIH/NIDDK F32 DK137487. K.E.W. is supported by NIH K01DK133637. L.S. is supported by funds from the Ministry of Education and Science of Poland within the ‘Excellence Initiative—Research University’ project, by the Ministry of Health of Poland within the ‘Center of Artificial Intelligence in Medicine at the Medical University of Bialystok’ project and by American Diabetes Association grant 11-22-PDFPM-03. M.C. is supported by the Novo Nordisk Foundation (NNF21SA0072102) and NIDDK UM1 DK126185. J.M.M. is supported by American Diabetes Association Innovative and Clinical Translational Award 1-19-ICTS-068, American Diabetes Association grant 11-22-ICTSPM-16 and National Human Genome Research Institute U01HG011723. M.S.U. is supported by NIDDK K23DK114551, NIDDK R03DK131249 and Doris Duke Foundation Award 2022063. The authors thank the participants of the All of Us Research Program and MGB Biobank. In addition, we thank the Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC) for providing pre-publication access to GWAS summary statistics for post-challenge insulin resistance measures. Finally, we also thank J. Dupuis for assistance with statistical analysis.

Author information

Author notes

  1. Timothy D. Majarian

    Present address: Vertex Pharmaceuticals, Boston, MA, USA

  2. These authors contributed equally: Kirk Smith, Aaron J. Deutsch.

Authors and Affiliations

  1. Diabetes Unit, Endocrine Division, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA

    Kirk Smith, Aaron J. Deutsch, Hyunkyung Kim, Alicia Huerta-Chagoya, Ravi Mandla, Philip H. Schroeder, Lukasz Szczerbinski, Varinderpal Kaur, Melina Claussnitzer, Jose C. Florez, Josep M. Mercader & Miriam S. Udler

  2. Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA

    Kirk Smith, Aaron J. Deutsch, Hyunkyung Kim, Alicia Huerta-Chagoya, Ravi Mandla, Philip H. Schroeder, Lukasz Szczerbinski, Varinderpal Kaur, Melina Claussnitzer, Jose C. Florez, Josep M. Mercader & Miriam S. Udler

  3. Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Kirk Smith, Aaron J. Deutsch, Hyunkyung Kim, Sarah Hsu, Alicia Huerta-Chagoya, Ravi Mandla, Philip H. Schroeder, Kenneth E. Westerman, Lukasz Szczerbinski, Timothy D. Majarian, Varinderpal Kaur, Melina Claussnitzer, Jose C. Florez, Alisa K. Manning, Josep M. Mercader & Miriam S. Udler

  4. Department of Medicine, Harvard Medical School, Boston, MA, USA

    Aaron J. Deutsch, Kenneth E. Westerman, Melina Claussnitzer, Jose C. Florez, Alisa K. Manning, Josep M. Mercader & Miriam S. Udler

  5. Biomedical Sciences Graduate Program, University of California, San Diego, La Jolla, CA, USA

    Carolyn McGrail

  6. Committee on Genetics, Genomics and Systems Biology, University of Chicago, Chicago, IL, USA

    Hyunkyung Kim

  7. Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA

    Hyunkyung Kim

  8. Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA

    Sarah Hsu, Kenneth E. Westerman, Timothy D. Majarian & Alisa K. Manning

  9. Department of Endocrinology, Diabetology and Internal Medicine, Medical University of Bialystok, Bialystok, Poland

    Lukasz Szczerbinski

  10. Clinical Research Centre, Medical University of Bialystok, Bialystok, Poland

    Lukasz Szczerbinski

  11. Precision Healthcare University Research Institute, Queen Mary University of London, London, UK

    Alice Williamson

  12. MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK

    Alice Williamson

  13. Department of Neurology, University of California, Los Angeles, Los Angeles, CA, USA

    Noah Zaitlen

  14. Department of Computational Medicine, University of California, Los Angeles, Los Angeles, CA, USA

    Noah Zaitlen

  15. Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA

    Noah Zaitlen

  16. Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Melina Claussnitzer

  17. Department of Pediatrics, University of California, San Diego, San Diego, CA, USA

    Kyle J. Gaulton

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Contributions

Conceived and designed the study: K.S., A.J.D. and M.S.U. Conducted analysis: K.S., A.J.D., C.M. and A.H.-C. Curated data: K.S., A.J.D., H.K., S.H., R.M., P.H.S., K.E.W., L.S., T.D.M., V.K., A.W., A.K.M. and J.M.M. Provided feedback on analysis: N.Z., M.C., J.C.F., A.K.M., J.M.M., K.J.G. and M.S.U. Wrote the initial manuscript draft: K.S., A.J.D. and M.S.U. All authors approved the final version of the manuscript.

Corresponding author

Correspondence to Miriam S. Udler.

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Competing interests

T.D.M. currently works for Vertex Pharmaceuticals. K.S., M.C., J.C.F., J.M.M. and M.S.U. are currently part of a collaboration project between the Broad Institute and Novo Nordisk. M.S.U. is an unfunded collaborator with Nightingale and AstraZeneca. None of the other authors declare competing interests.

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Nature Medicine thanks Constantin Polychronakos and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Anna Maria Ranzoni, in collaboration with the Nature Medicine team.

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Extended data

Extended Data Fig. 1 Overview of high-throughput bNMF pipeline for multi-ancestry (MA) clusters.

Flowchart of the steps implemented in the clustering pipeline. Steps include: 1) extract variants from diverse set of T2D GWAS datasets, 2) apply LD-pruning across reference panels for all populations included, to ensure independent genetic signals, 3) find proxy variants for variants that are multi-allelic, ambiguous, or have low trait counts, 4) align variants to risk increasing alleles in largest MA T2D GWAS and remove if their P value in this GWAS does not meet a Bonferroni threshold, 5) filter trait GWAS by minimum sample size, 6) filter trait GWAS by a minimum Bonferroni-corrected P value across the selected variants, 7) filter by correlation between traits and 8) generate the variant by trait association matrix which serves as the bNMF input.

Extended Data Fig. 2 The multi-ancestry clusters recapture several key pathways that were identified in our previous papers.

Correlation heatmap for trait cluster weights from the multi-ancestry clusters versus the (a) Udler et al. clusters3 and (b) Kim et al. clusters4. Correlation coefficients are displayed for trait pairs where R > 0.

Extended Data Fig. 3 Common T2D genetic clusters are shared across individual ancestry groups.

Heatmaps display the correlation of the trait cluster weights in the European, East Asian, African and Admixed American clusters versus the (a) multi-ancestry and (b) Udler et al. clusters3.

Extended Data Fig. 4 Sex-stratified association of multi-ancestry T2D genetic clusters with anthropometric traits.

Each dot displays the sex-stratified association between selected multi-ancestry T2D cluster pPS and selected anthropometric traits. (a) Visceral adipose tissue [VAT], subcutaneous adipose tissue [SAT], and VAT/SAT ratio, measured in a subset of approximately 9,000 MGB Biobank participants with available data. (b) Waist circumference, hip circumference, and waist/hip ratio, measured in the All of Us Cohort. Each outcome was normalized to a standard normal distribution (for all participants, females only, or males only). Each dot indicates the effect per one standard deviation increase in the pPS. Error bars denote the standard error. P values were obtained from two-sided t tests, and asterisks indicate P < 0.05. Complete statistics (including exact P values and the number of individuals measured for each outcome) are provided in Supplementary Table 13.

Extended Data Fig. 5 Variation in distribution of multi-ancestry T2D genetic clusters across ancestry groups.

Each histogram displays the distribution of the pPS for the indicated multi-ancestry T2D genetic cluster. For each cluster, the pPS for the entire cohort was normalized to a standard normal distribution, and a separate curve is displayed for each genetically inferred ancestry group. All analyses were performed in a meta-analysis of All of Us and MGB Biobank. AFR, African; AMR, Admixed American; EAS, East Asian; EUR, European; SAS, South Asian.

Extended Data Fig. 6 Proportion of total T2D genetic risk attributable to each multi-ancestry T2D cluster.

For each individual, the total T2D genetic risk was calculated as the sum of the pPS across each of the 12 multi-ancestry T2D genetic clusters. All individuals were then grouped according to genetically inferred ancestry. Each bar displays the proportion of the total T2D genetic risk conferred by each specific cluster. This graph represents a meta-analysis of All of Us and MGB Biobank. AFR, African; AMR, Admixed American; EAS, East Asian; EUR, European; SAS, South Asian.

Extended Data Fig. 7 Validation of relationship between T2D genetic clusters, BMI, and T2D risk.

(a, b) Relationship between BMI and T2D risk, classified by genetic ancestry. T2D risk was controlled for age, sex, BMI, and genetic ancestry group. (A) T2D risk in all participants (B) T2D risk in a subset of participants with similar Lipodystrophy 1 pPS (ranging from 0.5–1.5, representing 22.8% of the overall population). Analyses represent a meta-analysis of MGB Biobank and All of Us. (c) Relationship between BMI and T2D risk, assessed within EUR ancestry participants in All of Us. Covariates include age, sex, BMI, and 10 principal components. T2D risk was calculated separately for individuals in the top and bottom deciles of the Lipodystrophy 1 pPS. In all panels, the horizontal dashed line is chosen at a representative level to indicate the outcome at a BMI of 30 kg/m2 in the EUR ancestry group.

Extended Data Fig. 8 Comparison of cluster-specific risk allele frequencies (RAF) in EUR and other ancestry groups.

We compared the T2D risk allele frequencies between the EUR ancestry group and each of the other groups (AFR, AMR, EAS, and SAS). For each multi-ancestry cluster, we plotted the allele frequency for cluster-defining variants (those above the bNMF weight cutoff). In both the Lipodystrophy 1 and Lipodystrophy 2 clusters, the majority of variants had higher risk allele frequency in EAS compared to EUR ancestry, consistent with our observation that the pPS distributions for these two clusters were shifted to the right in EAS ancestry.

Extended Data Fig. 9 Ancestry-specific variation in adipose volume and triglycerides.

(a, b) Relationship between VAT/SAT ratio and T2D risk, classified by genetic ancestry. (A) Unadjusted T2D risk, controlling for age, sex, BMI, and genetic ancestry group. (B) Adjusted T2D risk, after additionally controlling for Lipodystrophy 1 pPS and Lipodystrophy 2 pPS. Analyses represent a subset of ~9,000 MGB Biobank participants with available VAT/SAT data. (c,d) Relationship between BMI and triglyceride levels, classified by genetic ancestry. (C) Unadjusted triglyceride level, controlling for age, sex, BMI, and genetic ancestry group. (D) Adjusted triglyceride level, after additionally controlling for Lipodystrophy 1 pPS and Lipodystrophy 2 pPS. Analyses represent a meta-analysis of MGB Biobank and All of Us. (e) Relationship between BMI and triglyceride levels, assessed within EUR ancestry participants in All of Us. Covariates include age, sex, BMI, and 10 principal components. Triglyceride levels were calculated separately for individuals in the top and bottom deciles of the Lipodystrophy 1 pPS. In all panels, the horizontal dashed line is chosen at a representative level to indicate the outcome at a BMI of 30 kg/m2 in the EUR ancestry group.

Extended Data Fig. 10 Conservation of biological pathways between the multi-ancestry and T2DGGI clusters.

Variants in the T2DGGI clusters (NSNP = 1,289) were assigned a proxy variant from the multi-ancestry variant set (NSNP = 650) based on linkage disequilibrium values (r2 > 0.5). The clusters were then cross-examined using a two-sided Wilcoxon rank-sum test based on the multi-ancestry bNMF variant weights.

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Smith, K., Deutsch, A.J., McGrail, C. et al. Multi-ancestry polygenic mechanisms of type 2 diabetes. Nat Med 30, 1065–1074 (2024). https://doi.org/10.1038/s41591-024-02865-3

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