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Prioritization of genes associated with type 2 diabetes mellitus for functional studies

Nature Reviews Endocrinology volume 19, pages 477–486 (2023)Cite this article

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Abstract

Existing therapies for type 2 diabetes mellitus (T2DM) show limited efficacy or have adverse effects. Numerous genetic variants associated with T2DM have been identified, but progress in translating these findings into potential drug targets has been limited. Here, we describe the tools and platforms available to identify effector genes from T2DM-associated coding and non-coding variants and prioritize them for functional studies. We discuss QSER1 and SLC12A8 as examples of genes that have been identified as possible T2DM candidate genes using these tools and platforms. We suggest further approaches, including the use of sequencing data with increased sample size and ethnic diversity, single-cell omics data for analyses, glycaemic trait associations to predict gene function and, potentially, human induced pluripotent stem cell ‘village’ cultures, to strengthen current gene functionalization workflows. Effective prioritization of T2DM-associated genes for experimental validation could expedite our understanding of the genetic mechanisms responsible for T2DM to facilitate the use of precision medicine in its treatment.

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Fig. 1: Framework for the prioritization of T2DM-causing genes from genome-wide association studies.
Fig. 2: Proposed mechanism(s) of action of QSER1 and SLC12A8.
Fig. 3: Use of human induced pluripotent stem cell ‘village’ cultures to study T2DM gene variants.

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References

  1. International Diabetes Federation. Diabetes facts & figures. International Diabetes Foundation https://www.idf.org/aboutdiabetes/what-is-diabetes/facts-figures.html (2021).

  2. Philipson, L. H. Harnessing heterogeneity in type 2 diabetes mellitus. Nat. Rev. Endocrinol. 16, 79–80 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Del Prato, S. Heterogeneity of diabetes: heralding the era of precision medicine. Lancet Diabetes Endocrinol. 7, 659–661 (2019).

    Article  PubMed  Google Scholar 

  4. Udler, M. S., McCarthy, M. I., Florez, J. C. & Mahajan, A. Genetic risk scores for diabetes diagnosis and precision medicine. Endocr. Rev. 40, 1500–1520 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  5. Grant, R. W. & Wexler, D. J. Personalized medicine in type 2 diabetes: what does the future hold? Diabetes Manag. 2, 199–204 (2012).

    Article  CAS  Google Scholar 

  6. Prasad, R. B. & Groop, L. Precision medicine in type 2 diabetes. J. Intern. Med. 285, 40–48 (2019).

    Article  CAS  PubMed  Google Scholar 

  7. Ahlqvist, E. et al. Novel subgroups of adult-onset diabetes and their association with outcomes: a data-driven cluster analysis of six variables. Lancet Diabetes Endocrinol. 6, 361–369 (2018).

    Article  PubMed  Google Scholar 

  8. Dennis, J. M., Shields, B. M., Henley, W. E., Jones, A. G. & Hattersley, A. T. Disease progression and treatment response in data-driven subgroups of type 2 diabetes compared with models based on simple clinical features: an analysis using clinical trial data. Lancet Diabetes Endocrinol. 7, 442–451 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  9. Gale, E. A. Is type 2 diabetes a category error? Lancet 381, 1956–1957 (2013).

    Article  PubMed  Google Scholar 

  10. McCarthy, M. I. Painting a new picture of personalised medicine for diabetes. Diabetologia 60, 793–799 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  11. Hattersley, A. T. & Patel, K. A. Precision diabetes: learning from monogenic diabetes. Diabetologia 60, 769–777 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  12. Deng, X. & Nakamura, Y. Cancer precision medicine: from cancer screening to drug selection and personalized immunotherapy. Trends Pharmacol. Sci. 38, 15–24 (2017).

    Article  PubMed  Google Scholar 

  13. Jones, A. G. et al. Markers of β-cell failure predict poor glycemic response to GLP-1 receptor agonist therapy in type 2 diabetes. Diabetes Care 39, 250–257 (2016).

    Article  CAS  PubMed  Google Scholar 

  14. Newman, B. et al. Concordance for type 2 (non-insulin-dependent) diabetes mellitus in male twins. Diabetologia 30, 763–768 (1987).

    Article  CAS  PubMed  Google Scholar 

  15. Nelson, M. R. et al. The support of human genetic evidence for approved drug indications. Nat. Genet. 47, 856–860 (2015).

    Article  CAS  PubMed  Google Scholar 

  16. Scott, R. A. et al. An expanded genome-wide association study of type 2 diabetes in Europeans. Diabetes 66, 2888–2902 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Mahajan, A. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Suzuki, K. et al. Identification of 28 new susceptibility loci for type 2 diabetes in the Japanese population. Nat. Genet. 51, 379–386 (2019).

    Article  CAS  PubMed  Google Scholar 

  19. Spracklen, C. N. et al. Identification of type 2 diabetes loci in 433,540 East Asian individuals. Nature 582, 240–245 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Vujkovic, M. et al. Discovery of 318 new risk loci for type 2 diabetes and related vascular outcomes among 1.4 million participants in a multi-ancestry meta-analysis. Nat. Genet. 52, 680–691 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Mahajan, A. et al. Multi-ancestry genetic study of type 2 diabetes highlights the power of diverse populations for discovery and translation. Nat. Genet. 54, 560–572 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Hindorff, L. A. et al. Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc. Natl Acad. Sci. USA 106, 9362–9367 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Gusev, A. et al. Partitioning heritability of regulatory and cell-type-specific variants across 11 common diseases. Am. J. Hum. Genet. 95, 535–552 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Fuchsberger, C. et al. The genetic architecture of type 2 diabetes. Nature 536, 41–47 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Flannick, J. et al. Exome sequencing of 20,791 cases of type 2 diabetes and 24,440 controls. Nature 570, 71–76 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Mahajan, A. et al. Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes. Nat. Genet. 50, 559–571 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Goodwin, S., McPherson, J. D. & McCombie, W. R. Coming of age: ten years of next-generation sequencing technologies. Nat. Rev. Genet. 17, 333–351 (2016).

    Article  CAS  PubMed  Google Scholar 

  28. Das, S., Abecasis, G. R. & Browning, B. L. Genotype imputation from large reference panels. Annu. Rev. Genomics Hum. Genet. 19, 73–96 (2018).

    Article  CAS  PubMed  Google Scholar 

  29. Schaid, D. J., Chen, W. & Larson, N. B. From genome-wide associations to candidate causal variants by statistical fine-mapping. Nat. Rev. Genet. 19, 491–504 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Stephens, M. & Balding, D. J. Bayesian statistical methods for genetic association studies. Nat. Rev. Genet. 10, 681–690 (2009).

    Article  CAS  PubMed  Google Scholar 

  31. Spain, S. L. & Barrett, J. C. Strategies for fine-mapping complex traits. Hum. Mol. Genet. 24, R111–R119 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Udler, M. S. et al. Type 2 diabetes genetic loci informed by multi-trait associations point to disease mechanisms and subtypes: a soft clustering analysis. PLoS Med. 15, e1002654 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Torres, J. M. et al. A multi-omic integrative scheme characterizes tissues of action at loci associated with type 2 diabetes. Am. J. Hum. Genet. 107, 1011–1028 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Estrada, K. et al. Association of a low-frequency variant in HNF1A with type 2 diabetes in a Latino population. JAMA 311, 2305–2314 (2014).

    Article  PubMed  Google Scholar 

  35. Majithia, A. R. et al. Rare variants in PPARG with decreased activity in adipocyte differentiation are associated with increased risk of type 2 diabetes. Proc. Natl Acad. Sci. USA 111, 13127–13132 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Flannick, J. et al. Loss-of-function mutations in SLC30A8 protect against type 2 diabetes. Nat. Genet. 46, 357–363 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Lee, S., Abecasis, G. R., Boehnke, M. & Lin, X. Rare-variant association analysis: study designs and statistical tests. Am. J. Hum. Genet. 95, 5–23 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Lee, S., Wu, M. C. & Lin, X. Optimal tests for rare variant effects in sequencing association studies. Biostatistics 13, 762–775 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  39. Goswami, C., Chattopadhyay, A. & Chuang, E. Y. Rare variants: data types and analysis strategies. Ann. Transl. Med. 9, 961 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  40. Wu, M. C. et al. Rare-variant association testing for sequencing data with the sequence kernel association test. Am. J. Hum. Genet. 89, 82–93 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Type 2 Diabetes Knowledge Portal. Curated T2D effector gene predictions. T2DKP https://t2d.hugeamp.org/method.html?trait=t2d&dataset=mccarthy (2022).

  42. Manning, A. et al. A low-frequency inactivating AKT2 variant enriched in the Finnish population is associated with fasting insulin levels and type 2 diabetes risk. Diabetes 66, 2019–2032 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Latva-Rasku, A. et al. A partial loss-of-function variant in AKT2 is associated with reduced insulin-mediated glucose uptake in multiple insulin-sensitive tissues: a genotype-based callback positron emission tomography study. Diabetes 67, 334–342 (2018).

    Article  CAS  PubMed  Google Scholar 

  44. Katsonis, P., Wilhelm, K., Williams, A. & Lichtarge, O. Genome interpretation using in silico predictors of variant impact. Hum. Genet. 141, 1549–1577 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  45. Yazar, M. & Özbek, P. In silico tools and approaches for the prediction of functional and structural effects of single-nucleotide polymorphisms on proteins: an expert review. OMICS 25, 23–37 (2021).

    Article  CAS  PubMed  Google Scholar 

  46. Vaser, R., Adusumalli, S., Leng, S. N., Sikic, M. & Ng, P. C. SIFT missense predictions for genomes. Nat. Protoc. 11, 1–9 (2016).

    Article  CAS  PubMed  Google Scholar 

  47. Mi, H., Muruganujan, A., Ebert, D., Huang, X. & Thomas, P. D. PANTHER version 14: more genomes, a new PANTHER GO-slim and improvements in enrichment analysis tools. Nucleic Acids Res. 47, D419–D426 (2019).

    Article  CAS  PubMed  Google Scholar 

  48. Reva, B., Antipin, Y. & Sander, C. Predicting the functional impact of protein mutations: application to cancer genomics. Nucleic Acids Res. 39, e118 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Kulshreshtha, S., Chaudhary, V., Goswami, G. K. & Mathur, N. Computational approaches for predicting mutant protein stability. J. Comput. Mol. Des. 30, 401–412 (2016).

    Article  CAS  Google Scholar 

  50. Adzhubei, I. A. et al. A method and server for predicting damaging missense mutations. Nat. Methods 7, 248–249 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Rentzsch, P., Witten, D., Cooper, G. M., Shendure, J. & Kircher, M. CADD: predicting the deleteriousness of variants throughout the human genome. Nucleic Acids Res. 47, D886–D894 (2019).

    Article  CAS  PubMed  Google Scholar 

  52. Capriotti, E., Calabrese, R. & Casadio, R. Predicting the insurgence of human genetic diseases associated to single point protein mutations with support vector machines and evolutionary information. Bioinformatics 22, 2729–2734 (2006).

    Article  CAS  PubMed  Google Scholar 

  53. Bromberg, Y. & Rost, B. SNAP: predict effect of non-synonymous polymorphisms on function. Nucleic Acids Res. 35, 3823–3835 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Bendl, J. et al. PredictSNP: robust and accurate consensus classifier for prediction of disease-related mutations. PLoS Comput. Biol. 10, e1003440 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  55. Flanagan, S. E., Patch, A.-M. & Ellard, S. Using SIFT and PolyPhen to predict loss-of-function and gain-of-function mutations. Genet. Test. Mol. Biomark. 14, 533–537 (2010).

    Article  CAS  Google Scholar 

  56. Gilad, Y., Rifkin, S. A. & Pritchard, J. K. Revealing the architecture of gene regulation: the promise of eQTL studies. Trends Genet. 24, 408–415 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. He, X. et al. Sherlock: detecting gene-disease associations by matching patterns of expression QTL and GWAS. Am. J. Hum. Genet. 92, 667–680 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Plagnol, V., Smyth, D. J., Todd, J. A. & Clayton, D. G. Statistical independence of the colocalized association signals for type 1 diabetes and RPS26 gene expression on chromosome 12q13. Biostatistics 10, 327–334 (2009).

    Article  PubMed  Google Scholar 

  59. Wallace, C. et al. Statistical colocalization of monocyte gene expression and genetic risk variants for type 1 diabetes. Hum. Mol. Genet. 21, 2815–2824 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. GTEx Consortium. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science 369, 1318–1330 (2020).

    Article  Google Scholar 

  61. Alonso, L. et al. TIGER: The gene expression regulatory variation landscape of human pancreatic islets. Cell Rep. 37, 109807 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Nica, A. C. & Dermitzakis, E. T. Expression quantitative trait loci: present and future. Philos. Trans. R. Soc. B Biol. Sci. 368, 20120362 (2013).

    Article  Google Scholar 

  63. Mendenhall, E. M. & Bernstein, B. E. Chromatin state maps: new technologies, new insights. Curr. Opin. Genet. Dev. 18, 109–115 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Kang, B., Kang, B., Roh, T.-Y., Seong, R. H. & Kim, W. The chromatin accessibility landscape of nonalcoholic fatty liver disease progression. Mol. Cell 45, 343–352 (2022).

    Article  CAS  Google Scholar 

  65. Li, S., Zong, X., Zhang, L., Li, L. & Wu, J. A chromatin accessibility landscape during early adipogenesis of human adipose-derived stem cells. Adipocyte 11, 239–249 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Scott, L. J. et al. The genetic regulatory signature of type 2 diabetes in human skeletal muscle. Nat. Commun. 7, 11764 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Rai, V. et al. Single-cell ATAC-Seq in human pancreatic islets and deep learning upscaling of rare cells reveals cell-specific type 2 diabetes regulatory signatures. Mol. Metab. 32, 109–121 (2020).

    Article  CAS  PubMed  Google Scholar 

  68. Pasquali, L. et al. Pancreatic islet enhancer clusters enriched in type 2 diabetes risk-associated variants. Nat. Genet. 46, 136–143 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Greenwald, W. W. et al. Pancreatic islet chromatin accessibility and conformation reveals distal enhancer networks of type 2 diabetes risk. Nat. Commun. 10, 2078 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  70. Pan, D. Z. et al. Integration of human adipocyte chromosomal interactions with adipose gene expression prioritizes obesity-related genes from GWAS. Nat. Commun. 9, 1512 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  71. Zhang, N. et al. Muscle progenitor specification and myogenic differentiation are associated with changes in chromatin topology. Nat. Commun. 11, 6222 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Schmitt, A. D. et al. A compendium of chromatin contact maps reveals spatially active regions in the human genome. Cell Rep. 17, 2042–2059 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Su, C. et al. 3D chromatin maps of the human pancreas reveal lineage-specific regulatory architecture of T2D risk. Cell Metab. 34, 1394–1409 (2022).

    Article  CAS  PubMed  Google Scholar 

  74. Mularoni, L., Ramos-Rodríguez, M. & Pasquali, L. The pancreatic Islet Regulome Browser. Front. Genet. 8, 13 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  75. Piñero, J. et al. DisGeNET: a discovery platform for the dynamical exploration of human diseases and their genes. Database 2015, bav028 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  76. Dai, H. J., Wu, J. C., Tsai, R. T., Pan, W. H. & Hsu, W. L. T-HOD: a literature-based candidate gene database for hypertension, obesity and diabetes. Database 2013, bas061 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  77. Agrawal, S. et al. T2D-Db: an integrated platform to study the molecular basis of type 2 diabetes. BMC Genomics 9, 320 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  78. Rani, J. et al. T2DiACoD: a gene atlas of type 2 diabetes mellitus associated complex disorders. Sci. Rep. 7, 6892 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  79. Lim, J. E. et al. Type 2 diabetes genetic association database manually curated for the study design and odds ratio. BMC Med. Inf. Decis. Mak. 10, 76 (2010).

    Article  Google Scholar 

  80. Yang, Z. et al. T2D@ZJU: a knowledgebase integrating heterogeneous connections associated with type 2 diabetes mellitus. Database 2013, bat052 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  81. Type 2 Diabetes Knowledge Portal. About the AMP T2DKP project. T2DKP https://t2d.hugeamp.org/about.html (2022).

  82. Karczewski, K. J. et al. Systematic single-variant and gene-based association testing of thousands of phenotypes in 394,841 UK Biobank exomes. Cell Genomics 2, 100168 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Landrum, M. J. et al. ClinVar: improving access to variant interpretations and supporting evidence. Nucleic Acids Res. 46, D1062–D1067 (2018).

    Article  CAS  PubMed  Google Scholar 

  84. Karczewski, K. J. et al. The mutational constraint spectrum quantified from variation in 141,456 humans. Nature 581, 434–443 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Kopanos, C. et al. VarSome: the human genomic variant search engine. Bioinformatics 35, 1978 (2019).

    Article  CAS  PubMed  Google Scholar 

  86. Type 2 Diabetes Knowledge Portal. T2D effector prediction summary. T2DKP https://t2d.hugeamp.org/method.html?trait=t2d&dataset=egls (2022).

  87. Dixon, G. et al. QSER1 protects DNA methylation valleys from de novo methylation. Science 372, eabd0875 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Geusz, R. J. et al. Pancreatic progenitor epigenome maps prioritize type 2 diabetes risk genes with roles in development. Elife 10, e59067 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Grozio, A. et al. Slc12a8 is a nicotinamide mononucleotide transporter. Nat. Metab. 1, 47–57 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Ramsey, K. M., Mills, K. F., Satoh, A. & Imai, S.-I. Age‐associated loss of Sirt1‐mediated enhancement of glucose‐stimulated insulin secretion in beta cell‐specific Sirt1‐overexpressing (BESTO) mice. Aging Cell 7, 78–88 (2008).

    Article  CAS  PubMed  Google Scholar 

  91. Revollo, J. R. et al. Nampt/PBEF/visfatin regulates insulin secretion in β cells as a systemic NAD biosynthetic enzyme. Cell Metab. 6, 363–375 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Yoshino, J., Mills, K. F., Yoon, M. J. & Imai, S.-I. Nicotinamide mononucleotide, a key NAD+ intermediate, treats the pathophysiology of diet- and age-induced diabetes in mice. Cell Metab. 14, 528–536 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Caton, P. W., Kieswich, J., Yaqoob, M., Holness, M. & Sugden, M. Nicotinamide mononucleotide protects against pro-inflammatory cytokine-mediated impairment of mouse islet function. Diabetologia 54, 3083–3092 (2011).

    Article  CAS  PubMed  Google Scholar 

  94. Gray, J. P., Alavian, K. N., Jonas, E. A. & Heart, E. A. NAD kinase regulates the size of the NADPH pool and insulin secretion in pancreatic β-cells. Am. J. Physiol. Endocrinol. Metab. 303, E191–E199 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Schmidt, M. S. & Brenner, C. Absence of evidence that Slc12a8 encodes a nicotinamide mononucleotide transporter. Nat. Metab. 1, 660–661 (2019).

    Article  PubMed  Google Scholar 

  96. Graham, S. E. et al. The power of genetic diversity in genome-wide association studies of lipids. Nature 600, 675–679 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. Wu, D. et al. Large-scale whole-genome sequencing of three diverse Asian populations in Singapore. Cell 179, 736–749 (2019).

    Article  CAS  PubMed  Google Scholar 

  98. Eraslan, G. et al. Single-nucleus cross-tissue molecular reference maps toward understanding disease gene function. Science 376, eabl4290 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. Alasoo, K. et al. Shared genetic effects on chromatin and gene expression indicate a role for enhancer priming in immune response. Nat. Genet. 50, 424–431 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  100. Glastonbury, C. A., Alves, A. C., Moustafa, J. S. E.-S. & Small, K. S. Cell-type heterogeneity in adipose tissue is associated with complex traits and reveals disease-relevant cell-specific eQTLs. Am. J. Hum. Genet. 104, 1013–1024 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  101. Kim-Hellmuth, S. et al. Cell type-specific genetic regulation of gene expression across human tissues. Science 369, eaaz8528 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  102. Yazar, S. et al. Single-cell eQTL mapping identifies cell type-specific genetic control of autoimmune disease. Science 376, eabf3041 (2022).

    Article  CAS  PubMed  Google Scholar 

  103. Battle, A. et al. Impact of regulatory variation from RNA to protein. Science 347, 664–667 (2015).

    Article  CAS  PubMed  Google Scholar 

  104. Smagris, E. et al. Pnpla3I148M knockin mice accumulate PNPLA3 on lipid droplets and develop hepatic steatosis. Hepatology 61, 108–118 (2015).

    Article  CAS  PubMed  Google Scholar 

  105. Neavin, D. R. et al. Village in a dish: a model system for population-scale hiPSC studies. Preprint at bioRxiv https://www.biorxiv.org/content/10.1101/2021.08.19.457030v1 (2021).

  106. Mitchell, J. M. et al. Mapping genetic effects on cellular phenotypes with “cell villages”. Preprint at bioRxiv https://doi.org/10.1101/2020.06.29.174383 (2020).

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Acknowledgements

The authors thank Tai E. Shyong and members of the Teo laboratory for their critical reading of this manuscript. W.X.T. is supported by the National University of Singapore (NUS) Research Scholarship (RS) and the Paris-NUS PhD mobility grant (ANR-18-IDEX-0001). A.K.K.T. is supported by IMCB, A*STAR, FY2019 SingHealth Duke-NUS Surgery Academic Clinical Programme Research Support Programme Grant, Precision Medicine and Personalized Therapeutics Joint Research Grant 2019, the 2nd A*STAR-AMED Joint Grant Call 192B9002, HLTRP/2022/NUS-IMCB-02, Paris-NUS grant 2021-06-R/UP-NUS (ANR-18-IDEX-0001), OFIRG21jun-0097, CSASI21jun-0006, MTCIRG21-0071, SDDC/FY2021/EX/93-A147, FY 2022 Interstellar Initiative Beyond grant, H22G0a0005 and I22D1AG053.

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  1. Stem Cells and Diabetes Laboratory, Institute of Molecular and Cell Biology (IMCB), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore

    Wei Xuan Tan & Adrian K. K. Teo

  2. Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore

    Wei Xuan Tan, Chin Meng Khoo & Adrian K. K. Teo

  3. Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore

    Xueling Sim

  4. Precision Medicine Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore

    Adrian K. K. Teo

  5. Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore

    Adrian K. K. Teo

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A.K.K.T. and W.X.T. researched data for the article, contributed substantially to discussion of content, wrote the manuscript and reviewed and edited the manuscript before submission. X.S. and C.M.K. reviewed and edited the manuscript before submission.

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Tan, W.X., Sim, X., Khoo, C.M. et al. Prioritization of genes associated with type 2 diabetes mellitus for functional studies. Nat Rev Endocrinol 19, 477–486 (2023). https://doi.org/10.1038/s41574-023-00836-1

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