Pathogenic variants in actionable MODY genes are associated with type 2 diabetes.
Journal
Nature metabolism
ISSN: 2522-5812
Titre abrégé: Nat Metab
Pays: Germany
ID NLM: 101736592
Informations de publication
Date de publication:
10 2020
10 2020
Historique:
received:
07
05
2020
accepted:
08
09
2020
pubmed:
14
10
2020
medline:
31
12
2020
entrez:
13
10
2020
Statut:
ppublish
Résumé
Genome-wide association studies have identified 240 independent loci associated with type 2 diabetes (T2D) risk, but this knowledge has not advanced precision medicine. In contrast, the genetic diagnosis of monogenic forms of diabetes (including maturity-onset diabetes of the young (MODY)) are textbook cases of genomic medicine. Recent studies trying to bridge the gap between monogenic diabetes and T2D have been inconclusive. Here, we show a significant burden of pathogenic variants in genes linked with monogenic diabetes among people with common T2D, particularly in actionable MODY genes, thus implying that there should be a substantial change in care for carriers with T2D. We show that, among 74,629 individuals, this burden is probably driven by the pathogenic variants found in GCK, and to a lesser extent in HNF4A, KCNJ11, HNF1B and ABCC8. The carriers with T2D are leaner, which evidences a functional metabolic effect of these mutations. Pathogenic variants in actionable MODY genes are more frequent than was previously expected in common T2D. These results open avenues for future interventions assessing the clinical interest of these pathogenic mutations in precision medicine.
Identifiants
pubmed: 33046911
doi: 10.1038/s42255-020-00294-3
pii: 10.1038/s42255-020-00294-3
doi:
Substances chimiques
Germinal Center Kinases
0
MAP4K2 protein, human
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
1126-1134Références
Mathers, C. D. & Loncar, D. Projections of global mortality and burden of disease from 2002 to 2030. PLoS Med. 3, e442 (2006).
pubmed: 17132052
pmcid: 1664601
doi: 10.1371/journal.pmed.0030442
Abajobir, A. A. et al. Global, regional, and national under-5 mortality, adult mortality, age-specific mortality, and life expectancy, 1970–2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet 390, 1084–1150 (2017).
doi: 10.1016/S0140-6736(17)31833-0
Dieleman, J. L. et al. US spending on personal health care and public health, 1996–2013. JAMA 316, 2627–2646 (2016).
pubmed: 28027366
pmcid: 5551483
doi: 10.1001/jama.2016.16885
Willemsen, G. et al. The concordance and heritability of type 2 diabetes in 34,166 twin pairs from international twin registers: The discordant twin (DISCOTWIN) consortium. Twin Res. Hum. Genet. 18, 762–771 (2015).
pubmed: 26678054
doi: 10.1017/thg.2015.83
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).
pubmed: 30297969
pmcid: 6287706
doi: 10.1038/s41588-018-0241-6
Bonnefond, A. & Froguel, P. Rare and common genetic events in type 2 diabetes: what should biologists know? Cell Metab. 21, 357–368 (2015).
pubmed: 25640731
doi: 10.1016/j.cmet.2014.12.020
Vaxillaire, M. & Froguel, P. Monogenic diabetes: implementation of translational genomic research towards precision medicine. J. Diabetes 8, 782–795 (2016).
pubmed: 27390143
doi: 10.1111/1753-0407.12446
Babenko, A. P. et al. Activating mutations in the ABCC8 gene in neonatal diabetes mellitus. N. Engl. J. Med. 355, 456–466 (2006).
pubmed: 16885549
doi: 10.1056/NEJMoa055068
Pearson, E. R. et al. Switching from insulin to oral sulfonylureas in patients with diabetes due to Kir6.2 mutations. N. Engl. J. Med. 355, 467–477 (2006).
pubmed: 16885550
doi: 10.1056/NEJMoa061759
Shepherd, M., Shields, B., Ellard, S., Rubio-Cabezas, O. & Hattersley, A. T. A genetic diagnosis of HNF1A diabetes alters treatment and improves glycaemic control in the majority of insulin-treated patients. Diabet. Med. 26, 437–441 (2009).
pubmed: 19388975
doi: 10.1111/j.1464-5491.2009.02690.x
Pearson, E. R. et al. Genetic cause of hyperglycaemia and response to treatment in diabetes. Lancet 362, 1275–1281 (2003).
pubmed: 14575972
doi: 10.1016/S0140-6736(03)14571-0
Pearson, E. R. et al. Molecular genetics and phenotypic characteristics of MODY caused by hepatocyte nuclear factor 4α mutations in a large European collection. Diabetologia 48, 878–885 (2005).
pubmed: 15830177
doi: 10.1007/s00125-005-1738-y
Stride, A. et al. Cross-sectional and longitudinal studies suggest pharmacological treatment used in patients with glucokinase mutations does not alter glycaemia. Diabetologia 57, 54–56 (2014).
pubmed: 24092492
doi: 10.1007/s00125-013-3075-x
Garg, V. et al. GATA4 mutations cause human congenital heart defects and reveal an interaction with TBX5. Nature 424, 443–447 (2003).
pubmed: 12845333
doi: 10.1038/nature01827
Bockenhauer, D. & Jaureguiberry, G. HNF1B-associated clinical phenotypes: the kidney and beyond. Pediatr. Nephrol. 31, 707–714 (2016).
pubmed: 26160100
doi: 10.1007/s00467-015-3142-2
Kodo, K. et al. GATA6 mutations cause human cardiac outflow tract defects by disrupting semaphorin-plexin signaling. Proc. Natl Acad. Sci. USA 106, 13933–13938 (2009).
pubmed: 19666519
doi: 10.1073/pnas.0904744106
pmcid: 2728998
Naylor, R. N. et al. Cost-effectiveness of MODY genetic testing: translating genomic advances into practical health applications. Diabetes Care 37, 202–209 (2014).
pubmed: 24026547
doi: 10.2337/dc13-0410
GoodSmith, M. S., Skandari, M. R., Huang, E. S. & Naylor, R. N. The impact of biomarker screening and cascade genetic testing on the cost-effectiveness of MODY genetic testing. Diabetes Care 42, 2247–2255 (2019).
pubmed: 31558549
doi: 10.2337/dc19-0486
pmcid: 6868460
Fuchsberger, C. et al. The genetic architecture of type 2 diabetes. Nature 536, 41–47 (2016).
pubmed: 27398621
pmcid: 5034897
doi: 10.1038/nature18642
Flannick, J. et al. Exome sequencing of 20,791 cases of type 2 diabetes and 24,440 controls. Nature 570, 71–76 (2019).
pubmed: 31118516
pmcid: 6699738
doi: 10.1038/s41586-019-1231-2
Lek, M. et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature 536, 285–291 (2016).
pubmed: 27535533
pmcid: 5018207
doi: 10.1038/nature19057
Sun, J., Zheng, Y. & Hsu, L. A unified mixed-effects model for rare-variant association in sequencing studies. Genet. Epidemiol. 37, 334–344 (2013).
pubmed: 23483651
pmcid: 3740585
doi: 10.1002/gepi.21717
Bansal, V. et al. Spectrum of mutations in monogenic diabetes genes identified from high-throughput DNA sequencing of 6,888 individuals. BMC Med. 15, 213 (2017).
pubmed: 29207974
pmcid: 5717832
doi: 10.1186/s12916-017-0977-3
Froguel, P. et al. Close linkage of glucokinase locus on chromosome 7p to early-onset non-insulin-dependent diabetes mellitus. Nature 356, 162–164 (1992).
pubmed: 1545870
doi: 10.1038/356162a0
Flannick, J. et al. Assessing the phenotypic effects in the general population of rare variants in genes for a dominant Mendelian form of diabetes. Nat. Genet. 45, 1380–1385 (2013).
pubmed: 24097065
pmcid: 4051627
doi: 10.1038/ng.2794
Bonnefond, A. et al. Whole-exome sequencing and high throughput genotyping identified KCNJ11 as the thirteenth MODY gene. PLoS ONE 7, e37423 (2012).
pubmed: 22701567
pmcid: 3372463
doi: 10.1371/journal.pone.0037423
Bonnefond, A. et al. GATA6 inactivating mutations are associated with heart defects and, inconsistently, with pancreatic agenesis and diabetes. Diabetologia 55, 2845–2847 (2012).
pubmed: 22806356
doi: 10.1007/s00125-012-2645-7
De Franco, E. et al. GATA6 mutations cause a broad phenotypic spectrum of diabetes from pancreatic agenesis to adult-onset diabetes without exocrine insufficiency. Diabetes 62, 993–997 (2013).
pubmed: 23223019
pmcid: 3581234
doi: 10.2337/db12-0885
Meur, G. et al. Insulin gene mutations resulting in early-onset diabetes: marked differences in clinical presentation, metabolic status, and pathogenic effect through endoplasmic reticulum retention. Diabetes 59, 653–661 (2010).
pubmed: 20007936
doi: 10.2337/db09-1091
Castel, S. E. et al. Modified penetrance of coding variants by cis-regulatory variation contributes to disease risk. Nat. Genet. 50, 1327–1334 (2018).
pubmed: 30127527
pmcid: 6119105
doi: 10.1038/s41588-018-0192-y
Khera, A. V. et al. Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat. Genet. 50, 1219–1224 (2018).
pubmed: 30104762
pmcid: 6128408
doi: 10.1038/s41588-018-0183-z
Baier, L. J. et al. ABCC8 R1420H loss-of-function variant in a southwest American Indian community: association with increased birth weight and doubled risk of type 2 diabetes. Diabetes 64, 4322–4332 (2015).
pubmed: 26246406
pmcid: 4657583
doi: 10.2337/db15-0459
Balkau, B. [An epidemiologic survey from a network of French Health Examination Centres, (D.E.S.I.R.): epidemiologic data on the insulin resistance syndrome]. Rev. Epidemiol. Sante Publique 44, 373–375 (1996).
pubmed: 8927780
Sladek, R. et al. A genome-wide association study identifies novel risk loci for type 2 diabetes. Nature 445, 881–885 (2007).
pubmed: 17293876
doi: 10.1038/nature05616
Meyre, D. et al. Genome-wide association study for early-onset and morbid adult obesity identifies three new risk loci in European populations. Nat. Genet. 41, 157–159 (2009).
pubmed: 19151714
doi: 10.1038/ng.301
Romon, M. et al. Relationships between physical activity and plasma leptin levels in healthy children: the Fleurbaix–Laventie Ville Santé II Study. Int. J. Obes. Relat. Metab. Disord. 28, 1227–1232 (2004).
pubmed: 15314633
doi: 10.1038/sj.ijo.0802725
Bycroft, C. et al. The UK Biobank resource with deep phenotyping and genomic data. Nature 562, 203–209 (2018).
pubmed: 30305743
pmcid: 6786975
doi: 10.1038/s41586-018-0579-z
Van Hout, C. V. et al. Whole exome sequencing and characterization of coding variation in 49,960 individuals in the UK Biobank. Preprint at bioRxiv https://doi.org/10.1101/572347 (2019).
Cirulli, E. T. et al. Genome-wide rare variant analysis for thousands of phenotypes in over 70,000 exomes from two cohorts. Nat. Commun. 11, 542 (2020).
pubmed: 31992710
pmcid: 6987107
doi: 10.1038/s41467-020-14288-y
Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).
pubmed: 19451168
pmcid: 2705234
doi: 10.1093/bioinformatics/btp324
McKenna, A. et al. The genome analysis toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).
pubmed: 20644199
pmcid: 2928508
doi: 10.1101/gr.107524.110
Sherry, S. T. et al. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 29, 308–311 (2001).
pubmed: 11125122
pmcid: 29783
doi: 10.1093/nar/29.1.308
Liu, X., Wu, C., Li, C. & Boerwinkle, E. dbNSFP v3.0: a one-stop database of functional predictions and annotations for human nonsynonymous and splice-site SNVs. Hum. Mutat. 37, 235–241 (2016).
pubmed: 26555599
pmcid: 4752381
doi: 10.1002/humu.22932
Regier, A. A. et al. Functional equivalence of genome sequencing analysis pipelines enables harmonized variant calling across human genetics projects. Nat. Commun. 9, 4038 (2018).
pubmed: 30279509
pmcid: 6168605
doi: 10.1038/s41467-018-06159-4
McLaren, W. et al. The ensembl variant effect predictor. Genome Biol. 17, 122 (2016).
pubmed: 27268795
pmcid: 4893825
doi: 10.1186/s13059-016-0974-4
Kendig, K. I. et al. Sentieon DNASeq variant calling workflow demonstrates strong computational performance and accuracy. Front. Genet. 10, 736 (2019).
pubmed: 31481971
pmcid: 6710408
doi: 10.3389/fgene.2019.00736
Richards, S. et al. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet. Med. 17, 405–424 (2015).
pubmed: 25741868
pmcid: 4544753
doi: 10.1038/gim.2015.30
Adzhubei, I. A. et al. A method and server for predicting damaging missense mutations. Nat. Methods 7, 248–249 (2010).
pubmed: 20354512
pmcid: 2855889
doi: 10.1038/nmeth0410-248
Vaser, R., Adusumalli, S., Leng, S. N., Sikic, M. & Ng, P. C. SIFT missense predictions for genomes. Nat. Protoc. 11, 1–9 (2016).
pubmed: 26633127
doi: 10.1038/nprot.2015.123
Schwarz, J. M., Cooper, D. N., Schuelke, M. & Seelow, D. MutationTaster2: mutation prediction for the deep-sequencing age. Nat. Methods 11, 361–362 (2014).
pubmed: 24681721
doi: 10.1038/nmeth.2890
Ellard, S., Colclough, K., Patel, K. A. & Hattersley, A. T. Prediction algorithms: pitfalls in interpreting genetic variants of autosomal dominant monogenic diabetes. J. Clin. Invest. 130, 14–16 (2020).
pubmed: 31815736
doi: 10.1172/JCI133516
Abraham, G. & Inouye, M. Fast principal component analysis of large-scale genome-wide data. PLoS ONE 9, e93766 (2014).
pubmed: 24718290
pmcid: 3981753
doi: 10.1371/journal.pone.0093766
Siva, N. 1000 Genomes project. Nat. Biotechnol. 26, 256 (2008).
pubmed: 18327223
doi: 10.1038/nbt0308-256b
Alexander, D. H., Novembre, J. & Lange, K. Fast model-based estimation of ancestry in unrelated individuals. Genome Res. 19, 1655–1664 (2009).
pubmed: 19648217
pmcid: 2752134
doi: 10.1101/gr.094052.109
Chen, H. et al. Efficient variant set mixed model association tests for continuous and binary traits in large-scale whole-genome sequencing studies. Am. J. Hum. Genet. 104, 260–274 (2019).
pubmed: 30639324
pmcid: 6372261
doi: 10.1016/j.ajhg.2018.12.012
Moutsianas, L. et al. The power of gene-based rare variant methods to detect disease-associated variation and test hypotheses about complex disease. PLoS Genet. 11, e1005165 (2015).
pubmed: 25906071
pmcid: 4407972
doi: 10.1371/journal.pgen.1005165
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).
pubmed: 24995866
pmcid: 4085641
doi: 10.1016/j.ajhg.2014.06.009
Balduzzi, S., Rücker, G. & Schwarzer, G. How to perform a meta-analysis with R: a practical tutorial. Evid. Based Ment. Health 22, 153–160 (2019).
pubmed: 31563865
doi: 10.1136/ebmental-2019-300117