Gene-level analysis of rare variants in 379,066 whole exome sequences identifies an association of GIGYF1 loss of function with type 2 diabetes

Deaton, Aimée M.; Parker, Margaret M.; Ward, Lucas D.; Flynn-Carroll, Alexander O.; BonDurant, Lucas; Hinkle, Gregory; Boutkov, Boris; Krasheninina, Olga; Lanche, Rouel; Mansfield, Adam; Nafde, Mona; O’Keeffe, Sean; Orelus, Max; Panea, Razvan; Polanco, Tommy; Rasool, Ayesha; Locke, Adam E.; Verweij, Niek; Nielsen, Jonas B.; Bovijn, Jonas; De, Tanima; Haas, Mary E.; Akbari, Parsa; Sosina, Olukayode A.; LeBlanc, Michelle G.; Abecasis, Gonçalo R.; Cantor, Michael; Coppola, Giovanni; Economides, Aris N.; Lotta, Luca A.; Overton, John D.; Reid, Jeffrey G.; Shuldiner, Alan R.; Beechert, Christina; Forsythe, Caitlin; Fuller, Erin D.; Gu, Zhenhua; Lattari, Michael; Lopez, Alexander; Schleicher, Thomas D.; Padilla, Maria Sotiropoulos; Toledo, Karina; Widom, Louis; Wolf, Sarah; Pradhan, Manasi; Manoochehri, Kia; Ulloa, Ricardo H.; Bai, Xiaodong; Balasubramanian, Suganthi; Barnard, Leland; Blumenfeld, Andrew; Eom, Gisu; Habegger, Lukas; Hawes, Alicia; Khalid, Shareef; Maxwell, Evan K.; Salerno, William; Staples, Jeffrey; Yadav, Ashish; Li, Dadong; Jones, Marcus B.; Mitnaul, Lyndon J.; Mighty, Jason; Deubler, Andrew; Karalis, Katia; Siminovitch, Katherine A.; Adams, Lance J.; Blank, Jackie; Bodian, Dale L.; Boris, Derek; Buchanan, Adam H.; Carey, David J.; Colonie, Ryan; Davis, F. Daniel; Hartzel, Dustin N.; Kelly, Melissa; Kirchner, H. Lester; Leader, Joseph B.; Ledbetter, David H.; Manus, Jean-Marie; Martin, Christa Lese; Metpally, Raghu; Meyer, Michelle N.; Mirshahi, Tooraj; Oetjens, Matthew T.; Person, Thomas N.; Still, Christopher D.; Strande, Natasha T.; Sturm, Amy C.; Wagner, Jen; Williams, Marc S.; Baras, Aris; Nioi, Paul

doi:10.1038/s41598-021-99091-5

Cited by 28 publications

(34 citation statements)

References 64 publications

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“…To ensure our results were not biassed by our approach, we implemented burden tests using STAAR 16 and a logistic model and arrived at substantially similar conclusions (Supplementary Figure 2; Supplementary Table 1; methods). We confirmed the T2D associations at all three genes identified by three previous studies of T2D risk that incorporated European genetic-ancestry individuals from the UKBB study 11,12,14 : GCK (N=35 carriers; OR=58.5 [95% CI=25.5-134.5]; p=2.0x10 -58 ), HNF1A (N=33; OR=12.7 [6.2-25.8]; p=4.4x10 -20 ), and GIGYF1 (N=133; OR=4.7 [3.1-7.0]; p=4.4x10 -…”

Section: Exome-wide Burden Testing In the Uk Biobanksupporting

confidence: 86%

“…Firstly, our result is not attributable to a single variant of large effect as evidenced by the strength of association with singleton variants (Figure 1A). Secondly, aside from a single individual carrying two balanced deletions, inspection of the underlying ES reads did not reveal a markedly increased error rate in variant calling or genotyping in TNRC6B as was suggested by Deaton et al 11 . Thirdly, the association persisted after excluding 14 individuals who carry a singleton PTV in a potentially non-constitutive exon as measured by PEXT (p=3.6x10 -7 ) 17 .…”

mentioning

confidence: 64%

“…This offers an unprecedented opportunity to explore the contribution of rare coding variation to the risk of T2D with much greater power than previously possible [6][7][8] . Initial exome-wide association analyses of these data have identified gene-based associations with increased risk of T2D for GCK, HNF1A, HNF4A, GIGYF1, CCAR2, TNRC6B and PAM, and protective effects for variants in FAM234A and MAP3K15 5,[9][10][11][12][13][14] . In this study, we combined multiple sources of health record data to identify additional T2D cases and used an extended range of variant classes and allele frequency cutoffs in order to directly implicate novel genes in the aetiology of T2D.…”

Section: Introductionmentioning

confidence: 99%

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Damaging missense variants inIGF1Rimplicate a role for IGF-1 resistance in the aetiology of type 2 diabetes

Gardner

Kentistou

Stankovic

et al. 2022

Preprint

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Type 2 diabetes (T2D) is a chronic metabolic disorder with a significant genetic component. While large-scale population studies have identified hundreds of common genetic variants associated with T2D susceptibility, the role of rare (minor allele frequency < 0.1%) protein coding variation is less clear. To this end, we performed a gene burden analysis of 18,691 genes in 418,436 (n=32,374 T2D cases) individuals sequenced by the UK Biobank (UKBB) study to assess the impact of rare genetic variants on T2D risk. Our analysis identified T2D associations at exome-wide significance (P < 6.9x10-7) with rare, damaging variants within previously identified genes including GCK, GIGYF1, HNF1A, and TNRC6B. In addition, individuals with rare, damaging missense variants in the genes ZEB2 (N=31 carriers; OR=5.5 [95% CI=2.5-12.0]; p=6.4x10-7), MLXIPL (N=245; OR=2.3 [1.6-3.2]; p=3.2x10-7), and IGF1R (N=394; OR=2.4 [1.8-3.2]; p=1.3x10-10) have higher risk of T2D. Carriers of damaging missense variants within IGF1R were also shorter (-2.2cm [-1.8-2.7]; p=1.2x10-19) and had higher circulating protein levels of insulin-like growth factor-1 (IGF-1; 2.3 nmol/L [1.7-2.9] p=2.8x10-14), indicating relative IGF-1 resistance. A likely causal role of IGF-1 resistance on T2D was further supported by Mendelian randomisation analyses using common variants. Our results increase our understanding of the genetic architecture of T2D and highlight a potential therapeutic benefit of targeting the Growth Hormone/IGF-1 axis.

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Section: Exome-wide Burden Testing In the Uk Biobanksupporting

confidence: 86%

mentioning

confidence: 64%

Section: Introductionmentioning

confidence: 99%

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Damaging missense variants inIGF1Rimplicate a role for IGF-1 resistance in the aetiology of type 2 diabetes

Gardner

Kentistou

Stankovic

et al. 2022

Preprint

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“…As a result, 477 up regulated and 383 down regulated genes, at least 4-fold change between T1DM and normal control samples. Wang et al [42], Mishra et al [43], Deaton et al [44] and You et al [45] demonstrated that altered expression of SPHK1, WDR13, GIGYF1 and DNMT3A were found to be substantially related to type 2 diabetes mellitus. Altered expression of SPHK1 [46] was easily found in hypertension.…”

Section: Discussionmentioning

confidence: 99%

Bioinformatics Analysis of next generation sequencing data for Risk Prediction in Patients with Type 1 diabetes mellitus

Vastrad¹,

Vastrad²

2022

Preprint

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Type 1 diabetes mellitus (T1DM) comprise the most common forms of autoimmune disease. The aim of this investigation was to apply a bioinformatics approach to reveal related pathways or genes involved in the development of T1DM. The next generation sequencing (NGS) dataset GSE182870 was downloaded from the gene expression omnibus (GEO) database. Differentially expressed gene (DEG) analysis was performed using DESeq2. The g:Profiler was utilized to analyze the functional enrichment, gene ontology (GO) and REACTOME pathway of the differentially expressed genes. Protein-protein interaction (PPI) network, modules, miRNA-hub gene regulatory network, and TF-hub gene regulatory network were conducted via comprehensive target prediction and network analyses. Finally, hub genes were validated by using receiver operating characteristic curve analysis. A total of 860 DEGs were screened out from NGS dataset, among which 477 genes were up regulated and 383 genes were down regulated. GO enrichment analysis indicated that up regulated genes were mainly involved in cellular metabolic process, intracellular anatomical structure and catalytic activity, and the down regulated genes were significantly enriched in cellular nitrogen compound biosynthetic process, protein containing complex and heterocyclic compound binding. REACTOME pathway enrichment analysis showed that the up regulated genes were mainly enriched in metabolism of carbohydrates and the down regulated genes were significantly enriched in metabolism of RNA. A PPI network, modules, miRNA-hub gene regulatory network and TF-hub gene regulatory network were constructed, and hub genes (MAPK14, RHOC, MAD2L1, TAF1, TRAF2, HSP90AA1, TP53, HSP90AB1, UBA52 and RACK1) were identified. This study provides further insights into the underlying pathogenesis of T1DM.

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“…To examine the association between the full continuum of HNF1A function score and diabetes status in the UK Biobank, we performed logistic mixed effects regression (lme4 and computed for European ancestry participants as previously described 86 ). To examine the association between function bins (i.e.…”

Section: Genotype-function-phenotype Association Analysesmentioning

confidence: 99%

Human gain-of-function variants in HNF1A confer protection from diabetes but independently increase hepatic secretion of multiple cardiovascular disease risk factors

DeForest

Kavitha

et al. 2022

Preprint

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Loss-of-function mutations in Hepatocyte Nuclear Factor 1A (HNF1A) are known to cause rare forms of diabetes and alter hepatic physiology through unclear mechanisms. In the general population, 1:100 individuals carry a rare protein-coding variant in HNF1A, most of unknown functional consequence. To characterize the full allelic series, we performed deep mutational scanning of 11,970 protein-coding HNF1A variants in human hepatocytes and clinical correlation with 553,246 exome-sequenced individuals. Surprisingly, we found ~1:5 rare protein-coding HNF1A variants found in the general population cause molecular gain-of-function (GOF), increasing the transcriptional activity of HNF1A by up to 50%. GOF in HNF1A conferred protection from type 2 diabetes (T2D) (OR=0.60, p=8.4 x 10-7), but not against coronary artery disease. Independently of T2D, increased hepatic expression of HNF1A promoted a pro-inflammatory and pro-atherogenic serum profile mediated in part by enhanced transcription of risk genes including PCSK9. In summary, ~1:300 individuals carry a GOF variant in HNF1A that protects carriers from diabetes but enhances hepatic secretion of metabolic disease risk factors.

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Gene-level analysis of rare variants in 379,066 whole exome sequences identifies an association of GIGYF1 loss of function with type 2 diabetes

Cited by 28 publications

References 64 publications

Damaging missense variants inIGF1Rimplicate a role for IGF-1 resistance in the aetiology of type 2 diabetes

Damaging missense variants inIGF1Rimplicate a role for IGF-1 resistance in the aetiology of type 2 diabetes

Bioinformatics Analysis of next generation sequencing data for Risk Prediction in Patients with Type 1 diabetes mellitus

Human gain-of-function variants in HNF1A confer protection from diabetes but independently increase hepatic secretion of multiple cardiovascular disease risk factors

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