biostudies-literatureSuzuki KNIDDK NIH HHSVersus ArthritisNIA NIH HHSNHGRI NIH HHSNational Institute for Health Research (NIHR)347-357https://www.ebi.ac.uk/biostudies/studies/S-EPMC10937372biostudies-literatureUnknown627(8003)Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes^1,2 and molecular mechanisms that are often specific to cell type^3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P < 5 × 10^-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores⁵ in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care.NatureGenetic drivers of heterogeneity in type 2 diabetes pathophysiology.PMC1093737221754R03 DK131249U01 DK105535L30 DK126146R01 DK134575K23 DK114551UM1 DK126194U01 HG011723R00 AG066849NIHR203308R01 DK118011Psaty BMKardia SLRBowden DWLamri AJukema JWFranco OHKawaguchi TVujkovic MLuan Jvan Dijk KWSonehara KCanouil MLynch JAMatsuda FStancakova AShin DMButterworth ASBroadaway KAMatsuda KNongmaithem SSSandow KKottgen Ade Silva HJLocke AEHuerta-Chagoya ABelow JERitchie MDStrauch KCho YSWickremasinghe ARPrins BPPankow JSvan Heel DAVanderwerff BHwu CMZeggini ELin KScott RAWang YXThorsteinsdottir UTuomi TCollins FSGordon-Larsen PMillwood IYChuang LMvan Klinken JBKadowaki TKatsuya TAhsan HLoos RJFBharadwaj DFiner SHsueh WKamanu FKYajnik CSKim DHRaffel LJNakatochi MTaylor KDRoden MHatzikotoulas KDedoussis GNg MCYvan Dam RMWitte DRKuusisto JRayner NWKandeel FRGenter PBoehnke MCruz MYu CEngert JCHerder CMohlke KLRuff CTKasturiratne APeters AGrallert HEvans DSDupuis JRotter JIThorleifsson GDu SKabagambe ERich SSKronenberg FHai YNousome DMandla RIslam MTWilson JGGiedraitis VGonzalez-Villalpando MEBocher OChen ZOrozco LPatel SRSabanayagam CTakeuchi FLee JJAhluwalia TSMoon SSchonherr SGieger CZawistowski MMagi RTsao PSChen JMaeda SChen GWalters RGChen CHKim BJLi LWacher-Rodarte NYoon KLi YPei PMamakou VLiu CTGoodarzi MOChen SHFornage MKamali ZSheu WHHMercader JMIchihara SLiu JZollner SParra EJShahriar MGonzalez-Villalpando CJiang GGerstein HCSuzuki KLiu SChee MLTam CHTPark KSSankareswaran ALiu YMorris ADLind LDanesh JDas SKHaiman CAChandak GRMorris APYin XKooner JSYamauchi TPedersen OKibriya MGMetspalu AStilp AMPreuss MHLouie TBuchanan TAPetty LETusie-Luna TAhlqvist EZhang WHansen TNtalla ILigthart SThorand BLorenz KMTran TCSaleheen DLuo XPorneala BFroguel PTrompet SMcCarthy MIProvince MAHayes MGFlorez JCBertoni AGuo XCook JPMelander OBielak LFShen BZhang LEmery LSTai ESAnand SSMa RCWMahajan ALee NRChang KMIkram MAVarma RMook-Kanamori DOFrayling TMMansuri SRBragg FRotimi CNFloyd JSAn PPare GPirie FJZheng WMarston NATaliun DRedline STsai FJChen WMLee MSAdeyemo AHattersley ATJasmine FNoordam RFischer KSarnowski CLaakso MNalls MAVoight BFMelloni GEMYanek LRWheeler EWuttke MMeigs JBLee JGraff MSchroeder PHan SSattar NMo HWu JYLi-Gao RKoh WPDoumatey APNano JRasmussen-Torvik LJWareham NJArruda ALKhor CCKanoni SDimitrov LSmith JAHakaste LSoutham LYuan JMReiner APKanona SBrandslund ITong LCui JNayak UAguilar-Salinas CAHung YJBecker DMLv JYokota MMotala AANamba SHuang WKeaton JMKooperberg CGrarup NChang LCLee KMPolikowsky HGShi JLuk AODenny JCFord IKaur VPalmer CNALyssenko VJang HMStefansson KBurant CFEckardt KUYusuf SHuang MVA Million Veteran ProgramHanis CLLindgren CMChai JFRohde RSpracklen CNSun MMoura FAZonderman ABLeong ABonnefond AIgase MPatil SHackinger SMeitinger TKho ANChan JCNYamamoto KYao JHoward AGRaffield LMTan JThangam MIsono MChoi HSFreedman BIEvans MKCheng CYWong TYPrasad GSale MMRoll KBrody JAHorikoshi MPeyser PALange LATuomilehto JHwang MYAdair LSUdler MSShojima NRader DJSo WYJoo YYLinneberg ATomlinson BLong JIngelsson EPereira MATaylor HJSteinthorsdottir VTabara YKim YJSofer TWilliamson AIpp EKato NIngelsson MNicolas AMcKean-Cowdin RValladares-Salgado ALim VJYGhanbari MCushman MGross MLee SSKShu XOXiang AHDuan QGuare LALangenberg CSim XKals MChambers JCPan IKwak SHCade BENadler JLOkada YChen YTJonas JBJorgensen TLall KChen YIBork-Jensen JNorth KEfalseGenetic drivers of heterogeneity in type 2 diabetes pathophysiology.Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes^1,2 and molecular mechanisms that are often specific to cell type^3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P < 5 × 10^-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores⁵ in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care.2024-01-01T00:00:00Z2024 Mar2025-04-18T20:15:39.884Z2025-04-07T08:10:22.19ZS-EPMC109373723837425610.1038/s41586-024-07019-6