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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

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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 ARTICLES https://doi.org/10.1038/s41588-020-0637-y 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 Marijana Vujkovic 1,2,112, Jacob M. Keaton3,4,5,6,112, Julie A. Lynch 7,8, Donald R. Miller9,10, Jin Zhou 11,12, Catherine Tcheandjieu13,14,15, Jennifer E. Huffman 16, Themistocles L. Assimes 13,14, Kimberly Lorenz 1,17,18, Xiang Zhu 13,19, Austin T. Hilliard 13,14, Renae L. Judy1,20, Jie Huang16,21, Kyung M. Lee 7, Derek Klarin 16,22,23,24, Saiju Pyarajan16,25,26, John Danesh27, Olle Melander28, Asif Rasheed29, Nadeem H. Mallick 30, Shahid Hameed30, Irshad H. Qureshi31,32, Muhammad Naeem Afzal31,32, Uzma Malik31,32, Anjum Jalal33, Shahid Abbas33, Xin Sheng 2, Long Gao17, Klaus H. Kaestner17, Katalin Susztak 2, Yan V. Sun 34,35, Scott L. DuVall 7,36, Kelly Cho16,25, Jennifer S. Lee13,14, J. Michael Gaziano16,25, Lawrence S. Phillips 34,37, James B. Meigs23,26,38, Peter D. Reaven 11,39, Peter W. Wilson34,40, Todd L. Edwards 4,41, Daniel J. Rader 2,17, Scott M. Damrauer 1,20, Christopher J. O’Donnell 16,25,26, Philip S. Tsao 13,14, The HPAP Consortium*, Regeneron Genetics Center*, VA Million Veteran Program*, Kyong-Mi Chang 1,2,42,112, Benjamin F. Voight 1,17,18,112 ✉ and Danish Saleheen 29,43,44,112 ✉ We investigated type 2 diabetes (T2D) genetic susceptibility via multi-ancestry meta-analysis of 228,499 cases and 1,178,783 controls in the Million Veteran Program (MVP), DIAMANTE, Biobank Japan and other studies. We report 568 associations, including 286 autosomal, 7 X-chromosomal and 25 identified in ancestry-specific analyses that were previously unreported. Transcriptome-wide association analysis detected 3,568 T2D associations with genetically predicted gene expression in 687 novel genes; of these, 54 are known to interact with FDA-approved drugs. A polygenic risk score (PRS) was strongly associated with increased risk of T2D-related retinopathy and modestly associated with chronic kidney disease (CKD), peripheral artery disease (PAD) and neuropathy. We investigated the genetic etiology of T2D-related vascular outcomes in the MVP and observed statistical SNP–T2D interactions at 13 variants, including coronary heart disease (CHD), CKD, PAD and neuropathy. These find- ings may help to identify potential therapeutic targets for T2D and genomic pathways that link T2D to vascular outcomes. 2D, a leading cause of morbidity globally, is projected to affect stroke and PAD) and three microvascular diseases (CKD, retinopa- up to 629 million people by 2045 (ref. 1). People with T2D are thy and neuropathy) in the MVP5. Subsequently, we conducted a Tat increased risk of developing a wide range of macro- and genome-wide SNP–T2D interaction analysis in the MVP to identify microvascular outcomes2 and there are large disparities in preva- genetic variants where the effect of the SNP on the vascular out- lence, severity and comorbidities across global populations. Over 400 come depends on the context of T2D presence. We also performed common variants have been identified that confer disease suscepti- association analyses of genetically predicted expression levels and bility3,4, yet because most studies have been performed in cohorts expression quantitative trait–T2D colocalization analyses to iden- of European or Asian ancestry, the impact of these variants across tify the effects of gene–tissue pairs that influence T2D risk through all ancestry groups needs to be quantified. Identification of genetic inter-individual variation in expression. factors and genes that underlie T2D-related complications could This study complements prior genetic studies of T2D through inform clinical management strategies, including patient stratifica- the use of large-scale clinical data in conjunction with polygenic tion or optimization of study design of randomized controlled trials. scores and evaluation of context specificity for genetic effects on The lack of large, multi-ancestry richly phenotyped cohorts linked to T2D vascular sequelae, and by describing the regulatory circuits genetic data has made it difficult to address these questions. that influence T2D risk. We conducted a multi-ancestry association study of T2D risk comprising 228,499 individuals with T2D and 1,178,783 control Results individuals of European, African American, Hispanic, South Asian Study populations. We performed a genome-wide, multi-ancestry and East Asian ancestry. We investigated the association of a T2D PRS T2D-association analysis (228,499 cases and 1,178,783 controls) that with major T2D-related macrovascular outcomes (CHD, ischemic encompassed five ancestral groups (Europeans, African Americans, A full list of affiliations appears at the end of the paper. NATURE GENETICS | www.nature.com/naturegenetics ARTICLES NATURE GENETICS KREMEN1 C21orf58 ARFRP1 RGS19 OPRL1 ACTL10 PCNT OSER1 RTEL1 LIME1 YBEY SNRPD2 MYPOP DIDO1 DMWD ATP13A1 FBXO17 SAE1 CACNA1A ZNF738 PVRL2 TM6SF2 GIPR KLHL21 TRIM63 FAM110D SH3BGRL3 FNDC5 MAU2 BMP8A OXCT2P1 PABPC4 OXCT2 CRTC1 CDKN2C CD101 NOTCH2 FCGR1A BOLA1 SV2A CALR ECM1 FARSA THBS3 LRRC37A16P GBAP1 GCDH GBA FAM189B FDPS C18orf8 ASH1L SH3GL1P3 NPC1KLF1 MSTO2P USP36 LMOD1 CYTH1 DSTYK RAB7L1 SRGAP2 HMGN2P19 MAP3K3DDX42 TBCE ASXL2 C2orf70 1 SLC5A6 ATRAID ACE X CAD ATP5G1UBE2ZSNF8 30 SNX17 ZNF513 TTLL6 PPM1G SNX11 NRBP1 SCRN2 KRTCAP3 Trans-ancestry GPN1 WNT3 FTOP1 CNTNAP1MAPT ZFP36L2 BECN1 22 20 THADA FAM134C PLEKHH2 TUBG2 ABCG5 COASY SLC3A1 HSD17B1P1HSD17B1 BCL11A 21 CEP68 ZEB2 HSPB9 RBM43 10 NMI PGAP3 TNFAIP6 FBXL20PNMT ITGB6 GGNBP2 2 GRB14 20 COBLL1 MYO19 1 SCN2A KCNJ12 PLEKHA3 ALKBH5 IRS1 Phet ACTG1P12 SLC25A11TOM1L2DRG2 TIMP4 PPARG 19 TSEN2 GP1BA 1 C3orf83 ZZEF1 TMEM158 ATP2A3 GTEx S PrediXcan CDHR4 ZNF276 FAM212A CLEC18ACFDP1 Skeletal muscle UBA7 MST1R Pancreas RBM6 IRX3 Adipose ITIH4 18 10 FTO Artery AKTIP SFMBT1 RBL2 SERBP1P3 FBRS RFT1 YPEL3 ADAMTS9 C3orf38 TBX6 DOC2A STX19 INO80E ZBTB20 TMEM219 20 ADCY5 SLC12A8 KCTD13 3 CPNE4 TUFM 17 PPP2R3A CDC37P1EIF3C NME9 MRAS NPIPB7 ZBTB38 EIF3CL TM4SF4 0.8 NPIPP1 Abs (beta) YEATS2 NTAN1 ABCC5 CLUAP1 EHHADH TMEM8A MAP3K13 PDIA2 0.6 ST6GAL1 ARHGDIG 16 WFS1 RGS11 DCAF16 ITFG3 LCORL Z69890.1 SGCB 0.4 FAM13A HBQ1 MANBA RHBDF1 Hispanics Europeans LRRC37A15P VPS33B KRT8P46 RCCD1 UBE2D3 UNC45A 0.2 CISD2 MAN2A2 BDH2 AP3S2 CENPE 4 FAM103A1 15 0.50.4 0.3 0.2 0.1 MAF PGRMC2 HMG20A 0 0.100.200.3 .4 .5 PDGFC TSPAN3 MAF ANKH C15orf27 GCNT4 UBE2Q2 HMGCR IMP3 POLK SNUPN 0.2 POC5 DMGDH MAN2C1 Asians African Americans NEIL1 BHMT PAM HERC1 EIF3KP1 FBXL22 0.4 GIN1 MYO5C 14 PPIP5K2 LRRC57 DCP2 PLA2G4B MCC JMJD7 0.6 TXNDC15 MAPKBP1 CDKAL1 LTK IER3 OIP5 HCG22 KLC1 Abs (beta) CCHCR1 CKB 0.8 TCF19 MARK3 HCG27 YY1 HLA C NPC2 5 HLA B WBP4 BAG6 13 GPANK1 CYCSP34 ZNF268 LY6G5B ZNF10 VWA7 HSPA1L ZNF891 C2 ZNF84 CFB ZNF605 NELFE ANKLE2 DXO FAM101A STK19 ZNF664 CYP21A2 CCDC92 ATF6B SETD8 PRRT1 NOTCH4 CDK2AP1 HLA DRA C12orf65 GLP1R ARL6IP4ABCB9 12 UNC5CL HPD GOPC P2RX4 NUS1 6 CENPW MED23 C12orf43SPPL3 MLEC C1GALT1 TOMM7 METAP2 JAZF1 WIF1 CHN2 TSPAN8 AEBP1 PRIM1 GSAP STEAP2 C12orf54H1FNT GIGYF1 POLR2J3 KLHL42 SPDYE2 11 SLC26A5 MRPS35 KLF14 BHLHE41KDM5A AOC1 HMBS UBE3C FAM86B3P ALG1L13P SLC37A4SMCO4 SGK223 7 CLDN23 STARD10PDE2A MFHAS1 NUMA1 ERI1 TPCN2 FIBP PPP1R3B BRMS1 SOX7 MTMR9 CTSW C8orf12 10 BLK SNX32SIPA1 ENPP7P12 LONRF1 ANK1 POLB PCNXL3KCNK7 SMIM19 LTBP3 8 EYA1 INTS8 CCNE2 9 NDUFAF6 MALAT1NEAT1 KB 1507C5.2 NF4 HSF1 FADS1 CYHR1 KIFC2 NUP160FNBP4 PPP1R16A SLC25A45 AF186192.5 ZNF251 ZNF34 ZNF7 COMMD5 CELF1 HAUS6 C1QT FOCAD PTPMT1 RGP1 FAM180B RABGAP1 RAPSN DENND1A BRD3 PSMC3 NACC2 APIP GPSM1 DNLZ SDCCAG3 SEC16A CAMK1D NUDT5 PCBD1 TH VDA BEND7 ZNF408 EIF3M SLC39A13 KCNJ11 HSD17B12 ARL14EP C2 HHEX NCR3LG1 TCF7L2 PHBP9 OR7E14P SEC23IP FRAT1 TNKS2 PLEKHA1 ZDHHC6 CWF19L1 ZFYVE27 EIF2S2P3 MARK2P9 ARHGAP19 Fig. 1 | Trans-ancestry GWAS meta-analysis identifies 318 loci associated with T2D. The graph represents a circos plot performed in 228,499 T2D cases and 1,178,783 controls. The outer track corresponds to –log10(P) for association with T2D in the trans-ancestry meta-analysis using a fixed-effects model with inverse-variance weighting of log odds ratios (y axis truncated at 30), by chromosomal position. The red line indicates genome-wide significance (P = 5.0 × 10−8). Purple gene labels indicate genes that were identified in skeletal muscle eQTLs by S-PrediXcan analysis, red gene names indicate genes identified in adipose eQTLs, black gene names indicate genes identified in pancreas eQTLs, and blue gene names indicate genes that were identified in eQTLs from arteries. The green band corresponds to measures of heterogeneity related to the index SNPs associated with T2D that were generated 2 using Cochran’s Q statistic. Dot sizes are proportional to I or ancestry-related heterogeneity. The inner track corresponds to –log10(P) for association with skeletal muscle, adipose, pancreas and artery tissue eQTLs from S-PrediXcan analysis (y axis truncated at 20), by chromosomal position. The red line indicates genome-wide significance (P = 5.0 × 10−8). Inset, effects of all 318 index SNPs on T2D by minor allele frequency, stratified and colored by ancestral group. Hispanics, South Asians and East Asians) by meta-analyzing Diet and Cancer Study8, Medstar9 and PennCath9 (Methods and genome-wide association study (GWAS) summary statistics derived Supplementary Tables 1 and 2). MVP participants (n = 273,409) from the MVP5 and other studies with non-overlapping partici- comprised predominantly male subjects (91.6%) and were classi- pants: the DIAMANTE Consortium3,
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