06391nas a2201909 4500008004100000022001400041245012500055210006900180260001600249300000900265490000700274520109900281100002101380700001901401700001701420700002301437700002001460700002801480700002201508700001801530700002101548700001901569700001901588700002001607700002201627700001401649700002301663700002301686700002101709700002001730700002301750700002001773700002601793700001801819700002401837700001801861700001701879700001601896700001701912700001501929700001701944700002901961700002001990700002402010700002502034700001402059700002102073700002102094700001702115700002502132700002002157700001402177700002102191700002802212700002502240700001902265700002602284700002102310700001702331700002002348700001802368700001702386700001902403700001902422700002502441700001802466700002402484700002102508700001602529700002402545700001502569700002002584700002402604700002202628700002202650700002002672700002102692700002102713700001802734700002002752700002202772700002102794700001502815700001702830700002002847700002302867700001802890700002002908700002202928700002102950700002102971700001902992700002003011700002003031700002103051700002303072700002003095700002503115700001903140700002103159700002103180700002203201700002503223700001903248700002303267700001603290700002403306700001703330700002403347700002203371700002003393700002803413700002303441700001903464700002603483700002403509700002103533700001703554700002303571700002103594700001803615700001603633700002203649700001903671700001903690700002203709700001903731700002703750700002403777700002603801700001703827700002103844700001503865700002103880700002103901700002203922700002403944700001903968700002103987700002804008700002004036700002604056700001904082700002004101700002004121700001804141700001604159700002504175700002004200700001804220700001704238700002104255700001804276700002404294700002404318700001804342700002404360700002004384700002204404700001904426856003604445 2019 eng d a2041-172300aMulti-ancestry sleep-by-SNP interaction analysis in 126,926 individuals reveals lipid loci stratified by sleep duration.0 aMultiancestry sleepbySNP interaction analysis in 126926 individu c2019 Nov 12 a51210 v103 a
Both short and long sleep are associated with an adverse lipid profile, likely through different biological pathways. To elucidate the biology of sleep-associated adverse lipid profile, we conduct multi-ancestry genome-wide sleep-SNP interaction analyses on three lipid traits (HDL-c, LDL-c and triglycerides). In the total study sample (discovery + replication) of 126,926 individuals from 5 different ancestry groups, when considering either long or short total sleep time interactions in joint analyses, we identify 49 previously unreported lipid loci, and 10 additional previously unreported lipid loci in a restricted sample of European-ancestry cohorts. In addition, we identify new gene-sleep interactions for known lipid loci such as LPL and PCSK9. The previously unreported lipid loci have a modest explained variance in lipid levels: most notable, gene-short-sleep interactions explain 4.25% of the variance in triglyceride level. Collectively, these findings contribute to our understanding of the biological mechanisms involved in sleep-associated adverse lipid profiles.
1 aNoordam, Raymond1 aBos, Maxime, M1 aWang, Heming1 aWinkler, Thomas, W1 aBentley, Amy, R1 aKilpeläinen, Tuomas, O1 ade Vries, Paul, S1 aSung, Yun, Ju1 aSchwander, Karen1 aCade, Brian, E1 aManning, Alisa1 aAschard, Hugues1 aBrown, Michael, R1 aChen, Han1 aFranceschini, Nora1 aMusani, Solomon, K1 aRichard, Melissa1 aVojinovic, Dina1 aAslibekyan, Stella1 aBartz, Traci, M1 aFuentes, Lisa, de Las1 aFeitosa, Mary1 aHorimoto, Andrea, R1 aIlkov, Marjan1 aKho, Minjung1 aKraja, Aldi1 aLi, Changwei1 aLim, Elise1 aLiu, Yongmei1 aMook-Kanamori, Dennis, O1 aRankinen, Tuomo1 aTajuddin, Salman, M1 avan der Spek, Ashley1 aWang, Zhe1 aMarten, Jonathan1 aLaville, Vincent1 aAlver, Maris1 aEvangelou, Evangelos1 aGraff, Maria, E1 aHe, Meian1 aKuhnel, Brigitte1 aLyytikäinen, Leo-Pekka1 aMarques-Vidal, Pedro1 aNolte, Ilja, M1 aPalmer, Nicholette, D1 aRauramaa, Rainer1 aShu, Xiao-Ou1 aSnieder, Harold1 aWeiss, Stefan1 aWen, Wanqing1 aYanek, Lisa, R1 aAdolfo, Correa1 aBallantyne, Christie1 aBielak, Larry1 aBiermasz, Nienke, R1 aBoerwinkle, Eric1 aDimou, Niki1 aEiriksdottir, Gudny1 aGao, Chuan1 aGharib, Sina, A1 aGottlieb, Daniel, J1 aHaba-Rubio, José1 aHarris, Tamara, B1 aHeikkinen, Sami1 aHeinzer, Raphael1 aHixson, James, E1 aHomuth, Georg1 aIkram, Arfan, M1 aKomulainen, Pirjo1 aKrieger, Jose, E1 aLee, Jiwon1 aLiu, Jingmin1 aLohman, Kurt, K1 aLuik, Annemarie, I1 aMägi, Reedik1 aMartin, Lisa, W1 aMeitinger, Thomas1 aMetspalu, Andres1 aMilaneschi, Yuri1 aNalls, Mike, A1 aO'Connell, Jeff1 aPeters, Annette1 aPeyser, Patricia1 aRaitakari, Olli, T1 aReiner, Alex, P1 aRensen, Patrick, C N1 aRice, Treva, K1 aRich, Stephen, S1 aRoenneberg, Till1 aRotter, Jerome, I1 aSchreiner, Pamela, J1 aShikany, James1 aSidney, Stephen, S1 aSims, Mario1 aSitlani, Colleen, M1 aSofer, Tamar1 aStrauch, Konstantin1 aSwertz, Morris, A1 aTaylor, Kent, D1 aUitterlinden, André, G1 aDuijn, Cornelia, M1 aVölzke, Henry1 aWaldenberger, Melanie1 aWallance, Robert, B1 aDijk, Ko Willems1 aYu, Caizheng1 aZonderman, Alan, B1 aBecker, Diane, M1 aElliott, Paul1 aEsko, Tõnu1 aGieger, Christian1 aGrabe, Hans, J1 aLakka, Timo, A1 aLehtimäki, Terho1 aNorth, Kari, E1 aPenninx, Brenda, W J H1 aVollenweider, Peter1 aWagenknecht, Lynne, E1 aWu, Tangchun1 aXiang, Yong-Bing1 aZheng, Wei1 aArnett, Donna, K1 aBouchard, Claude1 aEvans, Michele, K1 aGudnason, Vilmundur1 aKardia, Sharon1 aKelly, Tanika, N1 aKritchevsky, Stephen, B1 aLoos, Ruth, J F1 aPereira, Alexandre, C1 aProvince, Mike1 aPsaty, Bruce, M1 aRotimi, Charles1 aZhu, Xiaofeng1 aAmin, Najaf1 aCupples, Adrienne, L1 aFornage, Myriam1 aFox, Ervin, F1 aGuo, Xiuqing1 aGauderman, James1 aRice, Kenneth1 aKooperberg, Charles1 aMunroe, Patricia, B1 aLiu, Ching-Ti1 aMorrison, Alanna, C1 aRao, Dabeeru, C1 avan Heemst, Diana1 aRedline, Susan uhttps://chs-nhlbi.org/node/820204607nas a2201201 4500008004100000022001400041245012100055210006900176260001300245300001400258490000700272520112800279100002001407700001901427700002101446700003201467700001601499700001501515700002301530700002801553700002001581700001701601700002401618700002201642700002201664700002101686700001801707700002801725700001701753700001701770700002001787700002001807700002801827700002001855700002101875700002801896700002401924700001701948700001901965700002101984700002002005700002302025700002102048700001702069700002402086700002402110700002202134700001902156700002302175700002202198700001902220700003302239700001802272700002202290700002302312700002002335700002002355700002602375700002102401700002902422700001702451700002102468700002102489700002002510700002902530700002102559700001602580700001802596700002502614700003002639700002202669700002702691700002002718700002302738700002402761700002402785700002202809700002002831700001802851700002102869700002002890700002102910700002202931700002302953700002302976700002002999700002003019700002803039700002303067700001903090700002303109700002003132700001903152700002203171700002803193700003003221700002403251700002403275700002103299700002803320700002103348856003603369 2023 eng d a1546-171800aMulti-ancestry genome-wide study identifies effector genes and druggable pathways for coronary artery calcification.0 aMultiancestry genomewide study identifies effector genes and dru c2023 Oct a1651-16640 v553 aCoronary artery calcification (CAC), a measure of subclinical atherosclerosis, predicts future symptomatic coronary artery disease (CAD). Identifying genetic risk factors for CAC may point to new therapeutic avenues for prevention. Currently, there are only four known risk loci for CAC identified from genome-wide association studies (GWAS) in the general population. Here we conducted the largest multi-ancestry GWAS meta-analysis of CAC to date, which comprised 26,909 individuals of European ancestry and 8,867 individuals of African ancestry. We identified 11 independent risk loci, of which eight were new for CAC and five had not been reported for CAD. These new CAC loci are related to bone mineralization, phosphate catabolism and hormone metabolic pathways. Several new loci harbor candidate causal genes supported by multiple lines of functional evidence and are regulators of smooth muscle cell-mediated calcification ex vivo and in vitro. Together, these findings help refine the genetic architecture of CAC and extend our understanding of the biological and potential druggable pathways underlying CAC.
1 aKavousi, Maryam1 aBos, Maxime, M1 aBarnes, Hanna, J1 aCardenas, Christian, L Lino1 aWong, Doris1 aLu, Haojie1 aHodonsky, Chani, J1 aLandsmeer, Lennart, P L1 aTurner, Adam, W1 aKho, Minjung1 aHasbani, Natalie, R1 ade Vries, Paul, S1 aBowden, Donald, W1 aChopade, Sandesh1 aDeelen, Joris1 aBenavente, Ernest, Diez1 aGuo, Xiuqing1 aHofer, Edith1 aHwang, Shih-Jen1 aLutz, Sharon, M1 aLyytikäinen, Leo-Pekka1 aSlenders, Lotte1 aSmith, Albert, V1 aStanislawski, Maggie, A1 avan Setten, Jessica1 aWong, Quenna1 aYanek, Lisa, R1 aBecker, Diane, M1 aBeekman, Marian1 aBudoff, Matthew, J1 aFeitosa, Mary, F1 aFinan, Chris1 aHilliard, Austin, T1 aKardia, Sharon, L R1 aKovacic, Jason, C1 aKral, Brian, G1 aLangefeld, Carl, D1 aLauner, Lenore, J1 aMalik, Shaista1 aHoesein, Firdaus, A A Mohame1 aMokry, Michal1 aSchmidt, Reinhold1 aSmith, Jennifer, A1 aTaylor, Kent, D1 aTerry, James, G1 avan der Grond, Jeroen1 avan Meurs, Joyce1 aVliegenthart, Rozemarijn1 aXu, Jianzhao1 aYoung, Kendra, A1 aZilhão, Nuno, R1 aZweiker, Robert1 aAssimes, Themistocles, L1 aBecker, Lewis, C1 aBos, Daniel1 aCarr, Jeffrey1 aCupples, Adrienne, L1 ade Kleijn, Dominique, P V1 ade Winther, Menno1 aRuijter, Hester, M den1 aFornage, Myriam1 aFreedman, Barry, I1 aGudnason, Vilmundur1 aHingorani, Aroon, D1 aHokanson, John, E1 aIkram, Arfan, M1 aIšgum, Ivana1 aJacobs, David, R1 aKähönen, Mika1 aLange, Leslie, A1 aLehtimäki, Terho1 aPasterkamp, Gerard1 aRaitakari, Olli, T1 aSchmidt, Helena1 aSlagboom, Eline1 aUitterlinden, André, G1 aVernooij, Meike, W1 aBis, Joshua, C1 aFranceschini, Nora1 aPsaty, Bruce, M1 aPost, Wendy, S1 aRotter, Jerome, I1 aBjörkegren, Johan, L M1 aO'Donnell, Christopher, J1 aBielak, Lawrence, F1 aPeyser, Patricia, A1 aMalhotra, Rajeev1 avan der Laan, Sander, W1 aMiller, Clint, L uhttps://chs-nhlbi.org/node/950105739nas a2200901 4500008004100000245012500041210006900166260001600235520316300251100002003414700003103434700002303465700002103488700001803509700001503527700002303542700001303565700001703578700002203595700001803617700001803635700002303653700001503676700002203691700001703713700001803730700002503748700001703773700001803790700002103808700001903829700002203848700002003870700001503890700002003905700001603925700002503941700002403966700001903990700002404009700002404033700002504057700002104082700002404103700002404127700002204151700002004173700002104193700002104214700003004235700002004265700002104285700001204306700002104318700001904339700002004358700001904378700002104397700002304418700002104441700001604462700002504478700002104503700002404524700001904548700002204567700002004589700002304609700002304632700002404655700001904679700002104698700002204719700001804741700002204759700002004781856003604801 2023 eng d00aTime-to-Event Genome-Wide Association Study for Incident Cardiovascular Disease in People with Type 2 Diabetes Mellitus.0 aTimetoEvent GenomeWide Association Study for Incident Cardiovasc c2023 Jul 283 aBACKGROUND: Type 2 diabetes mellitus (T2D) confers a two- to three-fold increased risk of cardiovascular disease (CVD). However, the mechanisms underlying increased CVD risk among people with T2D are only partially understood. We hypothesized that a genetic association study among people with T2D at risk for developing incident cardiovascular complications could provide insights into molecular genetic aspects underlying CVD.
METHODS: From 16 studies of the Cohorts for Heart & Aging Research in Genomic Epidemiology (CHARGE) Consortium, we conducted a multi-ancestry time-to-event genome-wide association study (GWAS) for incident CVD among people with T2D using Cox proportional hazards models. Incident CVD was defined based on a composite of coronary artery disease (CAD), stroke, and cardiovascular death that occurred at least one year after the diagnosis of T2D. Cohort-level estimated effect sizes were combined using inverse variance weighted fixed effects meta-analysis. We also tested 204 known CAD variants for association with incident CVD among patients with T2D.
RESULTS: A total of 49,230 participants with T2D were included in the analyses (31,118 European ancestries and 18,112 non-European ancestries) which consisted of 8,956 incident CVD cases over a range of mean follow-up duration between 3.2 and 33.7 years (event rate 18.2%). We identified three novel, distinct genetic loci for incident CVD among individuals with T2D that reached the threshold for genome-wide significance ( <5.0×10 ): rs147138607 (intergenic variant between and ) with a hazard ratio (HR) 1.23, 95% confidence interval (CI) 1.15 - 1.32, =3.6×10 , rs11444867 (intergenic variant near ) with HR 1.89, 95% CI 1.52 - 2.35, =9.9×10 , and rs335407 (intergenic variant between and ) HR 1.25, 95% CI 1.16 - 1.35, =1.5×10 . Among 204 known CAD loci, 32 were associated with incident CVD in people with T2D with <0.05, and 5 were significant after Bonferroni correction ( <0.00024, 0.05/204). A polygenic score of these 204 variants was significantly associated with incident CVD with HR 1.14 (95% CI 1.12 - 1.16) per 1 standard deviation increase ( =1.0×10 ).
CONCLUSIONS: The data point to novel and known genomic regions associated with incident CVD among individuals with T2D.
CLINICAL PERSPECTIVE: We conducted a large-scale multi-ancestry time-to-event GWAS to identify genetic variants associated with CVD among people with T2D. Three variants were significantly associated with incident CVD in people with T2D: rs147138607 (intergenic variant between and ), rs11444867 (intergenic variant near ), and rs335407 (intergenic variant between and ). A polygenic score composed of known CAD variants identified in the general population was significantly associated with the risk of CVD in people with T2D. There are genetic risk factors specific to T2D that could at least partially explain the excess risk of CVD in people with T2D.In addition, we show that people with T2D have enrichment of known CAD association signals which could also explain the excess risk of CVD.
1 aKwak, Soo, Heon1 aHernandez-Cancela, Ryan, B1 aDiCorpo, Daniel, A1 aCondon, David, E1 aMerino, Jordi1 aWu, Peitao1 aBrody, Jennifer, A1 aYao, Jie1 aGuo, Xiuqing1 aAhmadizar, Fariba1 aMeyer, Mariah1 aSincan, Murat1 aMercader, Josep, M1 aLee, Sujin1 aHaessler, Jeffrey1 aVy, Ha, My T1 aLin, Zhaotong1 aArmstrong, Nicole, D1 aGu, Shaopeng1 aTsao, Noah, L1 aLange, Leslie, A1 aWang, Ningyuan1 aWiggins, Kerri, L1 aTrompet, Stella1 aLiu, Simin1 aLoos, Ruth, J F1 aJudy, Renae1 aSchroeder, Philip, H1 aHasbani, Natalie, R1 aBos, Maxime, M1 aMorrison, Alanna, C1 aJackson, Rebecca, D1 aReiner, Alexander, P1 aManson, JoAnn, E1 aChaudhary, Ninad, S1 aCarmichael, Lynn, K1 aChen, Yii-Der Ida1 aTaylor, Kent, D1 aGhanbari, Mohsen1 avan Meurs, Joyce1 aPitsillides, Achilleas, N1 aPsaty, Bruce, M1 aNoordam, Raymond1 aDo, Ron1 aPark, Kyong, Soo1 aJukema, Wouter1 aKavousi, Maryam1 aCorrea, Adolfo1 aRich, Stephen, S1 aDamrauer, Scott, M1 aHajek, Catherine1 aCho, Nam, H1 aIrvin, Marguerite, R1 aPankow, James, S1 aNadkarni, Girish, N1 aSladek, Robert1 aGoodarzi, Mark, O1 aFlorez, Jose, C1 aChasman, Daniel, I1 aHeckbert, Susan, R1 aKooperberg, Charles1 aDupuis, Josée1 aMalhotra, Rajeev1 ade Vries, Paul, S1 aLiu, Ching-Ti1 aRotter, Jerome, I1 aMeigs, James, B uhttps://chs-nhlbi.org/node/9450