05839nas a2201429 4500008004100000022001400041245013600055210006900191260000900260300001300269490000600282520178500288653001102073653003402084653001102118653002002129653001902149653000902168653001402177653002602191653003602217653002402253653002402277653001802301653001602319653001402335100002002349700001802369700002002387700002402407700002102431700002102452700002002473700001802493700002402511700002302535700001602558700001602574700002002590700001902610700002602629700002002655700002002675700001802695700002502713700002002738700002302758700002202781700002402803700002302827700002502850700001702875700002802892700001402920700001602934700002502950700002102975700002002996700002603016700001803042700001903060700003603079700002303115700001903138700002603157700001503183700002303198700002203221700002103243700002503264700002203289700002703311700001903338700001803357700002003375700002003395700002303415700002003438700002603458700001703484700001903501700001903520700002503539700002003564700001903584700002003603700002303623700002003646700003003666700002203696700002203718700002303740700001903763700001903782700002603801700002103827700001803848700002103866700001803887700002303905700002003928700002103948700001903969700002703988700002604015700002404041700002304065700002304088700002804111700001804139700002004157700003004177700002304207700002204230700002004252700002104272700002104293700001804314700002204332700001904354856003604373 2013 eng d a1553-740400aA meta-analysis of thyroid-related traits reveals novel loci and gender-specific differences in the regulation of thyroid function.0 ametaanalysis of thyroidrelated traits reveals novel loci and gen c2013 ae10032660 v93 a
Thyroid hormone is essential for normal metabolism and development, and overt abnormalities in thyroid function lead to common endocrine disorders affecting approximately 10% of individuals over their life span. In addition, even mild alterations in thyroid function are associated with weight changes, atrial fibrillation, osteoporosis, and psychiatric disorders. To identify novel variants underlying thyroid function, we performed a large meta-analysis of genome-wide association studies for serum levels of the highly heritable thyroid function markers TSH and FT4, in up to 26,420 and 17,520 euthyroid subjects, respectively. Here we report 26 independent associations, including several novel loci for TSH (PDE10A, VEGFA, IGFBP5, NFIA, SOX9, PRDM11, FGF7, INSR, ABO, MIR1179, NRG1, MBIP, ITPK1, SASH1, GLIS3) and FT4 (LHX3, FOXE1, AADAT, NETO1/FBXO15, LPCAT2/CAPNS2). Notably, only limited overlap was detected between TSH and FT4 associated signals, in spite of the feedback regulation of their circulating levels by the hypothalamic-pituitary-thyroid axis. Five of the reported loci (PDE8B, PDE10A, MAF/LOC440389, NETO1/FBXO15, and LPCAT2/CAPNS2) show strong gender-specific differences, which offer clues for the known sexual dimorphism in thyroid function and related pathologies. Importantly, the TSH-associated loci contribute not only to variation within the normal range, but also to TSH values outside the reference range, suggesting that they may be involved in thyroid dysfunction. Overall, our findings explain, respectively, 5.64% and 2.30% of total TSH and FT4 trait variance, and they improve the current knowledge of the regulation of hypothalamic-pituitary-thyroid axis function and the consequences of genetic variation for hypo- or hyperthyroidism.
10aFemale10aGenome-Wide Association Study10aHumans10aHyperthyroidism10aHypothyroidism10aMale10aPhenotype10aPolymorphism, Genetic10aPolymorphism, Single Nucleotide10aSex Characteristics10aSignal Transduction10aThyroid Gland10aThyrotropin10aThyroxine1 aPorcu, Eleonora1 aMedici, Marco1 aPistis, Giorgio1 aVolpato, Claudia, B1 aWilson, Scott, G1 aCappola, Anne, R1 aBos, Steffan, D1 aDeelen, Joris1 aHeijer, Martin, den1 aFreathy, Rachel, M1 aLahti, Jari1 aLiu, Chunyu1 aLopez, Lorna, M1 aNolte, Ilja, M1 aO'Connell, Jeffrey, R1 aTanaka, Toshiko1 aTrompet, Stella1 aArnold, Alice1 aBandinelli, Stefania1 aBeekman, Marian1 aBöhringer, Stefan1 aBrown, Suzanne, J1 aBuckley, Brendan, M1 aCamaschella, Clara1 ade Craen, Anton, J M1 aDavies, Gail1 ade Visser, Marieke, C H1 aFord, Ian1 aForsen, Tom1 aFrayling, Timothy, M1 aFugazzola, Laura1 aGögele, Martin1 aHattersley, Andrew, T1 aHermus, Ad, R1 aHofman, Albert1 aHouwing-Duistermaat, Jeanine, J1 aJensen, Richard, A1 aKajantie, Eero1 aKloppenburg, Margreet1 aLim, Ee, M1 aMasciullo, Corrado1 aMariotti, Stefano1 aMinelli, Cosetta1 aMitchell, Braxton, D1 aNagaraja, Ramaiah1 aNetea-Maier, Romana, T1 aPalotie, Aarno1 aPersani, Luca1 aPiras, Maria, G1 aPsaty, Bruce, M1 aRäikkönen, Katri1 aRichards, Brent1 aRivadeneira, Fernando1 aSala, Cinzia1 aSabra, Mona, M1 aSattar, Naveed1 aShields, Beverley, M1 aSoranzo, Nicole1 aStarr, John, M1 aStott, David, J1 aSweep, Fred, C G J1 aUsala, Gianluca1 avan der Klauw, Melanie, M1 avan Heemst, Diana1 avan Mullem, Alies1 aVermeulen, Sita, H1 aVisser, Edward1 aWalsh, John, P1 aWestendorp, Rudi, G J1 aWiden, Elisabeth1 aZhai, Guangju1 aCucca, Francesco1 aDeary, Ian, J1 aEriksson, Johan, G1 aFerrucci, Luigi1 aFox, Caroline, S1 aJukema, Wouter1 aKiemeney, Lambertus, A1 aPramstaller, Peter, P1 aSchlessinger, David1 aShuldiner, Alan, R1 aSlagboom, Eline, P1 aUitterlinden, André, G1 aVaidya, Bijay1 aVisser, Theo, J1 aWolffenbuttel, Bruce, H R1 aMeulenbelt, Ingrid1 aRotter, Jerome, I1 aSpector, Tim, D1 aHicks, Andrew, A1 aToniolo, Daniela1 aSanna, Serena1 aPeeters, Robin, P1 aNaitza, Silvia uhttps://chs-nhlbi.org/node/587709013nas a2202797 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2015 eng d a2041-172300aLow-frequency and rare exome chip variants associate with fasting glucose and type 2 diabetes susceptibility.0 aLowfrequency and rare exome chip variants associate with fasting c2015 a58970 v63 aFasting glucose and insulin are intermediate traits for type 2 diabetes. Here we explore the role of coding variation on these traits by analysis of variants on the HumanExome BeadChip in 60,564 non-diabetic individuals and in 16,491 T2D cases and 81,877 controls. We identify a novel association of a low-frequency nonsynonymous SNV in GLP1R (A316T; rs10305492; MAF=1.4%) with lower FG (β=-0.09±0.01 mmol l(-1), P=3.4 × 10(-12)), T2D risk (OR[95%CI]=0.86[0.76-0.96], P=0.010), early insulin secretion (β=-0.07±0.035 pmolinsulin mmolglucose(-1), P=0.048), but higher 2-h glucose (β=0.16±0.05 mmol l(-1), P=4.3 × 10(-4)). We identify a gene-based association with FG at G6PC2 (pSKAT=6.8 × 10(-6)) driven by four rare protein-coding SNVs (H177Y, Y207S, R283X and S324P). We identify rs651007 (MAF=20%) in the first intron of ABO at the putative promoter of an antisense lncRNA, associating with higher FG (β=0.02±0.004 mmol l(-1), P=1.3 × 10(-8)). Our approach identifies novel coding variant associations and extends the allelic spectrum of variation underlying diabetes-related quantitative traits and T2D susceptibility.
10aAfrican Continental Ancestry Group10aBlood Glucose10aDiabetes Mellitus, Type 210aEuropean Continental Ancestry Group10aExome10aFasting10aGenetic Association Studies10aGenetic Loci10aGenetic Predisposition to Disease10aGenetic Variation10aGlucagon-Like Peptide-1 Receptor10aGlucose-6-Phosphatase10aHumans10aInsulin10aMutation Rate10aOligonucleotide Array Sequence Analysis10aPolymorphism, Single Nucleotide1 aWessel, Jennifer1 aChu, Audrey, Y1 aWillems, Sara, M1 aWang, Shuai1 aYaghootkar, Hanieh1 aBrody, Jennifer, A1 aDauriz, Marco1 aHivert, Marie-France1 aRaghavan, Sridharan1 aLipovich, Leonard1 aHidalgo, Bertha1 aFox, Keolu1 aHuffman, Jennifer, E1 aAn, Ping1 aLu, Yingchang1 aRasmussen-Torvik, Laura, J1 aGrarup, Niels1 aEhm, Margaret, G1 aLi, Li1 aBaldridge, Abigail, S1 aStančáková, Alena1 aAbrol, Ravinder1 aBesse, Céline1 aBoland, Anne1 aBork-Jensen, Jette1 aFornage, Myriam1 aFreitag, Daniel, F1 aGarcia, Melissa, E1 aGuo, Xiuqing1 aHara, Kazuo1 aIsaacs, Aaron1 aJakobsdottir, Johanna1 aLange, Leslie, A1 aLayton, Jill, C1 aLi, Man1 aZhao, Jing, Hua1 aMeidtner, Karina1 aMorrison, Alanna, C1 aNalls, Mike, A1 aPeters, Marjolein, J1 aSabater-Lleal, Maria1 aSchurmann, Claudia1 aSilveira, Angela1 aSmith, Albert, V1 aSoutham, Lorraine1 aStoiber, Marcus, H1 aStrawbridge, Rona, J1 aTaylor, Kent, D1 aVarga, Tibor, V1 aAllin, Kristine, H1 aAmin, Najaf1 aAponte, Jennifer, L1 aAung, Tin1 aBarbieri, Caterina1 aBihlmeyer, Nathan, A1 aBoehnke, Michael1 aBombieri, Cristina1 aBowden, Donald, W1 aBurns, Sean, M1 aChen, Yuning1 aChen, Yii-DerI1 aCheng, Ching-Yu1 aCorrea, Adolfo1 aCzajkowski, Jacek1 aDehghan, Abbas1 aEhret, Georg, B1 aEiriksdottir, Gudny1 aEscher, Stefan, A1 aFarmaki, Aliki-Eleni1 aFrånberg, Mattias1 aGambaro, Giovanni1 aGiulianini, Franco1 aGoddard, William, A1 aGoel, Anuj1 aGottesman, Omri1 aGrove, Megan, L1 aGustafsson, Stefan1 aHai, Yang1 aHallmans, Göran1 aHeo, Jiyoung1 aHoffmann, Per1 aIkram, Mohammad, K1 aJensen, Richard, A1 aJørgensen, Marit, E1 aJørgensen, Torben1 aKaraleftheri, Maria1 aKhor, Chiea, C1 aKirkpatrick, Andrea1 aKraja, Aldi, T1 aKuusisto, Johanna1 aLange, Ethan, M1 aLee, I, T1 aLee, Wen-Jane1 aLeong, Aaron1 aLiao, Jiemin1 aLiu, Chunyu1 aLiu, Yongmei1 aLindgren, Cecilia, M1 aLinneberg, Allan1 aMalerba, Giovanni1 aMamakou, Vasiliki1 aMarouli, Eirini1 aMaruthur, Nisa, M1 aMatchan, Angela1 aMcKean-Cowdin, Roberta1 aMcLeod, Olga1 aMetcalf, Ginger, A1 aMohlke, Karen, L1 aMuzny, Donna, M1 aNtalla, Ioanna1 aPalmer, Nicholette, D1 aPasko, Dorota1 aPeter, Andreas1 aRayner, Nigel, W1 aRenstrom, Frida1 aRice, Ken1 aSala, Cinzia, F1 aSennblad, Bengt1 aSerafetinidis, Ioannis1 aSmith, Jennifer, A1 aSoranzo, Nicole1 aSpeliotes, Elizabeth, K1 aStahl, Eli, A1 aStirrups, Kathleen1 aTentolouris, Nikos1 aThanopoulou, Anastasia1 aTorres, Mina1 aTraglia, Michela1 aTsafantakis, Emmanouil1 aJavad, Sundas1 aYanek, Lisa, R1 aZengini, Eleni1 aBecker, Diane, M1 aBis, Joshua, C1 aBrown, James, B1 aCupples, Adrienne, L1 aHansen, Torben1 aIngelsson, Erik1 aKarter, Andrew, J1 aLorenzo, Carlos1 aMathias, Rasika, A1 aNorris, Jill, M1 aPeloso, Gina, M1 aSheu, Wayne, H-H1 aToniolo, Daniela1 aVaidya, Dhananjay1 aVarma, Rohit1 aWagenknecht, Lynne, E1 aBoeing, Heiner1 aBottinger, Erwin, P1 aDedoussis, George1 aDeloukas, Panos1 aFerrannini, Ele1 aFranco, Oscar, H1 aFranks, Paul, W1 aGibbs, Richard, A1 aGudnason, Vilmundur1 aHamsten, Anders1 aHarris, Tamara, B1 aHattersley, Andrew, T1 aHayward, Caroline1 aHofman, Albert1 aJansson, Jan-Håkan1 aLangenberg, Claudia1 aLauner, Lenore, J1 aLevy, Daniel1 aOostra, Ben, A1 aO'Donnell, Christopher, J1 aO'Rahilly, Stephen1 aPadmanabhan, Sandosh1 aPankow, James, S1 aPolasek, Ozren1 aProvince, Michael, A1 aRich, Stephen, S1 aRidker, Paul, M1 aRudan, Igor1 aSchulze, Matthias, B1 aSmith, Blair, H1 aUitterlinden, André, G1 aWalker, Mark1 aWatkins, Hugh1 aWong, Tien, Y1 aZeggini, Eleftheria1 aLaakso, Markku1 aBorecki, Ingrid, B1 aChasman, Daniel, I1 aPedersen, Oluf1 aPsaty, Bruce, M1 aTai, Shyong, E1 aDuijn, Cornelia, M1 aWareham, Nicholas, J1 aWaterworth, Dawn, M1 aBoerwinkle, Eric1 aKao, Linda, W H1 aFlorez, Jose, C1 aLoos, Ruth, J F1 aWilson, James, G1 aFrayling, Timothy, M1 aSiscovick, David, S1 aDupuis, Josée1 aRotter, Jerome, I1 aMeigs, James, B1 aScott, Robert, A1 aGoodarzi, Mark, O1 aEPIC-InterAct Consortium uhttps://chs-nhlbi.org/node/668603786nas a2200841 4500008004100000022001400041245009800055210006900153260001300222300001300235490000700248520140700255100001801662700002101680700002401701700002801725700002001753700001901773700001801792700001801810700002001828700002301848700001901871700002001890700001801910700001901928700001801947700002201965700001701987700002502004700002002029700001902049700002102068700002102089700002302110700002402133700002002157700001402177700001402191700001602205700002202221700001502243700002602258700002802284700001702312700002102329700001902350700002602369700002802395700002002423700002402443700002602467700001902493700001702512700001702529700002002546700002502566700001902591700001802610700002102628700002102649700002102670700002902691700001802720700002702738700002302765700002302788710002602811710002302837710002202860710002602882856003602908 2016 eng d a1553-740400aDiscovery of Genetic Variation on Chromosome 5q22 Associated with Mortality in Heart Failure.0 aDiscovery of Genetic Variation on Chromosome 5q22 Associated wit c2016 May ae10060340 v123 aFailure of the human heart to maintain sufficient output of blood for the demands of the body, heart failure, is a common condition with high mortality even with modern therapeutic alternatives. To identify molecular determinants of mortality in patients with new-onset heart failure, we performed a meta-analysis of genome-wide association studies and follow-up genotyping in independent populations. We identified and replicated an association for a genetic variant on chromosome 5q22 with 36% increased risk of death in subjects with heart failure (rs9885413, P = 2.7x10-9). We provide evidence from reporter gene assays, computational predictions and epigenomic marks that this polymorphism increases activity of an enhancer region active in multiple human tissues. The polymorphism was further reproducibly associated with a DNA methylation signature in whole blood (P = 4.5x10-40) that also associated with allergic sensitization and expression in blood of the cytokine TSLP (P = 1.1x10-4). Knockdown of the transcription factor predicted to bind the enhancer region (NHLH1) in a human cell line (HEK293) expressing NHLH1 resulted in lower TSLP expression. In addition, we observed evidence of recent positive selection acting on the risk allele in populations of African descent. Our findings provide novel genetic leads to factors that influence mortality in patients with heart failure.
1 aSmith, Gustav1 aFelix, Janine, F1 aMorrison, Alanna, C1 aKalogeropoulos, Andreas1 aTrompet, Stella1 aWilk, Jemma, B1 aGidlöf, Olof1 aWang, Xinchen1 aMorley, Michael1 aMendelson, Michael1 aJoehanes, Roby1 aLigthart, Symen1 aShan, Xiaoyin1 aBis, Joshua, C1 aWang, Ying, A1 aSjögren, Marketa1 aNgwa, Julius1 aBrandimarto, Jeffrey1 aStott, David, J1 aAguilar, David1 aRice, Kenneth, M1 aSesso, Howard, D1 aDemissie, Serkalem1 aBuckley, Brendan, M1 aTaylor, Kent, D1 aFord, Ian1 aYao, Chen1 aLiu, Chunyu1 aSotoodehnia, Nona1 aHarst, Pim1 aStricker, Bruno, H Ch1 aKritchevsky, Stephen, B1 aLiu, Yongmei1 aGaziano, Michael1 aHofman, Albert1 aMoravec, Christine, S1 aUitterlinden, André, G1 aKellis, Manolis1 avan Meurs, Joyce, B1 aMargulies, Kenneth, B1 aDehghan, Abbas1 aLevy, Daniel1 aOlde, Björn1 aPsaty, Bruce, M1 aCupples, Adrienne, L1 aJukema, Wouter1 aDjoussé, Luc1 aFranco, Oscar, H1 aBoerwinkle, Eric1 aBoyer, Laurie, A1 aNewton-Cheh, Christopher1 aButler, Javed1 aVasan, Ramachandran, S1 aCappola, Thomas, P1 aSmith, Nicholas, L1 aCHARGE-SCD consortium1 aEchoGen consortium1 aQT-IGC consortium1 aCHARGE-QRS consortium uhttps://chs-nhlbi.org/node/714405220nas a2201201 4500008004100000022001400041245010300055210006900158260001600227300000800243490000700251520181700258100002002075700001802095700002302113700002602136700002302162700002002185700002002205700002202225700001902247700001702266700002302283700001602306700002202322700001702344700001802361700001802379700002802397700001902425700001902444700002002463700002202483700002202505700001702527700002302544700001302567700002302580700001902603700002302622700002202645700002102667700002102688700001802709700001602727700001402743700002402757700002902781700002102810700002502831700001802856700002002874700002002894700002102914700001902935700001902954700002202973700002102995700001403016700001603030700002003046700001903066700001903085700002003104700001903124700002503143700002203168700002403190700001903214700002303233700002403256700002403280700002103304700002503325700002503350700002503375700002003400700002603420700002203446700002203468700002303490700002803513700002603541700002103567700002203588700001703610700002303627700001803650700002203668700001903690700002003709700001603729700002903745700002103774700002103795700002103816700002603837700002403863700001903887710002703906710004903933856003603982 2016 eng d a1474-760X00aDNA methylation signatures of chronic low-grade inflammation are associated with complex diseases.0 aDNA methylation signatures of chronic lowgrade inflammation are c2016 Dec 12 a2550 v173 aBACKGROUND: Chronic low-grade inflammation reflects a subclinical immune response implicated in the pathogenesis of complex diseases. Identifying genetic loci where DNA methylation is associated with chronic low-grade inflammation may reveal novel pathways or therapeutic targets for inflammation.
RESULTS: We performed a meta-analysis of epigenome-wide association studies (EWAS) of serum C-reactive protein (CRP), which is a sensitive marker of low-grade inflammation, in a large European population (n = 8863) and trans-ethnic replication in African Americans (n = 4111). We found differential methylation at 218 CpG sites to be associated with CRP (P < 1.15 × 10(-7)) in the discovery panel of European ancestry and replicated (P < 2.29 × 10(-4)) 58 CpG sites (45 unique loci) among African Americans. To further characterize the molecular and clinical relevance of the findings, we examined the association with gene expression, genetic sequence variants, and clinical outcomes. DNA methylation at nine (16%) CpG sites was associated with whole blood gene expression in cis (P < 8.47 × 10(-5)), ten (17%) CpG sites were associated with a nearby genetic variant (P < 2.50 × 10(-3)), and 51 (88%) were also associated with at least one related cardiometabolic entity (P < 9.58 × 10(-5)). An additive weighted score of replicated CpG sites accounted for up to 6% inter-individual variation (R2) of age-adjusted and sex-adjusted CRP, independent of known CRP-related genetic variants.
CONCLUSION: We have completed an EWAS of chronic low-grade inflammation and identified many novel genetic loci underlying inflammation that may serve as targets for the development of novel therapeutic interventions for inflammation.
1 aLigthart, Symen1 aMarzi, Carola1 aAslibekyan, Stella1 aMendelson, Michael, M1 aConneely, Karen, N1 aTanaka, Toshiko1 aColicino, Elena1 aWaite, Lindsay, L1 aJoehanes, Roby1 aGuan, Weihua1 aBrody, Jennifer, A1 aElks, Cathy1 aMarioni, Riccardo1 aJhun, Min, A1 aAgha, Golareh1 aBressler, Jan1 aWard-Caviness, Cavin, K1 aChen, Brian, H1 aHuan, Tianxiao1 aBakulski, Kelly1 aSalfati, Elias, L1 aFiorito, Giovanni1 aWahl, Simone1 aSchramm, Katharina1 aSha, Jin1 aHernandez, Dena, G1 aJust, Allan, C1 aSmith, Jennifer, A1 aSotoodehnia, Nona1 aPilling, Luke, C1 aPankow, James, S1 aTsao, Phil, S1 aLiu, Chunyu1 aZhao, Wei1 aGuarrera, Simonetta1 aMichopoulos, Vasiliki, J1 aSmith, Alicia, K1 aPeters, Marjolein, J1 aMelzer, David1 aVokonas, Pantel1 aFornage, Myriam1 aProkisch, Holger1 aBis, Joshua, C1 aChu, Audrey, Y1 aHerder, Christian1 aGrallert, Harald1 aYao, Chen1 aShah, Sonia1 aMcRae, Allan, F1 aLin, Honghuang1 aHorvath, Steve1 aFallin, Daniele1 aHofman, Albert1 aWareham, Nicholas, J1 aWiggins, Kerri, L1 aFeinberg, Andrew, P1 aStarr, John, M1 aVisscher, Peter, M1 aMurabito, Joanne, M1 aKardia, Sharon, L R1 aAbsher, Devin, M1 aBinder, Elisabeth, B1 aSingleton, Andrew, B1 aBandinelli, Stefania1 aPeters, Annette1 aWaldenberger, Melanie1 aMatullo, Giuseppe1 aSchwartz, Joel, D1 aDemerath, Ellen, W1 aUitterlinden, André, G1 avan Meurs, Joyce, B J1 aFranco, Oscar, H1 aChen, Yii-Der Ida1 aLevy, Daniel1 aTurner, Stephen, T1 aDeary, Ian, J1 aRessler, Kerry, J1 aDupuis, Josée1 aFerrucci, Luigi1 aOng, Ken, K1 aAssimes, Themistocles, L1 aBoerwinkle, Eric1 aKoenig, Wolfgang1 aArnett, Donna, K1 aBaccarelli, Andrea, A1 aBenjamin, Emelia, J1 aDehghan, Abbas1 aWHI-EMPC Investigators1 aCHARGE epigenetics of Coronary Heart Disease uhttps://chs-nhlbi.org/node/734905010nas a2201129 4500008004100000022001400041245004800055210004700103260001300150300001200163490000600175520193900181100001902120700001902139700002502158700002102183700002502204700002402229700001702253700001202270700001902282700002302301700002902324700002302353700002302376700002102399700001802420700002102438700001702459700001902476700002002495700001702515700001302532700002102545700002002566700001402586700002302600700001802623700002002641700001902661700001602680700002602696700001402722700002102736700002002757700002202777700002302799700001902822700001902841700002002860700002502880700002502905700002502930700001502955700002002970700001802990700002403008700002803032700001903060700001903079700002003098700001603118700002303134700002303157700002503180700002503205700002303230700001803253700002103271700002103292700002503313700002003338700002003358700002003378700002403398700001803422700001903440700002403459700001903483700002203502700002303524700002203547700002403569700001803593700002603611700002603637700002103663700002103684700001603705700001703721700002603738700001803764700002003782700001703802700002503819856003603844 2016 eng d a1942-326800aEpigenetic Signatures of Cigarette Smoking.0 aEpigenetic Signatures of Cigarette Smoking c2016 Oct a436-4470 v93 aBACKGROUND: DNA methylation leaves a long-term signature of smoking exposure and is one potential mechanism by which tobacco exposure predisposes to adverse health outcomes, such as cancers, osteoporosis, lung, and cardiovascular disorders.
METHODS AND RESULTS: To comprehensively determine the association between cigarette smoking and DNA methylation, we conducted a meta-analysis of genome-wide DNA methylation assessed using the Illumina BeadChip 450K array on 15 907 blood-derived DNA samples from participants in 16 cohorts (including 2433 current, 6518 former, and 6956 never smokers). Comparing current versus never smokers, 2623 cytosine-phosphate-guanine sites (CpGs), annotated to 1405 genes, were statistically significantly differentially methylated at Bonferroni threshold of P<1×10(-7) (18 760 CpGs at false discovery rate <0.05). Genes annotated to these CpGs were enriched for associations with several smoking-related traits in genome-wide studies including pulmonary function, cancers, inflammatory diseases, and heart disease. Comparing former versus never smokers, 185 of the CpGs that differed between current and never smokers were significant P<1×10(-7) (2623 CpGs at false discovery rate <0.05), indicating a pattern of persistent altered methylation, with attenuation, after smoking cessation. Transcriptomic integration identified effects on gene expression at many differentially methylated CpGs.
CONCLUSIONS: Cigarette smoking has a broad impact on genome-wide methylation that, at many loci, persists many years after smoking cessation. Many of the differentially methylated genes were novel genes with respect to biological effects of smoking and might represent therapeutic targets for prevention or treatment of tobacco-related diseases. Methylation at these sites could also serve as sensitive and stable biomarkers of lifetime exposure to tobacco smoke.
1 aJoehanes, Roby1 aJust, Allan, C1 aMarioni, Riccardo, E1 aPilling, Luke, C1 aReynolds, Lindsay, M1 aMandaviya, Pooja, R1 aGuan, Weihua1 aXu, Tao1 aElks, Cathy, E1 aAslibekyan, Stella1 aMoreno-Macias, Hortensia1 aSmith, Jennifer, A1 aBrody, Jennifer, A1 aDhingra, Radhika1 aYousefi, Paul1 aPankow, James, S1 aKunze, Sonja1 aShah, Sonia, H1 aMcRae, Allan, F1 aLohman, Kurt1 aSha, Jin1 aAbsher, Devin, M1 aFerrucci, Luigi1 aZhao, Wei1 aDemerath, Ellen, W1 aBressler, Jan1 aGrove, Megan, L1 aHuan, Tianxiao1 aLiu, Chunyu1 aMendelson, Michael, M1 aYao, Chen1 aKiel, Douglas, P1 aPeters, Annette1 aWang-Sattler, Rui1 aVisscher, Peter, M1 aWray, Naomi, R1 aStarr, John, M1 aDing, Jingzhong1 aRodriguez, Carlos, J1 aWareham, Nicholas, J1 aIrvin, Marguerite, R1 aZhi, Degui1 aBarrdahl, Myrto1 aVineis, Paolo1 aAmbatipudi, Srikant1 aUitterlinden, André, G1 aHofman, Albert1 aSchwartz, Joel1 aColicino, Elena1 aHou, Lifang1 aVokonas, Pantel, S1 aHernandez, Dena, G1 aSingleton, Andrew, B1 aBandinelli, Stefania1 aTurner, Stephen, T1 aWare, Erin, B1 aSmith, Alicia, K1 aKlengel, Torsten1 aBinder, Elisabeth, B1 aPsaty, Bruce, M1 aTaylor, Kent, D1 aGharib, Sina, A1 aSwenson, Brenton, R1 aLiang, Liming1 aDeMeo, Dawn, L1 aO'Connor, George, T1 aHerceg, Zdenko1 aRessler, Kerry, J1 aConneely, Karen, N1 aSotoodehnia, Nona1 aKardia, Sharon, L R1 aMelzer, David1 aBaccarelli, Andrea, A1 avan Meurs, Joyce, B J1 aRomieu, Isabelle1 aArnett, Donna, K1 aOng, Ken, K1 aLiu, Yongmei1 aWaldenberger, Melanie1 aDeary, Ian, J1 aFornage, Myriam1 aLevy, Daniel1 aLondon, Stephanie, J uhttps://chs-nhlbi.org/node/726105543nas a2201657 4500008004100000022001400041245013300055210007000188260001600258300001600274490000800290520094500298100002101243700001601264700001901280700001201299700001901311700001301330700002101343700001601364700001601380700001701396700002601413700001801439700002301457700002601480700002101506700002101527700001901548700002001567700002101587700002101608700001901629700001701648700002401665700001901689700002001708700002101728700002001749700002101769700001701790700001801807700002801825700002101853700002101874700001801895700002101913700002201934700002101956700002401977700002402001700002002025700001802045700002302063700001602086700001702102700001902119700002202138700002302160700001802183700002602201700001902227700002202246700002002268700002802288700002402316700002602340700001802366700002202384700002202406700001902428700001602447700001302463700001802476700001902494700002302513700001902536700002302555700002102578700001702599700001502616700002302631700001602654700002202670700002102692700002202713700002202735700001902757700002402776700002002800700002202820700002802842700002102870700001902891700001902910700001902929700002702948700001902975700001902994700002203013700002103035700001603056700002403072700002203096700001703118700002403135700002103159700002103180700002003201700002003221700001703241700002003258700001703278700001803295700002203313700002203335700002003357700002003377700001703397700002003414700002103434700001503455700002303470700002503493700002403518700002503542700002303567700001803590700002503608700002103633700002503654700001303679700002103692700002603713700002303739700002603762700002603788700001703814700001803831856003603849 2016 eng d a1091-649000aKLB is associated with alcohol drinking, and its gene product β-Klotho is necessary for FGF21 regulation of alcohol preference.0 aKLB is associated with alcohol drinking and its gene product βKl c2016 Dec 13 a14372-143770 v1133 aExcessive alcohol consumption is a major public health problem worldwide. Although drinking habits are known to be inherited, few genes have been identified that are robustly linked to alcohol drinking. We conducted a genome-wide association metaanalysis and replication study among >105,000 individuals of European ancestry and identified β-Klotho (KLB) as a locus associated with alcohol consumption (rs11940694; P = 9.2 × 10(-12)). β-Klotho is an obligate coreceptor for the hormone FGF21, which is secreted from the liver and implicated in macronutrient preference in humans. We show that brain-specific β-Klotho KO mice have an increased alcohol preference and that FGF21 inhibits alcohol drinking by acting on the brain. These data suggest that a liver-brain endocrine axis may play an important role in the regulation of alcohol drinking behavior and provide a unique pharmacologic target for reducing alcohol consumption.
1 aSchumann, Gunter1 aLiu, Chunyu1 aO'Reilly, Paul1 aGao, He1 aSong, Parkyong1 aXu, Bing1 aRuggeri, Barbara1 aAmin, Najaf1 aJia, Tianye1 aPreis, Sarah1 aLepe, Marcelo, Segura1 aAkira, Shizuo1 aBarbieri, Caterina1 aBaumeister, Sebastian1 aCauchi, Stephane1 aClarke, Toni-Kim1 aEnroth, Stefan1 aFischer, Krista1 aHällfors, Jenni1 aHarris, Sarah, E1 aHieber, Saskia1 aHofer, Edith1 aHottenga, Jouke-Jan1 aJohansson, Asa1 aJoshi, Peter, K1 aKaartinen, Niina1 aLaitinen, Jaana1 aLemaitre, Rozenn1 aLoukola, Anu1 aLuan, Jian'an1 aLyytikäinen, Leo-Pekka1 aMangino, Massimo1 aManichaikul, Ani1 aMbarek, Hamdi1 aMilaneschi, Yuri1 aMoayyeri, Alireza1 aMukamal, Kenneth1 aNelson, Christopher1 aNettleton, Jennifer1 aPartinen, Eemil1 aRawal, Rajesh1 aRobino, Antonietta1 aRose, Lynda1 aSala, Cinzia1 aSatoh, Takashi1 aSchmidt, Reinhold1 aSchraut, Katharina1 aScott, Robert1 aSmith, Albert, Vernon1 aStarr, John, M1 aTeumer, Alexander1 aTrompet, Stella1 aUitterlinden, André, G1 aVenturini, Cristina1 aVergnaud, Anne-Claire1 aVerweij, Niek1 aVitart, Veronique1 aVuckovic, Dragana1 aWedenoja, Juho1 aYengo, Loic1 aYu, Bing1 aZhang, Weihua1 aZhao, Jing Hua1 aBoomsma, Dorret, I1 aChambers, John1 aChasman, Daniel, I1 aDaniela, Toniolo1 ade Geus, Eco1 aDeary, Ian1 aEriksson, Johan, G1 aEsko, Tõnu1 aEulenburg, Volker1 aFranco, Oscar, H1 aFroguel, Philippe1 aGieger, Christian1 aGrabe, Hans, J1 aGudnason, Vilmundur1 aGyllensten, Ulf1 aHarris, Tamara, B1 aHartikainen, Anna-Liisa1 aHeath, Andrew, C1 aHocking, Lynne1 aHofman, Albert1 aHuth, Cornelia1 aJarvelin, Marjo-Riitta1 aJukema, Wouter1 aKaprio, Jaakko1 aKooner, Jaspal, S1 aKutalik, Zoltán1 aLahti, Jari1 aLangenberg, Claudia1 aLehtimäki, Terho1 aLiu, Yongmei1 aMadden, Pamela, A F1 aMartin, Nicholas1 aMorrison, Alanna1 aPenninx, Brenda1 aPirastu, Nicola1 aPsaty, Bruce1 aRaitakari, Olli1 aRidker, Paul1 aRose, Richard1 aRotter, Jerome, I1 aSamani, Nilesh, J1 aSchmidt, Helena1 aSpector, Tim, D1 aStott, David1 aStrachan, David1 aTzoulaki, Ioanna1 aHarst, Pim1 aDuijn, Cornelia, M1 aMarques-Vidal, Pedro1 aVollenweider, Peter1 aWareham, Nicholas, J1 aWhitfield, John, B1 aWilson, James1 aWolffenbuttel, Bruce1 aBakalkin, Georgy1 aEvangelou, Evangelos1 aLiu, Yun1 aRice, Kenneth, M1 aDesrivières, Sylvane1 aKliewer, Steven, A1 aMangelsdorf, David, J1 aMüller, Christian, P1 aLevy, Daniel1 aElliott, Paul uhttps://chs-nhlbi.org/node/725604937nas a2201345 4500008004100000022001400041245012400055210006900179260001300248300001200261490000700273520113500280100001601415700001901431700002301450700002301473700002301496700001901519700001801538700002401556700001801580700001801598700001701616700001901633700002001652700002201672700002301694700001901717700001901736700001801755700001901773700001601792700001401808700002601822700001501848700002601863700001601889700001301905700001301918700001901931700002201950700002001972700002101992700002002013700001402033700001902047700001802066700002402084700002202108700001902130700002402149700002002173700001202193700002702205700002202232700002002254700002402274700002802298700002402326700002102350700002402371700002102395700002202416700002802438700002102466700002702487700003002514700002002544700001502564700002402579700002002603700001702623700002302640700001902663700001902682700002202701700002202723700001802745700002202763700001902785700001902804700001402823700001802837700001402855700002102869700002302890700002802913700001702941700001902958700001902977700001702996700002003013700002103033700002403054700002503078700002303103700002303126700002103149700002603170700002203196700002003218700002003238700002003258700002003278700002903298700001703327700002303344710002603367710002303393710002603416710002503442710006603467710002203533856003603555 2016 eng d a1546-171800aMeta-analysis identifies common and rare variants influencing blood pressure and overlapping with metabolic trait loci.0 aMetaanalysis identifies common and rare variants influencing blo c2016 Oct a1162-700 v483 aMeta-analyses of association results for blood pressure using exome-centric single-variant and gene-based tests identified 31 new loci in a discovery stage among 146,562 individuals, with follow-up and meta-analysis in 180,726 additional individuals (total n = 327,288). These blood pressure-associated loci are enriched for known variants for cardiometabolic traits. Associations were also observed for the aggregation of rare and low-frequency missense variants in three genes, NPR1, DBH, and PTPMT1. In addition, blood pressure associations at 39 previously reported loci were confirmed. The identified variants implicate biological pathways related to cardiometabolic traits, vascular function, and development. Several new variants are inferred to have roles in transcription or as hubs in protein-protein interaction networks. Genetic risk scores constructed from the identified variants were strongly associated with coronary disease and myocardial infarction. This large collection of blood pressure-associated loci suggests new therapeutic strategies for hypertension, emphasizing a link with cardiometabolic risk.
1 aLiu, Chunyu1 aKraja, Aldi, T1 aSmith, Jennifer, A1 aBrody, Jennifer, A1 aFranceschini, Nora1 aBis, Joshua, C1 aRice, Kenneth1 aMorrison, Alanna, C1 aLu, Yingchang1 aWeiss, Stefan1 aGuo, Xiuqing1 aPalmas, Walter1 aMartin, Lisa, W1 aChen, Yii-Der Ida1 aSurendran, Praveen1 aDrenos, Fotios1 aCook, James, P1 aAuer, Paul, L1 aChu, Audrey, Y1 aGiri, Ayush1 aZhao, Wei1 aJakobsdottir, Johanna1 aLin, Li-An1 aStafford, Jeanette, M1 aAmin, Najaf1 aMei, Hao1 aYao, Jie1 aVoorman, Arend1 aLarson, Martin, G1 aGrove, Megan, L1 aSmith, Albert, V1 aHwang, Shih-Jen1 aChen, Han1 aHuan, Tianxiao1 aKosova, Gulum1 aStitziel, Nathan, O1 aKathiresan, Sekar1 aSamani, Nilesh1 aSchunkert, Heribert1 aDeloukas, Panos1 aLi, Man1 aFuchsberger, Christian1 aPattaro, Cristian1 aGorski, Mathias1 aKooperberg, Charles1 aPapanicolaou, George, J1 aRossouw, Jacques, E1 aFaul, Jessica, D1 aKardia, Sharon, L R1 aBouchard, Claude1 aRaffel, Leslie, J1 aUitterlinden, André, G1 aFranco, Oscar, H1 aVasan, Ramachandran, S1 aO'Donnell, Christopher, J1 aTaylor, Kent, D1 aLiu, Kiang1 aBottinger, Erwin, P1 aGottesman, Omri1 aDaw, Warwick1 aGiulianini, Franco1 aGanesh, Santhi1 aSalfati, Elias1 aHarris, Tamara, B1 aLauner, Lenore, J1 aDörr, Marcus1 aFelix, Stephan, B1 aRettig, Rainer1 aVölzke, Henry1 aKim, Eric1 aLee, Wen-Jane1 aLee, I-Te1 aSheu, Wayne, H-H1 aTsosie, Krystal, S1 aEdwards, Digna, R Velez1 aLiu, Yongmei1 aCorrea, Adolfo1 aWeir, David, R1 aVölker, Uwe1 aRidker, Paul, M1 aBoerwinkle, Eric1 aGudnason, Vilmundur1 aReiner, Alexander, P1 aDuijn, Cornelia, M1 aBorecki, Ingrid, B1 aEdwards, Todd, L1 aChakravarti, Aravinda1 aRotter, Jerome, I1 aPsaty, Bruce, M1 aLoos, Ruth, J F1 aFornage, Myriam1 aEhret, Georg, B1 aNewton-Cheh, Christopher1 aLevy, Daniel1 aChasman, Daniel, I1 aCHD Exome+ Consortium1 aExomeBP Consortium1 aGoT2DGenes Consortium1 aT2D-GENES Consortium1 aMyocardial Infarction Genetics and CARDIoGRAM Exome Consortia1 aCKDGen Consortium uhttps://chs-nhlbi.org/node/726403468nas a2200541 4500008004100000022001400041245007100055210006900126260001300195300001000208490000600218520192500224100002302149700001702172700002602189700001602215700002602231700002002257700002302277700002502300700002202325700001802347700001902365700002002384700002202404700002202426700002002448700002402468700002202492700001402514700002002528700002402548700002502572700001602597700001902613700002802632700001602660700001902676700001902695700002402714700002202738700002102760700002302781700002002804700001802824710004802842856003602890 2016 eng d a1942-326800aNovel Genetic Loci Associated With Retinal Microvascular Diameter.0 aNovel Genetic Loci Associated With Retinal Microvascular Diamete c2016 Feb a45-540 v93 aBACKGROUND: There is increasing evidence that retinal microvascular diameters are associated with cardiovascular and cerebrovascular conditions. The shared genetic effects of these associations are currently unknown. The aim of this study was to increase our understanding of the genetic factors that mediate retinal vessel size.
METHODS AND RESULTS: This study extends previous genome-wide association study results using 24 000+ multiethnic participants from 7 discovery cohorts and 5000+ subjects of European ancestry from 2 replication cohorts. Using the Illumina HumanExome BeadChip, we investigate the association of single-nucleotide polymorphisms and variants collectively across genes with summary measures of retinal vessel diameters, referred to as the central retinal venule equivalent and the central retinal arteriole equivalent. We report 4 new loci associated with central retinal venule equivalent, one of which is also associated with central retinal arteriole equivalent. The 4 single-nucleotide polymorphisms are rs7926971 in TEAD1 (P=3.1×10(-) (11); minor allele frequency=0.43), rs201259422 in TSPAN10 (P=4.4×10(-9); minor allele frequency=0.27), rs5442 in GNB3 (P=7.0×10(-10); minor allele frequency=0.05), and rs1800407 in OCA2 (P=3.4×10(-8); minor allele frequency=0.05). The latter single-nucleotide polymorphism, rs1800407, was also associated with central retinal arteriole equivalent (P=6.5×10(-12)). Results from the gene-based burden tests were null. In phenotype look-ups, single-nucleotide polymorphism rs201255422 was associated with both systolic (P=0.001) and diastolic blood pressures (P=8.3×10(-04)).
CONCLUSIONS: Our study expands the understanding of genetic factors influencing the size of the retinal microvasculature. These findings may also provide insight into the relationship between retinal and systemic microvascular disease.
1 aJensen, Richard, A1 aSim, Xueling1 aSmith, Albert, Vernon1 aLi, Xiaohui1 aJakobsdottir, Johanna1 aCheng, Ching-Yu1 aBrody, Jennifer, A1 aCotch, Mary, Frances1 aMcKnight, Barbara1 aKlein, Ronald1 aWang, Jie, Jin1 aKifley, Annette1 aHarris, Tamara, B1 aLauner, Lenore, J1 aTaylor, Kent, D1 aKlein, Barbara, E K1 aRaffel, Leslie, J1 aLi, Xiang1 aIkram, Arfan, M1 aKlaver, Caroline, C1 avan der Lee, Sven, J1 aMutlu, Unal1 aHofman, Albert1 aUitterlinden, André, G1 aLiu, Chunyu1 aKraja, Aldi, T1 aMitchell, Paul1 aGudnason, Vilmundur1 aRotter, Jerome, I1 aBoerwinkle, Eric1 aDuijn, Cornelia, M1 aPsaty, Bruce, M1 aWong, Tien, Y1 aCHARGE Exome Chip Blood Pressure Consortium uhttps://chs-nhlbi.org/node/690006988nas a2201993 4500008004100000022001400041245011600055210006900171260001600240520137500256100001201631700001301643700001801656700002201674700002201696700002501718700001801743700002701761700002001788700002801808700001901836700002001855700002101875700001801896700001601914700002801930700001901958700002501977700002302002700002302025700002302048700002502071700001902096700002102115700002102136700002202157700002102179700002302200700001502223700001802238700001902256700002102275700001702296700001702313700001602330700002002346700002802366700001802394700001902412700002002431700001702451700001802468700001802486700002202504700002102526700002102547700002902568700002302597700002202620700001702642700002202659700003202681700002102713700002302734700002102757700003102778700001402809700002202823700001902845700001602864700002102880700002102901700002302922700002302945700002402968700001902992700002103011700002303032700002103055700002003076700002403096700001803120700001703138700002003155700001803175700002003193700002403213700002103237700002103258700002503279700002203304700001603326700002103342700001703363700002203380700002303402700002003425700001803445700002403463700002203487700002203509700001903531700001903550700001703569700002803586700002403614700002303638700002503661700002003686700002403706700002103730700002403751700002203775700002203797700002303819700002203842700001703864700002103881700002103902700001703923700002003940700001803960700002503978700001904003700002104022700001904043700002004062700002104082700002504103700001804128700001904146700002004165700001904185700001904204700002004223700002904243700002004272700001104292700002304303700002004326700001904346700002004365700002604385700002404411700001904435700002104454700002004475700002404495700002104519700002304540700002804563700001804591700002304609700001704632700001904649700002604668700001904694700001904713700001804732700002304750700002104773700002304794700002304817700001904840700001904859710003904878710004104917856003604958 2016 eng d a1533-345000aSOS2 and ACP1 Loci Identified through Large-Scale Exome Chip Analysis Regulate Kidney Development and Function.0 aSOS2 and ACP1 Loci Identified through LargeScale Exome Chip Anal c2016 Dec 053 aGenome-wide association studies have identified >50 common variants associated with kidney function, but these variants do not fully explain the variation in eGFR. We performed a two-stage meta-analysis of associations between genotypes from the Illumina exome array and eGFR on the basis of serum creatinine (eGFRcrea) among participants of European ancestry from the CKDGen Consortium (nStage1: 111,666; nStage2: 48,343). In single-variant analyses, we identified single nucleotide polymorphisms at seven new loci associated with eGFRcrea (PPM1J, EDEM3, ACP1, SPEG, EYA4, CYP1A1, and ATXN2L; PStage1<3.7×10(-7)), of which most were common and annotated as nonsynonymous variants. Gene-based analysis identified associations of functional rare variants in three genes with eGFRcrea, including a novel association with the SOS Ras/Rho guanine nucleotide exchange factor 2 gene, SOS2 (P=5.4×10(-8) by sequence kernel association test). Experimental follow-up in zebrafish embryos revealed changes in glomerular gene expression and renal tubule morphology in the embryonic kidney of acp1- and sos2-knockdowns. These developmental abnormalities associated with altered blood clearance rate and heightened prevalence of edema. This study expands the number of loci associated with kidney function and identifies novel genes with potential roles in kidney formation.
1 aLi, Man1 aLi, Yong1 aWeeks, Olivia1 aMijatovic, Vladan1 aTeumer, Alexander1 aHuffman, Jennifer, E1 aTromp, Gerard1 aFuchsberger, Christian1 aGorski, Mathias1 aLyytikäinen, Leo-Pekka1 aNutile, Teresa1 aSedaghat, Sanaz1 aSorice, Rossella1 aTin, Adrienne1 aYang, Qiong1 aAhluwalia, Tarunveer, S1 aArking, Dan, E1 aBihlmeyer, Nathan, A1 aBöger, Carsten, A1 aCarroll, Robert, J1 aChasman, Daniel, I1 aCornelis, Marilyn, C1 aDehghan, Abbas1 aFaul, Jessica, D1 aFeitosa, Mary, F1 aGambaro, Giovanni1 aGasparini, Paolo1 aGiulianini, Franco1 aHeid, Iris1 aHuang, Jinyan1 aImboden, Medea1 aJackson, Anne, U1 aJeff, Janina1 aJhun, Min, A1 aKatz, Ronit1 aKifley, Annette1 aKilpeläinen, Tuomas, O1 aKumar, Ashish1 aLaakso, Markku1 aLi-Gao, Ruifang1 aLohman, Kurt1 aLu, Yingchang1 aMägi, Reedik1 aMalerba, Giovanni1 aMihailov, Evelin1 aMohlke, Karen, L1 aMook-Kanamori, Dennis, O1 aRobino, Antonietta1 aRuderfer, Douglas1 aSalvi, Erika1 aSchick, Ursula, M1 aSchulz, Christina-Alexandra1 aSmith, Albert, V1 aSmith, Jennifer, A1 aTraglia, Michela1 aYerges-Armstrong, Laura, M1 aZhao, Wei1 aGoodarzi, Mark, O1 aKraja, Aldi, T1 aLiu, Chunyu1 aWessel, Jennifer1 aBoerwinkle, Eric1 aBorecki, Ingrid, B1 aBork-Jensen, Jette1 aBottinger, Erwin, P1 aBraga, Daniele1 aBrandslund, Ivan1 aBrody, Jennifer, A1 aCampbell, Archie1 aCarey, David, J1 aChristensen, Cramer1 aCoresh, Josef1 aCrook, Errol1 aCurhan, Gary, C1 aCusi, Daniele1 ade Boer, Ian, H1 ade Vries, Aiko, P J1 aDenny, Joshua, C1 aDevuyst, Olivier1 aDreisbach, Albert, W1 aEndlich, Karlhans1 aEsko, Tõnu1 aFranco, Oscar, H1 aFulop, Tibor1 aGerhard, Glenn, S1 aGlümer, Charlotte1 aGottesman, Omri1 aGrarup, Niels1 aGudnason, Vilmundur1 aHarris, Tamara, B1 aHayward, Caroline1 aHocking, Lynne1 aHofman, Albert1 aHu, Frank, B1 aHusemoen, Lise, Lotte N1 aJackson, Rebecca, D1 aJørgensen, Torben1 aJørgensen, Marit, E1 aKähönen, Mika1 aKardia, Sharon, L R1 aKönig, Wolfgang1 aKooperberg, Charles1 aKriebel, Jennifer1 aLauner, Lenore, J1 aLauritzen, Torsten1 aLehtimäki, Terho1 aLevy, Daniel1 aLinksted, Pamela1 aLinneberg, Allan1 aLiu, Yongmei1 aLoos, Ruth, J F1 aLupo, Antonio1 aMeisinger, Christine1 aMelander, Olle1 aMetspalu, Andres1 aMitchell, Paul1 aNauck, Matthias1 aNürnberg, Peter1 aOrho-Melander, Marju1 aParsa, Afshin1 aPedersen, Oluf1 aPeters, Annette1 aPeters, Ulrike1 aPolasek, Ozren1 aPorteous, David1 aProbst-Hensch, Nicole, M1 aPsaty, Bruce, M1 aQi, Lu1 aRaitakari, Olli, T1 aReiner, Alex, P1 aRettig, Rainer1 aRidker, Paul, M1 aRivadeneira, Fernando1 aRossouw, Jacques, E1 aSchmidt, Frank1 aSiscovick, David1 aSoranzo, Nicole1 aStrauch, Konstantin1 aToniolo, Daniela1 aTurner, Stephen, T1 aUitterlinden, André, G1 aUlivi, Sheila1 aVelayutham, Dinesh1 aVölker, Uwe1 aVölzke, Henry1 aWaldenberger, Melanie1 aWang, Jie, Jin1 aWeir, David, R1 aWitte, Daniel1 aKuivaniemi, Helena1 aFox, Caroline, S1 aFranceschini, Nora1 aGoessling, Wolfram1 aKöttgen, Anna1 aChu, Audrey, Y1 aCHARGE Glycemic-T2D Working Group,1 aCHARGE Blood Pressure Working Group, uhttps://chs-nhlbi.org/node/725504860nas a2201081 4500008004100000022001400041245007600055210006900131260001600200300001200216490000800228520186200236653000902098653001902107653001602126653002802142653002002170653002402190653002202214653003402236653001102270653003702281653001602318653002602334653002802360653001702388100002402405700001902429700002002448700002002468700001702488700002302505700002502528700002202553700001902575700002002594700002202614700001702636700001902653700002202672700002102694700001702715700002202732700001802754700002402772700001602796700002602812700002802838700002402866700002102890700002002911700001202931700002202943700001902965700002002984700002203004700002403026700001403050700002303064700001803087700002303105700001903128700002003147700001603167700001903183700002203202700002303224700002003247700002003267700002003287700002303307700002203330700001903352700001703371700001803388700001903406700002603425700001703451700002003468700002903488700002203517700002303539700002103562700001803583700002603601700002103627700001803648700001903666700001703685700002003702710002003722856003603742 2017 eng d a1537-660500aDNA Methylation Analysis Identifies Loci for Blood Pressure Regulation.0 aDNA Methylation Analysis Identifies Loci for Blood Pressure Regu c2017 Dec 07 a888-9020 v1013 aGenome-wide association studies have identified hundreds of genetic variants associated with blood pressure (BP), but sequence variation accounts for a small fraction of the phenotypic variance. Epigenetic changes may alter the expression of genes involved in BP regulation and explain part of the missing heritability. We therefore conducted a two-stage meta-analysis of the cross-sectional associations of systolic and diastolic BP with blood-derived genome-wide DNA methylation measured on the Infinium HumanMethylation450 BeadChip in 17,010 individuals of European, African American, and Hispanic ancestry. Of 31 discovery-stage cytosine-phosphate-guanine (CpG) dinucleotides, 13 replicated after Bonferroni correction (discovery: N = 9,828, p < 1.0 × 10-7; replication: N = 7,182, p < 1.6 × 10-3). The replicated methylation sites are heritable (h2 > 30%) and independent of known BP genetic variants, explaining an additional 1.4% and 2.0% of the interindividual variation in systolic and diastolic BP, respectively. Bidirectional Mendelian randomization among up to 4,513 individuals of European ancestry from 4 cohorts suggested that methylation at cg08035323 (TAF1B-YWHAQ) influences BP, while BP influences methylation at cg00533891 (ZMIZ1), cg00574958 (CPT1A), and cg02711608 (SLC1A5). Gene expression analyses further identified six genes (TSPAN2, SLC7A11, UNC93B1, CPT1A, PTMS, and LPCAT3) with evidence of triangular associations between methylation, gene expression, and BP. Additional integrative Mendelian randomization analyses of gene expression and DNA methylation suggested that the expression of TSPAN2 is a putative mediator of association between DNA methylation at cg23999170 and BP. These findings suggest that heritable DNA methylation plays a role in regulating BP independently of previously known genetic variants.
10aAged10aBlood Pressure10aCpG Islands10aCross-Sectional Studies10aDNA Methylation10aEpigenesis, Genetic10aGenetic Variation10aGenome-Wide Association Study10aHumans10aMendelian Randomization Analysis10aMiddle Aged10aNerve Tissue Proteins10aQuantitative Trait Loci10aTetraspanins1 aRichard, Melissa, A1 aHuan, Tianxiao1 aLigthart, Symen1 aGondalia, Rahul1 aJhun, Min, A1 aBrody, Jennifer, A1 aIrvin, Marguerite, R1 aMarioni, Riccardo1 aShen, Jincheng1 aTsai, Pei-Chien1 aMontasser, May, E1 aJia, Yucheng1 aSyme, Catriona1 aSalfati, Elias, L1 aBoerwinkle, Eric1 aGuan, Weihua1 aMosley, Thomas, H1 aBressler, Jan1 aMorrison, Alanna, C1 aLiu, Chunyu1 aMendelson, Michael, M1 aUitterlinden, André, G1 avan Meurs, Joyce, B1 aFranco, Oscar, H1 aZhang, Guosheng1 aLi, Yun1 aStewart, James, D1 aBis, Joshua, C1 aPsaty, Bruce, M1 aChen, Yii-Der Ida1 aKardia, Sharon, L R1 aZhao, Wei1 aTurner, Stephen, T1 aAbsher, Devin1 aAslibekyan, Stella1 aStarr, John, M1 aMcRae, Allan, F1 aHou, Lifang1 aJust, Allan, C1 aSchwartz, Joel, D1 aVokonas, Pantel, S1 aMenni, Cristina1 aSpector, Tim, D1 aShuldiner, Alan1 aDamcott, Coleen, M1 aRotter, Jerome, I1 aPalmas, Walter1 aLiu, Yongmei1 aPaus, Tomáš1 aHorvath, Steve1 aO'Connell, Jeffrey, R1 aGuo, Xiuqing1 aPausova, Zdenka1 aAssimes, Themistocles, L1 aSotoodehnia, Nona1 aSmith, Jennifer, A1 aArnett, Donna, K1 aDeary, Ian, J1 aBaccarelli, Andrea, A1 aBell, Jordana, T1 aWhitsel, Eric1 aDehghan, Abbas1 aLevy, Daniel1 aFornage, Myriam1 aBIOS Consortium uhttps://chs-nhlbi.org/node/758307019nas a2201849 4500008004100000022001400041245009300055210006900148260001300217490000700230520169900237100001901936700001901955700002101974700002301995700001602018700002502034700002202059700001802081700001902099700001702118700002602135700001602161700003002177700002302207700002102230700002502251700002002276700002102296700002302317700001602340700002302356700002002379700001702399700002102416700002302437700002202460700001402482700002502496700002902521700002402550700002102574700001802595700001702613700002502630700001802655700001802673700002002691700002202711700002502733700001302758700002002771700002402791700002502815700001902840700001902859700002102878700002702899700001902926700001402945700002402959700002302983700002203006700002503028700002603053700002303079700002203102700002103124700002003145700001803165700002603183700001703209700001803226700002303244700002403267700001203291700002103303700002103324700002203345700002103367700002703388700002303415700001803438700002203456700002003478700002403498700001803522700001903540700002103559700001603580700002003596700002303616700002003639700002203659700001603681700002103697700001903718700002203737700001703759700002003776700002303796700002703819700002403846700001903870700002203889700001903911700002403930700001503954700002003969700001903989700002204008700001904030700002104049700002204070700002304092700001904115700001904134700002104153700002504174700002004199700002004219700002204239700001604261700002004277700002204297700002004319700001604339700002604355700001904381700001504400700002404415700002304439700002604462700002404488700002504512700002104537700002304558700002104581700002404602700002104626700002504647700001704672700002704689700002004716700002004736700002304756700002304779700001804802700002504820700001704845700002904862700002404891700002404915710004304939710015104982856003605133 2017 eng d a1942-326800aNew Blood Pressure-Associated Loci Identified in Meta-Analyses of 475 000 Individuals.0 aNew Blood PressureAssociated Loci Identified in MetaAnalyses of c2017 Oct0 v103 aBACKGROUND: Genome-wide association studies have recently identified >400 loci that harbor DNA sequence variants that influence blood pressure (BP). Our earlier studies identified and validated 56 single nucleotide variants (SNVs) associated with BP from meta-analyses of exome chip genotype data. An additional 100 variants yielded suggestive evidence of association.
METHODS AND RESULTS: Here, we augment the sample with 140 886 European individuals from the UK Biobank, in whom 77 of the 100 suggestive SNVs were available for association analysis with systolic BP or diastolic BP or pulse pressure. We performed 2 meta-analyses, one in individuals of European, South Asian, African, and Hispanic descent (pan-ancestry, ≈475 000), and the other in the subset of individuals of European descent (≈423 000). Twenty-one SNVs were genome-wide significant (P<5×10-8) for BP, of which 4 are new BP loci: rs9678851 (missense, SLC4A1AP), rs7437940 (AFAP1), rs13303 (missense, STAB1), and rs1055144 (7p15.2). In addition, we identified a potentially independent novel BP-associated SNV, rs3416322 (missense, SYNPO2L) at a known locus, uncorrelated with the previously reported SNVs. Two SNVs are associated with expression levels of nearby genes, and SNVs at 3 loci are associated with other traits. One SNV with a minor allele frequency <0.01, (rs3025380 at DBH) was genome-wide significant.
CONCLUSIONS: We report 4 novel loci associated with BP regulation, and 1 independent variant at an established BP locus. This analysis highlights several candidate genes with variation that alter protein function or gene expression for potential follow-up.
1 aKraja, Aldi, T1 aCook, James, P1 aWarren, Helen, R1 aSurendran, Praveen1 aLiu, Chunyu1 aEvangelou, Evangelos1 aManning, Alisa, K1 aGrarup, Niels1 aDrenos, Fotios1 aSim, Xueling1 aSmith, Albert, Vernon1 aAmin, Najaf1 aBlakemore, Alexandra, I F1 aBork-Jensen, Jette1 aBrandslund, Ivan1 aFarmaki, Aliki-Eleni1 aFava, Cristiano1 aFerreira, Teresa1 aHerzig, Karl-Heinz1 aGiri, Ayush1 aGiulianini, Franco1 aGrove, Megan, L1 aGuo, Xiuqing1 aHarris, Sarah, E1 aHave, Christian, T1 aHavulinna, Aki, S1 aZhang, He1 aJørgensen, Marit, E1 aKäräjämäki, AnneMari1 aKooperberg, Charles1 aLinneberg, Allan1 aLittle, Louis1 aLiu, Yongmei1 aBonnycastle, Lori, L1 aLu, Yingchang1 aMägi, Reedik1 aMahajan, Anubha1 aMalerba, Giovanni1 aMarioni, Riccardo, E1 aMei, Hao1 aMenni, Cristina1 aMorrison, Alanna, C1 aPadmanabhan, Sandosh1 aPalmas, Walter1 aPoveda, Alaitz1 aRauramaa, Rainer1 aRayner, Nigel, William1 aRiaz, Muhammad1 aRice, Ken1 aRichard, Melissa, A1 aSmith, Jennifer, A1 aSoutham, Lorraine1 aStančáková, Alena1 aStirrups, Kathleen, E1 aTragante, Vinicius1 aTuomi, Tiinamaija1 aTzoulaki, Ioanna1 aVarga, Tibor, V1 aWeiss, Stefan1 aYiorkas, Andrianos, M1 aYoung, Robin1 aZhang, Weihua1 aBarnes, Michael, R1 aCabrera, Claudia, P1 aGao, He1 aBoehnke, Michael1 aBoerwinkle, Eric1 aChambers, John, C1 aConnell, John, M1 aChristensen, Cramer, K1 ade Boer, Rudolf, A1 aDeary, Ian, J1 aDedoussis, George1 aDeloukas, Panos1 aDominiczak, Anna, F1 aDörr, Marcus1 aJoehanes, Roby1 aEdwards, Todd, L1 aEsko, Tõnu1 aFornage, Myriam1 aFranceschini, Nora1 aFranks, Paul, W1 aGambaro, Giovanni1 aGroop, Leif1 aHallmans, Göran1 aHansen, Torben1 aHayward, Caroline1 aHeikki, Oksa1 aIngelsson, Erik1 aTuomilehto, Jaakko1 aJarvelin, Marjo-Riitta1 aKardia, Sharon, L R1 aKarpe, Fredrik1 aKooner, Jaspal, S1 aLakka, Timo, A1 aLangenberg, Claudia1 aLind, Lars1 aLoos, Ruth, J F1 aLaakso, Markku1 aMcCarthy, Mark, I1 aMelander, Olle1 aMohlke, Karen, L1 aMorris, Andrew, P1 aPalmer, Colin, N A1 aPedersen, Oluf1 aPolasek, Ozren1 aPoulter, Neil, R1 aProvince, Michael, A1 aPsaty, Bruce, M1 aRidker, Paul, M1 aRotter, Jerome, I1 aRudan, Igor1 aSalomaa, Veikko1 aSamani, Nilesh, J1 aSever, Peter, J1 aSkaaby, Tea1 aStafford, Jeanette, M1 aStarr, John, M1 aHarst, Pim1 avan der Meer, Peter1 aDuijn, Cornelia, M1 aVergnaud, Anne-Claire1 aGudnason, Vilmundur1 aWareham, Nicholas, J1 aWilson, James, G1 aWiller, Cristen, J1 aWitte, Daniel, R1 aZeggini, Eleftheria1 aSaleheen, Danish1 aButterworth, Adam, S1 aDanesh, John1 aAsselbergs, Folkert, W1 aWain, Louise, V1 aEhret, Georg, B1 aChasman, Daniel, I1 aCaulfield, Mark, J1 aElliott, Paul1 aLindgren, Cecilia, M1 aLevy, Daniel1 aNewton-Cheh, Christopher1 aMunroe, Patricia, B1 aHowson, Joanna, M M1 aUnderstanding Society Scientific Group1 aCHARGE EXOME BP, CHD Exome+, Exome BP, GoT2D:T2DGenes Consortia, The UK Biobank Cardio-Metabolic Traits Consortium Blood Pressure Working Group† uhttps://chs-nhlbi.org/node/756903942nas a2200757 4500008004100000022001400041245010600055210006900161260001600230520175200246100002801998700002502026700001702051700002002068700002202088700001602110700001702126700002302143700002102166700001202187700002502199700002202224700002602246700002202272700001602294700002102310700002102331700003002352700001702382700001602399700001702415700002102432700003202453700001802485700002402503700002102527700002202548700002202570700001702592700002002609700002102629700002202650700002302672700002102695700001802716700001402734700001802748700002402766700002602790700002802816700002102844700001802865700002802883700002102911700002302932700001902955700002902974700001903003700001903022700002303041700001903064700002203083700002303105700002003128856003603148 2018 eng d a1528-002000aDNA methylation age is associated with an altered hemostatic profile in a multi-ethnic meta-analysis.0 aDNA methylation age is associated with an altered hemostatic pro c2018 Jul 243 aMany hemostatic factors are associated with age and age-related diseases, however much remains unknown about the biological mechanisms linking aging and hemostatic factors. DNA methylation is a novel means by which to assess epigenetic aging, which is a measure of age and the aging processes as determined by altered epigenetic states. We used a meta-analysis approach to examine the association between measures of epigenetic aging and hemostatic factors, as well as a clotting time measure. For fibrinogen, we used European and African-ancestry participants who were meta-analyzed separately and combined via a random effects meta-analysis. All other measures only included participants of European-ancestry. We found that 1-year higher extrinsic epigenetic age as compared to chronological age was associated with higher fibrinogen (0.004 g/L per year; 95% CI: 0.001, 0.007; P = 0.01) and plasminogen activator inhibitor 1 (PAI-1; 0.13 U/mL per year; 95% CI: 0.07, 0.20; P = 6.6x10-5) concentrations as well as lower activated partial thromboplastin time, a measure of clotting time. We replicated PAI-1 associations using an independent cohort. To further elucidate potential functional mechanisms we associated epigenetic aging with expression levels of the PAI-1 protein encoding gene (SERPINE1) and the three fibrinogen subunit-encoding genes (FGA, FGG, and FGB), in both peripheral blood and aorta intima-media samples. We observed associations between accelerated epigenetic aging and transcription of FGG in both tissues. Collectively, our results indicate that accelerated epigenetic aging is associated with a pro-coagulation hemostatic profile, and that epigenetic aging may regulate hemostasis in part via gene transcription.
1 aWard-Caviness, Cavin, K1 aHuffman, Jennifer, E1 aEvertt, Karl1 aGermain, Marine1 avan Dongen, Jenny1 aHill, David1 aJhun, Min, A1 aBrody, Jennifer, A1 aGhanbari, Mohsen1 aDu, Lei1 aRoetker, Nicholas, S1 ade Vries, Paul, S1 aWaldenberger, Melanie1 aGieger, Christian1 aWolf, Petra1 aProkisch, Holger1 aKoenig, Wolfgang1 aO'Donnell, Christopher, J1 aLevy, Daniel1 aLiu, Chunyu1 aTruong, Vinh1 aWells, Philip, S1 aTrégouët, David-Alexandre1 aTang, Weihong1 aMorrison, Alanna, C1 aBoerwinkle, Eric1 aWiggins, Kerri, L1 aMcKnight, Barbara1 aGuo, Xiuqing1 aPsaty, Bruce, M1 aSotoodenia, Nona1 aBoomsa, Dorret, I1 aWillemsen, Gonneke1 aLigthart, Lannie1 aDeary, Ian, J1 aZhao, Wei1 aWare, Erin, B1 aKardia, Sharon, L R1 avan Meurs, Joyce, B J1 aUitterlinden, André, G1 aFranco, Oscar, H1 aEriksson, Per1 aFranco-Cereceda, Anders1 aPankow, James, S1 aJohnson, Andrew, D1 aGagnon, France1 aMorange, Pierre-Emmanuel1 aGeus, Eco, J C1 aStarr, John, M1 aSmith, Jennifer, A1 aDehghan, Abbas1 aBjörck, Hanna, M1 aSmith, Nicholas, L1 aPeters, Annette uhttps://chs-nhlbi.org/node/781604760nas a2201057 4500008004100000022001400041245011000055210006900165260001500234300001200249490000800261520187700269653001002146653000902156653001902165653002102184653001602205653002002221653001102241653001102252653003402263653001102297653001402308653001502322653000902337653001602346653002602362653002202388653001402410653002402424653000902448653001802457100001802475700002602493700002802519700001902547700001902566700002002585700001902605700002302624700002202647700001802669700001902687700002002706700002402726700002002750700001902770700001702789700002202806700002102828700002002849700002302869700002102892700002502913700002402938700002002962700002202982700002003004700001203024700002003036700002203056700002003078700002203098700002203120700002203142700002003164700002003184700002003204700002103224700002003245700002103265700002003286700001603306700002503322700002103347700001903368700002203387700002003409700002403429700001603453700002003469700002203489700002203511700002003533700002003553700002903573700002103602700001703623700002603640856003603666 2019 eng d a1524-453900aBlood Leukocyte DNA Methylation Predicts Risk of Future Myocardial Infarction and Coronary Heart Disease.0 aBlood Leukocyte DNA Methylation Predicts Risk of Future Myocardi c2019 08 20 a645-6570 v1403 aBACKGROUND: DNA methylation is implicated in coronary heart disease (CHD), but current evidence is based on small, cross-sectional studies. We examined blood DNA methylation in relation to incident CHD across multiple prospective cohorts.
METHODS: Nine population-based cohorts from the United States and Europe profiled epigenome-wide blood leukocyte DNA methylation using the Illumina Infinium 450k microarray, and prospectively ascertained CHD events including coronary insufficiency/unstable angina, recognized myocardial infarction, coronary revascularization, and coronary death. Cohorts conducted race-specific analyses adjusted for age, sex, smoking, education, body mass index, blood cell type proportions, and technical variables. We conducted fixed-effect meta-analyses across cohorts.
RESULTS: Among 11 461 individuals (mean age 64 years, 67% women, 35% African American) free of CHD at baseline, 1895 developed CHD during a mean follow-up of 11.2 years. Methylation levels at 52 CpG (cytosine-phosphate-guanine) sites were associated with incident CHD or myocardial infarction (false discovery rate<0.05). These CpGs map to genes with key roles in calcium regulation (ATP2B2, CASR, GUCA1B, HPCAL1), and genes identified in genome- and epigenome-wide studies of serum calcium (CASR), serum calcium-related risk of CHD (CASR), coronary artery calcified plaque (PTPRN2), and kidney function (CDH23, HPCAL1), among others. Mendelian randomization analyses supported a causal effect of DNA methylation on incident CHD; these CpGs map to active regulatory regions proximal to long non-coding RNA transcripts.
CONCLUSION: Methylation of blood-derived DNA is associated with risk of future CHD across diverse populations and may serve as an informative tool for gaining further insight on the development of CHD.
10aAdult10aAged10aCohort Studies10aCoronary Disease10aCpG Islands10aDNA Methylation10aEurope10aFemale10aGenome-Wide Association Study10aHumans10aIncidence10aLeukocytes10aMale10aMiddle Aged10aMyocardial Infarction10aPopulation Groups10aPrognosis10aProspective Studies10aRisk10aUnited States1 aAgha, Golareh1 aMendelson, Michael, M1 aWard-Caviness, Cavin, K1 aJoehanes, Roby1 aHuan, Tianxiao1 aGondalia, Rahul1 aSalfati, Elias1 aBrody, Jennifer, A1 aFiorito, Giovanni1 aBressler, Jan1 aChen, Brian, H1 aLigthart, Symen1 aGuarrera, Simonetta1 aColicino, Elena1 aJust, Allan, C1 aWahl, Simone1 aGieger, Christian1 aVandiver, Amy, R1 aTanaka, Toshiko1 aHernandez, Dena, G1 aPilling, Luke, C1 aSingleton, Andrew, B1 aSacerdote, Carlotta1 aKrogh, Vittorio1 aPanico, Salvatore1 aTumino, Rosario1 aLi, Yun1 aZhang, Guosheng1 aStewart, James, D1 aFloyd, James, S1 aWiggins, Kerri, L1 aRotter, Jerome, I1 aMulthaup, Michael1 aBakulski, Kelly1 aHorvath, Steven1 aTsao, Philip, S1 aAbsher, Devin, M1 aVokonas, Pantel1 aHirschhorn, Joel1 aFallin, Daniele1 aLiu, Chunyu1 aBandinelli, Stefania1 aBoerwinkle, Eric1 aDehghan, Abbas1 aSchwartz, Joel, D1 aPsaty, Bruce, M1 aFeinberg, Andrew, P1 aHou, Lifang1 aFerrucci, Luigi1 aSotoodehnia, Nona1 aMatullo, Giuseppe1 aPeters, Annette1 aFornage, Myriam1 aAssimes, Themistocles, L1 aWhitsel, Eric, A1 aLevy, Daniel1 aBaccarelli, Andrea, A uhttps://chs-nhlbi.org/node/850704711nas a2201357 4500008004100000022001400041245009500055210006900150260001300219300001000232490000700242520077200249100001601021700002501037700002101062700001701083700002001100700002101120700002401141700002201165700001601187700002001203700003001223700002701253700002201280700002301302700002301325700002101348700001801369700001501387700001301402700002501415700002401440700002001464700002101484700002001505700001901525700003301544700002301577700002101600700002001621700001801641700001901659700002101678700002701699700001501726700002701741700001901768700002001787700001801807700002301825700002101848700001901869700002101888700002301909700001901932700002401951700002101975700001601996700002102012700002302033700001602056700002202072700002002094700002102114700001902135700002402154700001702178700002102195700002402216700002502240700001702265700003002282700001602312700002302328700002002351700001902371700003202390700002302422700002102445700002402466700002202490700002002512700002702532700002302559700002502582700001602607700002102623700001502644700002402659700002002683700002202703700002302725700002202748700002102770700002002791700002402811700002402835700002902859700002302888700001802911700002102929700001802950700002302968700002002991700002803011700002103039700003003060700002103090700002103111710004303132710004803175710006603223710002803289856003603317 2019 eng d a1546-171800aTrans-ethnic association study of blood pressure determinants in over 750,000 individuals.0 aTransethnic association study of blood pressure determinants in c2019 Jan a51-620 v513 aIn this trans-ethnic multi-omic study, we reinterpret the genetic architecture of blood pressure to identify genes, tissues, phenomes and medication contexts of blood pressure homeostasis. We discovered 208 novel common blood pressure SNPs and 53 rare variants in genome-wide association studies of systolic, diastolic and pulse pressure in up to 776,078 participants from the Million Veteran Program (MVP) and collaborating studies, with analysis of the blood pressure clinical phenome in MVP. Our transcriptome-wide association study detected 4,043 blood pressure associations with genetically predicted gene expression of 840 genes in 45 tissues, and mouse renal single-cell RNA sequencing identified upregulated blood pressure genes in kidney tubule cells.
1 aGiri, Ayush1 aHellwege, Jacklyn, N1 aKeaton, Jacob, M1 aPark, Jihwan1 aQiu, Chengxiang1 aWarren, Helen, R1 aTorstenson, Eric, S1 aKovesdy, Csaba, P1 aSun, Yan, V1 aWilson, Otis, D1 aRobinson-Cohen, Cassianne1 aRoumie, Christianne, L1 aChung, Cecilia, P1 aBirdwell, Kelly, A1 aDamrauer, Scott, M1 aDuVall, Scott, L1 aKlarin, Derek1 aCho, Kelly1 aWang, Yu1 aEvangelou, Evangelos1 aCabrera, Claudia, P1 aWain, Louise, V1 aShrestha, Rojesh1 aMautz, Brian, S1 aAkwo, Elvis, A1 aSargurupremraj, Muralidharan1 aDebette, Stephanie1 aBoehnke, Michael1 aScott, Laura, J1 aLuan, Jian'an1 aZhao, Jing-Hua1 aWillems, Sara, M1 aThériault, Sébastien1 aShah, Nabi1 aOldmeadow, Christopher1 aAlmgren, Peter1 aLi-Gao, Ruifang1 aVerweij, Niek1 aBoutin, Thibaud, S1 aMangino, Massimo1 aNtalla, Ioanna1 aFeofanova, Elena1 aSurendran, Praveen1 aCook, James, P1 aKarthikeyan, Savita1 aLahrouchi, Najim1 aLiu, Chunyu1 aSepúlveda, Nuno1 aRichardson, Tom, G1 aKraja, Aldi1 aAmouyel, Philippe1 aFarrall, Martin1 aPoulter, Neil, R1 aLaakso, Markku1 aZeggini, Eleftheria1 aSever, Peter1 aScott, Robert, A1 aLangenberg, Claudia1 aWareham, Nicholas, J1 aConen, David1 aPalmer, Colin, Neil Alexa1 aAttia, John1 aChasman, Daniel, I1 aRidker, Paul, M1 aMelander, Olle1 aMook-Kanamori, Dennis, Owen1 avan der Harst, Pim1 aCucca, Francesco1 aSchlessinger, David1 aHayward, Caroline1 aSpector, Tim, D1 aJarvelin, Marjo-Riitta1 aHennig, Branwen, J1 aTimpson, Nicholas, J1 aWei, Wei-Qi1 aSmith, Joshua, C1 aXu, Yaomin1 aMatheny, Michael, E1 aSiew, Edward, E1 aLindgren, Cecilia1 aHerzig, Karl-Heinz1 aDedoussis, George1 aDenny, Joshua, C1 aPsaty, Bruce, M1 aHowson, Joanna, M M1 aMunroe, Patricia, B1 aNewton-Cheh, Christopher1 aCaulfield, Mark, J1 aElliott, Paul1 aGaziano, Michael1 aConcato, John1 aWilson, Peter, W F1 aTsao, Philip, S1 aEdwards, Digna, R Velez1 aSusztak, Katalin1 aO'Donnell, Christopher, J1 aHung, Adriana, M1 aEdwards, Todd, L1 aUnderstanding Society Scientific Group1 aInternational Consortium for Blood Pressure1 aBlood Pressure-International Consortium of Exome Chip Studies1 aMillion Veteran Program uhttps://chs-nhlbi.org/node/791403309nas a2200421 4500008004100000022001400041245012200055210006900177260001600246300000700262490000700269520208500276100002902361700002402390700002202414700002402436700001802460700002002478700001802498700002302516700002002539700002002559700002002579700001902599700001802618700001802636700002102654700002002675700002002695700002002715700002102735700001602756700001702772700002202789700002102811700001902832856003602851 2020 eng d a1756-994X00aMitochondrial DNA copy number can influence mortality and cardiovascular disease via methylation of nuclear DNA CpGs.0 aMitochondrial DNA copy number can influence mortality and cardio c2020 Sep 28 a840 v123 aBACKGROUND: Mitochondrial DNA copy number (mtDNA-CN) has been associated with a variety of aging-related diseases, including all-cause mortality. However, the mechanism by which mtDNA-CN influences disease is not currently understood. One such mechanism may be through regulation of nuclear gene expression via the modification of nuclear DNA (nDNA) methylation.
METHODS: To investigate this hypothesis, we assessed the relationship between mtDNA-CN and nDNA methylation in 2507 African American (AA) and European American (EA) participants from the Atherosclerosis Risk in Communities (ARIC) study. To validate our findings, we assayed an additional 2528 participants from the Cardiovascular Health Study (CHS) (N = 533) and Framingham Heart Study (FHS) (N = 1995). We further assessed the effect of experimental modification of mtDNA-CN through knockout of TFAM, a regulator of mtDNA replication, via CRISPR-Cas9.
RESULTS: Thirty-four independent CpGs were associated with mtDNA-CN at genome-wide significance (P < 5 × 10). Meta-analysis across all cohorts identified six mtDNA-CN-associated CpGs at genome-wide significance (P < 5 × 10). Additionally, over half of these CpGs were associated with phenotypes known to be associated with mtDNA-CN, including coronary heart disease, cardiovascular disease, and mortality. Experimental modification of mtDNA-CN demonstrated that modulation of mtDNA-CN results in changes in nDNA methylation and gene expression of specific CpGs and nearby transcripts. Strikingly, the "neuroactive ligand receptor interaction" KEGG pathway was found to be highly overrepresented in the ARIC cohort (P = 5.24 × 10), as well as the TFAM knockout methylation (P = 4.41 × 10) and expression (P = 4.30 × 10) studies.
CONCLUSIONS: These results demonstrate that changes in mtDNA-CN influence nDNA methylation at specific loci and result in differential expression of specific genes that may impact human health and disease via altered cell signaling.
1 aCastellani, Christina, A1 aLongchamps, Ryan, J1 aSumpter, Jason, A1 aNewcomb, Charles, E1 aLane, John, A1 aGrove, Megan, L1 aBressler, Jan1 aBrody, Jennifer, A1 aFloyd, James, S1 aBartz, Traci, M1 aTaylor, Kent, D1 aWang, Penglong1 aTin, Adrienne1 aCoresh, Josef1 aPankow, James, S1 aFornage, Myriam1 aGuallar, Eliseo1 aO'Rourke, Brian1 aPankratz, Nathan1 aLiu, Chunyu1 aLevy, Daniel1 aSotoodehnia, Nona1 aBoerwinkle, Eric1 aArking, Dan, E uhttps://chs-nhlbi.org/node/848004340nas a2200733 4500008004100000022001400041245013200055210006900187260001300256300001200269490000700281520227500288100001602563700002202579700002002601700002502621700002302646700001302669700002702682700001902709700001602728700002602744700001902770700001702789700002202806700002002828700001702848700002802865700002102893700002402914700001702938700002402955700001802979700002602997700002003023700002003043700002103063700002403084700002003108700002203128700002303150700002403173700002003197700001603217700001803233700001803251700001503269700002003284700002103304700002103325700001503346700001803361700001603379700002303395700001703418700001603435700001803451700002703469700001703496700002003513700002003533700001703553856003603570 2020 eng d a2574-830000aWhole Blood DNA Methylation Signatures of Diet Are Associated With Cardiovascular Disease Risk Factors and All-Cause Mortality.0 aWhole Blood DNA Methylation Signatures of Diet Are Associated Wi c2020 Aug ae0027660 v133 aBACKGROUND: DNA methylation patterns associated with habitual diet have not been well studied.
METHODS: Diet quality was characterized using a Mediterranean-style diet score and the Alternative Healthy Eating Index score. We conducted ethnicity-specific and trans-ethnic epigenome-wide association analyses for diet quality and leukocyte-derived DNA methylation at over 400 000 CpGs (cytosine-guanine dinucleotides) in 5 population-based cohorts including 6662 European ancestry, 2702 African ancestry, and 360 Hispanic ancestry participants. For diet-associated CpGs identified in epigenome-wide analyses, we conducted Mendelian randomization (MR) analysis to examine their relations to cardiovascular disease risk factors and examined their longitudinal associations with all-cause mortality.
RESULTS: We identified 30 CpGs associated with either Mediterranean-style diet score or Alternative Healthy Eating Index, or both, in European ancestry participants. Among these CpGs, 12 CpGs were significantly associated with all-cause mortality (Bonferroni corrected <1.6×10). Hypermethylation of cg18181703 () was associated with higher scores of both Mediterranean-style diet score and Alternative Healthy Eating Index and lower risk for all-cause mortality (=5.7×10). Ten additional diet-associated CpGs were nominally associated with all-cause mortality (<0.05). MR analysis revealed 8 putatively causal associations for 6 CpGs with 4 cardiovascular disease risk factors (body mass index, triglycerides, high-density lipoprotein cholesterol concentrations, and type 2 diabetes mellitus; Bonferroni corrected MR <4.5×10). For example, hypermethylation of cg11250194 () was associated with lower triglyceride concentrations (MR, =1.5×10).and hypermethylation of cg02079413 (; ) was associated with body mass index (corrected MR, =1×10).
CONCLUSIONS: Habitual diet quality was associated with differential peripheral leukocyte DNA methylation levels of 30 CpGs, most of which were also associated with multiple health outcomes, in European ancestry individuals. These findings demonstrate that integrative genomic analysis of dietary information may reveal molecular targets for disease prevention and treatment.
1 aMa, Jiantao1 aRebholz, Casey, M1 aBraun, Kim, V E1 aReynolds, Lindsay, M1 aAslibekyan, Stella1 aXia, Rui1 aBiligowda, Niranjan, G1 aHuan, Tianxiao1 aLiu, Chunyu1 aMendelson, Michael, M1 aJoehanes, Roby1 aHu, Emily, A1 aVitolins, Mara, Z1 aWood, Alexis, C1 aLohman, Kurt1 aOchoa-Rosales, Carolina1 avan Meurs, Joyce1 aUitterlinden, Andre1 aLiu, Yongmei1 aElhadad, Mohamed, A1 aHeier, Margit1 aWaldenberger, Melanie1 aPeters, Annette1 aColicino, Elena1 aWhitsel, Eric, A1 aBaldassari, Antoine1 aGharib, Sina, A1 aSotoodehnia, Nona1 aBrody, Jennifer, A1 aSitlani, Colleen, M1 aTanaka, Toshiko1 aHill, David1 aCorley, Janie1 aDeary, Ian, J1 aZhang, Yan1 aSchöttker, Ben1 aBrenner, Hermann1 aWalker, Maura, E1 aYe, Shumao1 aNguyen, Steve1 aPankow, Jim1 aDemerath, Ellen, W1 aZheng, Yinan1 aHou, Lifang1 aLiang, Liming1 aLichtenstein, Alice, H1 aHu, Frank, B1 aFornage, Myriam1 aVoortman, Trudy1 aLevy, Daniel uhttps://chs-nhlbi.org/node/844602650nas a2200589 4500008004100000022001400041245008000055210006900135260001600204490000600220520090300226100001301129700002401142700002201166700002301188700002401211700001401235700002701249700002501276700001301301700001701314700002501331700002401356700002101380700002101401700002001422700002401442700002301466700002001489700002501509700002101534700002001555700002401575700002301599700002701622700001701649700002801666700002001694700001401714700001901728700002201747700002001769700002101789700001701810700002201827700001901849700002601868700001901894700001601913710009501929856003602024 2021 eng d a2666-979X00aAssociation of mitochondrial DNA copy number with cardiometabolic diseases.0 aAssociation of mitochondrial DNA copy number with cardiometaboli c2021 Oct 130 v13 aMitochondrial DNA (mtDNA) is present in multiple copies in human cells. We evaluated cross-sectional associations of whole blood mtDNA copy number (CN) with several cardiometabolic disease traits in 408,361 participants of multiple ancestries in TOPMed and UK Biobank. Age showed a threshold association with mtDNA CN: among younger participants (<65 years of age), each additional 10 years of age was associated with 0.03 standard deviation (s.d.) higher level of mtDNA CN ( = 0.0014) versus a 0.14 s.d. lower level of mtDNA CN ( = 1.82 × 10) among older participants (≥65 years). At lower mtDNA CN levels, we found age-independent associations with increased odds of obesity ( = 5.6 × 10), hypertension ( = 2.8 × 10), diabetes ( = 3.6 × 10), and hyperlipidemia ( = 6.3 × 10). The observed decline in mtDNA CN after 65 years of age may be a key to understanding age-related diseases.
1 aLiu, Xue1 aLongchamps, Ryan, J1 aWiggins, Kerri, L1 aRaffield, Laura, M1 aBielak, Lawrence, F1 aZhao, Wei1 aPitsillides, Achilleas1 aBlackwell, Thomas, W1 aYao, Jie1 aGuo, Xiuqing1 aKurniansyah, Nuzulul1 aThyagarajan, Bharat1 aPankratz, Nathan1 aRich, Stephen, S1 aTaylor, Kent, D1 aPeyser, Patricia, A1 aHeckbert, Susan, R1 aSeshadri, Sudha1 aCupples, Adrienne, L1 aBoerwinkle, Eric1 aGrove, Megan, L1 aLarson, Nicholas, B1 aSmith, Jennifer, A1 aVasan, Ramachandran, S1 aSofer, Tamar1 aFitzpatrick, Annette, L1 aFornage, Myriam1 aDing, Jun1 aCorrea, Adolfo1 aAbecasis, Goncalo1 aPsaty, Bruce, M1 aWilson, James, G1 aLevy, Daniel1 aRotter, Jerome, I1 aBis, Joshua, C1 aSatizabal, Claudia, L1 aArking, Dan, E1 aLiu, Chunyu1 aTOPMed mtDNA Working Group in NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium uhttps://chs-nhlbi.org/node/899708483nas a2202413 4500008004100000022001400041245007200055210006900127260001200196300001200208490000800220520169100228100001901919700002201938700002401960700002201984700002402006700001702030700003002047700002002077700002702097700002002124700003002144700002202174700002002196700001802216700002302234700001802257700002202275700002102297700002302318700002002341700002502361700002102386700001702407700001802424700002402442700001802466700002002484700002202504700001902526700002302545700001902568700002302587700001802610700001802628700001702646700001902663700002102682700002102703700002402724700002402748700001902772700002102791700002202812700002302834700002502857700001902882700002202901700002202923700002302945700002202968700002002990700002203010700001903032700002003051700001903071700002203090700001803112700001903130700001903149700002303168700001903191700002203210700002403232700002103256700001703277700001803294700002103312700001703333700002003350700002303370700002703393700002803420700001803448700002103466700002403487700001703511700002103528700001403549700002603563700002303589700002503612700002103637700002303658700001903681700002403700700001803724700001903742700002103761700002103782700002003803700002303823700002403846700001903870700002103889700002203910700001703932700001603949700001803965700001603983700002003999700001704019700002104036700002204057700002404079700001904103700002104122700002204143700002304165700002204188700002504210700002004235700002304255700002204278700002204300700002504322700002504347700002204372700002504394700002404419700002404443700001904467700002304486700002104509700001904530700002604549700002604575700002104601700002004622700002404642700002004666700001904686700002004705700001404725700001904739700002504758700001504783700002204798700002004820700002104840700002604861700002304887700001904910700002004929700002304949700001904972700002404991700002305015700002305038700002205061700002305083700001805106700002005124700001905144700002505163700002205188700002705210700002705237700003005264700001805294700002105312700001905333700002005352700001805372700002305390700001805413700001705431700002105448700002805469700002405497700002105521700002005542700001905562700002105581700002105602700002405623700001805647700001605665700002805681700002605709700002405735700002105759700002405780700002105804700002505825700002105850700002405871700002305895700002505918700002505943710006505968856003606033 2021 eng d a1476-468700aSequencing of 53,831 diverse genomes from the NHLBI TOPMed Program.0 aSequencing of 53831 diverse genomes from the NHLBI TOPMed Progra c2021 02 a290-2990 v5903 aThe Trans-Omics for Precision Medicine (TOPMed) programme seeks to elucidate the genetic architecture and biology of heart, lung, blood and sleep disorders, with the ultimate goal of improving diagnosis, treatment and prevention of these diseases. The initial phases of the programme focused on whole-genome sequencing of individuals with rich phenotypic data and diverse backgrounds. Here we describe the TOPMed goals and design as well as the available resources and early insights obtained from the sequence data. The resources include a variant browser, a genotype imputation server, and genomic and phenotypic data that are available through dbGaP (Database of Genotypes and Phenotypes). In the first 53,831 TOPMed samples, we detected more than 400 million single-nucleotide and insertion or deletion variants after alignment with the reference genome. Additional previously undescribed variants were detected through assembly of unmapped reads and customized analysis in highly variable loci. Among the more than 400 million detected variants, 97% have frequencies of less than 1% and 46% are singletons that are present in only one individual (53% among unrelated individuals). These rare variants provide insights into mutational processes and recent human evolutionary history. The extensive catalogue of genetic variation in TOPMed studies provides unique opportunities for exploring the contributions of rare and noncoding sequence variants to phenotypic variation. Furthermore, combining TOPMed haplotypes with modern imputation methods improves the power and reach of genome-wide association studies to include variants down to a frequency of approximately 0.01%.
1 aTaliun, Daniel1 aHarris, Daniel, N1 aKessler, Michael, D1 aCarlson, Jedidiah1 aSzpiech, Zachary, A1 aTorres, Raul1 aTaliun, Sarah, A Gagliano1 aCorvelo, André1 aGogarten, Stephanie, M1 aKang, Hyun, Min1 aPitsillides, Achilleas, N1 aLeFaive, Jonathon1 aLee, Seung-Been1 aTian, Xiaowen1 aBrowning, Brian, L1 aDas, Sayantan1 aEmde, Anne-Katrin1 aClarke, Wayne, E1 aLoesch, Douglas, P1 aShetty, Amol, C1 aBlackwell, Thomas, W1 aSmith, Albert, V1 aWong, Quenna1 aLiu, Xiaoming1 aConomos, Matthew, P1 aBobo, Dean, M1 aAguet, Francois1 aAlbert, Christine1 aAlonso, Alvaro1 aArdlie, Kristin, G1 aArking, Dan, E1 aAslibekyan, Stella1 aAuer, Paul, L1 aBarnard, John1 aBarr, Graham1 aBarwick, Lucas1 aBecker, Lewis, C1 aBeer, Rebecca, L1 aBenjamin, Emelia, J1 aBielak, Lawrence, F1 aBlangero, John1 aBoehnke, Michael1 aBowden, Donald, W1 aBrody, Jennifer, A1 aBurchard, Esteban, G1 aCade, Brian, E1 aCasella, James, F1 aChalazan, Brandon1 aChasman, Daniel, I1 aChen, Yii-Der Ida1 aCho, Michael, H1 aChoi, Seung, Hoan1 aChung, Mina, K1 aClish, Clary, B1 aCorrea, Adolfo1 aCurran, Joanne, E1 aCuster, Brian1 aDarbar, Dawood1 aDaya, Michelle1 ade Andrade, Mariza1 aDeMeo, Dawn, L1 aDutcher, Susan, K1 aEllinor, Patrick, T1 aEmery, Leslie, S1 aEng, Celeste1 aFatkin, Diane1 aFingerlin, Tasha1 aForer, Lukas1 aFornage, Myriam1 aFranceschini, Nora1 aFuchsberger, Christian1 aFullerton, Stephanie, M1 aGermer, Soren1 aGladwin, Mark, T1 aGottlieb, Daniel, J1 aGuo, Xiuqing1 aHall, Michael, E1 aHe, Jiang1 aHeard-Costa, Nancy, L1 aHeckbert, Susan, R1 aIrvin, Marguerite, R1 aJohnsen, Jill, M1 aJohnson, Andrew, D1 aKaplan, Robert1 aKardia, Sharon, L R1 aKelly, Tanika1 aKelly, Shannon1 aKenny, Eimear, E1 aKiel, Douglas, P1 aKlemmer, Robert1 aKonkle, Barbara, A1 aKooperberg, Charles1 aKöttgen, Anna1 aLange, Leslie, A1 aLasky-Su, Jessica1 aLevy, Daniel1 aLin, Xihong1 aLin, Keng-Han1 aLiu, Chunyu1 aLoos, Ruth, J F1 aGarman, Lori1 aGerszten, Robert1 aLubitz, Steven, A1 aLunetta, Kathryn, L1 aC Y Mak, Angel1 aManichaikul, Ani1 aManning, Alisa, K1 aMathias, Rasika, A1 aMcManus, David, D1 aMcGarvey, Stephen, T1 aMeigs, James, B1 aMeyers, Deborah, A1 aMikulla, Julie, L1 aMinear, Mollie, A1 aMitchell, Braxton, D1 aMohanty, Sanghamitra1 aMontasser, May, E1 aMontgomery, Courtney1 aMorrison, Alanna, C1 aMurabito, Joanne, M1 aNatale, Andrea1 aNatarajan, Pradeep1 aNelson, Sarah, C1 aNorth, Kari, E1 aO'Connell, Jeffrey, R1 aPalmer, Nicholette, D1 aPankratz, Nathan1 aPeloso, Gina, M1 aPeyser, Patricia, A1 aPleiness, Jacob1 aPost, Wendy, S1 aPsaty, Bruce, M1 aRao, D, C1 aRedline, Susan1 aReiner, Alexander, P1 aRoden, Dan1 aRotter, Jerome, I1 aRuczinski, Ingo1 aSarnowski, Chloe1 aSchoenherr, Sebastian1 aSchwartz, David, A1 aSeo, Jeong-Sun1 aSeshadri, Sudha1 aSheehan, Vivien, A1 aSheu, Wayne, H1 aShoemaker, Benjamin1 aSmith, Nicholas, L1 aSmith, Jennifer, A1 aSotoodehnia, Nona1 aStilp, Adrienne, M1 aTang, Weihong1 aTaylor, Kent, D1 aTelen, Marilyn1 aThornton, Timothy, A1 aTracy, Russell, P1 aVan Den Berg, David, J1 aVasan, Ramachandran, S1 aViaud-Martinez, Karine, A1 aVrieze, Scott1 aWeeks, Daniel, E1 aWeir, Bruce, S1 aWeiss, Scott, T1 aWeng, Lu-Chen1 aWiller, Cristen, J1 aZhang, Yingze1 aZhao, Xutong1 aArnett, Donna, K1 aAshley-Koch, Allison, E1 aBarnes, Kathleen, C1 aBoerwinkle, Eric1 aGabriel, Stacey1 aGibbs, Richard1 aRice, Kenneth, M1 aRich, Stephen, S1 aSilverman, Edwin, K1 aQasba, Pankaj1 aGan, Weiniu1 aPapanicolaou, George, J1 aNickerson, Deborah, A1 aBrowning, Sharon, R1 aZody, Michael, C1 aZöllner, Sebastian1 aWilson, James, G1 aCupples, Adrienne, L1 aLaurie, Cathy, C1 aJaquish, Cashell, E1 aHernandez, Ryan, D1 aO'Connor, Timothy, D1 aAbecasis, Goncalo, R1 aNHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium uhttps://chs-nhlbi.org/node/866605976nas a2201393 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2022 eng d a1524-456300aInsights From a Large-Scale Whole-Genome Sequencing Study of Systolic Blood Pressure, Diastolic Blood Pressure, and Hypertension.0 aInsights From a LargeScale WholeGenome Sequencing Study of Systo c2022 Jun 02 a101161HYPERTENSIONAHA122193243 aBACKGROUND: The availability of whole-genome sequencing data in large studies has enabled the assessment of coding and noncoding variants across the allele frequency spectrum for their associations with blood pressure.
METHODS: We conducted a multiancestry whole-genome sequencing analysis of blood pressure among 51 456 Trans-Omics for Precision Medicine and Centers for Common Disease Genomics program participants (stage-1). Stage-2 analyses leveraged array data from UK Biobank (N=383 145), Million Veteran Program (N=318 891), and Reasons for Geographic and Racial Differences in Stroke (N=10 643) participants, along with whole-exome sequencing data from UK Biobank (N=199 631) participants.
RESULTS: Two blood pressure signals achieved genome-wide significance in meta-analyses of stage-1 and stage-2 single variant findings (<5×10). Among them, a rare intergenic variant at novel locus, , was associated with lower systolic blood pressure in stage-1 (beta [SE]=-32.6 [6.0]; =4.99×10) but not stage-2 analysis (=0.11). Furthermore, a novel common variant at the known locus was suggestively associated with diastolic blood pressure in stage-1 (beta [SE]=-0.36 [0.07]; =4.18×10) and attained genome-wide significance in stage-2 (beta [SE]=-0.29 [0.03]; =7.28×10). Nineteen additional signals suggestively associated with blood pressure in meta-analysis of single and aggregate rare variant findings (<1×10 and <1×10, respectively).
DISCUSSION: We report one promising but unconfirmed rare variant for blood pressure and, more importantly, contribute insights for future blood pressure sequencing studies. Our findings suggest promise of aggregate analyses to complement single variant analysis strategies and the need for larger, diverse samples, and family studies to enable robust rare variant identification.
1 aKelly, Tanika, N1 aSun, Xiao1 aHe, Karen, Y1 aBrown, Michael, R1 aTaliun, Sarah, A Gagliano1 aHellwege, Jacklyn, N1 aIrvin, Marguerite, R1 aMi, Xuenan1 aBrody, Jennifer, A1 aFranceschini, Nora1 aGuo, Xiuqing1 aHwang, Shih-Jen1 ade Vries, Paul, S1 aGao, Yan1 aMoscati, Arden1 aNadkarni, Girish, N1 aYanek, Lisa, R1 aElfassy, Tali1 aSmith, Jennifer, A1 aChung, Ren-Hua1 aBeitelshees, Amber, L1 aPatki, Amit1 aAslibekyan, Stella1 aBlobner, Brandon, M1 aPeralta, Juan, M1 aAssimes, Themistocles, L1 aPalmas, Walter, R1 aLiu, Chunyu1 aBress, Adam, P1 aHuang, Zhijie1 aBecker, Lewis, C1 aHwa, Chii-Min1 aO'Connell, Jeffrey, R1 aCarlson, Jenna, C1 aWarren, Helen, R1 aDas, Sayantan1 aGiri, Ayush1 aMartin, Lisa, W1 aJohnson, Craig1 aFox, Ervin, R1 aBottinger, Erwin, P1 aRazavi, Alexander, C1 aVaidya, Dhananjay1 aChuang, Lee-Ming1 aChang, Yen-Pei, C1 aNaseri, Take1 aJain, Deepti1 aKang, Hyun, Min1 aHung, Adriana, M1 aSrinivasasainagendra, Vinodh1 aSnively, Beverly, M1 aGu, Dongfeng1 aMontasser, May, E1 aReupena, Muagututi'a, Sefuiva1 aHeavner, Benjamin, D1 aLeFaive, Jonathon1 aHixson, James, E1 aRice, Kenneth, M1 aWang, Fei, Fei1 aNielsen, Jonas, B1 aHuang, Jianfeng1 aKhan, Alyna, T1 aZhou, Wei1 aNierenberg, Jovia, L1 aLaurie, Cathy, C1 aArmstrong, Nicole, D1 aShi, Mengyao1 aPan, Yang1 aStilp, Adrienne, M1 aEmery, Leslie1 aWong, Quenna1 aHawley, Nicola, L1 aMinster, Ryan, L1 aCurran, Joanne, E1 aMunroe, Patricia, B1 aWeeks, Daniel, E1 aNorth, Kari, E1 aTracy, Russell, P1 aKenny, Eimear, E1 aShimbo, Daichi1 aChakravarti, Aravinda1 aRich, Stephen, S1 aReiner, Alex, P1 aBlangero, John1 aRedline, Susan1 aMitchell, Braxton, D1 aRao, Dabeeru, C1 aChen, Yii-Der, Ida1 aKardia, Sharon, L R1 aKaplan, Robert, C1 aMathias, Rasika, A1 aHe, Jiang1 aPsaty, Bruce, M1 aFornage, Myriam1 aLoos, Ruth, J F1 aCorrea, Adolfo1 aBoerwinkle, Eric1 aRotter, Jerome, I1 aKooperberg, Charles1 aEdwards, Todd, L1 aAbecasis, Goncalo, R1 aZhu, Xiaofeng1 aLevy, Daniel1 aArnett, Donna, K1 aMorrison, Alanna, C1 aNHLBI Trans-Omics for Precision Medicine TOPMed) Consortium, The Samoan Obesity, Lifestyle, and Genetic Adaptations Study (OLaGA) Group† uhttps://chs-nhlbi.org/node/909903954nas a2200805 4500008004100000022001400041245007700055210006900132260001300201300001100214490000700225520175800232653001501990653002802005653002002033653002402053653001602077653001102093653000902104653001402113100001902127700001802146700002002164700002802184700001602212700002302228700002102251700001502272700002402287700002802311700002002339700001702359700002602376700002002402700001602422700001402438700001602452700002002468700001902488700002002507700001802527700002602545700002202571700002302593700002102616700002302637700002002660700001902680700002002699700002202719700002402741700001702765700002202782700002002804700002602824700001802850700002002868700001402888700002202902700002702924700001602951700002502967700002002992700002103012700002303033700001803056700002103074700001703095856003603112 2022 eng d a1474-972600aIntegrative analysis of clinical and epigenetic biomarkers of mortality.0 aIntegrative analysis of clinical and epigenetic biomarkers of mo c2022 Jun ae136080 v213 aDNA methylation (DNAm) has been reported to be associated with many diseases and with mortality. We hypothesized that the integration of DNAm with clinical risk factors would improve mortality prediction. We performed an epigenome-wide association study of whole blood DNAm in relation to mortality in 15 cohorts (n = 15,013). During a mean follow-up of 10 years, there were 4314 deaths from all causes including 1235 cardiovascular disease (CVD) deaths and 868 cancer deaths. Ancestry-stratified meta-analysis of all-cause mortality identified 163 CpGs in European ancestry (EA) and 17 in African ancestry (AA) participants at p < 1 × 10 , of which 41 (EA) and 16 (AA) were also associated with CVD death, and 15 (EA) and 9 (AA) with cancer death. We built DNAm-based prediction models for all-cause mortality that predicted mortality risk after adjusting for clinical risk factors. The mortality prediction model trained by integrating DNAm with clinical risk factors showed an improvement in prediction of cancer death with 5% increase in the C-index in a replication cohort, compared with the model including clinical risk factors alone. Mendelian randomization identified 15 putatively causal CpGs in relation to longevity, CVD, or cancer risk. For example, cg06885782 (in KCNQ4) was positively associated with risk for prostate cancer (Beta = 1.2, P = 4.1 × 10 ) and negatively associated with longevity (Beta = -1.9, P = 0.02). Pathway analysis revealed that genes associated with mortality-related CpGs are enriched for immune- and cancer-related pathways. We identified replicable DNAm signatures of mortality and demonstrated the potential utility of CpGs as informative biomarkers for prediction of mortality risk.
10aBiomarkers10aCardiovascular Diseases10aDNA Methylation10aEpigenesis, Genetic10aEpigenomics10aHumans10aMale10aNeoplasms1 aHuan, Tianxiao1 aNguyen, Steve1 aColicino, Elena1 aOchoa-Rosales, Carolina1 aHill, David1 aBrody, Jennifer, A1 aSoerensen, Mette1 aZhang, Yan1 aBaldassari, Antoine1 aElhadad, Mohamed, Ahmed1 aToshiko, Tanaka1 aZheng, Yinan1 aDomingo-Relloso, Arce1 aLee, Dong, Heon1 aMa, Jiantao1 aYao, Chen1 aLiu, Chunyu1 aHwang, Shih-Jen1 aJoehanes, Roby1 aFornage, Myriam1 aBressler, Jan1 avan Meurs, Joyce, B J1 aDebrabant, Birgit1 aMengel-From, Jonas1 aHjelmborg, Jacob1 aChristensen, Kaare1 aVokonas, Pantel1 aSchwartz, Joel1 aGahrib, Sina, A1 aSotoodehnia, Nona1 aSitlani, Colleen, M1 aKunze, Sonja1 aGieger, Christian1 aPeters, Annette1 aWaldenberger, Melanie1 aDeary, Ian, J1 aFerrucci, Luigi1 aQu, Yishu1 aGreenland, Philip1 aLloyd-Jones, Donald, M1 aHou, Lifang1 aBandinelli, Stefania1 aVoortman, Trudy1 aHermann, Brenner1 aBaccarelli, Andrea1 aWhitsel, Eric1 aPankow, James, S1 aLevy, Daniel uhttps://chs-nhlbi.org/node/909403922nas a2200673 4500008004100000022001400041245014500055210006900200260001600269300001200285520190200297100001302199700001802212700001902230700001902249700001402268700002202282700002302304700002402327700001402351700002702365700002202392700001702414700002502431700001302456700001702469700001502486700002402501700002102525700002102546700002002567700002402587700002302611700002002634700002102654700002002675700002402695700002302719700002702742700002802769700002002797700001402817700002102831700002202852700001902874700002202893700002402915700001602939700002002955700002102975700001702996700002203013700001903035700002603054700001903080700001603099710009703115856003603212 2023 eng d a2047-998000aAssociation Between Whole Blood-Derived Mitochondrial DNA Copy Number, Low-Density Lipoprotein Cholesterol, and Cardiovascular Disease Risk.0 aAssociation Between Whole BloodDerived Mitochondrial DNA Copy Nu c2023 Oct 07 ae0290903 aBackground The relationship between mitochondrial DNA copy number (mtDNA CN) and cardiovascular disease remains elusive. Methods and Results We performed cross-sectional and prospective association analyses of blood-derived mtDNA CN and cardiovascular disease outcomes in 27 316 participants in 8 cohorts of multiple racial and ethnic groups with whole-genome sequencing. We also performed Mendelian randomization to explore causal relationships of mtDNA CN with coronary heart disease (CHD) and cardiometabolic risk factors (obesity, diabetes, hypertension, and hyperlipidemia). <0.01 was used for significance. We validated most of the previously reported associations between mtDNA CN and cardiovascular disease outcomes. For example, 1-SD unit lower level of mtDNA CN was associated with 1.08 (95% CI, 1.04-1.12; <0.001) times the hazard for developing incident CHD, adjusting for covariates. Mendelian randomization analyses showed no causal effect from a lower level of mtDNA CN to a higher CHD risk (β=0.091; =0.11) or in the reverse direction (β=-0.012; =0.076). Additional bidirectional Mendelian randomization analyses revealed that low-density lipoprotein cholesterol had a causal effect on mtDNA CN (β=-0.084; <0.001), but the reverse direction was not significant (=0.059). No causal associations were observed between mtDNA CN and obesity, diabetes, and hypertension, in either direction. Multivariable Mendelian randomization analyses showed no causal effect of CHD on mtDNA CN, controlling for low-density lipoprotein cholesterol level (=0.52), whereas there was a strong direct causal effect of higher low-density lipoprotein cholesterol on lower mtDNA CN, adjusting for CHD status (β=-0.092; <0.001). Conclusions Our findings indicate that high low-density lipoprotein cholesterol may underlie the complex relationships between mtDNA CN and vascular atherosclerosis.
1 aLiu, Xue1 aSun, Xianbang1 aZhang, Yuankai1 aJiang, Wenqing1 aLai, Meng1 aWiggins, Kerri, L1 aRaffield, Laura, M1 aBielak, Lawrence, F1 aZhao, Wei1 aPitsillides, Achilleas1 aHaessler, Jeffrey1 aZheng, Yinan1 aBlackwell, Thomas, W1 aYao, Jie1 aGuo, Xiuqing1 aQian, Yong1 aThyagarajan, Bharat1 aPankratz, Nathan1 aRich, Stephen, S1 aTaylor, Kent, D1 aPeyser, Patricia, A1 aHeckbert, Susan, R1 aSeshadri, Sudha1 aBoerwinkle, Eric1 aGrove, Megan, L1 aLarson, Nicholas, B1 aSmith, Jennifer, A1 aVasan, Ramachandran, S1 aFitzpatrick, Annette, L1 aFornage, Myriam1 aDing, Jun1 aCarson, April, P1 aAbecasis, Goncalo1 aDupuis, Josée1 aReiner, Alexander1 aKooperberg, Charles1 aHou, Lifang1 aPsaty, Bruce, M1 aWilson, James, G1 aLevy, Daniel1 aRotter, Jerome, I1 aBis, Joshua, C1 aSatizabal, Claudia, L1 aArking, Dan, E1 aLiu, Chunyu1 aTOPMed mtDNA Working Group in NHLBI Trans‐Omics for Precision Medicine (TOPMed) Consortium uhttps://chs-nhlbi.org/node/950204492nas a2200649 4500008004100000022001400041245014000055210006900195260001600264520258200280100001902862700001302881700002202894700002502916700001702941700002402958700001402982700001902996700002203015700002703037700002903064700001703093700001903110700002203129700002103151700001903172700002103191700001803212700002203230700001603252700002303268700002203291700001903313700001703332700001903349700002303368700002103391700002203412700002003434700001903454700002403473700002403497700002003521700002103541700002003562700002403582700002303606700002003629700002503649700001903674700002003693700002303713700002803736700001603764700002603780856003603806 2023 eng d a1526-632X00aAssociation of Mitochondrial DNA Copy Number With Brain MRI Markers and Cognitive Function: A Meta-analysis of Community-Based Cohorts.0 aAssociation of Mitochondrial DNA Copy Number With Brain MRI Mark c2023 Mar 163 aBACKGROUND AND OBJECTIVES: Previous studies suggest lower mitochondrial DNA (mtDNA) copy number (CN) is associated with neurodegenerative diseases. However, whether mtDNA CN in whole blood is related to endophenotypes of Alzheimer's disease (AD) and AD related dementia (AD/ADRD) needs further investigation. We assessed the association of mtDNA CN with cognitive function and MRI measures in community-based samples of middle-aged to older adults.
METHODS: We included dementia-free participants from nine diverse community-based cohorts with whole-genome sequencing in the Trans-Omics for Precision Medicine (TOPMed) program. Circulating mtDNA CN was estimated as twice the ratio of the average coverage of mtDNA to nuclear DNA. Brain MRI markers included total brain, hippocampal, and white matter hyperintensity volumes. General cognitive function was derived from distinct cognitive domains. We performed cohort-specific association analyses of mtDNA CN with AD/ADRD endophenotypes assessed within ±5 years (i.e., cross-sectional analyses) or 5 to 20 years after blood draw (i.e., prospective analyses) adjusting for potential confounders. We further explored associations stratified by sex and age (<60 vs. ≥60 years). Fixed-effects or sample size-weighted meta-analyses were performed to combine results. Finally, we performed Mendelian randomization (MR) analyses to assess causality.
RESULTS: We included up to 19,152 participants (mean age 59 years, 57% women). Higher mtDNA CN was cross-sectionally associated with better general cognitive function (Beta=0.04; 95% CI 0.02, 0.06) independent of age, sex, batch effects, race/ethnicity, time between blood draw and cognitive evaluation, cohort-specific variables, and education. Additional adjustment for blood cell counts or cardiometabolic traits led to slightly attenuated results. We observed similar significant associations with cognition in prospective analyses, although of reduced magnitude. We found no significant associations between mtDNA CN and brain MRI measures in meta-analyses. MR analyses did not reveal a causal relation between mtDNA CN in blood and cognition.
DISCUSSION: Higher mtDNA CN in blood is associated with better current and future general cognitive function in large and diverse communities across the US. Although MR analyses did not support a causal role, additional research is needed to assess causality. Circulating mtDNA CN could serve nevertheless as a biomarker of current and future cognitive function in the community.
1 aZhang, Yuankai1 aLiu, Xue1 aWiggins, Kerri, L1 aKurniansyah, Nuzulul1 aGuo, Xiuqing1 aRodrigue, Amanda, L1 aZhao, Wei1 aYanek, Lisa, R1 aRatliff, Scott, M1 aPitsillides, Achilleas1 aPatiño, Juan, Sebastian1 aSofer, Tamar1 aArking, Dan, E1 aAustin, Thomas, R1 aBeiser, Alexa, S1 aBlangero, John1 aBoerwinkle, Eric1 aBressler, Jan1 aCurran, Joanne, E1 aHou, Lifang1 aHughes, Timothy, M1 aKardia, Sharon, L1 aLauner, Lenore1 aLevy, Daniel1 aMosley, Tom, H1 aNasrallah, Ilya, M1 aRich, Stephen, S1 aRotter, Jerome, I1 aSeshadri, Sudha1 aTarraf, Wassim1 aGonzález, Kevin, A1 aRamachandran, Vasan1 aYaffe, Kristine1 aNyquist, Paul, A1 aPsaty, Bruce, M1 aDeCarli, Charles, S1 aSmith, Jennifer, A1 aGlahn, David, C1 aGonzález, Hector, M1 aBis, Joshua, C1 aFornage, Myriam1 aHeckbert, Susan, R1 aFitzpatrick, Annette, L1 aLiu, Chunyu1 aSatizabal, Claudia, L uhttps://chs-nhlbi.org/node/932304538nas a2200997 4500008004100000245012600041210006900167260001600236520164000252100001701892700003201909700001401941700001401955700002401969700002101993700001902014700001902033700002102052700002202073700001902095700002202114700002102136700002202157700002202179700002202201700002202223700002402245700002002269700002002289700002302309700002002332700001802352700002202370700001702392700001402409700002302423700002102446700001602467700002502483700001902508700002202527700002302549700002102572700001402593700002402607700001902631700001702650700001702667700001602684700002002700700001902720700002402739700002002763700002302783700002102806700002502827700002202852700002402874700002402898700001702922700002602939700002602965700002302991700002003014700002303034700002003057700001903077700002503096700002103121700003403142700002103176700002303197700001803220700002203238700002103260700003003281700001403311700001903325700001403344700002203358700001603380700002303396700002003419710006503439856003603504 2023 eng d00aRare variants in long non-coding RNAs are associated with blood lipid levels in the TOPMed Whole Genome Sequencing Study.0 aRare variants in long noncoding RNAs are associated with blood l c2023 Jun 293 aLong non-coding RNAs (lncRNAs) are known to perform important regulatory functions. Large-scale whole genome sequencing (WGS) studies and new statistical methods for variant set tests now provide an opportunity to assess the associations between rare variants in lncRNA genes and complex traits across the genome. In this study, we used high-coverage WGS from 66,329 participants of diverse ancestries with blood lipid levels (LDL-C, HDL-C, TC, and TG) in the National Heart, Lung, and Blood Institute (NHLBI) Trans-Omics for Precision Medicine (TOPMed) program to investigate the role of lncRNAs in lipid variability. We aggregated rare variants for 165,375 lncRNA genes based on their genomic locations and conducted rare variant aggregate association tests using the STAAR (variant-Set Test for Association using Annotation infoRmation) framework. We performed STAAR conditional analysis adjusting for common variants in known lipid GWAS loci and rare coding variants in nearby protein coding genes. Our analyses revealed 83 rare lncRNA variant sets significantly associated with blood lipid levels, all of which were located in known lipid GWAS loci (in a ±500 kb window of a Global Lipids Genetics Consortium index variant). Notably, 61 out of 83 signals (73%) were conditionally independent of common regulatory variations and rare protein coding variations at the same loci. We replicated 34 out of 61 (56%) conditionally independent associations using the independent UK Biobank WGS data. Our results expand the genetic architecture of blood lipids to rare variants in lncRNA, implicating new therapeutic opportunities.
1 aWang, Yuxuan1 aSelvaraj, Margaret, Sunitha1 aLi, Xihao1 aLi, Zilin1 aHoldcraft, Jacob, A1 aArnett, Donna, K1 aBis, Joshua, C1 aBlangero, John1 aBoerwinkle, Eric1 aBowden, Donald, W1 aCade, Brian, E1 aCarlson, Jenna, C1 aCarson, April, P1 aChen, Yii-Der Ida1 aCurran, Joanne, E1 ade Vries, Paul, S1 aDutcher, Susan, K1 aEllinor, Patrick, T1 aFloyd, James, S1 aFornage, Myriam1 aFreedman, Barry, I1 aGabriel, Stacey1 aGermer, Soren1 aGibbs, Richard, A1 aGuo, Xiuqing1 aHe, Jiang1 aHeard-Costa, Nancy1 aHildalgo, Bertha1 aHou, Lifang1 aIrvin, Marguerite, R1 aJoehanes, Roby1 aKaplan, Robert, C1 aKardia, Sharon, Lr1 aKelly, Tanika, N1 aKim, Ryan1 aKooperberg, Charles1 aKral, Brian, G1 aLevy, Daniel1 aLi, Changwei1 aLiu, Chunyu1 aLloyd-Jone, Don1 aLoos, Ruth, Jf1 aMahaney, Michael, C1 aMartin, Lisa, W1 aMathias, Rasika, A1 aMinster, Ryan, L1 aMitchell, Braxton, D1 aMontasser, May, E1 aMorrison, Alanna, C1 aMurabito, Joanne, M1 aNaseri, Take1 aO'Connell, Jeffrey, R1 aPalmer, Nicholette, D1 aPreuss, Michael, H1 aPsaty, Bruce, M1 aRaffield, Laura, M1 aRao, Dabeeru, C1 aRedline, Susan1 aReiner, Alexander, P1 aRich, Stephen, S1 aRuepena, Muagututi'a, Sefuiva1 aSheu, Wayne, H-H1 aSmith, Jennifer, A1 aSmith, Albert1 aTiwari, Hemant, K1 aTsai, Michael, Y1 aViaud-Martinez, Karine, A1 aWang, Zhe1 aYanek, Lisa, R1 aZhao, Wei1 aRotter, Jerome, I1 aLin, Xihong1 aNatarajan, Pradeep1 aPeloso, Gina, M1 aNHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium uhttps://chs-nhlbi.org/node/941802945nas a2200397 4500008004100000245009500041210006900136260001600205520178200221100001802003700002002021700001502041700001402056700003002070700001302100700001902113700001702132700001502149700002302164700002202187700002102209700002202230700002102252700002702273700001902300700002002319700002102339700002802360700002602388700001902414700001402433700001702447700001602464710003102480856003602511 2024 eng d00aAssociation analysis of mitochondrial DNA heteroplasmic variants: methods and application.0 aAssociation analysis of mitochondrial DNA heteroplasmic variants c2024 Jan 133 aWe rigorously assessed a comprehensive association testing framework for heteroplasmy, employing both simulated and real-world data. This framework employed a variant allele fraction (VAF) threshold and harnessed multiple gene-based tests for robust identification and association testing of heteroplasmy. Our simulation studies demonstrated that gene-based tests maintained an appropriate type I error rate at α=0.001. Notably, when 5% or more heteroplasmic variants within a target region were linked to an outcome, burden-extension tests (including the adaptive burden test, variable threshold burden test, and z-score weighting burden test) outperformed the sequence kernel association test (SKAT) and the original burden test. Applying this framework, we conducted association analyses on whole-blood derived heteroplasmy in 17,507 individuals of African and European ancestries (31% of African Ancestry, mean age of 62, with 58% women) with whole genome sequencing data. We performed both cohort- and ancestry-specific association analyses, followed by meta-analysis on bothpooled samples and within each ancestry group. Our results suggest that mtDNA-Enco ded genes/regions are likely to exhibit varying rates in somatic aging, with the notably strong associations observed between heteroplasmy in the and genes ( <0.001) and advance aging by the Original Burden test. In contrast, SKAT identified significant associations ( <0.001) between diabetes and the aggregated effects of heteroplasmy in several protein-coding genes. Further research is warranted to validate these findings. In summary, our proposed statistical framework represents a valuable tool for facilitating association testing of heteroplasmy with disease traits in large human populations.
1 aSun, Xianbang1 aBulekova, Katia1 aYang, Jian1 aLai, Meng1 aPitsillides, Achilleas, N1 aLiu, Xue1 aZhang, Yuankai1 aGuo, Xiuqing1 aYong, Qian1 aRaffield, Laura, M1 aRotter, Jerome, I1 aRich, Stephen, S1 aAbecasis, Goncalo1 aCarson, April, P1 aVasan, Ramachandran, S1 aBis, Joshua, C1 aPsaty, Bruce, M1 aBoerwinkle, Eric1 aFitzpatrick, Annette, L1 aSatizabal, Claudia, L1 aArking, Dan, E1 aDing, Jun1 aLevy, Daniel1 aLiu, Chunyu1 aTOPMed mtDNA working group uhttps://chs-nhlbi.org/node/9580