03584nas a2200373 4500008004100000022001400041245006300055210006100118260001600179300000700195490001400202520259400216653001002810653001202820653002802832653002102860653001902881653001102900653001902911653002002930653001802950653001302968653001102981653000902992653001603001653001403017653003603031653001003067653003103077100002403108700002403132700001903156856003503175 2007 eng d a1471-235000aGenome-wide association of sleep and circadian phenotypes.0 aGenomewide association of sleep and circadian phenotypes c2007 Sep 19 aS90 v8 Suppl 13 a
BACKGROUND: Numerous studies suggest genetic influences on sleepiness and circadian rhythms. The Sleep Heart Health Study collected questionnaire data on sleep habits and sleepiness from 2848 Framingham Heart Study Offspring Cohort participants. More than 700 participants were genotyped using the Affymetrix 100K SNP GeneChip, providing a unique opportunity to assess genetic linkage and association of these traits.
METHODS: Sleepiness (defined as the Epworth Sleepiness Scale score), usual bedtime and usual sleep duration were assessed by self-completion questionnaire. Standardized residual measures adjusted for age, sex and BMI were analyzed. Multipoint variance components linkage analysis was performed. Association of SNPs to sleep phenotypes was analyzed with both population-based and family-based association tests, with analysis limited to 70,987 autosomal SNPs with minor allele frequency > or =10%, call rate > or =80%, and no significant deviation from Hardy-Weinberg equilibrium (p > or = 0.001).
RESULTS: Heritability of sleepiness was 0.29, bedtime 0.22, and sleep duration 0.17. Both genotype and sleep phenotype data were available for 749 subjects. Linkage analysis revealed five linkage peaks of LOD >2: four to usual bedtime, one to sleep duration. These peaks include several candidate sleep-related genes, including CSNK2A2, encoding a known component of the circadian molecular clock, and PROK2, encoding a putative transmitter of the behavioral circadian rhythm from the suprachiasmatic nucleus. Association tests identified an association of usual bedtime with a non-synonymous coding SNP in NPSR1 that has been shown to encode a gain of function mutation of the neuropeptide S receptor, whose endogenous ligand is a potent promoter of wakefulness. Each copy of the minor allele of this SNP was associated with a 15 minute later mean bedtime. The lowest p value was for association of sleepiness with a SNP located in an intron of PDE4D, which encodes a cAMP-specific phosphodiesterase widely expressed in human brain. Full association results are posted at http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?id=phs000007 webcite.
CONCLUSION: This analysis confirms prior reports of significant heritability of sleepiness, usual bedtime, and usual sleep duration. Several genetic loci with suggestive linkage to these traits are identified, including linkage peaks containing circadian clock-related genes. Association tests identify NPSR1 and PDE4D as possible mediators of bedtime and sleepiness.
10aAdult10aAlleles10aCardiovascular Diseases10aCircadian Rhythm10aCohort Studies10aFemale10aGene Frequency10aGenetic Linkage10aGenome, Human10aGenotype10aHumans10aMale10aMiddle Aged10aPhenotype10aPolymorphism, Single Nucleotide10aSleep10aSurveys and Questionnaires1 aGottlieb, Daniel, J1 aO'Connor, George, T1 aWilk, Jemma, B uhttps://chs-nhlbi.org/node/98903039nas a2200553 4500008004100000022001400041245010400055210006900159260001300228300001100241490000600252520146700258653000901725653002201734653001101756653002201767653002801789653003501817653001801852653001301870653001101883653001201894653003301906653001401939653000901953653001701962653003601979653003402015653002402049100002502073700001602098700002002114700001702134700001902151700001702170700002502187700002402212700001902236700002502255700002002280700001602300700001802316700002402334700002302358700001802381700001402399710003602413856003602449 2009 eng d a1474-972600aAssociation of common genetic variation in the insulin/IGF1 signaling pathway with human longevity.0 aAssociation of common genetic variation in the insulinIGF1 signa c2009 Aug a460-720 v83 aThe insulin/IGF1 signaling pathways affect lifespan in several model organisms, including worms, flies and mice. To investigate whether common genetic variation in this pathway influences lifespan in humans, we genotyped 291 common variants in 30 genes encoding proteins in the insulin/IGF1 signaling pathway in a cohort of elderly Caucasian women selected from the Study of Osteoporotic Fractures (SOF). The cohort included 293 long-lived cases (lifespan > or = 92 years (y), mean +/- standard deviation (SD) = 95.3 +/- 2.2y) and 603 average-lifespan controls (lifespan < or = 79y, mean = 75.7 +/- 2.6y). Variants were selected for genotyping using a haplotype-tagging approach. We found a modest excess of variants nominally associated with longevity. Nominally significant variants were then replicated in two additional Caucasian cohorts including both males and females: the Cardiovascular Health Study and Ashkenazi Jewish Centenarians. An intronic single nucleotide polymorphism in AKT1, rs3803304, was significantly associated with lifespan in a meta-analysis across the three cohorts (OR = 0.78 95%CI = 0.68-0.89, adjusted P = 0.043); two intronic single nucleotide polymorphisms in FOXO3A demonstrated a significant lifespan association among women only (rs1935949, OR = 1.35, 95%CI = 1.15-1.57, adjusted P = 0.0093). These results demonstrate that common variants in several genes in the insulin/IGF1 pathway are associated with human lifespan.
10aAged10aAged, 80 and over10aFemale10aFollow-Up Studies10aForkhead Box Protein O310aForkhead Transcription Factors10aGenome, Human10aGenotype10aHumans10aInsulin10aInsulin-Like Growth Factor I10aLongevity10aMale10aOsteoporosis10aPolymorphism, Single Nucleotide10aProto-Oncogene Proteins c-akt10aSignal Transduction1 aPawlikowska, Ludmila1 aHu, Donglei1 aHuntsman, Scott1 aSung, Andrew1 aChu, Catherine1 aChen, Justin1 aJoyner, Alexander, H1 aSchork, Nicholas, J1 aHsueh, Wen-Chi1 aReiner, Alexander, P1 aPsaty, Bruce, M1 aAtzmon, Gil1 aBarzilai, Nir1 aCummings, Steven, R1 aBrowner, Warren, S1 aKwok, Pui-Yan1 aZiv, Elad1 aStudy of Osteoporotic Fractures uhttps://chs-nhlbi.org/node/110403137nas a2200673 4500008004100000022001400041245008300055210006900138260001300207300001200220490000700232520108000239653004101319653001001360653000901370653002501379653002301404653002701427653002701454653002701481653003701508653004001545653003801585653002201623653001801645653001101663653002801674653002701702653002001729653004001749653003601789653003801825653001701863653002001880100002901900700002201929700002101951700002501972700001701997700001902014700001902033700002302052700002602075700002502101700002202126700002302148700002202171700001702193700003002210700001902240700002302259700003002282700002802312700002002340700001902360700002202379700002602401856003602427 2009 eng d a1546-171800aCommon variants at ten loci influence QT interval duration in the QTGEN Study.0 aCommon variants at ten loci influence QT interval duration in th c2009 Apr a399-4060 v413 aQT interval duration, reflecting myocardial repolarization on the electrocardiogram, is a heritable risk factor for sudden cardiac death and drug-induced arrhythmias. We conducted a meta-analysis of three genome-wide association studies in 13,685 individuals of European ancestry from the Framingham Heart Study, the Rotterdam Study and the Cardiovascular Health Study, as part of the QTGEN consortium. We observed associations at P < 5 x 10(-8) with variants in NOS1AP, KCNQ1, KCNE1, KCNH2 and SCN5A, known to be involved in myocardial repolarization and mendelian long-QT syndromes. Associations were found at five newly identified loci, including 16q21 near NDRG4 and GINS3, 6q22 near PLN, 1p36 near RNF207, 16p13 near LITAF and 17q12 near LIG3 and RFFL. Collectively, the 14 independent variants at these 10 loci explain 5.4-6.5% of the variation in QT interval. These results, together with an accompanying paper, offer insights into myocardial repolarization and suggest candidate genes that could predispose to sudden cardiac death and drug-induced arrhythmias.
10aAdaptor Proteins, Signal Transducing10aAdult10aAged10aArrhythmias, Cardiac10aChromosome Mapping10aDeath, Sudden, Cardiac10aElectroencephalography10aERG1 Potassium Channel10aEther-A-Go-Go Potassium Channels10aEuropean Continental Ancestry Group10aGenetic Predisposition to Disease10aGenetic Variation10aGenome, Human10aHumans10aKCNQ1 Potassium Channel10aMeta-Analysis as Topic10aMuscle Proteins10aNAV1.5 Voltage-Gated Sodium Channel10aPolymorphism, Single Nucleotide10aPotassium Channels, Voltage-Gated10aRisk Factors10aSodium Channels1 aNewton-Cheh, Christopher1 aEijgelsheim, Mark1 aRice, Kenneth, M1 ade Bakker, Paul, I W1 aYin, Xiaoyan1 aEstrada, Karol1 aBis, Joshua, C1 aMarciante, Kristin1 aRivadeneira, Fernando1 aNoseworthy, Peter, A1 aSotoodehnia, Nona1 aSmith, Nicholas, L1 aRotter, Jerome, I1 aKors, Jan, A1 aWitteman, Jacqueline, C M1 aHofman, Albert1 aHeckbert, Susan, R1 aO'Donnell, Christopher, J1 aUitterlinden, André, G1 aPsaty, Bruce, M1 aLumley, Thomas1 aLarson, Martin, G1 aStricker, Bruno, H Ch uhttps://chs-nhlbi.org/node/108704054nas a2201033 4500008004100000022001400041245007700055210006900132260001300201300001100214490000700225520114900232653001901381653001401400653001901414653002201433653001701455653002001472653001801492653003401510653001101544653001701555653001401572653003601586653002801622100002201650700001901672700002601691700002001717700002101737700002201758700002001780700001901800700002201819700001901841700002101860700001901881700001601900700002001916700001801936700002101954700002601975700002202001700002102023700002202044700002002066700001802086700002102104700002002125700002402145700001702169700002202186700002102208700002202229700001902251700002602270700002302296700001602319700002502335700002502360700001802385700001902403700002802422700002302450700002202473700002502495700001902520700002302539700002402562700002002586700002302606700002002629700002002649700002002669700002202689700002402711700002302735700002002758700002002778700002602798700002402824700003002848700003002878700001702908700001802925700002202943700001902965856003602984 2009 eng d a1546-171800aMultiple loci influence erythrocyte phenotypes in the CHARGE Consortium.0 aMultiple loci influence erythrocyte phenotypes in the CHARGE Con c2009 Nov a1191-80 v413 aMeasurements of erythrocytes within the blood are important clinical traits and can indicate various hematological disorders. We report here genome-wide association studies (GWAS) for six erythrocyte traits, including hemoglobin concentration (Hb), hematocrit (Hct), mean corpuscular volume (MCV), mean corpuscular hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC) and red blood cell count (RBC). We performed an initial GWAS in cohorts of the CHARGE Consortium totaling 24,167 individuals of European ancestry and replication in additional independent cohorts of the HaemGen Consortium totaling 9,456 individuals. We identified 23 loci significantly associated with these traits in a meta-analysis of the discovery and replication cohorts (combined P values ranging from 5 x 10(-8) to 7 x 10(-86)). Our findings include loci previously associated with these traits (HBS1L-MYB, HFE, TMPRSS6, TFR2, SPTA1) as well as new associations (EPO, TFRC, SH2B3 and 15 other loci). This study has identified new determinants of erythrocyte traits, offering insight into common variants underlying variation in erythrocyte measures.
10aBlood Pressure10aCell Line10aCohort Studies10aEndothelial Cells10aErythrocytes10aGene Expression10aGenome, Human10aGenome-Wide Association Study10aHumans10aHypertension10aPhenotype10aPolymorphism, Single Nucleotide10aQuantitative Trait Loci1 aGanesh, Santhi, K1 aZakai, Neil, A1 avan Rooij, Frank, J A1 aSoranzo, Nicole1 aSmith, Albert, V1 aNalls, Michael, A1 aChen, Ming-Huei1 aKöttgen, Anna1 aGlazer, Nicole, L1 aDehghan, Abbas1 aKuhnel, Brigitte1 aAspelund, Thor1 aYang, Qiong1 aTanaka, Toshiko1 aJaffe, Andrew1 aBis, Joshua, C M1 aVerwoert, Germaine, C1 aTeumer, Alexander1 aFox, Caroline, S1 aGuralnik, Jack, M1 aEhret, Georg, B1 aRice, Kenneth1 aFelix, Janine, F1 aRendon, Augusto1 aEiriksdottir, Gudny1 aLevy, Daniel1 aPatel, Kushang, V1 aBoerwinkle, Eric1 aRotter, Jerome, I1 aHofman, Albert1 aSambrook, Jennifer, G1 aHernandez, Dena, G1 aZheng, Gang1 aBandinelli, Stefania1 aSingleton, Andrew, B1 aCoresh, Josef1 aLumley, Thomas1 aUitterlinden, André, G1 aVangils, Janine, M1 aLauner, Lenore, J1 aCupples, Adrienne, L1 aOostra, Ben, A1 aZwaginga, Jaap-Jan1 aOuwehand, Willem, H1 aThein, Swee-Lay1 aMeisinger, Christa1 aDeloukas, Panos1 aNauck, Matthias1 aSpector, Tim, D1 aGieger, Christian1 aGudnason, Vilmundur1 aDuijn, Cornelia, M1 aPsaty, Bruce, M1 aFerrucci, Luigi1 aChakravarti, Aravinda1 aGreinacher, Andreas1 aO'Donnell, Christopher, J1 aWitteman, Jacqueline, C M1 aFurth, Susan1 aCushman, Mary1 aHarris, Tamara, B1 aLin, Jing-Ping uhttps://chs-nhlbi.org/node/114105113nas a2201189 4500008004100000022001400041245009300055210006900148260001600217300001200233490000700245520181400252653001002066653000902076653001702085653001902102653001102121653001702132653001802149653003402167653001502201653001102216653000902227653001602236653003602252653000902288100002202297700002902319700002202348700002502370700002102395700002402416700002102440700001802461700001802479700001802497700001702515700002702532700001902559700001902578700001902597700002002616700001902636700002402655700001902679700002202698700002402720700002602744700002502770700002102795700002002816700001902836700002002855700001702875700002402892700002302916700001802939700001902957700001902976700002202995700002303017700002303040700001903063700002203082700002103104700001903125700002103144700002003165700002003185700001803205700001903223700002103242700001803263700001903281700002003300700002203320700001703342700003003359700002003389700002203409700002203431700001903453700002403472700002003496700002803516700002103544700002203565700002103587700002303608700002003631700002603651700002303677700002303700700002203723700002403745700001803769700002303787700002603810700002103836700003003857856003603887 2010 eng d a1460-208300aGenome-wide association analysis identifies multiple loci related to resting heart rate.0 aGenomewide association analysis identifies multiple loci related c2010 Oct 01 a3885-940 v193 aHigher resting heart rate is associated with increased cardiovascular disease and mortality risk. Though heritable factors play a substantial role in population variation, little is known about specific genetic determinants. This knowledge can impact clinical care by identifying novel factors that influence pathologic heart rate states, modulate heart rate through cardiac structure and function or by improving our understanding of the physiology of heart rate regulation. To identify common genetic variants associated with heart rate, we performed a meta-analysis of 15 genome-wide association studies (GWAS), including 38,991 subjects of European ancestry, estimating the association between age-, sex- and body mass-adjusted RR interval (inverse heart rate) and approximately 2.5 million markers. Results with P < 5 × 10(-8) were considered genome-wide significant. We constructed regression models with multiple markers to assess whether results at less stringent thresholds were likely to be truly associated with RR interval. We identified six novel associations with resting heart rate at six loci: 6q22 near GJA1; 14q12 near MYH7; 12p12 near SOX5, c12orf67, BCAT1, LRMP and CASC1; 6q22 near SLC35F1, PLN and c6orf204; 7q22 near SLC12A9 and UfSp1; and 11q12 near FADS1. Associations at 6q22 400 kb away from GJA1, at 14q12 MYH6 and at 1q32 near CD34 identified in previously published GWAS were confirmed. In aggregate, these variants explain approximately 0.7% of RR interval variance. A multivariant regression model including 20 variants with P < 10(-5) increased the explained variance to 1.6%, suggesting that some loci falling short of genome-wide significance are likely truly associated. Future research is warranted to elucidate underlying mechanisms that may impact clinical care.
10aAdult10aAged10aBase Pairing10aCohort Studies10aFemale10aGenetic Loci10aGenome, Human10aGenome-Wide Association Study10aHeart Rate10aHumans10aMale10aMiddle Aged10aPolymorphism, Single Nucleotide10aRest1 aEijgelsheim, Mark1 aNewton-Cheh, Christopher1 aSotoodehnia, Nona1 ade Bakker, Paul, I W1 aMüller, Martina1 aMorrison, Alanna, C1 aSmith, Albert, V1 aIsaacs, Aaron1 aSanna, Serena1 aDörr, Marcus1 aNavarro, Pau1 aFuchsberger, Christian1 aNolte, Ilja, M1 aGeus, Eco, J C1 aEstrada, Karol1 aHwang, Shih-Jen1 aBis, Joshua, C1 aRückert, Ina-Maria1 aAlonso, Alvaro1 aLauner, Lenore, J1 aHottenga, Jouke Jan1 aRivadeneira, Fernando1 aNoseworthy, Peter, A1 aRice, Kenneth, M1 aPerz, Siegfried1 aArking, Dan, E1 aSpector, Tim, D1 aKors, Jan, A1 aAulchenko, Yurii, S1 aTarasov, Kirill, V1 aHomuth, Georg1 aWild, Sarah, H1 aMarroni, Fabio1 aGieger, Christian1 aLicht, Carmilla, M1 aPrineas, Ronald, J1 aHofman, Albert1 aRotter, Jerome, I1 aHicks, Andrew, A1 aErnst, Florian1 aNajjar, Samer, S1 aWright, Alan, F1 aPeters, Annette1 aFox, Ervin, R1 aOostra, Ben, A1 aKroemer, Heyo, K1 aCouper, David1 aVölzke, Henry1 aCampbell, Harry1 aMeitinger, Thomas1 aUda, Manuela1 aWitteman, Jacqueline, C M1 aPsaty, Bruce, M1 aWichmann, H-Erich1 aHarris, Tamara, B1 aKääb, Stefan1 aSiscovick, David, S1 aJamshidi, Yalda1 aUitterlinden, André, G1 aFolsom, Aaron, R1 aLarson, Martin, G1 aWilson, James, F1 aPenninx, Brenda, W1 aSnieder, Harold1 aPramstaller, Peter, P1 aDuijn, Cornelia, M1 aLakatta, Edward, G1 aFelix, Stephan, B1 aGudnason, Vilmundur1 aPfeufer, Arne1 aHeckbert, Susan, R1 aStricker, Bruno, H Ch1 aBoerwinkle, Eric1 aO'Donnell, Christopher, J uhttps://chs-nhlbi.org/node/121704709nas a2201405 4500008004100000022001400041245010700055210006900162260001300231300001200244490000700256520077800263653002601041653001801067653002001085653001701105653003801122653002201160653001801182653003401200653001101234653003101245100001801276700002601294700002401320700001401344700002901358700001701387700002101404700002101425700001501446700002101461700002201482700001901504700002301523700002201546700002401568700002001592700001601612700002301628700001801651700001801669700002701687700002501714700002501739700002501764700002201789700001901811700002001830700001901850700002001869700002001889700002001909700002601929700002001955700002501975700001902000700002102019700001802040700002902058700001902087700002002106700001602126700002002142700001902162700002002181700002102201700001602222700002302238700002002261700001802281700002202299700002402321700002202345700002302367700002302390700001702413700001902430700001802449700001702467700001702484700002202501700001902523700001702542700002002559700002002579700001902599700002402618700002102642700002102663700002002684700002202704700001702726700001902743700002302762700002602785700002002811700002402831700001802855700002002873700002302893700002102916700001902937700001902956700002202975700002202997700002303019700002303042700002303065700002203088700002403110700001903134700002203153700001703175700001703192700002203209700001803231700001803249856003603267 2010 eng d a1546-171800aGenome-wide meta-analysis increases to 71 the number of confirmed Crohn's disease susceptibility loci.0 aGenomewide metaanalysis increases to 71 the number of confirmed c2010 Dec a1118-250 v423 aWe undertook a meta-analysis of six Crohn's disease genome-wide association studies (GWAS) comprising 6,333 affected individuals (cases) and 15,056 controls and followed up the top association signals in 15,694 cases, 14,026 controls and 414 parent-offspring trios. We identified 30 new susceptibility loci meeting genome-wide significance (P < 5 × 10⁻⁸). A series of in silico analyses highlighted particular genes within these loci and, together with manual curation, implicated functionally interesting candidate genes including SMAD3, ERAP2, IL10, IL2RA, TYK2, FUT2, DNMT3A, DENND1B, BACH2 and TAGAP. Combined with previously confirmed loci, these results identify 71 distinct loci with genome-wide significant evidence for association with Crohn's disease.
10aComputational Biology10aCrohn Disease10aGenetic Linkage10aGenetic Loci10aGenetic Predisposition to Disease10aGenetic Variation10aGenome, Human10aGenome-Wide Association Study10aHumans10aReproducibility of Results1 aFranke, Andre1 aMcGovern, Dermot, P B1 aBarrett, Jeffrey, C1 aWang, Kai1 aRadford-Smith, Graham, L1 aAhmad, Tariq1 aLees, Charlie, W1 aBalschun, Tobias1 aLee, James1 aRoberts, Rebecca1 aAnderson, Carl, A1 aBis, Joshua, C1 aBumpstead, Suzanne1 aEllinghaus, David1 aFesten, Eleonora, M1 aGeorges, Michel1 aGreen, Todd1 aHaritunians, Talin1 aJostins, Luke1 aLatiano, Anna1 aMathew, Christopher, G1 aMontgomery, Grant, W1 aPrescott, Natalie, J1 aRaychaudhuri, Soumya1 aRotter, Jerome, I1 aSchumm, Philip1 aSharma, Yashoda1 aSimms, Lisa, A1 aTaylor, Kent, D1 aWhiteman, David1 aWijmenga, Cisca1 aBaldassano, Robert, N1 aBarclay, Murray1 aBayless, Theodore, M1 aBrand, Stephan1 aBüning, Carsten1 aCohen, Albert1 aColombel, Jean-Frederick1 aCottone, Mario1 aStronati, Laura1 aDenson, Ted1 aDe Vos, Martine1 aD'Inca, Renata1 aDubinsky, Marla1 aEdwards, Cathryn1 aFlorin, Tim1 aFranchimont, Denis1 aGearry, Richard1 aGlas, Jürgen1 aVan Gossum, Andre1 aGuthery, Stephen, L1 aHalfvarson, Jonas1 aVerspaget, Hein, W1 aHugot, Jean-Pierre1 aKarban, Amir1 aLaukens, Debby1 aLawrance, Ian1 aLemann, Marc1 aLevine, Arie1 aLibioulle, Cecile1 aLouis, Edouard1 aMowat, Craig1 aNewman, William1 aPanés, Julián1 aPhillips, Anne1 aProctor, Deborah, D1 aRegueiro, Miguel1 aRussell, Richard1 aRutgeerts, Paul1 aSanderson, Jeremy1 aSans, Miquel1 aSeibold, Frank1 aSteinhart, Hillary1 aStokkers, Pieter, C F1 aTörkvist, Leif1 aKullak-Ublick, Gerd1 aWilson, David1 aWalters, Thomas1 aTargan, Stephan, R1 aBrant, Steven, R1 aRioux, John, D1 aD'Amato, Mauro1 aWeersma, Rinse, K1 aKugathasan, Subra1 aGriffiths, Anne, M1 aMansfield, John, C1 aVermeire, Severine1 aDuerr, Richard, H1 aSilverberg, Mark, S1 aSatsangi, Jack1 aSchreiber, Stefan1 aCho, Judy, H1 aAnnese, Vito1 aHakonarson, Hakon1 aDaly, Mark, J1 aParkes, Miles uhttps://chs-nhlbi.org/node/124912698nas a2203781 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2010 eng d a1476-468700aHundreds of variants clustered in genomic loci and biological pathways affect human height.0 aHundreds of variants clustered in genomic loci and biological pa c2010 Oct 14 a832-80 v4673 aMost common human traits and diseases have a polygenic pattern of inheritance: DNA sequence variants at many genetic loci influence the phenotype. Genome-wide association (GWA) studies have identified more than 600 variants associated with human traits, but these typically explain small fractions of phenotypic variation, raising questions about the use of further studies. Here, using 183,727 individuals, we show that hundreds of genetic variants, in at least 180 loci, influence adult height, a highly heritable and classic polygenic trait. The large number of loci reveals patterns with important implications for genetic studies of common human diseases and traits. First, the 180 loci are not random, but instead are enriched for genes that are connected in biological pathways (P = 0.016) and that underlie skeletal growth defects (P < 0.001). Second, the likely causal gene is often located near the most strongly associated variant: in 13 of 21 loci containing a known skeletal growth gene, that gene was closest to the associated variant. Third, at least 19 loci have multiple independently associated variants, suggesting that allelic heterogeneity is a frequent feature of polygenic traits, that comprehensive explorations of already-discovered loci should discover additional variants and that an appreciable fraction of associated loci may have been identified. Fourth, associated variants are enriched for likely functional effects on genes, being over-represented among variants that alter amino-acid structure of proteins and expression levels of nearby genes. Our data explain approximately 10% of the phenotypic variation in height, and we estimate that unidentified common variants of similar effect sizes would increase this figure to approximately 16% of phenotypic variation (approximately 20% of heritable variation). Although additional approaches are needed to dissect the genetic architecture of polygenic human traits fully, our findings indicate that GWA studies can identify large numbers of loci that implicate biologically relevant genes and pathways.
10aBody Height10aChromosomes, Human, Pair 310aGenetic Loci10aGenetic Predisposition to Disease10aGenome, Human10aGenome-Wide Association Study10aHumans10aMetabolic Networks and Pathways10aMultifactorial Inheritance10aPhenotype10aPolymorphism, Single Nucleotide1 aAllen, Hana, Lango1 aEstrada, Karol1 aLettre, Guillaume1 aBerndt, Sonja, I1 aWeedon, Michael, N1 aRivadeneira, Fernando1 aWiller, Cristen, J1 aJackson, Anne, U1 aVedantam, Sailaja1 aRaychaudhuri, Soumya1 aFerreira, Teresa1 aWood, Andrew, R1 aWeyant, Robert, J1 aSegrè, Ayellet, V1 aSpeliotes, Elizabeth, K1 aWheeler, Eleanor1 aSoranzo, Nicole1 aPark, Ju-Hyun1 aYang, Jian1 aGudbjartsson, Daniel1 aHeard-Costa, Nancy, L1 aRandall, Joshua, C1 aQi, Lu1 aSmith, Albert, Vernon1 aMägi, Reedik1 aPastinen, Tomi1 aLiang, Liming1 aHeid, Iris, M1 aLuan, Jian'an1 aThorleifsson, Gudmar1 aWinkler, Thomas, W1 aGoddard, Michael, E1 aLo, Ken, Sin1 aPalmer, Cameron1 aWorkalemahu, Tsegaselassie1 aAulchenko, Yurii, S1 aJohansson, Asa1 aZillikens, Carola, M1 aFeitosa, Mary, F1 aEsko, Tõnu1 aJohnson, Toby1 aKetkar, Shamika1 aKraft, Peter1 aMangino, Massimo1 aProkopenko, Inga1 aAbsher, Devin1 aAlbrecht, Eva1 aErnst, Florian1 aGlazer, Nicole, L1 aHayward, Caroline1 aHottenga, Jouke-Jan1 aJacobs, Kevin, B1 aKnowles, Joshua, W1 aKutalik, Zoltán1 aMonda, Keri, L1 aPolasek, Ozren1 aPreuss, Michael1 aRayner, Nigel, W1 aRobertson, Neil, R1 aSteinthorsdottir, Valgerdur1 aTyrer, Jonathan, P1 aVoight, Benjamin, F1 aWiklund, Fredrik1 aXu, Jianfeng1 aZhao, Jing Hua1 aNyholt, Dale, R1 aPellikka, Niina1 aPerola, Markus1 aPerry, John, R B1 aSurakka, Ida1 aTammesoo, Mari-Liis1 aAltmaier, Elizabeth, L1 aAmin, Najaf1 aAspelund, Thor1 aBhangale, Tushar1 aBoucher, Gabrielle1 aChasman, Daniel, I1 aChen, Constance1 aCoin, Lachlan1 aCooper, Matthew, N1 aDixon, Anna, L1 aGibson, Quince1 aGrundberg, Elin1 aHao, Ke1 aJunttila, Juhani1 aKaplan, Lee, M1 aKettunen, Johannes1 aKönig, Inke, R1 aKwan, Tony1 aLawrence, Robert, W1 aLevinson, Douglas, F1 aLorentzon, Mattias1 aMcKnight, Barbara1 aMorris, Andrew, P1 aMüller, Martina1 aNgwa, Julius, Suh1 aPurcell, Shaun1 aRafelt, Suzanne1 aSalem, Rany, M1 aSalvi, Erika1 aSanna, Serena1 aShi, Jianxin1 aSovio, Ulla1 aThompson, John, R1 aTurchin, Michael, C1 aVandenput, Liesbeth1 aVerlaan, Dominique, J1 aVitart, Veronique1 aWhite, Charles, C1 aZiegler, Andreas1 aAlmgren, Peter1 aBalmforth, Anthony, J1 aCampbell, Harry1 aCitterio, Lorena1 aDe Grandi, Alessandro1 aDominiczak, Anna1 aDuan, Jubao1 aElliott, Paul1 aElosua, Roberto1 aEriksson, Johan, G1 aFreimer, Nelson, B1 aGeus, Eco, J C1 aGlorioso, Nicola1 aHaiqing, Shen1 aHartikainen, Anna-Liisa1 aHavulinna, Aki, S1 aHicks, Andrew, A1 aHui, Jennie1 aIgl, Wilmar1 aIllig, Thomas1 aJula, Antti1 aKajantie, Eero1 aKilpeläinen, Tuomas, O1 aKoiranen, Markku1 aKolcic, Ivana1 aKoskinen, Seppo1 aKovacs, Peter1 aLaitinen, Jaana1 aLiu, Jianjun1 aLokki, Marja-Liisa1 aMarusic, Ana1 aMaschio, Andrea1 aMeitinger, Thomas1 aMulas, Antonella1 aParé, Guillaume1 aParker, Alex, N1 aPeden, John, F1 aPetersmann, Astrid1 aPichler, Irene1 aPietiläinen, Kirsi, H1 aPouta, Anneli1 aRidderstråle, Martin1 aRotter, Jerome, I1 aSambrook, Jennifer, G1 aSanders, Alan, R1 aSchmidt, Carsten, Oliver1 aSinisalo, Juha1 aSmit, Jan, H1 aStringham, Heather, M1 aWalters, Bragi1 aWiden, Elisabeth1 aWild, Sarah, H1 aWillemsen, Gonneke1 aZagato, Laura1 aZgaga, Lina1 aZitting, Paavo1 aAlavere, Helene1 aFarrall, Martin1 aMcArdle, Wendy, L1 aNelis, Mari1 aPeters, Marjolein, J1 aRipatti, Samuli1 avan Meurs, Joyce, B J1 aAben, Katja, K1 aArdlie, Kristin, G1 aBeckmann, Jacques, S1 aBeilby, John, P1 aBergman, Richard, N1 aBergmann, Sven1 aCollins, Francis, S1 aCusi, Daniele1 aHeijer, Martin, den1 aEiriksdottir, Gudny1 aGejman, Pablo, V1 aHall, Alistair, S1 aHamsten, Anders1 aHuikuri, Heikki, V1 aIribarren, Carlos1 aKähönen, Mika1 aKaprio, Jaakko1 aKathiresan, Sekar1 aKiemeney, Lambertus1 aKocher, Thomas1 aLauner, Lenore, J1 aLehtimäki, Terho1 aMelander, Olle1 aMosley, Tom, H1 aMusk, Arthur, W1 aNieminen, Markku, S1 aO'Donnell, Christopher, J1 aOhlsson, Claes1 aOostra, Ben1 aPalmer, Lyle, J1 aRaitakari, Olli1 aRidker, Paul, M1 aRioux, John, D1 aRissanen, Aila1 aRivolta, Carlo1 aSchunkert, Heribert1 aShuldiner, Alan, R1 aSiscovick, David, S1 aStumvoll, Michael1 aTönjes, Anke1 aTuomilehto, Jaakko1 avan Ommen, Gert-Jan1 aViikari, Jorma1 aHeath, Andrew, C1 aMartin, Nicholas, G1 aMontgomery, Grant, W1 aProvince, Michael, A1 aKayser, Manfred1 aArnold, Alice, M1 aAtwood, Larry, D1 aBoerwinkle, Eric1 aChanock, Stephen, J1 aDeloukas, Panos1 aGieger, Christian1 aGrönberg, Henrik1 aHall, Per1 aHattersley, Andrew, T1 aHengstenberg, Christian1 aHoffman, Wolfgang1 aLathrop, Mark, G1 aSalomaa, Veikko1 aSchreiber, Stefan1 aUda, Manuela1 aWaterworth, Dawn1 aWright, Alan, F1 aAssimes, Themistocles, L1 aBarroso, Inês1 aHofman, Albert1 aMohlke, Karen, L1 aBoomsma, Dorret, I1 aCaulfield, Mark, J1 aCupples, Adrienne, L1 aErdmann, Jeanette1 aFox, Caroline, S1 aGudnason, Vilmundur1 aGyllensten, Ulf1 aHarris, Tamara, B1 aHayes, Richard, B1 aJarvelin, Marjo-Riitta1 aMooser, Vincent1 aMunroe, Patricia, B1 aOuwehand, Willem, H1 aPenninx, Brenda, W1 aPramstaller, Peter, P1 aQuertermous, Thomas1 aRudan, Igor1 aSamani, Nilesh, J1 aSpector, Timothy, D1 aVölzke, Henry1 aWatkins, Hugh1 aWilson, James, F1 aGroop, Leif, C1 aHaritunians, Talin1 aHu, Frank, B1 aKaplan, Robert, C1 aMetspalu, Andres1 aNorth, Kari, E1 aSchlessinger, David1 aWareham, Nicholas, J1 aHunter, David, J1 aO'Connell, Jeffrey, R1 aStrachan, David, P1 aWichmann, H-Erich1 aBorecki, Ingrid, B1 aDuijn, Cornelia, M1 aSchadt, Eric, E1 aThorsteinsdottir, Unnur1 aPeltonen, Leena1 aUitterlinden, André, G1 aVisscher, Peter, M1 aChatterjee, Nilanjan1 aLoos, Ruth, J F1 aBoehnke, Michael1 aMcCarthy, Mark, I1 aIngelsson, Erik1 aLindgren, Cecilia, M1 aAbecasis, Goncalo, R1 aStefansson, Kari1 aFrayling, Timothy, M1 aHirschhorn, Joel, N uhttps://chs-nhlbi.org/node/123402822nas a2200637 4500008004100000022001400041245011200055210006900167260001300236300001000249490000700259520095900266653002301225653001101248653002901259653003801288653001801326653003401344653001101378653000901389653001801398653000901416653002701425653003601452653001501488653001901503100002101522700002201543700001901565700002001584700002001604700002601624700002301650700003001673700001901703700001701722700002501739700002101764700002201785700002001807700002301827700002201850700002801872700001901900700002301919700002601942700002401968700002101992700001902013700002302032700001902055700002502074700002402099700002502123856003602148 2010 eng d a1546-171800aMeta-analyses of genome-wide association studies identify multiple loci associated with pulmonary function.0 aMetaanalyses of genomewide association studies identify multiple c2010 Jan a45-520 v423 aSpirometric measures of lung function are heritable traits that reflect respiratory health and predict morbidity and mortality. We meta-analyzed genome-wide association studies for two clinically important lung-function measures: forced expiratory volume in the first second (FEV(1)) and its ratio to forced vital capacity (FEV(1)/FVC), an indicator of airflow obstruction. This meta-analysis included 20,890 participants of European ancestry from four CHARGE Consortium studies: Atherosclerosis Risk in Communities, Cardiovascular Health Study, Framingham Heart Study and Rotterdam Study. We identified eight loci associated with FEV(1)/FVC (HHIP, GPR126, ADAM19, AGER-PPT2, FAM13A, PTCH1, PID1 and HTR4) and one locus associated with FEV(1) (INTS12-GSTCD-NPNT) at or near genome-wide significance (P < 5 x 10(-8)) in the CHARGE Consortium dataset. Our findings may offer insights into pulmonary function and pathogenesis of chronic lung disease.
10aDatabases, Genetic10aFemale10aForced Expiratory Volume10aGenetic Predisposition to Disease10aGenome, Human10aGenome-Wide Association Study10aHumans10aLung10aLung Diseases10aMale10aMeta-Analysis as Topic10aPolymorphism, Single Nucleotide10aSpirometry10aVital Capacity1 aHancock, Dana, B1 aEijgelsheim, Mark1 aWilk, Jemma, B1 aGharib, Sina, A1 aLoehr, Laura, R1 aMarciante, Kristin, D1 aFranceschini, Nora1 avan Durme, Yannick, M T A1 aChen, Ting-Hsu1 aBarr, Graham1 aSchabath, Matthew, B1 aCouper, David, J1 aBrusselle, Guy, G1 aPsaty, Bruce, M1 aDuijn, Cornelia, M1 aRotter, Jerome, I1 aUitterlinden, André, G1 aHofman, Albert1 aPunjabi, Naresh, M1 aRivadeneira, Fernando1 aMorrison, Alanna, C1 aEnright, Paul, L1 aNorth, Kari, E1 aHeckbert, Susan, R1 aLumley, Thomas1 aStricker, Bruno, H C1 aO'Connor, George, T1 aLondon, Stephanie, J uhttps://chs-nhlbi.org/node/115011930nas a2203889 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2010 eng d a1546-171800aMeta-analysis identifies 13 new loci associated with waist-hip ratio and reveals sexual dimorphism in the genetic basis of fat distribution.0 aMetaanalysis identifies 13 new loci associated with waisthip rat c2010 Nov a949-600 v423 aWaist-hip ratio (WHR) is a measure of body fat distribution and a predictor of metabolic consequences independent of overall adiposity. WHR is heritable, but few genetic variants influencing this trait have been identified. We conducted a meta-analysis of 32 genome-wide association studies for WHR adjusted for body mass index (comprising up to 77,167 participants), following up 16 loci in an additional 29 studies (comprising up to 113,636 subjects). We identified 13 new loci in or near RSPO3, VEGFA, TBX15-WARS2, NFE2L3, GRB14, DNM3-PIGC, ITPR2-SSPN, LY86, HOXC13, ADAMTS9, ZNRF3-KREMEN1, NISCH-STAB1 and CPEB4 (P = 1.9 × 10⁻⁹ to P = 1.8 × 10⁻⁴⁰) and the known signal at LYPLAL1. Seven of these loci exhibited marked sexual dimorphism, all with a stronger effect on WHR in women than men (P for sex difference = 1.9 × 10⁻³ to P = 1.2 × 10⁻¹³). These findings provide evidence for multiple loci that modulate body fat distribution independent of overall adiposity and reveal strong gene-by-sex interactions.
10aAdipose Tissue10aAge Factors10aChromosome Mapping10aFemale10aGenome, Human10aGenome-Wide Association Study10aHumans10aMale10aMeta-Analysis as Topic10aPolymorphism, Single Nucleotide10aSex Characteristics10aWaist-Hip Ratio1 aHeid, Iris, M1 aJackson, Anne, U1 aRandall, Joshua, C1 aWinkler, Thomas, W1 aQi, Lu1 aSteinthorsdottir, Valgerdur1 aThorleifsson, Gudmar1 aZillikens, Carola, M1 aSpeliotes, Elizabeth, K1 aMägi, Reedik1 aWorkalemahu, Tsegaselassie1 aWhite, Charles, C1 aBouatia-Naji, Nabila1 aHarris, Tamara, B1 aBerndt, Sonja, I1 aIngelsson, Erik1 aWiller, Cristen, J1 aWeedon, Michael, N1 aLuan, Jian'an1 aVedantam, Sailaja1 aEsko, Tõnu1 aKilpeläinen, Tuomas, O1 aKutalik, Zoltán1 aLi, Shengxu1 aMonda, Keri, L1 aDixon, Anna, L1 aHolmes, Christopher, C1 aKaplan, Lee, M1 aLiang, Liming1 aMin, Josine, L1 aMoffatt, Miriam, F1 aMolony, Cliona1 aNicholson, George1 aSchadt, Eric, E1 aZondervan, Krina, T1 aFeitosa, Mary, F1 aFerreira, Teresa1 aAllen, Hana, Lango1 aWeyant, Robert, J1 aWheeler, Eleanor1 aWood, Andrew, R1 aEstrada, Karol1 aGoddard, Michael, E1 aLettre, Guillaume1 aMangino, Massimo1 aNyholt, Dale, R1 aPurcell, Shaun1 aSmith, Albert, Vernon1 aVisscher, Peter, M1 aYang, Jian1 aMcCarroll, Steven, A1 aNemesh, James1 aVoight, Benjamin, F1 aAbsher, Devin1 aAmin, Najaf1 aAspelund, Thor1 aCoin, Lachlan1 aGlazer, Nicole, L1 aHayward, Caroline1 aHeard-Costa, Nancy, L1 aHottenga, Jouke-Jan1 aJohansson, Asa1 aJohnson, Toby1 aKaakinen, Marika1 aKapur, Karen1 aKetkar, Shamika1 aKnowles, Joshua, W1 aKraft, Peter1 aKraja, Aldi, T1 aLamina, Claudia1 aLeitzmann, Michael, F1 aMcKnight, Barbara1 aMorris, Andrew, P1 aOng, Ken, K1 aPerry, John, R B1 aPeters, Marjolein, J1 aPolasek, Ozren1 aProkopenko, Inga1 aRayner, Nigel, W1 aRipatti, Samuli1 aRivadeneira, Fernando1 aRobertson, Neil, R1 aSanna, Serena1 aSovio, Ulla1 aSurakka, Ida1 aTeumer, Alexander1 avan Wingerden, Sophie1 aVitart, Veronique1 aZhao, Jing Hua1 aCavalcanti-Proença, Christine1 aChines, Peter, S1 aFisher, Eva1 aKulzer, Jennifer, R1 aLecoeur, Cécile1 aNarisu, Narisu1 aSandholt, Camilla1 aScott, Laura, J1 aSilander, Kaisa1 aStark, Klaus1 aTammesoo, Mari-Liis1 aTeslovich, Tanya, M1 aTimpson, Nicholas, John1 aWatanabe, Richard, M1 aWelch, Ryan1 aChasman, Daniel, I1 aCooper, Matthew, N1 aJansson, John-Olov1 aKettunen, Johannes1 aLawrence, Robert, W1 aPellikka, Niina1 aPerola, Markus1 aVandenput, Liesbeth1 aAlavere, Helene1 aAlmgren, Peter1 aAtwood, Larry, D1 aBennett, Amanda, J1 aBiffar, Reiner1 aBonnycastle, Lori, L1 aBornstein, Stefan, R1 aBuchanan, Thomas, A1 aCampbell, Harry1 aDay, Ian, N M1 aDei, Mariano1 aDörr, Marcus1 aElliott, Paul1 aErdos, Michael, R1 aEriksson, Johan, G1 aFreimer, Nelson, B1 aFu, Mao1 aGaget, Stefan1 aGeus, Eco, J C1 aGjesing, Anette, P1 aGrallert, Harald1 aGrässler, Jürgen1 aGroves, Christopher, J1 aGuiducci, Candace1 aHartikainen, Anna-Liisa1 aHassanali, Neelam1 aHavulinna, Aki, S1 aHerzig, Karl-Heinz1 aHicks, Andrew, A1 aHui, Jennie1 aIgl, Wilmar1 aJousilahti, Pekka1 aJula, Antti1 aKajantie, Eero1 aKinnunen, Leena1 aKolcic, Ivana1 aKoskinen, Seppo1 aKovacs, Peter1 aKroemer, Heyo, K1 aKrzelj, Vjekoslav1 aKuusisto, Johanna1 aKvaloy, Kirsti1 aLaitinen, Jaana1 aLantieri, Olivier1 aLathrop, Mark, G1 aLokki, Marja-Liisa1 aLuben, Robert, N1 aLudwig, Barbara1 aMcArdle, Wendy, L1 aMcCarthy, Anne1 aMorken, Mario, A1 aNelis, Mari1 aNeville, Matt, J1 aParé, Guillaume1 aParker, Alex, N1 aPeden, John, F1 aPichler, Irene1 aPietiläinen, Kirsi, H1 aPlatou, Carl, G P1 aPouta, Anneli1 aRidderstråle, Martin1 aSamani, Nilesh, J1 aSaramies, Jouko1 aSinisalo, Juha1 aSmit, Jan, H1 aStrawbridge, Rona, J1 aStringham, Heather, M1 aSwift, Amy, J1 aTeder-Laving, Maris1 aThomson, Brian1 aUsala, Gianluca1 avan Meurs, Joyce, B J1 avan Ommen, Gert-Jan1 aVatin, Vincent1 aVolpato, Claudia, B1 aWallaschofski, Henri1 aWalters, Bragi, G1 aWiden, Elisabeth1 aWild, Sarah, H1 aWillemsen, Gonneke1 aWitte, Daniel, R1 aZgaga, Lina1 aZitting, Paavo1 aBeilby, John, P1 aJames, Alan, L1 aKähönen, Mika1 aLehtimäki, Terho1 aNieminen, Markku, S1 aOhlsson, Claes1 aPalmer, Lyle, J1 aRaitakari, Olli1 aRidker, Paul, M1 aStumvoll, Michael1 aTönjes, Anke1 aViikari, Jorma1 aBalkau, Beverley1 aBen-Shlomo, Yoav1 aBergman, Richard, N1 aBoeing, Heiner1 aSmith, George Davey1 aEbrahim, Shah1 aFroguel, Philippe1 aHansen, Torben1 aHengstenberg, Christian1 aHveem, Kristian1 aIsomaa, Bo1 aJørgensen, Torben1 aKarpe, Fredrik1 aKhaw, Kay-Tee1 aLaakso, Markku1 aLawlor, Debbie, A1 aMarre, Michel1 aMeitinger, Thomas1 aMetspalu, Andres1 aMidthjell, Kristian1 aPedersen, Oluf1 aSalomaa, Veikko1 aSchwarz, Peter, E H1 aTuomi, Tiinamaija1 aTuomilehto, Jaakko1 aValle, Timo, T1 aWareham, Nicholas, J1 aArnold, Alice, M1 aBeckmann, Jacques, S1 aBergmann, Sven1 aBoerwinkle, Eric1 aBoomsma, Dorret, I1 aCaulfield, Mark, J1 aCollins, Francis, S1 aEiriksdottir, Gudny1 aGudnason, Vilmundur1 aGyllensten, Ulf1 aHamsten, Anders1 aHattersley, Andrew, T1 aHofman, Albert1 aHu, Frank, B1 aIllig, Thomas1 aIribarren, Carlos1 aJarvelin, Marjo-Riitta1 aKao, Linda, W H1 aKaprio, Jaakko1 aLauner, Lenore, J1 aMunroe, Patricia, B1 aOostra, Ben1 aPenninx, Brenda, W1 aPramstaller, Peter, P1 aPsaty, Bruce, M1 aQuertermous, Thomas1 aRissanen, Aila1 aRudan, Igor1 aShuldiner, Alan, R1 aSoranzo, Nicole1 aSpector, Timothy, D1 aSyvänen, Ann-Christine1 aUda, Manuela1 aUitterlinden, Andre1 aVölzke, Henry1 aVollenweider, Peter1 aWilson, James, F1 aWitteman, Jacqueline, C1 aWright, Alan, F1 aAbecasis, Goncalo, R1 aBoehnke, Michael1 aBorecki, Ingrid, B1 aDeloukas, Panos1 aFrayling, Timothy, M1 aGroop, Leif, C1 aHaritunians, Talin1 aHunter, David, J1 aKaplan, Robert, C1 aNorth, Kari, E1 aO'Connell, Jeffrey, R1 aPeltonen, Leena1 aSchlessinger, David1 aStrachan, David, P1 aHirschhorn, Joel, N1 aAssimes, Themistocles, L1 aWichmann, H-Erich1 aThorsteinsdottir, Unnur1 aDuijn, Cornelia, M1 aStefansson, Kari1 aCupples, Adrienne, L1 aLoos, Ruth, J F1 aBarroso, Inês1 aMcCarthy, Mark, I1 aFox, Caroline, S1 aMohlke, Karen, L1 aLindgren, Cecilia, M1 aMAGIC uhttps://chs-nhlbi.org/node/123604476nas a2200937 4500008004100000022001400041245014100055210006900196260001300265300001300278490000600291520177500297653002202072653001502094653002102109653002302130653002102153653003002174653001102204653001902215653002202234653002502256653001802281653003402299653001302333653001102346653002702357653000902384653001502393653001402408653003602422653003302458653004702491653001302538100002102551700001802572700002202590700001802612700001702630700002002647700002002667700002002687700002402707700001802731700001702749700002102766700002502787700001602812700002502828700002302853700001902876700002102895700001602916700002802932700002402960700002802984700002003012700002203032700001503054700002003069700002303089700002103112700002203133700001903155700002203174700002403196700002203220700002803242700002303270700001903293700002003312700002403332700002403356700001703380700002703397700001703424700002003441700002103461700002003482856003603502 2011 eng d a1553-740400aEnhanced statistical tests for GWAS in admixed populations: assessment using African Americans from CARe and a Breast Cancer Consortium.0 aEnhanced statistical tests for GWAS in admixed populations asses c2011 Apr ae10013710 v73 aWhile genome-wide association studies (GWAS) have primarily examined populations of European ancestry, more recent studies often involve additional populations, including admixed populations such as African Americans and Latinos. In admixed populations, linkage disequilibrium (LD) exists both at a fine scale in ancestral populations and at a coarse scale (admixture-LD) due to chromosomal segments of distinct ancestry. Disease association statistics in admixed populations have previously considered SNP association (LD mapping) or admixture association (mapping by admixture-LD), but not both. Here, we introduce a new statistical framework for combining SNP and admixture association in case-control studies, as well as methods for local ancestry-aware imputation. We illustrate the gain in statistical power achieved by these methods by analyzing data of 6,209 unrelated African Americans from the CARe project genotyped on the Affymetrix 6.0 chip, in conjunction with both simulated and real phenotypes, as well as by analyzing the FGFR2 locus using breast cancer GWAS data from 5,761 African-American women. We show that, at typed SNPs, our method yields an 8% increase in statistical power for finding disease risk loci compared to the power achieved by standard methods in case-control studies. At imputed SNPs, we observe an 11% increase in statistical power for mapping disease loci when our local ancestry-aware imputation framework and the new scoring statistic are jointly employed. Finally, we show that our method increases statistical power in regions harboring the causal SNP in the case when the causal SNP is untyped and cannot be imputed. Our methods and our publicly available software are broadly applicable to GWAS in admixed populations.
10aAfrican Americans10aAlgorithms10aBreast Neoplasms10aChromosome Mapping10aCoronary Disease10aDiabetes Mellitus, Type 210aFemale10aGene Frequency10aGenetic Variation10aGenetics, Population10aGenome, Human10aGenome-Wide Association Study10aGenotype10aHumans10aLinkage Disequilibrium10aMale10aOdds Ratio10aPhenotype10aPolymorphism, Single Nucleotide10aPrincipal Component Analysis10aReceptor, Fibroblast Growth Factor, Type 210aSoftware1 aPasaniuc, Bogdan1 aZaitlen, Noah1 aLettre, Guillaume1 aChen, Gary, K1 aTandon, Arti1 aKao, Linda, W H1 aRuczinski, Ingo1 aFornage, Myriam1 aSiscovick, David, S1 aZhu, Xiaofeng1 aLarkin, Emma1 aLange, Leslie, A1 aCupples, Adrienne, L1 aYang, Qiong1 aAkylbekova, Ermeg, L1 aMusani, Solomon, K1 aDivers, Jasmin1 aMychaleckyj, Joe1 aLi, Mingyao1 aPapanicolaou, George, J1 aMillikan, Robert, C1 aAmbrosone, Christine, B1 aJohn, Esther, M1 aBernstein, Leslie1 aZheng, Wei1 aHu, Jennifer, J1 aZiegler, Regina, G1 aNyante, Sarah, J1 aBandera, Elisa, V1 aIngles, Sue, A1 aPress, Michael, F1 aChanock, Stephen, J1 aDeming, Sandra, L1 aRodriguez-Gil, Jorge, L1 aPalmer, Cameron, D1 aBuxbaum, Sarah1 aEkunwe, Lynette1 aHirschhorn, Joel, N1 aHenderson, Brian, E1 aMyers, Simon1 aHaiman, Christopher, A1 aReich, David1 aPatterson, Nick1 aWilson, James, G1 aPrice, Alkes, L uhttps://chs-nhlbi.org/node/128802615nas a2200481 4500008004100000022001400041245014100055210006900196260001300265300001300278490000600291520118600297653002701483653001901510653001101529653001901540653001701559653001801576653003401594653001301628653001101641653002101652653003801673653001101711653002201722653002001744653003101764653003601795653001301831653003001844100002101874700002001895700001601915700001901931700001901950700001901969700001901988700002402007700001802031700002502049700002302074856003602097 2011 eng d a1553-740400aGenome-wide association analysis of soluble ICAM-1 concentration reveals novel associations at the NFKBIK, PNPLA3, RELA, and SH2B3 loci.0 aGenomewide association analysis of soluble ICAM1 concentration r c2011 Apr ae10013740 v73 aSoluble ICAM-1 (sICAM-1) is an endothelium-derived inflammatory marker that has been associated with diverse conditions such as myocardial infarction, diabetes, stroke, and malaria. Despite evidence for a heritable component to sICAM-1 levels, few genetic loci have been identified so far. To comprehensively address this issue, we performed a genome-wide association analysis of sICAM-1 concentration in 22,435 apparently healthy women from the Women's Genome Health Study. While our results confirm the previously reported associations at the ABO and ICAM1 loci, four novel associations were identified in the vicinity of NFKBIK (rs3136642, P = 5.4 × 10(-9)), PNPLA3 (rs738409, P = 5.8 × 10(-9)), RELA (rs1049728, P = 2.7 × 10(-16)), and SH2B3 (rs3184504, P = 2.9 × 10(-17)). Two loci, NFKBIB and RELA, are involved in NFKB signaling pathway; PNPLA3 is known for its association with fatty liver disease; and SH3B2 has been associated with a multitude of traits and disease including myocardial infarction. These associations provide insights into the genetic regulation of sICAM-1 levels and implicate these loci in the regulation of endothelial function.
10aABO Blood-Group System10aCohort Studies10aFemale10aGene Frequency10aGenetic Loci10aGenome, Human10aGenome-Wide Association Study10aGenotype10aHumans10aI-kappa B Kinase10aIntercellular Adhesion Molecule-110aLipase10aMembrane Proteins10aModels, Genetic10aMultifactorial Inheritance10aPolymorphism, Single Nucleotide10aProteins10aTranscription Factor RelA1 aParé, Guillaume1 aRidker, Paul, M1 aRose, Lynda1 aBarbalic, Maja1 aDupuis, Josée1 aDehghan, Abbas1 aBis, Joshua, C1 aBenjamin, Emelia, J1 aShiffman, Dov1 aParker, Alexander, N1 aChasman, Daniel, I uhttps://chs-nhlbi.org/node/128703188nas a2200553 4500008004100000022001400041245012300055210006900178260001300247300000900260490000700269520163300276653001001909653000901919653002001928653002501948653003001973653001602003653001202019653001102031653001802042653003402060653001302094653001102107653001202118653002702130653000902157653002702166653002702193653001602220653003602236653001502272100002202287700002202309700001802331700001802349700001302367700001702380700001902397700002802416700002202444700002502466700002302491700002002514700002002534700002502554700001902579856003602598 2011 eng d a1098-227200aMeta-analysis of gene-environment interaction: joint estimation of SNP and SNP × environment regression coefficients.0 aMetaanalysis of geneenvironment interaction joint estimation of c2011 Jan a11-80 v353 aINTRODUCTION: Genetic discoveries are validated through the meta-analysis of genome-wide association scans in large international consortia. Because environmental variables may interact with genetic factors, investigation of differing genetic effects for distinct levels of an environmental exposure in these large consortia may yield additional susceptibility loci undetected by main effects analysis. We describe a method of joint meta-analysis (JMA) of SNP and SNP by Environment (SNP × E) regression coefficients for use in gene-environment interaction studies.
METHODS: In testing SNP × E interactions, one approach uses a two degree of freedom test to identify genetic variants that influence the trait of interest. This approach detects both main and interaction effects between the trait and the SNP. We propose a method to jointly meta-analyze the SNP and SNP × E coefficients using multivariate generalized least squares. This approach provides confidence intervals of the two estimates, a joint significance test for SNP and SNP × E terms, and a test of homogeneity across samples.
RESULTS: We present a simulation study comparing this method to four other methods of meta-analysis and demonstrate that the JMA performs better than the others when both main and interaction effects are present. Additionally, we implemented our methods in a meta-analysis of the association between SNPs from the type 2 diabetes-associated gene PPARG and log-transformed fasting insulin levels and interaction by body mass index in a combined sample of 19,466 individuals from five cohorts.
10aAdult10aAged10aBody Mass Index10aConfidence Intervals10aDiabetes Mellitus, Type 210aEnvironment10aFasting10aFemale10aGenome, Human10aGenome-Wide Association Study10aGenotype10aHumans10aInsulin10aLeast-Squares Analysis10aMale10aMathematical Computing10aMeta-Analysis as Topic10aMiddle Aged10aPolymorphism, Single Nucleotide10aPPAR gamma1 aManning, Alisa, K1 aLaValley, Michael1 aLiu, Ching-Ti1 aRice, Kenneth1 aAn, Ping1 aLiu, Yongmei1 aMiljkovic, Iva1 aRasmussen-Torvik, Laura1 aHarris, Tamara, B1 aProvince, Michael, A1 aBorecki, Ingrid, B1 aFlorez, Jose, C1 aMeigs, James, B1 aCupples, Adrienne, L1 aDupuis, Josée uhttps://chs-nhlbi.org/node/125805459nas a2201501 4500008004100000022001400041245016600055210006900221260001600290300001000306490000700316520120500323653001001528653000901538653001001547653002001557653003501577653001901612653002801631653004001659653001701699653003801716653001801754653003401772653001301806653001001819653001101829653001601840653001401856653002801870653003601898653001701934100001901951700002001970700002301990700001802013700002502031700001802056700001902074700002102093700002502114700002002139700002202159700002502181700002202206700001802228700002002246700002302266700001902289700001602308700001602324700001702340700002502357700002202382700002002404700002302424700002002447700001802467700001902485700002002504700002002524700002102544700001802565700002602583700001702609700001902626700002302645700002002668700002302688700002402711700001802735700001802753700001902771700002302790700002202813700002402835700001702859700002402876700002102900700002102921700002002942700001802962700002102980700001703001700001903018700002303037700002403060700002203084700002203106700001903128700002103147700001803168700002003186700001903206700002203225700002503247700002303272700002503295700002003320700002103340700002303361700002703384700002203411700002003433700002003453700002103473700001203494700002303506700001703529700002103546700001803567700002003585700002103605700001903626700002103645700002403666700002003690700002503710700001903735700002403754700002003778700001803798700002503816700002403841700003003865710002603895856003603921 2011 eng d a1546-171800aMeta-analysis of genome-wide association studies from the CHARGE consortium identifies common variants associated with carotid intima media thickness and plaque.0 aMetaanalysis of genomewide association studies from the CHARGE c c2011 Sep 11 a940-70 v433 aCarotid intima media thickness (cIMT) and plaque determined by ultrasonography are established measures of subclinical atherosclerosis that each predicts future cardiovascular disease events. We conducted a meta-analysis of genome-wide association data in 31,211 participants of European ancestry from nine large studies in the setting of the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium. We then sought additional evidence to support our findings among 11,273 individuals using data from seven additional studies. In the combined meta-analysis, we identified three genomic regions associated with common carotid intima media thickness and two different regions associated with the presence of carotid plaque (P < 5 × 10(-8)). The associated SNPs mapped in or near genes related to cellular signaling, lipid metabolism and blood pressure homeostasis, and two of the regions were associated with coronary artery disease (P < 0.006) in the Coronary Artery Disease Genome-Wide Replication and Meta-Analysis (CARDIoGRAM) consortium. Our findings may provide new insight into pathways leading to subclinical atherosclerosis and subsequent cardiovascular events.
10aAdult10aAged10aAging10aAtherosclerosis10aCarotid Intima-Media Thickness10aCohort Studies10aCoronary Artery Disease10aEuropean Continental Ancestry Group10aGenetic Loci10aGenetic Predisposition to Disease10aGenome, Human10aGenome-Wide Association Study10aGenotype10aHeart10aHumans10aMiddle Aged10aPhenotype10aPlaque, Atherosclerotic10aPolymorphism, Single Nucleotide10aRisk Factors1 aBis, Joshua, C1 aKavousi, Maryam1 aFranceschini, Nora1 aIsaacs, Aaron1 aAbecasis, Goncalo, R1 aSchminke, Ulf1 aPost, Wendy, S1 aSmith, Albert, V1 aCupples, Adrienne, L1 aMarkus, Hugh, S1 aSchmidt, Reinhold1 aHuffman, Jennifer, E1 aLehtimäki, Terho1 aBaumert, Jens1 aMünzel, Thomas1 aHeckbert, Susan, R1 aDehghan, Abbas1 aNorth, Kari1 aOostra, Ben1 aBevan, Steve1 aStoegerer, Eva-Maria1 aHayward, Caroline1 aRaitakari, Olli1 aMeisinger, Christa1 aSchillert, Arne1 aSanna, Serena1 aVölzke, Henry1 aCheng, Yu-Ching1 aThorsson, Bolli1 aFox, Caroline, S1 aRice, Kenneth1 aRivadeneira, Fernando1 aNambi, Vijay1 aHalperin, Eran1 aPetrovic, Katja, E1 aPeltonen, Leena1 aWichmann, Erich, H1 aSchnabel, Renate, B1 aDörr, Marcus1 aParsa, Afshin1 aAspelund, Thor1 aDemissie, Serkalem1 aKathiresan, Sekar1 aReilly, Muredach, P1 aTaylor, Kent1 aUitterlinden, Andre1 aCouper, David, J1 aSitzer, Matthias1 aKähönen, Mika1 aIllig, Thomas1 aWild, Philipp, S1 aOrrù, Marco1 aLüdemann, Jan1 aShuldiner, Alan, R1 aEiriksdottir, Gudny1 aWhite, Charles, C1 aRotter, Jerome, I1 aHofman, Albert1 aSeissler, Jochen1 aZeller, Tanja1 aUsala, Gianluca1 aErnst, Florian1 aLauner, Lenore, J1 aD'Agostino, Ralph, B1 aO'Leary, Daniel, H1 aBallantyne, Christie1 aThiery, Joachim1 aZiegler, Andreas1 aLakatta, Edward, G1 aChilukoti, Ravi, Kumar1 aHarris, Tamara, B1 aWolf, Philip, A1 aPsaty, Bruce, M1 aPolak, Joseph, F1 aLi, Xia1 aRathmann, Wolfgang1 aUda, Manuela1 aBoerwinkle, Eric1 aKlopp, Norman1 aSchmidt, Helena1 aWilson, James, F1 aViikari, Jorma1 aKoenig, Wolfgang1 aBlankenberg, Stefan1 aNewman, Anne, B1 aWitteman, Jacqueline1 aHeiss, Gerardo1 avan Duijn, Cornelia1 aScuteri, Angelo1 aHomuth, Georg1 aMitchell, Braxton, D1 aGudnason, Vilmundur1 aO'Donnell, Christopher, J1 aCARDIoGRAM consortium uhttps://chs-nhlbi.org/node/132307496nas a2202305 4500008004100000022001400041245006700055210006600122260001600188300001000204490000800214520114000222653001201362653002001374653001401394653002801408653002401436653001101460653003001471653001901501653001801520653003401538653001801572653001101590653001901601653001901620653002901639653002701668653001401695653002301709100002201732700002601754700001601780700001801796700002001814700002001834700002901854700001901883700002301902700001801925700002001943700001801963700002001981700002002001700002002021700002102041700002202062700001702084700001802101700002602119700001902145700002202164700002002186700002402206700002402230700001802254700002202272700001802294700001602312700002002328700002202348700002102370700001802391700002302409700001502432700002602447700002002473700002002493700002102513700002502534700001702559700002902576700001902605700001902624700002302643700002202666700002202688700002102710700001702731700001702748700002302765700001902788700002002807700001802827700002202845700002002867700001802887700001402905700002102919700001902940700002202959700002202981700001903003700002803022700002303050700002803073700001803101700001803119700002003137700002103157700001903178700002103197700003103218700002003249700002503269700001903294700002103313700002003334700002403354700002203378700001803400700002503418700002503443700002103468700002003489700001803509700002003527700001903547700002303566700001803589700002603607700001803633700001903651700002203670700002203692700001903714700001703733700002203750700002303772700002403795700002203819700002403841700001703865700002303882700002003905700001903925700002203944700002303966700002203989700001704011700001904028700002104047700001804068700001704086700001804103700002804121700002304149700001704172700002204189700002204211700002304233700001804256700002004274700001904294700002104313700002504334700002104359700002204380700002104402700001904423700002304442700002004465700002304485700002304508700001804531700001804549700002204567700002204589700002704611700002404638700002304662700002004685700002404705700002504729700002804754700001904782700002004801700002404821700001604845700002204861700001904883700002204902700002104924700002104945700001904966700002104985700002105006700001805027700002105045700002005066700002405086700002405110700002005134856003605154 2011 eng d a1476-468700aNew gene functions in megakaryopoiesis and platelet formation.0 aNew gene functions in megakaryopoiesis and platelet formation c2011 Nov 30 a201-80 v4803 aPlatelets are the second most abundant cell type in blood and are essential for maintaining haemostasis. Their count and volume are tightly controlled within narrow physiological ranges, but there is only limited understanding of the molecular processes controlling both traits. Here we carried out a high-powered meta-analysis of genome-wide association studies (GWAS) in up to 66,867 individuals of European ancestry, followed by extensive biological and functional assessment. We identified 68 genomic loci reliably associated with platelet count and volume mapping to established and putative novel regulators of megakaryopoiesis and platelet formation. These genes show megakaryocyte-specific gene expression patterns and extensive network connectivity. Using gene silencing in Danio rerio and Drosophila melanogaster, we identified 11 of the genes as novel regulators of blood cell formation. Taken together, our findings advance understanding of novel gene functions controlling fate-determining events during megakaryopoiesis and platelet formation, providing a new example of successful translation of GWAS to function.
10aAnimals10aBlood Platelets10aCell Size10aDrosophila melanogaster10aDrosophila Proteins10aEurope10aGene Expression Profiling10aGene Silencing10aGenome, Human10aGenome-Wide Association Study10aHematopoiesis10aHumans10aMegakaryocytes10aPlatelet Count10aProtein Interaction Maps10aTranscription, Genetic10aZebrafish10aZebrafish Proteins1 aGieger, Christian1 aRadhakrishnan, Aparna1 aCvejic, Ana1 aTang, Weihong1 aPorcu, Eleonora1 aPistis, Giorgio1 aSerbanovic-Canic, Jovana1 aElling, Ulrich1 aGoodall, Alison, H1 aLabrune, Yann1 aLopez, Lorna, M1 aMägi, Reedik1 aMeacham, Stuart1 aOkada, Yukinori1 aPirastu, Nicola1 aSorice, Rossella1 aTeumer, Alexander1 aVoss, Katrin1 aZhang, Weihua1 aRamirez-Solis, Ramiro1 aBis, Joshua, C1 aEllinghaus, David1 aGögele, Martin1 aHottenga, Jouke-Jan1 aLangenberg, Claudia1 aKovacs, Peter1 aO'Reilly, Paul, F1 aShin, So-Youn1 aEsko, Tõnu1 aHartiala, Jaana1 aKanoni, Stavroula1 aMurgia, Federico1 aParsa, Afshin1 aStephens, Jonathan1 aHarst, Pim1 avan der Schoot, Ellen1 aAllayee, Hooman1 aAttwood, Antony1 aBalkau, Beverley1 aBastardot, François1 aBasu, Saonli1 aBaumeister, Sebastian, E1 aBiino, Ginevra1 aBomba, Lorenzo1 aBonnefond, Amélie1 aCambien, Francois1 aChambers, John, C1 aCucca, Francesco1 aD'Adamo, Pio1 aDavies, Gail1 ade Boer, Rudolf, A1 aGeus, Eco, J C1 aDöring, Angela1 aElliott, Paul1 aErdmann, Jeanette1 aEvans, David, M1 aFalchi, Mario1 aFeng, Wei1 aFolsom, Aaron, R1 aFrazer, Ian, H1 aGibson, Quince, D1 aGlazer, Nicole, L1 aHammond, Chris1 aHartikainen, Anna-Liisa1 aHeckbert, Susan, R1 aHengstenberg, Christian1 aHersch, Micha1 aIllig, Thomas1 aLoos, Ruth, J F1 aJolley, Jennifer1 aKhaw, Kay, Tee1 aKuhnel, Brigitte1 aKyrtsonis, Marie-Christine1 aLagou, Vasiliki1 aLloyd-Jones, Heather1 aLumley, Thomas1 aMangino, Massimo1 aMaschio, Andrea1 aLeach, Irene, Mateo1 aMcKnight, Barbara1 aMemari, Yasin1 aMitchell, Braxton, D1 aMontgomery, Grant, W1 aNakamura, Yusuke1 aNauck, Matthias1 aNavis, Gerjan1 aNöthlings, Ute1 aNolte, Ilja, M1 aPorteous, David, J1 aPouta, Anneli1 aPramstaller, Peter, P1 aPullat, Janne1 aRing, Susan, M1 aRotter, Jerome, I1 aRuggiero, Daniela1 aRuokonen, Aimo1 aSala, Cinzia1 aSamani, Nilesh, J1 aSambrook, Jennifer1 aSchlessinger, David1 aSchreiber, Stefan1 aSchunkert, Heribert1 aScott, James1 aSmith, Nicholas, L1 aSnieder, Harold1 aStarr, John, M1 aStumvoll, Michael1 aTakahashi, Atsushi1 aTang, W, H Wilson1 aTaylor, Kent1 aTenesa, Albert1 aThein, Swee, Lay1 aTönjes, Anke1 aUda, Manuela1 aUlivi, Sheila1 avan Veldhuisen, Dirk, J1 aVisscher, Peter, M1 aVölker, Uwe1 aWichmann, H-Erich1 aWiggins, Kerri, L1 aWillemsen, Gonneke1 aYang, Tsun-Po1 aZhao, Jing, Hua1 aZitting, Paavo1 aBradley, John, R1 aDedoussis, George, V1 aGasparini, Paolo1 aHazen, Stanley, L1 aMetspalu, Andres1 aPirastu, Mario1 aShuldiner, Alan, R1 avan Pelt, Joost1 aZwaginga, Jaap-Jan1 aBoomsma, Dorret, I1 aDeary, Ian, J1 aFranke, Andre1 aFroguel, Philippe1 aGanesh, Santhi, K1 aJarvelin, Marjo-Riitta1 aMartin, Nicholas, G1 aMeisinger, Christa1 aPsaty, Bruce, M1 aSpector, Timothy, D1 aWareham, Nicholas, J1 aAkkerman, Jan-Willem, N1 aCiullo, Marina1 aDeloukas, Panos1 aGreinacher, Andreas1 aJupe, Steve1 aKamatani, Naoyuki1 aKhadake, Jyoti1 aKooner, Jaspal, S1 aPenninger, Josef1 aProkopenko, Inga1 aStemple, Derek1 aToniolo, Daniela1 aWernisch, Lorenz1 aSanna, Serena1 aHicks, Andrew, A1 aRendon, Augusto1 aFerreira, Manuel, A1 aOuwehand, Willem, H1 aSoranzo, Nicole uhttps://chs-nhlbi.org/node/135503388nas a2200553 4500008004100000022001400041245012700055210006900182260001300251300001300264490000600277520170800283653002201991653002302013653001802036653001802054653004002072653003202112653003802144653001802182653001102200653002302211653002302234653001402257653002602271653003602297653003402333653003002367653002202397100002202419700001802441700001902459700002102478700002102499700002002520700002302540700002402563700002002587700002102607700002602628700002002654700002402674700002202698700002002720700002202740700001902762700001702781856003602798 2011 eng d a1553-740400aA phenomics-based strategy identifies loci on APOC1, BRAP, and PLCG1 associated with metabolic syndrome phenotype domains.0 aphenomicsbased strategy identifies loci on APOC1 BRAP and PLCG1 c2011 Oct ae10023220 v73 aDespite evidence of the clustering of metabolic syndrome components, current approaches for identifying unifying genetic mechanisms typically evaluate clinical categories that do not provide adequate etiological information. Here, we used data from 19,486 European American and 6,287 African American Candidate Gene Association Resource Consortium participants to identify loci associated with the clustering of metabolic phenotypes. Six phenotype domains (atherogenic dyslipidemia, vascular dysfunction, vascular inflammation, pro-thrombotic state, central obesity, and elevated plasma glucose) encompassing 19 quantitative traits were examined. Principal components analysis was used to reduce the dimension of each domain such that >55% of the trait variance was represented within each domain. We then applied a statistically efficient and computational feasible multivariate approach that related eight principal components from the six domains to 250,000 imputed SNPs using an additive genetic model and including demographic covariates. In European Americans, we identified 606 genome-wide significant SNPs representing 19 loci. Many of these loci were associated with only one trait domain, were consistent with results in African Americans, and overlapped with published findings, for instance central obesity and FTO. However, our approach, which is applicable to any set of interval scale traits that is heritable and exhibits evidence of phenotypic clustering, identified three new loci in or near APOC1, BRAP, and PLCG1, which were associated with multiple phenotype domains. These pleiotropic loci may help characterize metabolic dysregulation and identify targets for intervention.
10aAfrican Americans10aApolipoprotein C-I10aBlood Glucose10aDyslipidemias10aEuropean Continental Ancestry Group10aGenetic Association Studies10aGenetic Predisposition to Disease10aGenome, Human10aHumans10aMetabolic Syndrome10aObesity, Abdominal10aPhenotype10aPhospholipase C gamma10aPolymorphism, Single Nucleotide10aQuantitative Trait, Heritable10aUbiquitin-Protein Ligases10aVascular Diseases1 aAvery, Christy, L1 aHe, Qianchuan1 aNorth, Kari, E1 aAmbite, José, L1 aBoerwinkle, Eric1 aFornage, Myriam1 aHindorff, Lucia, A1 aKooperberg, Charles1 aMeigs, James, B1 aPankow, James, S1 aPendergrass, Sarah, A1 aPsaty, Bruce, M1 aRitchie, Marylyn, D1 aRotter, Jerome, I1 aTaylor, Kent, D1 aWilkens, Lynne, R1 aHeiss, Gerardo1 aLin, Dan, Yu uhttps://chs-nhlbi.org/node/134502787nas a2200649 4500008004100000022001400041245009900055210006900154260001600223300000900239490000800248520092400256653002201180653001201202653004001214653002501254653001001279653001101289653001901300653003201319653003801351653002201389653001801411653004201429653001101471653000901482653003601491653002201527653002301549100002401572700002301596700002501619700001601644700002101660700001801681700001601699700001201715700001801727700001301745700002001758700001901778700002101797700002001818700002001838700002201858700002101880700002601901700002101927700002001948700002601968700002401994700002002018710001302038710001502051710003502066856003602101 2012 eng d a1095-920300aEvolution and functional impact of rare coding variation from deep sequencing of human exomes.0 aEvolution and functional impact of rare coding variation from de c2012 Jul 06 a64-90 v3373 aAs a first step toward understanding how rare variants contribute to risk for complex diseases, we sequenced 15,585 human protein-coding genes to an average median depth of 111× in 2440 individuals of European (n = 1351) and African (n = 1088) ancestry. We identified over 500,000 single-nucleotide variants (SNVs), the majority of which were rare (86% with a minor allele frequency less than 0.5%), previously unknown (82%), and population-specific (82%). On average, 2.3% of the 13,595 SNVs each person carried were predicted to affect protein function of ~313 genes per genome, and ~95.7% of SNVs predicted to be functionally important were rare. This excess of rare functional variants is due to the combined effects of explosive, recent accelerated population growth and weak purifying selection. Furthermore, we show that large sample sizes will be required to associate rare variants with complex traits.
10aAfrican Americans10aDisease10aEuropean Continental Ancestry Group10aEvolution, Molecular10aExome10aFemale10aGene Frequency10aGenetic Association Studies10aGenetic Predisposition to Disease10aGenetic Variation10aGenome, Human10aHigh-Throughput Nucleotide Sequencing10aHumans10aMale10aPolymorphism, Single Nucleotide10aPopulation Growth10aSelection, Genetic1 aTennessen, Jacob, A1 aBigham, Abigail, W1 aO'Connor, Timothy, D1 aFu, Wenqing1 aKenny, Eimear, E1 aGravel, Simon1 aMcGee, Sean1 aDo, Ron1 aLiu, Xiaoming1 aJun, Goo1 aKang, Hyun, Min1 aJordan, Daniel1 aLeal, Suzanne, M1 aGabriel, Stacey1 aRieder, Mark, J1 aAbecasis, Goncalo1 aAltshuler, David1 aNickerson, Deborah, A1 aBoerwinkle, Eric1 aSunyaev, Shamil1 aBustamante, Carlos, D1 aBamshad, Michael, J1 aAkey, Joshua, M1 aBroad GO1 aSeattle GO1 aNHLBI Exome Sequencing Project uhttps://chs-nhlbi.org/node/138705536nas a2201405 4500008004100000022001400041245012000055210006900175260000900244300001300253490000600266520151700272653002901789653002001818653001801838653003401856653002001890653002301910653001101933653000901944653002601953653003601979653004402015653004302059653002802102653001202130653003002142653001902172100002102191700002602212700002002238700001802258700002102276700002602297700001802323700001902341700001602360700002202376700002102398700002202419700001702441700001602458700001902474700001802493700001902511700002002530700002402550700001902574700002202593700001802615700001702633700002202650700002102672700001802693700001902711700001802730700002002748700001702768700002002785700002202805700002602827700001902853700002202872700002402894700002202918700001902940700002402959700002302983700002203006700002203028700002003050700001903070700002003089700001903109700002103128700001903149700002403168700002303192700002603215700002103241700002803262700001703290700001903307700002003326700002003346700002803366700002003394700002103414700002003435700002403455700001903479700002103498700001903519700002203538700001803560700002803578700002803606700001803634700002303652700001703675700002503692700001903717700002503736700002403761700002903785700002003814700002703834700002303861700002403884700001903908700001703927700002503944700002303969700002003992700001704012700001904029700002104048700002504069856003604094 2012 eng d a1553-740400aGenome-wide joint meta-analysis of SNP and SNP-by-smoking interaction identifies novel loci for pulmonary function.0 aGenomewide joint metaanalysis of SNP and SNPbysmoking interactio c2012 ae10030980 v83 aGenome-wide association studies have identified numerous genetic loci for spirometic measures of pulmonary function, forced expiratory volume in one second (FEV(1)), and its ratio to forced vital capacity (FEV(1)/FVC). Given that cigarette smoking adversely affects pulmonary function, we conducted genome-wide joint meta-analyses (JMA) of single nucleotide polymorphism (SNP) and SNP-by-smoking (ever-smoking or pack-years) associations on FEV(1) and FEV(1)/FVC across 19 studies (total N = 50,047). We identified three novel loci not previously associated with pulmonary function. SNPs in or near DNER (smallest P(JMA = )5.00×10(-11)), HLA-DQB1 and HLA-DQA2 (smallest P(JMA = )4.35×10(-9)), and KCNJ2 and SOX9 (smallest P(JMA = )1.28×10(-8)) were associated with FEV(1)/FVC or FEV(1) in meta-analysis models including SNP main effects, smoking main effects, and SNP-by-smoking (ever-smoking or pack-years) interaction. The HLA region has been widely implicated for autoimmune and lung phenotypes, unlike the other novel loci, which have not been widely implicated. We evaluated DNER, KCNJ2, and SOX9 and found them to be expressed in human lung tissue. DNER and SOX9 further showed evidence of differential expression in human airway epithelium in smokers compared to non-smokers. Our findings demonstrated that joint testing of SNP and SNP-by-environment interaction identified novel loci associated with complex traits that are missed when considering only the genetic main effects.
10aForced Expiratory Volume10aGene Expression10aGenome, Human10aGenome-Wide Association Study10aHLA-DQ Antigens10aHLA-DQ beta-Chains10aHumans10aLung10aNerve Tissue Proteins10aPolymorphism, Single Nucleotide10aPotassium Channels, Inwardly Rectifying10aPulmonary Disease, Chronic Obstructive10aReceptors, Cell Surface10aSmoking10aSOX9 Transcription Factor10aVital Capacity1 aHancock, Dana, B1 aArtigas, Maria, Soler1 aGharib, Sina, A1 aHenry, Amanda1 aManichaikul, Ani1 aRamasamy, Adaikalavan1 aLoth, Daan, W1 aImboden, Medea1 aKoch, Beate1 aMcArdle, Wendy, L1 aSmith, Albert, V1 aSmolonska, Joanna1 aSood, Akshay1 aTang, Wenbo1 aWilk, Jemma, B1 aZhai, Guangju1 aZhao, Jing Hua1 aAschard, Hugues1 aBurkart, Kristin, M1 aCurjuric, Ivan1 aEijgelsheim, Mark1 aElliott, Paul1 aGu, Xiangjun1 aHarris, Tamara, B1 aJanson, Christer1 aHomuth, Georg1 aHysi, Pirro, G1 aLiu, Jason, Z1 aLoehr, Laura, R1 aLohman, Kurt1 aLoos, Ruth, J F1 aManning, Alisa, K1 aMarciante, Kristin, D1 aObeidat, Ma'en1 aPostma, Dirkje, S1 aAldrich, Melinda, C1 aBrusselle, Guy, G1 aChen, Ting-Hsu1 aEiriksdottir, Gudny1 aFranceschini, Nora1 aHeinrich, Joachim1 aRotter, Jerome, I1 aWijmenga, Cisca1 aWilliams, Dale1 aBentley, Amy, R1 aHofman, Albert1 aLaurie, Cathy, C1 aLumley, Thomas1 aMorrison, Alanna, C1 aJoubert, Bonnie, R1 aRivadeneira, Fernando1 aCouper, David, J1 aKritchevsky, Stephen, B1 aLiu, Yongmei1 aWjst, Matthias1 aWain, Louise, V1 aVonk, Judith, M1 aUitterlinden, André, G1 aRochat, Thierry1 aRich, Stephen, S1 aPsaty, Bruce, M1 aO'Connor, George, T1 aNorth, Kari, E1 aMirel, Daniel, B1 aMeibohm, Bernd1 aLauner, Lenore, J1 aKhaw, Kay-Tee1 aHartikainen, Anna-Liisa1 aHammond, Christopher, J1 aGläser, Sven1 aMarchini, Jonathan1 aKraft, Peter1 aWareham, Nicholas, J1 aVölzke, Henry1 aStricker, Bruno, H C1 aSpector, Timothy, D1 aProbst-Hensch, Nicole, M1 aJarvis, Deborah1 aJarvelin, Marjo-Riitta1 aHeckbert, Susan, R1 aGudnason, Vilmundur1 aBoezen, Marike1 aBarr, Graham1 aCassano, Patricia, A1 aStrachan, David, P1 aFornage, Myriam1 aHall, Ian, P1 aDupuis, Josée1 aTobin, Martin, D1 aLondon, Stephanie, J uhttps://chs-nhlbi.org/node/608804494nas a2200925 4500008004100000022001400041245008800055210006900143260001300212300001100225490000600236520191100242653001002153653002202163653000902185653002402194653004002218653001102258653002702269653002202296653001802318653003402336653001102370653000902381653001602390653003602406100001802442700002202460700002102482700002202503700001702525700001902542700002002561700002002581700001702601700002002618700001802638700002202656700002202678700002402700700002402724700001202748700002402760700002202784700001402806700001902820700001602839700001902855700002202874700001702896700002002913700002402933700001902957700002502976700002103001700002403022700002503046700002003071700002403091700002203115700002803137700001903165700002003184700001903204700002003223700001903243700002303262700002203285700002103307700002003328700001903348700002203367700001803389700002203407700002303429700002103452700002903473710003003502856003603532 2012 eng d a1942-326800aImpact of ancestry and common genetic variants on QT interval in African Americans.0 aImpact of ancestry and common genetic variants on QT interval in c2012 Dec a647-550 v53 aBACKGROUND: Ethnic differences in cardiac arrhythmia incidence have been reported, with a particularly high incidence of sudden cardiac death and low incidence of atrial fibrillation in individuals of African ancestry. We tested the hypotheses that African ancestry and common genetic variants are associated with prolonged duration of cardiac repolarization, a central pathophysiological determinant of arrhythmia, as measured by the electrocardiographic QT interval.
METHODS AND RESULTS: First, individual estimates of African and European ancestry were inferred from genome-wide single-nucleotide polymorphism (SNP) data in 7 population-based cohorts of African Americans (n=12,097) and regressed on measured QT interval from ECGs. Second, imputation was performed for 2.8 million SNPs, and a genome-wide association study of QT interval was performed in 10 cohorts (n=13,105). There was no evidence of association between genetic ancestry and QT interval (P=0.94). Genome-wide significant associations (P<2.5 × 10(-8)) were identified with SNPs at 2 loci, upstream of the genes NOS1AP (rs12143842, P=2 × 10(-15)) and ATP1B1 (rs1320976, P=2 × 10(-10)). The most significant SNP in NOS1AP was the same as the strongest SNP previously associated with QT interval in individuals of European ancestry. Low probability values (P<10(-5)) were observed for SNPs at several other loci previously identified in genome-wide association studies in individuals of European ancestry, including KCNQ1, KCNH2, LITAF, and PLN.
CONCLUSIONS: We observed no difference in duration of cardiac repolarization with global genetic indices of African American ancestry. In addition, our genome-wide association study extends the association of polymorphisms at several loci associated with repolarization in individuals of European ancestry to include individuals of African ancestry.
10aAdult10aAfrican Americans10aAged10aElectrocardiography10aEuropean Continental Ancestry Group10aFemale10aGenealogy and Heraldry10aGenetic Variation10aGenome, Human10aGenome-Wide Association Study10aHumans10aMale10aMiddle Aged10aPolymorphism, Single Nucleotide1 aSmith, Gustav1 aAvery, Christy, L1 aEvans, Daniel, S1 aNalls, Michael, A1 aMeng, Yan, A1 aSmith, Erin, N1 aPalmer, Cameron1 aTanaka, Toshiko1 aMehra, Reena1 aButler, Anne, M1 aYoung, Taylor1 aBuxbaum, Sarah, G1 aKerr, Kathleen, F1 aBerenson, Gerald, S1 aSchnabel, Renate, B1 aLi, Guo1 aEllinor, Patrick, T1 aMagnani, Jared, W1 aChen, Wei1 aBis, Joshua, C1 aCurb, David1 aHsueh, Wen-Chi1 aRotter, Jerome, I1 aLiu, Yongmei1 aNewman, Anne, B1 aLimacher, Marian, C1 aNorth, Kari, E1 aReiner, Alexander, P1 aQuibrera, Miguel1 aSchork, Nicholas, J1 aSingleton, Andrew, B1 aPsaty, Bruce, M1 aSoliman, Elsayed, Z1 aSolomon, Allen, J1 aSrinivasan, Sathanur, R1 aAlonso, Alvaro1 aWallace, Robert1 aRedline, Susan1 aZhang, Zhu-Ming1 aPost, Wendy, S1 aZonderman, Alan, B1 aTaylor, Herman, A1 aMurray, Sarah, S1 aFerrucci, Luigi1 aArking, Dan, E1 aEvans, Michele, K1 aFox, Ervin, R1 aSotoodehnia, Nona1 aHeckbert, Susan, R1 aWhitsel, Eric, A1 aNewton-Cheh, Christopher1 aCARe and COGENT consortia uhttps://chs-nhlbi.org/node/617903691nas a2200649 4500008004100000022001400041245014200055210006900197260001300266300001100279490000800290520184900298653001202147653001202159653001602171653002002187653001002207653002302217653000902240653002502249653001102274653002002285653002202305653001802327653001302345653001102358653002502369653000902394653000902403653001302412653001402425653003602439653000902475653001702484653001602501100002802517700001802545700002202563700002102585700002302606700002402629700002202653700001802675700002302693700002002716700002302736700002102759700002202780700002102802700001902823700002802842700002502870700002402895700002402919710006202943856003603005 2012 eng d a1943-263100aUltraconserved elements in the human genome: association and transmission analyses of highly constrained single-nucleotide polymorphisms.0 aUltraconserved elements in the human genome association and tran c2012 Sep a253-660 v1923 aUltraconserved elements in the human genome likely harbor important biological functions as they are dosage sensitive and are able to direct tissue-specific expression. Because they are under purifying selection, variants in these elements may have a lower frequency in the population but a higher likelihood of association with complex traits. We tested a set of highly constrained SNPs (hcSNPs) distributed genome-wide among ultraconserved and nearly ultraconserved elements for association with seven traits related to reproductive (age at natural menopause, number of children, age at first child, and age at last child) and overall [longevity, body mass index (BMI), and height] fitness. Using up to 24,047 European-American samples from the National Heart, Lung, and Blood Institute Candidate Gene Association Resource (CARe), we observed an excess of associations with BMI and height. In an independent replication panel the most strongly associated SNPs showed an 8.4-fold enrichment of associations at the nominal level, including three variants in previously identified loci and one in a locus (DENND1A) previously shown to be associated with polycystic ovary syndrome. Finally, using 1430 family trios, we showed that the transmissions from heterozygous parents to offspring of the derived alleles of rare (frequency ≤ 0.5%) hcSNPs are not biased, particularly after adjusting for the rates of genotype missingness and error in the data. The lack of transmission bias ruled out an immediately and strongly deleterious effect due to the rare derived alleles, consistent with the observation that mice homozygous for the deletion of ultraconserved elements showed no overt phenotype. Our study also illustrated the importance of carefully modeling potential technical confounders when analyzing genotype data of rare variants.
10aAlleles10aAnimals10aBody Height10aBody Mass Index10aChild10aConserved Sequence10aDogs10aEvolution, Molecular10aFemale10aGenetic Fitness10aGenetic Variation10aGenome, Human10aGenotype10aHumans10aInheritance Patterns10aMale10aMice10aPedigree10aPhenotype10aPolymorphism, Single Nucleotide10aRats10aReproduction10aYoung Adult1 aChiang, Charleston, W K1 aLiu, Ching-Ti1 aLettre, Guillaume1 aLange, Leslie, A1 aJorgensen, Neal, W1 aKeating, Brendan, J1 aVedantam, Sailaja1 aNock, Nora, L1 aFranceschini, Nora1 aReiner, Alex, P1 aDemerath, Ellen, W1 aBoerwinkle, Eric1 aRotter, Jerome, I1 aWilson, James, G1 aNorth, Kari, E1 aPapanicolaou, George, J1 aCupples, Adrienne, L1 aMurabito, Joanne, M1 aHirschhorn, Joel, N1 aGenetic Investigation of ANthropometric Traits Consortium uhttps://chs-nhlbi.org/node/154403380nas a2200565 4500008004100000022001400041245015900055210006900214260001300283300001100296490000600307520168100313653001501994653001002009653000902019653002802028653003102056653001402087653001002101653001102111653002602122653002002148653001702168653001802185653001102203653000902214653001602223653002002239653002302259653001502282653001302297653002002310653002702330653001602357100001902373700001802392700002402410700001702434700002002451700002102471700001702492700002202509700002002531700002402551700001802575700002602593700002402619710013502643856003602778 2013 eng d a1942-326800aExome sequencing and genome-wide linkage analysis in 17 families illustrate the complex contribution of TTN truncating variants to dilated cardiomyopathy.0 aExome sequencing and genomewide linkage analysis in 17 families c2013 Apr a144-530 v63 aBACKGROUND- Familial dilated cardiomyopathy (DCM) is a genetically heterogeneous disease with >30 known genes. TTN truncating variants were recently implicated in a candidate gene study to cause 25% of familial and 18% of sporadic DCM cases. METHODS AND RESULTS- We used an unbiased genome-wide approach using both linkage analysis and variant filtering across the exome sequences of 48 individuals affected with DCM from 17 families to identify genetic cause. Linkage analysis ranked the TTN region as falling under the second highest genome-wide multipoint linkage peak, multipoint logarithm of odds, 1.59. We identified 6 TTN truncating variants carried by individuals affected with DCM in 7 of 17 DCM families (logarithm of odds, 2.99); 2 of these 7 families also had novel missense variants that segregated with disease. Two additional novel truncating TTN variants did not segregate with DCM. Nucleotide diversity at the TTN locus, including missense variants, was comparable with 5 other known DCM genes. The average number of missense variants in the exome sequences from the DCM cases or the ≈5400 cases from the Exome Sequencing Project was ≈23 per individual. The average number of TTN truncating variants in the Exome Sequencing Project was 0.014 per individual. We also identified a region (chr9q21.11-q22.31) with no known DCM genes with a maximum heterogeneity logarithm of odds score of 1.74. CONCLUSIONS- These data suggest that TTN truncating variants contribute to DCM cause. However, the lack of segregation of all identified TTN truncating variants illustrates the challenge of determining variant pathogenicity even with full exome sequencing.
10aAdolescent10aAdult10aAged10aCardiomyopathy, Dilated10aChromosomes, Human, Pair 910aConnectin10aExome10aFemale10aGenetic Heterogeneity10aGenetic Linkage10aGenetic Loci10aGenome, Human10aHumans10aMale10aMiddle Aged10aMuscle Proteins10aMutation, Missense10aOdds Ratio10aPedigree10aProtein Kinases10aSequence Analysis, DNA10aYoung Adult1 aNorton, Nadine1 aLi, Duanxiang1 aRampersaud, Evadnie1 aMorales, Ana1 aMartin, Eden, R1 aZuchner, Stephan1 aGuo, Shengru1 aGonzalez, Michael1 aHedges, Dale, J1 aRobertson, Peggy, D1 aKrumm, Niklas1 aNickerson, Deborah, A1 aHershberger, Ray, E1 aNational Heart, Lung, and Blood Institute GO Exome Sequencing Project and the Exome Sequencing Project Family Studies Project Team uhttps://chs-nhlbi.org/node/613804096nas a2200901 4500008004100000022001400041245007000055210006900125260001500194300001100209490000700220520164400227653001001871653002201881653000901903653002201912653002001934653001101954653001701965653003801982653001802020653003402038653001302072653001102085653002702096653000902123653001602132653001202148653003602160653001602196100001502212700002502227700001502252700002302267700001902290700001902309700002802328700002102356700002102377700002002398700001702418700002102435700002102456700002502477700002002502700001702522700001602539700002002555700002502575700002102600700001602621700002102637700001602658700001902674700001802693700001902711700001702730700002602747700002002773700002202793700002102815700002002836700002402856700001502880700002002895700001602915700003002931700002202961700002102983700002403004700002003028700002303048700002203071700002703093700001903120700001903139856003603158 2013 eng d a1537-660500aFine Mapping and Identification of BMI Loci in African Americans.0 aFine Mapping and Identification of BMI Loci in African Americans c2013 Oct 3 a661-710 v933 aGenome-wide association studies (GWASs) primarily performed in European-ancestry (EA) populations have identified numerous loci associated with body mass index (BMI). However, it is still unclear whether these GWAS loci can be generalized to other ethnic groups, such as African Americans (AAs). Furthermore, the putative functional variant or variants in these loci mostly remain under investigation. The overall lower linkage disequilibrium in AA compared to EA populations provides the opportunity to narrow in or fine-map these BMI-related loci. Therefore, we used the Metabochip to densely genotype and evaluate 21 BMI GWAS loci identified in EA studies in 29,151 AAs from the Population Architecture using Genomics and Epidemiology (PAGE) study. Eight of the 21 loci (SEC16B, TMEM18, ETV5, GNPDA2, TFAP2B, BDNF, FTO, and MC4R) were found to be associated with BMI in AAs at 5.8 × 10(-5). Within seven out of these eight loci, we found that, on average, a substantially smaller number of variants was correlated (r(2) > 0.5) with the most significant SNP in AA than in EA populations (16 versus 55). Conditional analyses revealed GNPDA2 harboring a potential additional independent signal. Moreover, Metabochip-wide discovery analyses revealed two BMI-related loci, BRE (rs116612809, p = 3.6 × 10(-8)) and DHX34 (rs4802349, p = 1.2 × 10(-7)), which were significant when adjustment was made for the total number of SNPs tested across the chip. These results demonstrate that fine mapping in AAs is a powerful approach for both narrowing in on the underlying causal variants in known loci and discovering BMI-related loci.
10aAdult10aAfrican Americans10aAged10aAged, 80 and over10aBody Mass Index10aFemale10aGenetic Loci10aGenetic Predisposition to Disease10aGenome, Human10aGenome-Wide Association Study10aGenotype10aHumans10aLinkage Disequilibrium10aMale10aMiddle Aged10aObesity10aPolymorphism, Single Nucleotide10aYoung Adult1 aGong, Jian1 aSchumacher, Fredrick1 aLim, Unhee1 aHindorff, Lucia, A1 aHaessler, Jeff1 aBuyske, Steven1 aCarlson, Christopher, S1 aRosse, Stephanie1 aBůzková, Petra1 aFornage, Myriam1 aGross, Myron1 aPankratz, Nathan1 aPankow, James, S1 aSchreiner, Pamela, J1 aCooper, Richard1 aEhret, Georg1 aGu, Charles1 aHouston, Denise1 aIrvin, Marguerite, R1 aJackson, Rebecca1 aKuller, Lew1 aHenderson, Brian1 aCheng, Iona1 aWilkens, Lynne1 aLeppert, Mark1 aLewis, Cora, E1 aLi, Rongling1 aNguyen, Khanh-Dung, H1 aGoodloe, Robert1 aFarber-Eger, Eric1 aBoston, Jonathan1 aDilks, Holli, H1 aRitchie, Marylyn, D1 aFowke, Jay1 aPooler, Loreall1 aGraff, Misa1 aFernandez-Rhodes, Lindsay1 aCochrane, Barbara1 aBoerwinkle, Eric1 aKooperberg, Charles1 aMatise, Tara, C1 aLe Marchand, Loïc1 aCrawford, Dana, C1 aHaiman, Christopher, A1 aNorth, Kari, E1 aPeters, Ulrike uhttps://chs-nhlbi.org/node/662603673nas a2200601 4500008004100000022001400041245015800055210006900213260000900282300000700291490000700298520191500305653001102220653002602231653001802257653003402275653001102309653001102320653000902331653003602340653002202376100002002398700001902418700002202437700002102459700002102480700002002501700002402521700002202545700002102567700002102588700002202609700002402631700001902655700002302674700002202697700002102719700002102740700001602761700002202777700002602799700002102825700002302846700002402869700002302893700002402916700002202940700001902962700001902981700002003000710001503020856003603035 2013 eng d a1471-215600aInvestigation of gene-by-sex interactions for lipid traits in diverse populations from the population architecture using genomics and epidemiology study.0 aInvestigation of genebysex interactions for lipid traits in dive c2013 a330 v143 aBACKGROUND: High-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and triglyceride (TG) levels are influenced by both genes and the environment. Genome-wide association studies (GWAS) have identified ~100 common genetic variants associated with HDL-C, LDL-C, and/or TG levels, mostly in populations of European descent, but little is known about the modifiers of these associations. Here, we investigated whether GWAS-identified SNPs for lipid traits exhibited heterogeneity by sex in the Population Architecture using Genomics and Epidemiology (PAGE) study.
RESULTS: A sex-stratified meta-analysis was performed for 49 GWAS-identified SNPs for fasting HDL-C, LDL-C, and ln(TG) levels among adults self-identified as European American (25,013). Heterogeneity by sex was established when phet < 0.001. There was evidence for heterogeneity by sex for two SNPs for ln(TG) in the APOA1/C3/A4/A5/BUD13 gene cluster: rs28927680 (p(het) = 7.4 x 10(-7)) and rs3135506 (p(het) = 4.3 x 10(-4)one SNP in PLTP for HDL levels (rs7679; p(het) = 9.9 x 10(-4)), and one in HMGCR for LDL levels (rs12654264; p(het) = 3.1 x 10(-5)). We replicated heterogeneity by sex in five of seventeen loci previously reported by genome-wide studies (binomial p = 0.0009). We also present results for other racial/ethnic groups in the supplementary materials, to provide a resource for future meta-analyses.
CONCLUSIONS: We provide further evidence for sex-specific effects of SNPs in the APOA1/C3/A4/A5/BUD13 gene cluster, PLTP, and HMGCR on fasting triglyceride levels in European Americans from the PAGE study. Our findings emphasize the need for considering context-specific effects when interpreting genetic associations emerging from GWAS, and also highlight the difficulties in replicating interaction effects across studies and across racial/ethnic groups.
10aFemale10aGenetic Heterogeneity10aGenome, Human10aGenome-Wide Association Study10aHumans10aLipids10aMale10aPolymorphism, Single Nucleotide10aPopulation Groups1 aTaylor, Kira, C1 aCarty, Cara, L1 aDumitrescu, Logan1 aBůzková, Petra1 aCole, Shelley, A1 aHindorff, Lucia1 aSchumacher, Fred, R1 aWilkens, Lynne, R1 aShohet, Ralph, V1 aQuibrera, Miguel1 aJohnson, Karen, C1 aHenderson, Brian, E1 aHaessler, Jeff1 aFranceschini, Nora1 aEaton, Charles, B1 aDuggan, David, J1 aCochran, Barbara1 aCheng, Iona1 aCarlson, Chris, S1 aBrown-Gentry, Kristin1 aAnderson, Garnet1 aAmbite, Jose, Luis1 aHaiman, Christopher1 aLe Marchand, Loïc1 aKooperberg, Charles1 aCrawford, Dana, C1 aBuyske, Steven1 aNorth, Kari, E1 aFornage, Myriam1 aPAGE Study uhttps://chs-nhlbi.org/node/662711004nas a2203505 4500008004100000022001400041245014700055210006900202260001300271300001300284490000600297520115200303653001801455653001601473653002001489653001601509653003001525653001101555653001701566653001801583653003401601653001101635653000901646653003601655653002401691653002401715653002001739100002301759700002301782700002101805700002101826700002101847700001901868700002801887700001601915700001801931700001601949700003101965700002101996700003002017700001802047700001502065700002102080700002302101700001902124700002002143700001902163700002202182700002002204700001802224700003202242700002502274700003002299700002302329700001902352700001902371700002202390700002202412700001902434700001802453700001902471700002202490700002302512700002302535700002202558700002402580700002102604700002202625700002202647700002602669700002302695700002202718700002302740700002402763700002202787700002202809700001202831700002502843700001802868700002802886700002402914700001802938700002502956700001902981700001603000700001903016700001903035700002503054700002503079700002603104700001903130700002003149700001703169700002403186700002003210700002303230700001603253700002003269700002203289700002803311700002103339700002403360700002003384700002303404700001803427700002203445700002103467700001903488700002603507700002203533700002803555700002003583700002803603700002703631700002003658700002303678700002003701700002103721700002403742700002003766700001503786700002803801700002603829700001903855700002803874700001903902700002503921700002403946700002703970700002003997700001404017700002004031700002204051700001904073700001804092700001804110700002104128700002004149700002204169700001604191700002204207700002204229700002604251700002604277700001704303700002004320700002304340700001904363700002304382700002004405700002304425700001904448700001804467700002304485700002504508700001904533700001904552700002004571700003004591700003504621700002104656700001704677700002104694700001904715700002504734700002404759700001804783700001504801700002404816700002204840700002504862700001504887700002304902700002004925700001904945700002204964700002004986700001805006700002205024700002005046700002105066700002105087700001705108700002105125700002005146700001605166700002105182700001905203700002505222700002405247700002305271700001905294700002205313700001805335700002005353700002005373700001905393700002105412700002405433700001805457700002005475700002105495700002005516700002505536700002105561700002105582700002405603700001805627700001605645700002105661700003705682700002205719700001805741700002005759700002105779700002005800700002605820700002305846700001905869700002005888700002305908700002405931700002005955700001905975700002005994700002406014700002506038700001906063700002106082700002106103700001806124700002106142700003006163700002006193700002306213700002206236700002006258700002806278700002206306700001906328700002406347700002206371700002006393700001906413700002306432700001506455700001706470700001506487700001806502700001906520700002706539700002306566700002206589700002106611700002206632700002206654700002306676700002506699700002106724700001906745700001806764700001906782700002106801700001906822700001806841700002906859700001906888700002106907700002306928700002006951700002106971700002206992700001907014700002307033700001807056700002007074700001907094700002107113700002607134700002407160700002307184700002107207700002307228700002507251700002207276700002407298700001107322700002007333700002507353700001907378700001807397710002307415710002407438856003607462 2013 eng d a1553-740400aSex-stratified genome-wide association studies including 270,000 individuals show sexual dimorphism in genetic loci for anthropometric traits.0 aSexstratified genomewide association studies including 270000 in c2013 Jun ae10035000 v93 aGiven the anthropometric differences between men and women and previous evidence of sex-difference in genetic effects, we conducted a genome-wide search for sexually dimorphic associations with height, weight, body mass index, waist circumference, hip circumference, and waist-to-hip-ratio (133,723 individuals) and took forward 348 SNPs into follow-up (additional 137,052 individuals) in a total of 94 studies. Seven loci displayed significant sex-difference (FDR<5%), including four previously established (near GRB14/COBLL1, LYPLAL1/SLC30A10, VEGFA, ADAMTS9) and three novel anthropometric trait loci (near MAP3K1, HSD17B4, PPARG), all of which were genome-wide significant in women (P<5×10(-8)), but not in men. Sex-differences were apparent only for waist phenotypes, not for height, weight, BMI, or hip circumference. Moreover, we found no evidence for genetic effects with opposite directions in men versus women. The PPARG locus is of specific interest due to its role in diabetes genetics and therapy. Our results demonstrate the value of sex-specific GWAS to unravel the sexually dimorphic genetic underpinning of complex traits.
10aAnthropometry10aBody Height10aBody Mass Index10aBody Weight10aBody Weights and Measures10aFemale10aGenetic Loci10aGenome, Human10aGenome-Wide Association Study10aHumans10aMale10aPolymorphism, Single Nucleotide10aSex Characteristics10aWaist Circumference10aWaist-Hip Ratio1 aRandall, Joshua, C1 aWinkler, Thomas, W1 aKutalik, Zoltán1 aBerndt, Sonja, I1 aJackson, Anne, U1 aMonda, Keri, L1 aKilpeläinen, Tuomas, O1 aEsko, Tõnu1 aMägi, Reedik1 aLi, Shengxu1 aWorkalemahu, Tsegaselassie1 aFeitosa, Mary, F1 aCroteau-Chonka, Damien, C1 aDay, Felix, R1 aFall, Tove1 aFerreira, Teresa1 aGustafsson, Stefan1 aLocke, Adam, E1 aMathieson, Iain1 aScherag, Andre1 aVedantam, Sailaja1 aWood, Andrew, R1 aLiang, Liming1 aSteinthorsdottir, Valgerdur1 aThorleifsson, Gudmar1 aDermitzakis, Emmanouil, T1 aDimas, Antigone, S1 aKarpe, Fredrik1 aMin, Josine, L1 aNicholson, George1 aClegg, Deborah, J1 aPerson, Thomas1 aKrohn, Jon, P1 aBauer, Sabrina1 aBuechler, Christa1 aEisinger, Kristina1 aBonnefond, Amélie1 aFroguel, Philippe1 aHottenga, Jouke-Jan1 aProkopenko, Inga1 aWaite, Lindsay, L1 aHarris, Tamara, B1 aSmith, Albert, Vernon1 aShuldiner, Alan, R1 aMcArdle, Wendy, L1 aCaulfield, Mark, J1 aMunroe, Patricia, B1 aGrönberg, Henrik1 aChen, Yii-Der Ida1 aLi, Guo1 aBeckmann, Jacques, S1 aJohnson, Toby1 aThorsteinsdottir, Unnur1 aTeder-Laving, Maris1 aKhaw, Kay-Tee1 aWareham, Nicholas, J1 aZhao, Jing Hua1 aAmin, Najaf1 aOostra, Ben, A1 aKraja, Aldi, T1 aProvince, Michael, A1 aCupples, Adrienne, L1 aHeard-Costa, Nancy, L1 aKaprio, Jaakko1 aRipatti, Samuli1 aSurakka, Ida1 aCollins, Francis, S1 aSaramies, Jouko1 aTuomilehto, Jaakko1 aJula, Antti1 aSalomaa, Veikko1 aErdmann, Jeanette1 aHengstenberg, Christian1 aLoley, Christina1 aSchunkert, Heribert1 aLamina, Claudia1 aWichmann, Erich, H1 aAlbrecht, Eva1 aGieger, Christian1 aHicks, Andrew, A1 aJohansson, Asa1 aPramstaller, Peter, P1 aKathiresan, Sekar1 aSpeliotes, Elizabeth, K1 aPenninx, Brenda1 aHartikainen, Anna-Liisa1 aJarvelin, Marjo-Riitta1 aGyllensten, Ulf1 aBoomsma, Dorret, I1 aCampbell, Harry1 aWilson, James, F1 aChanock, Stephen, J1 aFarrall, Martin1 aGoel, Anuj1 aMedina-Gómez, Carolina1 aRivadeneira, Fernando1 aEstrada, Karol1 aUitterlinden, André, G1 aHofman, Albert1 aZillikens, Carola, M1 aHeijer, Martin, den1 aKiemeney, Lambertus, A1 aMaschio, Andrea1 aHall, Per1 aTyrer, Jonathan1 aTeumer, Alexander1 aVölzke, Henry1 aKovacs, Peter1 aTönjes, Anke1 aMangino, Massimo1 aSpector, Tim, D1 aHayward, Caroline1 aRudan, Igor1 aHall, Alistair, S1 aSamani, Nilesh, J1 aAttwood, Antony, Paul1 aSambrook, Jennifer, G1 aHung, Joseph1 aPalmer, Lyle, J1 aLokki, Marja-Liisa1 aSinisalo, Juha1 aBoucher, Gabrielle1 aHuikuri, Heikki1 aLorentzon, Mattias1 aOhlsson, Claes1 aEklund, Niina1 aEriksson, Johan, G1 aBarlassina, Cristina1 aRivolta, Carlo1 aNolte, Ilja, M1 aSnieder, Harold1 avan der Klauw, Melanie, M1 avan Vliet-Ostaptchouk, Jana, V1 aGejman, Pablo, V1 aShi, Jianxin1 aJacobs, Kevin, B1 aWang, Zhaoming1 aBakker, Stephan, J L1 aLeach, Irene, Mateo1 aNavis, Gerjan1 aHarst, Pim1 aMartin, Nicholas, G1 aMedland, Sarah, E1 aMontgomery, Grant, W1 aYang, Jian1 aChasman, Daniel, I1 aRidker, Paul, M1 aRose, Lynda, M1 aLehtimäki, Terho1 aRaitakari, Olli1 aAbsher, Devin1 aIribarren, Carlos1 aBasart, Hanneke1 aHovingh, Kees, G1 aHyppönen, Elina1 aPower, Chris1 aAnderson, Denise1 aBeilby, John, P1 aHui, Jennie1 aJolley, Jennifer1 aSager, Hendrik1 aBornstein, Stefan, R1 aSchwarz, Peter, E H1 aKristiansson, Kati1 aPerola, Markus1 aLindström, Jaana1 aSwift, Amy, J1 aUusitupa, Matti1 aAtalay, Mustafa1 aLakka, Timo, A1 aRauramaa, Rainer1 aBolton, Jennifer, L1 aFowkes, Gerry1 aFraser, Ross, M1 aPrice, Jackie, F1 aFischer, Krista1 aKov, Kaarel, Krjutå1 aMetspalu, Andres1 aMihailov, Evelin1 aLangenberg, Claudia1 aLuan, Jian'an1 aOng, Ken, K1 aChines, Peter, S1 aKeinanen-Kiukaanniemi, Sirkka, M1 aSaaristo, Timo, E1 aEdkins, Sarah1 aFranks, Paul, W1 aHallmans, Göran1 aShungin, Dmitry1 aMorris, Andrew, David1 aPalmer, Colin, N A1 aErbel, Raimund1 aMoebus, Susanne1 aNöthen, Markus, M1 aPechlivanis, Sonali1 aHveem, Kristian1 aNarisu, Narisu1 aHamsten, Anders1 aHumphries, Steve, E1 aStrawbridge, Rona, J1 aTremoli, Elena1 aGrallert, Harald1 aThorand, Barbara1 aIllig, Thomas1 aKoenig, Wolfgang1 aMüller-Nurasyid, Martina1 aPeters, Annette1 aBoehm, Bernhard, O1 aKleber, Marcus, E1 aMärz, Winfried1 aWinkelmann, Bernhard, R1 aKuusisto, Johanna1 aLaakso, Markku1 aArveiler, Dominique1 aCesana, Giancarlo1 aKuulasmaa, Kari1 aVirtamo, Jarmo1 aYarnell, John, W G1 aKuh, Diana1 aWong, Andrew1 aLind, Lars1 ade Faire, Ulf1 aGigante, Bruna1 aMagnusson, Patrik, K E1 aPedersen, Nancy, L1 aDedoussis, George1 aDimitriou, Maria1 aKolovou, Genovefa1 aKanoni, Stavroula1 aStirrups, Kathleen1 aBonnycastle, Lori, L1 aNjølstad, Inger1 aWilsgaard, Tom1 aGanna, Andrea1 aRehnberg, Emil1 aHingorani, Aroon1 aKivimaki, Mika1 aKumari, Meena1 aAssimes, Themistocles, L1 aBarroso, Inês1 aBoehnke, Michael1 aBorecki, Ingrid, B1 aDeloukas, Panos1 aFox, Caroline, S1 aFrayling, Timothy1 aGroop, Leif, C1 aHaritunians, Talin1 aHunter, David1 aIngelsson, Erik1 aKaplan, Robert1 aMohlke, Karen, L1 aO'Connell, Jeffrey, R1 aSchlessinger, David1 aStrachan, David, P1 aStefansson, Kari1 aDuijn, Cornelia, M1 aAbecasis, Goncalo, R1 aMcCarthy, Mark, I1 aHirschhorn, Joel, N1 aQi, Lu1 aLoos, Ruth, J F1 aLindgren, Cecilia, M1 aNorth, Kari, E1 aHeid, Iris, M1 aDIAGRAM Consortium1 aMAGIC investigators uhttps://chs-nhlbi.org/node/602802102nas a2200469 4500008004100000022001400041245008200055210006900137260001300206300001200219490000700231520073300238653002100971653002600992653002301018653002201041653001801063653003401081653001301115653001701128653001101145653002401156100002401180700001901204700002301223700001801246700001201264700001801276700001701294700001301311700001801324700001901342700001601361700001901377700003001396700002001426700002501446700001901471700002101490710008501511856003601596 2013 eng d a1546-171800aWhole-genome sequence-based analysis of high-density lipoprotein cholesterol.0 aWholegenome sequencebased analysis of highdensity lipoprotein ch c2013 Aug a899-9010 v453 aWe describe initial steps for interrogating whole-genome sequence data to characterize the genetic architecture of a complex trait, levels of high-density lipoprotein cholesterol (HDL-C). We report whole-genome sequencing and analysis of 962 individuals from the Cohorts for Heart and Aging Research in Genetic Epidemiology (CHARGE) studies. From this analysis, we estimate that common variation contributes more to heritability of HDL-C levels than rare variation, and screening for mendelian variants for dyslipidemia identified individuals with extreme HDL-C levels. Whole-genome sequencing analyses highlight the value of regulatory and non-protein-coding regions of the genome in addition to protein-coding regions.
10aCholesterol, HDL10aComputational Biology10aDatabases, Genetic10aGenetic Variation10aGenome, Human10aGenome-Wide Association Study10aGenomics10aHeterozygote10aHumans10aOpen Reading Frames1 aMorrison, Alanna, C1 aVoorman, Arend1 aJohnson, Andrew, D1 aLiu, Xiaoming1 aYu, Jin1 aLi, Alexander1 aMuzny, Donna1 aYu, Fuli1 aRice, Kenneth1 aZhu, Chengsong1 aBis, Joshua1 aHeiss, Gerardo1 aO'Donnell, Christopher, J1 aPsaty, Bruce, M1 aCupples, Adrienne, L1 aGibbs, Richard1 aBoerwinkle, Eric1 aCohorts for Heart and Aging Research in Genetic Epidemiology (CHARGE) Consortium uhttps://chs-nhlbi.org/node/628307147nas a2202257 4500008004100000022001400041245010000055210006900155260001300224300001100237490000700248520092000255653001901175653002301194653002201217653002901239653001701268653003801285653001801323653003401341653001101375653001801386653002701404653003601431653001401467653002801481653003101509653001501540653001901555100001801574700002601592700002001618700002001638700002301658700001601681700002301697700002601720700001501746700002101761700002001782700002301802700001901825700002501844700002201869700002601891700002501917700001901942700001801961700001901979700002001998700002202018700001802040700002002058700001702078700002402095700003102119700001902150700001902169700002002188700001502208700001802223700002202241700002002263700002002283700001202303700001902315700001602334700002102350700001902371700002202390700001902412700002402431700001602455700001702471700001902488700002002507700002402527700002002551700001802571700001802589700002102607700002702628700001702655700001702672700002702689700001902716700002202735700001702757700002002774700002102794700001802815700001902833700001902852700002002871700002602891700001902917700001902936700002402955700002402979700002203003700001303025700002203038700002003060700002103080700001903101700001903120700001803139700002003157700002003177700001703197700002503214700002803239700002003267700002303287700002203310700001503332700001803347700002303365700001603388700002603404700001703430700001603447700001703463700001903480700002303499700001903522700002003541700002403561700002003585700002103605700002103626700002103647700002203668700001903690700002003709700002103729700002203750700001903772700002003791700002003811700002203831700001803853700002803871700002403899700001703923700002803940700002403968700002103992700001904013700001904032700002204051700001804073700002204091700001904113700001804132700002304150700002504173700002004198700001904218700002404237700001904261700001604280700001904296700002204315700001804337700001904355700001704374700002704391700002104418700001504439700002304454700002204477700002404499700001704523700002504540700002104565700001704586700001904603700002204622700001704644700002404661700002504685700001704710700002204727700001904749700001704768700002204785700002104807700002504828856003604853 2014 eng d a1546-171800aGenome-wide association analysis identifies six new loci associated with forced vital capacity.0 aGenomewide association analysis identifies six new loci associat c2014 Jul a669-770 v463 aForced vital capacity (FVC), a spirometric measure of pulmonary function, reflects lung volume and is used to diagnose and monitor lung diseases. We performed genome-wide association study meta-analysis of FVC in 52,253 individuals from 26 studies and followed up the top associations in 32,917 additional individuals of European ancestry. We found six new regions associated at genome-wide significance (P < 5 × 10(-8)) with FVC in or near EFEMP1, BMP6, MIR129-2-HSD17B12, PRDM11, WWOX and KCNJ2. Two loci previously associated with spirometric measures (GSTCD and PTCH1) were related to FVC. Newly implicated regions were followed up in samples from African-American, Korean, Chinese and Hispanic individuals. We detected transcripts for all six newly implicated genes in human lung tissue. The new loci may inform mechanisms involved in lung development and the pathogenesis of restrictive lung disease.
10aCohort Studies10aDatabases, Genetic10aFollow-Up Studies10aForced Expiratory Volume10aGenetic Loci10aGenetic Predisposition to Disease10aGenome, Human10aGenome-Wide Association Study10aHumans10aLung Diseases10aMeta-Analysis as Topic10aPolymorphism, Single Nucleotide10aPrognosis10aQuantitative Trait Loci10aRespiratory Function Tests10aSpirometry10aVital Capacity1 aLoth, Daan, W1 aArtigas, Maria, Soler1 aGharib, Sina, A1 aWain, Louise, V1 aFranceschini, Nora1 aKoch, Beate1 aPottinger, Tess, D1 aSmith, Albert, Vernon1 aDuan, Qing1 aOldmeadow, Chris1 aLee, Mi, Kyeong1 aStrachan, David, P1 aJames, Alan, L1 aHuffman, Jennifer, E1 aVitart, Veronique1 aRamasamy, Adaikalavan1 aWareham, Nicholas, J1 aKaprio, Jaakko1 aWang, Xin-Qun1 aTrochet, Holly1 aKähönen, Mika1 aFlexeder, Claudia1 aAlbrecht, Eva1 aLopez, Lorna, M1 ade Jong, Kim1 aThyagarajan, Bharat1 aAlves, Alexessander, Couto1 aEnroth, Stefan1 aOmenaas, Ernst1 aJoshi, Peter, K1 aFall, Tove1 aViñuela, Ana1 aLauner, Lenore, J1 aLoehr, Laura, R1 aFornage, Myriam1 aLi, Guo1 aWilk, Jemma, B1 aTang, Wenbo1 aManichaikul, Ani1 aLahousse, Lies1 aHarris, Tamara, B1 aNorth, Kari, E1 aRudnicka, Alicja, R1 aHui, Jennie1 aGu, Xiangjun1 aLumley, Thomas1 aWright, Alan, F1 aHastie, Nicholas, D1 aCampbell, Susan1 aKumar, Rajesh1 aPin, Isabelle1 aScott, Robert, A1 aPietiläinen, Kirsi, H1 aSurakka, Ida1 aLiu, Yongmei1 aHolliday, Elizabeth, G1 aSchulz, Holger1 aHeinrich, Joachim1 aDavies, Gail1 aVonk, Judith, M1 aWojczynski, Mary1 aPouta, Anneli1 aJohansson, Asa1 aWild, Sarah, H1 aIngelsson, Erik1 aRivadeneira, Fernando1 aVölzke, Henry1 aHysi, Pirro, G1 aEiriksdottir, Gudny1 aMorrison, Alanna, C1 aRotter, Jerome, I1 aGao, Wei1 aPostma, Dirkje, S1 aWhite, Wendy, B1 aRich, Stephen, S1 aHofman, Albert1 aAspelund, Thor1 aCouper, David1 aSmith, Lewis, J1 aPsaty, Bruce, M1 aLohman, Kurt1 aBurchard, Esteban, G1 aUitterlinden, André, G1 aGarcia, Melissa1 aJoubert, Bonnie, R1 aMcArdle, Wendy, L1 aMusk, Bill1 aHansel, Nadia1 aHeckbert, Susan, R1 aZgaga, Lina1 avan Meurs, Joyce, B J1 aNavarro, Pau1 aRudan, Igor1 aOh, Yeon-Mok1 aRedline, Susan1 aJarvis, Deborah, L1 aZhao, Jing Hua1 aRantanen, Taina1 aO'Connor, George, T1 aRipatti, Samuli1 aScott, Rodney, J1 aKarrasch, Stefan1 aGrallert, Harald1 aGaddis, Nathan, C1 aStarr, John, M1 aWijmenga, Cisca1 aMinster, Ryan, L1 aLederer, David, J1 aPekkanen, Juha1 aGyllensten, Ulf1 aCampbell, Harry1 aMorris, Andrew, P1 aGläser, Sven1 aHammond, Christopher, J1 aBurkart, Kristin, M1 aBeilby, John1 aKritchevsky, Stephen, B1 aGudnason, Vilmundur1 aHancock, Dana, B1 aWilliams, Dale1 aPolasek, Ozren1 aZemunik, Tatijana1 aKolcic, Ivana1 aPetrini, Marcy, F1 aWjst, Matthias1 aKim, Woo, Jin1 aPorteous, David, J1 aScotland, Generation1 aSmith, Blair, H1 aViljanen, Anne1 aHeliövaara, Markku1 aAttia, John, R1 aSayers, Ian1 aHampel, Regina1 aGieger, Christian1 aDeary, Ian, J1 aBoezen, Marike1 aNewman, Anne1 aJarvelin, Marjo-Riitta1 aWilson, James, F1 aLind, Lars1 aStricker, Bruno, H1 aTeumer, Alexander1 aSpector, Timothy, D1 aMelén, Erik1 aPeters, Marjolein, J1 aLange, Leslie, A1 aBarr, Graham1 aBracke, Ken, R1 aVerhamme, Fien, M1 aSung, Joohon1 aHiemstra, Pieter, S1 aCassano, Patricia, A1 aSood, Akshay1 aHayward, Caroline1 aDupuis, Josée1 aHall, Ian, P1 aBrusselle, Guy, G1 aTobin, Martin, D1 aLondon, Stephanie, J uhttps://chs-nhlbi.org/node/658204315nas a2200901 4500008004100000022001400041245006300055210006000118260001600178300001200194490000700206520174600213653002201959653003701981653001802018653004002036653001802076653003402094653001302128653001102141653002002152653001502172653002702187653001402214653003602228653002802264100002302292700002502315700002002340700002602360700002302386700002002409700002102429700002202450700002002472700002002492700002302512700002202535700001902557700002002576700002502596700001802621700001902639700002102658700002102679700002402700700002102724700001602745700001402761700002102775700002302796700002002819700002202839700002102861700001902882700001502901700001902916700002102935700001902956700002802975700002303003700002103026700001803047700002503065700002003090700002303110700002503133700002203158700003003180700002303210700002103233700002203254700001903276710002203295710001103317710004903328856003603377 2014 eng d a1460-208300aTrans-ethnic meta-analysis of white blood cell phenotypes.0 aTransethnic metaanalysis of white blood cell phenotypes c2014 Dec 20 a6944-600 v233 aWhite blood cell (WBC) count is a common clinical measure used as a predictor of certain aspects of human health, including immunity and infection status. WBC count is also a complex trait that varies among individuals and ancestry groups. Differences in linkage disequilibrium structure and heterogeneity in allelic effects are expected to play a role in the associations observed between populations. Prior genome-wide association study (GWAS) meta-analyses have identified genomic loci associated with WBC and its subtypes, but much of the heritability of these phenotypes remains unexplained. Using GWAS summary statistics for over 50 000 individuals from three diverse populations (Japanese, African-American and European ancestry), a Bayesian model methodology was employed to account for heterogeneity between ancestry groups. This approach was used to perform a trans-ethnic meta-analysis of total WBC, neutrophil and monocyte counts. Ten previously known associations were replicated and six new loci were identified, including several regions harboring genes related to inflammation and immune cell function. Ninety-five percent credible interval regions were calculated to narrow the association signals and fine-map the putatively causal variants within loci. Finally, a conditional analysis was performed on the most significant SNPs identified by the trans-ethnic meta-analysis (MA), and nine secondary signals within loci previously associated with WBC or its subtypes were identified. This work illustrates the potential of trans-ethnic analysis and ascribes a critical role to multi-ethnic cohorts and consortia in exploring complex phenotypes with respect to variants that lie outside the European-biased GWAS pool.
10aAfrican Americans10aAsian Continental Ancestry Group10aBayes Theorem10aEuropean Continental Ancestry Group10aGenome, Human10aGenome-Wide Association Study10aGenotype10aHumans10aLeukocyte Count10aLeukocytes10aLinkage Disequilibrium10aPhenotype10aPolymorphism, Single Nucleotide10aQuantitative Trait Loci1 aKeller, Margaux, F1 aReiner, Alexander, P1 aOkada, Yukinori1 avan Rooij, Frank, J A1 aJohnson, Andrew, D1 aChen, Ming-Huei1 aSmith, Albert, V1 aMorris, Andrew, P1 aTanaka, Toshiko1 aFerrucci, Luigi1 aZonderman, Alan, B1 aLettre, Guillaume1 aHarris, Tamara1 aGarcia, Melissa1 aBandinelli, Stefania1 aQayyum, Rehan1 aYanek, Lisa, R1 aBecker, Diane, M1 aBecker, Lewis, C1 aKooperberg, Charles1 aKeating, Brendan1 aReis, Jared1 aTang, Hua1 aBoerwinkle, Eric1 aKamatani, Yoichiro1 aMatsuda, Koichi1 aKamatani, Naoyuki1 aNakamura, Yusuke1 aKubo, Michiaki1 aLiu, Simin1 aDehghan, Abbas1 aFelix, Janine, F1 aHofman, Albert1 aUitterlinden, André, G1 aDuijn, Cornelia, M1 aFranco, Oscar, H1 aLongo, Dan, L1 aSingleton, Andrew, B1 aPsaty, Bruce, M1 aEvans, Michelle, K1 aCupples, Adrienne, L1 aRotter, Jerome, I1 aO'Donnell, Christopher, J1 aTakahashi, Atsushi1 aWilson, James, G1 aGanesh, Santhi, K1 aNalls, Mike, A1 aCHARGE Hematology1 aCOGENT1 aBioBank Japan Project (RIKEN) Working Groups uhttps://chs-nhlbi.org/node/657302719nas a2200469 4500008004100000022001400041245010800055210006900163260001600232300001100248490000700259520132600266653000901592653001001601653002801611653001901639653002401658653002801682653003401710653003301744653002201777653001801799653003401817653001101851653002501862653002701887653002001914653002101934653002001955653001801975100002401993700002102017700001902038700002202057700002502079700002202104700002102126700002502147700002102172700002002193856003602213 2015 eng d a1097-025800aGeneralized estimating equations for genome-wide association studies using longitudinal phenotype data.0 aGeneralized estimating equations for genomewide association stud c2015 Jan 15 a118-300 v343 aMany longitudinal cohort studies have both genome-wide measures of genetic variation and repeated measures of phenotypes and environmental exposures. Genome-wide association study analyses have typically used only cross-sectional data to evaluate quantitative phenotypes and binary traits. Incorporation of repeated measures may increase power to detect associations, but also requires specialized analysis methods. Here, we discuss one such method-generalized estimating equations (GEE)-in the contexts of analysis of main effects of rare genetic variants and analysis of gene-environment interactions. We illustrate the potential for increased power using GEE analyses instead of cross-sectional analyses. We also address challenges that arise, such as the need for small-sample corrections when the minor allele frequency of a genetic variant and/or the prevalence of an environmental exposure is low. To illustrate methods for detection of gene-drug interactions on a genome-wide scale, using repeated measures data, we conduct single-study analyses and meta-analyses across studies in three large cohort studies participating in the Cohorts for Heart and Aging Research in Genomic Epidemiology consortium-the Atherosclerosis Risk in Communities study, the Cardiovascular Health Study, and the Rotterdam Study.
10aAged10aAging10aCardiovascular Diseases10aCohort Studies10aComputer Simulation10aCross-Sectional Studies10aEpidemiologic Research Design10aGene-Environment Interaction10aGenetic Variation10aGenome, Human10aGenome-Wide Association Study10aHumans10aLongitudinal Studies10aMeta-Analysis as Topic10aModels, Genetic10aPharmacogenetics10aRisk Assessment10aUnited States1 aSitlani, Colleen, M1 aRice, Kenneth, M1 aLumley, Thomas1 aMcKnight, Barbara1 aCupples, Adrienne, L1 aAvery, Christy, L1 aNoordam, Raymond1 aStricker, Bruno, H C1 aWhitsel, Eric, A1 aPsaty, Bruce, M uhttps://chs-nhlbi.org/node/660203088nas a2200289 4500008004100000022001400041245008600055210006900141260001600210300000800226490000700234520224200241653002302483653001802506653001302524653004202537653001102579100001802590700001902608700002902627700002002656700001902676700002102695700003302716700001302749856003602762 2016 eng d a1471-210500aA hybrid computational strategy to address WGS variant analysis in >5000 samples.0 ahybrid computational strategy to address WGS variant analysis in c2016 Sep 10 a3610 v173 aBACKGROUND: The decreasing costs of sequencing are driving the need for cost effective and real time variant calling of whole genome sequencing data. The scale of these projects are far beyond the capacity of typical computing resources available with most research labs. Other infrastructures like the cloud AWS environment and supercomputers also have limitations due to which large scale joint variant calling becomes infeasible, and infrastructure specific variant calling strategies either fail to scale up to large datasets or abandon joint calling strategies.
RESULTS: We present a high throughput framework including multiple variant callers for single nucleotide variant (SNV) calling, which leverages hybrid computing infrastructure consisting of cloud AWS, supercomputers and local high performance computing infrastructures. We present a novel binning approach for large scale joint variant calling and imputation which can scale up to over 10,000 samples while producing SNV callsets with high sensitivity and specificity. As a proof of principle, we present results of analysis on Cohorts for Heart And Aging Research in Genomic Epidemiology (CHARGE) WGS freeze 3 dataset in which joint calling, imputation and phasing of over 5300 whole genome samples was produced in under 6 weeks using four state-of-the-art callers. The callers used were SNPTools, GATK-HaplotypeCaller, GATK-UnifiedGenotyper and GotCloud. We used Amazon AWS, a 4000-core in-house cluster at Baylor College of Medicine, IBM power PC Blue BioU at Rice and Rhea at Oak Ridge National Laboratory (ORNL) for the computation. AWS was used for joint calling of 180 TB of BAM files, and ORNL and Rice supercomputers were used for the imputation and phasing step. All other steps were carried out on the local compute cluster. The entire operation used 5.2 million core hours and only transferred a total of 6 TB of data across the platforms.
CONCLUSIONS: Even with increasing sizes of whole genome datasets, ensemble joint calling of SNVs for low coverage data can be accomplished in a scalable, cost effective and fast manner by using heterogeneous computing platforms without compromising on the quality of variants.
10aDatabases, Genetic10aGenome, Human10aGenomics10aHigh-Throughput Nucleotide Sequencing10aHumans1 aHuang, Zhuoyi1 aRustagi, Navin1 aVeeraraghavan, Narayanan1 aCarroll, Andrew1 aGibbs, Richard1 aBoerwinkle, Eric1 aVenkata, Manjunath, Gorentla1 aYu, Fuli uhttps://chs-nhlbi.org/node/856404416nas a2200913 4500008004100000022001400041245012600055210006900181260001600250300001200266490000800278520169200286653003701978653002902015653001902044653003802063653001802101653003402119653001102153653002302164653003602187653001702223653001402240653002502254100002402279700001702303700001902320700001902339700001802358700002802376700002002404700002802424700001902452700003002471700002402501700002102525700002402546700002102570700002302591700002602614700001902640700002002659700002202679700002202701700002602723700001802749700002302767700002102790700002502811700002202836700002702858700002102885700002602906700001702932700002702949700002102976700002002997700001903017700002303036700002403059700002403083700002003107700001503127700002003142700002903162700002303191700002203214700001903236700002003255700003103275700002303306700002103329700002503350700002603375700002003401700002503421700002003446856003603466 2017 eng d a1537-660500aLow-Frequency Synonymous Coding Variation in CYP2R1 Has Large Effects on Vitamin D Levels and Risk of Multiple Sclerosis.0 aLowFrequency Synonymous Coding Variation in CYP2R1 Has Large Eff c2017 Aug 03 a227-2380 v1013 aVitamin D insufficiency is common, correctable, and influenced by genetic factors, and it has been associated with risk of several diseases. We sought to identify low-frequency genetic variants that strongly increase the risk of vitamin D insufficiency and tested their effect on risk of multiple sclerosis, a disease influenced by low vitamin D concentrations. We used whole-genome sequencing data from 2,619 individuals through the UK10K program and deep-imputation data from 39,655 individuals genotyped genome-wide. Meta-analysis of the summary statistics from 19 cohorts identified in CYP2R1 the low-frequency (minor allele frequency = 2.5%) synonymous coding variant g.14900931G>A (p.Asp120Asp) (rs117913124[A]), which conferred a large effect on 25-hydroxyvitamin D (25OHD) levels (-0.43 SD of standardized natural log-transformed 25OHD per A allele; p value = 1.5 × 10(-88)). The effect on 25OHD was four times larger and independent of the effect of a previously described common variant near CYP2R1. By analyzing 8,711 individuals, we showed that heterozygote carriers of this low-frequency variant have an increased risk of vitamin D insufficiency (odds ratio [OR] = 2.2, 95% confidence interval [CI] = 1.78-2.78, p = 1.26 × 10(-12)). Individuals carrying one copy of this variant also had increased odds of multiple sclerosis (OR = 1.4, 95% CI = 1.19-1.64, p = 2.63 × 10(-5)) in a sample of 5,927 case and 5,599 control subjects. In conclusion, we describe a low-frequency CYP2R1 coding variant that exerts the largest effect upon 25OHD levels identified to date in the general European population and implicates vitamin D in the etiology of multiple sclerosis.
10aCholestanetriol 26-Monooxygenase10aCytochrome P450 Family 210aGene Frequency10aGenetic Predisposition to Disease10aGenome, Human10aGenome-Wide Association Study10aHumans10aMultiple Sclerosis10aPolymorphism, Single Nucleotide10aRisk Factors10aVitamin D10aVitamin D Deficiency1 aManousaki, Despoina1 aDudding, Tom1 aHaworth, Simon1 aHsu, Yi-Hsiang1 aLiu, Ching-Ti1 aMedina-Gómez, Carolina1 aVoortman, Trudy1 avan der Velde, Nathalie1 aMelhus, Håkan1 aRobinson-Cohen, Cassianne1 aCousminer, Diana, L1 aNethander, Maria1 aVandenput, Liesbeth1 aNoordam, Raymond1 aForgetta, Vincenzo1 aGreenwood, Celia, M T1 aBiggs, Mary, L1 aPsaty, Bruce, M1 aRotter, Jerome, I1 aZemel, Babette, S1 aMitchell, Jonathan, A1 aTaylor, Bruce1 aLorentzon, Mattias1 aKarlsson, Magnus1 aJaddoe, Vincent, V W1 aTiemeier, Henning1 aCampos-Obando, Natalia1 aFranco, Oscar, H1 aUtterlinden, Andre, G1 aBroer, Linda1 avan Schoor, Natasja, M1 aHam, Annelies, C1 aIkram, Arfan, M1 aKarasik, David1 ade Mutsert, Renée1 aRosendaal, Frits, R1 aHeijer, Martin, den1 aWang, Thomas, J1 aLind, Lars1 aOrwoll, Eric, S1 aMook-Kanamori, Dennis, O1 aMichaëlsson, Karl1 aKestenbaum, Bryan1 aOhlsson, Claes1 aMellström, Dan1 ade Groot, Lisette, C P G M1 aGrant, Struan, F A1 aKiel, Douglas, P1 aZillikens, Carola, M1 aRivadeneira, Fernando1 aSawcer, Stephen1 aTimpson, Nicholas, J1 aRichards, Brent uhttps://chs-nhlbi.org/node/748703066nas a2200577 4500008004100000022001400041245008100055210006900136260001300205300001300218490000700231520143400238653001101672653002301683653001801706653001101724653004301735653001901778653001701797653001801814653003401832653001101866653001901877653001801896653002001914653000901934653002301943653001401966653001601980653001901996653003602015653003302051653002102084653002302105653002702128100001802155700002302173700002002196700001302216700002502229700002102254700001802275700002002293700001802313700002302331700002502354700002202379700002502401710002602426856003602452 2017 eng d a1553-740400aRare coding variants pinpoint genes that control human hematological traits.0 aRare coding variants pinpoint genes that control human hematolog c2017 Aug ae10069250 v133 aThe identification of rare coding or splice site variants remains the most straightforward strategy to link genes with human phenotypes. Here, we analyzed the association between 137,086 rare (minor allele frequency (MAF) <1%) coding or splice site variants and 15 hematological traits in up to 308,572 participants. We found 56 such rare coding or splice site variants at P<5x10-8, including 31 that are associated with a blood-cell phenotype for the first time. All but one of these 31 new independent variants map to loci previously implicated in hematopoiesis by genome-wide association studies (GWAS). This includes a rare splice acceptor variant (rs146597587, MAF = 0.5%) in interleukin 33 (IL33) associated with reduced eosinophil count (P = 2.4x10-23), and lower risk of asthma (P = 2.6x10-7, odds ratio [95% confidence interval] = 0.56 [0.45-0.70]) and allergic rhinitis (P = 4.2x10-4, odds ratio = 0.55 [0.39-0.76]). The single new locus identified in our study is defined by a rare p.Arg172Gly missense variant (rs145535174, MAF = 0.05%) in plasminogen (PLG) associated with increased platelet count (P = 6.8x10-9), and decreased D-dimer concentration (P = 0.018) and platelet reactivity (P<0.03). Finally, our results indicate that searching for rare coding or splice site variants in very large sample sizes can help prioritize causal genes at many GWAS loci associated with complex human diseases and traits.
10aAsthma10aDatabases, Genetic10aEndometriosis10aFemale10aFibrin Fibrinogen Degradation Products10aGene Frequency10aGenetic Loci10aGenome, Human10aGenome-Wide Association Study10aHumans10aInterleukin-3310aLinear Models10aLogistic Models10aMale10aMutation, Missense10aPhenotype10aPlasminogen10aPlatelet Count10aPolymorphism, Single Nucleotide10aPrincipal Component Analysis10aProtein Splicing10aRhinitis, Allergic10aSequence Analysis, DNA1 aMousas, Abdou1 aNtritsos, Georgios1 aChen, Ming-Huei1 aSong, Ci1 aHuffman, Jennifer, E1 aTzoulaki, Ioanna1 aElliott, Paul1 aPsaty, Bruce, M1 aAuer, Paul, L1 aJohnson, Andrew, D1 aEvangelou, Evangelos1 aLettre, Guillaume1 aReiner, Alexander, P1 aBlood-Cell Consortium uhttps://chs-nhlbi.org/node/757702919nas a2200421 4500008004100000022001400041245010800055210006900163260001500232300000700247490000700254520170100261653003801962653001802000653001102018653001302029100002202042700002202064700002302086700002202109700002102131700002002152700002102172700002202193700001702215700002902232700001602261700001702277700001602294700001902310700002702329700002302356700001802379700002102397700002402418700001902442856003602461 2019 eng d a1474-760X00aSystematic analysis of dark and camouflaged genes reveals disease-relevant genes hiding in plain sight.0 aSystematic analysis of dark and camouflaged genes reveals diseas c2019 05 20 a970 v203 aBACKGROUND: The human genome contains "dark" gene regions that cannot be adequately assembled or aligned using standard short-read sequencing technologies, preventing researchers from identifying mutations within these gene regions that may be relevant to human disease. Here, we identify regions with few mappable reads that we call dark by depth, and others that have ambiguous alignment, called camouflaged. We assess how well long-read or linked-read technologies resolve these regions.
RESULTS: Based on standard whole-genome Illumina sequencing data, we identify 36,794 dark regions in 6054 gene bodies from pathways important to human health, development, and reproduction. Of these gene bodies, 8.7% are completely dark and 35.2% are ≥ 5% dark. We identify dark regions that are present in protein-coding exons across 748 genes. Linked-read or long-read sequencing technologies from 10x Genomics, PacBio, and Oxford Nanopore Technologies reduce dark protein-coding regions to approximately 50.5%, 35.6%, and 9.6%, respectively. We present an algorithm to resolve most camouflaged regions and apply it to the Alzheimer's Disease Sequencing Project. We rescue a rare ten-nucleotide frameshift deletion in CR1, a top Alzheimer's disease gene, found in disease cases but not in controls.
CONCLUSIONS: While we could not formally assess the association of the CR1 frameshift mutation with Alzheimer's disease due to insufficient sample-size, we believe it merits investigating in a larger cohort. There remain thousands of potentially important genomic regions overlooked by short-read sequencing that are largely resolved by long-read technologies.
10aGenetic Predisposition to Disease10aGenome, Human10aHumans10aMutation1 aEbbert, Mark, T W1 aJensen, Tanner, D1 aJansen-West, Karen1 aSens, Jonathon, P1 aReddy, Joseph, S1 aRidge, Perry, G1 aKauwe, John, S K1 aBelzil, Veronique1 aPregent, Luc1 aCarrasquillo, Minerva, M1 aKeene, Dirk1 aLarson, Eric1 aCrane, Paul1 aAsmann, Yan, W1 aErtekin-Taner, Nilufer1 aYounkin, Steven, G1 aRoss, Owen, A1 aRademakers, Rosa1 aPetrucelli, Leonard1 aFryer, John, D uhttps://chs-nhlbi.org/node/810002490nas a2200481 4500008004100000022001400041245010000055210006900155260001500224300000900239490000700248520108200255653001501337653001401352653002401366653003701390653002201427653001701449653003801466653001801504653003401522653001101556653002701567653001801594653002001612653003601632653003101668653002801699100001501727700001501742700001501757700001801772700001601790700002401806700001301830700002401843700001401867700002401881700002001905700002201925700002501947856003601972 2021 eng d a2041-172300aIdentification of putative causal loci in whole-genome sequencing data via knockoff statistics.0 aIdentification of putative causal loci in wholegenome sequencing c2021 05 25 a31520 v123 aThe analysis of whole-genome sequencing studies is challenging due to the large number of rare variants in noncoding regions and the lack of natural units for testing. We propose a statistical method to detect and localize rare and common risk variants in whole-genome sequencing studies based on a recently developed knockoff framework. It can (1) prioritize causal variants over associations due to linkage disequilibrium thereby improving interpretability; (2) help distinguish the signal due to rare variants from shadow effects of significant common variants nearby; (3) integrate multiple knockoffs for improved power, stability, and reproducibility; and (4) flexibly incorporate state-of-the-art and future association tests to achieve the benefits proposed here. In applications to whole-genome sequencing data from the Alzheimer's Disease Sequencing Project (ADSP) and COPDGene samples from NHLBI Trans-Omics for Precision Medicine (TOPMed) Program we show that our method compared with conventional association tests can lead to substantially more discoveries.
10aAlgorithms10aCausality10aComputer Simulation10aData Interpretation, Statistical10aDatasets as Topic10aGenetic Loci10aGenetic Predisposition to Disease10aGenome, Human10aGenome-Wide Association Study10aHumans10aLinkage Disequilibrium10aMarkov Chains10aModels, Genetic10aPolymorphism, Single Nucleotide10aReproducibility of Results10aWhole Genome Sequencing1 aHe, Zihuai1 aLiu, Linxi1 aWang, Chen1 aLe Guen, Yann1 aLee, Justin1 aGogarten, Stephanie1 aLu, Fred1 aMontgomery, Stephen1 aTang, Hua1 aSilverman, Edwin, K1 aCho, Michael, H1 aGreicius, Michael1 aIonita-Laza, Iuliana uhttps://chs-nhlbi.org/node/877805763nas a2201477 4500008004100000022001400041245012500055210006900180260001500249300001400264490000800278520153200286653001101818653001501829653002301844653003801867653001801905653003401923653001101957653001501968653005301983653001402036653003602050653001402086653001302100653004302113653002802156653001902184653001802203653002802221100002402249700002302273700002102296700002302317700002902340700002502369700002302394700001602417700002002433700002002453700002402473700001502497700002202512700001902534700002002553700002002573700002302593700002502616700002002641700001802661700001702679700002102696700002802717700001502745700002002760700002302780700002002803700001902823700002102842700001402863700002302877700002202900700002002922700001402942700002002956700001902976700001502995700002503010700001803035700002403053700002003077700002103097700001903118700002103137700002503158700001903183700002003202700002003222700001903242700001503261700001903276700002003295700002003315700002403335700001603359700002303375700002003398700001903418700002403437700001803461700002303479700002203502700002103524700001703545700001203562700002703574700002003601700002103621700002303642700002503665700002403690700001503714700002603729700002303755700001903778700002603797700002203823700002103845700002003866700002003886700002103906700002003927700002203947700002403969700002303993700001404016700002204030700002504052700002704077700001404104700002304118700002504141700001804166710006504184856003604249 2021 eng d a1537-660500aWhole-genome sequencing in diverse subjects identifies genetic correlates of leukocyte traits: The NHLBI TOPMed program.0 aWholegenome sequencing in diverse subjects identifies genetic co c2021 10 07 a1836-18510 v1083 aMany common and rare variants associated with hematologic traits have been discovered through imputation on large-scale reference panels. However, the majority of genome-wide association studies (GWASs) have been conducted in Europeans, and determining causal variants has proved challenging. We performed a GWAS of total leukocyte, neutrophil, lymphocyte, monocyte, eosinophil, and basophil counts generated from 109,563,748 variants in the autosomes and the X chromosome in the Trans-Omics for Precision Medicine (TOPMed) program, which included data from 61,802 individuals of diverse ancestry. We discovered and replicated 7 leukocyte trait associations, including (1) the association between a chromosome X, pseudo-autosomal region (PAR), noncoding variant located between cytokine receptor genes (CSF2RA and CLRF2) and lower eosinophil count; and (2) associations between single variants found predominantly among African Americans at the S1PR3 (9q22.1) and HBB (11p15.4) loci and monocyte and lymphocyte counts, respectively. We further provide evidence indicating that the newly discovered eosinophil-lowering chromosome X PAR variant might be associated with reduced susceptibility to common allergic diseases such as atopic dermatitis and asthma. Additionally, we found a burden of very rare FLT3 (13q12.2) variants associated with monocyte counts. Together, these results emphasize the utility of whole-genome sequencing in diverse samples in identifying associations missed by European-ancestry-driven GWASs.
10aAsthma10aBiomarkers10aDermatitis, Atopic10aGenetic Predisposition to Disease10aGenome, Human10aGenome-Wide Association Study10aHumans10aLeukocytes10aNational Heart, Lung, and Blood Institute (U.S.)10aPhenotype10aPolymorphism, Single Nucleotide10aPrognosis10aProteome10aPulmonary Disease, Chronic Obstructive10aQuantitative Trait Loci10aUnited Kingdom10aUnited States10aWhole Genome Sequencing1 aMikhaylova, Anna, V1 aMcHugh, Caitlin, P1 aPolfus, Linda, M1 aRaffield, Laura, M1 aBoorgula, Meher, Preethi1 aBlackwell, Thomas, W1 aBrody, Jennifer, A1 aBroome, Jai1 aChami, Nathalie1 aChen, Ming-Huei1 aConomos, Matthew, P1 aCox, Corey1 aCurran, Joanne, E1 aDaya, Michelle1 aEkunwe, Lynette1 aGlahn, David, C1 aHeard-Costa, Nancy1 aHighland, Heather, M1 aHobbs, Brian, D1 aIlboudo, Yann1 aJain, Deepti1 aLange, Leslie, A1 aMiller-Fleming, Tyne, W1 aMin, Nancy1 aMoon, Jee-Young1 aPreuss, Michael, H1 aRosen, Jonathon1 aRyan, Kathleen1 aSmith, Albert, V1 aSun, Quan1 aSurendran, Praveen1 ade Vries, Paul, S1 aWalter, Klaudia1 aWang, Zhe1 aWheeler, Marsha1 aYanek, Lisa, R1 aZhong, Xue1 aAbecasis, Goncalo, R1 aAlmasy, Laura1 aBarnes, Kathleen, C1 aBeaty, Terri, H1 aBecker, Lewis, C1 aBlangero, John1 aBoerwinkle, Eric1 aButterworth, Adam, S1 aChavan, Sameer1 aCho, Michael, H1 aChoquet, Helene1 aCorrea, Adolfo1 aCox, Nancy1 aDeMeo, Dawn, L1 aFaraday, Nauder1 aFornage, Myriam1 aGerszten, Robert, E1 aHou, Lifang1 aJohnson, Andrew, D1 aJorgenson, Eric1 aKaplan, Robert1 aKooperberg, Charles1 aKundu, Kousik1 aLaurie, Cecelia, A1 aLettre, Guillaume1 aLewis, Joshua, P1 aLi, Bingshan1 aLi, Yun1 aLloyd-Jones, Donald, M1 aLoos, Ruth, J F1 aManichaikul, Ani1 aMeyers, Deborah, A1 aMitchell, Braxton, D1 aMorrison, Alanna, C1 aNgo, Debby1 aNickerson, Deborah, A1 aNongmaithem, Suraj1 aNorth, Kari, E1 aO'Connell, Jeffrey, R1 aOrtega, Victor, E1 aPankratz, Nathan1 aPerry, James, A1 aPsaty, Bruce, M1 aRich, Stephen, S1 aSoranzo, Nicole1 aRotter, Jerome, I1 aSilverman, Edwin, K1 aSmith, Nicholas, L1 aTang, Hua1 aTracy, Russell, P1 aThornton, Timothy, A1 aVasan, Ramachandran, S1 aZein, Joe1 aMathias, Rasika, A1 aReiner, Alexander, P1 aAuer, Paul, L1 aNHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium uhttps://chs-nhlbi.org/node/891403311nas a2200829 4500008004100000022001400041245009300055210006900148260001300217300001400230490000700244520101000251653001701261653001801278653003401296653002301330653001101353653001401364653002301378100002501401700001501426700002001441700001701461700002001478700001701498700001801515700002101533700001901554700002101573700002301594700002101617700002301638700001701661700001701678700002401695700001501719700002301734700002001757700001701777700002401794700002101818700002001839700001701859700002601876700002201902700002301924700002501947700002601972700001601998700002102014700002402035700002302059700001702082700002302099700002702122700001902149700002102168700002202189700002402211700002302235700002002258700001902278700002002297700001502317700001202332700001802344700002302362700002502385700001702410700001802427856003602445 2023 eng d a1546-171800aMosaic chromosomal alterations in blood across ancestries using whole-genome sequencing.0 aMosaic chromosomal alterations in blood across ancestries using c2023 Nov a1912-19190 v553 aMegabase-scale mosaic chromosomal alterations (mCAs) in blood are prognostic markers for a host of human diseases. Here, to gain a better understanding of mCA rates in genetically diverse populations, we analyzed whole-genome sequencing data from 67,390 individuals from the National Heart, Lung, and Blood Institute Trans-Omics for Precision Medicine program. We observed higher sensitivity with whole-genome sequencing data, compared with array-based data, in uncovering mCAs at low mutant cell fractions and found that individuals of European ancestry have the highest rates of autosomal mCAs and the lowest rates of chromosome X mCAs, compared with individuals of African or Hispanic ancestry. Although further studies in diverse populations will be needed to replicate our findings, we report three loci associated with loss of chromosome X, associations between autosomal mCAs and rare variants in DCPS, ADM17, PPP1R16B and TET2 and ancestry-specific variants in ATM and MPL with mCAs in cis.
10aBlack People10aGenome, Human10aGenome-Wide Association Study10aHispanic or Latino10aHumans10aMosaicism10aPrecision Medicine1 aJakubek, Yasminka, A1 aZhou, Ying1 aStilp, Adrienne1 aBacon, Jason1 aWong, Justin, W1 aOzcan, Zuhal1 aArnett, Donna1 aBarnes, Kathleen1 aBis, Joshua, C1 aBoerwinkle, Eric1 aBrody, Jennifer, A1 aCarson, April, P1 aChasman, Daniel, I1 aChen, Jiawen1 aCho, Michael1 aConomos, Matthew, P1 aCox, Nancy1 aDoyle, Margaret, F1 aFornage, Myriam1 aGuo, Xiuqing1 aKardia, Sharon, L R1 aLewis, Joshua, P1 aLoos, Ruth, J F1 aMa, Xiaolong1 aMachiela, Mitchell, J1 aMack, Taralynn, M1 aMathias, Rasika, A1 aMitchell, Braxton, D1 aMychaleckyj, Josyf, C1 aNorth, Kari1 aPankratz, Nathan1 aPeyser, Patricia, A1 aPreuss, Michael, H1 aPsaty, Bruce1 aRaffield, Laura, M1 aVasan, Ramachandran, S1 aRedline, Susan1 aRich, Stephen, S1 aRotter, Jerome, I1 aSilverman, Edwin, K1 aSmith, Jennifer, A1 aSmith, Aaron, P1 aTaub, Margaret1 aTaylor, Kent, D1 aYun, Jeong1 aLi, Yun1 aDesai, Pinkal1 aBick, Alexander, G1 aReiner, Alexander, P1 aScheet, Paul1 aAuer, Paul, L uhttps://chs-nhlbi.org/node/9538