03344nas a2200517 4500008004100000022001400041245014800055210006900203260001600272300001100288490000800299520172900307653002602036653003402062653001802096653003202114653002502146653003402171653001102205653003302216653003102249653005202280653001402332653001902346653002002365653001702385653001802402100002002420700002302440700001902463700002802482700002102510700002202531700002702553700001902580700002402599700002202623700002102645700001902666700001902685700002402704700002302728700002402751710001502775856003602790 2011 eng d a1476-625600aThe Next PAGE in understanding complex traits: design for the analysis of Population Architecture Using Genetics and Epidemiology (PAGE) Study.0 aNext PAGE in understanding complex traits design for the analysi c2011 Oct 01 a849-590 v1743 a
Genetic studies have identified thousands of variants associated with complex traits. However, most association studies are limited to populations of European descent and a single phenotype. The Population Architecture using Genomics and Epidemiology (PAGE) Study was initiated in 2008 by the National Human Genome Research Institute to investigate the epidemiologic architecture of well-replicated genetic variants associated with complex diseases in several large, ethnically diverse population-based studies. Combining DNA samples and hundreds of phenotypes from multiple cohorts, PAGE is well-suited to address generalization of associations and variability of effects in diverse populations; identify genetic and environmental modifiers; evaluate disease subtypes, intermediate phenotypes, and biomarkers; and investigate associations with novel phenotypes. PAGE investigators harmonize phenotypes across studies where possible and perform coordinated cohort-specific analyses and meta-analyses. PAGE researchers are genotyping thousands of genetic variants in up to 121,000 DNA samples from African-American, white, Hispanic/Latino, Asian/Pacific Islander, and American Indian participants. Initial analyses will focus on single nucleotide polymorphisms (SNPs) associated with obesity, lipids, cardiovascular disease, type 2 diabetes, inflammation, various cancers, and related biomarkers. PAGE SNPs are also assessed for pleiotropy using the "phenome-wide association study" approach, testing each SNP for associations with hundreds of phenotypes. PAGE data will be deposited into the National Center for Biotechnology Information's Database of Genotypes and Phenotypes and made available via a custom browser.
10aEpidemiologic Methods10aEpidemiologic Research Design10aEthnic Groups10aGenetic Association Studies10aGenetics, Population10aGenome-Wide Association Study10aHumans10aInterinstitutional Relations10aMultifactorial Inheritance10aNational Human Genome Research Institute (U.S.)10aPhenotype10aPilot Projects10aResearch Design10aRisk Factors10aUnited States1 aMatise, Tara, C1 aAmbite, Jose, Luis1 aBuyske, Steven1 aCarlson, Christopher, S1 aCole, Shelley, A1 aCrawford, Dana, C1 aHaiman, Christopher, A1 aHeiss, Gerardo1 aKooperberg, Charles1 aLe Marchand, Loic1 aManolio, Teri, A1 aNorth, Kari, E1 aPeters, Ulrike1 aRitchie, Marylyn, D1 aHindorff, Lucia, A1 aHaines, Jonathan, L1 aPAGE Study uhttps://chs-nhlbi.org/node/131303141nas a2200685 4500008004100000022001400041245018100055210006900236260001300305300001100318490000700329520115600336653001001492653000901502653002201511653001201533653003001545653001101575653003801586653003401624653001301658653001101671653000901682653001701691653001601708653002201724653000901746653001701755100002701772700002501799700002201824700002101846700002201867700001501889700001901904700002301923700002401946700002301970700002401993700002002017700002502037700001902062700002302081700001502104700002102119700002002140700001802160700002402178700002602202700001902228700002202247700002302269700002302292700001902315700001902334700002202353700002302375700002102398856003602419 2012 eng d a1939-327X00aConsistent directions of effect for established type 2 diabetes risk variants across populations: the population architecture using Genomics and Epidemiology (PAGE) Consortium.0 aConsistent directions of effect for established type 2 diabetes c2012 Jun a1642-70 v613 aCommon genetic risk variants for type 2 diabetes (T2D) have primarily been identified in populations of European and Asian ancestry. We tested whether the direction of association with 20 T2D risk variants generalizes across six major racial/ethnic groups in the U.S. as part of the Population Architecture using Genomics and Epidemiology Consortium (16,235 diabetes case and 46,122 control subjects of European American, African American, Hispanic, East Asian, American Indian, and Native Hawaiian ancestry). The percentage of positive (odds ratio [OR] >1 for putative risk allele) associations ranged from 69% in American Indians to 100% in European Americans. Of the nine variants where we observed significant heterogeneity of effect by racial/ethnic group (P(heterogeneity) < 0.05), eight were positively associated with risk (OR >1) in at least five groups. The marked directional consistency of association observed for most genetic variants across populations implies a shared functional common variant in each region. Fine-mapping of all loci will be required to reveal markers of risk that are important within and across populations.
10aAdult10aAged10aAged, 80 and over10aAlleles10aDiabetes Mellitus, Type 210aFemale10aGenetic Predisposition to Disease10aGenome-Wide Association Study10aGenotype10aHumans10aMale10aMetagenomics10aMiddle Aged10aPopulation Groups10aRisk10aRisk Factors1 aHaiman, Christopher, A1 aFesinmeyer, Megan, D1 aSpencer, Kylee, L1 aBůzková, Petra1 aVoruganti, Saroja1 aWan, Peggy1 aHaessler, Jeff1 aFranceschini, Nora1 aMonroe, Kristine, R1 aHoward, Barbara, V1 aJackson, Rebecca, D1 aFlorez, Jose, C1 aKolonel, Laurence, N1 aBuyske, Steven1 aGoodloe, Robert, J1 aLiu, Simin1 aManson, JoAnn, E1 aMeigs, James, B1 aWaters, Kevin1 aMukamal, Kenneth, J1 aPendergrass, Sarah, A1 aShrader, Peter1 aWilkens, Lynne, R1 aHindorff, Lucia, A1 aAmbite, Jose, Luis1 aNorth, Kari, E1 aPeters, Ulrike1 aCrawford, Dana, C1 aLe Marchand, Loïc1 aPankow, James, S uhttps://chs-nhlbi.org/node/663303528nas a2200721 4500008004100000022001400041245014600055210006900201260000900270300001100279490000600290520139200296653002201688653002801710653004001738653002101778653002101799653002301820653001901843653001901862653003401881653001301915653001101928653002301939653003601962653002801998100001902026700001302045700001902058700001602077700002902093700002202122700002302144700002202167700002302189700002102212700002102233700002202254700002102276700001802297700002202315700002502337700002302362700002202385700001702407700002002424700002402444700001202468700002302480700002002503700002602523700002202549700002802571700002402599700001802623700002102641700002102662700002702683700001902710700002202729700001902751856003602770 2012 eng d a1932-620300aEvaluation of the metabochip genotyping array in African Americans and implications for fine mapping of GWAS-identified loci: the PAGE study.0 aEvaluation of the metabochip genotyping array in African America c2012 ae356510 v73 aThe Metabochip is a custom genotyping array designed for replication and fine mapping of metabolic, cardiovascular, and anthropometric trait loci and includes low frequency variation content identified from the 1000 Genomes Project. It has 196,725 SNPs concentrated in 257 genomic regions. We evaluated the Metabochip in 5,863 African Americans; 89% of all SNPs passed rigorous quality control with a call rate of 99.9%. Two examples illustrate the value of fine mapping with the Metabochip in African-ancestry populations. At CELSR2/PSRC1/SORT1, we found the strongest associated SNP for LDL-C to be rs12740374 (p = 3.5 × 10(-11)), a SNP indistinguishable from multiple SNPs in European ancestry samples due to high correlation. Its distinct signal supports functional studies elsewhere suggesting a causal role in LDL-C. At CETP we found rs17231520, with risk allele frequency 0.07 in African Americans, to be associated with HDL-C (p = 7.2 × 10(-36)). This variant is very rare in Europeans and not tagged in common GWAS arrays, but was identified as associated with HDL-C in African Americans in a single-gene study. Our results, one narrowing the risk interval and the other revealing an associated variant not found in Europeans, demonstrate the advantages of high-density genotyping of common and rare variation for fine mapping of trait loci in African American samples.
10aAfrican Americans10aCardiovascular Diseases10aCholesterol Ester Transfer Proteins10aCholesterol, HDL10aCholesterol, LDL10aChromosomes, Human10aCohort Studies10aGene Frequency10aGenome-Wide Association Study10aGenotype10aHumans10aMetabolic Diseases10aPolymorphism, Single Nucleotide10aQuantitative Trait Loci1 aBuyske, Steven1 aWu, Ying1 aCarty, Cara, L1 aCheng, Iona1 aAssimes, Themistocles, L1 aDumitrescu, Logan1 aHindorff, Lucia, A1 aMitchell, Sabrina1 aAmbite, Jose, Luis1 aBoerwinkle, Eric1 aBůzková, Petra1 aCarlson, Chris, S1 aCochran, Barbara1 aDuggan, David1 aEaton, Charles, B1 aFesinmeyer, Megan, D1 aFranceschini, Nora1 aHaessler, Jeffrey1 aJenny, Nancy1 aKang, Hyun, Min1 aKooperberg, Charles1 aLin, Yi1 aLe Marchand, Loïc1 aMatise, Tara, C1 aRobinson, Jennifer, G1 aRodriguez, Carlos1 aSchumacher, Fredrick, R1 aVoight, Benjamin, F1 aYoung, Alicia1 aManolio, Teri, A1 aMohlke, Karen, L1 aHaiman, Christopher, A1 aPeters, Ulrike1 aCrawford, Dana, C1 aNorth, Kari, E uhttps://chs-nhlbi.org/node/663403996nas a2200625 4500008004100000022001400041245008800055210006900143260000900212300001300221490000600234520220300240653002202443653000902465653002602474653002402500653004002524653001102564653003802575653003402613653001102647653002702658653000902685653001702694653001602711653003602727653002802763653003402791653001702825653001602842653001802858100002202876700002402898700001902922700001802941700001602959700001902975700001902994700001803013700002503031700002303056700002003079700001703099700002303116700001903139700002103158700002203179700002103201700002403222700002703246700002103273700002103294700001903315856003603334 2012 eng d a1553-740400aFine-mapping and initial characterization of QT interval loci in African Americans.0 aFinemapping and initial characterization of QT interval loci in c2012 ae10028700 v83 aThe QT interval (QT) is heritable and its prolongation is a risk factor for ventricular tachyarrhythmias and sudden death. Most genetic studies of QT have examined European ancestral populations; however, the increased genetic diversity in African Americans provides opportunities to narrow association signals and identify population-specific variants. We therefore evaluated 6,670 SNPs spanning eleven previously identified QT loci in 8,644 African American participants from two Population Architecture using Genomics and Epidemiology (PAGE) studies: the Atherosclerosis Risk in Communities study and Women's Health Initiative Clinical Trial. Of the fifteen known independent QT variants at the eleven previously identified loci, six were significantly associated with QT in African American populations (P≤1.20×10(-4)): ATP1B1, PLN1, KCNQ1, NDRG4, and two NOS1AP independent signals. We also identified three population-specific signals significantly associated with QT in African Americans (P≤1.37×10(-5)): one at NOS1AP and two at ATP1B1. Linkage disequilibrium (LD) patterns in African Americans assisted in narrowing the region likely to contain the functional variants for several loci. For example, African American LD patterns showed that 0 SNPs were in LD with NOS1AP signal rs12143842, compared with European LD patterns that indicated 87 SNPs, which spanned 114.2 Kb, were in LD with rs12143842. Finally, bioinformatic-based characterization of the nine African American signals pointed to functional candidates located exclusively within non-coding regions, including predicted binding sites for transcription factors such as TBX5, which has been implicated in cardiac structure and conductance. In this detailed evaluation of QT loci, we identified several African Americans SNPs that better define the association with QT and successfully narrowed intervals surrounding established loci. These results demonstrate that the same loci influence variation in QT across multiple populations, that novel signals exist in African Americans, and that the SNPs identified as strong candidates for functional evaluation implicate gene regulatory dysfunction in QT prolongation.
10aAfrican Americans10aAged10aComputational Biology10aElectrocardiography10aEuropean Continental Ancestry Group10aFemale10aGenetic Predisposition to Disease10aGenome-Wide Association Study10aHumans10aLinkage Disequilibrium10aMale10aMetagenomics10aMiddle Aged10aPolymorphism, Single Nucleotide10aQuantitative Trait Loci10aQuantitative Trait, Heritable10aRisk Factors10aTachycardia10aUnited States1 aAvery, Christy, L1 aSethupathy, Praveen1 aBuyske, Steven1 aHe, Qianchuan1 aLin, Dan-Yu1 aArking, Dan, E1 aCarty, Cara, L1 aDuggan, David1 aFesinmeyer, Megan, D1 aHindorff, Lucia, A1 aJeff, Janina, M1 aKlein, Liviu1 aPatton, Kristen, K1 aPeters, Ulrike1 aShohet, Ralph, V1 aSotoodehnia, Nona1 aYoung, Alicia, M1 aKooperberg, Charles1 aHaiman, Christopher, A1 aMohlke, Karen, L1 aWhitsel, Eric, A1 aNorth, Kari, E uhttps://chs-nhlbi.org/node/608303734nas a2200745 4500008004100000022001400041245012200055210006900177260001600246300000600262490000700268520160700275653001501882653001001897653002201907653000901929653005001938653002001988653004002008653001102048653003802059653001102097653000902108653002202117653001602139653001202155653003602167653001302203653001702216653001202233653001602245100002502261700001902286700001502305700002102320700002202341700002202363700002002385700001802405700002002423700002202443700001602465700002802481700002102509700002002530700002102550700002402571700001902595700002202614700001502636700003002651700002402681700002402705700002202729700002502751700002102776700001802797700002302815700002302838700002202861700002702883700002302910700001902933856003602952 2013 eng d a1471-235000aEffects of smoking on the genetic risk of obesity: the population architecture using genomics and epidemiology study.0 aEffects of smoking on the genetic risk of obesity the population c2013 Jan 11 a60 v143 aBACKGROUND: Although smoking behavior is known to affect body mass index (BMI), the potential for smoking to influence genetic associations with BMI is largely unexplored.
METHODS: As part of the 'Population Architecture using Genomics and Epidemiology (PAGE)' Consortium, we investigated interaction between genetic risk factors associated with BMI and smoking for 10 single nucleotide polymorphisms (SNPs) previously identified in genome-wide association studies. We included 6 studies with a total of 56,466 subjects (16,750 African Americans (AA) and 39,716 European Americans (EA)). We assessed effect modification by testing an interaction term for each SNP and smoking (current vs. former/never) in the linear regression and by stratified analyses.
RESULTS: We did not observe strong evidence for interactions and only observed two interactions with p-values <0.1: for rs6548238/TMEM18, the risk allele (C) was associated with BMI only among AA females who were former/never smokers (β = 0.018, p = 0.002), vs. current smokers (β = 0.001, p = 0.95, p(interaction) = 0.10). For rs9939609/FTO, the A allele was more strongly associated with BMI among current smoker EA females (β = 0.017, p = 3.5 x 10(-5)), vs. former/never smokers (β = 0.006, p = 0.05, p(interaction) = 0.08).
CONCLUSIONS: These analyses provide limited evidence that smoking status may modify genetic effects of previously identified genetic risk factors for BMI. Larger studies are needed to follow up our results.
CLINICAL TRIAL REGISTRATION: NCT00000611.
10aAdolescent10aAdult10aAfrican Americans10aAged10aAlpha-Ketoglutarate-Dependent Dioxygenase FTO10aBody Mass Index10aEuropean Continental Ancestry Group10aFemale10aGenetic Predisposition to Disease10aHumans10aMale10aMembrane Proteins10aMiddle Aged10aObesity10aPolymorphism, Single Nucleotide10aProteins10aRisk Factors10aSmoking10aYoung Adult1 aFesinmeyer, Megan, D1 aNorth, Kari, E1 aLim, Unhee1 aBůzková, Petra1 aCrawford, Dana, C1 aHaessler, Jeffrey1 aGross, Myron, D1 aFowke, Jay, H1 aGoodloe, Robert1 aLove, Shelley-Ann1 aGraff, Misa1 aCarlson, Christopher, S1 aKuller, Lewis, H1 aMatise, Tara, C1 aHong, Ching-Ping1 aHenderson, Brian, E1 aAllen, Melissa1 aRohde, Rebecca, R1 aMayo, Ping1 aSchnetz-Boutaud, Nathalie1 aMonroe, Kristine, R1 aRitchie, Marylyn, D1 aPrentice, Ross, L1 aKolonel, Lawrence, N1 aManson, JoAnn, E1 aPankow, James1 aHindorff, Lucia, A1 aFranceschini, Nora1 aWilkens, Lynne, R1 aHaiman, Christopher, A1 aLe Marchand, Loïc1 aPeters, Ulrike uhttps://chs-nhlbi.org/node/606504096nas 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/662603887nas a2200601 4500008004100000022001400041245013100055210006900186260001300255300001300268490000700281520205500288653002202343653002002365653002002385653003002405653004002435653001902475653003802494653002202532653003402554653002302588653001102611653002802622653001102650653001702661653002702678653003602705100002802741700002002769700001902789700002702808700002502835700001902860700002802879700001902907700002302926700002402949700002102973700002202994700002203016700002203038700002103060700001803081700001603099700001903115700002303134700002203157700002303179700002703202710002003229856003603249 2013 eng d a1545-788500aGeneralization and dilution of association results from European GWAS in populations of non-European ancestry: the PAGE study.0 aGeneralization and dilution of association results from European c2013 Sep ae10016610 v113 aThe vast majority of genome-wide association study (GWAS) findings reported to date are from populations with European Ancestry (EA), and it is not yet clear how broadly the genetic associations described will generalize to populations of diverse ancestry. The Population Architecture Using Genomics and Epidemiology (PAGE) study is a consortium of multi-ancestry, population-based studies formed with the objective of refining our understanding of the genetic architecture of common traits emerging from GWAS. In the present analysis of five common diseases and traits, including body mass index, type 2 diabetes, and lipid levels, we compare direction and magnitude of effects for GWAS-identified variants in multiple non-EA populations against EA findings. We demonstrate that, in all populations analyzed, a significant majority of GWAS-identified variants have allelic associations in the same direction as in EA, with none showing a statistically significant effect in the opposite direction, after adjustment for multiple testing. However, 25% of tagSNPs identified in EA GWAS have significantly different effect sizes in at least one non-EA population, and these differential effects were most frequent in African Americans where all differential effects were diluted toward the null. We demonstrate that differential LD between tagSNPs and functional variants within populations contributes significantly to dilute effect sizes in this population. Although most variants identified from GWAS in EA populations generalize to all non-EA populations assessed, genetic models derived from GWAS findings in EA may generate spurious results in non-EA populations due to differential effect sizes. Regardless of the origin of the differential effects, caution should be exercised in applying any genetic risk prediction model based on tagSNPs outside of the ancestry group in which it was derived. Models based directly on functional variation may generalize more robustly, but the identification of functional variants remains challenging.
10aAfrican Americans10aAsian Americans10aBody Mass Index10aDiabetes Mellitus, Type 210aEuropean Continental Ancestry Group10aGene Frequency10aGenetic Predisposition to Disease10aGenetic Variation10aGenome-Wide Association Study10aHispanic Americans10aHumans10aIndians, North American10aLipids10aMetagenomics10aOceanic Ancestry Group10aPolymorphism, Single Nucleotide1 aCarlson, Christopher, S1 aMatise, Tara, C1 aNorth, Kari, E1 aHaiman, Christopher, A1 aFesinmeyer, Megan, D1 aBuyske, Steven1 aSchumacher, Fredrick, R1 aPeters, Ulrike1 aFranceschini, Nora1 aRitchie, Marylyn, D1 aDuggan, David, J1 aSpencer, Kylee, L1 aDumitrescu, Logan1 aEaton, Charles, B1 aThomas, Fridtjof1 aYoung, Alicia1 aCarty, Cara1 aHeiss, Gerardo1 aLe Marchand, Loïc1 aCrawford, Dana, C1 aHindorff, Lucia, A1 aKooperberg, Charles, L1 aPAGE Consortium uhttps://chs-nhlbi.org/node/628903914nas a2200673 4500008004100000022001400041245016900055210006900224260001300293300001100306490000700317520191200324653001202236653002002248653001802268653001902286653001702305653003802322653003402360653001102394653002702405653001702432653001202449653001402461653003602475653001702511100002502528700001902553700002402572700001502596700002302611700002202634700002002656700002102676700002002697700002102717700003002738700001402768700001802782700001702800700002802817700002202845700002102867700002102888700002002909700002102929700002502950700002302975700002502998700002403023700002403047700002203071700002303093700001903116700002703135700002303162700001903185856003603204 2013 eng d a1930-739X00aGenetic risk factors for BMI and obesity in an ethnically diverse population: results from the population architecture using genomics and epidemiology (PAGE) study.0 aGenetic risk factors for BMI and obesity in an ethnically divers c2013 Apr a835-460 v213 aOBJECTIVE: Several genome-wide association studies (GWAS) have demonstrated that common genetic variants contribute to obesity. However, studies of this complex trait have focused on ancestrally European populations, despite the high prevalence of obesity in some minority groups.
DESIGN AND METHODS: As part of the "Population Architecture using Genomics and Epidemiology (PAGE)" Consortium, we investigated the association between 13 GWAS-identified single-nucleotide polymorphisms (SNPs) and BMI and obesity in 69,775 subjects, including 6,149 American Indians, 15,415 African-Americans, 2,438 East Asians, 7,346 Hispanics, 604 Pacific Islanders, and 37,823 European Americans. For the BMI-increasing allele of each SNP, we calculated β coefficients using linear regression (for BMI) and risk estimates using logistic regression (for obesity defined as BMI ≥ 30) followed by fixed-effects meta-analysis to combine results across PAGE sites. Analyses stratified by racial/ethnic group assumed an additive genetic model and were adjusted for age, sex, and current smoking. We defined "replicating SNPs" (in European Americans) and "generalizing SNPs" (in other racial/ethnic groups) as those associated with an allele frequency-specific increase in BMI.
RESULTS: By this definition, we replicated 9/13 SNP associations (5 out of 8 loci) in European Americans. We also generalized 8/13 SNP associations (5/8 loci) in East Asians, 7/13 (5/8 loci) in African Americans, 6/13 (4/8 loci) in Hispanics, 5/8 in Pacific Islanders (5/8 loci), and 5/9 (4/8 loci) in American Indians.
CONCLUSION: Linkage disequilibrium patterns suggest that tagSNPs selected for European Americans may not adequately tag causal variants in other ancestry groups. Accordingly, fine-mapping in large samples is needed to comprehensively explore these loci in diverse populations.
10aAlleles10aBody Mass Index10aEthnic Groups10aGene Frequency10aGenetic Loci10aGenetic Predisposition to Disease10aGenome-Wide Association Study10aHumans10aLinkage Disequilibrium10aMetagenomics10aObesity10aPhenotype10aPolymorphism, Single Nucleotide10aRisk Factors1 aFesinmeyer, Megan, D1 aNorth, Kari, E1 aRitchie, Marylyn, D1 aLim, Unhee1 aFranceschini, Nora1 aWilkens, Lynne, R1 aGross, Myron, D1 aBůzková, Petra1 aGlenn, Kimberly1 aQuibrera, Miguel1 aFernandez-Rhodes, Lindsay1 aLi, Qiong1 aFowke, Jay, H1 aLi, Rongling1 aCarlson, Christopher, S1 aPrentice, Ross, L1 aKuller, Lewis, H1 aManson, JoAnn, E1 aMatise, Tara, C1 aCole, Shelley, A1 aChen, Christina, T L1 aHoward, Barbara, V1 aKolonel, Laurence, N1 aHenderson, Brian, E1 aMonroe, Kristine, R1 aCrawford, Dana, C1 aHindorff, Lucia, A1 aBuyske, Steven1 aHaiman, Christopher, A1 aLe Marchand, Loïc1 aPeters, Ulrike uhttps://chs-nhlbi.org/node/663103932nas a2200769 4500008004100000022001400041245020400055210006900259260001600328300000700344490000700351520160000358653004101958653001001999653002202009653000902031653001202040653003702052653001802089653003002107653004002137653001102177653001902188653001702207653003402224653001302258653002302271653001102294653002802305653001202333653000902345653001602354653003602370653004202406100002502448700002002473700001902493700002802512700002102540700002302561700002202584700002002606700002202626700003102648700002302679700002102702700002502723700001502748700002102763700002402784700002102808700002002829700002602849700001702875700001502892700002202907700002302929700002302952700001902975700002002994700002203014700002303036700002703059700001903086700002103105856003603126 2013 eng d a1471-235000aGenetic variants associated with fasting glucose and insulin concentrations in an ethnically diverse population: results from the Population Architecture using Genomics and Epidemiology (PAGE) study.0 aGenetic variants associated with fasting glucose and insulin con c2013 Sep 25 a980 v143 aBACKGROUND: Multiple genome-wide association studies (GWAS) within European populations have implicated common genetic variants associated with insulin and glucose concentrations. In contrast, few studies have been conducted within minority groups, which carry the highest burden of impaired glucose homeostasis and type 2 diabetes in the U.S.
METHODS: As part of the 'Population Architecture using Genomics and Epidemiology (PAGE) Consortium, we investigated the association of up to 10 GWAS-identified single nucleotide polymorphisms (SNPs) in 8 genetic regions with glucose or insulin concentrations in up to 36,579 non-diabetic subjects including 23,323 European Americans (EA) and 7,526 African Americans (AA), 3,140 Hispanics, 1,779 American Indians (AI), and 811 Asians. We estimated the association between each SNP and fasting glucose or log-transformed fasting insulin, followed by meta-analysis to combine results across PAGE sites.
RESULTS: Overall, our results show that 9/9 GWAS SNPs are associated with glucose in EA (p = 0.04 to 9 × 10-15), versus 3/9 in AA (p= 0.03 to 6 × 10-5), 3/4 SNPs in Hispanics, 2/4 SNPs in AI, and 1/2 SNPs in Asians. For insulin we observed a significant association with rs780094/GCKR in EA, Hispanics and AI only.
CONCLUSIONS: Generalization of results across multiple racial/ethnic groups helps confirm the relevance of some of these loci for glucose and insulin metabolism. Lack of association in non-EA groups may be due to insufficient power, or to unique patterns of linkage disequilibrium.
10aAdaptor Proteins, Signal Transducing10aAdult10aAfrican Americans10aAged10aAlleles10aAsian Continental Ancestry Group10aBlood Glucose10aDiabetes Mellitus, Type 210aEuropean Continental Ancestry Group10aFemale10aGene Frequency10aGenetic Loci10aGenome-Wide Association Study10aGenomics10aHispanic Americans10aHumans10aIndians, North American10aInsulin10aMale10aMiddle Aged10aPolymorphism, Single Nucleotide10aTranscription Factor 7-Like 2 Protein1 aFesinmeyer, Megan, D1 aMeigs, James, B1 aNorth, Kari, E1 aSchumacher, Fredrick, R1 aBůzková, Petra1 aFranceschini, Nora1 aHaessler, Jeffrey1 aGoodloe, Robert1 aSpencer, Kylee, L1 aVoruganti, Venkata, Saroja1 aHoward, Barbara, V1 aJackson, Rebecca1 aKolonel, Laurence, N1 aLiu, Simin1 aManson, JoAnn, E1 aMonroe, Kristine, R1 aMukamal, Kenneth1 aDilks, Holli, H1 aPendergrass, Sarah, A1 aNato, Andrew1 aWan, Peggy1 aWilkens, Lynne, R1 aLe Marchand, Loïc1 aAmbite, Jose, Luis1 aBuyske, Steven1 aFlorez, Jose, C1 aCrawford, Dana, C1 aHindorff, Lucia, A1 aHaiman, Christopher, A1 aPeters, Ulrike1 aPankow, James, S uhttps://chs-nhlbi.org/node/629003572nas a2200745 4500008004100000022001400041245011600055210006900171260001300240300001100253490000700264520148800271653001501759653001001774653000901784653002201793653001001815653002001825653001901845653002801864653004001892653001101932653003201943653001901975653001101994653000902005653001602014653001202030653003602042653001302078653001802091653001602109100002202125700002502147700001502172700001802187700002102205700002202226700002202248700002402270700002402294700002202318700001602340700002102356700002102377700002302398700002102421700002502442700001902467700002402486700001902510700002002529700002002549700002302569700002802592700001902620700002102639700002302660700002002683700002202703700002702725700001902752700001902771856003602790 2013 eng d a1939-327X00aThe influence of obesity-related single nucleotide polymorphisms on BMI across the life course: the PAGE study.0 ainfluence of obesityrelated single nucleotide polymorphisms on B c2013 May a1763-70 v623 aEvidence is limited as to whether heritable risk of obesity varies throughout adulthood. Among >34,000 European Americans, aged 18-100 years, from multiple U.S. studies in the Population Architecture using Genomics and Epidemiology (PAGE) Consortium, we examined evidence for heterogeneity in the associations of five established obesity risk variants (near FTO, GNPDA2, MTCH2, TMEM18, and NEGR1) with BMI across four distinct epochs of adulthood: 1) young adulthood (ages 18-25 years), adulthood (ages 26-49 years), middle-age adulthood (ages 50-69 years), and older adulthood (ages ≥70 years); or 2) by menopausal status in women and stratification by age 50 years in men. Summary-effect estimates from each meta-analysis were compared for heterogeneity across the life epochs. We found heterogeneity in the association of the FTO (rs8050136) variant with BMI across the four adulthood epochs (P = 0.0006), with larger effects in young adults relative to older adults (β [SE] = 1.17 [0.45] vs. 0.09 [0.09] kg/m², respectively, per A allele) and smaller intermediate effects. We found no evidence for heterogeneity in the association of GNPDA2, MTCH2, TMEM18, and NEGR1 with BMI across adulthood. Genetic predisposition to obesity may have greater effects on body weight in young compared with older adulthood for FTO, suggesting changes by age, generation, or secular trends. Future research should compare and contrast our findings with results using longitudinal data.
10aAdolescent10aAdult10aAged10aAged, 80 and over10aAging10aBody Mass Index10aCohort Studies10aCross-Sectional Studies10aEuropean Continental Ancestry Group10aFemale10aGenetic Association Studies10aHealth Surveys10aHumans10aMale10aMiddle Aged10aObesity10aPolymorphism, Single Nucleotide10aProteins10aUnited States10aYoung Adult1 aGraff, Mariaelisa1 aGordon-Larsen, Penny1 aLim, Unhee1 aFowke, Jay, H1 aLove, Shelly-Ann1 aFesinmeyer, Megan1 aWilkens, Lynne, R1 aVertilus, Shawyntee1 aRitchie, Marilyn, D1 aPrentice, Ross, L1 aPankow, Jim1 aMonroe, Kristine1 aManson, JoAnn, E1 aLe Marchand, Loïc1 aKuller, Lewis, H1 aKolonel, Laurence, N1 aHong, Ching, P1 aHenderson, Brian, E1 aHaessler, Jeff1 aGross, Myron, D1 aGoodloe, Robert1 aFranceschini, Nora1 aCarlson, Christopher, S1 aBuyske, Steven1 aBůzková, Petra1 aHindorff, Lucia, A1 aMatise, Tara, C1 aCrawford, Dana, C1 aHaiman, Christopher, A1 aPeters, Ulrike1 aNorth, Kari, E uhttps://chs-nhlbi.org/node/663009008nas a2202809 4500008004100000022001400041245010800055210006900163260001300232300001000245490000700255520108900262653002201351653002001373653002501393653001901418653001701437653003801454653003401492653001101526653002701537653001201564653003601576100001901612700001801631700002001649700002001669700002101689700002101710700001901731700002601750700002401776700002401800700001801824700002201842700002501864700001801889700001201907700001701919700001701936700001801953700002201971700001601993700002402009700001902033700002402052700002202076700002502098700002202123700002102145700002002166700002102186700001602207700002202223700002202245700001802267700001802285700001802303700001502321700002102336700002802357700002402385700002002409700003202429700002002461700002202481700002102503700001302524700002302537700002902560700002002589700001702609700002302626700001902649700002202668700002402690700002302714700002202737700002302759700002302782700002402805700002202829700001702851700002402868700001802892700002102910700002102931700002302952700002202975700001702997700001803014700001603032700002103048700002103069700001703090700002403107700002603131700002003157700002603177700001803203700002803221700001503249700002003264700002203284700001603306700002003322700002903342700002203371700001803393700002503411700002203436700002703458700002303485700002903508700002103537700002803558700002603586700002203612700002503634700003103659700002803690700002203718700002003740700002103760700002303781700002103804700001903825700003203844700002803876700002303904700002003927700001903947700002203966700002403988700002704012700002804039700001904067700002104086700002304107700001904130700002204149700001504171700001904186700002104205700002104226700001904247700001804266700002304284700001904307700001404326700001904340700001404359700002304373700002104396700002604417700002204443700002004465700001904485700002004504700002804524700001704552700002104569700001804590700002104608700002104629700002204650700002104672700002304693700002404716700002204740700001904762700002404781700002304805700002004828700001804848700002204866700001804888700002004906700002704926700002204953700002304975700001504998700001405013700002005027700001705047700001905064700002005083700002205103700002505125700002405150700002105174700002405195700001905219700002505238700002305263700001505286700002205301700002405323700002205347700002705369700002005396700002105416700001905437700002405456700002405480700002005504700001905524700002305543700002505566700002205591700002505613700002405638700002505662700002305687700001905710700002105729700001905750700001505769700002005784700002305804700002005827700001805847700002305865700002405888700002805912700002405940700002005964700002405984700002006008700001906028700002706047710002106074710002106095710002606116710002006142856003606162 2013 eng d a1546-171800aA meta-analysis identifies new loci associated with body mass index in individuals of African ancestry.0 ametaanalysis identifies new loci associated with body mass index c2013 Jun a690-60 v453 aGenome-wide association studies (GWAS) have identified 36 loci associated with body mass index (BMI), predominantly in populations of European ancestry. We conducted a meta-analysis to examine the association of >3.2 million SNPs with BMI in 39,144 men and women of African ancestry and followed up the most significant associations in an additional 32,268 individuals of African ancestry. We identified one new locus at 5q33 (GALNT10, rs7708584, P = 3.4 × 10(-11)) and another at 7p15 when we included data from the GIANT consortium (MIR148A-NFE2L3, rs10261878, P = 1.2 × 10(-10)). We also found suggestive evidence of an association at a third locus at 6q16 in the African-ancestry sample (KLHL32, rs974417, P = 6.9 × 10(-8)). Thirty-two of the 36 previously established BMI variants showed directionally consistent effect estimates in our GWAS (binomial P = 9.7 × 10(-7)), five of which reached genome-wide significance. These findings provide strong support for shared BMI loci across populations, as well as for the utility of studying ancestrally diverse populations.
10aAfrican Americans10aBody Mass Index10aCase-Control Studies10aGene Frequency10aGenetic Loci10aGenetic Predisposition to Disease10aGenome-Wide Association Study10aHumans10aLinkage Disequilibrium10aObesity10aPolymorphism, Single Nucleotide1 aMonda, Keri, L1 aChen, Gary, K1 aTaylor, Kira, C1 aPalmer, Cameron1 aEdwards, Todd, L1 aLange, Leslie, A1 aC Y Ng, Maggie1 aAdeyemo, Adebowale, A1 aAllison, Matthew, A1 aBielak, Lawrence, F1 aChen, Guanjie1 aGraff, Mariaelisa1 aIrvin, Marguerite, R1 aRhie, Suhn, K1 aLi, Guo1 aLiu, Yongmei1 aLiu, Youfang1 aLu, Yingchang1 aNalls, Michael, A1 aSun, Yan, V1 aWojczynski, Mary, K1 aYanek, Lisa, R1 aAldrich, Melinda, C1 aAdemola, Adeyinka1 aAmos, Christopher, I1 aBandera, Elisa, V1 aBock, Cathryn, H1 aBritton, Angela1 aBroeckel, Ulrich1 aCai, Quiyin1 aCaporaso, Neil, E1 aCarlson, Chris, S1 aCarpten, John1 aCasey, Graham1 aChen, Wei-Min1 aChen, Fang1 aChen, Yii-der, I1 aChiang, Charleston, W K1 aCoetzee, Gerhard, A1 aDemerath, Ellen1 aDeming-Halverson, Sandra, L1 aDriver, Ryan, W1 aDubbert, Patricia1 aFeitosa, Mary, F1 aFeng, Ye1 aFreedman, Barry, I1 aGillanders, Elizabeth, M1 aGottesman, Omri1 aGuo, Xiuqing1 aHaritunians, Talin1 aHarris, Tamara1 aHarris, Curtis, C1 aHennis, Anselm, J M1 aHernandez, Dena, G1 aMcNeill, Lorna, H1 aHoward, Timothy, D1 aHoward, Barbara, V1 aHoward, Virginia, J1 aJohnson, Karen, C1 aKang, Sun, J1 aKeating, Brendan, J1 aKolb, Suzanne1 aKuller, Lewis, H1 aKutlar, Abdullah1 aLangefeld, Carl, D1 aLettre, Guillaume1 aLohman, Kurt1 aLotay, Vaneet1 aLyon, Helen1 aManson, JoAnn, E1 aMaixner, William1 aMeng, Yan, A1 aMonroe, Kristine, R1 aMorhason-Bello, Imran1 aMurphy, Adam, B1 aMychaleckyj, Josyf, C1 aNadukuru, Raj1 aNathanson, Katherine, L1 aNayak, Uma1 aN'diaye, Amidou1 aNemesure, Barbara1 aWu, Suh-Yuh1 aLeske, Cristina1 aNeslund-Dudas, Christine1 aNeuhouser, Marian1 aNyante, Sarah1 aOchs-Balcom, Heather1 aOgunniyi, Adesola1 aOgundiran, Temidayo, O1 aOjengbede, Oladosu1 aOlopade, Olufunmilayo, I1 aPalmer, Julie, R1 aRuiz-Narvaez, Edward, A1 aPalmer, Nicholette, D1 aPress, Michael, F1 aRampersaud, Evandine1 aRasmussen-Torvik, Laura, J1 aRodriguez-Gil, Jorge, L1 aSalako, Babatunde1 aSchadt, Eric, E1 aSchwartz, Ann, G1 aShriner, Daniel, A1 aSiscovick, David1 aSmith, Shad, B1 aWassertheil-Smoller, Sylvia1 aSpeliotes, Elizabeth, K1 aSpitz, Margaret, R1 aSucheston, Lara1 aTaylor, Herman1 aTayo, Bamidele, O1 aTucker, Margaret, A1 aVan Den Berg, David, J1 aEdwards, Digna, R Velez1 aWang, Zhaoming1 aWiencke, John, K1 aWinkler, Thomas, W1 aWitte, John, S1 aWrensch, Margaret1 aWu, Xifeng1 aYang, James, J1 aLevin, Albert, M1 aYoung, Taylor, R1 aZakai, Neil, A1 aCushman, Mary1 aZanetti, Krista, A1 aZhao, Jing Hua1 aZhao, Wei1 aZheng, Yonglan1 aZhou, Jie1 aZiegler, Regina, G1 aZmuda, Joseph, M1 aFernandes, Jyotika, K1 aGilkeson, Gary, S1 aKamen, Diane, L1 aHunt, Kelly, J1 aSpruill, Ida, J1 aAmbrosone, Christine, B1 aAmbs, Stefan1 aArnett, Donna, K1 aAtwood, Larry1 aBecker, Diane, M1 aBerndt, Sonja, I1 aBernstein, Leslie1 aBlot, William, J1 aBorecki, Ingrid, B1 aBottinger, Erwin, P1 aBowden, Donald, W1 aBurke, Gregory1 aChanock, Stephen, J1 aCooper, Richard, S1 aDing, Jingzhong1 aDuggan, David1 aEvans, Michele, K1 aFox, Caroline1 aGarvey, Timothy1 aBradfield, Jonathan, P1 aHakonarson, Hakon1 aGrant, Struan, F A1 aHsing, Ann1 aChu, Lisa1 aHu, Jennifer, J1 aHuo, Dezheng1 aIngles, Sue, A1 aJohn, Esther, M1 aJordan, Joanne, M1 aKabagambe, Edmond, K1 aKardia, Sharon, L R1 aKittles, Rick, A1 aGoodman, Phyllis, J1 aKlein, Eric, A1 aKolonel, Laurence, N1 aLe Marchand, Loïc1 aLiu, Simin1 aMcKnight, Barbara1 aMillikan, Robert, C1 aMosley, Thomas, H1 aPadhukasahasram, Badri1 aWilliams, Keoki1 aPatel, Sanjay, R1 aPeters, Ulrike1 aPettaway, Curtis, A1 aPeyser, Patricia, A1 aPsaty, Bruce, M1 aRedline, Susan1 aRotimi, Charles, N1 aRybicki, Benjamin, A1 aSale, Michèle, M1 aSchreiner, Pamela, J1 aSignorello, Lisa, B1 aSingleton, Andrew, B1 aStanford, Janet, L1 aStrom, Sara, S1 aThun, Michael, J1 aVitolins, Mara1 aZheng, Wei1 aMoore, Jason, H1 aWilliams, Scott, M1 aKetkar, Shamika1 aZhu, Xiaofeng1 aZonderman, Alan, B1 aKooperberg, Charles1 aPapanicolaou, George, J1 aHenderson, Brian, E1 aReiner, Alex, P1 aHirschhorn, Joel, N1 aLoos, Ruth, J F1 aNorth, Kari, E1 aHaiman, Christopher, A1 aNABEC Consortium1 aUKBEC Consortium1 aBioBank Japan Project1 aAGEN Consortium uhttps://chs-nhlbi.org/node/607804247nas a2200757 4500008004100000022001400041245023300055210006900288260000900357300001300366490000600379520199400385653004102379653001002420653002202430653000902452653002202461653001202483653002002495653002302515653003402538653004002572653001102612653003802623653003402661653001102695653002702706653000902733653001702742653001602759653001202775653001302787100001902800700001902819700002402838700001802862700001902880700001502899700002502914700002402939700001902963700002502982700001503007700001603022700002103038700001903059700001703078700001603095700001703111700002603128700002003154700001903174700001803193700002503211700001603236700002003252700002103272700002103293700002003314700002303334700002303357700002203380700002703402700002403429856003603453 2013 eng d a1553-740400aA systematic mapping approach of 16q12.2/FTO and BMI in more than 20,000 African Americans narrows in on the underlying functional variation: results from the Population Architecture using Genomics and Epidemiology (PAGE) study.0 asystematic mapping approach of 16q122FTO and BMI in more than 20 c2013 ae10031710 v93 aGenetic variants in intron 1 of the fat mass- and obesity-associated (FTO) gene have been consistently associated with body mass index (BMI) in Europeans. However, follow-up studies in African Americans (AA) have shown no support for some of the most consistently BMI-associated FTO index single nucleotide polymorphisms (SNPs). This is most likely explained by different race-specific linkage disequilibrium (LD) patterns and lower correlation overall in AA, which provides the opportunity to fine-map this region and narrow in on the functional variant. To comprehensively explore the 16q12.2/FTO locus and to search for second independent signals in the broader region, we fine-mapped a 646-kb region, encompassing the large FTO gene and the flanking gene RPGRIP1L by investigating a total of 3,756 variants (1,529 genotyped and 2,227 imputed variants) in 20,488 AAs across five studies. We observed associations between BMI and variants in the known FTO intron 1 locus: the SNP with the most significant p-value, rs56137030 (8.3 × 10(-6)) had not been highlighted in previous studies. While rs56137030was correlated at r(2)>0.5 with 103 SNPs in Europeans (including the GWAS index SNPs), this number was reduced to 28 SNPs in AA. Among rs56137030 and the 28 correlated SNPs, six were located within candidate intronic regulatory elements, including rs1421085, for which we predicted allele-specific binding affinity for the transcription factor CUX1, which has recently been implicated in the regulation of FTO. We did not find strong evidence for a second independent signal in the broader region. In summary, this large fine-mapping study in AA has substantially reduced the number of common alleles that are likely to be functional candidates of the known FTO locus. Importantly our study demonstrated that comprehensive fine-mapping in AA provides a powerful approach to narrow in on the functional candidate(s) underlying the initial GWAS findings in European populations.
10aAdaptor Proteins, Signal Transducing10aAdult10aAfrican Americans10aAged10aAged, 80 and over10aAlleles10aBody Mass Index10aChromosome Mapping10aContinental Population Groups10aEuropean Continental Ancestry Group10aFemale10aGenetic Predisposition to Disease10aGenome-Wide Association Study10aHumans10aLinkage Disequilibrium10aMale10aMetagenomics10aMiddle Aged10aObesity10aProteins1 aPeters, Ulrike1 aNorth, Kari, E1 aSethupathy, Praveen1 aBuyske, Steve1 aHaessler, Jeff1 aJiao, Shuo1 aFesinmeyer, Megan, D1 aJackson, Rebecca, D1 aKuller, Lew, H1 aRajkovic, Aleksandar1 aLim, Unhee1 aCheng, Iona1 aSchumacher, Fred1 aWilkens, Lynne1 aLi, Rongling1 aMonda, Keri1 aEhret, Georg1 aNguyen, Khanh-Dung, H1 aCooper, Richard1 aLewis, Cora, E1 aLeppert, Mark1 aIrvin, Marguerite, R1 aGu, Charles1 aHouston, Denise1 aBůzková, Petra1 aRitchie, Marylyn1 aMatise, Tara, C1 aLe Marchand, Loïc1 aHindorff, Lucia, A1 aCrawford, Dana, C1 aHaiman, Christopher, A1 aKooperberg, Charles uhttps://chs-nhlbi.org/node/662805266nas a2201201 4500008004100000022001400041245015400055210006900209260001300278300001300291490000600304520181500310653002202125653002202147653002102169653002102190653004002211653003402251653001102285653002202296653002202318653002702340653002602367653001802393100001302411700002202424700002102446700002102467700001902488700001802507700002102525700002102546700002502567700001902592700001602611700002102627700003002648700002202678700002202700700002302722700001702745700002402762700002302786700001402809700002002823700002202843700001802865700002302883700001202906700002202918700002802940700002502968700001902993700002603012700002103038700002503059700002003084700001703104700001803121700002003139700001903159700001903178700002103197700002003218700002803238700002103266700002603287700002403313700001803337700002103355700001703376700001703393700002003410700002003430700002303450700002603473700002203499700001903521700001903540700001403559700002103573700002003594700002003614700002103634700001903655700002403674700002103698700002203719700002003741700002303761700002003784700002003804700002103824700002703845700002103872700002403893700002903917700002203946700002003968700001903988700002104007856003604028 2013 eng d a1553-740400aTrans-ethnic fine-mapping of lipid loci identifies population-specific signals and allelic heterogeneity that increases the trait variance explained.0 aTransethnic finemapping of lipid loci identifies populationspeci c2013 Mar ae10033790 v93 aGenome-wide association studies (GWAS) have identified ~100 loci associated with blood lipid levels, but much of the trait heritability remains unexplained, and at most loci the identities of the trait-influencing variants remain unknown. We conducted a trans-ethnic fine-mapping study at 18, 22, and 18 GWAS loci on the Metabochip for their association with triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C), respectively, in individuals of African American (n = 6,832), East Asian (n = 9,449), and European (n = 10,829) ancestry. We aimed to identify the variants with strongest association at each locus, identify additional and population-specific signals, refine association signals, and assess the relative significance of previously described functional variants. Among the 58 loci, 33 exhibited evidence of association at P<1 × 10(-4) in at least one ancestry group. Sequential conditional analyses revealed that ten, nine, and four loci in African Americans, Europeans, and East Asians, respectively, exhibited two or more signals. At these loci, accounting for all signals led to a 1.3- to 1.8-fold increase in the explained phenotypic variance compared to the strongest signals. Distinct signals across ancestry groups were identified at PCSK9 and APOA5. Trans-ethnic analyses narrowed the signals to smaller sets of variants at GCKR, PPP1R3B, ABO, LCAT, and ABCA1. Of 27 variants reported previously to have functional effects, 74% exhibited the strongest association at the respective signal. In conclusion, trans-ethnic high-density genotyping and analysis confirm the presence of allelic heterogeneity, allow the identification of population-specific variants, and limit the number of candidate SNPs for functional studies.
10aAfrican Americans10aApolipoproteins A10aCholesterol, HDL10aCholesterol, LDL10aEuropean Continental Ancestry Group10aGenome-Wide Association Study10aHumans10aLipoproteins, HDL10aLipoproteins, LDL10aProprotein Convertases10aSerine Endopeptidases10aTriglycerides1 aWu, Ying1 aWaite, Lindsay, L1 aJackson, Anne, U1 aSheu, Wayne, H-H1 aBuyske, Steven1 aAbsher, Devin1 aArnett, Donna, K1 aBoerwinkle, Eric1 aBonnycastle, Lori, L1 aCarty, Cara, L1 aCheng, Iona1 aCochran, Barbara1 aCroteau-Chonka, Damien, C1 aDumitrescu, Logan1 aEaton, Charles, B1 aFranceschini, Nora1 aGuo, Xiuqing1 aHenderson, Brian, E1 aHindorff, Lucia, A1 aKim, Eric1 aKinnunen, Leena1 aKomulainen, Pirjo1 aLee, Wen-Jane1 aLe Marchand, Loïc1 aLin, Yi1 aLindström, Jaana1 aLingaas-Holmen, Oddgeir1 aMitchell, Sabrina, L1 aNarisu, Narisu1 aRobinson, Jennifer, G1 aSchumacher, Fred1 aStančáková, Alena1 aSundvall, Jouko1 aSung, Yun-Ju1 aSwift, Amy, J1 aWang, Wen-Chang1 aWilkens, Lynne1 aWilsgaard, Tom1 aYoung, Alicia, M1 aAdair, Linda, S1 aBallantyne, Christie, M1 aBůzková, Petra1 aChakravarti, Aravinda1 aCollins, Francis, S1 aDuggan, David1 aFeranil, Alan, B1 aHo, Low-Tone1 aHung, Yi-Jen1 aHunt, Steven, C1 aHveem, Kristian1 aJuang, Jyh-Ming, J1 aKesäniemi, Antero, Y1 aKuusisto, Johanna1 aLaakso, Markku1 aLakka, Timo, A1 aLee, I-Te1 aLeppert, Mark, F1 aMatise, Tara, C1 aMoilanen, Leena1 aNjølstad, Inger1 aPeters, Ulrike1 aQuertermous, Thomas1 aRauramaa, Rainer1 aRotter, Jerome, I1 aSaramies, Jouko1 aTuomilehto, Jaakko1 aUusitupa, Matti1 aWang, Tzung-Dau1 aBoehnke, Michael1 aHaiman, Christopher, A1 aChen, Yii-der, I1 aKooperberg, Charles1 aAssimes, Themistocles, L1 aCrawford, Dana, C1 aHsiung, Chao, A1 aNorth, Kari, E1 aMohlke, Karen, L uhttps://chs-nhlbi.org/node/662905943nas a2201657 4500008004100000022001400041245014100055210006900196260001600265300001100281490000700292520126000299653005101559653001001610653003901620653000901659653001201668653001201680653002101692653002101713653001901734653002101753653004001774653001101814653001901825653003201844653001701876653002201893653001101915653001801926653000901944653000901953653002301962653003601985653001602021653001402037653002702051653001602078653001802094100002002112700001802132700001902150700001902169700002402188700002402212700002302236700002502259700002002284700002002304700001802324700002602342700002102368700001702389700002502406700002102431700001702452700001702469700001802486700002102504700002002525700001802545700001902563700002302582700001602605700001902621700002502640700002402665700002202689700002202711700002602733700002402759700002502783700002602808700002102834700001902855700002802874700002202902700002002924700002602944700002502970700002502995700001903020700002103039700002303060700001803083700001803101700002003119700002403139700001903163700002503182700002003207700001803227700002903245700002003274700001503294700002103309700002203330700002303352700001903375700002203394700001603416700002103432700002403453700002003477700001803497700002103515700001903536700001503555700002103570700002203591700001903613700002503632700002103657700001903678700001603697700002403713700002203737700002103759700001703780700001903797700002503816700002203841700001903863700001803882700002303900700001703923700002403940700002103964700002303985700002404008700002104032700002004053700002204073700003004095700001804125700002104143700002204164700002504186710003804211856003604249 2014 eng d a1537-660500aAssociation of low-frequency and rare coding-sequence variants with blood lipids and coronary heart disease in 56,000 whites and blacks.0 aAssociation of lowfrequency and rare codingsequence variants wit c2014 Feb 06 a223-320 v943 aLow-frequency coding DNA sequence variants in the proprotein convertase subtilisin/kexin type 9 gene (PCSK9) lower plasma low-density lipoprotein cholesterol (LDL-C), protect against risk of coronary heart disease (CHD), and have prompted the development of a new class of therapeutics. It is uncertain whether the PCSK9 example represents a paradigm or an isolated exception. We used the "Exome Array" to genotype >200,000 low-frequency and rare coding sequence variants across the genome in 56,538 individuals (42,208 European ancestry [EA] and 14,330 African ancestry [AA]) and tested these variants for association with LDL-C, high-density lipoprotein cholesterol (HDL-C), and triglycerides. Although we did not identify new genes associated with LDL-C, we did identify four low-frequency (frequencies between 0.1% and 2%) variants (ANGPTL8 rs145464906 [c.361C>T; p.Gln121*], PAFAH1B2 rs186808413 [c.482C>T; p.Ser161Leu], COL18A1 rs114139997 [c.331G>A; p.Gly111Arg], and PCSK7 rs142953140 [c.1511G>A; p.Arg504His]) with large effects on HDL-C and/or triglycerides. None of these four variants was associated with risk for CHD, suggesting that examples of low-frequency coding variants with robust effects on both lipids and CHD will be limited.
10a1-Alkyl-2-acetylglycerophosphocholine Esterase10aAdult10aAfrican Continental Ancestry Group10aAged10aAlleles10aAnimals10aCholesterol, HDL10aCholesterol, LDL10aCohort Studies10aCoronary Disease10aEuropean Continental Ancestry Group10aFemale10aGene Frequency10aGenetic Association Studies10aGenetic Code10aGenetic Variation10aHumans10aLinear Models10aMale10aMice10aMice, Inbred C57BL10aMicrotubule-Associated Proteins10aMiddle Aged10aPhenotype10aSequence Analysis, DNA10aSubtilisins10aTriglycerides1 aPeloso, Gina, M1 aAuer, Paul, L1 aBis, Joshua, C1 aVoorman, Arend1 aMorrison, Alanna, C1 aStitziel, Nathan, O1 aBrody, Jennifer, A1 aKhetarpal, Sumeet, A1 aCrosby, Jacy, R1 aFornage, Myriam1 aIsaacs, Aaron1 aJakobsdottir, Johanna1 aFeitosa, Mary, F1 aDavies, Gail1 aHuffman, Jennifer, E1 aManichaikul, Ani1 aDavis, Brian1 aLohman, Kurt1 aJoon, Aron, Y1 aSmith, Albert, V1 aGrove, Megan, L1 aZanoni, Paolo1 aRedon, Valeska1 aDemissie, Serkalem1 aLawson, Kim1 aPeters, Ulrike1 aCarlson, Christopher1 aJackson, Rebecca, D1 aRyckman, Kelli, K1 aMackey, Rachel, H1 aRobinson, Jennifer, G1 aSiscovick, David, S1 aSchreiner, Pamela, J1 aMychaleckyj, Josyf, C1 aPankow, James, S1 aHofman, Albert1 aUitterlinden, André, G1 aHarris, Tamara, B1 aTaylor, Kent, D1 aStafford, Jeanette, M1 aReynolds, Lindsay, M1 aMarioni, Riccardo, E1 aDehghan, Abbas1 aFranco, Oscar, H1 aPatel, Aniruddh, P1 aLu, Yingchang1 aHindy, George1 aGottesman, Omri1 aBottinger, Erwin, P1 aMelander, Olle1 aOrho-Melander, Marju1 aLoos, Ruth, J F1 aDuga, Stefano1 aMerlini, Piera, Angelica1 aFarrall, Martin1 aGoel, Anuj1 aAsselta, Rosanna1 aGirelli, Domenico1 aMartinelli, Nicola1 aShah, Svati, H1 aKraus, William, E1 aLi, Mingyao1 aRader, Daniel, J1 aReilly, Muredach, P1 aMcPherson, Ruth1 aWatkins, Hugh1 aArdissino, Diego1 aZhang, Qunyuan1 aWang, Judy1 aTsai, Michael, Y1 aTaylor, Herman, A1 aCorrea, Adolfo1 aGriswold, Michael, E1 aLange, Leslie, A1 aStarr, John, M1 aRudan, Igor1 aEiriksdottir, Gudny1 aLauner, Lenore, J1 aOrdovas, Jose, M1 aLevy, Daniel1 aChen, Y-D, Ida1 aReiner, Alexander, P1 aHayward, Caroline1 aPolasek, Ozren1 aDeary, Ian, J1 aBorecki, Ingrid, B1 aLiu, Yongmei1 aGudnason, Vilmundur1 aWilson, James, G1 aDuijn, Cornelia, M1 aKooperberg, Charles1 aRich, Stephen, S1 aPsaty, Bruce, M1 aRotter, Jerome, I1 aO'Donnell, Christopher, J1 aRice, Kenneth1 aBoerwinkle, Eric1 aKathiresan, Sekar1 aCupples, Adrienne, L1 aNHLBI GO Exome Sequencing Project uhttps://chs-nhlbi.org/node/659004043nas a2200769 4500008004100000022001400041245014200055210006900197260001300266300001100279490000800290520183700298653001002135653002202145653000902167653001502176653002302191653002802214653001802242653001102260653001702271653003802288653002502326653003402351653001102385653002702396653001602423653003602439653001702475100001802492700002002510700001202530700001902542700001802561700002302579700002402602700001702626700001802643700002302661700001702684700001802701700002302719700002402742700002102766700001702787700002002804700001902824700001502843700002702858700002302885700002102908700002102929700002802950700002202978700002803000700002003028700002203048700002103070700001903091700002103110700002203131700001903153700002003172700002403192700002103216856003603237 2014 eng d a1432-120300aLarge multiethnic Candidate Gene Study for C-reactive protein levels: identification of a novel association at CD36 in African Americans.0 aLarge multiethnic Candidate Gene Study for Creactive protein lev c2014 Aug a985-950 v1333 aC-reactive protein (CRP) is a heritable biomarker of systemic inflammation and a predictor of cardiovascular disease (CVD). Large-scale genetic association studies for CRP have largely focused on individuals of European descent. We sought to uncover novel genetic variants for CRP in a multiethnic sample using the ITMAT Broad-CARe (IBC) array, a custom 50,000 SNP gene-centric array having dense coverage of over 2,000 candidate CVD genes. We performed analyses on 7,570 African Americans (AA) from the Candidate gene Association Resource (CARe) study and race-combined meta-analyses that included 29,939 additional individuals of European descent from CARe, the Women's Health Initiative (WHI) and KORA studies. We observed array-wide significance (p < 2.2 × 10(-6)) for four loci in AA, three of which have been reported previously in individuals of European descent (IL6R, p = 2.0 × 10(-6); CRP, p = 4.2 × 10(-71); APOE, p = 1.6 × 10(-6)). The fourth significant locus, CD36 (p = 1.6 × 10(-6)), was observed at a functional variant (rs3211938) that is extremely rare in individuals of European descent. We replicated the CD36 finding (p = 1.8 × 10(-5)) in an independent sample of 8,041 AA women from WHI; a meta-analysis combining the CARe and WHI AA results at rs3211938 reached genome-wide significance (p = 1.5 × 10(-10)). In the race-combined meta-analyses, 13 loci reached significance, including ten (CRP, TOMM40/APOE/APOC1, HNF1A, LEPR, GCKR, IL6R, IL1RN, NLRP3, HNF4A and BAZ1B/BCL7B) previously associated with CRP, and one (ARNTL) previously reported to be nominally associated with CRP. Two novel loci were also detected (RPS6KB1, p = 2.0 × 10(-6); CD36, p = 1.4 × 10(-6)). These results highlight both shared and unique genetic risk factors for CRP in AA compared to populations of European descent.
10aAdult10aAfrican Americans10aAged10aBiomarkers10aC-Reactive Protein10aCardiovascular Diseases10aCD36 Antigens10aFemale10aGenetic Loci10aGenetic Predisposition to Disease10aGenetics, Population10aGenome-Wide Association Study10aHumans10aMeta-Analysis as Topic10aMiddle Aged10aPolymorphism, Single Nucleotide10aRisk Factors1 aEllis, Jaclyn1 aLange, Ethan, M1 aLi, Jin1 aDupuis, Josée1 aBaumert, Jens1 aWalston, Jeremy, D1 aKeating, Brendan, J1 aDurda, Peter1 aFox, Ervin, R1 aPalmer, Cameron, D1 aMeng, Yan, A1 aYoung, Taylor1 aFarlow, Deborah, N1 aSchnabel, Renate, B1 aMarzi, Carola, S1 aLarkin, Emma1 aMartin, Lisa, W1 aBis, Joshua, C1 aAuer, Paul1 aRamachandran, Vasan, S1 aGabriel, Stacey, B1 aWillis, Monte, S1 aPankow, James, S1 aPapanicolaou, George, J1 aRotter, Jerome, I1 aBallantyne, Christie, M1 aGross, Myron, D1 aLettre, Guillaume1 aWilson, James, G1 aPeters, Ulrike1 aKoenig, Wolfgang1 aTracy, Russell, P1 aRedline, Susan1 aReiner, Alex, P1 aBenjamin, Emelia, J1 aLange, Leslie, A uhttps://chs-nhlbi.org/node/655804252nas a2200745 4500008004100000022001400041245016200055210006900217260001300286300001100299490000600310520207600316653001002392653003902402653000902441653003702450653002302487653001102510653002202521653003402543653002302577653001102600653002802611653001702639653000902656653001602665653003602681653001802717653001602735100002602751700002602777700001902803700002102822700002802843700001602871700001702887700002302904700001702927700001902944700001502963700002602978700002003004700002603024700001803050700001803068700002103086700002103107700002103128700002203149700002103171700002303192700002003215700002203235700002703257700002503284700002003309700001603329700002103345700002303366700002303389700002203412700001703434700001903451856003603470 2014 eng d a1942-326800aMultiancestral analysis of inflammation-related genetic variants and C-reactive protein in the population architecture using genomics and epidemiology study.0 aMultiancestral analysis of inflammationrelated genetic variants c2014 Apr a178-880 v73 aBACKGROUND: C-reactive protein (CRP) is a biomarker of inflammation. Genome-wide association studies (GWAS) have identified single-nucleotide polymorphisms (SNPs) associated with CRP concentrations and inflammation-related traits such as cardiovascular disease, type 2 diabetes mellitus, and obesity. We aimed to replicate previous CRP-SNP associations, assess whether these associations generalize to additional race/ethnicity groups, and evaluate inflammation-related SNPs for a potentially pleiotropic association with CRP.
METHODS AND RESULTS: We selected and analyzed 16 CRP-associated and 250 inflammation-related GWAS SNPs among 40 473 African American, American Indian, Asian/Pacific Islander, European American, and Hispanic participants from 7 studies collaborating in the Population Architecture using Genomics and Epidemiology (PAGE) study. Fixed-effect meta-analyses combined study-specific race/ethnicity-stratified linear regression estimates to evaluate the association between each SNP and high-sensitivity CRP. Overall, 18 SNPs in 8 loci were significantly associated with CRP (Bonferroni-corrected P<3.1×10(-3) for replication, P<2.0×10(-4) for pleiotropy): Seven of these were specific to European Americans, while 9 additionally generalized to African Americans (1), Hispanics (5), or both (3); 1 SNP was seen only in African Americans and Hispanics. Two SNPs in the CELSR2/PSRC1/SORT1 locus showed a potentially novel association with CRP: rs599839 (P=2.0×10(-6)) and rs646776 (P=3.1×10(-5)).
CONCLUSIONS: We replicated 16 SNP-CRP associations, 10 of which generalized to African Americans and/or Hispanics. We also identified potentially novel pleiotropic associations with CRP for two SNPs previously associated with coronary artery disease and/or low-density lipoprotein-cholesterol. These findings demonstrate the benefit of evaluating genotype-phenotype associations in multiple race/ethnicity groups and looking for pleiotropic relationships among SNPs previously associated with related phenotypes.
10aAdult10aAfrican Continental Ancestry Group10aAged10aAsian Continental Ancestry Group10aC-Reactive Protein10aFemale10aGenetic Variation10aGenome-Wide Association Study10aHispanic Americans10aHumans10aIndians, North American10aInflammation10aMale10aMiddle Aged10aPolymorphism, Single Nucleotide10aUnited States10aYoung Adult1 aKocarnik, Jonathan, M1 aPendergrass, Sarah, A1 aCarty, Cara, L1 aPankow, James, S1 aSchumacher, Fredrick, R1 aCheng, Iona1 aDurda, Peter1 aAmbite, Jose, Luis1 aDeelman, Ewa1 aCook, Nancy, R1 aLiu, Simin1 aWactawski-Wende, Jean1 aHutter, Carolyn1 aBrown-Gentry, Kristin1 aWilson, Sarah1 aBest, Lyle, G1 aPankratz, Nathan1 aHong, Ching-Ping1 aCole, Shelley, A1 aVoruganti, Saroja1 aBůzková, Petra1 aJorgensen, Neal, W1 aJenny, Nancy, S1 aWilkens, Lynne, R1 aHaiman, Christopher, A1 aKolonel, Laurence, N1 aLaCroix, Andrea1 aNorth, Kari1 aJackson, Rebecca1 aLe Marchand, Loïc1 aHindorff, Lucia, A1 aCrawford, Dana, C1 aGross, Myron1 aPeters, Ulrike uhttps://chs-nhlbi.org/node/636003227nas a2200505 4500008004100000022001400041245012600055210006900181260001600250300001200266490000700278520166100285653003501946653002301981653004002004653001002044653001102054653003202065653002102097653002602118100002002144700002002164700002302184700002402207700002902231700001802260700002502278700001902303700002602322700002302348700001702371700002202388700002202410700002002432700002402452700002502476700003002501700002502531700002102556700002002577700002402597700002602621710003802647856003602685 2014 eng d a1460-208300aQuantifying rare, deleterious variation in 12 human cytochrome P450 drug-metabolism genes in a large-scale exome dataset.0 aQuantifying rare deleterious variation in 12 human cytochrome P4 c2014 Apr 15 a1957-630 v233 aThe study of genetic influences on drug response and efficacy ('pharmacogenetics') has existed for over 50 years. Yet, we still lack a complete picture of how genetic variation, both common and rare, affects each individual's responses to medications. Exome sequencing is a promising alternative method for pharmacogenetic discovery as it provides information on both common and rare variation in large numbers of individuals. Using exome data from 2203 AA and 4300 Caucasian individuals through the NHLBI Exome Sequencing Project, we conducted a survey of coding variation within 12 Cytochrome P450 (CYP) genes that are collectively responsible for catalyzing nearly 75% of all known Phase I drug oxidation reactions. In addition to identifying many polymorphisms with known pharmacogenetic effects, we discovered over 730 novel nonsynonymous alleles across the 12 CYP genes of interest. These alleles include many with diverse functional effects such as premature stop codons, aberrant splicesites and mutations at conserved active site residues. Our analysis considering both novel, predicted functional alleles as well as known, actionable CYP alleles reveals that rare, deleterious variation contributes markedly to the overall burden of pharmacogenetic alleles within the populations considered, and that the contribution of rare variation to this burden is over three times greater in AA individuals as compared with Caucasians. While most of these impactful alleles are individually rare, 7.6-11.7% of individuals interrogated in the study carry at least one newly described potentially deleterious alleles in a major drug-metabolizing CYP.
10aCytochrome P-450 Enzyme System10aDatabases, Genetic10aEuropean Continental Ancestry Group10aExome10aHumans10aPharmaceutical Preparations10aPharmacogenetics10aPolymorphism, Genetic1 aGordon, Adam, S1 aTabor, Holly, K1 aJohnson, Andrew, D1 aSnively, Beverly, M1 aAssimes, Themistocles, L1 aAuer, Paul, L1 aIoannidis, John, P A1 aPeters, Ulrike1 aRobinson, Jennifer, G1 aSucheston, Lara, E1 aWang, Danxin1 aSotoodehnia, Nona1 aRotter, Jerome, I1 aPsaty, Bruce, M1 aJackson, Rebecca, D1 aHerrington, David, M1 aO'Donnell, Christopher, J1 aReiner, Alexander, P1 aRich, Stephen, S1 aRieder, Mark, J1 aBamshad, Michael, J1 aNickerson, Deborah, A1 aNHLBI GO Exome Sequencing Project uhttps://chs-nhlbi.org/node/656505955nas a2201657 4500008004100000022001400041245011000055210006900165260001600234300001100250490000700261520138800268653001001656653000901666653002201675653002101697653001901718653001801737653001001755653001101765653002201776653001901798653001701817653003401834653001301868653001101881653001101892653000901903653001601912653001401928653003601942653002801978653002702006653001902033653002702052653002602079100002102105700001402126700001402140700001602154700002202170700002202192700001702214700002002231700002102251700002102272700001302293700002002306700001702326700001502343700001702358700002002375700001402395700002702409700001802436700002202454700001602476700001902492700001702511700002102528700002302549700002002572700001802592700002202610700001802632700001502650700001202665700001702677700001502694700001802709700001902727700001902746700001902765700002502784700002602809700002102835700002502856700002102881700001902902700002502921700001702946700002502963700001202988700002303000700002003023700002603043700002903069700002303098700002803121700001803149700002003167700002303187700002103210700001903231700001803250700002803268700001903296700002403315700002303339700002803362700001703390700002203407700002203429700002403451700002403475700002003499700002303519700002203542700001603564700001703580700002403597700002003621700001803641700002003659700002103679700001603700700001903716700002203735700002803757700002303785700003003808700001903838700001903857700002503876700002103901700002003922700002103942700002203963700001603985700002004001700002504021700002404046700002104070700002604091700002504117700002104142700002204163700002304185710005304208856003604261 2014 eng d a1537-660500aWhole-exome sequencing identifies rare and low-frequency coding variants associated with LDL cholesterol.0 aWholeexome sequencing identifies rare and lowfrequency coding va c2014 Feb 06 a233-450 v943 aElevated low-density lipoprotein cholesterol (LDL-C) is a treatable, heritable risk factor for cardiovascular disease. Genome-wide association studies (GWASs) have identified 157 variants associated with lipid levels but are not well suited to assess the impact of rare and low-frequency variants. To determine whether rare or low-frequency coding variants are associated with LDL-C, we exome sequenced 2,005 individuals, including 554 individuals selected for extreme LDL-C (>98(th) or <2(nd) percentile). Follow-up analyses included sequencing of 1,302 additional individuals and genotype-based analysis of 52,221 individuals. We observed significant evidence of association between LDL-C and the burden of rare or low-frequency variants in PNPLA5, encoding a phospholipase-domain-containing protein, and both known and previously unidentified variants in PCSK9, LDLR and APOB, three known lipid-related genes. The effect sizes for the burden of rare variants for each associated gene were substantially higher than those observed for individual SNPs identified from GWASs. We replicated the PNPLA5 signal in an independent large-scale sequencing study of 2,084 individuals. In conclusion, this large whole-exome-sequencing study for LDL-C identified a gene not known to be implicated in LDL-C and provides unique insight into the design and analysis of similar experiments.
10aAdult10aAged10aApolipoproteins E10aCholesterol, LDL10aCohort Studies10aDyslipidemias10aExome10aFemale10aFollow-Up Studies10aGene Frequency10aGenetic Code10aGenome-Wide Association Study10aGenotype10aHumans10aLipase10aMale10aMiddle Aged10aPhenotype10aPolymorphism, Single Nucleotide10aProprotein Convertase 910aProprotein Convertases10aReceptors, LDL10aSequence Analysis, DNA10aSerine Endopeptidases1 aLange, Leslie, A1 aHu, Youna1 aZhang, He1 aXue, Chenyi1 aSchmidt, Ellen, M1 aTang, Zheng-Zheng1 aBizon, Chris1 aLange, Ethan, M1 aSmith, Joshua, D1 aTurner, Emily, H1 aJun, Goo1 aKang, Hyun, Min1 aPeloso, Gina1 aAuer, Paul1 aLi, Kuo-Ping1 aFlannick, Jason1 aZhang, Ji1 aFuchsberger, Christian1 aGaulton, Kyle1 aLindgren, Cecilia1 aLocke, Adam1 aManning, Alisa1 aSim, Xueling1 aRivas, Manuel, A1 aHolmen, Oddgeir, L1 aGottesman, Omri1 aLu, Yingchang1 aRuderfer, Douglas1 aStahl, Eli, A1 aDuan, Qing1 aLi, Yun1 aDurda, Peter1 aJiao, Shuo1 aIsaacs, Aaron1 aHofman, Albert1 aBis, Joshua, C1 aCorrea, Adolfo1 aGriswold, Michael, E1 aJakobsdottir, Johanna1 aSmith, Albert, V1 aSchreiner, Pamela, J1 aFeitosa, Mary, F1 aZhang, Qunyuan1 aHuffman, Jennifer, E1 aCrosby, Jacy1 aWassel, Christina, L1 aDo, Ron1 aFranceschini, Nora1 aMartin, Lisa, W1 aRobinson, Jennifer, G1 aAssimes, Themistocles, L1 aCrosslin, David, R1 aRosenthal, Elisabeth, A1 aTsai, Michael1 aRieder, Mark, J1 aFarlow, Deborah, N1 aFolsom, Aaron, R1 aLumley, Thomas1 aFox, Ervin, R1 aCarlson, Christopher, S1 aPeters, Ulrike1 aJackson, Rebecca, D1 aDuijn, Cornelia, M1 aUitterlinden, André, G1 aLevy, Daniel1 aRotter, Jerome, I1 aTaylor, Herman, A1 aGudnason, Vilmundur1 aSiscovick, David, S1 aFornage, Myriam1 aBorecki, Ingrid, B1 aHayward, Caroline1 aRudan, Igor1 aChen, Eugene1 aBottinger, Erwin, P1 aLoos, Ruth, J F1 aSætrom, Pål1 aHveem, Kristian1 aBoehnke, Michael1 aGroop, Leif1 aMcCarthy, Mark1 aMeitinger, Thomas1 aBallantyne, Christie, M1 aGabriel, Stacey, B1 aO'Donnell, Christopher, J1 aPost, Wendy, S1 aNorth, Kari, E1 aReiner, Alexander, P1 aBoerwinkle, Eric1 aPsaty, Bruce, M1 aAltshuler, David1 aKathiresan, Sekar1 aLin, Dan-Yu1 aJarvik, Gail, P1 aCupples, Adrienne, L1 aKooperberg, Charles1 aWilson, James, G1 aNickerson, Deborah, A1 aAbecasis, Goncalo, R1 aRich, Stephen, S1 aTracy, Russell, P1 aWiller, Cristen, J1 aNHLBI Grand Opportunity Exome Sequencing Project uhttps://chs-nhlbi.org/node/657704486nas a2200901 4500008004100000022001400041245009600055210006900151260001600220300001100236490000700247520184700254653001002101653002202111653002302133653002802156653001902184653004002203653001002243653001102253653001902264653003802283653003402321653003802355653001102393653000902404653001102413653003602424653002902460653001702489100002202506700001802528700001902546700001902565700001402584700002102598700002102619700002002640700002402660700001902684700002802703700002002731700002202751700001202773700002402785700002402809700002102833700002002854700002102874700001902895700002102914700001602935700002002951700001502971700001902986700002003005700001803025700002103043700001803064700002203082700002403104700002303128700002303151700001903174700002603193700002203219700001903241700001903260700002103279700002103300700002403321700002403345700002003369700002003389710006503409710007403474856003603548 2015 eng d a1460-208300aAssociation of exome sequences with plasma C-reactive protein levels in >9000 participants.0 aAssociation of exome sequences with plasma Creactive protein lev c2015 Jan 15 a559-710 v243 aC-reactive protein (CRP) concentration is a heritable systemic marker of inflammation that is associated with cardiovascular disease risk. Genome-wide association studies have identified CRP-associated common variants associated in ∼25 genes. Our aims were to apply exome sequencing to (1) assess whether the candidate loci contain rare coding variants associated with CRP levels and (2) perform an exome-wide search for rare variants in novel genes associated with CRP levels. We exome-sequenced 6050 European-Americans (EAs) and 3109 African-Americans (AAs) from the NHLBI-ESP and the CHARGE consortia, and performed association tests of sequence data with measured CRP levels. In single-variant tests across candidate loci, a novel rare (minor allele frequency = 0.16%) CRP-coding variant (rs77832441-A; p.Thr59Met) was associated with 53% lower mean CRP levels (P = 2.9 × 10(-6)). We replicated the association of rs77832441 in an exome array analysis of 11 414 EAs (P = 3.0 × 10(-15)). Despite a strong effect on CRP levels, rs77832441 was not associated with inflammation-related phenotypes including coronary heart disease. We also found evidence for an AA-specific association of APOE-ε2 rs7214 with higher CRP levels. At the exome-wide significance level (P < 5.0 × 10(-8)), we confirmed associations for reported common variants of HNF1A, CRP, IL6R and TOMM40-APOE. In gene-based tests, a burden of rare/lower frequency variation in CRP in EAs (P ≤ 6.8 × 10(-4)) and in retinoic acid receptor-related orphan receptor α (RORA) in AAs (P = 1.7 × 10(-3)) were associated with CRP levels at the candidate gene level (P < 2.0 × 10(-3)). This inquiry did not elucidate novel genes, but instead demonstrated that variants distributed across the allele frequency spectrum within candidate genes contribute to CRP levels.
10aAdult10aAfrican Americans10aC-Reactive Protein10aCardiovascular Diseases10aCohort Studies10aEuropean Continental Ancestry Group10aExome10aFemale10aGene Frequency10aGenetic Predisposition to Disease10aGenome-Wide Association Study10aHepatocyte Nuclear Factor 1-alpha10aHumans10aMale10aPlasma10aPolymorphism, Single Nucleotide10aReceptors, Interleukin-610aRisk Factors1 aSchick, Ursula, M1 aAuer, Paul, L1 aBis, Joshua, C1 aLin, Honghuang1 aWei, Peng1 aPankratz, Nathan1 aLange, Leslie, A1 aBrody, Jennifer1 aStitziel, Nathan, O1 aKim, Daniel, S1 aCarlson, Christopher, S1 aFornage, Myriam1 aHaessler, Jeffery1 aHsu, Li1 aJackson, Rebecca, D1 aKooperberg, Charles1 aLeal, Suzanne, M1 aPsaty, Bruce, M1 aBoerwinkle, Eric1 aTracy, Russell1 aArdissino, Diego1 aShah, Svati1 aWiller, Cristen1 aLoos, Ruth1 aMelander, Olle1 aMcPherson, Ruth1 aHovingh, Kees1 aReilly, Muredach1 aWatkins, Hugh1 aGirelli, Domenico1 aFontanillas, Pierre1 aChasman, Daniel, I1 aGabriel, Stacey, B1 aGibbs, Richard1 aNickerson, Deborah, A1 aKathiresan, Sekar1 aPeters, Ulrike1 aDupuis, Josée1 aWilson, James, G1 aRich, Stephen, S1 aMorrison, Alanna, C1 aBenjamin, Emelia, J1 aGross, Myron, D1 aReiner, Alex, P1 aCohorts for Heart and Aging Research in Genomic Epidemiology1 aNational Heart, Lung, and Blood Institute GO Exome Sequencing Project uhttps://chs-nhlbi.org/node/659704841nas a2200685 4500008004100000022001400041245011900055210006900174260001300243300001000256490000700266520281800273653000903091653001903100653001003119653001103129653003803140653002203178653003403200653001103234653000903245653001603254653002003270653005303290653002103343653002403364653002803388653001103416653001803427100001803445700001603463700002203479700002503501700002003526700002003546700002403566700002403590700002803614700001903642700001803661700002503679700002403704700002003728700001603748700001203764700002403776700002003800700001903820700002903839700002103868700002103889700002203910700002503932700002503957700002603982700001904008700002104027710007104048856003604119 2015 eng d a2168-615700aRare and Coding Region Genetic Variants Associated With Risk of Ischemic Stroke: The NHLBI Exome Sequence Project.0 aRare and Coding Region Genetic Variants Associated With Risk of c2015 Jul a781-80 v723 aIMPORTANCE: Stroke is the second leading cause of death and the third leading cause of years of life lost. Genetic factors contribute to stroke prevalence, and candidate gene and genome-wide association studies (GWAS) have identified variants associated with ischemic stroke risk. These variants often have small effects without obvious biological significance. Exome sequencing may discover predicted protein-altering variants with a potentially large effect on ischemic stroke risk.
OBJECTIVE: To investigate the contribution of rare and common genetic variants to ischemic stroke risk by targeting the protein-coding regions of the human genome.
DESIGN, SETTING, AND PARTICIPANTS: The National Heart, Lung, and Blood Institute (NHLBI) Exome Sequencing Project (ESP) analyzed approximately 6000 participants from numerous cohorts of European and African ancestry. For discovery, 365 cases of ischemic stroke (small-vessel and large-vessel subtypes) and 809 European ancestry controls were sequenced; for replication, 47 affected sibpairs concordant for stroke subtype and an African American case-control series were sequenced, with 1672 cases and 4509 European ancestry controls genotyped. The ESP's exome sequencing and genotyping started on January 1, 2010, and continued through June 30, 2012. Analyses were conducted on the full data set between July 12, 2012, and July 13, 2013.
MAIN OUTCOMES AND MEASURES: Discovery of new variants or genes contributing to ischemic stroke risk and subtype (primary analysis) and determination of support for protein-coding variants contributing to risk in previously published candidate genes (secondary analysis).
RESULTS: We identified 2 novel genes associated with an increased risk of ischemic stroke: a protein-coding variant in PDE4DIP (rs1778155; odds ratio, 2.15; P = 2.63 × 10(-8)) with an intracellular signal transduction mechanism and in ACOT4 (rs35724886; odds ratio, 2.04; P = 1.24 × 10(-7)) with a fatty acid metabolism; confirmation of PDE4DIP was observed in affected sibpair families with large-vessel stroke subtype and in African Americans. Replication of protein-coding variants in candidate genes was observed for 2 previously reported GWAS associations: ZFHX3 (cardioembolic stroke) and ABCA1 (large-vessel stroke).
CONCLUSIONS AND RELEVANCE: Exome sequencing discovered 2 novel genes and mechanisms, PDE4DIP and ACOT4, associated with increased risk for ischemic stroke. In addition, ZFHX3 and ABCA1 were discovered to have protein-coding variants associated with ischemic stroke. These results suggest that genetic variation in novel pathways contributes to ischemic stroke risk and serves as a target for prediction, prevention, and therapy.
10aAged10aBrain Ischemia10aExome10aFemale10aGenetic Predisposition to Disease10aGenetic Variation10aGenome-Wide Association Study10aHumans10aMale10aMiddle Aged10aMuscle Proteins10aNational Heart, Lung, and Blood Institute (U.S.)10aNuclear Proteins10aOpen Reading Frames10aPalmitoyl-CoA Hydrolase10aStroke10aUnited States1 aAuer, Paul, L1 aNalls, Mike1 aMeschia, James, F1 aWorrall, Bradford, B1 aLongstreth, W T1 aSeshadri, Sudha1 aKooperberg, Charles1 aBurger, Kathleen, M1 aCarlson, Christopher, S1 aCarty, Cara, L1 aChen, Wei-Min1 aCupples, Adrienne, L1 aDeStefano, Anita, L1 aFornage, Myriam1 aHardy, John1 aHsu, Li1 aJackson, Rebecca, D1 aJarvik, Gail, P1 aKim, Daniel, S1 aLakshminarayan, Kamakshi1 aLange, Leslie, A1 aManichaikul, Ani1 aQuinlan, Aaron, R1 aSingleton, Andrew, B1 aThornton, Timothy, A1 aNickerson, Deborah, A1 aPeters, Ulrike1 aRich, Stephen, S1 aNational Heart, Lung, and Blood Institute Exome Sequencing Project uhttps://chs-nhlbi.org/node/684906988nas a2201993 4500008004100000022001400041245011600055210006900171260001600240520137500256100001201631700001301643700001801656700002201674700002201696700002501718700001801743700002701761700002001788700002801808700001901836700002001855700002101875700001801896700001601914700002801930700001901958700002501977700002302002700002302025700002302048700002502071700001902096700002102115700002102136700002202157700002102179700002302200700001502223700001802238700001902256700002102275700001702296700001702313700001602330700002002346700002802366700001802394700001902412700002002431700001702451700001802468700001802486700002202504700002102526700002102547700002902568700002302597700002202620700001702642700002202659700003202681700002102713700002302734700002102757700003102778700001402809700002202823700001902845700001602864700002102880700002102901700002302922700002302945700002402968700001902992700002103011700002303032700002103055700002003076700002403096700001803120700001703138700002003155700001803175700002003193700002403213700002103237700002103258700002503279700002203304700001603326700002103342700001703363700002203380700002303402700002003425700001803445700002403463700002203487700002203509700001903531700001903550700001703569700002803586700002403614700002303638700002503661700002003686700002403706700002103730700002403751700002203775700002203797700002303819700002203842700001703864700002103881700002103902700001703923700002003940700001803960700002503978700001904003700002104022700001904043700002004062700002104082700002504103700001804128700001904146700002004165700001904185700001904204700002004223700002904243700002004272700001104292700002304303700002004326700001904346700002004365700002604385700002404411700001904435700002104454700002004475700002404495700002104519700002304540700002804563700001804591700002304609700001704632700001904649700002604668700001904694700001904713700001804732700002304750700002104773700002304794700002304817700001904840700001904859710003904878710004104917856003604958 2016 eng d a1533-345000aSOS2 and ACP1 Loci Identified through Large-Scale Exome Chip Analysis Regulate Kidney Development and Function.0 aSOS2 and ACP1 Loci Identified through LargeScale Exome Chip Anal c2016 Dec 053 aGenome-wide association studies have identified >50 common variants associated with kidney function, but these variants do not fully explain the variation in eGFR. We performed a two-stage meta-analysis of associations between genotypes from the Illumina exome array and eGFR on the basis of serum creatinine (eGFRcrea) among participants of European ancestry from the CKDGen Consortium (nStage1: 111,666; nStage2: 48,343). In single-variant analyses, we identified single nucleotide polymorphisms at seven new loci associated with eGFRcrea (PPM1J, EDEM3, ACP1, SPEG, EYA4, CYP1A1, and ATXN2L; PStage1<3.7×10(-7)), of which most were common and annotated as nonsynonymous variants. Gene-based analysis identified associations of functional rare variants in three genes with eGFRcrea, including a novel association with the SOS Ras/Rho guanine nucleotide exchange factor 2 gene, SOS2 (P=5.4×10(-8) by sequence kernel association test). Experimental follow-up in zebrafish embryos revealed changes in glomerular gene expression and renal tubule morphology in the embryonic kidney of acp1- and sos2-knockdowns. These developmental abnormalities associated with altered blood clearance rate and heightened prevalence of edema. This study expands the number of loci associated with kidney function and identifies novel genes with potential roles in kidney formation.
1 aLi, Man1 aLi, Yong1 aWeeks, Olivia1 aMijatovic, Vladan1 aTeumer, Alexander1 aHuffman, Jennifer, E1 aTromp, Gerard1 aFuchsberger, Christian1 aGorski, Mathias1 aLyytikäinen, Leo-Pekka1 aNutile, Teresa1 aSedaghat, Sanaz1 aSorice, Rossella1 aTin, Adrienne1 aYang, Qiong1 aAhluwalia, Tarunveer, S1 aArking, Dan, E1 aBihlmeyer, Nathan, A1 aBöger, Carsten, A1 aCarroll, Robert, J1 aChasman, Daniel, I1 aCornelis, Marilyn, C1 aDehghan, Abbas1 aFaul, Jessica, D1 aFeitosa, Mary, F1 aGambaro, Giovanni1 aGasparini, Paolo1 aGiulianini, Franco1 aHeid, Iris1 aHuang, Jinyan1 aImboden, Medea1 aJackson, Anne, U1 aJeff, Janina1 aJhun, Min, A1 aKatz, Ronit1 aKifley, Annette1 aKilpeläinen, Tuomas, O1 aKumar, Ashish1 aLaakso, Markku1 aLi-Gao, Ruifang1 aLohman, Kurt1 aLu, Yingchang1 aMägi, Reedik1 aMalerba, Giovanni1 aMihailov, Evelin1 aMohlke, Karen, L1 aMook-Kanamori, Dennis, O1 aRobino, Antonietta1 aRuderfer, Douglas1 aSalvi, Erika1 aSchick, Ursula, M1 aSchulz, Christina-Alexandra1 aSmith, Albert, V1 aSmith, Jennifer, A1 aTraglia, Michela1 aYerges-Armstrong, Laura, M1 aZhao, Wei1 aGoodarzi, Mark, O1 aKraja, Aldi, T1 aLiu, Chunyu1 aWessel, Jennifer1 aBoerwinkle, Eric1 aBorecki, Ingrid, B1 aBork-Jensen, Jette1 aBottinger, Erwin, P1 aBraga, Daniele1 aBrandslund, Ivan1 aBrody, Jennifer, A1 aCampbell, Archie1 aCarey, David, J1 aChristensen, Cramer1 aCoresh, Josef1 aCrook, Errol1 aCurhan, Gary, C1 aCusi, Daniele1 ade Boer, Ian, H1 ade Vries, Aiko, P J1 aDenny, Joshua, C1 aDevuyst, Olivier1 aDreisbach, Albert, W1 aEndlich, Karlhans1 aEsko, Tõnu1 aFranco, Oscar, H1 aFulop, Tibor1 aGerhard, Glenn, S1 aGlümer, Charlotte1 aGottesman, Omri1 aGrarup, Niels1 aGudnason, Vilmundur1 aHarris, Tamara, B1 aHayward, Caroline1 aHocking, Lynne1 aHofman, Albert1 aHu, Frank, B1 aHusemoen, Lise, Lotte N1 aJackson, Rebecca, D1 aJørgensen, Torben1 aJørgensen, Marit, E1 aKähönen, Mika1 aKardia, Sharon, L R1 aKönig, Wolfgang1 aKooperberg, Charles1 aKriebel, Jennifer1 aLauner, Lenore, J1 aLauritzen, Torsten1 aLehtimäki, Terho1 aLevy, Daniel1 aLinksted, Pamela1 aLinneberg, Allan1 aLiu, Yongmei1 aLoos, Ruth, J F1 aLupo, Antonio1 aMeisinger, Christine1 aMelander, Olle1 aMetspalu, Andres1 aMitchell, Paul1 aNauck, Matthias1 aNürnberg, Peter1 aOrho-Melander, Marju1 aParsa, Afshin1 aPedersen, Oluf1 aPeters, Annette1 aPeters, Ulrike1 aPolasek, Ozren1 aPorteous, David1 aProbst-Hensch, Nicole, M1 aPsaty, Bruce, M1 aQi, Lu1 aRaitakari, Olli, T1 aReiner, Alex, P1 aRettig, Rainer1 aRidker, Paul, M1 aRivadeneira, Fernando1 aRossouw, Jacques, E1 aSchmidt, Frank1 aSiscovick, David1 aSoranzo, Nicole1 aStrauch, Konstantin1 aToniolo, Daniela1 aTurner, Stephen, T1 aUitterlinden, André, G1 aUlivi, Sheila1 aVelayutham, Dinesh1 aVölker, Uwe1 aVölzke, Henry1 aWaldenberger, Melanie1 aWang, Jie, Jin1 aWeir, David, R1 aWitte, Daniel1 aKuivaniemi, Helena1 aFox, Caroline, S1 aFranceschini, Nora1 aGoessling, Wolfram1 aKöttgen, Anna1 aChu, Audrey, Y1 aCHARGE Glycemic-T2D Working Group,1 aCHARGE Blood Pressure Working Group, uhttps://chs-nhlbi.org/node/725503442nas a2200469 4500008004100000022001400041245018400055210006900239260001300308300001200321490000700333520199400340100002202334700002502356700002402381700002502405700002002430700001702450700002402467700002002491700002602511700001302537700002302550700002002573700002702593700001902620700002202639700001902661700002102680700002302701700002002724700002102744700002202765700001702787700002102804700001902825700002702844700002202871700002402893700001902917856003602936 2017 eng d a1556-387100aFine mapping of QT interval regions in global populations refines previously identified QT interval loci and identifies signals unique to African and Hispanic descent populations.0 aFine mapping of QT interval regions in global populations refine c2017 Apr a572-5800 v143 aBACKGROUND: The electrocardiographically measured QT interval (QT) is heritable and its prolongation is an established risk factor for several cardiovascular diseases. Yet, most QT genetic studies have been performed in European ancestral populations, possibly reducing their global relevance.
OBJECTIVE: To leverage diversity and improve biological insight, we fine mapped 16 of the 35 previously identified QT loci (46%) in populations of African American (n = 12,410) and Hispanic/Latino (n = 14,837) ancestry.
METHODS: Racial/ethnic-specific multiple linear regression analyses adjusted for heart rate and clinical covariates were examined separately and in combination after inverse-variance weighted trans-ethnic meta-analysis.
RESULTS: The 16 fine-mapped QT loci included on the Illumina Metabochip represented 21 independent signals, of which 16 (76%) were significantly (P-value≤9.1×10(-5)) associated with QT. Through sequential conditional analysis we also identified three trans-ethnic novel SNPs at ATP1B1, SCN5A-SCN10A, and KCNQ1 and three Hispanic/Latino-specific novel SNPs at NOS1AP and SCN5A-SCN10A (two novel SNPs) with evidence of associations with QT independent of previous identified GWAS lead SNPs. Linkage disequilibrium patterns helped to narrow the region likely to contain the functional variants at several loci, including NOS1AP, USP50-TRPM7, and PRKCA, although intervals surrounding SLC35F1-PLN and CNOT1 remained broad in size (>100 kb). Finally, bioinformatics-based functional characterization suggested a regulatory function in cardiac tissues for the majority of independent signals that generalized and the novel SNPs.
CONCLUSION: Our findings suggest that a majority of identified SNPs implicate gene regulatory dysfunction in QT prolongation, that the same loci influence variation in QT across global populations, and that additional, novel, population-specific QT signals exist.
1 aAvery, Christy, L1 aWassel, Christina, L1 aRichard, Melissa, A1 aHighland, Heather, M1 aBien, Stephanie1 aZubair, Niha1 aSoliman, Elsayed, Z1 aFornage, Myriam1 aBielinski, Suzette, J1 aTao, Ran1 aSeyerle, Amanda, A1 aShah, Sanjiv, J1 aLloyd-Jones, Donald, M1 aBuyske, Steven1 aRotter, Jerome, I1 aPost, Wendy, S1 aRich, Stephen, S1 aHindorff, Lucia, A1 aJeff, Janina, M1 aShohet, Ralph, V1 aSotoodehnia, Nona1 aLin, Dan, Yu1 aWhitsel, Eric, A1 aPeters, Ulrike1 aHaiman, Christopher, A1 aCrawford, Dana, C1 aKooperberg, Charles1 aNorth, Kari, E uhttps://chs-nhlbi.org/node/746304643nas a2200985 4500008004100000022001400041245023800055210006900293260001300362300001200375490000800387520178500395653002002180653001802200653002502218653001102243653001202254100003002266700001502296700002202311700002302333700002202356700002802378700001602406700002502422700002202447700002102469700002002490700002402510700001902534700001702553700002002570700002102590700001302611700001802624700002702642700002102669700002202690700002002712700001602732700001702748700002602765700001902791700001802810700002502828700001502853700002702868700002302895700002502918700001902943700001802962700002402980700002003004700002103024700001703045700001803062700001403080700001703094700001803111700001603129700002003145700001403165700001603179700002003195700002403215700002303239700002203262700002603284700002003310700002203330700001903352700002103371700002103392700002403413700002003437700002503457700002503482700001703507700002003524700001903544700002003563700001903583700001903602856003603621 2017 eng d a1432-120300aTrans-ethnic fine-mapping of genetic loci for body mass index in the diverse ancestral populations of the Population Architecture using Genomics and Epidemiology (PAGE) Study reveals evidence for multiple signals at established loci.0 aTransethnic finemapping of genetic loci for body mass index in t c2017 Jun a771-8000 v1363 aMost body mass index (BMI) genetic loci have been identified in studies of primarily European ancestries. The effect of these loci in other racial/ethnic groups is less clear. Thus, we aimed to characterize the generalizability of 170 established BMI variants, or their proxies, to diverse US populations and trans-ethnically fine-map 36 BMI loci using a sample of >102,000 adults of African, Hispanic/Latino, Asian, European and American Indian/Alaskan Native descent from the Population Architecture using Genomics and Epidemiology Study. We performed linear regression of the natural log of BMI (18.5-70 kg/m(2)) on the additive single nucleotide polymorphisms (SNPs) at BMI loci on the MetaboChip (Illumina, Inc.), adjusting for age, sex, population stratification, study site, or relatedness. We then performed fixed-effect meta-analyses and a Bayesian trans-ethnic meta-analysis to empirically cluster by allele frequency differences. Finally, we approximated conditional and joint associations to test for the presence of secondary signals. We noted directional consistency with the previously reported risk alleles beyond what would have been expected by chance (binomial p < 0.05). Nearly, a quarter of the previously described BMI index SNPs and 29 of 36 densely-genotyped BMI loci on the MetaboChip replicated/generalized in trans-ethnic analyses. We observed multiple signals at nine loci, including the description of seven loci with novel multiple signals. This study supports the generalization of most common genetic loci to diverse ancestral populations and emphasizes the importance of dense multiethnic genomic data in refining the functional variation at genetic loci of interest and describing several loci with multiple underlying genetic variants.
10aBody Mass Index10aEthnic Groups10aGenetics, Population10aHumans10aObesity1 aFernandez-Rhodes, Lindsay1 aGong, Jian1 aHaessler, Jeffrey1 aFranceschini, Nora1 aGraff, Mariaelisa1 aNishimura, Katherine, K1 aWang, Yujie1 aHighland, Heather, M1 aYoneyama, Sachiko1 aBush, William, S1 aGoodloe, Robert1 aRitchie, Marylyn, D1 aCrawford, Dana1 aGross, Myron1 aFornage, Myriam1 aBůzková, Petra1 aTao, Ran1 aIsasi, Carmen1 aAvilés-Santa, Larissa1 aDaviglus, Martha1 aMackey, Rachel, H1 aHouston, Denise1 aGu, Charles1 aEhret, Georg1 aNguyen, Khanh-Dung, H1 aLewis, Cora, E1 aLeppert, Mark1 aIrvin, Marguerite, R1 aLim, Unhee1 aHaiman, Christopher, A1 aLe Marchand, Loïc1 aSchumacher, Fredrick1 aWilkens, Lynne1 aLu, Yingchang1 aBottinger, Erwin, P1 aLoos, Ruth, J L1 aSheu, Wayne, H-H1 aGuo, Xiuqing1 aLee, Wen-Jane1 aHai, Yang1 aHung, Yi-Jen1 aAbsher, Devin1 aWu, I-Chien1 aTaylor, Kent, D1 aLee, I-Te1 aLiu, Yeheng1 aWang, Tzung-Dau1 aQuertermous, Thomas1 aJuang, Jyh-Ming, J1 aRotter, Jerome, I1 aAssimes, Themistocles1 aHsiung, Chao, A1 aChen, Yii-Der Ida1 aPrentice, Ross1 aKuller, Lewis, H1 aManson, JoAnn, E1 aKooperberg, Charles1 aSmokowski, Paul1 aRobinson, Whitney, R1 aGordon-Larsen, Penny1 aLi, Rongling1 aHindorff, Lucia1 aBuyske, Steven1 aMatise, Tara, C1 aPeters, Ulrike1 aNorth, Kari, E uhttps://chs-nhlbi.org/node/746503689nas a2200553 4500008004100000022001400041245023400055210006900289260001600358300001400374490000700388520195100395100002602346700002102372700001602393700002202409700002002431700001902451700001902470700002502489700002202514700002802536700002302564700002902587700001902616700002102635700001802656700001302674700001902687700002502706700002402731700003002755700001902785700002102804700002102825700001802846700002302864700001602887700002202903700002302925700002002948700001902968700001902987700002703006700001903033700002303052700002403075856003603099 2018 eng d a1460-208300aDiscovery, fine-mapping, and conditional analyses of genetic variants associated with C-reactive protein in multiethnic populations using the Metabochip in the Population Architecture using Genomics and Epidemiology (PAGE) study.0 aDiscovery finemapping and conditional analyses of genetic varian c2018 Aug 15 a2940-29530 v273 aC-reactive protein (CRP) is a circulating biomarker indicative of systemic inflammation. We aimed to evaluate genetic associations with CRP levels among non-European-ancestry populations through discovery, fine-mapping and conditional analyses. A total of 30 503 non-European-ancestry participants from 6 studies participating in the Population Architecture using Genomics and Epidemiology study had serum high-sensitivity CRP measurements and ∼200 000 single nucleotide polymorphisms (SNPs) genotyped on the Metabochip. We evaluated the association between each SNP and log-transformed CRP levels using multivariate linear regression, with additive genetic models adjusted for age, sex, the first four principal components of genetic ancestry, and study-specific factors. Differential linkage disequilibrium patterns between race/ethnicity groups were used to fine-map regions associated with CRP levels. Conditional analyses evaluated for multiple independent signals within genetic regions. One hundred and sixty-three unique variants in 12 loci in overall or race/ethnicity-stratified Metabochip-wide scans reached a Bonferroni-corrected P-value <2.5E-7. Three loci have no (HACL1, OLFML2B) or only limited (PLA2G6) previous associations with CRP levels. Six loci had different top hits in race/ethnicity-specific versus overall analyses. Fine-mapping refined the signal in six loci, particularly in HNF1A. Conditional analyses provided evidence for secondary signals in LEPR, IL1RN and HNF1A, and for multiple independent signals in CRP and APOE. We identified novel variants and loci associated with CRP levels, generalized known CRP associations to a multiethnic study population, refined association signals at several loci and found evidence for multiple independent signals at several well-known loci. This study demonstrates the benefit of conducting inclusive genetic association studies in large multiethnic populations.
1 aKocarnik, Jonathan, M1 aRichard, Melissa1 aGraff, Misa1 aHaessler, Jeffrey1 aBien, Stephanie1 aCarlson, Chris1 aCarty, Cara, L1 aReiner, Alexander, P1 aAvery, Christy, L1 aBallantyne, Christie, M1 aLaCroix, Andrea, Z1 aAssimes, Themistocles, L1 aBarbalic, Maja1 aPankratz, Nathan1 aTang, Weihong1 aTao, Ran1 aChen, Dongquan1 aTalavera, Gregory, A1 aDaviglus, Martha, L1 aChirinos-Medina, Diana, A1 aPereira, Rocio1 aNishimura, Katie1 aBůzková, Petra1 aBest, Lyle, G1 aAmbite, Jose, Luis1 aCheng, Iona1 aCrawford, Dana, C1 aHindorff, Lucia, A1 aFornage, Myriam1 aHeiss, Gerardo1 aNorth, Kari, E1 aHaiman, Christopher, A1 aPeters, Ulrike1 aLe Marchand, Loïc1 aKooperberg, Charles uhttps://chs-nhlbi.org/node/779802530nas a2200313 4500008004100000022001400041245008500055210006900140260001600209520156200225100002801787700002101815700002301836700002401859700002401883700002001907700002201927700002601949700002601975700002102001700001702022700002202039700002002061700002202081700001902103700002002122710003802142856003602180 2018 eng d a1432-120300aRare loss of function variants in candidate genes and risk of colorectal cancer.0 aRare loss of function variants in candidate genes and risk of co c2018 Sep 283 aAlthough ~ 25% of colorectal cancer or polyp (CRC/P) cases show familial aggregation, current germline genetic testing identifies a causal genotype in the 16 major genes associated with high penetrance CRC/P in only 20% of these cases. As there are likely other genes underlying heritable CRC/P, we evaluated the association of variation at novel loci with CRC/P. We evaluated 158 a priori selected candidate genes by comparing the number of rare potentially disruptive variants (PDVs) found in 84 CRC/P cases without an identified CRC/P risk-associated variant and 2440 controls. We repeated this analysis using an additional 73 CRC/P cases. We also compared the frequency of PDVs in select genes among CRC/P cases with two publicly available data sets. We found a significant enrichment of PDVs in cases vs. controls: 20% of cases vs. 11.5% of controls with ≥ 1 PDV (OR = 1.9, p = 0.01) in the original set of cases. Among the second cohort of CRC/P cases, 18% had a PDV, significantly different from 11.5% (p = 0.02). Logistic regression, adjusting for ancestry and multiple testing, indicated association between CRC/P and PDVs in NTHL1 (p = 0.0001), BRCA2 (p = 0.01) and BRIP1 (p = 0.04). However, there was no significant difference in the frequency of PDVs at each of these genes between all 157 CRC/P cases and two publicly available data sets. These results suggest an increased presence of PDVs in CRC/P cases and support further investigation of the association of NTHL1, BRCA2 and BRIP1 variation with CRC/P.
1 aRosenthal, Elisabeth, A1 aShirts, Brian, H1 aAmendola, Laura, M1 aHorike-Pyne, Martha1 aRobertson, Peggy, D1 aHisama, Fuki, M1 aBennett, Robin, L1 aDorschner, Michael, O1 aNickerson, Deborah, A1 aStanaway, Ian, B1 aNassir, Rami1 aVickers, Kathy, T1 aLi, Christopher1 aGrady, William, M1 aPeters, Ulrike1 aJarvik, Gail, P1 aNHLBI GO Exome Sequencing Project uhttps://chs-nhlbi.org/node/784707958nas a2202377 4500008004100000022001400041245011500055210006900170260001600239300000900255490000700264520122900271100001901500700001801519700002501537700002401562700002701586700002201613700002201635700002101657700002201678700002301700700002001723700002101743700002201764700002101786700002201807700002401829700002101853700002001874700001801894700001901912700001601931700002401947700003201971700002902003700002002032700002902052700002602081700002002107700002002127700002202147700002602169700001602195700002002211700002102231700001902252700001502271700002502286700002202311700002702333700002202360700002202382700002402404700001802428700002002446700002502466700001802491700001702509700002302526700002102549700002502570700002202595700002302617700001902640700002302659700002602682700001702708700001802725700002702743700002602770700002202796700002802818700002302846700002602869700002002895700002002915700002102935700001802956700001702974700002302991700001903014700002403033700002203057700002203079700002103101700002403122700002003146700002403166700002303190700001903213700001303232700001803245700002203263700003003285700002003315700002603335700002103361700002403382700002203406700002703428700001503455700001803470700001903488700002303507700002203530700002403552700002303576700001803599700001803617700001903635700001803654700002403672700001803696700001903714700001903733700002303752700002003775700001803795700002203813700002603835700002703861700002203888700002603910700001403936700001903950700002503969700002003994700001804014700002204032700001704054700001804071700002204089700001604111700002504127700002004152700001704172700002304189700001804212700002204230700002004252700001304272700002304285700001804308700002904326700002104355700002104376700001404397700002304411700001504434700002304449700001704472700001904489700003204508700002404540700002204564700002304586700002004609700002304629700002404652700002804676700002204704700002404726700002404750700001904774700002504793700002104818700001904839700001904858700002104877700002004898700002404918700002204942700002704964700002204991700002105013700002305034700002005057700001705077700001905094700002405113700002205137700001905159700001905178700002905197700002005226700002805246700001605274700001905290700002505309700002805334700002805362700002405390700001905414700002105433700002405454700002005478700002205498700002405520856003605544 2020 eng d a2041-172300aMulti-ancestry GWAS of the electrocardiographic PR interval identifies 202 loci underlying cardiac conduction.0 aMultiancestry GWAS of the electrocardiographic PR interval ident c2020 May 21 a25420 v113 aThe electrocardiographic PR interval reflects atrioventricular conduction, and is associated with conduction abnormalities, pacemaker implantation, atrial fibrillation (AF), and cardiovascular mortality. Here we report a multi-ancestry (N = 293,051) genome-wide association meta-analysis for the PR interval, discovering 202 loci of which 141 have not previously been reported. Variants at identified loci increase the percentage of heritability explained, from 33.5% to 62.6%. We observe enrichment for cardiac muscle developmental/contractile and cytoskeletal genes, highlighting key regulation processes for atrioventricular conduction. Additionally, 8 loci not previously reported harbor genes underlying inherited arrhythmic syndromes and/or cardiomyopathies suggesting a role for these genes in cardiovascular pathology in the general population. We show that polygenic predisposition to PR interval duration is an endophenotype for cardiovascular disease, including distal conduction disease, AF, and atrioventricular pre-excitation. These findings advance our understanding of the polygenic basis of cardiac conduction, and the genetic relationship between PR interval duration and cardiovascular disease.
1 aNtalla, Ioanna1 aWeng, Lu-Chen1 aCartwright, James, H1 aHall, Amelia, Weber1 aSveinbjornsson, Gardar1 aTucker, Nathan, R1 aChoi, Seung, Hoan1 aChaffin, Mark, D1 aRoselli, Carolina1 aBarnes, Michael, R1 aMifsud, Borbala1 aWarren, Helen, R1 aHayward, Caroline1 aMarten, Jonathan1 aCranley, James, J1 aConcas, Maria, Pina1 aGasparini, Paolo1 aBoutin, Thibaud1 aKolcic, Ivana1 aPolasek, Ozren1 aRudan, Igor1 aAraujo, Nathalia, M1 aLima-Costa, Maria, Fernanda1 aRibeiro, Antonio, Luiz P1 aSouza, Renan, P1 aTarazona-Santos, Eduardo1 aGiedraitis, Vilmantas1 aIngelsson, Erik1 aMahajan, Anubha1 aMorris, Andrew, P1 aM, Fabiola, del Greco1 aFoco, Luisa1 aGögele, Martin1 aHicks, Andrew, A1 aCook, James, P1 aLind, Lars1 aLindgren, Cecilia, M1 aSundström, Johan1 aNelson, Christopher, P1 aRiaz, Muhammad, B1 aSamani, Nilesh, J1 aSinagra, Gianfranco1 aUlivi, Sheila1 aKähönen, Mika1 aMishra, Pashupati, P1 aMononen, Nina1 aNikus, Kjell1 aCaulfield, Mark, J1 aDominiczak, Anna1 aPadmanabhan, Sandosh1 aMontasser, May, E1 aO'Connell, Jeff, R1 aRyan, Kathleen1 aShuldiner, Alan, R1 aAeschbacher, Stefanie1 aConen, David1 aRisch, Lorenz1 aThériault, Sébastien1 aHutri-Kähönen, Nina1 aLehtimäki, Terho1 aLyytikäinen, Leo-Pekka1 aRaitakari, Olli, T1 aBarnes, Catriona, L K1 aCampbell, Harry1 aJoshi, Peter, K1 aWilson, James, F1 aIsaacs, Aaron1 aKors, Jan, A1 aDuijn, Cornelia, M1 aHuang, Paul, L1 aGudnason, Vilmundur1 aHarris, Tamara, B1 aLauner, Lenore, J1 aSmith, Albert, V1 aBottinger, Erwin, P1 aLoos, Ruth, J F1 aNadkarni, Girish, N1 aPreuss, Michael, H1 aCorrea, Adolfo1 aMei, Hao1 aWilson, James1 aMeitinger, Thomas1 aMüller-Nurasyid, Martina1 aPeters, Annette1 aWaldenberger, Melanie1 aMangino, Massimo1 aSpector, Timothy, D1 aRienstra, Michiel1 avan de Vegte, Yordi, J1 aHarst, Pim1 aVerweij, Niek1 aKääb, Stefan1 aSchramm, Katharina1 aSinner, Moritz, F1 aStrauch, Konstantin1 aCutler, Michael, J1 aFatkin, Diane1 aLondon, Barry1 aOlesen, Morten1 aRoden, Dan, M1 aShoemaker, Benjamin1 aSmith, Gustav1 aBiggs, Mary, L1 aBis, Joshua, C1 aBrody, Jennifer, A1 aPsaty, Bruce, M1 aRice, Kenneth1 aSotoodehnia, Nona1 aDe Grandi, Alessandro1 aFuchsberger, Christian1 aPattaro, Cristian1 aPramstaller, Peter, P1 aFord, Ian1 aJukema, Wouter1 aMacfarlane, Peter, W1 aTrompet, Stella1 aDörr, Marcus1 aFelix, Stephan, B1 aVölker, Uwe1 aWeiss, Stefan1 aHavulinna, Aki, S1 aJula, Antti1 aSääksjärvi, Katri1 aSalomaa, Veikko1 aGuo, Xiuqing1 aHeckbert, Susan, R1 aLin, Henry, J1 aRotter, Jerome, I1 aTaylor, Kent, D1 aYao, Jie1 ade Mutsert, Renée1 aMaan, Arie, C1 aMook-Kanamori, Dennis, O1 aNoordam, Raymond1 aCucca, Francesco1 aDing, Jun1 aLakatta, Edward, G1 aQian, Yong1 aTarasov, Kirill, V1 aLevy, Daniel1 aLin, Honghuang1 aNewton-Cheh, Christopher, H1 aLunetta, Kathryn, L1 aMurray, Alison, D1 aPorteous, David, J1 aSmith, Blair, H1 aStricker, Bruno, H1 aUitterlinden, Andre1 avan den Berg, Marten, E1 aHaessler, Jeffrey1 aJackson, Rebecca, D1 aKooperberg, Charles1 aPeters, Ulrike1 aReiner, Alexander, P1 aWhitsel, Eric, A1 aAlonso, Alvaro1 aArking, Dan, E1 aBoerwinkle, Eric1 aEhret, Georg, B1 aSoliman, Elsayed, Z1 aAvery, Christy, L1 aGogarten, Stephanie, M1 aKerr, Kathleen, F1 aLaurie, Cathy, C1 aSeyerle, Amanda, A1 aStilp, Adrienne1 aAssa, Solmaz1 aSaid, Abdullah1 avan der Ende, Yldau1 aLambiase, Pier, D1 aOrini, Michele1 aRamirez, Julia1 aVan Duijvenboden, Stefan1 aArnar, David, O1 aGudbjartsson, Daniel, F1 aHolm, Hilma1 aSulem, Patrick1 aThorleifsson, Gudmar1 aThorolfsdottir, Rosa, B1 aThorsteinsdottir, Unnur1 aBenjamin, Emelia, J1 aTinker, Andrew1 aStefansson, Kari1 aEllinor, Patrick, T1 aJamshidi, Yalda1 aLubitz, Steven, A1 aMunroe, Patricia, B uhttps://chs-nhlbi.org/node/836811212nas a2203445 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2021 eng d a1476-468700aGenetic insights into biological mechanisms governing human ovarian ageing.0 aGenetic insights into biological mechanisms governing human ovar c2021 Aug a393-3970 v5963 aReproductive longevity is essential for fertility and influences healthy ageing in women, but insights into its underlying biological mechanisms and treatments to preserve it are limited. Here we identify 290 genetic determinants of ovarian ageing, assessed using normal variation in age at natural menopause (ANM) in about 200,000 women of European ancestry. These common alleles were associated with clinical extremes of ANM; women in the top 1% of genetic susceptibility have an equivalent risk of premature ovarian insufficiency to those carrying monogenic FMR1 premutations. The identified loci implicate a broad range of DNA damage response (DDR) processes and include loss-of-function variants in key DDR-associated genes. Integration with experimental models demonstrates that these DDR processes act across the life-course to shape the ovarian reserve and its rate of depletion. Furthermore, we demonstrate that experimental manipulation of DDR pathways highlighted by human genetics increases fertility and extends reproductive life in mice. Causal inference analyses using the identified genetic variants indicate that extending reproductive life in women improves bone health and reduces risk of type 2 diabetes, but increases the risk of hormone-sensitive cancers. These findings provide insight into the mechanisms that govern ovarian ageing, when they act, and how they might be targeted by therapeutic approaches to extend fertility and prevent disease.
1 aRuth, Katherine, S1 aDay, Felix, R1 aHussain, Jazib1 aMartínez-Marchal, Ana1 aAiken, Catherine, E1 aAzad, Ajuna1 aThompson, Deborah, J1 aKnoblochova, Lucie1 aAbe, Hironori1 aTarry-Adkins, Jane, L1 aGonzalez, Javier, Martin1 aFontanillas, Pierre1 aClaringbould, Annique1 aBakker, Olivier, B1 aSulem, Patrick1 aWalters, Robin, G1 aTerao, Chikashi1 aTuron, Sandra1 aHorikoshi, Momoko1 aLin, Kuang1 aOnland-Moret, Charlotte, N1 aSankar, Aditya1 aHertz, Emil, Peter Thra1 aTimshel, Pascal, N1 aShukla, Vallari1 aBorup, Rehannah1 aOlsen, Kristina, W1 aAguilera, Paula1 aFerrer-Roda, Mònica1 aHuang, Yan1 aStankovic, Stasa1 aTimmers, Paul, R H J1 aAhearn, Thomas, U1 aAlizadeh, Behrooz, Z1 aNaderi, Elnaz1 aAndrulis, Irene, L1 aArnold, Alice, M1 aAronson, Kristan, J1 aAugustinsson, Annelie1 aBandinelli, Stefania1 aBarbieri, Caterina, M1 aBeaumont, Robin, N1 aBecher, Heiko1 aBeckmann, Matthias, W1 aBenonisdottir, Stefania1 aBergmann, Sven1 aBochud, Murielle1 aBoerwinkle, Eric1 aBojesen, Stig, E1 aBolla, Manjeet, K1 aBoomsma, Dorret, I1 aBowker, Nicholas1 aBrody, Jennifer, A1 aBroer, Linda1 aBuring, Julie, E1 aCampbell, Archie1 aCampbell, Harry1 aCastelao, Jose, E1 aCatamo, Eulalia1 aChanock, Stephen, J1 aChenevix-Trench, Georgia1 aCiullo, Marina1 aCorre, Tanguy1 aCouch, Fergus, J1 aCox, Angela1 aCrisponi, Laura1 aCross, Simon, S1 aCucca, Francesco1 aCzene, Kamila1 aSmith, George Davey1 ade Geus, Eco, J C N1 ade Mutsert, Renée1 aDe Vivo, Immaculata1 aDemerath, Ellen, W1 aDennis, Joe1 aDunning, Alison, M1 aDwek, Miriam1 aEriksson, Mikael1 aEsko, Tõnu1 aFasching, Peter, A1 aFaul, Jessica, D1 aFerrucci, Luigi1 aFranceschini, Nora1 aFrayling, Timothy, M1 aGago-Dominguez, Manuela1 aMezzavilla, Massimo1 aGarcía-Closas, Montserrat1 aGieger, Christian1 aGiles, Graham, G1 aGrallert, Harald1 aGudbjartsson, Daniel, F1 aGudnason, Vilmundur1 aGuénel, Pascal1 aHaiman, Christopher, A1 aHåkansson, Niclas1 aHall, Per1 aHayward, Caroline1 aHe, Chunyan1 aHe, Wei1 aHeiss, Gerardo1 aHøffding, Miya, K1 aHopper, John, L1 aHottenga, Jouke, J1 aHu, Frank1 aHunter, David1 aIkram, Mohammad, A1 aJackson, Rebecca, D1 aJoaquim, Micaella, D R1 aJohn, Esther, M1 aJoshi, Peter, K1 aKarasik, David1 aKardia, Sharon, L R1 aKartsonaki, Christiana1 aKarlsson, Robert1 aKitahara, Cari, M1 aKolcic, Ivana1 aKooperberg, Charles1 aKraft, Peter1 aKurian, Allison, W1 aKutalik, Zoltán1 aLa Bianca, Martina1 aLachance, Genevieve1 aLangenberg, Claudia1 aLauner, Lenore, J1 aLaven, Joop, S E1 aLawlor, Deborah, A1 aLe Marchand, Loïc1 aLi, Jingmei1 aLindblom, Annika1 aLindström, Sara1 aLindstrom, Tricia1 aLinet, Martha1 aLiu, Yongmei1 aLiu, Simin1 aLuan, Jian'an1 aMägi, Reedik1 aMagnusson, Patrik, K E1 aMangino, Massimo1 aMannermaa, Arto1 aMarco, Brumat1 aMarten, Jonathan1 aMartin, Nicholas, G1 aMbarek, Hamdi1 aMcKnight, Barbara1 aMedland, Sarah, E1 aMeisinger, Christa1 aMeitinger, Thomas1 aMenni, Cristina1 aMetspalu, Andres1 aMilani, Lili1 aMilne, Roger, L1 aMontgomery, Grant, W1 aMook-Kanamori, Dennis, O1 aMulas, Antonella1 aMulligan, Anna, M1 aMurray, Alison1 aNalls, Mike, A1 aNewman, Anne1 aNoordam, Raymond1 aNutile, Teresa1 aNyholt, Dale, R1 aOlshan, Andrew, F1 aOlsson, Håkan1 aPainter, Jodie, N1 aPatel, Alpa, V1 aPedersen, Nancy, L1 aPerjakova, Natalia1 aPeters, Annette1 aPeters, Ulrike1 aPharoah, Paul, D P1 aPolasek, Ozren1 aPorcu, Eleonora1 aPsaty, Bruce, M1 aRahman, Iffat1 aRennert, Gad1 aRennert, Hedy, S1 aRidker, Paul, M1 aRing, Susan, M1 aRobino, Antonietta1 aRose, Lynda, M1 aRosendaal, Frits, R1 aRossouw, Jacques1 aRudan, Igor1 aRueedi, Rico1 aRuggiero, Daniela1 aSala, Cinzia, F1 aSaloustros, Emmanouil1 aSandler, Dale, P1 aSanna, Serena1 aSawyer, Elinor, J1 aSarnowski, Chloe1 aSchlessinger, David1 aSchmidt, Marjanka, K1 aSchoemaker, Minouk, J1 aSchraut, Katharina, E1 aScott, Christopher1 aShekari, Saleh1 aShrikhande, Amruta1 aSmith, Albert, V1 aSmith, Blair, H1 aSmith, Jennifer, A1 aSorice, Rossella1 aSouthey, Melissa, C1 aSpector, Tim, D1 aSpinelli, John, J1 aStampfer, Meir1 aStöckl, Doris1 avan Meurs, Joyce, B J1 aStrauch, Konstantin1 aStyrkarsdottir, Unnur1 aSwerdlow, Anthony, J1 aTanaka, Toshiko1 aTeras, Lauren, R1 aTeumer, Alexander1 aÞorsteinsdottir, Unnur1 aTimpson, Nicholas, J1 aToniolo, Daniela1 aTraglia, Michela1 aTroester, Melissa, A1 aTruong, Thérèse1 aTyrrell, Jessica1 aUitterlinden, André, G1 aUlivi, Sheila1 aVachon, Celine, M1 aVitart, Veronique1 aVölker, Uwe1 aVollenweider, Peter1 aVölzke, Henry1 aWang, Qin1 aWareham, Nicholas, J1 aWeinberg, Clarice, R1 aWeir, David, R1 aWilcox, Amber, N1 aDijk, Ko Willems1 aWillemsen, Gonneke1 aWilson, James, F1 aWolffenbuttel, Bruce, H R1 aWolk, Alicja1 aWood, Andrew, R1 aZhao, Wei1 aZygmunt, Marek1 aChen, Zhengming1 aLi, Liming1 aFranke, Lude1 aBurgess, Stephen1 aDeelen, Patrick1 aPers, Tune, H1 aGrøndahl, Marie, Louise1 aAndersen, Claus, Yding1 aPujol, Anna1 aLopez-Contreras, Andres, J1 aDaniel, Jeremy, A1 aStefansson, Kari1 aChang-Claude, Jenny1 aSchouw, Yvonne, T1 aLunetta, Kathryn, L1 aChasman, Daniel, I1 aEaston, Douglas, F1 aVisser, Jenny, A1 aOzanne, Susan, E1 aNamekawa, Satoshi, H1 aSolc, Petr1 aMurabito, Joanne, M1 aOng, Ken, K1 aHoffmann, Eva, R1 aMurray, Anna1 aRoig, Ignasi1 aPerry, John, R B1 aBiobank-based Integrative Omics Study (BIOS) Consortium1 aeQTLGen Consortium1 aBioBank Japan Project1 aChina Kadoorie Biobank Collaborative Group1 akConFab Investigators1 aLifeLines Cohort Study1 aInterAct Consortium1 a23andMe Research Team uhttps://chs-nhlbi.org/node/883502453nas a2200253 4500008004100000022001400041245012100055210006900176260001300245300001100258490000700269520168400276100001901960700002501979700001902004700001902023700001802042700002302060700001702083700002202100700001902122700002202141856003602163 2022 eng d a1877-783X00aSleep problems and risk of cancer incidence and mortality in an older cohort: The Cardiovascular Health Study (CHS).0 aSleep problems and risk of cancer incidence and mortality in an c2022 Feb a1020570 v763 aBACKGROUND: Sleep problems (SP) can indicate underlying sleep disorders, such as obstructive sleep apnea, which may adversely impact cancer risk and mortality.
METHODS: We assessed the association of baseline and longitudinal sleep apnea and insomnia symptoms with incident cancer (N = 3930) and cancer mortality (N = 4580) in the Cardiovascular Health Study. We used Cox proportional hazards regression to calculate adjusted hazard ratios (HR) and 95% confidence intervals (CI) to evaluate the associations.
RESULTS: Overall, 885 incident cancers and 804 cancer deaths were identified over a median follow-up of 12 and 14 years, respectively. Compared to participants who reported no sleep apnea symptoms, the risk of incident cancer was inversely associated [(HR (95%CI)] with snoring [0.84 (0.71, 0.99)]. We noted an elevated prostate cancer incidence for apnea [2.34 (1.32, 4.15)] and snoring [1.69 (1.11, 2.57)]. We also noted an elevated HR for lymphatic or hematopoietic cancers [daytime sleepiness: 1.81 (1.06, 3.08)]. We found an inverse relationship for cancer mortality with respect to snoring [0.73 (0.62, 0.8)] and apnea [(0.69 (0.51, 0.94))]. We noted a significant inverse relationship between difficulty falling asleep and colorectal cancer death [0.32 (0.15, 0.69)] and snoring with lung cancer death [0.56 (0.35, 0.89)].
CONCLUSIONS: The relationship between SP and cancer risk and mortality was heterogeneous. Larger prospective studies addressing more cancer sites, molecular type-specific associations, and better longitudinal SP assessments are needed for improved delineation of SP-cancer risk dyad.
1 aSillah, Arthur1 aWatson, Nathaniel, F1 aPeters, Ulrike1 aBiggs, Mary, L1 aNieto, Javier1 aLi, Christopher, I1 aGozal, David1 aThornton, Timothy1 aBarrie, Sonnah1 aPhipps, Amanda, I uhttps://chs-nhlbi.org/node/900305493nas a2201573 4500008004100000245011200041210006900153260001600222520100600238100001801244700002301262700002201285700002501307700002001332700001501352700001401367700002001381700002101401700002201422700001401444700001401458700002401472700002101496700002401517700001901541700002901560700002201589700001901611700001801630700002801648700002001676700001901696700001901715700002101734700002401755700001901779700001701798700002801815700001701843700002201860700002301882700002401905700001701929700001801946700002501964700002001989700002102009700001602030700002002046700001602066700001302082700001802095700002402113700001702137700002102154700002402175700002102199700002002220700002002240700001702260700002202277700002802299700002302327700002402350700001702374700002302391700003002414700002602444700002102470700002002491700001902511700002302530700001402553700002402567700002402591700001802615700002802633700002402661700002302685700002202708700002702730700001702757700002102774700002102795700002202816700002202838700002302860700001902883700002302902700001402925700002402939700002102963700002802984700002403012700001903036700002103055700002503076700002103101700002303122700002203145700002203167700002003189700002303209700001403232700002303246700001603269700002503285700002403310700002103334700002503355700001903380700002003399700002303419700002503442700002103467700002203488700002403510700002303534700002003557700002203577700002003599700001803619700002503637700001603662700001603678700002503694700002103719700001803740700002003758700001903778700002103797710006503818856003603883 2023 eng d00aWHOLE GENOME SEQUENCING ANALYSIS OF BODY MASS INDEX IDENTIFIES NOVEL AFRICAN ANCESTRY-SPECIFIC RISK ALLELE.0 aWHOLE GENOME SEQUENCING ANALYSIS OF BODY MASS INDEX IDENTIFIES N c2023 Aug 223 aObesity is a major public health crisis associated with high mortality rates. Previous genome-wide association studies (GWAS) investigating body mass index (BMI) have largely relied on imputed data from European individuals. This study leveraged whole-genome sequencing (WGS) data from 88,873 participants from the Trans-Omics for Precision Medicine (TOPMed) Program, of which 51% were of non-European population groups. We discovered 18 BMI-associated signals ( < 5 × 10 ). Notably, we identified and replicated a novel low frequency single nucleotide polymorphism (SNP) in that was common in individuals of African descent. Using a diverse study population, we further identified two novel secondary signals in known BMI loci and pinpointed two likely causal variants in the and loci. Our work demonstrates the benefits of combining WGS and diverse cohorts in expanding current catalog of variants and genes confer risk for obesity, bringing us one step closer to personalized medicine.
1 aZhang, Xinruo1 aBrody, Jennifer, A1 aGraff, Mariaelisa1 aHighland, Heather, M1 aChami, Nathalie1 aXu, Hanfei1 aWang, Zhe1 aFerrier, Kendra1 aChittoor, Geetha1 aJosyula, Navya, S1 aLi, Xihao1 aLi, Zilin1 aAllison, Matthew, A1 aBecker, Diane, M1 aBielak, Lawrence, F1 aBis, Joshua, C1 aBoorgula, Meher, Preethi1 aBowden, Donald, W1 aBroome, Jai, G1 aButh, Erin, J1 aCarlson, Christopher, S1 aChang, Kyong-Mi1 aChavan, Sameer1 aChiu, Yen-Feng1 aChuang, Lee-Ming1 aConomos, Matthew, P1 aDeMeo, Dawn, L1 aDu, Margaret1 aDuggirala, Ravindranath1 aEng, Celeste1 aFohner, Alison, E1 aFreedman, Barry, I1 aGarrett, Melanie, E1 aGuo, Xiuqing1 aHaiman, Chris1 aHeavner, Benjamin, D1 aHidalgo, Bertha1 aHixson, James, E1 aHo, Yuk-Lam1 aHobbs, Brian, D1 aHu, Donglei1 aHui, Qin1 aHwu, Chii-Min1 aJackson, Rebecca, D1 aJain, Deepti1 aKalyani, Rita, R1 aKardia, Sharon, L R1 aKelly, Tanika, N1 aLange, Ethan, M1 aLeNoir, Michael1 aLi, Changwei1 aLe Marchand, Loic1 aMcDonald, Merry-Lynn, N1 aMcHugh, Caitlin, P1 aMorrison, Alanna, C1 aNaseri, Take1 aO'Connell, Jeffrey1 aO'Donnell, Christopher, J1 aPalmer, Nicholette, D1 aPankow, James, S1 aPerry, James, A1 aPeters, Ulrike1 aPreuss, Michael, H1 aRao, D, C1 aRegan, Elizabeth, A1 aReupena, Sefuiva, M1 aRoden, Dan, M1 aRodriguez-Santana, Jose1 aSitlani, Colleen, M1 aSmith, Jennifer, A1 aTiwari, Hemant, K1 aVasan, Ramachandran, S1 aWang, Zeyuan1 aWeeks, Daniel, E1 aWessel, Jennifer1 aWiggins, Kerri, L1 aWilkens, Lynne, R1 aWilson, Peter, W F1 aYanek, Lisa, R1 aYoneda, Zachary, T1 aZhao, Wei1 aZöllner, Sebastian1 aArnett, Donna, K1 aAshley-Koch, Allison, E1 aBarnes, Kathleen, C1 aBlangero, John1 aBoerwinkle, Eric1 aBurchard, Esteban, G1 aCarson, April, P1 aChasman, Daniel, I1 aChen, Yii-Der Ida1 aCurran, Joanne, E1 aFornage, Myriam1 aGordeuk, Victor, R1 aHe, Jiang1 aHeckbert, Susan, R1 aHou, Lifang1 aIrvin, Marguerite, R1 aKooperberg, Charles1 aMinster, Ryan, L1 aMitchell, Braxton, D1 aNouraie, Mehdi1 aPsaty, Bruce, M1 aRaffield, Laura, M1 aReiner, Alexander, P1 aRich, Stephen, S1 aRotter, Jerome, I1 aShoemaker, Benjamin1 aSmith, Nicholas, L1 aTaylor, Kent, D1 aTelen, Marilyn, J1 aWeiss, Scott, T1 aZhang, Yingze1 aCosta, Nancy, Heard-1 aSun, Yan, V1 aLin, Xihong1 aCupples, Adrienne, L1 aLange, Leslie, A1 aLiu, Ching-Ti1 aLoos, Ruth, J F1 aNorth, Kari, E1 aJustice, Anne, E1 aNHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium uhttps://chs-nhlbi.org/node/948403779nas a2200673 4500008004100000245013300041210006900174260001600243520177100259100001902030700002202049700001402071700002002085700002002105700001502125700001902140700002202159700002702181700001402208700002102222700002002243700001902263700002102282700001902303700001702322700002202339700002102361700002202382700002502404700002102429700002002450700002502470700002402495700002202519700001902541700001602560700002302576700002002599700001902619700001502638700002102653700002302674700002202697700002402719700002002743700001902763700001902782700001602801700002002817700002402837700002502861700002302886700001602909700001802925700002302943710006502966710003803031856003603069 2023 eng d00aWhole Genome Sequencing Based Analysis of Inflammation Biomarkers in the Trans-Omics for Precision Medicine (TOPMed) Consortium.0 aWhole Genome Sequencing Based Analysis of Inflammation Biomarker c2023 Sep 123 aInflammation biomarkers can provide valuable insight into the role of inflammatory processes in many diseases and conditions. Sequencing based analyses of such biomarkers can also serve as an exemplar of the genetic architecture of quantitative traits. To evaluate the biological insight, which can be provided by a multi-ancestry, whole-genome based association study, we performed a comprehensive analysis of 21 inflammation biomarkers from up to 38,465 individuals with whole-genome sequencing from the Trans-Omics for Precision Medicine (TOPMed) program. We identified 22 distinct single-variant associations across 6 traits - E-selectin, intercellular adhesion molecule 1, interleukin-6, lipoprotein-associated phospholipase A2 activity and mass, and P-selectin - that remained significant after conditioning on previously identified associations for these inflammatory biomarkers. We further expanded upon known biomarker associations by pairing the single-variant analysis with a rare variant set-based analysis that further identified 19 significant rare variant set-based associations with 5 traits. These signals were distinct from both significant single variant association signals within TOPMed and genetic signals observed in prior studies, demonstrating the complementary value of performing both single and rare variant analyses when analyzing quantitative traits. We also confirm several previously reported signals from semi-quantitative proteomics platforms. Many of these signals demonstrate the extensive allelic heterogeneity and ancestry-differentiated variant-trait associations common for inflammation biomarkers, a characteristic we hypothesize will be increasingly observed with well-powered, large-scale analyses of complex traits.
1 aJiang, Min-Zhi1 aGaynor, Sheila, M1 aLi, Xihao1 aVan Buren, Eric1 aStilp, Adrienne1 aButh, Erin1 aWang, Fei, Fei1 aManansala, Regina1 aGogarten, Stephanie, M1 aLi, Zilin1 aPolfus, Linda, M1 aSalimi, Shabnam1 aBis, Joshua, C1 aPankratz, Nathan1 aYanek, Lisa, R1 aDurda, Peter1 aTracy, Russell, P1 aRich, Stephen, S1 aRotter, Jerome, I1 aMitchell, Braxton, D1 aLewis, Joshua, P1 aPsaty, Bruce, M1 aPratte, Katherine, A1 aSilverman, Edwin, K1 aKaplan, Robert, C1 aAvery, Christy1 aNorth, Kari1 aMathias, Rasika, A1 aFaraday, Nauder1 aLin, Honghuang1 aWang, Biqi1 aCarson, April, P1 aNorwood, Arnita, F1 aGibbs, Richard, A1 aKooperberg, Charles1 aLundin, Jessica1 aPeters, Ulrike1 aDupuis, Josée1 aHou, Lifang1 aFornage, Myriam1 aBenjamin, Emelia, J1 aReiner, Alexander, P1 aBowler, Russell, P1 aLin, Xihong1 aAuer, Paul, L1 aRaffield, Laura, M1 aNHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium1 aTOPMed Inflammation Working Group uhttps://chs-nhlbi.org/node/9500