03141nas 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 a
Common 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/606503887nas 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/629004247nas 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/6628