04377nas a2200829 4500008004100000022001400041245014300055210006900198260001300267300001300280490000600293520197700299653001502276653001002291653000902301653002202310653003402332653001102366653001902377653002502396653003402421653001102455653002702466653002102493653002202514653002202536653000902558653001602567653002702583653003602610653002802646653001702674653001802691653001602709100002202725700001902747700001702766700002802783700002302811700002102834700002102855700001802876700002602894700002102920700002802941700002102969700002102990700002503011700001703036700002203053700002003075700002303095700001903118700002303137700002203160700001903182700002503201700001803226700002203244700002103266700002603287700002103313700002103334700002203355700002703377700002303404700001903427700002403446700001903470700002203489856003603511 2011 eng d a1553-740400aGenetic determinants of lipid traits in diverse populations from the population architecture using genomics and epidemiology (PAGE) study.0 aGenetic determinants of lipid traits in diverse populations from c2011 Jun ae10021380 v73 a
For the past five years, genome-wide association studies (GWAS) have identified hundreds of common variants associated with human diseases and traits, including high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and triglyceride (TG) levels. Approximately 95 loci associated with lipid levels have been identified primarily among populations of European ancestry. The Population Architecture using Genomics and Epidemiology (PAGE) study was established in 2008 to characterize GWAS-identified variants in diverse population-based studies. We genotyped 49 GWAS-identified SNPs associated with one or more lipid traits in at least two PAGE studies and across six racial/ethnic groups. We performed a meta-analysis testing for SNP associations with fasting HDL-C, LDL-C, and ln(TG) levels in self-identified European American (~20,000), African American (~9,000), American Indian (~6,000), Mexican American/Hispanic (~2,500), Japanese/East Asian (~690), and Pacific Islander/Native Hawaiian (~175) adults, regardless of lipid-lowering medication use. We replicated 55 of 60 (92%) SNP associations tested in European Americans at p<0.05. Despite sufficient power, we were unable to replicate ABCA1 rs4149268 and rs1883025, CETP rs1864163, and TTC39B rs471364 previously associated with HDL-C and MAFB rs6102059 previously associated with LDL-C. Based on significance (p<0.05) and consistent direction of effect, a majority of replicated genotype-phentoype associations for HDL-C, LDL-C, and ln(TG) in European Americans generalized to African Americans (48%, 61%, and 57%), American Indians (45%, 64%, and 77%), and Mexican Americans/Hispanics (57%, 56%, and 86%). Overall, 16 associations generalized across all three populations. For the associations that did not generalize, differences in effect sizes, allele frequencies, and linkage disequilibrium offer clues to the next generation of association studies for these traits.
10aAdolescent10aAdult10aAged10aAged, 80 and over10aContinental Population Groups10aFemale10aGene Frequency10aGenetics, Population10aGenome-Wide Association Study10aHumans10aLinkage Disequilibrium10aLipid Metabolism10aLipoproteins, HDL10aLipoproteins, LDL10aMale10aMiddle Aged10aMolecular Epidemiology10aPolymorphism, Single Nucleotide10aQuantitative Trait Loci10aRisk Factors10aTriglycerides10aYoung Adult1 aDumitrescu, Logan1 aCarty, Cara, L1 aTaylor, Kira1 aSchumacher, Fredrick, R1 aHindorff, Lucia, A1 aAmbite, José, L1 aAnderson, Garnet1 aBest, Lyle, G1 aBrown-Gentry, Kristin1 aBůzková, Petra1 aCarlson, Christopher, S1 aCochran, Barbara1 aCole, Shelley, A1 aDevereux, Richard, B1 aDuggan, Dave1 aEaton, Charles, B1 aFornage, Myriam1 aFranceschini, Nora1 aHaessler, Jeff1 aHoward, Barbara, V1 aJohnson, Karen, C1 aLaston, Sandra1 aKolonel, Laurence, N1 aLee, Elisa, T1 aMacCluer, Jean, W1 aManolio, Teri, A1 aPendergrass, Sarah, A1 aQuibrera, Miguel1 aShohet, Ralph, V1 aWilkens, Lynne, R1 aHaiman, Christopher, A1 aLe Marchand, Loïc1 aBuyske, Steven1 aKooperberg, Charles1 aNorth, Kari, E1 aCrawford, Dana, C uhttps://chs-nhlbi.org/node/130303388nas a2200553 4500008004100000022001400041245012700055210006900182260001300251300001300264490000600277520170800283653002201991653002302013653001802036653001802054653004002072653003202112653003802144653001802182653001102200653002302211653002302234653001402257653002602271653003602297653003402333653003002367653002202397100002202419700001802441700001902459700002102478700002102499700002002520700002302540700002402563700002002587700002102607700002602628700002002654700002402674700002202698700002002720700002202740700001902762700001702781856003602798 2011 eng d a1553-740400aA phenomics-based strategy identifies loci on APOC1, BRAP, and PLCG1 associated with metabolic syndrome phenotype domains.0 aphenomicsbased strategy identifies loci on APOC1 BRAP and PLCG1 c2011 Oct ae10023220 v73 aDespite evidence of the clustering of metabolic syndrome components, current approaches for identifying unifying genetic mechanisms typically evaluate clinical categories that do not provide adequate etiological information. Here, we used data from 19,486 European American and 6,287 African American Candidate Gene Association Resource Consortium participants to identify loci associated with the clustering of metabolic phenotypes. Six phenotype domains (atherogenic dyslipidemia, vascular dysfunction, vascular inflammation, pro-thrombotic state, central obesity, and elevated plasma glucose) encompassing 19 quantitative traits were examined. Principal components analysis was used to reduce the dimension of each domain such that >55% of the trait variance was represented within each domain. We then applied a statistically efficient and computational feasible multivariate approach that related eight principal components from the six domains to 250,000 imputed SNPs using an additive genetic model and including demographic covariates. In European Americans, we identified 606 genome-wide significant SNPs representing 19 loci. Many of these loci were associated with only one trait domain, were consistent with results in African Americans, and overlapped with published findings, for instance central obesity and FTO. However, our approach, which is applicable to any set of interval scale traits that is heritable and exhibits evidence of phenotypic clustering, identified three new loci in or near APOC1, BRAP, and PLCG1, which were associated with multiple phenotype domains. These pleiotropic loci may help characterize metabolic dysregulation and identify targets for intervention.
10aAfrican Americans10aApolipoprotein C-I10aBlood Glucose10aDyslipidemias10aEuropean Continental Ancestry Group10aGenetic Association Studies10aGenetic Predisposition to Disease10aGenome, Human10aHumans10aMetabolic Syndrome10aObesity, Abdominal10aPhenotype10aPhospholipase C gamma10aPolymorphism, Single Nucleotide10aQuantitative Trait, Heritable10aUbiquitin-Protein Ligases10aVascular Diseases1 aAvery, Christy, L1 aHe, Qianchuan1 aNorth, Kari, E1 aAmbite, José, L1 aBoerwinkle, Eric1 aFornage, Myriam1 aHindorff, Lucia, A1 aKooperberg, Charles1 aMeigs, James, B1 aPankow, James, S1 aPendergrass, Sarah, A1 aPsaty, Bruce, M1 aRitchie, Marylyn, D1 aRotter, Jerome, I1 aTaylor, Kent, D1 aWilkens, Lynne, R1 aHeiss, Gerardo1 aLin, Dan, Yu uhttps://chs-nhlbi.org/node/134503141nas 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/663303734nas 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/606503914nas 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/663003673nas a2200601 4500008004100000022001400041245015800055210006900213260000900282300000700291490000700298520191500305653001102220653002602231653001802257653003402275653001102309653001102320653000902331653003602340653002202376100002002398700001902418700002202437700002102459700002102480700002002501700002402521700002202545700002102567700002102588700002202609700002402631700001902655700002302674700002202697700002102719700002102740700001602761700002202777700002602799700002102825700002302846700002402869700002302893700002402916700002202940700001902962700001902981700002003000710001503020856003603035 2013 eng d a1471-215600aInvestigation of gene-by-sex interactions for lipid traits in diverse populations from the population architecture using genomics and epidemiology study.0 aInvestigation of genebysex interactions for lipid traits in dive c2013 a330 v143 aBACKGROUND: High-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and triglyceride (TG) levels are influenced by both genes and the environment. Genome-wide association studies (GWAS) have identified ~100 common genetic variants associated with HDL-C, LDL-C, and/or TG levels, mostly in populations of European descent, but little is known about the modifiers of these associations. Here, we investigated whether GWAS-identified SNPs for lipid traits exhibited heterogeneity by sex in the Population Architecture using Genomics and Epidemiology (PAGE) study.
RESULTS: A sex-stratified meta-analysis was performed for 49 GWAS-identified SNPs for fasting HDL-C, LDL-C, and ln(TG) levels among adults self-identified as European American (25,013). Heterogeneity by sex was established when phet < 0.001. There was evidence for heterogeneity by sex for two SNPs for ln(TG) in the APOA1/C3/A4/A5/BUD13 gene cluster: rs28927680 (p(het) = 7.4 x 10(-7)) and rs3135506 (p(het) = 4.3 x 10(-4)one SNP in PLTP for HDL levels (rs7679; p(het) = 9.9 x 10(-4)), and one in HMGCR for LDL levels (rs12654264; p(het) = 3.1 x 10(-5)). We replicated heterogeneity by sex in five of seventeen loci previously reported by genome-wide studies (binomial p = 0.0009). We also present results for other racial/ethnic groups in the supplementary materials, to provide a resource for future meta-analyses.
CONCLUSIONS: We provide further evidence for sex-specific effects of SNPs in the APOA1/C3/A4/A5/BUD13 gene cluster, PLTP, and HMGCR on fasting triglyceride levels in European Americans from the PAGE study. Our findings emphasize the need for considering context-specific effects when interpreting genetic associations emerging from GWAS, and also highlight the difficulties in replicating interaction effects across studies and across racial/ethnic groups.
10aFemale10aGenetic Heterogeneity10aGenome, Human10aGenome-Wide Association Study10aHumans10aLipids10aMale10aPolymorphism, Single Nucleotide10aPopulation Groups1 aTaylor, Kira, C1 aCarty, Cara, L1 aDumitrescu, Logan1 aBůzková, Petra1 aCole, Shelley, A1 aHindorff, Lucia1 aSchumacher, Fred, R1 aWilkens, Lynne, R1 aShohet, Ralph, V1 aQuibrera, Miguel1 aJohnson, Karen, C1 aHenderson, Brian, E1 aHaessler, Jeff1 aFranceschini, Nora1 aEaton, Charles, B1 aDuggan, David, J1 aCochran, Barbara1 aCheng, Iona1 aCarlson, Chris, S1 aBrown-Gentry, Kristin1 aAnderson, Garnet1 aAmbite, Jose, Luis1 aHaiman, Christopher1 aLe Marchand, Loïc1 aKooperberg, Charles1 aCrawford, Dana, C1 aBuyske, Steven1 aNorth, Kari, E1 aFornage, Myriam1 aPAGE Study uhttps://chs-nhlbi.org/node/662702686nas a2200697 4500008004100000022001400041245012800055210006900183260001300252300001200265490000800277520066900285653002100954653002100975653001900996653001801015653001101033653001901044653003301063653002501096653003401121653001101155653002101166653000901187653003601196653001501232653001201247653001801259653001601277100002201293700001901315700002301334700002301357700002101380700002101401700002801422700002201450700002301472700002101495700001901516700002101535700002401556700001601580700002201596700002201618700002201640700002601662700002301688700002101711700002101732700002301753700002201776700002301798700002701821700001901848700002401867700001901891700002001910700002201930856003601952 2013 eng d a1432-120300aNo evidence of interaction between known lipid-associated genetic variants and smoking in the multi-ethnic PAGE population.0 aNo evidence of interaction between known lipidassociated genetic c2013 Dec a1427-310 v1323 aGenome-wide association studies (GWAS) have identified many variants that influence high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, and/or triglycerides. However, environmental modifiers, such as smoking, of these known genotype-phenotype associations are just recently emerging in the literature. We have tested for interactions between smoking and 49 GWAS-identified variants in over 41,000 racially/ethnically diverse samples with lipid levels from the Population Architecture Using Genomics and Epidemiology (PAGE) study. Despite their biological plausibility, we were unable to detect significant SNP × smoking interactions.
10aCholesterol, HDL10aCholesterol, LDL10aCohort Studies10aEthnic Groups10aFemale10aGene Frequency10aGene-Environment Interaction10aGenetics, Population10aGenome-Wide Association Study10aHumans10aLipid Metabolism10aMale10aPolymorphism, Single Nucleotide10aPrevalence10aSmoking10aTriglycerides10aYoung Adult1 aDumitrescu, Logan1 aCarty, Cara, L1 aFranceschini, Nora1 aHindorff, Lucia, A1 aCole, Shelley, A1 aBůzková, Petra1 aSchumacher, Fredrick, R1 aEaton, Charles, B1 aGoodloe, Robert, J1 aDuggan, David, J1 aHaessler, Jeff1 aCochran, Barbara1 aHenderson, Brian, E1 aCheng, Iona1 aJohnson, Karen, C1 aCarlson, Chris, S1 aLove, Shelly-Anne1 aBrown-Gentry, Kristin1 aNato, Alejandro, Q1 aQuibrera, Miguel1 aShohet, Ralph, V1 aAmbite, Jose, Luis1 aWilkens, Lynne, R1 aLe Marchand, Loïc1 aHaiman, Christopher, A1 aBuyske, Steven1 aKooperberg, Charles1 aNorth, Kari, E1 aFornage, Myriam1 aCrawford, Dana, C uhttps://chs-nhlbi.org/node/629203380nas a2200673 4500008004100000022001400041245020100055210006900256260001300325300001100338490000700349520133800356653001001694653000901704653004001713653001101753653003201764653003401796653001101830653001101841653000901852653001601861653003601877653002801913653003401941653001701975100002201992700001902014700002302033700002302056700002102079700002102100700002802121700002202149700002302171700002102194700001902215700002102234700002402255700001602279700002202295700002202317700002102339700002602360700002302386700002102409700002102430700002102451700002302472700002202495700002202517700002702539700001902566700002402585700001902609700002002628700002202648856003602670 2013 eng d a1469-180900aPost-genome-wide association study challenges for lipid traits: describing age as a modifier of gene-lipid associations in the Population Architecture using Genomics and Epidemiology (PAGE) study.0 aPostgenomewide association study challenges for lipid traits des c2013 Sep a416-250 v773 aNumerous common genetic variants that influence plasma high-density lipoprotein cholesterol, low-density lipoprotein cholesterol (LDL-C), and triglyceride distributions have been identified via genome-wide association studies (GWAS). However, whether or not these associations are age-dependent has largely been overlooked. We conducted an association study and meta-analysis in more than 22,000 European Americans between 49 previously identified GWAS variants and the three lipid traits, stratified by age (males: <50 or ≥50 years of age; females: pre- or postmenopausal). For each variant, a test of heterogeneity was performed between the two age strata and significant Phet values were used as evidence of age-specific genetic effects. We identified seven associations in females and eight in males that displayed suggestive heterogeneity by age (Phet < 0.05). The association between rs174547 (FADS1) and LDL-C in males displayed the most evidence for heterogeneity between age groups (Phet = 1.74E-03, I(2) = 89.8), with a significant association in older males (P = 1.39E-06) but not younger males (P = 0.99). However, none of the suggestive modifying effects survived adjustment for multiple testing, highlighting the challenges of identifying modifiers of modest SNP-trait associations despite large sample sizes.
10aAdult10aAged10aEuropean Continental Ancestry Group10aFemale10aGenetic Association Studies10aGenome-Wide Association Study10aHumans10aLipids10aMale10aMiddle Aged10aPolymorphism, Single Nucleotide10aQuantitative Trait Loci10aQuantitative Trait, Heritable10aRisk Factors1 aDumitrescu, Logan1 aCarty, Cara, L1 aFranceschini, Nora1 aHindorff, Lucia, A1 aCole, Shelley, A1 aBůzková, Petra1 aSchumacher, Fredrick, R1 aEaton, Charles, B1 aGoodloe, Robert, J1 aDuggan, David, J1 aHaessler, Jeff1 aCochran, Barbara1 aHenderson, Brian, E1 aCheng, Iona1 aJohnson, Karen, C1 aCarlson, Chris, S1 aLove, Shelly-Ann1 aBrown-Gentry, Kristin1 aNato, Alejandro, Q1 aQuibrera, Miguel1 aAnderson, Garnet1 aShohet, Ralph, V1 aAmbite, Jose, Luis1 aWilkens, Lynne, R1 aLe Marchand, Loic1 aHaiman, Christopher, A1 aBuyske, Steven1 aKooperberg, Charles1 aNorth, Kari, E1 aFornage, Myriam1 aCrawford, Dana, C uhttps://chs-nhlbi.org/node/611104252nas 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/636005087nas a2201237 4500008004100000022001400041245010900055210006900164260001200233300001400245490000700259520156200266653002801828653003801856653003401894653001101928653003601939653001701975100002701992700001502019700002402034700002002058700002302078700001602101700002002117700001802137700001402155700001802169700001802187700002402205700002002229700002102249700002302270700001802293700002202311700002802333700002502361700002302386700002202409700002202431700002302453700002002476700001502496700001802511700001602529700001902545700002402564700002202588700002002610700001202630700002002642700002502662700001902687700002202706700002202728700002702750700002302777700002202800700001902822700002102841700001702862700002102879700001602900700002002916700002302936700002102959700002002980700002003000700002003020700001903040700002403059700001503083700002403098700002303122700002203145700002403167700002203191700001603213700001903229700002203248700002503270700002203295700002103317700002103338700002103359700001503380700002103395700002303416700002503439700002403464700002103488700002003509700002003529700002303549700002303572700001403595700001603609700002003625700003003645700002903675710003003704710003303734710001803767710002803785856003603813 2022 eng d a1546-170X00aLarge-scale genome-wide association study of coronary artery disease in genetically diverse populations.0 aLargescale genomewide association study of coronary artery disea c2022 08 a1679-16920 v283 aWe report a genome-wide association study (GWAS) of coronary artery disease (CAD) incorporating nearly a quarter of a million cases, in which existing studies are integrated with data from cohorts of white, Black and Hispanic individuals from the Million Veteran Program. We document near equivalent heritability of CAD across multiple ancestral groups, identify 95 novel loci, including nine on the X chromosome, detect eight loci of genome-wide significance in Black and Hispanic individuals, and demonstrate that two common haplotypes at the 9p21 locus are responsible for risk stratification in all populations except those of African origin, in which these haplotypes are virtually absent. Moreover, in the largest GWAS for angiographically derived coronary atherosclerosis performed to date, we find 15 loci of genome-wide significance that robustly overlap with established loci for clinical CAD. Phenome-wide association analyses of novel loci and polygenic risk scores (PRSs) augment signals related to insulin resistance, extend pleiotropic associations of these loci to include smoking and family history, and precisely document the markedly reduced transferability of existing PRSs to Black individuals. Downstream integrative analyses reinforce the critical roles of vascular endothelial, fibroblast, and smooth muscle cells in CAD susceptibility, but also point to a shared biology between atherosclerosis and oncogenesis. This study highlights the value of diverse populations in further characterizing the genetic architecture of CAD.
10aCoronary Artery Disease10aGenetic Predisposition to Disease10aGenome-Wide Association Study10aHumans10aPolymorphism, Single Nucleotide10aRisk Factors1 aTcheandjieu, Catherine1 aZhu, Xiang1 aHilliard, Austin, T1 aClarke, Shoa, L1 aNapolioni, Valerio1 aMa, Shining1 aLee, Kyung, Min1 aFang, Huaying1 aChen, Fei1 aLu, Yingchang1 aTsao, Noah, L1 aRaghavan, Sridharan1 aKoyama, Satoshi1 aGorman, Bryan, R1 aVujkovic, Marijana1 aKlarin, Derek1 aLevin, Michael, G1 aSinnott-Armstrong, Nasa1 aWojcik, Genevieve, L1 aPlomondon, Mary, E1 aMaddox, Thomas, M1 aWaldo, Stephen, W1 aBick, Alexander, G1 aPyarajan, Saiju1 aHuang, Jie1 aSong, Rebecca1 aHo, Yuk-Lam1 aBuyske, Steven1 aKooperberg, Charles1 aHaessler, Jeffrey1 aLoos, Ruth, J F1 aDo, Ron1 aVerbanck, Marie1 aChaudhary, Kumardeep1 aNorth, Kari, E1 aAvery, Christy, L1 aGraff, Mariaelisa1 aHaiman, Christopher, A1 aLe Marchand, Loïc1 aWilkens, Lynne, R1 aBis, Joshua, C1 aLeonard, Hampton1 aShen, Botong1 aLange, Leslie, A1 aGiri, Ayush1 aDikilitas, Ozan1 aKullo, Iftikhar, J1 aStanaway, Ian, B1 aJarvik, Gail, P1 aGordon, Adam, S1 aHebbring, Scott1 aNamjou, Bahram1 aKaufman, Kenneth, M1 aIto, Kaoru1 aIshigaki, Kazuyoshi1 aKamatani, Yoichiro1 aVerma, Shefali, S1 aRitchie, Marylyn, D1 aKember, Rachel, L1 aBaras, Aris1 aLotta, Luca, A1 aKathiresan, Sekar1 aHauser, Elizabeth, R1 aMiller, Donald, R1 aLee, Jennifer, S1 aSaleheen, Danish1 aReaven, Peter, D1 aCho, Kelly1 aGaziano, Michael1 aNatarajan, Pradeep1 aHuffman, Jennifer, E1 aVoight, Benjamin, F1 aRader, Daniel, J1 aChang, Kyong-Mi1 aLynch, Julie, A1 aDamrauer, Scott, M1 aWilson, Peter, W F1 aTang, Hua1 aSun, Yan, V1 aTsao, Philip, S1 aO'Donnell, Christopher, J1 aAssimes, Themistocles, L1 aRegeneron Genetics Center1 aCARDIoGRAMplusC4D Consortium1 aBiobank Japan1 aMillion Veteran Program uhttps://chs-nhlbi.org/node/917605493nas 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/9484