%0 Journal Article %J N Engl J Med %D 2010 %T Genetic ancestry in lung-function predictions. %A Kumar, Rajesh %A Seibold, Max A %A Aldrich, Melinda C %A Williams, L Keoki %A Reiner, Alex P %A Colangelo, Laura %A Galanter, Joshua %A Gignoux, Christopher %A Hu, Donglei %A Sen, Saunak %A Choudhry, Shweta %A Peterson, Edward L %A Rodriguez-Santana, Jose %A Rodriguez-Cintron, William %A Nalls, Michael A %A Leak, Tennille S %A O'Meara, Ellen %A Meibohm, Bernd %A Kritchevsky, Stephen B %A Li, Rongling %A Harris, Tamara B %A Nickerson, Deborah A %A Fornage, Myriam %A Enright, Paul %A Ziv, Elad %A Smith, Lewis J %A Liu, Kiang %A Burchard, Esteban González %K Adolescent %K Adult %K African Americans %K Aged %K Aged, 80 and over %K Female %K Forced Expiratory Volume %K Genetic Markers %K Genotype %K Humans %K Linear Models %K Male %K Middle Aged %K Oligonucleotide Array Sequence Analysis %K Reference Values %K Respiratory Function Tests %K Vital Capacity %K Young Adult %X

BACKGROUND: Self-identified race or ethnic group is used to determine normal reference standards in the prediction of pulmonary function. We conducted a study to determine whether the genetically determined percentage of African ancestry is associated with lung function and whether its use could improve predictions of lung function among persons who identified themselves as African American.

METHODS: We assessed the ancestry of 777 participants self-identified as African American in the Coronary Artery Risk Development in Young Adults (CARDIA) study and evaluated the relation between pulmonary function and ancestry by means of linear regression. We performed similar analyses of data for two independent cohorts of subjects identifying themselves as African American: 813 participants in the Health, Aging, and Body Composition (HABC) study and 579 participants in the Cardiovascular Health Study (CHS). We compared the fit of two types of models to lung-function measurements: models based on the covariates used in standard prediction equations and models incorporating ancestry. We also evaluated the effect of the ancestry-based models on the classification of disease severity in two asthma-study populations.

RESULTS: African ancestry was inversely related to forced expiratory volume in 1 second (FEV(1)) and forced vital capacity in the CARDIA cohort. These relations were also seen in the HABC and CHS cohorts. In predicting lung function, the ancestry-based model fit the data better than standard models. Ancestry-based models resulted in the reclassification of asthma severity (based on the percentage of the predicted FEV(1)) in 4 to 5% of participants.

CONCLUSIONS: Current predictive equations, which rely on self-identified race alone, may misestimate lung function among subjects who identify themselves as African American. Incorporating ancestry into normative equations may improve lung-function estimates and more accurately categorize disease severity. (Funded by the National Institutes of Health and others.)

%B N Engl J Med %V 363 %P 321-30 %8 2010 Jul 22 %G eng %N 4 %1 http://www.ncbi.nlm.nih.gov/pubmed/20647190?dopt=Abstract %R 10.1056/NEJMoa0907897 %0 Journal Article %J Hum Mol Genet %D 2011 %T Association of genetic variation with systolic and diastolic blood pressure among African Americans: the Candidate Gene Association Resource study. %A Fox, Ervin R %A Young, J Hunter %A Li, Yali %A Dreisbach, Albert W %A Keating, Brendan J %A Musani, Solomon K %A Liu, Kiang %A Morrison, Alanna C %A Ganesh, Santhi %A Kutlar, Abdullah %A Ramachandran, Vasan S %A Polak, Josef F %A Fabsitz, Richard R %A Dries, Daniel L %A Farlow, Deborah N %A Redline, Susan %A Adeyemo, Adebowale %A Hirschorn, Joel N %A Sun, Yan V %A Wyatt, Sharon B %A Penman, Alan D %A Palmas, Walter %A Rotter, Jerome I %A Townsend, Raymond R %A Doumatey, Ayo P %A Tayo, Bamidele O %A Mosley, Thomas H %A Lyon, Helen N %A Kang, Sun J %A Rotimi, Charles N %A Cooper, Richard S %A Franceschini, Nora %A Curb, J David %A Martin, Lisa W %A Eaton, Charles B %A Kardia, Sharon L R %A Taylor, Herman A %A Caulfield, Mark J %A Ehret, Georg B %A Johnson, Toby %A Chakravarti, Aravinda %A Zhu, Xiaofeng %A Levy, Daniel %K Adult %K African Americans %K Aged %K Blood Pressure %K Cohort Studies %K Diastole %K European Continental Ancestry Group %K Female %K Genetic Loci %K Genome-Wide Association Study %K Genotype %K Humans %K Hypertension %K Male %K Middle Aged %K Phenotype %K Polymorphism, Single Nucleotide %K Systole %X

The prevalence of hypertension in African Americans (AAs) is higher than in other US groups; yet, few have performed genome-wide association studies (GWASs) in AA. Among people of European descent, GWASs have identified genetic variants at 13 loci that are associated with blood pressure. It is unknown if these variants confer susceptibility in people of African ancestry. Here, we examined genome-wide and candidate gene associations with systolic blood pressure (SBP) and diastolic blood pressure (DBP) using the Candidate Gene Association Resource (CARe) consortium consisting of 8591 AAs. Genotypes included genome-wide single-nucleotide polymorphism (SNP) data utilizing the Affymetrix 6.0 array with imputation to 2.5 million HapMap SNPs and candidate gene SNP data utilizing a 50K cardiovascular gene-centric array (ITMAT-Broad-CARe [IBC] array). For Affymetrix data, the strongest signal for DBP was rs10474346 (P= 3.6 × 10(-8)) located near GPR98 and ARRDC3. For SBP, the strongest signal was rs2258119 in C21orf91 (P= 4.7 × 10(-8)). The top IBC association for SBP was rs2012318 (P= 6.4 × 10(-6)) near SLC25A42 and for DBP was rs2523586 (P= 1.3 × 10(-6)) near HLA-B. None of the top variants replicated in additional AA (n = 11 882) or European-American (n = 69 899) cohorts. We replicated previously reported European-American blood pressure SNPs in our AA samples (SH2B3, P= 0.009; TBX3-TBX5, P= 0.03; and CSK-ULK3, P= 0.0004). These genetic loci represent the best evidence of genetic influences on SBP and DBP in AAs to date. More broadly, this work supports that notion that blood pressure among AAs is a trait with genetic underpinnings but also with significant complexity.

%B Hum Mol Genet %V 20 %P 2273-84 %8 2011 Jun 01 %G eng %N 11 %1 http://www.ncbi.nlm.nih.gov/pubmed/21378095?dopt=Abstract %R 10.1093/hmg/ddr092 %0 Journal Article %J JAMA %D 2012 %T Association of weight status with mortality in adults with incident diabetes. %A Carnethon, Mercedes R %A De Chavez, Peter John D %A Biggs, Mary L %A Lewis, Cora E %A Pankow, James S %A Bertoni, Alain G %A Golden, Sherita H %A Liu, Kiang %A Mukamal, Kenneth J %A Campbell-Jenkins, Brenda %A Dyer, Alan R %K Adult %K Aged %K Aged, 80 and over %K Body Mass Index %K Body Weight %K Cardiovascular Diseases %K Cause of Death %K Diabetes Mellitus, Type 2 %K Female %K Humans %K Longitudinal Studies %K Male %K Middle Aged %K Obesity %K Overweight %K United States %X

CONTEXT: Type 2 diabetes in normal-weight adults (body mass index [BMI] <25) is a representation of the metabolically obese normal-weight phenotype with unknown mortality consequences.

OBJECTIVE: To test the association of weight status with mortality in adults with new-onset diabetes in order to minimize the influence of diabetes duration and voluntary weight loss on mortality.

DESIGN, SETTING, AND PARTICIPANTS: Pooled analysis of 5 longitudinal cohort studies: Atherosclerosis Risk in Communities study, 1990-2006; Cardiovascular Health Study, 1992-2008; Coronary Artery Risk Development in Young Adults, 1987-2011; Framingham Offspring Study, 1979-2007; and Multi-Ethnic Study of Atherosclerosis, 2002-2011. A total of 2625 participants with incident diabetes contributed 27,125 person-years of follow-up. Included were men and women (age >40 years) who developed incident diabetes based on fasting glucose 126 mg/dL or greater or newly initiated diabetes medication and who had concurrent measurements of BMI. Participants were classified as normal weight if their BMI was 18.5 to 24.99 or overweight/obese if BMI was 25 or greater.

MAIN OUTCOME MEASURES: Total, cardiovascular, and noncardiovascular mortality.

RESULTS: The proportion of adults who were normal weight at the time of incident diabetes ranged from 9% to 21% (overall 12%). During follow-up, 449 participants died: 178 from cardiovascular causes and 253 from noncardiovascular causes (18 were not classified). The rates of total, cardiovascular, and noncardiovascular mortality were higher in normal-weight participants (284.8, 99.8, and 198.1 per 10,000 person-years, respectively) than in overweight/obese participants (152.1, 67.8, and 87.9 per 10,000 person-years, respectively). After adjustment for demographic characteristics and blood pressure, lipid levels, waist circumference, and smoking status, hazard ratios comparing normal-weight participants with overweight/obese participants for total, cardiovascular, and noncardiovascular mortality were 2.08 (95% CI, 1.52-2.85), 1.52 (95% CI, 0.89-2.58), and 2.32 (95% CI, 1.55-3.48), respectively.

CONCLUSION: Adults who were normal weight at the time of incident diabetes had higher mortality than adults who are overweight or obese.

%B JAMA %V 308 %P 581-90 %8 2012 Aug 08 %G eng %N 6 %1 http://www.ncbi.nlm.nih.gov/pubmed/22871870?dopt=Abstract %R 10.1001/jama.2012.9282 %0 Journal Article %J PLoS One %D 2012 %T Multi-ethnic analysis of lipid-associated loci: the NHLBI CARe project. %A Musunuru, Kiran %A Romaine, Simon P R %A Lettre, Guillaume %A Wilson, James G %A Volcik, Kelly A %A Tsai, Michael Y %A Taylor, Herman A %A Schreiner, Pamela J %A Rotter, Jerome I %A Rich, Stephen S %A Redline, Susan %A Psaty, Bruce M %A Papanicolaou, George J %A Ordovas, Jose M %A Liu, Kiang %A Krauss, Ronald M %A Glazer, Nicole L %A Gabriel, Stacey B %A Fornage, Myriam %A Cupples, L Adrienne %A Buxbaum, Sarah G %A Boerwinkle, Eric %A Ballantyne, Christie M %A Kathiresan, Sekar %A Rader, Daniel J %K African Americans %K Cholesterol, HDL %K Cholesterol, LDL %K European Continental Ancestry Group %K Genetic Association Studies %K Genetic Loci %K Humans %K Polymorphism, Single Nucleotide %K Triglycerides %X

BACKGROUND: Whereas it is well established that plasma lipid levels have substantial heritability within populations, it remains unclear how many of the genetic determinants reported in previous studies (largely performed in European American cohorts) are relevant in different ethnicities.

METHODOLOGY/PRINCIPAL FINDINGS: We tested a set of ∼50,000 polymorphisms from ∼2,000 candidate genes and genetic loci from genome-wide association studies (GWAS) for association with low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglycerides (TG) in 25,000 European Americans and 9,000 African Americans in the National Heart, Lung, and Blood Institute (NHLBI) Candidate Gene Association Resource (CARe). We replicated associations for a number of genes in one or both ethnicities and identified a novel lipid-associated variant in a locus harboring ICAM1. We compared the architecture of genetic loci associated with lipids in both African Americans and European Americans and found that the same genes were relevant across ethnic groups but the specific associated variants at each gene often differed.

CONCLUSIONS/SIGNIFICANCE: We identify or provide further evidence for a number of genetic determinants of plasma lipid levels through population association studies. In many loci the determinants appear to differ substantially between African Americans and European Americans.

%B PLoS One %V 7 %P e36473 %8 2012 %G eng %N 5 %1 http://www.ncbi.nlm.nih.gov/pubmed/22629316?dopt=Abstract %R 10.1371/journal.pone.0036473 %0 Journal Article %J Am J Epidemiol %D 2013 %T Association of functional polymorphism rs2231142 (Q141K) in the ABCG2 gene with serum uric acid and gout in 4 US populations: the PAGE Study. %A Zhang, Lili %A Spencer, Kylee L %A Voruganti, V Saroja %A Jorgensen, Neal W %A Fornage, Myriam %A Best, Lyle G %A Brown-Gentry, Kristin D %A Cole, Shelley A %A Crawford, Dana C %A Deelman, Ewa %A Franceschini, Nora %A Gaffo, Angelo L %A Glenn, Kimberly R %A Heiss, Gerardo %A Jenny, Nancy S %A Köttgen, Anna %A Li, Qiong %A Liu, Kiang %A Matise, Tara C %A North, Kari E %A Umans, Jason G %A Kao, W H Linda %K Adult %K African Americans %K Age Distribution %K ATP-Binding Cassette Transporters %K Comorbidity %K European Continental Ancestry Group %K Female %K Genetic Predisposition to Disease %K Genetics, Population %K Genome-Wide Association Study %K Gout %K Hormone Replacement Therapy %K Humans %K Indians, North American %K Male %K Mexican Americans %K Middle Aged %K Neoplasm Proteins %K Polymorphism, Genetic %K Postmenopause %K Sex Distribution %K United States %K Uric Acid %X

A loss-of-function mutation (Q141K, rs2231142) in the ATP-binding cassette, subfamily G, member 2 gene (ABCG2) has been shown to be associated with serum uric acid levels and gout in Asians, Europeans, and European and African Americans; however, less is known about these associations in other populations. Rs2231142 was genotyped in 22,734 European Americans, 9,720 African Americans, 3,849 Mexican Americans, and 3,550 American Indians in the Population Architecture using Genomics and Epidemiology (PAGE) Study (2008-2012). Rs2231142 was significantly associated with serum uric acid levels (P = 2.37 × 10(-67), P = 3.98 × 10(-5), P = 6.97 × 10(-9), and P = 5.33 × 10(-4) in European Americans, African Americans, Mexican Americans, and American Indians, respectively) and gout (P = 2.83 × 10(-10), P = 0.01, and P = 0.01 in European Americans, African Americans, and Mexican Americans, respectively). Overall, the T allele was associated with a 0.24-mg/dL increase in serum uric acid level (P = 1.37 × 10(-80)) and a 1.75-fold increase in the odds of gout (P = 1.09 × 10(-12)). The association between rs2231142 and serum uric acid was significantly stronger in men, postmenopausal women, and hormone therapy users compared with their counterparts. The association with gout was also significantly stronger in men than in women. These results highlight a possible role of sex hormones in the regulation of ABCG2 urate transporter and its potential implications for the prevention, diagnosis, and treatment of hyperuricemia and gout.

%B Am J Epidemiol %V 177 %P 923-32 %8 2013 May 1 %G eng %N 9 %1 http://www.ncbi.nlm.nih.gov/pubmed/23552988?dopt=Abstract %R 10.1093/aje/kws330 %0 Journal Article %J Nat Genet %D 2013 %T Genome-wide association analyses identify 18 new loci associated with serum urate concentrations. %A Köttgen, Anna %A Albrecht, Eva %A Teumer, Alexander %A Vitart, Veronique %A Krumsiek, Jan %A Hundertmark, Claudia %A Pistis, Giorgio %A Ruggiero, Daniela %A O'Seaghdha, Conall M %A Haller, Toomas %A Yang, Qiong %A Tanaka, Toshiko %A Johnson, Andrew D %A Kutalik, Zoltán %A Smith, Albert V %A Shi, Julia %A Struchalin, Maksim %A Middelberg, Rita P S %A Brown, Morris J %A Gaffo, Angelo L %A Pirastu, Nicola %A Li, Guo %A Hayward, Caroline %A Zemunik, Tatijana %A Huffman, Jennifer %A Yengo, Loic %A Zhao, Jing Hua %A Demirkan, Ayse %A Feitosa, Mary F %A Liu, Xuan %A Malerba, Giovanni %A Lopez, Lorna M %A van der Harst, Pim %A Li, Xinzhong %A Kleber, Marcus E %A Hicks, Andrew A %A Nolte, Ilja M %A Johansson, Asa %A Murgia, Federico %A Wild, Sarah H %A Bakker, Stephan J L %A Peden, John F %A Dehghan, Abbas %A Steri, Maristella %A Tenesa, Albert %A Lagou, Vasiliki %A Salo, Perttu %A Mangino, Massimo %A Rose, Lynda M %A Lehtimäki, Terho %A Woodward, Owen M %A Okada, Yukinori %A Tin, Adrienne %A Müller, Christian %A Oldmeadow, Christopher %A Putku, Margus %A Czamara, Darina %A Kraft, Peter %A Frogheri, Laura %A Thun, Gian Andri %A Grotevendt, Anne %A Gislason, Gauti Kjartan %A Harris, Tamara B %A Launer, Lenore J %A McArdle, Patrick %A Shuldiner, Alan R %A Boerwinkle, Eric %A Coresh, Josef %A Schmidt, Helena %A Schallert, Michael %A Martin, Nicholas G %A Montgomery, Grant W %A Kubo, Michiaki %A Nakamura, Yusuke %A Tanaka, Toshihiro %A Munroe, Patricia B %A Samani, Nilesh J %A Jacobs, David R %A Liu, Kiang %A D'Adamo, Pio %A Ulivi, Sheila %A Rotter, Jerome I %A Psaty, Bruce M %A Vollenweider, Peter %A Waeber, Gérard %A Campbell, Susan %A Devuyst, Olivier %A Navarro, Pau %A Kolcic, Ivana %A Hastie, Nicholas %A Balkau, Beverley %A Froguel, Philippe %A Esko, Tõnu %A Salumets, Andres %A Khaw, Kay Tee %A Langenberg, Claudia %A Wareham, Nicholas J %A Isaacs, Aaron %A Kraja, Aldi %A Zhang, Qunyuan %A Wild, Philipp S %A Scott, Rodney J %A Holliday, Elizabeth G %A Org, Elin %A Viigimaa, Margus %A Bandinelli, Stefania %A Metter, Jeffrey E %A Lupo, Antonio %A Trabetti, Elisabetta %A Sorice, Rossella %A Döring, Angela %A Lattka, Eva %A Strauch, Konstantin %A Theis, Fabian %A Waldenberger, Melanie %A Wichmann, H-Erich %A Davies, Gail %A Gow, Alan J %A Bruinenberg, Marcel %A Stolk, Ronald P %A Kooner, Jaspal S %A Zhang, Weihua %A Winkelmann, Bernhard R %A Boehm, Bernhard O %A Lucae, Susanne %A Penninx, Brenda W %A Smit, Johannes H %A Curhan, Gary %A Mudgal, Poorva %A Plenge, Robert M %A Portas, Laura %A Persico, Ivana %A Kirin, Mirna %A Wilson, James F %A Mateo Leach, Irene %A van Gilst, Wiek H %A Goel, Anuj %A Ongen, Halit %A Hofman, Albert %A Rivadeneira, Fernando %A Uitterlinden, André G %A Imboden, Medea %A von Eckardstein, Arnold %A Cucca, Francesco %A Nagaraja, Ramaiah %A Piras, Maria Grazia %A Nauck, Matthias %A Schurmann, Claudia %A Budde, Kathrin %A Ernst, Florian %A Farrington, Susan M %A Theodoratou, Evropi %A Prokopenko, Inga %A Stumvoll, Michael %A Jula, Antti %A Perola, Markus %A Salomaa, Veikko %A Shin, So-Youn %A Spector, Tim D %A Sala, Cinzia %A Ridker, Paul M %A Kähönen, Mika %A Viikari, Jorma %A Hengstenberg, Christian %A Nelson, Christopher P %A Meschia, James F %A Nalls, Michael A %A Sharma, Pankaj %A Singleton, Andrew B %A Kamatani, Naoyuki %A Zeller, Tanja %A Burnier, Michel %A Attia, John %A Laan, Maris %A Klopp, Norman %A Hillege, Hans L %A Kloiber, Stefan %A Choi, Hyon %A Pirastu, Mario %A Tore, Silvia %A Probst-Hensch, Nicole M %A Völzke, Henry %A Gudnason, Vilmundur %A Parsa, Afshin %A Schmidt, Reinhold %A Whitfield, John B %A Fornage, Myriam %A Gasparini, Paolo %A Siscovick, David S %A Polasek, Ozren %A Campbell, Harry %A Rudan, Igor %A Bouatia-Naji, Nabila %A Metspalu, Andres %A Loos, Ruth J F %A van Duijn, Cornelia M %A Borecki, Ingrid B %A Ferrucci, Luigi %A Gambaro, Giovanni %A Deary, Ian J %A Wolffenbuttel, Bruce H R %A Chambers, John C %A März, Winfried %A Pramstaller, Peter P %A Snieder, Harold %A Gyllensten, Ulf %A Wright, Alan F %A Navis, Gerjan %A Watkins, Hugh %A Witteman, Jacqueline C M %A Sanna, Serena %A Schipf, Sabine %A Dunlop, Malcolm G %A Tönjes, Anke %A Ripatti, Samuli %A Soranzo, Nicole %A Toniolo, Daniela %A Chasman, Daniel I %A Raitakari, Olli %A Kao, W H Linda %A Ciullo, Marina %A Fox, Caroline S %A Caulfield, Mark %A Bochud, Murielle %A Gieger, Christian %K Analysis of Variance %K European Continental Ancestry Group %K Gene Frequency %K Genetic Loci %K Genome-Wide Association Study %K Glucose %K Gout %K Humans %K Inhibins %K Polymorphism, Single Nucleotide %K Signal Transduction %K Uric Acid %X

Elevated serum urate concentrations can cause gout, a prevalent and painful inflammatory arthritis. By combining data from >140,000 individuals of European ancestry within the Global Urate Genetics Consortium (GUGC), we identified and replicated 28 genome-wide significant loci in association with serum urate concentrations (18 new regions in or near TRIM46, INHBB, SFMBT1, TMEM171, VEGFA, BAZ1B, PRKAG2, STC1, HNF4G, A1CF, ATXN2, UBE2Q2, IGF1R, NFAT5, MAF, HLF, ACVR1B-ACVRL1 and B3GNT4). Associations for many of the loci were of similar magnitude in individuals of non-European ancestry. We further characterized these loci for associations with gout, transcript expression and the fractional excretion of urate. Network analyses implicate the inhibins-activins signaling pathways and glucose metabolism in systemic urate control. New candidate genes for serum urate concentration highlight the importance of metabolic control of urate production and excretion, which may have implications for the treatment and prevention of gout.

%B Nat Genet %V 45 %P 145-54 %8 2013 Feb %G eng %N 2 %1 http://www.ncbi.nlm.nih.gov/pubmed/23263486?dopt=Abstract %R 10.1038/ng.2500 %0 Journal Article %J Am J Hum Genet %D 2014 %T Effects of long-term averaging of quantitative blood pressure traits on the detection of genetic associations. %A Ganesh, Santhi K %A Chasman, Daniel I %A Larson, Martin G %A Guo, Xiuqing %A Verwoert, Germain %A Bis, Joshua C %A Gu, Xiangjun %A Smith, Albert V %A Yang, Min-Lee %A Zhang, Yan %A Ehret, Georg %A Rose, Lynda M %A Hwang, Shih-Jen %A Papanicolau, George J %A Sijbrands, Eric J %A Rice, Kenneth %A Eiriksdottir, Gudny %A Pihur, Vasyl %A Ridker, Paul M %A Vasan, Ramachandran S %A Newton-Cheh, Christopher %A Raffel, Leslie J %A Amin, Najaf %A Rotter, Jerome I %A Liu, Kiang %A Launer, Lenore J %A Xu, Ming %A Caulfield, Mark %A Morrison, Alanna C %A Johnson, Andrew D %A Vaidya, Dhananjay %A Dehghan, Abbas %A Li, Guo %A Bouchard, Claude %A Harris, Tamara B %A Zhang, He %A Boerwinkle, Eric %A Siscovick, David S %A Gao, Wei %A Uitterlinden, André G %A Rivadeneira, Fernando %A Hofman, Albert %A Willer, Cristen J %A Franco, Oscar H %A Huo, Yong %A Witteman, Jacqueline C M %A Munroe, Patricia B %A Gudnason, Vilmundur %A Palmas, Walter %A van Duijn, Cornelia %A Fornage, Myriam %A Levy, Daniel %A Psaty, Bruce M %A Chakravarti, Aravinda %K Blood Pressure %K Genome-Wide Association Study %K Humans %K Longitudinal Studies %K Phenotype %K Polymorphism, Single Nucleotide %K Quantitative Trait Loci %X

Blood pressure (BP) is a heritable, quantitative trait with intraindividual variability and susceptibility to measurement error. Genetic studies of BP generally use single-visit measurements and thus cannot remove variability occurring over months or years. We leveraged the idea that averaging BP measured across time would improve phenotypic accuracy and thereby increase statistical power to detect genetic associations. We studied systolic BP (SBP), diastolic BP (DBP), mean arterial pressure (MAP), and pulse pressure (PP) averaged over multiple years in 46,629 individuals of European ancestry. We identified 39 trait-variant associations across 19 independent loci (p < 5 × 10(-8)); five associations (in four loci) uniquely identified by our LTA analyses included those of SBP and MAP at 2p23 (rs1275988, near KCNK3), DBP at 2q11.2 (rs7599598, in FER1L5), and PP at 6p21 (rs10948071, near CRIP3) and 7p13 (rs2949837, near IGFBP3). Replication analyses conducted in cohorts with single-visit BP data showed positive replication of associations and a nominal association (p < 0.05). We estimated a 20% gain in statistical power with long-term average (LTA) as compared to single-visit BP association studies. Using LTA analysis, we identified genetic loci influencing BP. LTA might be one way of increasing the power of genetic associations for continuous traits in extant samples for other phenotypes that are measured serially over time.

%B Am J Hum Genet %V 95 %P 49-65 %8 2014 Jul 03 %G eng %N 1 %1 http://www.ncbi.nlm.nih.gov/pubmed/24975945?dopt=Abstract %R 10.1016/j.ajhg.2014.06.002 %0 Journal Article %J Am J Hum Genet %D 2014 %T Gene-age interactions in blood pressure regulation: a large-scale investigation with the CHARGE, Global BPgen, and ICBP Consortia. %A Simino, Jeannette %A Shi, Gang %A Bis, Joshua C %A Chasman, Daniel I %A Ehret, Georg B %A Gu, Xiangjun %A Guo, Xiuqing %A Hwang, Shih-Jen %A Sijbrands, Eric %A Smith, Albert V %A Verwoert, Germaine C %A Bragg-Gresham, Jennifer L %A Cadby, Gemma %A Chen, Peng %A Cheng, Ching-Yu %A Corre, Tanguy %A de Boer, Rudolf A %A Goel, Anuj %A Johnson, Toby %A Khor, Chiea-Chuen %A Lluís-Ganella, Carla %A Luan, Jian'an %A Lyytikäinen, Leo-Pekka %A Nolte, Ilja M %A Sim, Xueling %A Sõber, Siim %A van der Most, Peter J %A Verweij, Niek %A Zhao, Jing Hua %A Amin, Najaf %A Boerwinkle, Eric %A Bouchard, Claude %A Dehghan, Abbas %A Eiriksdottir, Gudny %A Elosua, Roberto %A Franco, Oscar H %A Gieger, Christian %A Harris, Tamara B %A Hercberg, Serge %A Hofman, Albert %A James, Alan L %A Johnson, Andrew D %A Kähönen, Mika %A Khaw, Kay-Tee %A Kutalik, Zoltán %A Larson, Martin G %A Launer, Lenore J %A Li, Guo %A Liu, Jianjun %A Liu, Kiang %A Morrison, Alanna C %A Navis, Gerjan %A Ong, Rick Twee-Hee %A Papanicolau, George J %A Penninx, Brenda W %A Psaty, Bruce M %A Raffel, Leslie J %A Raitakari, Olli T %A Rice, Kenneth %A Rivadeneira, Fernando %A Rose, Lynda M %A Sanna, Serena %A Scott, Robert A %A Siscovick, David S %A Stolk, Ronald P %A Uitterlinden, André G %A Vaidya, Dhananjay %A van der Klauw, Melanie M %A Vasan, Ramachandran S %A Vithana, Eranga Nishanthie %A Völker, Uwe %A Völzke, Henry %A Watkins, Hugh %A Young, Terri L %A Aung, Tin %A Bochud, Murielle %A Farrall, Martin %A Hartman, Catharina A %A Laan, Maris %A Lakatta, Edward G %A Lehtimäki, Terho %A Loos, Ruth J F %A Lucas, Gavin %A Meneton, Pierre %A Palmer, Lyle J %A Rettig, Rainer %A Snieder, Harold %A Tai, E Shyong %A Teo, Yik-Ying %A van der Harst, Pim %A Wareham, Nicholas J %A Wijmenga, Cisca %A Wong, Tien Yin %A Fornage, Myriam %A Gudnason, Vilmundur %A Levy, Daniel %A Palmas, Walter %A Ridker, Paul M %A Rotter, Jerome I %A van Duijn, Cornelia M %A Witteman, Jacqueline C M %A Chakravarti, Aravinda %A Rao, Dabeeru C %K Adolescent %K Adult %K Age Factors %K Aged %K Blood Pressure %K Cohort Studies %K Humans %K Middle Aged %K Young Adult %X

Although age-dependent effects on blood pressure (BP) have been reported, they have not been systematically investigated in large-scale genome-wide association studies (GWASs). We leveraged the infrastructure of three well-established consortia (CHARGE, GBPgen, and ICBP) and a nonstandard approach (age stratification and metaregression) to conduct a genome-wide search of common variants with age-dependent effects on systolic (SBP), diastolic (DBP), mean arterial (MAP), and pulse (PP) pressure. In a two-staged design using 99,241 individuals of European ancestry, we identified 20 genome-wide significant (p ≤ 5 × 10(-8)) loci by using joint tests of the SNP main effect and SNP-age interaction. Nine of the significant loci demonstrated nominal evidence of age-dependent effects on BP by tests of the interactions alone. Index SNPs in the EHBP1L1 (DBP and MAP), CASZ1 (SBP and MAP), and GOSR2 (PP) loci exhibited the largest age interactions, with opposite directions of effect in the young versus the old. The changes in the genetic effects over time were small but nonnegligible (up to 1.58 mm Hg over 60 years). The EHBP1L1 locus was discovered through gene-age interactions only in whites but had DBP main effects replicated (p = 8.3 × 10(-4)) in 8,682 Asians from Singapore, indicating potential interethnic heterogeneity. A secondary analysis revealed 22 loci with evidence of age-specific effects (e.g., only in 20 to 29-year-olds). Age can be used to select samples with larger genetic effect sizes and more homogenous phenotypes, which may increase statistical power. Age-dependent effects identified through novel statistical approaches can provide insight into the biology and temporal regulation underlying BP associations.

%B Am J Hum Genet %V 95 %P 24-38 %8 2014 Jul 03 %G eng %N 1 %1 http://www.ncbi.nlm.nih.gov/pubmed/24954895?dopt=Abstract %R 10.1016/j.ajhg.2014.05.010 %0 Journal Article %J PLoS One %D 2014 %T No evidence for genome-wide interactions on plasma fibrinogen by smoking, alcohol consumption and body mass index: results from meta-analyses of 80,607 subjects. %A Baumert, Jens %A Huang, Jie %A McKnight, Barbara %A Sabater-Lleal, Maria %A Steri, Maristella %A Chu, Audrey Y %A Trompet, Stella %A Lopez, Lorna M %A Fornage, Myriam %A Teumer, Alexander %A Tang, Weihong %A Rudnicka, Alicja R %A Mälarstig, Anders %A Hottenga, Jouke-Jan %A Kavousi, Maryam %A Lahti, Jari %A Tanaka, Toshiko %A Hayward, Caroline %A Huffman, Jennifer E %A Morange, Pierre-Emmanuel %A Rose, Lynda M %A Basu, Saonli %A Rumley, Ann %A Stott, David J %A Buckley, Brendan M %A de Craen, Anton J M %A Sanna, Serena %A Masala, Marco %A Biffar, Reiner %A Homuth, Georg %A Silveira, Angela %A Sennblad, Bengt %A Goel, Anuj %A Watkins, Hugh %A Müller-Nurasyid, Martina %A Rückerl, Regina %A Taylor, Kent %A Chen, Ming-Huei %A de Geus, Eco J C %A Hofman, Albert %A Witteman, Jacqueline C M %A de Maat, Moniek P M %A Palotie, Aarno %A Davies, Gail %A Siscovick, David S %A Kolcic, Ivana %A Wild, Sarah H %A Song, Jaejoon %A McArdle, Wendy L %A Ford, Ian %A Sattar, Naveed %A Schlessinger, David %A Grotevendt, Anne %A Franzosi, Maria Grazia %A Illig, Thomas %A Waldenberger, Melanie %A Lumley, Thomas %A Tofler, Geoffrey H %A Willemsen, Gonneke %A Uitterlinden, André G %A Rivadeneira, Fernando %A Räikkönen, Katri %A Chasman, Daniel I %A Folsom, Aaron R %A Lowe, Gordon D %A Westendorp, Rudi G J %A Slagboom, P Eline %A Cucca, Francesco %A Wallaschofski, Henri %A Strawbridge, Rona J %A Seedorf, Udo %A Koenig, Wolfgang %A Bis, Joshua C %A Mukamal, Kenneth J %A van Dongen, Jenny %A Widen, Elisabeth %A Franco, Oscar H %A Starr, John M %A Liu, Kiang %A Ferrucci, Luigi %A Polasek, Ozren %A Wilson, James F %A Oudot-Mellakh, Tiphaine %A Campbell, Harry %A Navarro, Pau %A Bandinelli, Stefania %A Eriksson, Johan %A Boomsma, Dorret I %A Dehghan, Abbas %A Clarke, Robert %A Hamsten, Anders %A Boerwinkle, Eric %A Jukema, J Wouter %A Naitza, Silvia %A Ridker, Paul M %A Völzke, Henry %A Deary, Ian J %A Reiner, Alexander P %A Trégouët, David-Alexandre %A O'Donnell, Christopher J %A Strachan, David P %A Peters, Annette %A Smith, Nicholas L %K Alcohol Drinking %K Body Mass Index %K Fibrinogen %K Gene-Environment Interaction %K Genomics %K Humans %K Smoking %X

Plasma fibrinogen is an acute phase protein playing an important role in the blood coagulation cascade having strong associations with smoking, alcohol consumption and body mass index (BMI). Genome-wide association studies (GWAS) have identified a variety of gene regions associated with elevated plasma fibrinogen concentrations. However, little is yet known about how associations between environmental factors and fibrinogen might be modified by genetic variation. Therefore, we conducted large-scale meta-analyses of genome-wide interaction studies to identify possible interactions of genetic variants and smoking status, alcohol consumption or BMI on fibrinogen concentration. The present study included 80,607 subjects of European ancestry from 22 studies. Genome-wide interaction analyses were performed separately in each study for about 2.6 million single nucleotide polymorphisms (SNPs) across the 22 autosomal chromosomes. For each SNP and risk factor, we performed a linear regression under an additive genetic model including an interaction term between SNP and risk factor. Interaction estimates were meta-analysed using a fixed-effects model. No genome-wide significant interaction with smoking status, alcohol consumption or BMI was observed in the meta-analyses. The most suggestive interaction was found for smoking and rs10519203, located in the LOC123688 region on chromosome 15, with a p value of 6.2 × 10(-8). This large genome-wide interaction study including 80,607 participants found no strong evidence of interaction between genetic variants and smoking status, alcohol consumption or BMI on fibrinogen concentrations. Further studies are needed to yield deeper insight in the interplay between environmental factors and gene variants on the regulation of fibrinogen concentrations.

%B PLoS One %V 9 %P e111156 %8 2014 %G eng %N 12 %1 http://www.ncbi.nlm.nih.gov/pubmed/25551457?dopt=Abstract %R 10.1371/journal.pone.0111156 %0 Journal Article %J Nat Genet %D 2016 %T Meta-analysis identifies common and rare variants influencing blood pressure and overlapping with metabolic trait loci. %A Liu, Chunyu %A Kraja, Aldi T %A Smith, Jennifer A %A Brody, Jennifer A %A Franceschini, Nora %A Bis, Joshua C %A Rice, Kenneth %A Morrison, Alanna C %A Lu, Yingchang %A Weiss, Stefan %A Guo, Xiuqing %A Palmas, Walter %A Martin, Lisa W %A Chen, Yii-Der Ida %A Surendran, Praveen %A Drenos, Fotios %A Cook, James P %A Auer, Paul L %A Chu, Audrey Y %A Giri, Ayush %A Zhao, Wei %A Jakobsdottir, Johanna %A Lin, Li-An %A Stafford, Jeanette M %A Amin, Najaf %A Mei, Hao %A Yao, Jie %A Voorman, Arend %A Larson, Martin G %A Grove, Megan L %A Smith, Albert V %A Hwang, Shih-Jen %A Chen, Han %A Huan, Tianxiao %A Kosova, Gulum %A Stitziel, Nathan O %A Kathiresan, Sekar %A Samani, Nilesh %A Schunkert, Heribert %A Deloukas, Panos %A Li, Man %A Fuchsberger, Christian %A Pattaro, Cristian %A Gorski, Mathias %A Kooperberg, Charles %A Papanicolaou, George J %A Rossouw, Jacques E %A Faul, Jessica D %A Kardia, Sharon L R %A Bouchard, Claude %A Raffel, Leslie J %A Uitterlinden, André G %A Franco, Oscar H %A Vasan, Ramachandran S %A O'Donnell, Christopher J %A Taylor, Kent D %A Liu, Kiang %A Bottinger, Erwin P %A Gottesman, Omri %A Daw, E Warwick %A Giulianini, Franco %A Ganesh, Santhi %A Salfati, Elias %A Harris, Tamara B %A Launer, Lenore J %A Dörr, Marcus %A Felix, Stephan B %A Rettig, Rainer %A Völzke, Henry %A Kim, Eric %A Lee, Wen-Jane %A Lee, I-Te %A Sheu, Wayne H-H %A Tsosie, Krystal S %A Edwards, Digna R Velez %A Liu, Yongmei %A Correa, Adolfo %A Weir, David R %A Völker, Uwe %A Ridker, Paul M %A Boerwinkle, Eric %A Gudnason, Vilmundur %A Reiner, Alexander P %A van Duijn, Cornelia M %A Borecki, Ingrid B %A Edwards, Todd L %A Chakravarti, Aravinda %A Rotter, Jerome I %A Psaty, Bruce M %A Loos, Ruth J F %A Fornage, Myriam %A Ehret, Georg B %A Newton-Cheh, Christopher %A Levy, Daniel %A Chasman, Daniel I %X

Meta-analyses of association results for blood pressure using exome-centric single-variant and gene-based tests identified 31 new loci in a discovery stage among 146,562 individuals, with follow-up and meta-analysis in 180,726 additional individuals (total n = 327,288). These blood pressure-associated loci are enriched for known variants for cardiometabolic traits. Associations were also observed for the aggregation of rare and low-frequency missense variants in three genes, NPR1, DBH, and PTPMT1. In addition, blood pressure associations at 39 previously reported loci were confirmed. The identified variants implicate biological pathways related to cardiometabolic traits, vascular function, and development. Several new variants are inferred to have roles in transcription or as hubs in protein-protein interaction networks. Genetic risk scores constructed from the identified variants were strongly associated with coronary disease and myocardial infarction. This large collection of blood pressure-associated loci suggests new therapeutic strategies for hypertension, emphasizing a link with cardiometabolic risk.

%B Nat Genet %V 48 %P 1162-70 %8 2016 Oct %G eng %N 10 %R 10.1038/ng.3660 %0 Journal Article %J Circ Heart Fail %D 2016 %T Predicting Heart Failure With Preserved and Reduced Ejection Fraction: The International Collaboration on Heart Failure Subtypes. %A Ho, Jennifer E %A Enserro, Danielle %A Brouwers, Frank P %A Kizer, Jorge R %A Shah, Sanjiv J %A Psaty, Bruce M %A Bartz, Traci M %A Santhanakrishnan, Rajalakshmi %A Lee, Douglas S %A Chan, Cheeling %A Liu, Kiang %A Blaha, Michael J %A Hillege, Hans L %A van der Harst, Pim %A van Gilst, Wiek H %A Kop, Willem J %A Gansevoort, Ron T %A Vasan, Ramachandran S %A Gardin, Julius M %A Levy, Daniel %A Gottdiener, John S %A de Boer, Rudolf A %A Larson, Martin G %X

BACKGROUND: Heart failure (HF) is a prevalent and deadly disease, and preventive strategies focused on at-risk individuals are needed. Current HF prediction models have not examined HF subtypes. We sought to develop and validate risk prediction models for HF with preserved and reduced ejection fraction (HFpEF, HFrEF).

METHODS AND RESULTS: Of 28,820 participants from 4 community-based cohorts, 982 developed incident HFpEF and 909 HFrEF during a median follow-up of 12 years. Three cohorts were combined, and a 2:1 random split was used for derivation and internal validation, with the fourth cohort as external validation. Models accounted for multiple competing risks (death, other HF subtype, and unclassified HF). The HFpEF-specific model included age, sex, systolic blood pressure, body mass index, antihypertensive treatment, and previous myocardial infarction; it had good discrimination in derivation (c-statistic 0.80; 95% confidence interval [CI], 0.78-0.82) and validation samples (internal: 0.79; 95% CI, 0.77-0.82 and external: 0.76; 95% CI: 0.71-0.80). The HFrEF-specific model additionally included smoking, left ventricular hypertrophy, left bundle branch block, and diabetes mellitus; it had good discrimination in derivation (c-statistic 0.82; 95% CI, 0.80-0.84) and validation samples (internal: 0.80; 95% CI, 0.78-0.83 and external: 0.76; 95% CI, 0.71-0.80). Age was more strongly associated with HFpEF, and male sex, left ventricular hypertrophy, bundle branch block, previous myocardial infarction, and smoking with HFrEF (P value for each comparison ≤0.02).

CONCLUSIONS: We describe and validate risk prediction models for HF subtypes and show good discrimination in a large sample. Some risk factors differed between HFpEF and HFrEF, supporting the notion of pathogenetic differences among HF subtypes.

%B Circ Heart Fail %V 9 %8 2016 Jun %G eng %N 6 %R 10.1161/CIRCHEARTFAILURE.115.003116 %0 Journal Article %J JAMA Cardiol %D 2017 %T Association of Coronary Artery Calcium Score vs Age With Cardiovascular Risk in Older Adults: An Analysis of Pooled Population-Based Studies. %A Yano, Yuichiro %A O'Donnell, Christopher J %A Kuller, Lewis %A Kavousi, Maryam %A Erbel, Raimund %A Ning, Hongyan %A D'Agostino, Ralph %A Newman, Anne B %A Nasir, Khurram %A Hofman, Albert %A Lehmann, Nils %A Dhana, Klodian %A Blankstein, Ron %A Hoffmann, Udo %A Möhlenkamp, Stefan %A Massaro, Joseph M %A Mahabadi, Amir-Abbas %A Lima, João A C %A Ikram, M Arfan %A Jöckel, Karl-Heinz %A Franco, Oscar H %A Liu, Kiang %A Lloyd-Jones, Donald %A Greenland, Philip %X

Importance: Besides age, other discriminators of atherosclerotic cardiovascular disease (ASCVD) risk are needed in older adults.

Objectives: To examine the predictive ability of coronary artery calcium (CAC) score vs age for incident ASCVD and how risk prediction changes by adding CAC score and removing only age from prediction models.

Design, Setting, and Participants: We conducted an analysis of pooled US population-based studies, including the Framingham Heart Study, the Multi-Ethnic Study of Atherosclerosis, and the Cardiovascular Health Study. Results were compared with 2 European cohorts, the Rotterdam Study and the Heinz Nixdorf Recall Study. Participants underwent CAC scoring between 1998 and 2006 using cardiac computed tomography. The participants included adults older than 60 years without known ASCVD at baseline.

Exposures: Coronary artery calcium scores.

Main Outcomes and Measures: Incident ASCVD events including coronary heart disease (CHD) and stroke.

Results: The study included 4778 participants from 3 US cohorts, with a mean age of 70.1 years; 2582 (54.0%) were women, and 2431 (50.9%) were nonwhite. Over 11 years of follow-up (44 152 person-years), 405 CHD and 228 stroke events occurred. Coronary artery calcium score (vs age) had a greater association with incident CHD (C statistic, 0.733 vs 0.690; C statistics difference, 0.043; 95% CI of difference, 0.009-0.075) and modestly improved prediction of incident stroke (C statistic, 0.695 vs 0.670; C statistics difference, 0.025; 95% CI of difference, -0.015 to 0.064). Adding CAC score to models including traditional cardiovascular risk factors, with only age being removed, provided improved discrimination for incident CHD (C statistic, 0.735 vs 0.703; C statistics difference, 0.032; 95% CI of difference, 0.002-0.062) but not for stroke. Coronary artery calcium score was more likely than age to provide higher category-free net reclassification improvement among participants who experienced an ASCVD event (0.390; 95% CI, 0.312-0.467 vs 0.08; 95% CI -0.001 to 0.181) and to result in more accurate reclassification of risk for ASCVD events among these individuals. The findings were similar in the 2 European cohorts (n = 4990).

Conclusions and Relevance: Coronary artery calcium may be an alternative marker besides age to better discriminate between lower and higher CHD risk in older adults. Whether CAC score can assist in guiding the decision to initiate statin treatment for primary prevention in older adults requires further investigation.

%B JAMA Cardiol %8 2017 Jul 26 %G eng %R 10.1001/jamacardio.2017.2498 %0 Journal Article %J J Clin Invest %D 2017 %T Large-scale genome-wide analysis identifies genetic variants associated with cardiac structure and function. %A Wild, Philipp S %A Felix, Janine F %A Schillert, Arne %A Teumer, Alexander %A Chen, Ming-Huei %A Leening, Maarten J G %A Völker, Uwe %A Großmann, Vera %A Brody, Jennifer A %A Irvin, Marguerite R %A Shah, Sanjiv J %A Pramana, Setia %A Lieb, Wolfgang %A Schmidt, Reinhold %A Stanton, Alice V %A Malzahn, Dörthe %A Smith, Albert Vernon %A Sundström, Johan %A Minelli, Cosetta %A Ruggiero, Daniela %A Lyytikäinen, Leo-Pekka %A Tiller, Daniel %A Smith, J Gustav %A Monnereau, Claire %A Di Tullio, Marco R %A Musani, Solomon K %A Morrison, Alanna C %A Pers, Tune H %A Morley, Michael %A Kleber, Marcus E %A Aragam, Jayashri %A Benjamin, Emelia J %A Bis, Joshua C %A Bisping, Egbert %A Broeckel, Ulrich %A Cheng, Susan %A Deckers, Jaap W %A del Greco M, Fabiola %A Edelmann, Frank %A Fornage, Myriam %A Franke, Lude %A Friedrich, Nele %A Harris, Tamara B %A Hofer, Edith %A Hofman, Albert %A Huang, Jie %A Hughes, Alun D %A Kähönen, Mika %A Investigators, Knhi %A Kruppa, Jochen %A Lackner, Karl J %A Lannfelt, Lars %A Laskowski, Rafael %A Launer, Lenore J %A Leosdottir, Margrét %A Lin, Honghuang %A Lindgren, Cecilia M %A Loley, Christina %A MacRae, Calum A %A Mascalzoni, Deborah %A Mayet, Jamil %A Medenwald, Daniel %A Morris, Andrew P %A Müller, Christian %A Müller-Nurasyid, Martina %A Nappo, Stefania %A Nilsson, Peter M %A Nuding, Sebastian %A Nutile, Teresa %A Peters, Annette %A Pfeufer, Arne %A Pietzner, Diana %A Pramstaller, Peter P %A Raitakari, Olli T %A Rice, Kenneth M %A Rivadeneira, Fernando %A Rotter, Jerome I %A Ruohonen, Saku T %A Sacco, Ralph L %A Samdarshi, Tandaw E %A Schmidt, Helena %A Sharp, Andrew S P %A Shields, Denis C %A Sorice, Rossella %A Sotoodehnia, Nona %A Stricker, Bruno H %A Surendran, Praveen %A Thom, Simon %A Töglhofer, Anna M %A Uitterlinden, André G %A Wachter, Rolf %A Völzke, Henry %A Ziegler, Andreas %A Münzel, Thomas %A März, Winfried %A Cappola, Thomas P %A Hirschhorn, Joel N %A Mitchell, Gary F %A Smith, Nicholas L %A Fox, Ervin R %A Dueker, Nicole D %A Jaddoe, Vincent W V %A Melander, Olle %A Russ, Martin %A Lehtimäki, Terho %A Ciullo, Marina %A Hicks, Andrew A %A Lind, Lars %A Gudnason, Vilmundur %A Pieske, Burkert %A Barron, Anthony J %A Zweiker, Robert %A Schunkert, Heribert %A Ingelsson, Erik %A Liu, Kiang %A Arnett, Donna K %A Psaty, Bruce M %A Blankenberg, Stefan %A Larson, Martin G %A Felix, Stephan B %A Franco, Oscar H %A Zeller, Tanja %A Vasan, Ramachandran S %A Dörr, Marcus %X

BACKGROUND: Understanding the genetic architecture of cardiac structure and function may help to prevent and treat heart disease. This investigation sought to identify common genetic variations associated with inter-individual variability in cardiac structure and function.

METHODS: A GWAS meta-analysis of echocardiographic traits was performed, including 46,533 individuals from 30 studies (EchoGen consortium). The analysis included 16 traits of left ventricular (LV) structure, and systolic and diastolic function.

RESULTS: The discovery analysis included 21 cohorts for structural and systolic function traits (n = 32,212) and 17 cohorts for diastolic function traits (n = 21,852). Replication was performed in 5 cohorts (n = 14,321) and 6 cohorts (n = 16,308), respectively. Besides 5 previously reported loci, the combined meta-analysis identified 10 additional genome-wide significant SNPs: rs12541595 near MTSS1 and rs10774625 in ATXN2 for LV end-diastolic internal dimension; rs806322 near KCNRG, rs4765663 in CACNA1C, rs6702619 near PALMD, rs7127129 in TMEM16A, rs11207426 near FGGY, rs17608766 in GOSR2, and rs17696696 in CFDP1 for aortic root diameter; and rs12440869 in IQCH for Doppler transmitral A-wave peak velocity. Findings were in part validated in other cohorts and in GWAS of related disease traits. The genetic loci showed associations with putative signaling pathways, and with gene expression in whole blood, monocytes, and myocardial tissue.

CONCLUSION: The additional genetic loci identified in this large meta-analysis of cardiac structure and function provide insights into the underlying genetic architecture of cardiac structure and warrant follow-up in future functional studies.

FUNDING: For detailed information per study, see Acknowledgments.

%B J Clin Invest %V 127 %P 1798-1812 %8 2017 May 01 %G eng %N 5 %R 10.1172/JCI84840 %0 Journal Article %J Eur J Heart Fail %D 2017 %T Predictors and outcomes of heart failure with mid-range ejection fraction. %A Bhambhani, Vijeta %A Kizer, Jorge R %A Lima, João A C %A van der Harst, Pim %A Bahrami, Hossein %A Nayor, Matthew %A de Filippi, Christopher R %A Enserro, Danielle %A Blaha, Michael J %A Cushman, Mary %A Wang, Thomas J %A Gansevoort, Ron T %A Fox, Caroline S %A Gaggin, Hanna K %A Kop, Willem J %A Liu, Kiang %A Vasan, Ramachandran S %A Psaty, Bruce M %A Lee, Douglas S %A Brouwers, Frank P %A Hillege, Hans L %A Bartz, Traci M %A Benjamin, Emelia J %A Chan, Cheeling %A Allison, Matthew %A Gardin, Julius M %A Januzzi, James L %A Levy, Daniel %A Herrington, David M %A van Gilst, Wiek H %A Bertoni, Alain G %A Larson, Martin G %A de Boer, Rudolf A %A Gottdiener, John S %A Shah, Sanjiv J %A Ho, Jennifer E %X

AIMS: While heart failure with preserved (HFpEF) and reduced ejection fraction (HFrEF) are well described, determinants and outcomes of heart failure with mid-range ejection fraction (HFmrEF) remain unclear. We sought to examine clinical and biochemical predictors of incident HFmrEF in the community.

METHODS AND RESULTS: We pooled data from four community-based longitudinal cohorts, with ascertainment of new heart failure (HF) classified into HFmrEF [ejection fraction (EF) 41-49%], HFpEF (EF ≥50%), and HFrEF (EF ≤40%). Predictors of incident HF subtypes were assessed using multivariable Cox models. Among 28 820 participants free of HF followed for a median of 12 years, there were 200 new HFmrEF cases, compared with 811 HFpEF and 1048 HFrEF. Clinical predictors of HFmrEF included age, male sex, systolic blood pressure, diabetes mellitus, and prior myocardial infarction (multivariable adjusted P ≤ 0.003 for all). Biomarkers that predicted HFmrEF included natriuretic peptides, cystatin-C, and high-sensitivity troponin (P ≤ 0.0004 for all). Natriuretic peptides were stronger predictors of HFrEF [hazard ratio (HR) 2.00 per 1 standard deviation increase, 95% confidence interval (CI) 1.81-2.20] than of HFmrEF (HR 1.51, 95% CI 1.20-1.90, P = 0.01 for difference), and did not differ in their association with incident HFmrEF and HFpEF (HR 1.56, 95% CI 1.41-1.73, P = 0.68 for difference). All-cause mortality following the onset of HFmrEF was worse than that of HFpEF (50 vs. 39 events per 1000 person-years, P = 0.02), but comparable to that of HFrEF (46 events per 1000 person-years, P = 0.78).

CONCLUSIONS: We found overlap in predictors of incident HFmrEF with other HF subtypes. In contrast, mortality risk after HFmrEF was worse than HFpEF, and similar to HFrEF.

%B Eur J Heart Fail %8 2017 Dec 11 %G eng %R 10.1002/ejhf.1091 %0 Journal Article %J PLoS Genet %D 2017 %T Single-trait and multi-trait genome-wide association analyses identify novel loci for blood pressure in African-ancestry populations. %A Liang, Jingjing %A Le, Thu H %A Edwards, Digna R Velez %A Tayo, Bamidele O %A Gaulton, Kyle J %A Smith, Jennifer A %A Lu, Yingchang %A Jensen, Richard A %A Chen, Guanjie %A Yanek, Lisa R %A Schwander, Karen %A Tajuddin, Salman M %A Sofer, Tamar %A Kim, Wonji %A Kayima, James %A McKenzie, Colin A %A Fox, Ervin %A Nalls, Michael A %A Young, J Hunter %A Sun, Yan V %A Lane, Jacqueline M %A Cechova, Sylvia %A Zhou, Jie %A Tang, Hua %A Fornage, Myriam %A Musani, Solomon K %A Wang, Heming %A Lee, Juyoung %A Adeyemo, Adebowale %A Dreisbach, Albert W %A Forrester, Terrence %A Chu, Pei-Lun %A Cappola, Anne %A Evans, Michele K %A Morrison, Alanna C %A Martin, Lisa W %A Wiggins, Kerri L %A Hui, Qin %A Zhao, Wei %A Jackson, Rebecca D %A Ware, Erin B %A Faul, Jessica D %A Reiner, Alex P %A Bray, Michael %A Denny, Joshua C %A Mosley, Thomas H %A Palmas, Walter %A Guo, Xiuqing %A Papanicolaou, George J %A Penman, Alan D %A Polak, Joseph F %A Rice, Kenneth %A Taylor, Ken D %A Boerwinkle, Eric %A Bottinger, Erwin P %A Liu, Kiang %A Risch, Neil %A Hunt, Steven C %A Kooperberg, Charles %A Zonderman, Alan B %A Laurie, Cathy C %A Becker, Diane M %A Cai, Jianwen %A Loos, Ruth J F %A Psaty, Bruce M %A Weir, David R %A Kardia, Sharon L R %A Arnett, Donna K %A Won, Sungho %A Edwards, Todd L %A Redline, Susan %A Cooper, Richard S %A Rao, D C %A Rotter, Jerome I %A Rotimi, Charles %A Levy, Daniel %A Chakravarti, Aravinda %A Zhu, Xiaofeng %A Franceschini, Nora %K African Americans %K Animals %K Basic Helix-Loop-Helix Transcription Factors %K Blood Pressure %K Cadherins %K Case-Control Studies %K Female %K Genetic Loci %K Genome-Wide Association Study %K Humans %K Hypertension %K Male %K Membrane Proteins %K Mice %K Multifactorial Inheritance %K Polymorphism, Single Nucleotide %X

Hypertension is a leading cause of global disease, mortality, and disability. While individuals of African descent suffer a disproportionate burden of hypertension and its complications, they have been underrepresented in genetic studies. To identify novel susceptibility loci for blood pressure and hypertension in people of African ancestry, we performed both single and multiple-trait genome-wide association analyses. We analyzed 21 genome-wide association studies comprised of 31,968 individuals of African ancestry, and validated our results with additional 54,395 individuals from multi-ethnic studies. These analyses identified nine loci with eleven independent variants which reached genome-wide significance (P < 1.25×10-8) for either systolic and diastolic blood pressure, hypertension, or for combined traits. Single-trait analyses identified two loci (TARID/TCF21 and LLPH/TMBIM4) and multiple-trait analyses identified one novel locus (FRMD3) for blood pressure. At these three loci, as well as at GRP20/CDH17, associated variants had alleles common only in African-ancestry populations. Functional annotation showed enrichment for genes expressed in immune and kidney cells, as well as in heart and vascular cells/tissues. Experiments driven by these findings and using angiotensin-II induced hypertension in mice showed altered kidney mRNA expression of six genes, suggesting their potential role in hypertension. Our study provides new evidence for genes related to hypertension susceptibility, and the need to study African-ancestry populations in order to identify biologic factors contributing to hypertension.

%B PLoS Genet %V 13 %P e1006728 %8 2017 May %G eng %N 5 %R 10.1371/journal.pgen.1006728 %0 Journal Article %J JAMA Cardiol %D 2018 %T Association of Cardiovascular Biomarkers With Incident Heart Failure With Preserved and Reduced Ejection Fraction. %A de Boer, Rudolf A %A Nayor, Matthew %A deFilippi, Christopher R %A Enserro, Danielle %A Bhambhani, Vijeta %A Kizer, Jorge R %A Blaha, Michael J %A Brouwers, Frank P %A Cushman, Mary %A Lima, João A C %A Bahrami, Hossein %A van der Harst, Pim %A Wang, Thomas J %A Gansevoort, Ron T %A Fox, Caroline S %A Gaggin, Hanna K %A Kop, Willem J %A Liu, Kiang %A Vasan, Ramachandran S %A Psaty, Bruce M %A Lee, Douglas S %A Hillege, Hans L %A Bartz, Traci M %A Benjamin, Emelia J %A Chan, Cheeling %A Allison, Matthew %A Gardin, Julius M %A Januzzi, James L %A Shah, Sanjiv J %A Levy, Daniel %A Herrington, David M %A Larson, Martin G %A van Gilst, Wiek H %A Gottdiener, John S %A Bertoni, Alain G %A Ho, Jennifer E %X

Importance: Nearly half of all patients with heart failure have preserved ejection fraction (HFpEF) as opposed to reduced ejection fraction (HFrEF), yet associations of biomarkers with future heart failure subtype are incompletely understood.

Objective: To evaluate the associations of 12 cardiovascular biomarkers with incident HFpEF vs HFrEF among adults from the general population.

Design, Setting, and Participants: This study included 4 longitudinal community-based cohorts: the Cardiovascular Health Study (1989-1990; 1992-1993 for supplemental African-American cohort), the Framingham Heart Study (1995-1998), the Multi-Ethnic Study of Atherosclerosis (2000-2002), and the Prevention of Renal and Vascular End-stage Disease study (1997-1998). Each cohort had prospective ascertainment of incident HFpEF and HFrEF. Data analysis was performed from June 25, 2015, to November 9, 2017.

Exposures: The following biomarkers were examined: N-terminal pro B-type natriuretic peptide or brain natriuretic peptide, high-sensitivity troponin T or I, C-reactive protein (CRP), urinary albumin to creatinine ratio (UACR), renin to aldosterone ratio, D-dimer, fibrinogen, soluble suppressor of tumorigenicity, galectin-3, cystatin C, plasminogen activator inhibitor 1, and interleukin 6.

Main Outcomes and Measures: Development of incident HFpEF and incident HFrEF.

Results: Among the 22 756 participants in these 4 cohorts (12 087 women and 10 669 men; mean [SD] age, 60 [13] years) in the study, during a median follow-up of 12 years, 633 participants developed incident HFpEF, and 841 developed HFrEF. In models adjusted for clinical risk factors of heart failure, 2 biomarkers were significantly associated with incident HFpEF: UACR (hazard ratio [HR], 1.33; 95% CI, 1.20-1.48; P < .001) and natriuretic peptides (HR, 1.27; 95% CI, 1.16-1.40; P < .001), with suggestive associations for high-sensitivity troponin (HR, 1.11; 95% CI, 1.03-1.19; P = .008), plasminogen activator inhibitor 1 (HR, 1.22; 95% CI, 1.03-1.45; P = .02), and fibrinogen (HR, 1.12; 95% CI, 1.03-1.22; P = .01). By contrast, 6 biomarkers were associated with incident HFrEF: natriuretic peptides (HR, 1.54; 95% CI, 1.41-1.68; P < .001), UACR (HR, 1.21; 95% CI, 1.11-1.32; P < .001), high-sensitivity troponin (HR, 1.37; 95% CI, 1.29-1.46; P < .001), cystatin C (HR, 1.19; 95% CI, 1.11-1.27; P < .001), D-dimer (HR, 1.22; 95% CI, 1.11-1.35; P < .001), and CRP (HR, 1.19; 95% CI, 1.11-1.28; P < .001). When directly compared, natriuretic peptides, high-sensitivity troponin, and CRP were more strongly associated with HFrEF compared with HFpEF.

Conclusions and Relevance: Biomarkers of renal dysfunction, endothelial dysfunction, and inflammation were associated with incident HFrEF. By contrast, only natriuretic peptides and UACR were associated with HFpEF. These findings highlight the need for future studies focused on identifying novel biomarkers of the risk of HFpEF.

%B JAMA Cardiol %8 2018 Jan 10 %G eng %R 10.1001/jamacardio.2017.4987 %0 Journal Article %J Am J Hum Genet %D 2018 %T A Large-Scale Multi-ancestry Genome-wide Study Accounting for Smoking Behavior Identifies Multiple Significant Loci for Blood Pressure. %A Sung, Yun J %A Winkler, Thomas W %A de Las Fuentes, Lisa %A Bentley, Amy R %A Brown, Michael R %A Kraja, Aldi T %A Schwander, Karen %A Ntalla, Ioanna %A Guo, Xiuqing %A Franceschini, Nora %A Lu, Yingchang %A Cheng, Ching-Yu %A Sim, Xueling %A Vojinovic, Dina %A Marten, Jonathan %A Musani, Solomon K %A Li, Changwei %A Feitosa, Mary F %A Kilpeläinen, Tuomas O %A Richard, Melissa A %A Noordam, Raymond %A Aslibekyan, Stella %A Aschard, Hugues %A Bartz, Traci M %A Dorajoo, Rajkumar %A Liu, Yongmei %A Manning, Alisa K %A Rankinen, Tuomo %A Smith, Albert Vernon %A Tajuddin, Salman M %A Tayo, Bamidele O %A Warren, Helen R %A Zhao, Wei %A Zhou, Yanhua %A Matoba, Nana %A Sofer, Tamar %A Alver, Maris %A Amini, Marzyeh %A Boissel, Mathilde %A Chai, Jin Fang %A Chen, Xu %A Divers, Jasmin %A Gandin, Ilaria %A Gao, Chuan %A Giulianini, Franco %A Goel, Anuj %A Harris, Sarah E %A Hartwig, Fernando Pires %A Horimoto, Andrea R V R %A Hsu, Fang-Chi %A Jackson, Anne U %A Kähönen, Mika %A Kasturiratne, Anuradhani %A Kuhnel, Brigitte %A Leander, Karin %A Lee, Wen-Jane %A Lin, Keng-Hung %A 'an Luan, Jian %A McKenzie, Colin A %A Meian, He %A Nelson, Christopher P %A Rauramaa, Rainer %A Schupf, Nicole %A Scott, Robert A %A Sheu, Wayne H H %A Stančáková, Alena %A Takeuchi, Fumihiko %A van der Most, Peter J %A Varga, Tibor V %A Wang, Heming %A Wang, Yajuan %A Ware, Erin B %A Weiss, Stefan %A Wen, Wanqing %A Yanek, Lisa R %A Zhang, Weihua %A Zhao, Jing Hua %A Afaq, Saima %A Alfred, Tamuno %A Amin, Najaf %A Arking, Dan %A Aung, Tin %A Barr, R Graham %A Bielak, Lawrence F %A Boerwinkle, Eric %A Bottinger, Erwin P %A Braund, Peter S %A Brody, Jennifer A %A Broeckel, Ulrich %A Cabrera, Claudia P %A Cade, Brian %A Caizheng, Yu %A Campbell, Archie %A Canouil, Mickaël %A Chakravarti, Aravinda %A Chauhan, Ganesh %A Christensen, Kaare %A Cocca, Massimiliano %A Collins, Francis S %A Connell, John M %A de Mutsert, Renée %A de Silva, H Janaka %A Debette, Stephanie %A Dörr, Marcus %A Duan, Qing %A Eaton, Charles B %A Ehret, Georg %A Evangelou, Evangelos %A Faul, Jessica D %A Fisher, Virginia A %A Forouhi, Nita G %A Franco, Oscar H %A Friedlander, Yechiel %A Gao, He %A Gigante, Bruna %A Graff, Misa %A Gu, C Charles %A Gu, Dongfeng %A Gupta, Preeti %A Hagenaars, Saskia P %A Harris, Tamara B %A He, Jiang %A Heikkinen, Sami %A Heng, Chew-Kiat %A Hirata, Makoto %A Hofman, Albert %A Howard, Barbara V %A Hunt, Steven %A Irvin, Marguerite R %A Jia, Yucheng %A Joehanes, Roby %A Justice, Anne E %A Katsuya, Tomohiro %A Kaufman, Joel %A Kerrison, Nicola D %A Khor, Chiea Chuen %A Koh, Woon-Puay %A Koistinen, Heikki A %A Komulainen, Pirjo %A Kooperberg, Charles %A Krieger, Jose E %A Kubo, Michiaki %A Kuusisto, Johanna %A Langefeld, Carl D %A Langenberg, Claudia %A Launer, Lenore J %A Lehne, Benjamin %A Lewis, Cora E %A Li, Yize %A Lim, Sing Hui %A Lin, Shiow %A Liu, Ching-Ti %A Liu, Jianjun %A Liu, Jingmin %A Liu, Kiang %A Liu, Yeheng %A Loh, Marie %A Lohman, Kurt K %A Long, Jirong %A Louie, Tin %A Mägi, Reedik %A Mahajan, Anubha %A Meitinger, Thomas %A Metspalu, Andres %A Milani, Lili %A Momozawa, Yukihide %A Morris, Andrew P %A Mosley, Thomas H %A Munson, Peter %A Murray, Alison D %A Nalls, Mike A %A Nasri, Ubaydah %A Norris, Jill M %A North, Kari %A Ogunniyi, Adesola %A Padmanabhan, Sandosh %A Palmas, Walter R %A Palmer, Nicholette D %A Pankow, James S %A Pedersen, Nancy L %A Peters, Annette %A Peyser, Patricia A %A Polasek, Ozren %A Raitakari, Olli T %A Renstrom, Frida %A Rice, Treva K %A Ridker, Paul M %A Robino, Antonietta %A Robinson, Jennifer G %A Rose, Lynda M %A Rudan, Igor %A Sabanayagam, Charumathi %A Salako, Babatunde L %A Sandow, Kevin %A Schmidt, Carsten O %A Schreiner, Pamela J %A Scott, William R %A Seshadri, Sudha %A Sever, Peter %A Sitlani, Colleen M %A Smith, Jennifer A %A Snieder, Harold %A Starr, John M %A Strauch, Konstantin %A Tang, Hua %A Taylor, Kent D %A Teo, Yik Ying %A Tham, Yih Chung %A Uitterlinden, André G %A Waldenberger, Melanie %A Wang, Lihua %A Wang, Ya X %A Wei, Wen Bin %A Williams, Christine %A Wilson, Gregory %A Wojczynski, Mary K %A Yao, Jie %A Yuan, Jian-Min %A Zonderman, Alan B %A Becker, Diane M %A Boehnke, Michael %A Bowden, Donald W %A Chambers, John C %A Chen, Yii-Der Ida %A de Faire, Ulf %A Deary, Ian J %A Esko, Tõnu %A Farrall, Martin %A Forrester, Terrence %A Franks, Paul W %A Freedman, Barry I %A Froguel, Philippe %A Gasparini, Paolo %A Gieger, Christian %A Horta, Bernardo Lessa %A Hung, Yi-Jen %A Jonas, Jost B %A Kato, Norihiro %A Kooner, Jaspal S %A Laakso, Markku %A Lehtimäki, Terho %A Liang, Kae-Woei %A Magnusson, Patrik K E %A Newman, Anne B %A Oldehinkel, Albertine J %A Pereira, Alexandre C %A Redline, Susan %A Rettig, Rainer %A Samani, Nilesh J %A Scott, James %A Shu, Xiao-Ou %A van der Harst, Pim %A Wagenknecht, Lynne E %A Wareham, Nicholas J %A Watkins, Hugh %A Weir, David R %A Wickremasinghe, Ananda R %A Wu, Tangchun %A Zheng, Wei %A Kamatani, Yoichiro %A Laurie, Cathy C %A Bouchard, Claude %A Cooper, Richard S %A Evans, Michele K %A Gudnason, Vilmundur %A Kardia, Sharon L R %A Kritchevsky, Stephen B %A Levy, Daniel %A O'Connell, Jeff R %A Psaty, Bruce M %A van Dam, Rob M %A Sims, Mario %A Arnett, Donna K %A Mook-Kanamori, Dennis O %A Kelly, Tanika N %A Fox, Ervin R %A Hayward, Caroline %A Fornage, Myriam %A Rotimi, Charles N %A Province, Michael A %A van Duijn, Cornelia M %A Tai, E Shyong %A Wong, Tien Yin %A Loos, Ruth J F %A Reiner, Alex P %A Rotter, Jerome I %A Zhu, Xiaofeng %A Bierut, Laura J %A Gauderman, W James %A Caulfield, Mark J %A Elliott, Paul %A Rice, Kenneth %A Munroe, Patricia B %A Morrison, Alanna C %A Cupples, L Adrienne %A Rao, Dabeeru C %A Chasman, Daniel I %X

Genome-wide association analysis advanced understanding of blood pressure (BP), a major risk factor for vascular conditions such as coronary heart disease and stroke. Accounting for smoking behavior may help identify BP loci and extend our knowledge of its genetic architecture. We performed genome-wide association meta-analyses of systolic and diastolic BP incorporating gene-smoking interactions in 610,091 individuals. Stage 1 analysis examined ∼18.8 million SNPs and small insertion/deletion variants in 129,913 individuals from four ancestries (European, African, Asian, and Hispanic) with follow-up analysis of promising variants in 480,178 additional individuals from five ancestries. We identified 15 loci that were genome-wide significant (p < 5 × 10) in stage 1 and formally replicated in stage 2. A combined stage 1 and 2 meta-analysis identified 66 additional genome-wide significant loci (13, 35, and 18 loci in European, African, and trans-ancestry, respectively). A total of 56 known BP loci were also identified by our results (p < 5 × 10). Of the newly identified loci, ten showed significant interaction with smoking status, but none of them were replicated in stage 2. Several loci were identified in African ancestry, highlighting the importance of genetic studies in diverse populations. The identified loci show strong evidence for regulatory features and support shared pathophysiology with cardiometabolic and addiction traits. They also highlight a role in BP regulation for biological candidates such as modulators of vascular structure and function (CDKN1B, BCAR1-CFDP1, PXDN, EEA1), ciliopathies (SDCCAG8, RPGRIP1L), telomere maintenance (TNKS, PINX1, AKTIP), and central dopaminergic signaling (MSRA, EBF2).

%B Am J Hum Genet %V 102 %P 375-400 %8 2018 Mar 01 %G eng %N 3 %R 10.1016/j.ajhg.2018.01.015 %0 Journal Article %J Am J Epidemiol %D 2019 %T Multi-Ancestry Genome-Wide Association Study of Lipid Levels Incorporating Gene-Alcohol Interactions. %A de Vries, Paul S %A Brown, Michael R %A Bentley, Amy R %A Sung, Yun J %A Winkler, Thomas W %A Ntalla, Ioanna %A Schwander, Karen %A Kraja, Aldi T %A Guo, Xiuqing %A Franceschini, Nora %A Cheng, Ching-Yu %A Sim, Xueling %A Vojinovic, Dina %A Huffman, Jennifer E %A Musani, Solomon K %A Li, Changwei %A Feitosa, Mary F %A Richard, Melissa A %A Noordam, Raymond %A Aschard, Hugues %A Bartz, Traci M %A Bielak, Lawrence F %A Deng, Xuan %A Dorajoo, Rajkumar %A Lohman, Kurt K %A Manning, Alisa K %A Rankinen, Tuomo %A Smith, Albert V %A Tajuddin, Salman M %A Evangelou, Evangelos %A Graff, Mariaelisa %A Alver, Maris %A Boissel, Mathilde %A Chai, Jin Fang %A Chen, Xu %A Divers, Jasmin %A Gandin, Ilaria %A Gao, Chuan %A Goel, Anuj %A Hagemeijer, Yanick %A Harris, Sarah E %A Hartwig, Fernando P %A He, Meian %A Horimoto, Andrea R V R %A Hsu, Fang-Chi %A Jackson, Anne U %A Kasturiratne, Anuradhani %A Komulainen, Pirjo %A Kuhnel, Brigitte %A Laguzzi, Federica %A Lee, Joseph H %A Luan, Jian'an %A Lyytikäinen, Leo-Pekka %A Matoba, Nana %A Nolte, Ilja M %A Pietzner, Maik %A Riaz, Muhammad %A Said, M Abdullah %A Scott, Robert A %A Sofer, Tamar %A Stančáková, Alena %A Takeuchi, Fumihiko %A Tayo, Bamidele O %A van der Most, Peter J %A Varga, Tibor V %A Wang, Yajuan %A Ware, Erin B %A Wen, Wanqing %A Yanek, Lisa R %A Zhang, Weihua %A Zhao, Jing Hua %A Afaq, Saima %A Amin, Najaf %A Amini, Marzyeh %A Arking, Dan E %A Aung, Tin %A Ballantyne, Christie %A Boerwinkle, Eric %A Broeckel, Ulrich %A Campbell, Archie %A Canouil, Mickaël %A Charumathi, Sabanayagam %A Chen, Yii-Der Ida %A Connell, John M %A de Faire, Ulf %A de Las Fuentes, Lisa %A de Mutsert, Renée %A de Silva, H Janaka %A Ding, Jingzhong %A Dominiczak, Anna F %A Duan, Qing %A Eaton, Charles B %A Eppinga, Ruben N %A Faul, Jessica D %A Fisher, Virginia %A Forrester, Terrence %A Franco, Oscar H %A Friedlander, Yechiel %A Ghanbari, Mohsen %A Giulianini, Franco %A Grabe, Hans J %A Grove, Megan L %A Gu, C Charles %A Harris, Tamara B %A Heikkinen, Sami %A Heng, Chew-Kiat %A Hirata, Makoto %A Hixson, James E %A Howard, Barbara V %A Ikram, M Arfan %A Jacobs, David R %A Johnson, Craig %A Jonas, Jost Bruno %A Kammerer, Candace M %A Katsuya, Tomohiro %A Khor, Chiea Chuen %A Kilpeläinen, Tuomas O %A Koh, Woon-Puay %A Koistinen, Heikki A %A Kolcic, Ivana %A Kooperberg, Charles %A Krieger, Jose E %A Kritchevsky, Steve B %A Kubo, Michiaki %A Kuusisto, Johanna %A Lakka, Timo A %A Langefeld, Carl D %A Langenberg, Claudia %A Launer, Lenore J %A Lehne, Benjamin %A Lemaitre, Rozenn N %A Li, Yize %A Liang, Jingjing %A Liu, Jianjun %A Liu, Kiang %A Loh, Marie %A Louie, Tin %A Mägi, Reedik %A Manichaikul, Ani W %A McKenzie, Colin A %A Meitinger, Thomas %A Metspalu, Andres %A Milaneschi, Yuri %A Milani, Lili %A Mohlke, Karen L %A Mosley, Thomas H %A Mukamal, Kenneth J %A Nalls, Mike A %A Nauck, Matthias %A Nelson, Christopher P %A Sotoodehnia, Nona %A O'Connell, Jeff R %A Palmer, Nicholette D %A Pazoki, Raha %A Pedersen, Nancy L %A Peters, Annette %A Peyser, Patricia A %A Polasek, Ozren %A Poulter, Neil %A Raffel, Leslie J %A Raitakari, Olli T %A Reiner, Alex P %A Rice, Treva K %A Rich, Stephen S %A Robino, Antonietta %A Robinson, Jennifer G %A Rose, Lynda M %A Rudan, Igor %A Schmidt, Carsten O %A Schreiner, Pamela J %A Scott, William R %A Sever, Peter %A Shi, Yuan %A Sidney, Stephen %A Sims, Mario %A Smith, Blair H %A Smith, Jennifer A %A Snieder, Harold %A Starr, John M %A Strauch, Konstantin %A Tan, Nicholas %A Taylor, Kent D %A Teo, Yik Ying %A Tham, Yih Chung %A Uitterlinden, André G %A van Heemst, Diana %A Vuckovic, Dragana %A Waldenberger, Melanie %A Wang, Lihua %A Wang, Yujie %A Wang, Zhe %A Wei, Wen Bin %A Williams, Christine %A Wilson, Gregory %A Wojczynski, Mary K %A Yao, Jie %A Yu, Bing %A Yu, Caizheng %A Yuan, Jian-Min %A Zhao, Wei %A Zonderman, Alan B %A Becker, Diane M %A Boehnke, Michael %A Bowden, Donald W %A Chambers, John C %A Deary, Ian J %A Esko, Tõnu %A Farrall, Martin %A Franks, Paul W %A Freedman, Barry I %A Froguel, Philippe %A Gasparini, Paolo %A Gieger, Christian %A Horta, Bernardo L %A Kamatani, Yoichiro %A Kato, Norihiro %A Kooner, Jaspal S %A Laakso, Markku %A Leander, Karin %A Lehtimäki, Terho %A Magnusson, Patrik K E %A Penninx, Brenda %A Pereira, Alexandre C %A Rauramaa, Rainer %A Samani, Nilesh J %A Scott, James %A Shu, Xiao-Ou %A van der Harst, Pim %A Wagenknecht, Lynne E %A Wang, Ya Xing %A Wareham, Nicholas J %A Watkins, Hugh %A Weir, David R %A Wickremasinghe, Ananda R %A Zheng, Wei %A Elliott, Paul %A North, Kari E %A Bouchard, Claude %A Evans, Michele K %A Gudnason, Vilmundur %A Liu, Ching-Ti %A Liu, Yongmei %A Psaty, Bruce M %A Ridker, Paul M %A van Dam, Rob M %A Kardia, Sharon L R %A Zhu, Xiaofeng %A Rotimi, Charles N %A Mook-Kanamori, Dennis O %A Fornage, Myriam %A Kelly, Tanika N %A Fox, Ervin R %A Hayward, Caroline %A van Duijn, Cornelia M %A Tai, E Shyong %A Wong, Tien Yin %A Liu, Jingmin %A Rotter, Jerome I %A Gauderman, W James %A Province, Michael A %A Munroe, Patricia B %A Rice, Kenneth %A Chasman, Daniel I %A Cupples, L Adrienne %A Rao, Dabeeru C %A Morrison, Alanna C %X

An individual's lipid profile is influenced by genetic variants and alcohol consumption, but the contribution of interactions between these exposures has not been studied. We therefore incorporated gene-alcohol interactions into a multi-ancestry genome-wide association study of levels of high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, and triglycerides. We included 45 studies in Stage 1 (genome-wide discovery) and 66 studies in Stage 2 (focused follow-up), for a total of 394,584 individuals from five ancestry groups. Genetic main and interaction effects were jointly assessed by a 2 degrees of freedom (DF) test, and a 1 DF test was used to assess the interaction effects alone. Variants at 495 loci were at least suggestively associated (P < 1 × 10-6) with lipid levels in Stage 1 and were evaluated in Stage 2, followed by combined analyses of Stage 1 and Stage 2. In the combined analysis of Stage 1 and Stage 2, 147 independent loci were associated with lipid levels at P < 5 × 10-8 using 2 DF tests, of which 18 were novel. No genome-wide significant associations were found testing the interaction effect alone. The novel loci included several genes (PCSK5, VEGFB, and A1CF) with a putative role in lipid metabolism based on existing evidence from cellular and experimental models.

%B Am J Epidemiol %8 2019 Jan 29 %G eng %R 10.1093/aje/kwz005 %0 Journal Article %J Nat Genet %D 2019 %T Multi-ancestry genome-wide gene-smoking interaction study of 387,272 individuals identifies new loci associated with serum lipids. %A Bentley, Amy R %A Sung, Yun J %A Brown, Michael R %A Winkler, Thomas W %A Kraja, Aldi T %A Ntalla, Ioanna %A Schwander, Karen %A Chasman, Daniel I %A Lim, Elise %A Deng, Xuan %A Guo, Xiuqing %A Liu, Jingmin %A Lu, Yingchang %A Cheng, Ching-Yu %A Sim, Xueling %A Vojinovic, Dina %A Huffman, Jennifer E %A Musani, Solomon K %A Li, Changwei %A Feitosa, Mary F %A Richard, Melissa A %A Noordam, Raymond %A Baker, Jenna %A Chen, Guanjie %A Aschard, Hugues %A Bartz, Traci M %A Ding, Jingzhong %A Dorajoo, Rajkumar %A Manning, Alisa K %A Rankinen, Tuomo %A Smith, Albert V %A Tajuddin, Salman M %A Zhao, Wei %A Graff, Mariaelisa %A Alver, Maris %A Boissel, Mathilde %A Chai, Jin Fang %A Chen, Xu %A Divers, Jasmin %A Evangelou, Evangelos %A Gao, Chuan %A Goel, Anuj %A Hagemeijer, Yanick %A Harris, Sarah E %A Hartwig, Fernando P %A He, Meian %A Horimoto, Andrea R V R %A Hsu, Fang-Chi %A Hung, Yi-Jen %A Jackson, Anne U %A Kasturiratne, Anuradhani %A Komulainen, Pirjo %A Kuhnel, Brigitte %A Leander, Karin %A Lin, Keng-Hung %A Luan, Jian'an %A Lyytikäinen, Leo-Pekka %A Matoba, Nana %A Nolte, Ilja M %A Pietzner, Maik %A Prins, Bram %A Riaz, Muhammad %A Robino, Antonietta %A Said, M Abdullah %A Schupf, Nicole %A Scott, Robert A %A Sofer, Tamar %A Stančáková, Alena %A Takeuchi, Fumihiko %A Tayo, Bamidele O %A van der Most, Peter J %A Varga, Tibor V %A Wang, Tzung-Dau %A Wang, Yajuan %A Ware, Erin B %A Wen, Wanqing %A Xiang, Yong-Bing %A Yanek, Lisa R %A Zhang, Weihua %A Zhao, Jing Hua %A Adeyemo, Adebowale %A Afaq, Saima %A Amin, Najaf %A Amini, Marzyeh %A Arking, Dan E %A Arzumanyan, Zorayr %A Aung, Tin %A Ballantyne, Christie %A Barr, R Graham %A Bielak, Lawrence F %A Boerwinkle, Eric %A Bottinger, Erwin P %A Broeckel, Ulrich %A Brown, Morris %A Cade, Brian E %A Campbell, Archie %A Canouil, Mickaël %A Charumathi, Sabanayagam %A Chen, Yii-Der Ida %A Christensen, Kaare %A Concas, Maria Pina %A Connell, John M %A de Las Fuentes, Lisa %A de Silva, H Janaka %A de Vries, Paul S %A Doumatey, Ayo %A Duan, Qing %A Eaton, Charles B %A Eppinga, Ruben N %A Faul, Jessica D %A Floyd, James S %A Forouhi, Nita G %A Forrester, Terrence %A Friedlander, Yechiel %A Gandin, Ilaria %A Gao, He %A Ghanbari, Mohsen %A Gharib, Sina A %A Gigante, Bruna %A Giulianini, Franco %A Grabe, Hans J %A Gu, C Charles %A Harris, Tamara B %A Heikkinen, Sami %A Heng, Chew-Kiat %A Hirata, Makoto %A Hixson, James E %A Ikram, M Arfan %A Jia, Yucheng %A Joehanes, Roby %A Johnson, Craig %A Jonas, Jost Bruno %A Justice, Anne E %A Katsuya, Tomohiro %A Khor, Chiea Chuen %A Kilpeläinen, Tuomas O %A Koh, Woon-Puay %A Kolcic, Ivana %A Kooperberg, Charles %A Krieger, Jose E %A Kritchevsky, Stephen B %A Kubo, Michiaki %A Kuusisto, Johanna %A Lakka, Timo A %A Langefeld, Carl D %A Langenberg, Claudia %A Launer, Lenore J %A Lehne, Benjamin %A Lewis, Cora E %A Li, Yize %A Liang, Jingjing %A Lin, Shiow %A Liu, Ching-Ti %A Liu, Jianjun %A Liu, Kiang %A Loh, Marie %A Lohman, Kurt K %A Louie, Tin %A Luzzi, Anna %A Mägi, Reedik %A Mahajan, Anubha %A Manichaikul, Ani W %A McKenzie, Colin A %A Meitinger, Thomas %A Metspalu, Andres %A Milaneschi, Yuri %A Milani, Lili %A Mohlke, Karen L %A Momozawa, Yukihide %A Morris, Andrew P %A Murray, Alison D %A Nalls, Mike A %A Nauck, Matthias %A Nelson, Christopher P %A North, Kari E %A O'Connell, Jeffrey R %A Palmer, Nicholette D %A Papanicolau, George J %A Pedersen, Nancy L %A Peters, Annette %A Peyser, Patricia A %A Polasek, Ozren %A Poulter, Neil %A Raitakari, Olli T %A Reiner, Alex P %A Renstrom, Frida %A Rice, Treva K %A Rich, Stephen S %A Robinson, Jennifer G %A Rose, Lynda M %A Rosendaal, Frits R %A Rudan, Igor %A Schmidt, Carsten O %A Schreiner, Pamela J %A Scott, William R %A Sever, Peter %A Shi, Yuan %A Sidney, Stephen %A Sims, Mario %A Smith, Jennifer A %A Snieder, Harold %A Starr, John M %A Strauch, Konstantin %A Stringham, Heather M %A Tan, Nicholas Y Q %A Tang, Hua %A Taylor, Kent D %A Teo, Yik Ying %A Tham, Yih Chung %A Tiemeier, Henning %A Turner, Stephen T %A Uitterlinden, André G %A van Heemst, Diana %A Waldenberger, Melanie %A Wang, Heming %A Wang, Lan %A Wang, Lihua %A Wei, Wen Bin %A Williams, Christine A %A Wilson, Gregory %A Wojczynski, Mary K %A Yao, Jie %A Young, Kristin %A Yu, Caizheng %A Yuan, Jian-Min %A Zhou, Jie %A Zonderman, Alan B %A Becker, Diane M %A Boehnke, Michael %A Bowden, Donald W %A Chambers, John C %A Cooper, Richard S %A de Faire, Ulf %A Deary, Ian J %A Elliott, Paul %A Esko, Tõnu %A Farrall, Martin %A Franks, Paul W %A Freedman, Barry I %A Froguel, Philippe %A Gasparini, Paolo %A Gieger, Christian %A Horta, Bernardo L %A Juang, Jyh-Ming Jimmy %A Kamatani, Yoichiro %A Kammerer, Candace M %A Kato, Norihiro %A Kooner, Jaspal S %A Laakso, Markku %A Laurie, Cathy C %A Lee, I-Te %A Lehtimäki, Terho %A Magnusson, Patrik K E %A Oldehinkel, Albertine J %A Penninx, Brenda W J H %A Pereira, Alexandre C %A Rauramaa, Rainer %A Redline, Susan %A Samani, Nilesh J %A Scott, James %A Shu, Xiao-Ou %A van der Harst, Pim %A Wagenknecht, Lynne E %A Wang, Jun-Sing %A Wang, Ya Xing %A Wareham, Nicholas J %A Watkins, Hugh %A Weir, David R %A Wickremasinghe, Ananda R %A Wu, Tangchun %A Zeggini, Eleftheria %A Zheng, Wei %A Bouchard, Claude %A Evans, Michele K %A Gudnason, Vilmundur %A Kardia, Sharon L R %A Liu, Yongmei %A Psaty, Bruce M %A Ridker, Paul M %A van Dam, Rob M %A Mook-Kanamori, Dennis O %A Fornage, Myriam %A Province, Michael A %A Kelly, Tanika N %A Fox, Ervin R %A Hayward, Caroline %A van Duijn, Cornelia M %A Tai, E Shyong %A Wong, Tien Yin %A Loos, Ruth J F %A Franceschini, Nora %A Rotter, Jerome I %A Zhu, Xiaofeng %A Bierut, Laura J %A Gauderman, W James %A Rice, Kenneth %A Munroe, Patricia B %A Morrison, Alanna C %A Rao, Dabeeru C %A Rotimi, Charles N %A Cupples, L Adrienne %X

The concentrations of high- and low-density-lipoprotein cholesterol and triglycerides are influenced by smoking, but it is unknown whether genetic associations with lipids may be modified by smoking. We conducted a multi-ancestry genome-wide gene-smoking interaction study in 133,805 individuals with follow-up in an additional 253,467 individuals. Combined meta-analyses identified 13 new loci associated with lipids, some of which were detected only because association differed by smoking status. Additionally, we demonstrate the importance of including diverse populations, particularly in studies of interactions with lifestyle factors, where genomic and lifestyle differences by ancestry may contribute to novel findings.

%B Nat Genet %V 51 %P 636-648 %8 2019 Apr %G eng %N 4 %R 10.1038/s41588-019-0378-y %0 Journal Article %J Front Genet %D 2023 %T Gene-educational attainment interactions in a multi-population genome-wide meta-analysis identify novel lipid loci. %A de Las Fuentes, Lisa %A Schwander, Karen L %A Brown, Michael R %A Bentley, Amy R %A Winkler, Thomas W %A Sung, Yun Ju %A Munroe, Patricia B %A Miller, Clint L %A Aschard, Hugo %A Aslibekyan, Stella %A Bartz, Traci M %A Bielak, Lawrence F %A Chai, Jin Fang %A Cheng, Ching-Yu %A Dorajoo, Rajkumar %A Feitosa, Mary F %A Guo, Xiuqing %A Hartwig, Fernando P %A Horimoto, Andrea %A Kolcic, Ivana %A Lim, Elise %A Liu, Yongmei %A Manning, Alisa K %A Marten, Jonathan %A Musani, Solomon K %A Noordam, Raymond %A Padmanabhan, Sandosh %A Rankinen, Tuomo %A Richard, Melissa A %A Ridker, Paul M %A Smith, Albert V %A Vojinovic, Dina %A Zonderman, Alan B %A Alver, Maris %A Boissel, Mathilde %A Christensen, Kaare %A Freedman, Barry I %A Gao, Chuan %A Giulianini, Franco %A Harris, Sarah E %A He, Meian %A Hsu, Fang-Chi %A Kuhnel, Brigitte %A Laguzzi, Federica %A Li, Xiaoyin %A Lyytikäinen, Leo-Pekka %A Nolte, Ilja M %A Poveda, Alaitz %A Rauramaa, Rainer %A Riaz, Muhammad %A Robino, Antonietta %A Sofer, Tamar %A Takeuchi, Fumihiko %A Tayo, Bamidele O %A van der Most, Peter J %A Verweij, Niek %A Ware, Erin B %A Weiss, Stefan %A Wen, Wanqing %A Yanek, Lisa R %A Zhan, Yiqiang %A Amin, Najaf %A Arking, Dan E %A Ballantyne, Christie %A Boerwinkle, Eric %A Brody, Jennifer A %A Broeckel, Ulrich %A Campbell, Archie %A Canouil, Mickaël %A Chai, Xiaoran %A Chen, Yii-Der Ida %A Chen, Xu %A Chitrala, Kumaraswamy Naidu %A Concas, Maria Pina %A de Faire, Ulf %A de Mutsert, Renée %A de Silva, H Janaka %A de Vries, Paul S %A Do, Ahn %A Faul, Jessica D %A Fisher, Virginia %A Floyd, James S %A Forrester, Terrence %A Friedlander, Yechiel %A Girotto, Giorgia %A Gu, C Charles %A Hallmans, Göran %A Heikkinen, Sami %A Heng, Chew-Kiat %A Homuth, Georg %A Hunt, Steven %A Ikram, M Arfan %A Jacobs, David R %A Kavousi, Maryam %A Khor, Chiea Chuen %A Kilpeläinen, Tuomas O %A Koh, Woon-Puay %A Komulainen, Pirjo %A Langefeld, Carl D %A Liang, Jingjing %A Liu, Kiang %A Liu, Jianjun %A Lohman, Kurt %A Mägi, Reedik %A Manichaikul, Ani W %A McKenzie, Colin A %A Meitinger, Thomas %A Milaneschi, Yuri %A Nauck, Matthias %A Nelson, Christopher P %A O'Connell, Jeffrey R %A Palmer, Nicholette D %A Pereira, Alexandre C %A Perls, Thomas %A Peters, Annette %A Polasek, Ozren %A Raitakari, Olli T %A Rice, Kenneth %A Rice, Treva K %A Rich, Stephen S %A Sabanayagam, Charumathi %A Schreiner, Pamela J %A Shu, Xiao-Ou %A Sidney, Stephen %A Sims, Mario %A Smith, Jennifer A %A Starr, John M %A Strauch, Konstantin %A Tai, E Shyong %A Taylor, Kent D %A Tsai, Michael Y %A Uitterlinden, André G %A van Heemst, Diana %A Waldenberger, Melanie %A Wang, Ya-Xing %A Wei, Wen-Bin %A Wilson, Gregory %A Xuan, Deng %A Yao, Jie %A Yu, Caizheng %A Yuan, Jian-Min %A Zhao, Wei %A Becker, Diane M %A Bonnefond, Amélie %A Bowden, Donald W %A Cooper, Richard S %A Deary, Ian J %A Divers, Jasmin %A Esko, Tõnu %A Franks, Paul W %A Froguel, Philippe %A Gieger, Christian %A Jonas, Jost B %A Kato, Norihiro %A Lakka, Timo A %A Leander, Karin %A Lehtimäki, Terho %A Magnusson, Patrik K E %A North, Kari E %A Ntalla, Ioanna %A Penninx, Brenda %A Samani, Nilesh J %A Snieder, Harold %A Spedicati, Beatrice %A van der Harst, Pim %A Völzke, Henry %A Wagenknecht, Lynne E %A Weir, David R %A Wojczynski, Mary K %A Wu, Tangchun %A Zheng, Wei %A Zhu, Xiaofeng %A Bouchard, Claude %A Chasman, Daniel I %A Evans, Michele K %A Fox, Ervin R %A Gudnason, Vilmundur %A Hayward, Caroline %A Horta, Bernardo L %A Kardia, Sharon L R %A Krieger, Jose Eduardo %A Mook-Kanamori, Dennis O %A Peyser, Patricia A %A Province, Michael M %A Psaty, Bruce M %A Rudan, Igor %A Sim, Xueling %A Smith, Blair H %A van Dam, Rob M %A van Duijn, Cornelia M %A Wong, Tien Yin %A Arnett, Donna K %A Rao, Dabeeru C %A Gauderman, James %A Liu, Ching-Ti %A Morrison, Alanna C %A Rotter, Jerome I %A Fornage, Myriam %X

Educational attainment, widely used in epidemiologic studies as a surrogate for socioeconomic status, is a predictor of cardiovascular health outcomes. A two-stage genome-wide meta-analysis of low-density lipoprotein cholesterol (LDL), high-density lipoprotein cholesterol (HDL), and triglyceride (TG) levels was performed while accounting for gene-educational attainment interactions in up to 226,315 individuals from five population groups. We considered two educational attainment variables: "Some College" (yes/no, for any education beyond high school) and "Graduated College" (yes/no, for completing a 4-year college degree). Genome-wide significant ( < 5 × 10) and suggestive ( < 1 × 10) variants were identified in Stage 1 (in up to 108,784 individuals) through genome-wide analysis, and those variants were followed up in Stage 2 studies (in up to 117,531 individuals). In combined analysis of Stages 1 and 2, we identified 18 novel lipid loci (nine for LDL, seven for HDL, and two for TG) by two degree-of-freedom (2 DF) joint tests of main and interaction effects. Four loci showed significant interaction with educational attainment. Two loci were significant only in cross-population analyses. Several loci include genes with known or suggested roles in adipose (), brain (), and liver () biology, highlighting the potential importance of brain-adipose-liver communication in the regulation of lipid metabolism. An investigation of the potential druggability of genes in identified loci resulted in five gene targets shown to interact with drugs approved by the Food and Drug Administration, including genes with roles in adipose and brain tissue. Genome-wide interaction analysis of educational attainment identified novel lipid loci not previously detected by analyses limited to main genetic effects.

%B Front Genet %V 14 %P 1235337 %8 2023 %G eng %R 10.3389/fgene.2023.1235337