Search

Your search keyword '"Ngwa, Julius S."' showing total 10 results
Start Over Author "Ngwa, Julius S." Topic genetic predisposition to disease
10 results on '"Ngwa, Julius S."'

1. Genome-wide meta-analysis of 241,258 adults accounting for smoking behaviour identifies novel loci for obesity traits.

Author

Justice AE, Winkler TW, Feitosa MF, Graff M, Fisher VA, Young K, Barata L, Deng X, Czajkowski J, Hadley D, Ngwa JS, Ahluwalia TS, Chu AY, Heard-Costa NL, Lim E, Perez J, Eicher JD, Kutalik Z, Xue L, Mahajan A, Renström F, Wu J, Qi Q, Ahmad S, Alfred T, Amin N, Bielak LF, Bonnefond A, Bragg J, Cadby G, Chittani M, Coggeshall S, Corre T, Direk N, Eriksson J, Fischer K, Gorski M, Neergaard Harder M, Horikoshi M, Huang T, Huffman JE, Jackson AU, Justesen JM, Kanoni S, Kinnunen L, Kleber ME, Komulainen P, Kumari M, Lim U, Luan J, Lyytikäinen LP, Mangino M, Manichaikul A, Marten J, Middelberg RPS, Müller-Nurasyid M, Navarro P, Pérusse L, Pervjakova N, Sarti C, Smith AV, Smith JA, Stančáková A, Strawbridge RJ, Stringham HM, Sung YJ, Tanaka T, Teumer A, Trompet S, van der Laan SW, van der Most PJ, Van Vliet-Ostaptchouk JV, Vedantam SL, Verweij N, Vink JM, Vitart V, Wu Y, Yengo L, Zhang W, Hua Zhao J, Zimmermann ME, Zubair N, Abecasis GR, Adair LS, Afaq S, Afzal U, Bakker SJL, Bartz TM, Beilby J, Bergman RN, Bergmann S, Biffar R, Blangero J, Boerwinkle E, Bonnycastle LL, Bottinger E, Braga D, Buckley BM, Buyske S, Campbell H, Chambers JC, Collins FS, Curran JE, de Borst GJ, de Craen AJM, de Geus EJC, Dedoussis G, Delgado GE, den Ruijter HM, Eiriksdottir G, Eriksson AL, Esko T, Faul JD, Ford I, Forrester T, Gertow K, Gigante B, Glorioso N, Gong J, Grallert H, Grammer TB, Grarup N, Haitjema S, Hallmans G, Hamsten A, Hansen T, Harris TB, Hartman CA, Hassinen M, Hastie ND, Heath AC, Hernandez D, Hindorff L, Hocking LJ, Hollensted M, Holmen OL, Homuth G, Jan Hottenga J, Huang J, Hung J, Hutri-Kähönen N, Ingelsson E, James AL, Jansson JO, Jarvelin MR, Jhun MA, Jørgensen ME, Juonala M, Kähönen M, Karlsson M, Koistinen HA, Kolcic I, Kolovou G, Kooperberg C, Krämer BK, Kuusisto J, Kvaløy K, Lakka TA, Langenberg C, Launer LJ, Leander K, Lee NR, Lind L, Lindgren CM, Linneberg A, Lobbens S, Loh M, Lorentzon M, Luben R, Lubke G, Ludolph-Donislawski A, Lupoli S, Madden PAF, Männikkö R, Marques-Vidal P, Martin NG, McKenzie CA, McKnight B, Mellström D, Menni C, Montgomery GW, Musk AB, Narisu N, Nauck M, Nolte IM, Oldehinkel AJ, Olden M, Ong KK, Padmanabhan S, Peyser PA, Pisinger C, Porteous DJ, Raitakari OT, Rankinen T, Rao DC, Rasmussen-Torvik LJ, Rawal R, Rice T, Ridker PM, Rose LM, Bien SA, Rudan I, Sanna S, Sarzynski MA, Sattar N, Savonen K, Schlessinger D, Scholtens S, Schurmann C, Scott RA, Sennblad B, Siemelink MA, Silbernagel G, Slagboom PE, Snieder H, Staessen JA, Stott DJ, Swertz MA, Swift AJ, Taylor KD, Tayo BO, Thorand B, Thuillier D, Tuomilehto J, Uitterlinden AG, Vandenput L, Vohl MC, Völzke H, Vonk JM, Waeber G, Waldenberger M, Westendorp RGJ, Wild S, Willemsen G, Wolffenbuttel BHR, Wong A, Wright AF, Zhao W, Zillikens MC, Baldassarre D, Balkau B, Bandinelli S, Böger CA, Boomsma DI, Bouchard C, Bruinenberg M, Chasman DI, Chen YD, Chines PS, Cooper RS, Cucca F, Cusi D, Faire U, Ferrucci L, Franks PW, Froguel P, Gordon-Larsen P, Grabe HJ, Gudnason V, Haiman CA, Hayward C, Hveem K, Johnson AD, Wouter Jukema J, Kardia SLR, Kivimaki M, Kooner JS, Kuh D, Laakso M, Lehtimäki T, Marchand LL, März W, McCarthy MI, Metspalu A, Morris AP, Ohlsson C, Palmer LJ, Pasterkamp G, Pedersen O, Peters A, Peters U, Polasek O, Psaty BM, Qi L, Rauramaa R, Smith BH, Sørensen TIA, Strauch K, Tiemeier H, Tremoli E, van der Harst P, Vestergaard H, Vollenweider P, Wareham NJ, Weir DR, Whitfield JB, Wilson JF, Tyrrell J, Frayling TM, Barroso I, Boehnke M, Deloukas P, Fox CS, Hirschhorn JN, Hunter DJ, Spector TD, Strachan DP, van Duijn CM, Heid IM, Mohlke KL, Marchini J, Loos RJF, Kilpeläinen TO, Liu CT, Borecki IB, North KE, and Cupples LA

Subjects
Adiposity genetics, Adult, Body Fat Distribution, Body Mass Index, Epistasis, Genetic, Humans, Phenotype, Polymorphism, Single Nucleotide, Waist Circumference genetics, Waist-Hip Ratio, Genetic Predisposition to Disease genetics, Genome-Wide Association Study methods, Obesity genetics, Quantitative Trait Loci genetics, Smoking genetics
Abstract

Few genome-wide association studies (GWAS) account for environmental exposures, like smoking, potentially impacting the overall trait variance when investigating the genetic contribution to obesity-related traits. Here, we use GWAS data from 51,080 current smokers and 190,178 nonsmokers (87% European descent) to identify loci influencing BMI and central adiposity, measured as waist circumference and waist-to-hip ratio both adjusted for BMI. We identify 23 novel genetic loci, and 9 loci with convincing evidence of gene-smoking interaction (GxSMK) on obesity-related traits. We show consistent direction of effect for all identified loci and significance for 18 novel and for 5 interaction loci in an independent study sample. These loci highlight novel biological functions, including response to oxidative stress, addictive behaviour, and regulatory functions emphasizing the importance of accounting for environment in genetic analyses. Our results suggest that tobacco smoking may alter the genetic susceptibility to overall adiposity and body fat distribution.

Published
2017
Full Text
View/download PDF

2. Genome-wide meta-analysis identifies 11 new loci for anthropometric traits and provides insights into genetic architecture.

Author

Berndt SI, Gustafsson S, Mägi R, Ganna A, Wheeler E, Feitosa MF, Justice AE, Monda KL, Croteau-Chonka DC, Day FR, Esko T, Fall T, Ferreira T, Gentilini D, Jackson AU, Luan J, Randall JC, Vedantam S, Willer CJ, Winkler TW, Wood AR, Workalemahu T, Hu YJ, Lee SH, Liang L, Lin DY, Min JL, Neale BM, Thorleifsson G, Yang J, Albrecht E, Amin N, Bragg-Gresham JL, Cadby G, den Heijer M, Eklund N, Fischer K, Goel A, Hottenga JJ, Huffman JE, Jarick I, Johansson Å, Johnson T, Kanoni S, Kleber ME, König IR, Kristiansson K, Kutalik Z, Lamina C, Lecoeur C, Li G, Mangino M, McArdle WL, Medina-Gomez C, Müller-Nurasyid M, Ngwa JS, Nolte IM, Paternoster L, Pechlivanis S, Perola M, Peters MJ, Preuss M, Rose LM, Shi J, Shungin D, Smith AV, Strawbridge RJ, Surakka I, Teumer A, Trip MD, Tyrer J, Van Vliet-Ostaptchouk JV, Vandenput L, Waite LL, Zhao JH, Absher D, Asselbergs FW, Atalay M, Attwood AP, Balmforth AJ, Basart H, Beilby J, Bonnycastle LL, Brambilla P, Bruinenberg M, Campbell H, Chasman DI, Chines PS, Collins FS, Connell JM, Cookson WO, de Faire U, de Vegt F, Dei M, Dimitriou M, Edkins S, Estrada K, Evans DM, Farrall M, Ferrario MM, Ferrières J, Franke L, Frau F, Gejman PV, Grallert H, Grönberg H, Gudnason V, Hall AS, Hall P, Hartikainen AL, Hayward C, Heard-Costa NL, Heath AC, Hebebrand J, Homuth G, Hu FB, Hunt SE, Hyppönen E, Iribarren C, Jacobs KB, Jansson JO, Jula A, Kähönen M, Kathiresan S, Kee F, Khaw KT, Kivimäki M, Koenig W, Kraja AT, Kumari M, Kuulasmaa K, Kuusisto J, Laitinen JH, Lakka TA, Langenberg C, Launer LJ, Lind L, Lindström J, Liu J, Liuzzi A, Lokki ML, Lorentzon M, Madden PA, Magnusson PK, Manunta P, Marek D, März W, Mateo Leach I, McKnight B, Medland SE, Mihailov E, Milani L, Montgomery GW, Mooser V, Mühleisen TW, Munroe PB, Musk AW, Narisu N, Navis G, Nicholson G, Nohr EA, Ong KK, Oostra BA, Palmer CN, Palotie A, Peden JF, Pedersen N, Peters A, Polasek O, Pouta A, Pramstaller PP, Prokopenko I, Pütter C, Radhakrishnan A, Raitakari O, Rendon A, Rivadeneira F, Rudan I, Saaristo TE, Sambrook JG, Sanders AR, Sanna S, Saramies J, Schipf S, Schreiber S, Schunkert H, Shin SY, Signorini S, Sinisalo J, Skrobek B, Soranzo N, Stančáková A, Stark K, Stephens JC, Stirrups K, Stolk RP, Stumvoll M, Swift AJ, Theodoraki EV, Thorand B, Tregouet DA, Tremoli E, Van der Klauw MM, van Meurs JB, Vermeulen SH, Viikari J, Virtamo J, Vitart V, Waeber G, Wang Z, Widén E, Wild SH, Willemsen G, Winkelmann BR, Witteman JC, Wolffenbuttel BH, Wong A, Wright AF, Zillikens MC, Amouyel P, Boehm BO, Boerwinkle E, Boomsma DI, Caulfield MJ, Chanock SJ, Cupples LA, Cusi D, Dedoussis GV, Erdmann J, Eriksson JG, Franks PW, Froguel P, Gieger C, Gyllensten U, Hamsten A, Harris TB, Hengstenberg C, Hicks AA, Hingorani A, Hinney A, Hofman A, Hovingh KG, Hveem K, Illig T, Jarvelin MR, Jöckel KH, Keinanen-Kiukaanniemi SM, Kiemeney LA, Kuh D, Laakso M, Lehtimäki T, Levinson DF, Martin NG, Metspalu A, Morris AD, Nieminen MS, Njølstad I, Ohlsson C, Oldehinkel AJ, Ouwehand WH, Palmer LJ, Penninx B, Power C, Province MA, Psaty BM, Qi L, Rauramaa R, Ridker PM, Ripatti S, Salomaa V, Samani NJ, Snieder H, Sørensen TI, Spector TD, Stefansson K, Tönjes A, Tuomilehto J, Uitterlinden AG, Uusitupa M, van der Harst P, Vollenweider P, Wallaschofski H, Wareham NJ, Watkins H, Wichmann HE, Wilson JF, Abecasis GR, Assimes TL, Barroso I, Boehnke M, Borecki IB, Deloukas P, Fox CS, Frayling T, Groop LC, Haritunian T, Heid IM, Hunter D, Kaplan RC, Karpe F, Moffatt MF, Mohlke KL, O'Connell JR, Pawitan Y, Schadt EE, Schlessinger D, Steinthorsdottir V, Strachan DP, Thorsteinsdottir U, van Duijn CM, Visscher PM, Di Blasio AM, Hirschhorn JN, Lindgren CM, Morris AP, Meyre D, Scherag A, McCarthy MI, Speliotes EK, North KE, Loos RJ, and Ingelsson E

Subjects
Body Mass Index, Case-Control Studies, Genotype, Humans, Meta-Analysis as Topic, Phenotype, Waist-Hip Ratio, White People genetics, Anthropometry, Body Height genetics, Genetic Predisposition to Disease, Genome-Wide Association Study, Obesity genetics, Polymorphism, Single Nucleotide genetics, Quantitative Trait Loci
Abstract

Approaches exploiting trait distribution extremes may be used to identify loci associated with common traits, but it is unknown whether these loci are generalizable to the broader population. In a genome-wide search for loci associated with the upper versus the lower 5th percentiles of body mass index, height and waist-to-hip ratio, as well as clinical classes of obesity, including up to 263,407 individuals of European ancestry, we identified 4 new loci (IGFBP4, H6PD, RSRC1 and PPP2R2A) influencing height detected in the distribution tails and 7 new loci (HNF4G, RPTOR, GNAT2, MRPS33P4, ADCY9, HS6ST3 and ZZZ3) for clinical classes of obesity. Further, we find a large overlap in genetic structure and the distribution of variants between traits based on extremes and the general population and little etiological heterogeneity between obesity subgroups.

Published
2013
Full Text
View/download PDF

3. Discovery and fine mapping of serum protein loci through transethnic meta-analysis.

Author

Franceschini N, van Rooij FJ, Prins BP, Feitosa MF, Karakas M, Eckfeldt JH, Folsom AR, Kopp J, Vaez A, Andrews JS, Baumert J, Boraska V, Broer L, Hayward C, Ngwa JS, Okada Y, Polasek O, Westra HJ, Wang YA, Del Greco M F, Glazer NL, Kapur K, Kema IP, Lopez LM, Schillert A, Smith AV, Winkler CA, Zgaga L, Bandinelli S, Bergmann S, Boban M, Bochud M, Chen YD, Davies G, Dehghan A, Ding J, Doering A, Durda JP, Ferrucci L, Franco OH, Franke L, Gunjaca G, Hofman A, Hsu FC, Kolcic I, Kraja A, Kubo M, Lackner KJ, Launer L, Loehr LR, Li G, Meisinger C, Nakamura Y, Schwienbacher C, Starr JM, Takahashi A, Torlak V, Uitterlinden AG, Vitart V, Waldenberger M, Wild PS, Kirin M, Zeller T, Zemunik T, Zhang Q, Ziegler A, Blankenberg S, Boerwinkle E, Borecki IB, Campbell H, Deary IJ, Frayling TM, Gieger C, Harris TB, Hicks AA, Koenig W, O' Donnell CJ, Fox CS, Pramstaller PP, Psaty BM, Reiner AP, Rotter JI, Rudan I, Snieder H, Tanaka T, van Duijn CM, Vollenweider P, Waeber G, Wilson JF, Witteman JC, Wolffenbuttel BH, Wright AF, Wu Q, Liu Y, Jenny NS, North KE, Felix JF, Alizadeh BZ, Cupples LA, Perry JR, and Morris AP

Subjects
Adult, Aged, Alleles, Animals, Asian People genetics, Chromosome Mapping methods, Female, Genome-Wide Association Study methods, Humans, Linkage Disequilibrium genetics, Male, Mice, Middle Aged, Protein Biosynthesis genetics, Proteolysis, Ribosomes genetics, Serum Albumin genetics, White People genetics, Blood Proteins genetics, Blood Proteins metabolism, Genetic Loci, Genetic Predisposition to Disease genetics
Abstract

Many disorders are associated with altered serum protein concentrations, including malnutrition, cancer, and cardiovascular, kidney, and inflammatory diseases. Although these protein concentrations are highly heritable, relatively little is known about their underlying genetic determinants. Through transethnic meta-analysis of European-ancestry and Japanese genome-wide association studies, we identified six loci at genome-wide significance (p < 5 × 10(-8)) for serum albumin (HPN-SCN1B, GCKR-FNDC4, SERPINF2-WDR81, TNFRSF11A-ZCCHC2, FRMD5-WDR76, and RPS11-FCGRT, in up to 53,190 European-ancestry and 9,380 Japanese individuals) and three loci for total protein (TNFRS13B, 6q21.3, and ELL2, in up to 25,539 European-ancestry and 10,168 Japanese individuals). We observed little evidence of heterogeneity in allelic effects at these loci between groups of European and Japanese ancestry but obtained substantial improvements in the resolution of fine mapping of potential causal variants by leveraging transethnic differences in the distribution of linkage disequilibrium. We demonstrated a functional role for the most strongly associated serum albumin locus, HPN, for which Hpn knockout mice manifest low plasma albumin concentrations. Other loci associated with serum albumin harbor genes related to ribosome function, protein translation, and proteasomal degradation, whereas those associated with serum total protein include genes related to immune function. Our results highlight the advantages of transethnic meta-analysis for the discovery and fine mapping of complex trait loci and have provided initial insights into the underlying genetic architecture of serum protein concentrations and their association with human disease., (Copyright © 2012 The American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.)

Published
2012
Full Text
View/download PDF

4. Genome-wide meta-analysis of macronutrient intake of 91,114 European ancestry participants from the cohorts for heart and aging research in genomic epidemiology consortium

Author

Merino, Jordi, Dashti, Hassan S, Li, Sherly X, Sarnowski, Chloé, Justice, Anne E, Graff, Misa, Papoutsakis, Constantina, Smith, Caren E, Dedoussis, George V, Lemaitre, Rozenn N, Wojczynski, Mary K, Männistö, Satu, Ngwa, Julius S, Kho, Minjung, Ahluwalia, Tarunveer S, Pervjakova, Natalia, Houston, Denise K, Bouchard, Claude, Huang, Tao, Orho-Melander, Marju, Frazier-Wood, Alexis C, Mook-Kanamori, Dennis O, Pérusse, Louis, Pennell, Craig E, de Vries, Paul S, Voortman, Trudy, Li, Olivia, Kanoni, Stavroula, Rose, Lynda M, Lehtimäki, Terho, Zhao, Jing Hua, Feitosa, Mary F, Luan, Jian’an, McKeown, Nicola M, Smith, Jennifer A, Hansen, Torben, Eklund, Niina, Nalls, Mike A, Rankinen, Tuomo, Huang, Jinyan, Hernandez, Dena G, Schulz, Christina-Alexandra, Manichaikul, Ani, Li-Gao, Ruifang, Vohl, Marie-Claude, Wang, Carol A, van Rooij, Frank JA, Shin, Jean, Kalafati, Ioanna P, Day, Felix, Ridker, Paul M, Kähönen, Mika, Siscovick, David S, Langenberg, Claudia, Zhao, Wei, Astrup, Arne, Knekt, Paul, Garcia, Melissa, Rao, DC, Qi, Qibin, Ferrucci, Luigi, Ericson, Ulrika, Blangero, John, Hofman, Albert, Pausova, Zdenka, Mikkilä, Vera, Wareham, Nick J, Kardia, Sharon LR, Pedersen, Oluf, Jula, Antti, Curran, Joanne E, Zillikens, M Carola, Viikari, Jorma S, Forouhi, Nita G, Ordovás, José M, Lieske, John C, Rissanen, Harri, Uitterlinden, André G, Raitakari, Olli T, Kiefte-de Jong, Jessica C, Dupuis, Josée, Rotter, Jerome I, North, Kari E, Scott, Robert A, Province, Michael A, Perola, Markus, Cupples, L Adrienne, Turner, Stephen T, Sørensen, Thorkild IA, Salomaa, Veikko, Liu, Yongmei, Sung, Yun J, Qi, Lu, Bandinelli, Stefania, Rich, Stephen S, de Mutsert, Renée, Tremblay, Angelo, Oddy, Wendy H, Franco, Oscar H, and Paus, Tomas

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Obesity, Human Genome, Genetics, Prevention, Clinical Research, 2.1 Biological and endogenous factors, Aetiology, Cardiovascular, Metabolic and endocrine, Aged, Aging, Alpha-Ketoglutarate-Dependent Dioxygenase FTO, Cohort Studies, Energy Intake, Female, Fibroblast Growth Factors, Genetic Loci, Genetic Predisposition to Disease, Genome-Wide Association Study, Genomics, Genotype, Heart Diseases, Humans, Male, Membrane Proteins, Middle Aged, Nutrients, Polymorphism, Single Nucleotide, Receptors, Retinoic Acid, White People, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry, Clinical sciences, Biological psychology, Clinical and health psychology
Abstract

Macronutrient intake, the proportion of calories consumed from carbohydrate, fat, and protein, is an important risk factor for metabolic diseases with significant familial aggregation. Previous studies have identified two genetic loci for macronutrient intake, but incomplete coverage of genetic variation and modest sample sizes have hindered the discovery of additional loci. Here, we expanded the genetic landscape of macronutrient intake, identifying 12 suggestively significant loci (P

Published
2019

5. Genome-wide physical activity interactions in adiposity - A meta-analysis of 200,452 adults.

Author

Graff, Mariaelisa, Scott, Robert A, Justice, Anne E, Young, Kristin L, Feitosa, Mary F, Barata, Llilda, Winkler, Thomas W, Chu, Audrey Y, Mahajan, Anubha, Hadley, David, Xue, Luting, Workalemahu, Tsegaselassie, Heard-Costa, Nancy L, den Hoed, Marcel, Ahluwalia, Tarunveer S, Qi, Qibin, Ngwa, Julius S, Renström, Frida, Quaye, Lydia, Eicher, John D, Hayes, James E, Cornelis, Marilyn, Kutalik, Zoltan, Lim, Elise, Luan, Jian'an, Huffman, Jennifer E, Zhang, Weihua, Zhao, Wei, Griffin, Paula J, Haller, Toomas, Ahmad, Shafqat, Marques-Vidal, Pedro M, Bien, Stephanie, Yengo, Loic, Teumer, Alexander, Smith, Albert Vernon, Kumari, Meena, Harder, Marie Neergaard, Justesen, Johanne Marie, Kleber, Marcus E, Hollensted, Mette, Lohman, Kurt, Rivera, Natalia V, Whitfield, John B, Zhao, Jing Hua, Stringham, Heather M, Lyytikäinen, Leo-Pekka, Huppertz, Charlotte, Willemsen, Gonneke, Peyrot, Wouter J, Wu, Ying, Kristiansson, Kati, Demirkan, Ayse, Fornage, Myriam, Hassinen, Maija, Bielak, Lawrence F, Cadby, Gemma, Tanaka, Toshiko, Mägi, Reedik, van der Most, Peter J, Jackson, Anne U, Bragg-Gresham, Jennifer L, Vitart, Veronique, Marten, Jonathan, Navarro, Pau, Bellis, Claire, Pasko, Dorota, Johansson, Åsa, Snitker, Søren, Cheng, Yu-Ching, Eriksson, Joel, Lim, Unhee, Aadahl, Mette, Adair, Linda S, Amin, Najaf, Balkau, Beverley, Auvinen, Juha, Beilby, John, Bergman, Richard N, Bergmann, Sven, Bertoni, Alain G, Blangero, John, Bonnefond, Amélie, Bonnycastle, Lori L, Borja, Judith B, Brage, Søren, Busonero, Fabio, Buyske, Steve, Campbell, Harry, Chines, Peter S, Collins, Francis S, Corre, Tanguy, Smith, George Davey, Delgado, Graciela E, Dueker, Nicole, Dörr, Marcus, Ebeling, Tapani, Eiriksdottir, Gudny, Esko, Tõnu, and Faul, Jessica D

Subjects
CHARGE Consortium, EPIC-InterAct Consortium, PAGE Consortium, Humans, Obesity, Genetic Predisposition to Disease, Body Mass Index, Waist-Hip Ratio, Exercise, Genotype, Female, Male, Adiposity, Waist Circumference, Genome-Wide Association Study, Epigenomics, Alpha-Ketoglutarate-Dependent Dioxygenase FTO, Genetics, Developmental Biology
Abstract

Physical activity (PA) may modify the genetic effects that give rise to increased risk of obesity. To identify adiposity loci whose effects are modified by PA, we performed genome-wide interaction meta-analyses of BMI and BMI-adjusted waist circumference and waist-hip ratio from up to 200,452 adults of European (n = 180,423) or other ancestry (n = 20,029). We standardized PA by categorizing it into a dichotomous variable where, on average, 23% of participants were categorized as inactive and 77% as physically active. While we replicate the interaction with PA for the strongest known obesity-risk locus in the FTO gene, of which the effect is attenuated by ~30% in physically active individuals compared to inactive individuals, we do not identify additional loci that are sensitive to PA. In additional genome-wide meta-analyses adjusting for PA and interaction with PA, we identify 11 novel adiposity loci, suggesting that accounting for PA or other environmental factors that contribute to variation in adiposity may facilitate gene discovery.

Published
2017

6. Consumption of meat is associated with higher fasting glucose and insulin concentrations regardless of glucose and insulin genetic risk scores: a meta-analysis of 50,345 Caucasians 1 , 2

Author

Fretts, Amanda M, Follis, Jack L, Nettleton, Jennifer A, Lemaitre, Rozenn N, Ngwa, Julius S, Wojczynski, Mary K, Kalafati, Ioanna Panagiota, Varga, Tibor V, Frazier-Wood, Alexis C, Houston, Denise K, Lahti, Jari, Ericson, Ulrika, van den Hooven, Edith H, Mikkilä, Vera, Kiefte-de Jong, Jessica C, Mozaffarian, Dariush, Rice, Kenneth, Renström, Frida, North, Kari E, McKeown, Nicola M, Feitosa, Mary F, Kanoni, Stavroula, Smith, Caren E, Garcia, Melissa E, Tiainen, Anna-Maija, Sonestedt, Emily, Manichaikul, Ani, van Rooij, Frank JA, Dimitriou, Maria, Raitakari, Olli, Pankow, James S, Djoussé, Luc, Province, Michael A, Hu, Frank B, Lai, Chao-Qiang, Keller, Margaux F, Perälä, Mia-Maria, Rotter, Jerome I, Hofman, Albert, Graff, Misa, Kähönen, Mika, Mukamal, Kenneth, Johansson, Ingegerd, Ordovas, Jose M, Liu, Yongmei, Männistö, Satu, Uitterlinden, André G, Deloukas, Panos, Seppälä, Ilkka, Psaty, Bruce M, Cupples, L Adrienne, Borecki, Ingrid B, Franks, Paul W, Arnett, Donna K, Nalls, Mike A, Eriksson, Johan G, Orho-Melander, Marju, Franco, Oscar H, Lehtimäki, Terho, Dedoussis, George V, Meigs, James B, and Siscovick, David S

Subjects
Biomedical and Clinical Sciences, Nutrition and Dietetics, Prevention, Diabetes, Genetics, Aging, Cardiovascular, Nutrition, Metabolic and endocrine, Blood Glucose, Cohort Studies, Genetic Association Studies, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Hyperglycemia, Hyperinsulinism, Insulin, Insulin Resistance, Insulin Secretion, Insulin-Secreting Cells, Meat, Meat Products, Middle Aged, Polymorphism, Single Nucleotide, Risk Factors, diet, gene–diet interaction, glucose, insulin, meat intake, meta-analysis, Engineering, Medical and Health Sciences, Nutrition & Dietetics, Clinical sciences, Nutrition and dietetics
Abstract

BackgroundRecent studies suggest that meat intake is associated with diabetes-related phenotypes. However, whether the associations of meat intake and glucose and insulin homeostasis are modified by genes related to glucose and insulin is unknown.ObjectiveWe investigated the associations of meat intake and the interaction of meat with genotype on fasting glucose and insulin concentrations in Caucasians free of diabetes mellitus.DesignFourteen studies that are part of the Cohorts for Heart and Aging Research in Genomic Epidemiology consortium participated in the analysis. Data were provided for up to 50,345 participants. Using linear regression within studies and a fixed-effects meta-analysis across studies, we examined 1) the associations of processed meat and unprocessed red meat intake with fasting glucose and insulin concentrations; and 2) the interactions of processed meat and unprocessed red meat with genetic risk score related to fasting glucose or insulin resistance on fasting glucose and insulin concentrations.ResultsProcessed meat was associated with higher fasting glucose, and unprocessed red meat was associated with both higher fasting glucose and fasting insulin concentrations after adjustment for potential confounders [not including body mass index (BMI)]. For every additional 50-g serving of processed meat per day, fasting glucose was 0.021 mmol/L (95% CI: 0.011, 0.030 mmol/L) higher. Every additional 100-g serving of unprocessed red meat per day was associated with a 0.037-mmol/L (95% CI: 0.023, 0.051-mmol/L) higher fasting glucose concentration and a 0.049-ln-pmol/L (95% CI: 0.035, 0.063-ln-pmol/L) higher fasting insulin concentration. After additional adjustment for BMI, observed associations were attenuated and no longer statistically significant. The association of processed meat and fasting insulin did not reach statistical significance after correction for multiple comparisons. Observed associations were not modified by genetic loci known to influence fasting glucose or insulin resistance.ConclusionThe association of higher fasting glucose and insulin concentrations with meat consumption was not modified by an index of glucose- and insulin-related single-nucleotide polymorphisms. Six of the participating studies are registered at clinicaltrials.gov as NCT0000513 (Atherosclerosis Risk in Communities), NCT00149435 (Cardiovascular Health Study), NCT00005136 (Family Heart Study), NCT00005121 (Framingham Heart Study), NCT00083369 (Genetics of Lipid Lowering Drugs and Diet Network), and NCT00005487 (Multi-Ethnic Study of Atherosclerosis).

Published
2015

7. Genome-wide meta-analysis of observational studies shows common genetic variants associated with macronutrient intake1234

Author

Tanaka, Toshiko, Ngwa, Julius S, van Rooij, Frank JA, Zillikens, M Carola, Wojczynski, Mary K, Frazier-Wood, Alexis C, Houston, Denise K, Kanoni, Stavroula, Lemaitre, Rozenn N, Luan, Jian'an, Mikkilä, Vera, Renstrom, Frida, Sonestedt, Emily, Zhao, Jing Hua, Chu, Audrey Y, Qi, Lu, Chasman, Daniel I, de Oliveira Otto, Marcia C, Dhurandhar, Emily J, Feitosa, Mary F, Johansson, Ingegerd, Khaw, Kay-Tee, Lohman, Kurt K, Manichaikul, Ani, McKeown, Nicola M, Mozaffarian, Dariush, Singleton, Andrew, Stirrups, Kathleen, Viikari, Jorma, Ye, Zheng, Bandinelli, Stefania, Barroso, Inês, Deloukas, Panos, Forouhi, Nita G, Hofman, Albert, Liu, Yongmei, Lyytikäinen, Leo-Pekka, North, Kari E, Dimitriou, Maria, Hallmans, Goran, Kähönen, Mika, Langenberg, Claudia, Ordovas, Jose M, Uitterlinden, André G, Hu, Frank B, Kalafati, Ioanna-Panagiota, Raitakari, Olli, Franco, Oscar H, Johnson, Andrew, Emilsson, Valur, Schrack, Jennifer A, Semba, Richard D, Siscovick, David S, Arnett, Donna K, Borecki, Ingrid B, Franks, Paul W, Kritchevsky, Stephen B, Lehtimäki, Terho, Loos, Ruth JF, Orho-Melander, Marju, Rotter, Jerome I, Wareham, Nicholas J, Witteman, Jacqueline CM, Ferrucci, Luigi, Dedoussis, George, Cupples, L Adrienne, and Nettleton, Jennifer A

Subjects
Genotype, Quantitative Trait Loci, Atherosclerosis, Dietary Fats, Polymorphism, Single Nucleotide, White People, Body Mass Index, Fibroblast Growth Factors, Nutritional Epidemiology and Public Health, Surveys and Questionnaires, Dietary Carbohydrates, Humans, Gene-Environment Interaction, Genetic Predisposition to Disease, Dietary Proteins, Obesity, Prospective Studies, Energy Intake, Life Style, Alleles, Follow-Up Studies, Genome-Wide Association Study
Abstract

Background: Macronutrient intake varies substantially between individuals, and there is evidence that this variation is partly accounted for by genetic variants. Objective: The objective of the study was to identify common genetic variants that are associated with macronutrient intake. Design: We performed 2-stage genome-wide association (GWA) meta-analysis of macronutrient intake in populations of European descent. Macronutrients were assessed by using food-frequency questionnaires and analyzed as percentages of total energy consumption from total fat, protein, and carbohydrate. From the discovery GWA (n = 38,360), 35 independent loci associated with macronutrient intake at P < 5 × 10−6 were identified and taken forward to replication in 3 additional cohorts (n = 33,533) from the DietGen Consortium. For one locus, fat mass obesity-associated protein (FTO), cohorts with Illumina MetaboChip genotype data (n = 7724) provided additional replication data. Results: A variant in the chromosome 19 locus (rs838145) was associated with higher carbohydrate (β ± SE: 0.25 ± 0.04%; P = 1.68 × 10−8) and lower fat (β ± SE: −0.21 ± 0.04%; P = 1.57 × 10−9) consumption. A candidate gene in this region, fibroblast growth factor 21 (FGF21), encodes a fibroblast growth factor involved in glucose and lipid metabolism. The variants in this locus were associated with circulating FGF21 protein concentrations (P < 0.05) but not mRNA concentrations in blood or brain. The body mass index (BMI)–increasing allele of the FTO variant (rs1421085) was associated with higher protein intake (β ± SE: 0.10 ± 0.02%; P = 9.96 × 10−10), independent of BMI (after adjustment for BMI, β ± SE: 0.08 ± 0.02%; P = 3.15 × 10−7). Conclusion: Our results indicate that variants in genes involved in nutrient metabolism and obesity are associated with macronutrient consumption in humans. Trials related to this study were registered at clinicaltrials.gov as NCT00005131 (Atherosclerosis Risk in Communities), NCT00005133 (Cardiovascular Health Study), NCT00005136 (Family Heart Study), NCT00005121 (Framingham Heart Study), NCT00083369 (Genetic and Environmental Determinants of Triglycerides), NCT01331512 (InCHIANTI Study), and NCT00005487 (Multi-Ethnic Study of Atherosclerosis).

Published
2013

8. Consumption of meat is associated with higher fasting glucose and insulin concentrations regardless of glucose and insulin genetic risk scores: a meta-analysis of 50,345 Caucasians12

Author

Fretts, Amanda M, Follis, Jack L, Nettleton, Jennifer A, Lemaitre, Rozenn N, Ngwa, Julius S, Wojczynski, Mary K, Kalafati, Ioanna Panagiota, Varga, Tibor V, Frazier-Wood, Alexis C, Houston, Denise K, Lahti, Jari, Ericson, Ulrika, van den Hooven, Edith H, Mikkilä, Vera, Kiefte-de Jong, Jessica C, Mozaffarian, Dariush, Rice, Kenneth, Renström, Frida, North, Kari E, McKeown, Nicola M, Feitosa, Mary F, Kanoni, Stavroula, Smith, Caren E, Garcia, Melissa E, Tiainen, Anna-Maija, Sonestedt, Emily, Manichaikul, Ani, van Rooij, Frank JA, Dimitriou, Maria, Raitakari, Olli, Pankow, James S, Djoussé, Luc, Province, Michael A, Hu, Frank B, Lai, Chao-Qiang, Keller, Margaux F, Perälä, Mia-Maria, Rotter, Jerome I, Hofman, Albert, Graff, Misa, Kähönen, Mika, Mukamal, Kenneth, Johansson, Ingegerd, Ordovas, Jose M, Liu, Yongmei, Männistö, Satu, Uitterlinden, André G, Deloukas, Panos, Seppälä, Ilkka, Psaty, Bruce M, Cupples, L Adrienne, Borecki, Ingrid B, Franks, Paul W, Arnett, Donna K, Nalls, Mike A, Eriksson, Johan G, Orho-Melander, Marju, Franco, Oscar H, Lehtimäki, Terho, Dedoussis, George V, Meigs, James B, and Siscovick, David S

Subjects
Blood Glucose, Meat, Gene-Nutrient Interactions, food and beverages, Middle Aged, Polymorphism, Single Nucleotide, Cohort Studies, Meat Products, Risk Factors, Hyperglycemia, Hyperinsulinism, Insulin-Secreting Cells, Insulin Secretion, Humans, Insulin, Genetic Predisposition to Disease, Insulin Resistance, Genetic Association Studies, Genome-Wide Association Study
Abstract

Recent studies suggest that meat intake is associated with diabetes-related phenotypes. However, whether the associations of meat intake and glucose and insulin homeostasis are modified by genes related to glucose and insulin is unknown.We investigated the associations of meat intake and the interaction of meat with genotype on fasting glucose and insulin concentrations in Caucasians free of diabetes mellitus.Fourteen studies that are part of the Cohorts for Heart and Aging Research in Genomic Epidemiology consortium participated in the analysis. Data were provided for up to 50,345 participants. Using linear regression within studies and a fixed-effects meta-analysis across studies, we examined 1) the associations of processed meat and unprocessed red meat intake with fasting glucose and insulin concentrations; and 2) the interactions of processed meat and unprocessed red meat with genetic risk score related to fasting glucose or insulin resistance on fasting glucose and insulin concentrations.Processed meat was associated with higher fasting glucose, and unprocessed red meat was associated with both higher fasting glucose and fasting insulin concentrations after adjustment for potential confounders [not including body mass index (BMI)]. For every additional 50-g serving of processed meat per day, fasting glucose was 0.021 mmol/L (95% CI: 0.011, 0.030 mmol/L) higher. Every additional 100-g serving of unprocessed red meat per day was associated with a 0.037-mmol/L (95% CI: 0.023, 0.051-mmol/L) higher fasting glucose concentration and a 0.049-ln-pmol/L (95% CI: 0.035, 0.063-ln-pmol/L) higher fasting insulin concentration. After additional adjustment for BMI, observed associations were attenuated and no longer statistically significant. The association of processed meat and fasting insulin did not reach statistical significance after correction for multiple comparisons. Observed associations were not modified by genetic loci known to influence fasting glucose or insulin resistance.The association of higher fasting glucose and insulin concentrations with meat consumption was not modified by an index of glucose- and insulin-related single-nucleotide polymorphisms. Six of the participating studies are registered at clinicaltrials.gov as NCT0000513 (Atherosclerosis Risk in Communities), NCT00149435 (Cardiovascular Health Study), NCT00005136 (Family Heart Study), NCT00005121 (Framingham Heart Study), NCT00083369 (Genetics of Lipid Lowering Drugs and Diet Network), and NCT00005487 (Multi-Ethnic Study of Atherosclerosis).

Published
2015

9. Genome-wide physical activity interactions in adiposity - A meta-analysis of 200,452 adults

Author

Graff, Mariaelisa, Scott, Robert A, Justice, Anne E, Young, Kristin L, Feitosa, Mary F, Barata, Llilda, Winkler, Thomas W, Chu, Audrey Y, Mahajan, Anubha, Hadley, David, Xue, Luting, Workalemahu, Tsegaselassie, Heard-Costa, Nancy L, Den Hoed, Marcel, Ahluwalia, Tarunveer S, Qi, Qibin, Ngwa, Julius S, Renström, Frida, Quaye, Lydia, Eicher, John D, Hayes, James E, Cornelis, Marilyn, Kutalik, Zoltan, Lim, Elise, Luan, Jian'an, Huffman, Jennifer E, Zhang, Weihua, Zhao, Wei, Griffin, Paula J, Haller, Toomas, Ahmad, Shafqat, Marques-Vidal, Pedro M, Bien, Stephanie, Yengo, Loic, Teumer, Alexander, Smith, Albert Vernon, Kumari, Meena, Harder, Marie Neergaard, Justesen, Johanne Marie, Kleber, Marcus E, Hollensted, Mette, Lohman, Kurt, Rivera, Natalia V, Whitfield, John B, Zhao, Jing Hua, Stringham, Heather M, Lyytikäinen, Leo-Pekka, Huppertz, Charlotte, Willemsen, Gonneke, Peyrot, Wouter J, Wu, Ying, Kristiansson, Kati, Demirkan, Ayse, Fornage, Myriam, Hassinen, Maija, Bielak, Lawrence F, Cadby, Gemma, Tanaka, Toshiko, Mägi, Reedik, Van Der Most, Peter J, Jackson, Anne U, Bragg-Gresham, Jennifer L, Vitart, Veronique, Marten, Jonathan, Navarro, Pau, Bellis, Claire, Pasko, Dorota, Johansson, Åsa, Snitker, Søren, Cheng, Yu-Ching, Eriksson, Joel, Lim, Unhee, Aadahl, Mette, Adair, Linda S, Amin, Najaf, Balkau, Beverley, Auvinen, Juha, Beilby, John, Bergman, Richard N, Bergmann, Sven, Bertoni, Alain G, Blangero, John, Bonnefond, Amélie, Bonnycastle, Lori L, Borja, Judith B, Brage, Søren, Busonero, Fabio, Buyske, Steve, Campbell, Harry, Chines, Peter S, Collins, Francis S, Corre, Tanguy, Smith, George Davey, Delgado, Graciela E, Dueker, Nicole, Dörr, Marcus, Ebeling, Tapani, Eiriksdottir, Gudny, Esko, Tõnu, Faul, Jessica D, Fu, Mao, Færch, Kristine, Gieger, Christian, Gläser, Sven, Gong, Jian, Gordon-Larsen, Penny, Grallert, Harald, Grammer, Tanja B, Grarup, Niels, Van Grootheest, Gerard, Harald, Kennet, Hastie, Nicholas D, Havulinna, Aki S, Hernandez, Dena, Hindorff, Lucia, Hocking, Lynne J, Holmens, Oddgeir L, Holzapfel, Christina, Hottenga, Jouke Jan, Huang, Jie, Huang, Tao, Hui, Jennie, Huth, Cornelia, Hutri-Kähönen, Nina, James, Alan L, Jansson, John-Olov, Jhun, Min A, Juonala, Markus, Kinnunen, Leena, Koistinen, Heikki A, Kolcic, Ivana, Komulainen, Pirjo, Kuusisto, Johanna, Kvaløy, Kirsti, Kähönen, Mika, Lakka, Timo A, Launer, Lenore J, Lehne, Benjamin, Lindgren, Cecilia M, Lorentzon, Mattias, Luben, Robert, Marre, Michel, Milaneschi, Yuri, Monda, Keri L, Montgomery, Grant W, De Moor, Marleen HM, Mulas, Antonella, Müller-Nurasyid, Martina, Musk, AW, Männikkö, Reija, Männistö, Satu, Narisu, Narisu, Nauck, Matthias, Nettleton, Jennifer A, Nolte, Ilja M, Oldehinkel, Albertine J, Olden, Matthias, Ong, Ken K, Padmanabhan, Sandosh, Paternoster, Lavinia, Perez, Jeremiah, Perola, Markus, Peters, Annette, Peters, Ulrike, Peyser, Patricia A, Prokopenko, Inga, Puolijoki, Hannu, Raitakari, Olli T, Rankinen, Tuomo, Rasmussen-Torvik, Laura J, Rawal, Rajesh, Ridker, Paul M, Rose, Lynda M, Rudan, Igor, Sarti, Cinzia, Sarzynski, Mark A, Savonen, Kai, Scott, William R, Sanna, Serena, Shuldiner, Alan R, Sidney, Steve, Silbernagel, Günther, Smith, Blair H, Smith, Jennifer A, Snieder, Harold, Stančáková, Alena, Sternfeld, Barbara, Swift, Amy J, Tammelin, Tuija, Tan, Sian-Tsung, Thorand, Barbara, Thuillier, Dorothée, Vandenput, Liesbeth, Vestergaard, Henrik, Van Vliet-Ostaptchouk, Jana V, Vohl, Marie-Claude, Völker, Uwe, Waeber, Gérard, Walker, Mark, Wild, Sarah, Wong, Andrew, Wright, Alan F, Zillikens, M Carola, Zubair, Niha, Haiman, Christopher A, Lemarchand, Loic, Gyllensten, Ulf, Ohlsson, Claes, Hofman, Albert, Rivadeneira, Fernando, Uitterlinden, André G, Pérusse, Louis, Wilson, James F, Hayward, Caroline, Polasek, Ozren, Cucca, Francesco, Hveem, Kristian, Hartman, Catharina A, Tönjes, Anke, Bandinelli, Stefania, Palmer, Lyle J, Kardia, Sharon LR, Rauramaa, Rainer, Sørensen, Thorkild IA, Tuomilehto, Jaakko, Salomaa, Veikko, Penninx, Brenda WJH, De Geus, Eco JC, Boomsma, Dorret I, Lehtimäki, Terho, Mangino, Massimo, Laakso, Markku, Bouchard, Claude, Martin, Nicholas G, Kuh, Diana, Liu, Yongmei, Linneberg, Allan, März, Winfried, Strauch, Konstantin, Kivimäki, Mika, Harris, Tamara B, Gudnason, Vilmundur, Völzke, Henry, Qi, Lu, Järvelin, Marjo-Riitta, Chambers, John C, Kooner, Jaspal S, Froguel, Philippe, Kooperberg, Charles, Vollenweider, Peter, Hallmans, Göran, Hansen, Torben, Pedersen, Oluf, Metspalu, Andres, Wareham, Nicholas J, Langenberg, Claudia, Weir, David R, Porteous, David J, Boerwinkle, Eric, Chasman, Daniel I, CHARGE Consortium, EPIC-InterAct Consortium, PAGE Consortium, Abecasis, Gonçalo R, Barroso, Inês, McCarthy, Mark I, Frayling, Timothy M, O'Connell, Jeffrey R, Van Duijn, Cornelia M, Boehnke, Michael, Heid, Iris M, Mohlke, Karen L, Strachan, David P, Fox, Caroline S, Liu, Ching-Ti, Hirschhorn, Joel N, Klein, Robert J, Johnson, Andrew D, Borecki, Ingrid B, Franks, Paul W, North, Kari E, Cupples, L Adrienne, Loos, Ruth JF, and Kilpeläinen, Tuomas O

Subjects
Epigenomics, Male, Genotype, Waist-Hip Ratio, Alpha-Ketoglutarate-Dependent Dioxygenase FTO, 3. Good health, Body Mass Index, Humans, Female, Genetic Predisposition to Disease, Obesity, Waist Circumference, Exercise, Adiposity, Genome-Wide Association Study
Abstract

Physical activity (PA) may modify the genetic effects that give rise to increased risk of obesity. To identify adiposity loci whose effects are modified by PA, we performed genome-wide interaction meta-analyses of BMI and BMI-adjusted waist circumference and waist-hip ratio from up to 200,452 adults of European (n = 180,423) or other ancestry (n = 20,029). We standardized PA by categorizing it into a dichotomous variable where, on average, 23% of participants were categorized as inactive and 77% as physically active. While we replicate the interaction with PA for the strongest known obesity-risk locus in the FTO gene, of which the effect is attenuated by ~30% in physically active individuals compared to inactive individuals, we do not identify additional loci that are sensitive to PA. In additional genome-wide meta-analyses adjusting for PA and interaction with PA, we identify 11 novel adiposity loci, suggesting that accounting for PA or other environmental factors that contribute to variation in adiposity may facilitate gene discovery.

10. Genome-wide meta-analysis of macronutrient intake of 91,114 European ancestry participants from the cohorts for heart and aging research in genomic epidemiology consortium

Author

Merino, Jordi, Dashti, Hassan S, Li, Sherly X, Sarnowski, Chloé, Justice, Anne E, Graff, Misa, Papoutsakis, Constantina, Smith, Caren E, Dedoussis, George V, Lemaitre, Rozenn N, Wojczynski, Mary K, Männistö, Satu, Ngwa, Julius S, Kho, Minjung, Ahluwalia, Tarunveer S, Pervjakova, Natalia, Houston, Denise K, Bouchard, Claude, Huang, Tao, Orho-Melander, Marju, Frazier-Wood, Alexis C, Mook-Kanamori, Dennis O, Pérusse, Louis, Pennell, Craig E, De Vries, Paul S, Voortman, Trudy, Li, Olivia, Kanoni, Stavroula, Rose, Lynda M, Lehtimäki, Terho, Zhao, Jing Hua, Feitosa, Mary F, Luan, Jian'an, McKeown, Nicola M, Smith, Jennifer A, Hansen, Torben, Eklund, Niina, Nalls, Mike A, Rankinen, Tuomo, Huang, Jinyan, Hernandez, Dena G, Schulz, Christina-Alexandra, Manichaikul, Ani, Li-Gao, Ruifang, Vohl, Marie-Claude, Wang, Carol A, Van Rooij, Frank JA, Shin, Jean, Kalafati, Ioanna P, Day, Felix, Ridker, Paul M, Kähönen, Mika, Siscovick, David S, Langenberg, Claudia, Zhao, Wei, Astrup, Arne, Knekt, Paul, Garcia, Melissa, Rao, DC, Qi, Qibin, Ferrucci, Luigi, Ericson, Ulrika, Blangero, John, Hofman, Albert, Pausova, Zdenka, Mikkilä, Vera, Wareham, Nick J, Kardia, Sharon LR, Pedersen, Oluf, Jula, Antti, Curran, Joanne E, Zillikens, M Carola, Viikari, Jorma S, Forouhi, Nita G, Ordovás, José M, Lieske, John C, Rissanen, Harri, Uitterlinden, André G, Raitakari, Olli T, Kiefte-De Jong, Jessica C, Dupuis, Josée, Rotter, Jerome I, North, Kari E, Scott, Robert A, Province, Michael A, Perola, Markus, Cupples, L Adrienne, Turner, Stephen T, Sørensen, Thorkild IA, Salomaa, Veikko, Liu, Yongmei, Sung, Yun J, Qi, Lu, Bandinelli, Stefania, Rich, Stephen S, De Mutsert, Renée, Tremblay, Angelo, Oddy, Wendy H, Franco, Oscar H, Paus, Tomas, Florez, Jose C, Deloukas, Panos, Lyytikäinen, Leo-Pekka, Chasman, Daniel I, Chu, Audrey Y, and Tanaka, Toshiko

Subjects
2. Zero hunger, Male, Aging, Genotype, Heart Diseases, Receptors, Retinoic Acid, Alpha-Ketoglutarate-Dependent Dioxygenase FTO, Membrane Proteins, Genomics, Nutrients, Middle Aged, Polymorphism, Single Nucleotide, White People, 3. Good health, Cohort Studies, Fibroblast Growth Factors, Genetic Loci, Humans, Female, Genetic Predisposition to Disease, Obesity, Energy Intake, Aged, Genome-Wide Association Study
Abstract

Macronutrient intake, the proportion of calories consumed from carbohydrate, fat, and protein, is an important risk factor for metabolic diseases with significant familial aggregation. Previous studies have identified two genetic loci for macronutrient intake, but incomplete coverage of genetic variation and modest sample sizes have hindered the discovery of additional loci. Here, we expanded the genetic landscape of macronutrient intake, identifying 12 suggestively significant loci (P < 1 × 10-6) associated with intake of any macronutrient in 91,114 European ancestry participants. Four loci replicated and reached genome-wide significance in a combined meta-analysis including 123,659 European descent participants, unraveling two novel loci; a common variant in RARB locus for carbohydrate intake and a rare variant in DRAM1 locus for protein intake, and corroborating earlier FGF21 and FTO findings. In additional analysis of 144,770 participants from the UK Biobank, all identified associations from the two-stage analysis were confirmed except for DRAM1. Identified loci might have implications in brain and adipose tissue biology and have clinical impact in obesity-related phenotypes. Our findings provide new insight into biological functions related to macronutrient intake.

Catalog

Books, media, physical & digital resources
See catalog results