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52. Multi-omics Integration Identifies Genes Influencing Traits Associated with Cardiovascular Risks: The Long Life Family Study

Author

Acharya, Sandeep, primary, Liao, Shu, additional, Jung, Wooseok J., additional, Kang, Yu S., additional, Moghaddam, Vaha A., additional, Feitosa, Mary, additional, Wojczynski, Mary, additional, Lin, Shiow, additional, Anema, Jason A., additional, Schwander, Karen, additional, Connell, Jeff O, additional, Province, Mike, additional, and Brent, Michael R., additional

Published
2024
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53. Meta-analysis uncovers genome-wide significant variants for rapid kidney function decline

Author

Alizadeh, Behrooz Z., Boezen, H. Marike, Franke, Lude, van der Harst, Pim, Navis, Gerjan, Rots, Marianne, Snieder, Harold, Swertz, Morris, Wolffenbuttel, Bruce H.R., Wijmenga, Cisca, Abecasis, Goncalo, Baras, Aris, Cantor, Michael, Coppola, Giovanni, Economides, Aris, Lotta, Luca A., Overton, John D., Reid, Jeffrey G., Shuldiner, Alan, Beechert, Christina, Forsythe, Caitlin, Fuller, Erin D., Gu, Zhenhua, Lattari, Michael, Lopez, Alexander, Schleicher, Thomas D., Padilla, Maria Sotiropoulos, Toledo, Karina, Widom, Louis, Wolf, Sarah E., Pradhan, Manasi, Manoochehri, Kia, Ulloa, Ricardo H., Bai, Xiaodong, Balasubramanian, Suganthi, Barnard, Leland, Blumenfeld, Andrew, Eom, Gisu, Habegger, Lukas, Hawes, Alicia, Khalid, Shareef, Maxwell, Evan K., Salerno, William, Staples, Jeffrey C., Jones, Marcus B., Mitnaul, Lyndon J., Gorski, Mathias, Jung, Bettina, Li, Yong, Matias-Garcia, Pamela R., Wuttke, Matthias, Coassin, Stefan, Thio, Chris H.L., Kleber, Marcus E., Winkler, Thomas W., Wanner, Veronika, Chai, Jin-Fang, Chu, Audrey Y., Cocca, Massimiliano, Feitosa, Mary F., Ghasemi, Sahar, Hoppmann, Anselm, Horn, Katrin, Li, Man, Nutile, Teresa, Scholz, Markus, Sieber, Karsten B., Teumer, Alexander, Tin, Adrienne, Wang, Judy, Tayo, Bamidele O., Ahluwalia, Tarunveer S., Almgren, Peter, Bakker, Stephan J.L., Banas, Bernhard, Bansal, Nisha, Biggs, Mary L., Boerwinkle, Eric, Bottinger, Erwin P., Brenner, Hermann, Carroll, Robert J., Chalmers, John, Chee, Miao-Li, Chee, Miao-Ling, Cheng, Ching-Yu, Coresh, Josef, de Borst, Martin H., Degenhardt, Frauke, Eckardt, Kai-Uwe, Endlich, Karlhans, Franke, Andre, Freitag-Wolf, Sandra, Gampawar, Piyush, Gansevoort, Ron T., Ghanbari, Mohsen, Gieger, Christian, Hamet, Pavel, Ho, Kevin, Hofer, Edith, Holleczek, Bernd, Xian Foo, Valencia Hui, Hutri-Kähönen, Nina, Hwang, Shih-Jen, Ikram, M. Arfan, Josyula, Navya Shilpa, Kähönen, Mika, Khor, Chiea-Chuen, Koenig, Wolfgang, Kramer, Holly, Krämer, Bernhard K., Kühnel, Brigitte, Lange, Leslie A., Lehtimäki, Terho, Lieb, Wolfgang, Loos, Ruth J.F., Lukas, Mary Ann, Lyytikäinen, Leo-Pekka, Meisinger, Christa, Meitinger, Thomas, Melander, Olle, Milaneschi, Yuri, Mishra, Pashupati P., Mononen, Nina, Mychaleckyj, Josyf C., Nadkarni, Girish N., Nauck, Matthias, Nikus, Kjell, Ning, Boting, Nolte, Ilja M., O’Donoghue, Michelle L., Orho-Melander, Marju, Pendergrass, Sarah A., Penninx, Brenda W.J.H., Preuss, Michael H., Psaty, Bruce M., Raffield, Laura M., Raitakari, Olli T., Rettig, Rainer, Rheinberger, Myriam, Rice, Kenneth M., Rosenkranz, Alexander R., Rossing, Peter, Rotter, Jerome I., Sabanayagam, Charumathi, Schmidt, Helena, Schmidt, Reinhold, Schöttker, Ben, Schulz, Christina-Alexandra, Sedaghat, Sanaz, Shaffer, Christian M., Strauch, Konstantin, Szymczak, Silke, Taylor, Kent D., Tremblay, Johanne, Chaker, Layal, van der Most, Peter J., Verweij, Niek, Völker, Uwe, Waldenberger, Melanie, Wallentin, Lars, Waterworth, Dawn M., White, Harvey D., Wilson, James G., Wong, Tien-Yin, Woodward, Mark, Yang, Qiong, Yasuda, Masayuki, Yerges-Armstrong, Laura M., Zhang, Yan, Wanner, Christoph, Böger, Carsten A., Köttgen, Anna, Kronenberg, Florian, Pattaro, Cristian, and Heid, Iris M.

Published
2021
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54. Erratum: Large meta-analysis of genome-wide association studies identifies five loci for lean body mass.

Author

Zillikens, M Carola, Demissie, Serkalem, Hsu, Yi-Hsiang, Yerges-Armstrong, Laura M, Chou, Wen-Chi, Stolk, Lisette, Livshits, Gregory, Broer, Linda, Johnson, Toby, Koller, Daniel L, Kutalik, Zoltán, Luan, Jian'an, Malkin, Ida, Ried, Janina S, Smith, Albert V, Thorleifsson, Gudmar, Vandenput, Liesbeth, Hua Zhao, Jing, Zhang, Weihua, Aghdassi, Ali, Åkesson, Kristina, Amin, Najaf, Baier, Leslie J, Barroso, Inês, Bennett, David A, Bertram, Lars, Biffar, Rainer, Bochud, Murielle, Boehnke, Michael, Borecki, Ingrid B, Buchman, Aron S, Byberg, Liisa, Campbell, Harry, Campos Obanda, Natalia, Cauley, Jane A, Cawthon, Peggy M, Cederberg, Henna, Chen, Zhao, Cho, Nam H, Jin Choi, Hyung, Claussnitzer, Melina, Collins, Francis, Cummings, Steven R, De Jager, Philip L, Demuth, Ilja, Dhonukshe-Rutten, Rosalie AM, Diatchenko, Luda, Eiriksdottir, Gudny, Enneman, Anke W, Erdos, Mike, Eriksson, Johan G, Eriksson, Joel, Estrada, Karol, Evans, Daniel S, Feitosa, Mary F, Fu, Mao, Garcia, Melissa, Gieger, Christian, Girke, Thomas, Glazer, Nicole L, Grallert, Harald, Grewal, Jagvir, Han, Bok-Ghee, Hanson, Robert L, Hayward, Caroline, Hofman, Albert, Hoffman, Eric P, Homuth, Georg, Hsueh, Wen-Chi, Hubal, Monica J, Hubbard, Alan, Huffman, Kim M, Husted, Lise B, Illig, Thomas, Ingelsson, Erik, Ittermann, Till, Jansson, John-Olov, Jordan, Joanne M, Jula, Antti, Karlsson, Magnus, Khaw, Kay-Tee, Kilpeläinen, Tuomas O, Klopp, Norman, Kloth, Jacqueline SL, Koistinen, Heikki A, Kraus, William E, Kritchevsky, Stephen, Kuulasmaa, Teemu, Kuusisto, Johanna, Laakso, Markku, Lahti, Jari, Lang, Thomas, Langdahl, Bente L, Launer, Lenore J, Lee, Jong-Young, Lerch, Markus M, Lewis, Joshua R, Lind, Lars, Lindgren, Cecilia, and Liu, Yongmei

Subjects
Epidemiology, Biological Sciences, Health Sciences, Genetics
Abstract

A correction to this article has been published and is linked from the HTML version of this article.

Published
2017

55. Large meta-analysis of genome-wide association studies identifies five loci for lean body mass.

Author

Zillikens, M Carola, Demissie, Serkalem, Hsu, Yi-Hsiang, Yerges-Armstrong, Laura M, Chou, Wen-Chi, Stolk, Lisette, Livshits, Gregory, Broer, Linda, Johnson, Toby, Koller, Daniel L, Kutalik, Zoltán, Luan, Jian'an, Malkin, Ida, Ried, Janina S, Smith, Albert V, Thorleifsson, Gudmar, Vandenput, Liesbeth, Hua Zhao, Jing, Zhang, Weihua, Aghdassi, Ali, Åkesson, Kristina, Amin, Najaf, Baier, Leslie J, Barroso, Inês, Bennett, David A, Bertram, Lars, Biffar, Rainer, Bochud, Murielle, Boehnke, Michael, Borecki, Ingrid B, Buchman, Aron S, Byberg, Liisa, Campbell, Harry, Campos Obanda, Natalia, Cauley, Jane A, Cawthon, Peggy M, Cederberg, Henna, Chen, Zhao, Cho, Nam H, Jin Choi, Hyung, Claussnitzer, Melina, Collins, Francis, Cummings, Steven R, De Jager, Philip L, Demuth, Ilja, Dhonukshe-Rutten, Rosalie AM, Diatchenko, Luda, Eiriksdottir, Gudny, Enneman, Anke W, Erdos, Mike, Eriksson, Johan G, Eriksson, Joel, Estrada, Karol, Evans, Daniel S, Feitosa, Mary F, Fu, Mao, Garcia, Melissa, Gieger, Christian, Girke, Thomas, Glazer, Nicole L, Grallert, Harald, Grewal, Jagvir, Han, Bok-Ghee, Hanson, Robert L, Hayward, Caroline, Hofman, Albert, Hoffman, Eric P, Homuth, Georg, Hsueh, Wen-Chi, Hubal, Monica J, Hubbard, Alan, Huffman, Kim M, Husted, Lise B, Illig, Thomas, Ingelsson, Erik, Ittermann, Till, Jansson, John-Olov, Jordan, Joanne M, Jula, Antti, Karlsson, Magnus, Khaw, Kay-Tee, Kilpeläinen, Tuomas O, Klopp, Norman, Kloth, Jacqueline SL, Koistinen, Heikki A, Kraus, William E, Kritchevsky, Stephen, Kuulasmaa, Teemu, Kuusisto, Johanna, Laakso, Markku, Lahti, Jari, Lang, Thomas, Langdahl, Bente L, Launer, Lenore J, Lee, Jong-Young, Lerch, Markus M, Lewis, Joshua R, Lind, Lars, Lindgren, Cecilia, and Liu, Yongmei

Subjects
Humans, Thinness, 17-Hydroxysteroid Dehydrogenases, Aldehyde Oxidoreductases, Extracellular Matrix Proteins, Body Composition, Phenotype, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Regulatory Elements, Transcriptional, Versicans, Genome-Wide Association Study, Insulin Receptor Substrate Proteins, ADAMTS Proteins, Alpha-Ketoglutarate-Dependent Dioxygenase FTO, Human Genome, Genetics, 1.1 Normal biological development and functioning
Abstract

Lean body mass, consisting mostly of skeletal muscle, is important for healthy aging. We performed a genome-wide association study for whole body (20 cohorts of European ancestry with n = 38,292) and appendicular (arms and legs) lean body mass (n = 28,330) measured using dual energy X-ray absorptiometry or bioelectrical impedance analysis, adjusted for sex, age, height, and fat mass. Twenty-one single-nucleotide polymorphisms were significantly associated with lean body mass either genome wide (p

Published
2017

56. 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

57. Discovery and fine-mapping of adiposity loci using high density imputation of genome-wide association studies in individuals of African ancestry: African Ancestry Anthropometry Genetics Consortium.

Author

Ng, Maggie CY, Graff, Mariaelisa, Lu, Yingchang, Justice, Anne E, Mudgal, Poorva, Liu, Ching-Ti, Young, Kristin, Yanek, Lisa R, Feitosa, Mary F, Wojczynski, Mary K, Rand, Kristin, Brody, Jennifer A, Cade, Brian E, Dimitrov, Latchezar, Duan, Qing, Guo, Xiuqing, Lange, Leslie A, Nalls, Michael A, Okut, Hayrettin, Tajuddin, Salman M, Tayo, Bamidele O, Vedantam, Sailaja, Bradfield, Jonathan P, Chen, Guanjie, Chen, Wei-Min, Chesi, Alessandra, Irvin, Marguerite R, Padhukasahasram, Badri, Smith, Jennifer A, Zheng, Wei, Allison, Matthew A, Ambrosone, Christine B, Bandera, Elisa V, Bartz, Traci M, Berndt, Sonja I, Bernstein, Leslie, Blot, William J, Bottinger, Erwin P, Carpten, John, Chanock, Stephen J, Chen, Yii-Der Ida, Conti, David V, Cooper, Richard S, Fornage, Myriam, Freedman, Barry I, Garcia, Melissa, Goodman, Phyllis J, Hsu, Yu-Han H, Hu, Jennifer, Huff, Chad D, Ingles, Sue A, John, Esther M, Kittles, Rick, Klein, Eric, Li, Jin, McKnight, Barbara, Nayak, Uma, Nemesure, Barbara, Ogunniyi, Adesola, Olshan, Andrew, Press, Michael F, Rohde, Rebecca, Rybicki, Benjamin A, Salako, Babatunde, Sanderson, Maureen, Shao, Yaming, Siscovick, David S, Stanford, Janet L, Stevens, Victoria L, Stram, Alex, Strom, Sara S, Vaidya, Dhananjay, Witte, John S, Yao, Jie, Zhu, Xiaofeng, Ziegler, Regina G, Zonderman, Alan B, Adeyemo, Adebowale, Ambs, Stefan, Cushman, Mary, Faul, Jessica D, Hakonarson, Hakon, Levin, Albert M, Nathanson, Katherine L, Ware, Erin B, Weir, David R, Zhao, Wei, Zhi, Degui, Bone Mineral Density in Childhood Study (BMDCS) Group, Arnett, Donna K, Grant, Struan FA, Kardia, Sharon LR, Oloapde, Olufunmilayo I, Rao, DC, Rotimi, Charles N, Sale, Michele M, Williams, L Keoki, Zemel, Babette S, Becker, Diane M, and Borecki, Ingrid B

Subjects
Bone Mineral Density in Childhood Study (BMDCS) Group, Humans, Obesity, Genetic Predisposition to Disease, Serine Endopeptidases, Anthropometry, Body Mass Index, Waist-Hip Ratio, Chromosome Mapping, Gene Frequency, Linkage Disequilibrium, Polymorphism, Single Nucleotide, African Continental Ancestry Group, European Continental Ancestry Group, Female, Male, Adiposity, Genome-Wide Association Study, Transcription Factor 7-Like 2 Protein, Genetics, Human Genome, 2.1 Biological and endogenous factors, Developmental Biology
Abstract

Genome-wide association studies (GWAS) have identified >300 loci associated with measures of adiposity including body mass index (BMI) and waist-to-hip ratio (adjusted for BMI, WHRadjBMI), but few have been identified through screening of the African ancestry genomes. We performed large scale meta-analyses and replications in up to 52,895 individuals for BMI and up to 23,095 individuals for WHRadjBMI from the African Ancestry Anthropometry Genetics Consortium (AAAGC) using 1000 Genomes phase 1 imputed GWAS to improve coverage of both common and low frequency variants in the low linkage disequilibrium African ancestry genomes. In the sex-combined analyses, we identified one novel locus (TCF7L2/HABP2) for WHRadjBMI and eight previously established loci at P < 5×10-8: seven for BMI, and one for WHRadjBMI in African ancestry individuals. An additional novel locus (SPRYD7/DLEU2) was identified for WHRadjBMI when combined with European GWAS. In the sex-stratified analyses, we identified three novel loci for BMI (INTS10/LPL and MLC1 in men, IRX4/IRX2 in women) and four for WHRadjBMI (SSX2IP, CASC8, PDE3B and ZDHHC1/HSD11B2 in women) in individuals of African ancestry or both African and European ancestry. For four of the novel variants, the minor allele frequency was low (

Published
2017

58. Genome-wide association meta-analysis of fish and EPA+DHA consumption in 17 US and European cohorts

Author

Mozaffarian, Dariush, Dashti, Hassan S, Wojczynski, Mary K, Chu, Audrey Y, Nettleton, Jennifer A, Männistö, Satu, Kristiansson, Kati, Reedik, Mägi, Lahti, Jari, Houston, Denise K, Cornelis, Marilyn C, van Rooij, Frank JA, Dimitriou, Maria, Kanoni, Stavroula, Mikkilä, Vera, Steffen, Lyn M, de Oliveira Otto, Marcia C, Qi, Lu, Psaty, Bruce, Djousse, Luc, Rotter, Jerome I, Harald, Kennet, Perola, Markus, Rissanen, Harri, Jula, Antti, Krista, Fischer, Mihailov, Evelin, Feitosa, Mary F, Ngwa, Julius S, Xue, Luting, Jacques, Paul F, Perälä, Mia-Maria, Palotie, Aarno, Liu, Yongmei, Nalls, Nike A, Ferrucci, Luigi, Hernandez, Dena, Manichaikul, Ani, Tsai, Michael Y, Jong, Jessica C Kiefte-de, Hofman, Albert, Uitterlinden, André G, Rallidis, Loukianos, Ridker, Paul M, Rose, Lynda M, Buring, Julie E, Lehtimäki, Terho, Kähönen, Mika, Viikari, Jorma, Lemaitre, Rozenn, Salomaa, Veikko, Knekt, Paul, Metspalu, Andres, Borecki, Ingrid B, Cupples, L Adrienne, Eriksson, Johan G, Kritchevsky, Stephen B, Bandinelli, Stefania, Siscovick, David, Franco, Oscar H, Deloukas, Panos, Dedoussis, George, Chasman, Daniel I, Raitakari, Olli, and Tanaka, Toshiko

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Genetics, Epidemiology, Health Sciences, Nutrition and Dietetics, Complementary and Integrative Health, Human Genome, Nutrition, Aging, Prevention, Obesity, Stroke, Cardiovascular, Oral and gastrointestinal, Adult, Aged, Cohort Studies, Docosahexaenoic Acids, Eicosapentaenoic Acid, Europe, Female, Genome-Wide Association Study, Humans, Male, Middle Aged, Seafood, United States, White People, General Science & Technology
Abstract

BackgroundRegular fish and omega-3 consumption may have several health benefits and are recommended by major dietary guidelines. Yet, their intakes remain remarkably variable both within and across populations, which could partly owe to genetic influences.ObjectiveTo identify common genetic variants that influence fish and dietary eicosapentaenoic acid plus docosahexaenoic acid (EPA+DHA) consumption.DesignWe conducted genome-wide association (GWA) meta-analysis of fish (n = 86,467) and EPA+DHA (n = 62,265) consumption in 17 cohorts of European descent from the CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology) Consortium Nutrition Working Group. Results from cohort-specific GWA analyses (additive model) for fish and EPA+DHA consumption were adjusted for age, sex, energy intake, and population stratification, and meta-analyzed separately using fixed-effect meta-analysis with inverse variance weights (METAL software). Additionally, heritability was estimated in 2 cohorts.ResultsHeritability estimates for fish and EPA+DHA consumption ranged from 0.13-0.24 and 0.12-0.22, respectively. A significant GWA for fish intake was observed for rs9502823 on chromosome 6: each copy of the minor allele (FreqA = 0.015) was associated with 0.029 servings/day (~1 serving/month) lower fish consumption (P = 1.96x10-8). No significant association was observed for EPA+DHA, although rs7206790 in the obesity-associated FTO gene was among top hits (P = 8.18x10-7). Post-hoc calculations demonstrated 95% statistical power to detect a genetic variant associated with effect size of 0.05% for fish and 0.08% for EPA+DHA.ConclusionsThese novel findings suggest that non-genetic personal and environmental factors are principal determinants of the remarkable variation in fish consumption, representing modifiable targets for increasing intakes among all individuals. Genes underlying the signal at rs72838923 and mechanisms for the association warrant further investigation.

Published
2017

59. Multiethnic genome-wide meta-analysis of ectopic fat depots identifies loci associated with adipocyte development and differentiation

Author

Chu, Audrey Y, Deng, Xuan, Fisher, Virginia A, Drong, Alexander, Zhang, Yang, Feitosa, Mary F, Liu, Ching-Ti, Weeks, Olivia, Choh, Audrey C, Duan, Qing, Dyer, Thomas D, Eicher, John D, Guo, Xiuqing, Heard-Costa, Nancy L, Kacprowski, Tim, Kent, Jack W, Lange, Leslie A, Liu, Xinggang, Lohman, Kurt, Lu, Lingyi, Mahajan, Anubha, O'Connell, Jeffrey R, Parihar, Ankita, Peralta, Juan M, Smith, Albert V, Zhang, Yi, Homuth, Georg, Kissebah, Ahmed H, Kullberg, Joel, Laqua, René, Launer, Lenore J, Nauck, Matthias, Olivier, Michael, Peyser, Patricia A, Terry, James G, Wojczynski, Mary K, Yao, Jie, Bielak, Lawrence F, Blangero, John, Borecki, Ingrid B, Bowden, Donald W, Carr, John Jeffrey, Czerwinski, Stefan A, Ding, Jingzhong, Friedrich, Nele, Gudnason, Vilmunder, Harris, Tamara B, Ingelsson, Erik, Johnson, Andrew D, Kardia, Sharon LR, Langefeld, Carl D, Lind, Lars, Liu, Yongmei, Mitchell, Braxton D, Morris, Andrew P, Mosley, Thomas H, Rotter, Jerome I, Shuldiner, Alan R, Towne, Bradford, Völzke, Henry, Wallaschofski, Henri, Wilson, James G, Allison, Matthew, Lindgren, Cecilia M, Goessling, Wolfram, Cupples, L Adrienne, Steinhauser, Matthew L, and Fox, Caroline S

Subjects
Nutrition, Human Genome, Genetics, Adipocytes, Animals, Body Fat Distribution, Cell Differentiation, Cohort Studies, Ethnicity, Female, Genetic Loci, Genetic Markers, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Male, Mice, Mice, Inbred C57BL, Obesity, Phenotype, Polymorphism, Single Nucleotide, Biological Sciences, Medical and Health Sciences, Developmental Biology
Abstract

Variation in body fat distribution contributes to the metabolic sequelae of obesity. The genetic determinants of body fat distribution are poorly understood. The goal of this study was to gain new insights into the underlying genetics of body fat distribution by conducting sample-size-weighted fixed-effects genome-wide association meta-analyses in up to 9,594 women and 8,738 men of European, African, Hispanic and Chinese ancestry, with and without sex stratification, for six traits associated with ectopic fat (hereinafter referred to as ectopic-fat traits). In total, we identified seven new loci associated with ectopic-fat traits (ATXN1, UBE2E2, EBF1, RREB1, GSDMB, GRAMD3 and ENSA; P < 5 × 10-8; false discovery rate < 1%). Functional analysis of these genes showed that loss of function of either Atxn1 or Ube2e2 in primary mouse adipose progenitor cells impaired adipocyte differentiation, suggesting physiological roles for ATXN1 and UBE2E2 in adipogenesis. Future studies are necessary to further explore the mechanisms by which these genes affect adipocyte biology and how their perturbations contribute to systemic metabolic disease.

Published
2017

60. Multiethnic Exome-Wide Association Study of Subclinical Atherosclerosis

Author

Natarajan, Pradeep, Bis, Joshua C, Bielak, Lawrence F, Cox, Amanda J, Dörr, Marcus, Feitosa, Mary F, Franceschini, Nora, Guo, Xiuqing, Hwang, Shih-Jen, Isaacs, Aaron, Jhun, Min A, Kavousi, Maryam, Li-Gao, Ruifang, Lyytikäinen, Leo-Pekka, Marioni, Riccardo E, Schminke, Ulf, Stitziel, Nathan O, Tada, Hayato, van Setten, Jessica, Smith, Albert V, Vojinovic, Dina, Yanek, Lisa R, Yao, Jie, Yerges-Armstrong, Laura M, Amin, Najaf, Baber, Usman, Borecki, Ingrid B, Carr, J Jeffrey, Chen, Yii-Der Ida, Cupples, L Adrienne, de Jong, Pim A, de Koning, Harry, de Vos, Bob D, Demirkan, Ayse, Fuster, Valentin, Franco, Oscar H, Goodarzi, Mark O, Harris, Tamara B, Heckbert, Susan R, Heiss, Gerardo, Hoffmann, Udo, Hofman, Albert, Išgum, Ivana, Jukema, J Wouter, Kähönen, Mika, Kardia, Sharon LR, Kral, Brian G, Launer, Lenore J, Massaro, Joe, Mehran, Roxana, Mitchell, Braxton D, Mosley, Thomas H, de Mutsert, Renée, Newman, Anne B, Nguyen, Khanh-Dung, North, Kari E, O'Connell, Jeffrey R, Oudkerk, Matthijs, Pankow, James S, Peloso, Gina M, Post, Wendy, Province, Michael A, Raffield, Laura M, Raitakari, Olli T, Reilly, Dermot F, Rivadeneira, Fernando, Rosendaal, Frits, Sartori, Samantha, Taylor, Kent D, Teumer, Alexander, Trompet, Stella, Turner, Stephen T, Uitterlinden, Andre G, Vaidya, Dhananjay, van der Lugt, Aad, Völker, Uwe, Wardlaw, Joanna M, Wassel, Christina L, Weiss, Stefan, Wojczynski, Mary K, Becker, Diane M, Becker, Lewis C, Boerwinkle, Eric, Bowden, Donald W, Deary, Ian J, Dehghan, Abbas, Felix, Stephan B, Gudnason, Vilmundur, Lehtimäki, Terho, Mathias, Rasika, Mook-Kanamori, Dennis O, Psaty, Bruce M, Rader, Daniel J, Rotter, Jerome I, Wilson, James G, van Duijn, Cornelia M, Völzke, Henry, Kathiresan, Sekar, Peyser, Patricia A, and O'Donnell, Christopher J

Subjects
Biomedical and Clinical Sciences, Cardiovascular Medicine and Haematology, Heart Disease - Coronary Heart Disease, Aging, Human Genome, Atherosclerosis, Cardiovascular, Heart Disease, Clinical Research, Genetics, Aetiology, 2.1 Biological and endogenous factors, Good Health and Well Being, Apolipoprotein B-100, Apolipoprotein E2, Asymptomatic Diseases, Black People, Carotid Artery Diseases, Carotid Intima-Media Thickness, Cholesterol, LDL, Computed Tomography Angiography, Coronary Angiography, Coronary Artery Disease, Exome, Gene Frequency, Genetic Markers, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Odds Ratio, Oligonucleotide Array Sequence Analysis, Phenotype, Prognosis, Risk Assessment, Risk Factors, Vascular Calcification, White People, carotid intima-media thickness, coronary artery calcification, exome, genome-wide association study, genomics, CHARGE Consortium, carotid intima–media thickness, Medical Biotechnology, Cardiorespiratory Medicine and Haematology, Cardiovascular System & Hematology, Cardiovascular medicine and haematology
Abstract

BackgroundThe burden of subclinical atherosclerosis in asymptomatic individuals is heritable and associated with elevated risk of developing clinical coronary heart disease. We sought to identify genetic variants in protein-coding regions associated with subclinical atherosclerosis and the risk of subsequent coronary heart disease.Methods and resultsWe studied a total of 25 109 European ancestry and African ancestry participants with coronary artery calcification (CAC) measured by cardiac computed tomography and 52 869 participants with common carotid intima-media thickness measured by ultrasonography within the CHARGE Consortium (Cohorts for Heart and Aging Research in Genomic Epidemiology). Participants were genotyped for 247 870 DNA sequence variants (231 539 in exons) across the genome. A meta-analysis of exome-wide association studies was performed across cohorts for CAC and carotid intima-media thickness. APOB p.Arg3527Gln was associated with 4-fold excess CAC (P=3×10-10). The APOE ε2 allele (p.Arg176Cys) was associated with both 22.3% reduced CAC (P=1×10-12) and 1.4% reduced carotid intima-media thickness (P=4×10-14) in carriers compared with noncarriers. In secondary analyses conditioning on low-density lipoprotein cholesterol concentration, the ε2 protective association with CAC, although attenuated, remained strongly significant. Additionally, the presence of ε2 was associated with reduced risk for coronary heart disease (odds ratio 0.77; P=1×10-11).ConclusionsExome-wide association meta-analysis demonstrates that protein-coding variants in APOB and APOE associate with subclinical atherosclerosis. APOE ε2 represents the first significant association for multiple subclinical atherosclerosis traits across multiple ethnicities, as well as clinical coronary heart disease.

Published
2016

62. Meta-analysis of rare and common exome chip variants identifies S1PR4 and other loci influencing blood cell traits

Author

Pankratz, Nathan, Schick, Ursula M, Zhou, Yi, Zhou, Wei, Ahluwalia, Tarunveer Singh, Allende, Maria Laura, Auer, Paul L, Bork-Jensen, Jette, Brody, Jennifer A, Chen, Ming-Huei, Clavo, Vinna, Eicher, John D, Grarup, Niels, Hagedorn, Elliott J, Hu, Bella, Hunker, Kristina, Johnson, Andrew D, Leusink, Maarten, Lu, Yingchang, Lyytikainen, Leo-Pekka, Manichaikul, Ani, Marioni, Riccardo E, Nalls, Mike A, Pazoki, Raha, Smith, Albert Vernon, van Rooij, Frank JA, Yang, Min-Lee, Zhang, Xiaoling, Zhang, Yan, Asselbergs, Folkert W, Boerwinkle, Eric, Borecki, Ingrid B, Bottinger, Erwin P, Cushman, Mary, de Bakker, Paul IW, Deary, Ian J, Dong, Liguang, Feitosa, Mary F, Floyd, James S, Franceschini, Nora, Franco, Oscar H, Garcia, Melissa E, Grove, Megan L, Gudnason, Vilmundur, Hansen, Torben, Harris, Tamara B, Hofman, Albert, Jackson, Rebecca D, Jia, Jia, Kahonen, Mika, Launer, Lenore J, Lehtimaki, Terho, Liewald, David C, Linneberg, Allan, Liu, Yongmei, Loos, Ruth JF, Nguyen, Vy M, Numans, Mattijs E, Pedersen, Oluf, Psaty, Bruce M, Raitakari, Olli T, Rich, Stephen S, Rivadeneira, Fernando, Di Sant, Amanda M Rosa, Rotter, Jerome I, Starr, John M, Taylor, Kent D, Thuesen, Betina Heinsbaek, Tracy, Russell P, Uitterlinden, Andre G, Wang, Jiansong, Wang, Judy, Dehghan, Abbas, Huo, Yong, Cupples, L Adrienne, Wilson, James G, Proia, Richard L, Zon, Leonard I, O'Donnell, Christopher J, Reiner, Alex P, and Ganesh, Santhi K

Subjects
Biological Sciences, Genetics, Prevention, Human Genome, Clinical Research, Aetiology, 2.1 Biological and endogenous factors, Cardiovascular, Animals, Erythrocyte Count, Erythrocytes, Ethnicity, Exome, Female, Genetic Loci, Genome-Wide Association Study, Hematocrit, Humans, Male, Mice, Quantitative Trait Loci, Receptors, Lysosphingolipid, Zebrafish, CHARGE Consortium Hematology Working Group, Medical and Health Sciences, Developmental Biology, Agricultural biotechnology, Bioinformatics and computational biology
Abstract

Hematologic measures such as hematocrit and white blood cell (WBC) count are heritable and clinically relevant. We analyzed erythrocyte and WBC phenotypes in 52,531 individuals (37,775 of European ancestry, 11,589 African Americans, and 3,167 Hispanic Americans) from 16 population-based cohorts with Illumina HumanExome BeadChip genotypes. We then performed replication analyses of new discoveries in 18,018 European-American women and 5,261 Han Chinese. We identified and replicated four new erythrocyte trait-locus associations (CEP89, SHROOM3, FADS2, and APOE) and six new WBC loci for neutrophil count (S1PR4), monocyte count (BTBD8, NLRP12, and IL17RA), eosinophil count (IRF1), and total WBC count (MYB). The association of a rare missense variant in S1PR4 supports the role of sphingosine-1-phosphate signaling in leukocyte trafficking and circulating neutrophil counts. Loss-of-function experiments for S1pr4 in mouse and s1pr4 in zebrafish demonstrated phenotypes consistent with the association observed in humans and altered kinetics of neutrophil recruitment and resolution in response to tissue injury.

Published
2016

63. An Empirical Comparison of Joint and Stratified Frameworks for Studying G × E Interactions: Systolic Blood Pressure and Smoking in the CHARGE Gene‐Lifestyle Interactions Working Group

Author

Sung, Yun Ju, Winkler, Thomas W, Manning, Alisa K, Aschard, Hugues, Gudnason, Vilmundur, Harris, Tamara B, Smith, Albert V, Boerwinkle, Eric, Brown, Michael R, Morrison, Alanna C, Fornage, Myriam, Lin, Li-An, Richard, Melissa, Bartz, Traci M, Psaty, Bruce M, Hayward, Caroline, Polasek, Ozren, Marten, Jonathan, Rudan, Igor, Feitosa, Mary F, Kraja, Aldi T, Province, Michael A, Deng, Xuan, Fisher, Virginia A, Zhou, Yanhua, Bielak, Lawrence F, Smith, Jennifer, Huffman, Jennifer E, Padmanabhan, Sandosh, Smith, Blair H, Ding, Jingzhong, Liu, Yongmei, Lohman, Kurt, Bouchard, Claude, Rankinen, Tuomo, Rice, Treva K, Arnett, Donna, Schwander, Karen, Guo, Xiuqing, Palmas, Walter, Rotter, Jerome I, Alfred, Tamuno, Bottinger, Erwin P, Loos, Ruth JF, Amin, Najaf, Franco, Oscar H, van Duijn, Cornelia M, Vojinovic, Dina, Chasman, Daniel I, Ridker, Paul M, Rose, Lynda M, Kardia, Sharon, Zhu, Xiaofeng, Rice, Kenneth, Borecki, Ingrid B, Rao, Dabeeru C, Gauderman, W James, and Cupples, L Adrienne

Subjects
Epidemiology, Biological Sciences, Health Sciences, Genetics, Clinical Research, Human Genome, Blood Pressure, Cohort Studies, Databases, Factual, Family, Gene Frequency, Gene-Environment Interaction, Genome-Wide Association Study, Genotype, Humans, Phenotype, Smoking, gene-environment interaction, meta-analysis, low-frequency variants, Public Health and Health Services
Abstract

Studying gene-environment (G × E) interactions is important, as they extend our knowledge of the genetic architecture of complex traits and may help to identify novel variants not detected via analysis of main effects alone. The main statistical framework for studying G × E interactions uses a single regression model that includes both the genetic main and G × E interaction effects (the "joint" framework). The alternative "stratified" framework combines results from genetic main-effect analyses carried out separately within the exposed and unexposed groups. Although there have been several investigations using theory and simulation, an empirical comparison of the two frameworks is lacking. Here, we compare the two frameworks using results from genome-wide association studies of systolic blood pressure for 3.2 million low frequency and 6.5 million common variants across 20 cohorts of European ancestry, comprising 79,731 individuals. Our cohorts have sample sizes ranging from 456 to 22,983 and include both family-based and population-based samples. In cohort-specific analyses, the two frameworks provided similar inference for population-based cohorts. The agreement was reduced for family-based cohorts. In meta-analyses, agreement between the two frameworks was less than that observed in cohort-specific analyses, despite the increased sample size. In meta-analyses, agreement depended on (1) the minor allele frequency, (2) inclusion of family-based cohorts in meta-analysis, and (3) filtering scheme. The stratified framework appears to approximate the joint framework well only for common variants in population-based cohorts. We conclude that the joint framework is the preferred approach and should be used to control false positives when dealing with low-frequency variants and/or family-based cohorts.

Published
2016

64. Meta-analysis of 49 549 individuals imputed with the 1000 Genomes Project reveals an exonic damaging variant in ANGPTL4 determining fasting TG levels

Author

van Leeuwen, Elisabeth M, Sabo, Aniko, Bis, Joshua C, Huffman, Jennifer E, Manichaikul, Ani, Smith, Albert V, Feitosa, Mary F, Demissie, Serkalem, Joshi, Peter K, Duan, Qing, Marten, Jonathan, van Klinken, Jan B, Surakka, Ida, Nolte, Ilja M, Zhang, Weihua, Mbarek, Hamdi, Li-Gao, Ruifang, Trompet, Stella, Verweij, Niek, Evangelou, Evangelos, Lyytikäinen, Leo-Pekka, Tayo, Bamidele O, Deelen, Joris, van der Most, Peter J, van der Laan, Sander W, Arking, Dan E, Morrison, Alanna, Dehghan, Abbas, Franco, Oscar H, Hofman, Albert, Rivadeneira, Fernando, Sijbrands, Eric J, Uitterlinden, Andre G, Mychaleckyj, Josyf C, Campbell, Archie, Hocking, Lynne J, Padmanabhan, Sandosh, Brody, Jennifer A, Rice, Kenneth M, White, Charles C, Harris, Tamara, Isaacs, Aaron, Campbell, Harry, Lange, Leslie A, Rudan, Igor, Kolcic, Ivana, Navarro, Pau, Zemunik, Tatijana, Salomaa, Veikko, Study, The LifeLines Cohort, Kooner, Angad S, Kooner, Jaspal S, Lehne, Benjamin, Scott, William R, Tan, Sian-Tsung, de Geus, Eco J, Milaneschi, Yuri, Penninx, Brenda WJH, Willemsen, Gonneke, de Mutsert, Renée, Ford, Ian, Gansevoort, Ron T, Segura-Lepe, Marcelo P, Raitakari, Olli T, Viikari, Jorma S, Nikus, Kjell, Forrester, Terrence, McKenzie, Colin A, de Craen, Anton JM, de Ruijter, Hester M, Group, CHARGE Lipids Working, Pasterkamp, Gerard, Snieder, Harold, Oldehinkel, Albertine J, Slagboom, P Eline, Cooper, Richard S, Kähönen, Mika, Lehtimäki, Terho, Elliott, Paul, van der Harst, Pim, Jukema, J Wouter, Mook-Kanamori, Dennis O, Boomsma, Dorret I, Chambers, John C, Swertz, Morris, Ripatti, Samuli, van Dijk, Ko Willems, Vitart, Veronique, Polasek, Ozren, Hayward, Caroline, Wilson, James G, Wilson, James F, Gudnason, Vilmundur, Rich, Stephen S, Psaty, Bruce M, Borecki, Ingrid B, Boerwinkle, Eric, Rotter, Jerome I, Cupples, L Adrienne, and van Duijn, Cornelia M

Subjects
Biological Sciences, Genetics, Biotechnology, Human Genome, Aetiology, 2.1 Biological and endogenous factors, Angiopoietin-Like Protein 4, Angiopoietins, Exons, Fasting, Female, Genome, Human, Genome-Wide Association Study, Genotype, Humans, Male, Middle Aged, Polymorphism, Single Nucleotide, LifeLines Cohort Study, CHARGE Lipids Working Group, Complex traits, Epidemiology, Genome-wide, circulating lipid levels, Medical and Health Sciences, Genetics & Heredity, Clinical sciences
Abstract

BackgroundSo far, more than 170 loci have been associated with circulating lipid levels through genome-wide association studies (GWAS). These associations are largely driven by common variants, their function is often not known, and many are likely to be markers for the causal variants. In this study we aimed to identify more new rare and low-frequency functional variants associated with circulating lipid levels.MethodsWe used the 1000 Genomes Project as a reference panel for the imputations of GWAS data from ∼60 000 individuals in the discovery stage and ∼90 000 samples in the replication stage.ResultsOur study resulted in the identification of five new associations with circulating lipid levels at four loci. All four loci are within genes that can be linked biologically to lipid metabolism. One of the variants, rs116843064, is a damaging missense variant within the ANGPTL4 gene.ConclusionsThis study illustrates that GWAS with high-scale imputation may still help us unravel the biological mechanism behind circulating lipid levels.

Published
2016

65. Genome-wide association study identifies 74 loci associated with educational attainment

Author

Okbay, Aysu, Beauchamp, Jonathan P, Fontana, Mark Alan, Lee, James J, Pers, Tune H, Rietveld, Cornelius A, Turley, Patrick, Chen, Guo-Bo, Emilsson, Valur, Meddens, S Fleur W, Oskarsson, Sven, Pickrell, Joseph K, Thom, Kevin, Timshel, Pascal, de Vlaming, Ronald, Abdellaoui, Abdel, Ahluwalia, Tarunveer S, Bacelis, Jonas, Baumbach, Clemens, Bjornsdottir, Gyda, Brandsma, Johannes H, Pina Concas, Maria, Derringer, Jaime, Furlotte, Nicholas A, Galesloot, Tessel E, Girotto, Giorgia, Gupta, Richa, Hall, Leanne M, Harris, Sarah E, Hofer, Edith, Horikoshi, Momoko, Huffman, Jennifer E, Kaasik, Kadri, Kalafati, Ioanna P, Karlsson, Robert, Kong, Augustine, Lahti, Jari, Lee, Sven J van der, deLeeuw, Christiaan, Lind, Penelope A, Lindgren, Karl-Oskar, Liu, Tian, Mangino, Massimo, Marten, Jonathan, Mihailov, Evelin, Miller, Michael B, van der Most, Peter J, Oldmeadow, Christopher, Payton, Antony, Pervjakova, Natalia, Peyrot, Wouter J, Qian, Yong, Raitakari, Olli, Rueedi, Rico, Salvi, Erika, Schmidt, Börge, Schraut, Katharina E, Shi, Jianxin, Smith, Albert V, Poot, Raymond A, St Pourcain, Beate, Teumer, Alexander, Thorleifsson, Gudmar, Verweij, Niek, Vuckovic, Dragana, Wellmann, Juergen, Westra, Harm-Jan, Yang, Jingyun, Zhao, Wei, Zhu, Zhihong, Alizadeh, Behrooz Z, Amin, Najaf, Bakshi, Andrew, Baumeister, Sebastian E, Biino, Ginevra, Bønnelykke, Klaus, Boyle, Patricia A, Campbell, Harry, Cappuccio, Francesco P, Davies, Gail, De Neve, Jan-Emmanuel, Deloukas, Panos, Demuth, Ilja, Ding, Jun, Eibich, Peter, Eisele, Lewin, Eklund, Niina, Evans, David M, Faul, Jessica D, Feitosa, Mary F, Forstner, Andreas J, Gandin, Ilaria, Gunnarsson, Bjarni, Halldórsson, Bjarni V, Harris, Tamara B, Heath, Andrew C, Hocking, Lynne J, Holliday, Elizabeth G, Homuth, Georg, and Horan, Michael A

Subjects
Biological Sciences, Genetics, Epidemiology, Health Sciences, Statistics, Mathematical Sciences, Human Genome, Clinical Research, Alzheimer Disease, Bipolar Disorder, Brain, Cognition, Computational Biology, Educational Status, Fetus, Gene Expression Regulation, Gene-Environment Interaction, Genome-Wide Association Study, Humans, Molecular Sequence Annotation, Polymorphism, Single Nucleotide, Schizophrenia, United Kingdom, LifeLines Cohort Study, General Science & Technology
Abstract

Educational attainment is strongly influenced by social and other environmental factors, but genetic factors are estimated to account for at least 20% of the variation across individuals. Here we report the results of a genome-wide association study (GWAS) for educational attainment that extends our earlier discovery sample of 101,069 individuals to 293,723 individuals, and a replication study in an independent sample of 111,349 individuals from the UK Biobank. We identify 74 genome-wide significant loci associated with the number of years of schooling completed. Single-nucleotide polymorphisms associated with educational attainment are disproportionately found in genomic regions regulating gene expression in the fetal brain. Candidate genes are preferentially expressed in neural tissue, especially during the prenatal period, and enriched for biological pathways involved in neural development. Our findings demonstrate that, even for a behavioural phenotype that is mostly environmentally determined, a well-powered GWAS identifies replicable associated genetic variants that suggest biologically relevant pathways. Because educational attainment is measured in large numbers of individuals, it will continue to be useful as a proxy phenotype in efforts to characterize the genetic influences of related phenotypes, including cognition and neuropsychiatric diseases.

Published
2016

66. Genome-wide meta-analysis uncovers novel loci influencing circulating leptin levels.

Author

Kilpeläinen, Tuomas O, Carli, Jayne F Martin, Skowronski, Alicja A, Sun, Qi, Kriebel, Jennifer, Feitosa, Mary F, Hedman, Åsa K, Drong, Alexander W, Hayes, James E, Zhao, Jinghua, Pers, Tune H, Schick, Ursula, Grarup, Niels, Kutalik, Zoltán, Trompet, Stella, Mangino, Massimo, Kristiansson, Kati, Beekman, Marian, Lyytikäinen, Leo-Pekka, Eriksson, Joel, Henneman, Peter, Lahti, Jari, Tanaka, Toshiko, Luan, Jian'an, Del Greco M, Fabiola, Pasko, Dorota, Renström, Frida, Willems, Sara M, Mahajan, Anubha, Rose, Lynda M, Guo, Xiuqing, Liu, Yongmei, Kleber, Marcus E, Pérusse, Louis, Gaunt, Tom, Ahluwalia, Tarunveer S, Ju Sung, Yun, Ramos, Yolande F, Amin, Najaf, Amuzu, Antoinette, Barroso, Inês, Bellis, Claire, Blangero, John, Buckley, Brendan M, Böhringer, Stefan, I Chen, Yii-Der, de Craen, Anton JN, Crosslin, David R, Dale, Caroline E, Dastani, Zari, Day, Felix R, Deelen, Joris, Delgado, Graciela E, Demirkan, Ayse, Finucane, Francis M, Ford, Ian, Garcia, Melissa E, Gieger, Christian, Gustafsson, Stefan, Hallmans, Göran, Hankinson, Susan E, Havulinna, Aki S, Herder, Christian, Hernandez, Dena, Hicks, Andrew A, Hunter, David J, Illig, Thomas, Ingelsson, Erik, Ioan-Facsinay, Andreea, Jansson, John-Olov, Jenny, Nancy S, Jørgensen, Marit E, Jørgensen, Torben, Karlsson, Magnus, Koenig, Wolfgang, Kraft, Peter, Kwekkeboom, Joanneke, Laatikainen, Tiina, Ladwig, Karl-Heinz, LeDuc, Charles A, Lowe, Gordon, Lu, Yingchang, Marques-Vidal, Pedro, Meisinger, Christa, Menni, Cristina, Morris, Andrew P, Myers, Richard H, Männistö, Satu, Nalls, Mike A, Paternoster, Lavinia, Peters, Annette, Pradhan, Aruna D, Rankinen, Tuomo, Rasmussen-Torvik, Laura J, Rathmann, Wolfgang, Rice, Treva K, Brent Richards, J, Ridker, Paul M, Sattar, Naveed, and Savage, David B

Subjects
Adipose Tissue, Animals, Mice, Leptin, RNA, Messenger, Tissue Culture Techniques, Gene Expression Regulation, Male, Genome-Wide Association Study, Gene Knockdown Techniques, RNA, Messenger
Abstract

Leptin is an adipocyte-secreted hormone, the circulating levels of which correlate closely with overall adiposity. Although rare mutations in the leptin (LEP) gene are well known to cause leptin deficiency and severe obesity, no common loci regulating circulating leptin levels have been uncovered. Therefore, we performed a genome-wide association study (GWAS) of circulating leptin levels from 32,161 individuals and followed up loci reaching P

Published
2016

67. Genetic associations at 53 loci highlight cell types and biological pathways relevant for kidney function.

Author

Pattaro, Cristian, Teumer, Alexander, Gorski, Mathias, Chu, Audrey Y, Li, Man, Mijatovic, Vladan, Garnaas, Maija, Tin, Adrienne, Sorice, Rossella, Li, Yong, Taliun, Daniel, Olden, Matthias, Foster, Meredith, Yang, Qiong, Chen, Ming-Huei, Pers, Tune H, Johnson, Andrew D, Ko, Yi-An, Fuchsberger, Christian, Tayo, Bamidele, Nalls, Michael, Feitosa, Mary F, Isaacs, Aaron, Dehghan, Abbas, d'Adamo, Pio, Adeyemo, Adebowale, Dieffenbach, Aida Karina, Zonderman, Alan B, Nolte, Ilja M, van der Most, Peter J, Wright, Alan F, Shuldiner, Alan R, Morrison, Alanna C, Hofman, Albert, Smith, Albert V, Dreisbach, Albert W, Franke, Andre, Uitterlinden, Andre G, Metspalu, Andres, Tonjes, Anke, Lupo, Antonio, Robino, Antonietta, Johansson, Åsa, Demirkan, Ayse, Kollerits, Barbara, Freedman, Barry I, Ponte, Belen, Oostra, Ben A, Paulweber, Bernhard, Krämer, Bernhard K, Mitchell, Braxton D, Buckley, Brendan M, Peralta, Carmen A, Hayward, Caroline, Helmer, Catherine, Rotimi, Charles N, Shaffer, Christian M, Müller, Christian, Sala, Cinzia, van Duijn, Cornelia M, Saint-Pierre, Aude, Ackermann, Daniel, Shriner, Daniel, Ruggiero, Daniela, Toniolo, Daniela, Lu, Yingchang, Cusi, Daniele, Czamara, Darina, Ellinghaus, David, Siscovick, David S, Ruderfer, Douglas, Gieger, Christian, Grallert, Harald, Rochtchina, Elena, Atkinson, Elizabeth J, Holliday, Elizabeth G, Boerwinkle, Eric, Salvi, Erika, Bottinger, Erwin P, Murgia, Federico, Rivadeneira, Fernando, Ernst, Florian, Kronenberg, Florian, Hu, Frank B, Navis, Gerjan J, Curhan, Gary C, Ehret, George B, Homuth, Georg, Coassin, Stefan, Thun, Gian-Andri, Pistis, Giorgio, Gambaro, Giovanni, Malerba, Giovanni, Montgomery, Grant W, Eiriksdottir, Gudny, Jacobs, Gunnar, Li, Guo, Wichmann, H-Erich, Campbell, Harry, and Schmidt, Helena

Subjects
ICBP Consortium, AGEN Consortium, CARDIOGRAM, CHARGe-Heart Failure Group, ECHOGen Consortium, Humans, Genetic Predisposition to Disease, Gene Expression Regulation, Genotype, Renal Insufficiency, Chronic, Genome-Wide Association Study, Renal Insufficiency, Chronic
Abstract

Reduced glomerular filtration rate defines chronic kidney disease and is associated with cardiovascular and all-cause mortality. We conducted a meta-analysis of genome-wide association studies for estimated glomerular filtration rate (eGFR), combining data across 133,413 individuals with replication in up to 42,166 individuals. We identify 24 new and confirm 29 previously identified loci. Of these 53 loci, 19 associate with eGFR among individuals with diabetes. Using bioinformatics, we show that identified genes at eGFR loci are enriched for expression in kidney tissues and in pathways relevant for kidney development and transmembrane transporter activity, kidney structure, and regulation of glucose metabolism. Chromatin state mapping and DNase I hypersensitivity analyses across adult tissues demonstrate preferential mapping of associated variants to regulatory regions in kidney but not extra-renal tissues. These findings suggest that genetic determinants of eGFR are mediated largely through direct effects within the kidney and highlight important cell types and biological pathways.

Published
2016

73. 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

74. The Influence of Age and Sex on Genetic Associations with Adult Body Size and Shape: A Large-Scale Genome-Wide Interaction Study.

Author

Winkler, Thomas W, Justice, Anne E, Graff, Mariaelisa, Barata, Llilda, Feitosa, Mary F, Chu, Su, Czajkowski, Jacek, Esko, Tõnu, Fall, Tove, Kilpeläinen, Tuomas O, Lu, Yingchang, Mägi, Reedik, Mihailov, Evelin, Pers, Tune H, Rüeger, Sina, Teumer, Alexander, Ehret, Georg B, Ferreira, Teresa, Heard-Costa, Nancy L, Karjalainen, Juha, Lagou, Vasiliki, Mahajan, Anubha, Neinast, Michael D, Prokopenko, Inga, Simino, Jeannette, Teslovich, Tanya M, Jansen, Rick, Westra, Harm-Jan, White, Charles C, Absher, Devin, Ahluwalia, Tarunveer S, Ahmad, Shafqat, Albrecht, Eva, Alves, Alexessander Couto, Bragg-Gresham, Jennifer L, de Craen, Anton JM, Bis, Joshua C, Bonnefond, Amélie, Boucher, Gabrielle, Cadby, Gemma, Cheng, Yu-Ching, Chiang, Charleston WK, Delgado, Graciela, Demirkan, Ayse, Dueker, Nicole, Eklund, Niina, Eiriksdottir, Gudny, Eriksson, Joel, Feenstra, Bjarke, Fischer, Krista, Frau, Francesca, Galesloot, Tessel E, Geller, Frank, Goel, Anuj, Gorski, Mathias, Grammer, Tanja B, Gustafsson, Stefan, Haitjema, Saskia, Hottenga, Jouke-Jan, Huffman, Jennifer E, Jackson, Anne U, Jacobs, Kevin B, Johansson, Åsa, Kaakinen, Marika, Kleber, Marcus E, Lahti, Jari, Mateo Leach, Irene, Lehne, Benjamin, Liu, Youfang, Lo, Ken Sin, Lorentzon, Mattias, Luan, Jian'an, Madden, Pamela AF, Mangino, Massimo, McKnight, Barbara, Medina-Gomez, Carolina, Monda, Keri L, Montasser, May E, Müller, Gabriele, Müller-Nurasyid, Martina, Nolte, Ilja M, Panoutsopoulou, Kalliope, Pascoe, Laura, Paternoster, Lavinia, Rayner, Nigel W, Renström, Frida, Rizzi, Federica, Rose, Lynda M, Ryan, Kathy A, Salo, Perttu, Sanna, Serena, Scharnagl, Hubert, Shi, Jianxin, Smith, Albert Vernon, Southam, Lorraine, Stančáková, Alena, Steinthorsdottir, Valgerdur, Strawbridge, Rona J, Sung, Yun Ju, and Tachmazidou, Ioanna

Subjects
CHARGE Consortium, DIAGRAM Consortium, GLGC Consortium, Global-BPGen Consortium, ICBP Consortium, MAGIC Consortium, Humans, Genetic Predisposition to Disease, Body Mass Index, Body Size, Waist-Hip Ratio, Chromosome Mapping, Age Factors, Sex Characteristics, Polymorphism, Single Nucleotide, Adult, Aged, Middle Aged, European Continental Ancestry Group, Female, Male, Genome-Wide Association Study, Developmental Biology, Genetics
Abstract

Genome-wide association studies (GWAS) have identified more than 100 genetic variants contributing to BMI, a measure of body size, or waist-to-hip ratio (adjusted for BMI, WHRadjBMI), a measure of body shape. Body size and shape change as people grow older and these changes differ substantially between men and women. To systematically screen for age- and/or sex-specific effects of genetic variants on BMI and WHRadjBMI, we performed meta-analyses of 114 studies (up to 320,485 individuals of European descent) with genome-wide chip and/or Metabochip data by the Genetic Investigation of Anthropometric Traits (GIANT) Consortium. Each study tested the association of up to ~2.8M SNPs with BMI and WHRadjBMI in four strata (men ≤50y, men >50y, women ≤50y, women >50y) and summary statistics were combined in stratum-specific meta-analyses. We then screened for variants that showed age-specific effects (G x AGE), sex-specific effects (G x SEX) or age-specific effects that differed between men and women (G x AGE x SEX). For BMI, we identified 15 loci (11 previously established for main effects, four novel) that showed significant (FDR

Published
2015

75. Genome of The Netherlands population-specific imputations identify an ABCA6 variant associated with cholesterol levels.

Author

van Leeuwen, Elisabeth M, Karssen, Lennart C, Deelen, Joris, Isaacs, Aaron, Medina-Gomez, Carolina, Mbarek, Hamdi, Kanterakis, Alexandros, Trompet, Stella, Postmus, Iris, Verweij, Niek, van Enckevort, David J, Huffman, Jennifer E, White, Charles C, Feitosa, Mary F, Bartz, Traci M, Manichaikul, Ani, Joshi, Peter K, Peloso, Gina M, Deelen, Patrick, van Dijk, Freerk, Willemsen, Gonneke, de Geus, Eco J, Milaneschi, Yuri, Penninx, Brenda WJH, Francioli, Laurent C, Menelaou, Androniki, Pulit, Sara L, Rivadeneira, Fernando, Hofman, Albert, Oostra, Ben A, Franco, Oscar H, Mateo Leach, Irene, Beekman, Marian, de Craen, Anton JM, Uh, Hae-Won, Trochet, Holly, Hocking, Lynne J, Porteous, David J, Sattar, Naveed, Packard, Chris J, Buckley, Brendan M, Brody, Jennifer A, Bis, Joshua C, Rotter, Jerome I, Mychaleckyj, Josyf C, Campbell, Harry, Duan, Qing, Lange, Leslie A, Wilson, James F, Hayward, Caroline, Polasek, Ozren, Vitart, Veronique, Rudan, Igor, Wright, Alan F, Rich, Stephen S, Psaty, Bruce M, Borecki, Ingrid B, Kearney, Patricia M, Stott, David J, Adrienne Cupples, L, Genome of The Netherlands Consortium, Jukema, J Wouter, van der Harst, Pim, Sijbrands, Eric J, Hottenga, Jouke-Jan, Uitterlinden, Andre G, Swertz, Morris A, van Ommen, Gert-Jan B, de Bakker, Paul IW, Eline Slagboom, P, Boomsma, Dorret I, Wijmenga, Cisca, and van Duijn, Cornelia M

Subjects
Genome of The Netherlands Consortium, Humans, Cholesterol, ATP-Binding Cassette Transporters, Gene Frequency, Mutation, Missense, Netherlands, Genetic Association Studies, Mutation, Missense
Abstract

Variants associated with blood lipid levels may be population-specific. To identify low-frequency variants associated with this phenotype, population-specific reference panels may be used. Here we impute nine large Dutch biobanks (~35,000 samples) with the population-specific reference panel created by the Genome of The Netherlands Project and perform association testing with blood lipid levels. We report the discovery of five novel associations at four loci (P value

Published
2015

76. New genetic loci link adipose and insulin biology to body fat distribution

Author

Shungin, Dmitry, Winkler, Thomas W, Croteau-Chonka, Damien C, Ferreira, Teresa, Locke, Adam E, Mägi, Reedik, Strawbridge, Rona J, Pers, Tune H, Fischer, Krista, Justice, Anne E, Workalemahu, Tsegaselassie, Wu, Joseph MW, Buchkovich, Martin L, Heard-Costa, Nancy L, Roman, Tamara S, Drong, Alexander W, Song, Ci, Gustafsson, Stefan, Day, Felix R, Esko, Tonu, Fall, Tove, Kutalik, Zoltán, Luan, Jian’an, Randall, Joshua C, Scherag, André, Vedantam, Sailaja, Wood, Andrew R, Chen, Jin, Fehrmann, Rudolf, Karjalainen, Juha, Kahali, Bratati, Liu, Ching-Ti, Schmidt, Ellen M, Absher, Devin, Amin, Najaf, Anderson, Denise, Beekman, Marian, Bragg-Gresham, Jennifer L, Buyske, Steven, Demirkan, Ayse, Ehret, Georg B, Feitosa, Mary F, Goel, Anuj, Jackson, Anne U, Johnson, Toby, Kleber, Marcus E, Kristiansson, Kati, Mangino, Massimo, Mateo Leach, Irene, Medina-Gomez, Carolina, Palmer, Cameron D, Pasko, Dorota, Pechlivanis, Sonali, Peters, Marjolein J, Prokopenko, Inga, Stančáková, Alena, Ju Sung, Yun, Tanaka, Toshiko, Teumer, Alexander, Van Vliet-Ostaptchouk, Jana V, Yengo, Loïc, Zhang, Weihua, Albrecht, Eva, Ärnlöv, Johan, Arscott, Gillian M, Bandinelli, Stefania, Barrett, Amy, Bellis, Claire, Bennett, Amanda J, Berne, Christian, Blüher, Matthias, Böhringer, Stefan, Bonnet, Fabrice, Böttcher, Yvonne, Bruinenberg, Marcel, Carba, Delia B, Caspersen, Ida H, Clarke, Robert, Warwick Daw, E, Deelen, Joris, Deelman, Ewa, Delgado, Graciela, Doney, Alex SF, Eklund, Niina, Erdos, Michael R, Estrada, Karol, Eury, Elodie, Friedrich, Nele, Garcia, Melissa E, Giedraitis, Vilmantas, Gigante, Bruna, Go, Alan S, Golay, Alain, Grallert, Harald, Grammer, Tanja B, Gräßler, Jürgen, Grewal, Jagvir, Groves, Christopher J, Haller, Toomas, and Hallmans, Goran

Subjects
Genetics, Human Genome, Diabetes, Obesity, Nutrition, 2.1 Biological and endogenous factors, Aetiology, Metabolic and endocrine, Cardiovascular, Stroke, Adipocytes, Adipogenesis, Adipose Tissue, Age Factors, Body Fat Distribution, Body Mass Index, Epigenesis, Genetic, Europe, Female, Genome, Human, Genome-Wide Association Study, Humans, Insulin, Insulin Resistance, Male, Models, Biological, Neovascularization, Physiologic, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Racial Groups, Sex Characteristics, Transcription, Genetic, Waist-Hip Ratio, ADIPOGen Consortium, CARDIOGRAMplusC4D Consortium, CKDGen Consortium, GEFOS Consortium, GENIE Consortium, GLGC, ICBP, International Endogene Consortium, LifeLines Cohort Study, MAGIC Investigators, MuTHER Consortium, PAGE Consortium, ReproGen Consortium, General Science & Technology
Abstract

Body fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes, independent of overall adiposity. To increase our understanding of the genetic basis of body fat distribution and its molecular links to cardiometabolic traits, here we conduct genome-wide association meta-analyses of traits related to waist and hip circumferences in up to 224,459 individuals. We identify 49 loci (33 new) associated with waist-to-hip ratio adjusted for body mass index (BMI), and an additional 19 loci newly associated with related waist and hip circumference measures (P

Published
2015

77. Genetic studies of body mass index yield new insights for obesity biology

Author

Locke, Adam E, Kahali, Bratati, Berndt, Sonja I, Justice, Anne E, Pers, Tune H, Day, Felix R, Powell, Corey, Vedantam, Sailaja, Buchkovich, Martin L, Yang, Jian, Croteau-Chonka, Damien C, Esko, Tonu, Fall, Tove, Ferreira, Teresa, Gustafsson, Stefan, Kutalik, Zoltán, Luan, Jian’an, Mägi, Reedik, Randall, Joshua C, Winkler, Thomas W, Wood, Andrew R, Workalemahu, Tsegaselassie, Faul, Jessica D, Smith, Jennifer A, Hua Zhao, Jing, Zhao, Wei, Chen, Jin, Fehrmann, Rudolf, Hedman, Åsa K, Karjalainen, Juha, Schmidt, Ellen M, Absher, Devin, Amin, Najaf, Anderson, Denise, Beekman, Marian, Bolton, Jennifer L, Bragg-Gresham, Jennifer L, Buyske, Steven, Demirkan, Ayse, Deng, Guohong, Ehret, Georg B, Feenstra, Bjarke, Feitosa, Mary F, Fischer, Krista, Goel, Anuj, Gong, Jian, Jackson, Anne U, Kanoni, Stavroula, Kleber, Marcus E, Kristiansson, Kati, Lim, Unhee, Lotay, Vaneet, Mangino, Massimo, Mateo Leach, Irene, Medina-Gomez, Carolina, Medland, Sarah E, Nalls, Michael A, Palmer, Cameron D, Pasko, Dorota, Pechlivanis, Sonali, Peters, Marjolein J, Prokopenko, Inga, Shungin, Dmitry, Stančáková, Alena, Strawbridge, Rona J, Ju Sung, Yun, Tanaka, Toshiko, Teumer, Alexander, Trompet, Stella, van der Laan, Sander W, van Setten, Jessica, Van Vliet-Ostaptchouk, Jana V, Wang, Zhaoming, Yengo, Loïc, Zhang, Weihua, Isaacs, Aaron, Albrecht, Eva, Ärnlöv, Johan, Arscott, Gillian M, Attwood, Antony P, Bandinelli, Stefania, Barrett, Amy, Bas, Isabelita N, Bellis, Claire, Bennett, Amanda J, Berne, Christian, Blagieva, Roza, Blüher, Matthias, Böhringer, Stefan, Bonnycastle, Lori L, Böttcher, Yvonne, Boyd, Heather A, Bruinenberg, Marcel, Caspersen, Ida H, Ida Chen, Yii-Der, Clarke, Robert, Warwick Daw, E, de Craen, Anton JM, Delgado, Graciela, and Dimitriou, Maria

Subjects
Epidemiology, Biological Sciences, Health Sciences, Genetics, Human Genome, Nutrition, Obesity, Prevention, Aetiology, 2.1 Biological and endogenous factors, Stroke, Cardiovascular, Metabolic and endocrine, Oral and gastrointestinal, Cancer, Adipogenesis, Adiposity, Age Factors, Body Mass Index, Energy Metabolism, Europe, Female, Genetic Predisposition to Disease, Genome-Wide Association Study, Glutamic Acid, Humans, Insulin, Insulin Secretion, Male, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Racial Groups, Synapses, LifeLines Cohort Study, ADIPOGen Consortium, AGEN-BMI Working Group, CARDIOGRAMplusC4D Consortium, CKDGen Consortium, GLGC, ICBP, MAGIC Investigators, MuTHER Consortium, MIGen Consortium, PAGE Consortium, ReproGen Consortium, GENIE Consortium, International Endogene Consortium, General Science & Technology
Abstract

Obesity is heritable and predisposes to many diseases. To understand the genetic basis of obesity better, here we conduct a genome-wide association study and Metabochip meta-analysis of body mass index (BMI), a measure commonly used to define obesity and assess adiposity, in up to 339,224 individuals. This analysis identifies 97 BMI-associated loci (P 20% of BMI variation. Pathway analyses provide strong support for a role of the central nervous system in obesity susceptibility and implicate new genes and pathways, including those related to synaptic function, glutamate signalling, insulin secretion/action, energy metabolism, lipid biology and adipogenesis.

Published
2015

78. Fine mapping the CETP region reveals a common intronic insertion associated to HDL-C

Author

van Leeuwen, Elisabeth M, Huffman, Jennifer E, Bis, Joshua C, Isaacs, Aaron, Mulder, Monique, Sabo, Aniko, Smith, Albert V, Demissie, Serkalem, Manichaikul, Ani, Brody, Jennifer A, Feitosa, Mary F, Duan, Qing, Schraut, Katharina E, Navarro, Pau, van Vliet-Ostaptchouk, Jana V, Zhu, Gu, Mbarek, Hamdi, Trompet, Stella, Verweij, Niek, Lyytikäinen, Leo-Pekka, Deelen, Joris, Nolte, Ilja M, van der Laan, Sander W, Davies, Gail, Vermeij-Verdoold, Andrea JM, van Oosterhout, Andy ALJ, Vergeer-Drop, Jeannette M, Arking, Dan E, Trochet, Holly, Medina-Gomez, Carolina, Rivadeneira, Fernando, Uitterlinden, Andre G, Dehghan, Abbas, Franco, Oscar H, Sijbrands, Eric J, Hofman, Albert, White, Charles C, Mychaleckyj, Josyf C, Peloso, Gina M, Swertz, Morris A, Willemsen, Gonneke, de Geus, Eco J, Milaneschi, Yuri, Penninx, Brenda WJH, Ford, Ian, Buckley, Brendan M, de Craen, Anton JM, Starr, John M, Deary, Ian J, Pasterkamp, Gerard, Oldehinkel, Albertine J, Snieder, Harold, Slagboom, P Eline, Nikus, Kjell, Kähönen, Mika, Lehtimäki, Terho, Viikari, Jorma S, Raitakari, Olli T, van der Harst, Pim, Jukema, J Wouter, Hottenga, Jouke-Jan, Boomsma, Dorret I, Whitfield, John B, Montgomery, Grant, Martin, Nicholas G, Polasek, Ozren, Vitart, Veronique, Hayward, Caroline, Kolcic, Ivana, Wright, Alan F, Rudan, Igor, Joshi, Peter K, Wilson, James F, Lange, Leslie A, Wilson, James G, Gudnason, Vilmundur, Harris, Tamar B, Morrison, Alanna C, Borecki, Ingrid B, Rich, Stephen S, Padmanabhan, Sandosh, Psaty, Bruce M, Rotter, Jerome I, Smith, Blair H, Boerwinkle, Eric, Cupples, L Adrienne, and van Duijn, Cornelia

Subjects
Biological Sciences, Genetics, Aetiology, 2.1 Biological and endogenous factors, Generation Scotland, LifeLines Cohort Study, CHARGE Lipids Working Group, Biochemistry and Cell Biology, Medical Biotechnology, Clinical Sciences, Biochemistry and cell biology
Abstract

BackgroundIndividuals with exceptional longevity and their offspring have significantly larger high-density lipoprotein concentrations (HDL-C) particle sizes due to the increased homozygosity for the I405V variant in the cholesteryl ester transfer protein (CETP) gene. In this study, we investigate the association of CETP and HDL-C further to identify novel, independent CETP variants associated with HDL-C in humans.MethodsWe performed a meta-analysis of HDL-C within the CETP region using 59,432 individuals imputed with 1000 Genomes data. We performed replication in an independent sample of 47,866 individuals and validation was done by Sanger sequencing.ResultsThe meta-analysis of HDL-C within the CETP region identified five independent variants, including an exonic variant and a common intronic insertion. We replicated these 5 variants significantly in an independent sample of 47,866 individuals. Sanger sequencing of the insertion within a single family confirmed segregation of this variant. The strongest reported association between HDL-C and CETP variants, was rs3764261; however, after conditioning on the five novel variants we identified the support for rs3764261 was highly reduced (βunadjusted=3.179 mg/dl (P value=5.25×10-509), βadjusted=0.859 mg/dl (P value=9.51×10-25)), and this finding suggests that these five novel variants may partly explain the association of CETP with HDL-C. Indeed, three of the five novel variants (rs34065661, rs5817082, rs7499892) are independent of rs3764261.ConclusionsThe causal variants in CETP that account for the association with HDL-C remain unknown. We used studies imputed to the 1000 Genomes reference panel for fine mapping of the CETP region. We identified and validated five variants within this region that may partly account for the association of the known variant (rs3764261), as well as other sources of genetic contribution to HDL-C.

Published
2015

80. Association of the PHACTR1/EDN1 Genetic Locus With Spontaneous Coronary Artery Dissection

Author

Motreff, Pascal, Belle, Loïc, Dupouy, Patrick, Barnay, Pierre, Meneveau, Nicolas, Gilard, Martine, Rioufol, Gilles, Range, Grégoire, Brunel, Philippe, Delarche, Nicolas, Filippi, Emmanuelle, Le Bivic, Louis, Harbaoui, Brahim, Benamer, Hakim, Cayla, Guillaume, Varenne, Olivier, Manzo-Silberman, Stephane Peggy, Silvain, Johanne, Spaulding, Christian, Caussin, Christophe, Gerbaud, Edouard, Valy, Yann, Koning, René, Lhermusier, Thibault, Champin, Stanislas, Salengro, Emmanuel, Fluttaz, Arnaud, Zabalawi, Amer, Cottin, Yves, Teiger, Emmanuel, Saint-Etienne, Christophe, Ducrocq, Grégory, Marliere, Stéphanie, Boiffard, Emmanuel, Aubry, Pierre, Georges, Jean Louis, Bresson, Didier, De Poli, Fabien, Karrillon, Gaëtan, Roule, Vincent, Bali, Laurent, Valla, Mathieu, Gerbay, Antoine, Houpe, David, Dubreuil, Olivier, Monnier, Arsène, Mayaud, Norbert, Manchuelle, Aurélie, Commeau, Philippe, Bedossa, Marc, Nikpay, Majid, Goel, Anuj, Won, Hong-Hee, Hall, Leanne M., Willenborg, Christina, Kanoni, Stavroula, Saleheen, Danish, Kyriakou, Theodosios, Nelson, Christopher P., Hopewell, Jemma C., Webb, Thomas R., Zeng, Lingyao, Dehghan, Abbas, Alver, Maris, Armasu, Sebastian M., Auro, Kirsi, Bjonnes, Andrew, Chasman, Daniel I., Chen, Shufeng, Ford, Ian, Franceschini, Nora, Gieger, Christian, Grace, Christopher, Gustafsson, Stefan, Huang, Jie, Hwang, Shih-Jen, Kim, Yun Kyoung, Kleber, Marcus E., Lau, King Wai, Lu, Xiangfeng, Lu, Yingchang, Lyytikäinen, Leo P., Mihailov, Evelin, Morrison, Alanna, Pervjakova, Natalia, Qu, Liming, Rose, Lynda M., Salfati, Elias, Saxena, Richa, Scholz, Markus, Smith, Albert V., Tikkanen, Emmi, Uitterlinden, Andre, Yang, Xueli, Zhang, Weihua, Zhao, Wei, de Andrade, Mariza, de Vries, Paul S., van Zuydam, Natalie R., Anand, Sonia S., Bertram, Lars, Beutner, Frank, Dedoussis, George, Frossard, Philippe, Gauguier, Dominique, Goodall, Alison H., Gottesman, Omri, Haber, Marc, Han, Bok-Ghee, Huang, Jianfeng, Jalilzadeh, Shapour, Kessler, Thorsten, König, Inke R., Lannfelt, Lars, Lieb, Wolfgang, Lind, Lars, Lindgren, Cecilia M., Lokki, Maisa, Magnusson, Patrik K., Mallick, Nadeem H., Mehra, Narinder, Meitinger, Thomas, Memon, Fazal-ur-Rehman, Morris, Andrew P., Nieminen, Markku S., Pedersen, Nancy L., Peters, Annette, Rallidis, Loukianos S., Rasheed, Asif, Samuel, Maria, Shah, Svati H., Sinisalo, Juha, Stirrups, Kathleen E., Trompet, Stella, Wang, Laiyuan, Zaman, Khan S., Ardissino, Diego, Boerwinkle, Eric, Borecki, Ingrid B., Bottinger, Erwin P., Buring, Julie E., Chambers, John C., Collins, Rory, Cupples, L Adrienne, Danesh, John, Demuth, Ilja, Elosua, Roberto, Epstein, Stephen E., Esko, Tõnu, Feitosa, Mary F., Franco, Oscar H., Franzosi, Maria Grazia, Granger, Christopher B., Gu, Dongfeng, Gudnason, Vilmundur, Hall, Alistair S., Hamsten, Anders, Harris, Tamara B., Hazen, Stanley L., Hengstenberg, Christian, Hofman, Albert, Ingelsson, Erik, Iribarren, Carlos, Jukema, J Wouter, Karhunen, Pekka J., Kim, Bong-Jo, Kooner, Jaspal S., Kullo, Iftikhar J., Lehtimäki, Terho, Loos, Ruth J., Melander, Olle, Metspalu, Andres, März, Winfried, Palmer, Colin N., Perola, Markus, Quertermous, Thomas, Rader, Daniel J., Ridker, Paul M., Ripatti, Samuli, Roberts, Robert, Salomaa, Veikko, Sanghera, Dharambir K., Schwartz, Stephen M., Seedorf, Udo, Stewart, Alexandre F., Stott, David J., Thiery, Joachim, Zalloua, Pierre A., O'Donnell, Christopher J., Reilly, Muredach P., Assimes, Themistocles L., Thompson, John R., Erdmann, Jeanette, Clarke, Robert, Watkins, Hugh, Kathiresan, Sekar, McPherson, Ruth, Deloukas, Panos, Schunkert, Heribert, Samani, Nilesh J., Farrall, Martin, Adlam, David, Olson, Timothy M., Combaret, Nicolas, Kovacic, Jason C., Iismaa, Siiri E., Al-Hussaini, Abtehale, O'Byrne, Megan M., Bouajila, Sara, Georges, Adrien, Mishra, Ketan, Braund, Peter S., d’Escamard, Valentina, Huang, Siying, Margaritis, Marios, Kadian-Dodov, Daniella, Welch, Catherine A., Mazurkiewicz, Stephani, Jeunemaitre, Xavier, Wong, Claire Mei Yi, Giannoulatou, Eleni, Sweeting, Michael, Muller, David, Wood, Alice, McGrath-Cadell, Lucy, Fatkin, Diane, Dunwoodie, Sally L., Harvey, Richard, Holloway, Cameron, Empana, Jean-Philippe, Jouven, Xavier, Olin, Jeffrey W., Gulati, Rajiv, Tweet, Marysia S., Hayes, Sharonne N., Graham, Robert M., and Bouatia-Naji, Nabila

Published
2019
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81. Defining the role of common variation in the genomic and biological architecture of adult human height

Author

Wood, Andrew R, Esko, Tonu, Yang, Jian, Vedantam, Sailaja, Pers, Tune H, Gustafsson, Stefan, Chu, Audrey Y, Estrada, Karol, Luan, Jian'an, Kutalik, Zoltán, Amin, Najaf, Buchkovich, Martin L, Croteau-Chonka, Damien C, Day, Felix R, Duan, Yanan, Fall, Tove, Fehrmann, Rudolf, Ferreira, Teresa, Jackson, Anne U, Karjalainen, Juha, Lo, Ken Sin, Locke, Adam E, Mägi, Reedik, Mihailov, Evelin, Porcu, Eleonora, Randall, Joshua C, Scherag, André, Vinkhuyzen, Anna AE, Westra, Harm-Jan, Winkler, Thomas W, Workalemahu, Tsegaselassie, Zhao, Jing Hua, Absher, Devin, Albrecht, Eva, Anderson, Denise, Baron, Jeffrey, Beekman, Marian, Demirkan, Ayse, Ehret, Georg B, Feenstra, Bjarke, Feitosa, Mary F, Fischer, Krista, Fraser, Ross M, Goel, Anuj, Gong, Jian, Justice, Anne E, Kanoni, Stavroula, Kleber, Marcus E, Kristiansson, Kati, Lim, Unhee, Lotay, Vaneet, Lui, Julian C, Mangino, Massimo, Leach, Irene Mateo, Medina-Gomez, Carolina, Nalls, Michael A, Nyholt, Dale R, Palmer, Cameron D, Pasko, Dorota, Pechlivanis, Sonali, Prokopenko, Inga, Ried, Janina S, Ripke, Stephan, Shungin, Dmitry, Stancáková, Alena, Strawbridge, Rona J, Sung, Yun Ju, Tanaka, Toshiko, Teumer, Alexander, Trompet, Stella, van der Laan, Sander W, van Setten, Jessica, Van Vliet-Ostaptchouk, Jana V, Wang, Zhaoming, Yengo, Loïc, Zhang, Weihua, Afzal, Uzma, Ärnlöv, Johan, Arscott, Gillian M, Bandinelli, Stefania, Barrett, Amy, Bellis, Claire, Bennett, Amanda J, Berne, Christian, Blüher, Matthias, Bolton, Jennifer L, Böttcher, Yvonne, Boyd, Heather A, Bruinenberg, Marcel, Buckley, Brendan M, Buyske, Steven, Caspersen, Ida H, Chines, Peter S, Clarke, Robert, Claudi-Boehm, Simone, Cooper, Matthew, Daw, E Warwick, De Jong, Pim A, Deelen, Joris, and Delgado, Graciela

Subjects
Biological Sciences, Genetics, Biotechnology, Human Genome, 2.1 Biological and endogenous factors, Aetiology, Adult, Analysis of Variance, Body Height, Genetic Variation, Genetics, Population, Genome-Wide Association Study, Humans, Oligonucleotide Array Sequence Analysis, Polymorphism, Single Nucleotide, White People, Electronic Medical Records and Genomics (eMEMERGEGE) Consortium, MIGen Consortium, PAGEGE Consortium, LifeLines Cohort Study, Medical and Health Sciences, Developmental Biology, Agricultural biotechnology, Bioinformatics and computational biology
Abstract

Using genome-wide data from 253,288 individuals, we identified 697 variants at genome-wide significance that together explained one-fifth of the heritability for adult height. By testing different numbers of variants in independent studies, we show that the most strongly associated ∼2,000, ∼3,700 and ∼9,500 SNPs explained ∼21%, ∼24% and ∼29% of phenotypic variance. Furthermore, all common variants together captured 60% of heritability. The 697 variants clustered in 423 loci were enriched for genes, pathways and tissue types known to be involved in growth and together implicated genes and pathways not highlighted in earlier efforts, such as signaling by fibroblast growth factors, WNT/β-catenin and chondroitin sulfate-related genes. We identified several genes and pathways not previously connected with human skeletal growth, including mTOR, osteoglycin and binding of hyaluronic acid. Our results indicate a genetic architecture for human height that is characterized by a very large but finite number (thousands) of causal variants.

Published
2014

82. Pleiotropic genes for metabolic syndrome and inflammation

Author

Kraja, Aldi T, Chasman, Daniel I, North, Kari E, Reiner, Alexander P, Yanek, Lisa R, Kilpeläinen, Tuomas O, Smith, Jennifer A, Dehghan, Abbas, Dupuis, Josée, Johnson, Andrew D, Feitosa, Mary F, Tekola-Ayele, Fasil, Chu, Audrey Y, Nolte, Ilja M, Dastani, Zari, Morris, Andrew, Pendergrass, Sarah A, Sun, Yan V, Ritchie, Marylyn D, Vaez, Ahmad, Lin, Honghuang, Ligthart, Symen, Marullo, Letizia, Rohde, Rebecca, Shao, Yaming, Ziegler, Mark A, Im, Hae Kyung, Group, Cross Consortia Pleiotropy, Heart and, the Cohorts for, Epidemiology, Aging Research in Genetic, Consortium, the Genetic Investigation of Anthropometric Traits, Consortium, the Global Lipids Genetics, the Meta-Analyses of Glucose, Consortium, Insulin-related traits, Consortium, the Global BPgen, Consortium, The ADIPOGen, Study, the Women's Genome Health, Study, the Howard University Family, Schnabel, Renate B, Jørgensen, Torben, Jørgensen, Marit E, Hansen, Torben, Pedersen, Oluf, Stolk, Ronald P, Snieder, Harold, Hofman, Albert, Uitterlinden, Andre G, Franco, Oscar H, Ikram, M Arfan, Richards, J Brent, Rotimi, Charles, Wilson, James G, Lange, Leslie, Ganesh, Santhi K, Nalls, Mike, Rasmussen-Torvik, Laura J, Pankow, James S, Coresh, Josef, Tang, Weihong, Kao, WH Linda, Boerwinkle, Eric, Morrison, Alanna C, Ridker, Paul M, Becker, Diane M, Rotter, Jerome I, Kardia, Sharon LR, Loos, Ruth JF, Larson, Martin G, Hsu, Yi-Hsiang, Province, Michael A, Tracy, Russell, Voight, Benjamin F, Vaidya, Dhananjay, O'Donnell, Christopher J, Benjamin, Emelia J, Alizadeh, Behrooz Z, Prokopenko, Inga, Meigs, James B, and Borecki, Ingrid B

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Genetics, Nutrition, Diabetes, Clinical Research, Human Genome, Cardiovascular, Obesity, Prevention, 2.1 Biological and endogenous factors, Biomarkers, Computational Biology, Gene Regulatory Networks, Genetic Pleiotropy, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Inflammation, Meta-Analysis as Topic, Metabolic Syndrome, Phenotype, Quantitative Trait, Heritable, Metabolic syndrome, Inflammatory markers, Pleiotropic associations, Meta-analysis, Regulome, Cross Consortia Pleiotropy Group, Cohorts for Heart and, Aging Research in Genetic Epidemiology, Genetic Investigation of Anthropometric Traits Consortium, Global Lipids Genetics Consortium, Meta-Analyses of Glucose, Insulin-related traits Consortium, Global BPgen Consortium, ADIPOGen Consortium, Women's Genome Health Study, Howard University Family Study, Clinical Sciences, Genetics & Heredity, Clinical sciences
Abstract

Metabolic syndrome (MetS) has become a health and financial burden worldwide. The MetS definition captures clustering of risk factors that predict higher risk for diabetes mellitus and cardiovascular disease. Our study hypothesis is that additional to genes influencing individual MetS risk factors, genetic variants exist that influence MetS and inflammatory markers forming a predisposing MetS genetic network. To test this hypothesis a staged approach was undertaken. (a) We analyzed 17 metabolic and inflammatory traits in more than 85,500 participants from 14 large epidemiological studies within the Cross Consortia Pleiotropy Group. Individuals classified with MetS (NCEP definition), versus those without, showed on average significantly different levels for most inflammatory markers studied. (b) Paired average correlations between 8 metabolic traits and 9 inflammatory markers from the same studies as above, estimated with two methods, and factor analyses on large simulated data, helped in identifying 8 combinations of traits for follow-up in meta-analyses, out of 130,305 possible combinations between metabolic traits and inflammatory markers studied. (c) We performed correlated meta-analyses for 8 metabolic traits and 6 inflammatory markers by using existing GWAS published genetic summary results, with about 2.5 million SNPs from twelve predominantly largest GWAS consortia. These analyses yielded 130 unique SNPs/genes with pleiotropic associations (a SNP/gene associating at least one metabolic trait and one inflammatory marker). Of them twenty-five variants (seven loci newly reported) are proposed as MetS candidates. They map to genes MACF1, KIAA0754, GCKR, GRB14, COBLL1, LOC646736-IRS1, SLC39A8, NELFE, SKIV2L, STK19, TFAP2B, BAZ1B, BCL7B, TBL2, MLXIPL, LPL, TRIB1, ATXN2, HECTD4, PTPN11, ZNF664, PDXDC1, FTO, MC4R and TOMM40. Based on large data evidence, we conclude that inflammation is a feature of MetS and several gene variants show pleiotropic genetic associations across phenotypes and might explain a part of MetS correlated genetic architecture. These findings warrant further functional investigation.

Published
2014

83. Association of Low-Frequency and Rare Coding-Sequence Variants with Blood Lipids and Coronary Heart Disease in 56,000 Whites and Blacks

Author

Peloso, Gina M, Auer, Paul L, Bis, Joshua C, Voorman, Arend, Morrison, Alanna C, Stitziel, Nathan O, Brody, Jennifer A, Khetarpal, Sumeet A, Crosby, Jacy R, Fornage, Myriam, Isaacs, Aaron, Jakobsdottir, Johanna, Feitosa, Mary F, Davies, Gail, Huffman, Jennifer E, Manichaikul, Ani, Davis, Brian, Lohman, Kurt, Joon, Aron Y, Smith, Albert V, Grove, Megan L, Zanoni, Paolo, Redon, Valeska, Demissie, Serkalem, Lawson, Kim, Peters, Ulrike, Carlson, Christopher, Jackson, Rebecca D, Ryckman, Kelli K, Mackey, Rachel H, Robinson, Jennifer G, Siscovick, David S, Schreiner, Pamela J, Mychaleckyj, Josyf C, Pankow, James S, Hofman, Albert, Uitterlinden, Andre G, Harris, Tamara B, Taylor, Kent D, Stafford, Jeanette M, Reynolds, Lindsay M, Marioni, Riccardo E, Dehghan, Abbas, Franco, Oscar H, Patel, Aniruddh P, Lu, Yingchang, Hindy, George, Gottesman, Omri, Bottinger, Erwin P, Melander, Olle, Orho-Melander, Marju, Loos, Ruth JF, Duga, Stefano, Merlini, Piera Angelica, Farrall, Martin, Goel, Anuj, Asselta, Rosanna, Girelli, Domenico, Martinelli, Nicola, Shah, Svati H, Kraus, William E, Li, Mingyao, Rader, Daniel J, Reilly, Muredach P, McPherson, Ruth, Watkins, Hugh, Ardissino, Diego, Project, NHLBI GO Exome Sequencing, Zhang, Qunyuan, Wang, Judy, Tsai, Michael Y, Taylor, Herman A, Correa, Adolfo, Griswold, Michael E, Lange, Leslie A, Starr, John M, Rudan, Igor, Eiriksdottir, Gudny, Launer, Lenore J, Ordovas, Jose M, Levy, Daniel, Chen, Y-D Ida, Reiner, Alexander P, Hayward, Caroline, Polasek, Ozren, Deary, Ian J, Borecki, Ingrid B, Liu, Yongmei, Gudnason, Vilmundur, Wilson, James G, van Duijn, Cornelia M, Kooperberg, Charles, Rich, Stephen S, Psaty, Bruce M, Rotter, Jerome I, O’Donnell, Christopher J, Rice, Kenneth, Boerwinkle, Eric, Kathiresan, Sekar, and Cupples, L Adrienne

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Genetics, Heart Disease - Coronary Heart Disease, Human Genome, Cardiovascular, Heart Disease, Atherosclerosis, Aetiology, 2.1 Biological and endogenous factors, 1-Alkyl-2-acetylglycerophosphocholine Esterase, Adult, Aged, Alleles, Animals, Black People, Cholesterol, HDL, Cholesterol, LDL, Cohort Studies, Coronary Disease, Female, Gene Frequency, Genetic Association Studies, Genetic Code, Genetic Variation, Humans, Linear Models, Male, Mice, Mice, Inbred C57BL, Microtubule-Associated Proteins, Middle Aged, Phenotype, Sequence Analysis, DNA, Subtilisins, Triglycerides, White People, NHLBI GO Exome Sequencing Project, Medical and Health Sciences, Genetics & Heredity, Biological sciences, Biomedical and clinical sciences, Health sciences
Abstract

Low-frequency coding DNA sequence variants in the proprotein convertase subtilisin/kexin type 9 gene (PCSK9) lower plasma low-density lipoprotein cholesterol (LDL-C), protect against risk of coronary heart disease (CHD), and have prompted the development of a new class of therapeutics. It is uncertain whether the PCSK9 example represents a paradigm or an isolated exception. We used the "Exome Array" to genotype >200,000 low-frequency and rare coding sequence variants across the genome in 56,538 individuals (42,208 European ancestry [EA] and 14,330 African ancestry [AA]) and tested these variants for association with LDL-C, high-density lipoprotein cholesterol (HDL-C), and triglycerides. Although we did not identify new genes associated with LDL-C, we did identify four low-frequency (frequencies between 0.1% and 2%) variants (ANGPTL8 rs145464906 [c.361C>T; p.Gln121*], PAFAH1B2 rs186808413 [c.482C>T; p.Ser161Leu], COL18A1 rs114139997 [c.331G>A; p.Gly111Arg], and PCSK7 rs142953140 [c.1511G>A; p.Arg504His]) with large effects on HDL-C and/or triglycerides. None of these four variants was associated with risk for CHD, suggesting that examples of low-frequency coding variants with robust effects on both lipids and CHD will be limited.

Published
2014

84. Whole-Exome Sequencing Identifies Rare and Low-Frequency Coding Variants Associated with LDL Cholesterol

Author

Lange, Leslie A, Hu, Youna, Zhang, He, Xue, Chenyi, Schmidt, Ellen M, Tang, Zheng-Zheng, Bizon, Chris, Lange, Ethan M, Smith, Joshua D, Turner, Emily H, Jun, Goo, Kang, Hyun Min, Peloso, Gina, Auer, Paul, Li, Kuo-ping, Flannick, Jason, Zhang, Ji, Fuchsberger, Christian, Gaulton, Kyle, Lindgren, Cecilia, Locke, Adam, Manning, Alisa, Sim, Xueling, Rivas, Manuel A, Holmen, Oddgeir L, Gottesman, Omri, Lu, Yingchang, Ruderfer, Douglas, Stahl, Eli A, Duan, Qing, Li, Yun, Durda, Peter, Jiao, Shuo, Isaacs, Aaron, Hofman, Albert, Bis, Joshua C, Correa, Adolfo, Griswold, Michael E, Jakobsdottir, Johanna, Smith, Albert V, Schreiner, Pamela J, Feitosa, Mary F, Zhang, Qunyuan, Huffman, Jennifer E, Crosby, Jacy, Wassel, Christina L, Do, Ron, Franceschini, Nora, Martin, Lisa W, Robinson, Jennifer G, Assimes, Themistocles L, Crosslin, David R, Rosenthal, Elisabeth A, Tsai, Michael, Rieder, Mark J, Farlow, Deborah N, Folsom, Aaron R, Lumley, Thomas, Fox, Ervin R, Carlson, Christopher S, Peters, Ulrike, Jackson, Rebecca D, van Duijn, Cornelia M, Uitterlinden, André G, Levy, Daniel, Rotter, Jerome I, Taylor, Herman A, Gudnason, Vilmundur, Siscovick, David S, Fornage, Myriam, Borecki, Ingrid B, Hayward, Caroline, Rudan, Igor, Chen, Y Eugene, Bottinger, Erwin P, Loos, Ruth JF, Sætrom, Pål, Hveem, Kristian, Boehnke, Michael, Groop, Leif, McCarthy, Mark, Meitinger, Thomas, Ballantyne, Christie M, Gabriel, Stacey B, O’Donnell, Christopher J, Post, Wendy S, North, Kari E, Reiner, Alexander P, Boerwinkle, Eric, Psaty, Bruce M, Altshuler, David, Kathiresan, Sekar, Lin, Dan-Yu, Jarvik, Gail P, Cupples, L Adrienne, Kooperberg, Charles, Wilson, James G, Nickerson, Deborah A, Abecasis, Goncalo R, and Rich, Stephen S

Subjects
Epidemiology, Biological Sciences, Health Sciences, Genetics, Human Genome, Prevention, Clinical Research, Heart Disease, Cardiovascular, Atherosclerosis, Aetiology, 2.1 Biological and endogenous factors, Adult, Aged, Apolipoproteins E, Cholesterol, LDL, Cohort Studies, Dyslipidemias, Exome, Female, Follow-Up Studies, Gene Frequency, Genetic Code, Genome-Wide Association Study, Genotype, Humans, Lipase, Male, Middle Aged, Phenotype, Polymorphism, Single Nucleotide, Proprotein Convertase 9, Proprotein Convertases, Receptors, LDL, Sequence Analysis, DNA, Serine Endopeptidases, NHLBI Grand Opportunity Exome Sequencing Project, Medical and Health Sciences, Genetics & Heredity, Biological sciences, Biomedical and clinical sciences, Health sciences
Abstract

Elevated low-density lipoprotein cholesterol (LDL-C) is a treatable, heritable risk factor for cardiovascular disease. Genome-wide association studies (GWASs) have identified 157 variants associated with lipid levels but are not well suited to assess the impact of rare and low-frequency variants. To determine whether rare or low-frequency coding variants are associated with LDL-C, we exome sequenced 2,005 individuals, including 554 individuals selected for extreme LDL-C (>98(th) or

Published
2014

85. Genetic Pleiotropy Between Pulmonary Function and Age-Related Traits: The Long Life Family Study.

Author

Feitosa, Mary F, Wojczynski, Mary K, Anema, Jason A, Daw, E Warwick, Wang, Lihua, Santanasto, Adam J, Nygaard, Marianne, and Province, Michael A

Subjects
*GENETIC pleiotropy, *LONGEVITY, *FORCED expiratory volume, *GENETIC variation, *FAMILIES, *CARDIOVASCULAR diseases
Abstract

Background Pulmonary function (PF) progressively declines with aging. Forced expiratory volume in the first second (FEV1) and forced vital capacity (FVC) are predictors of morbidity of pulmonary and cardiovascular diseases and all-cause mortality. In addition, reduced PF is associated with elevated chronic low-grade systemic inflammation, glucose metabolism, body fatness, and low muscle strength. It may suggest pleiotropic genetic effects between PF with these age-related factors. Methods We evaluated whether FEV1 and FVC share common pleiotropic genetic effects with interleukin-6, high-sensitivity C-reactive protein, body mass index, muscle (grip) strength, plasma glucose, and glycosylated hemoglobin in 3 888 individuals (age range: 26–106). We employed sex-combined and sex-specific correlated meta-analyses to test whether combining genome-wide association p values from 2 or more traits enhances the ability to detect variants sharing effects on these correlated traits. Results We identified 32 loci for PF, including 29 novel pleiotropic loci associated with PF and (i) body fatness (CYP2U1/SGMS2), (ii) glucose metabolism (CBWD1/DOCK8 and MMUT/CENPQ), (iii) inflammatory markers (GLRA3/HPGD , TRIM9 , CALN1 , CTNNB1/ZNF621 , GATA5/SLCO4A1/NTSR1 , and NPVF/C7orf31/CYCS), and (iv) muscle strength (MAL2 , AC008825.1/LINC02103 , AL136418.1). Conclusions The identified genes/loci for PF and age-related traits suggest their underlying shared genetic effects, which can explain part of their phenotypic correlations. Integration of gene expression and genomic annotation data shows enrichment of our genetic variants in lung, blood, adipose, pancreas, and muscles, among others. Our findings highlight the critical roles of identified gene/locus in systemic inflammation, glucose metabolism, strength performance, PF, and pulmonary disease, which are involved in accelerated biological aging. [ABSTRACT FROM AUTHOR]

Published
2024
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86. Gene-educational attainment interactions in a multi-population genome-wide meta-analysis identify novel lipid loci

Author

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

Published
2023
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87. Discovery and refinement of loci associated with lipid levels

Author

Willer, Cristen J, Schmidt, Ellen M, Sengupta, Sebanti, Peloso, Gina M, Gustafsson, Stefan, Kanoni, Stavroula, Ganna, Andrea, Chen, Jin, Buchkovich, Martin L, Mora, Samia, Beckmann, Jacques S, Bragg-Gresham, Jennifer L, Chang, Hsing-Yi, Demirkan, Ayse, Den Hertog, Heleen M, Do, Ron, Donnelly, Louise A, Ehret, Georg B, Esko, Tonu, Feitosa, Mary F, Ferreira, Teresa, Fischer, Krista, Fontanillas, Pierre, Fraser, Ross M, Freitag, Daniel F, Gurdasani, Deepti, Heikkila, Kauko, Hyppoenen, Elina, Isaacs, Aaron, Jackson, Anne U, Johansson, Asa, Johnson, Toby, Kaakinen, Marika, Kettunen, Johannes, Kleber, Marcus E, Li, Xiaohui, Luan, Jian'an, Lyytikainen, Leo-Pekka, Magnusson, Patrik KE, Mangino, Massimo, Mihailov, Evelin, Montasser, May E, Mueller-Nurasyid, Martina, Nolte, Ilja M, O'Connell, Jeffrey R, Palmer, Cameron D, Perola, Markus, Petersen, Ann-Kristin, Sanna, Serena, Saxena, Richa, Service, Susan K, Shah, Sonia, Shungin, Dmitry, Sidore, Carlo, Song, Ci, Strawbridge, Rona J, Surakka, Ida, Tanaka, Toshiko, Teslovich, Tanya M, Thorleifsson, Gudmar, Van den Herik, Evita G, Voight, Benjamin F, Volcik, Kelly A, Waite, Lindsay L, Wong, Andrew, Wu, Ying, Zhang, Weihua, Absher, Devin, Asiki, Gershim, Barroso, Ines, Been, Latonya F, Bolton, Jennifer L, Bonnycastle, Lori L, Brambilla, Paolo, Burnett, Mary S, Cesana, Giancarlo, Dimitriou, Maria, Doney, Alex SF, Doering, Angela, Elliott, Paul, Epstein, Stephen E, Eyjolfsson, Gudmundur Ingi, Gigante, Bruna, Goodarzi, Mark O, Grallert, Harald, Gravito, Martha L, Groves, Christopher J, Hallmans, Goran, Hartikainen, Anna-Liisa, Hayward, Caroline, Hernandez, Dena, Hicks, Andrew A, Holm, Hilma, Hung, Yi-Jen, Illig, Thomas, Jones, Michelle R, Kaleebu, Pontiano, Kastelein, John JP, Khaw, Kay-Tee, and Kim, Eric

Subjects
Cardiovascular, Human Genome, Genetics, 2.1 Biological and endogenous factors, Aetiology, Asian People, Black People, Cholesterol, HDL, Cholesterol, LDL, Coronary Artery Disease, Genetic Predisposition to Disease, Genome-Wide Association Study, Genotype, Humans, Lipids, Triglycerides, White People, Global Lipids Genetics Consortium, Biological Sciences, Medical and Health Sciences, Developmental Biology
Abstract

Levels of low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterol, triglycerides and total cholesterol are heritable, modifiable risk factors for coronary artery disease. To identify new loci and refine known loci influencing these lipids, we examined 188,577 individuals using genome-wide and custom genotyping arrays. We identify and annotate 157 loci associated with lipid levels at P < 5 × 10(-8), including 62 loci not previously associated with lipid levels in humans. Using dense genotyping in individuals of European, East Asian, South Asian and African ancestry, we narrow association signals in 12 loci. We find that loci associated with blood lipid levels are often associated with cardiovascular and metabolic traits, including coronary artery disease, type 2 diabetes, blood pressure, waist-hip ratio and body mass index. Our results demonstrate the value of using genetic data from individuals of diverse ancestry and provide insights into the biological mechanisms regulating blood lipids to guide future genetic, biological and therapeutic research.

Published
2013

88. Common variants associated with plasma triglycerides and risk for coronary artery disease

Author

Do, Ron, Willer, Cristen J, Schmidt, Ellen M, Sengupta, Sebanti, Gao, Chi, Peloso, Gina M, Gustafsson, Stefan, Kanoni, Stavroula, Ganna, Andrea, Chen, Jin, Buchkovich, Martin L, Mora, Samia, Beckmann, Jacques S, Bragg-Gresham, Jennifer L, Chang, Hsing-Yi, Demirkan, Ayşe, Den Hertog, Heleen M, Donnelly, Louise A, Ehret, Georg B, Esko, Tõnu, Feitosa, Mary F, Ferreira, Teresa, Fischer, Krista, Fontanillas, Pierre, Fraser, Ross M, Freitag, Daniel F, Gurdasani, Deepti, Heikkilä, Kauko, Hyppönen, Elina, Isaacs, Aaron, Jackson, Anne U, Johansson, Åsa, Johnson, Toby, Kaakinen, Marika, Kettunen, Johannes, Kleber, Marcus E, Li, Xiaohui, Luan, Jian'an, Lyytikäinen, Leo-Pekka, Magnusson, Patrik KE, Mangino, Massimo, Mihailov, Evelin, Montasser, May E, Müller-Nurasyid, Martina, Nolte, Ilja M, O'Connell, Jeffrey R, Palmer, Cameron D, Perola, Markus, Petersen, Ann-Kristin, Sanna, Serena, Saxena, Richa, Service, Susan K, Shah, Sonia, Shungin, Dmitry, Sidore, Carlo, Song, Ci, Strawbridge, Rona J, Surakka, Ida, Tanaka, Toshiko, Teslovich, Tanya M, Thorleifsson, Gudmar, Van den Herik, Evita G, Voight, Benjamin F, Volcik, Kelly A, Waite, Lindsay L, Wong, Andrew, Wu, Ying, Zhang, Weihua, Absher, Devin, Asiki, Gershim, Barroso, Inês, Been, Latonya F, Bolton, Jennifer L, Bonnycastle, Lori L, Brambilla, Paolo, Burnett, Mary S, Cesana, Giancarlo, Dimitriou, Maria, Doney, Alex SF, Döring, Angela, Elliott, Paul, Epstein, Stephen E, Eyjolfsson, Gudmundur Ingi, Gigante, Bruna, Goodarzi, Mark O, Grallert, Harald, Gravito, Martha L, Groves, Christopher J, Hallmans, Göran, Hartikainen, Anna-Liisa, Hayward, Caroline, Hernandez, Dena, Hicks, Andrew A, Holm, Hilma, Hung, Yi-Jen, Illig, Thomas, Jones, Michelle R, Kaleebu, Pontiano, Kastelein, John JP, and Khaw, Kay-Tee

Subjects
Heart Disease - Coronary Heart Disease, Atherosclerosis, Prevention, Heart Disease, Cardiovascular, 2.1 Biological and endogenous factors, Aetiology, Biological Transport, Cholesterol, HDL, Cholesterol, LDL, Coronary Artery Disease, Humans, Polymorphism, Single Nucleotide, Risk Factors, Triglycerides, Biological Sciences, Medical and Health Sciences, Developmental Biology
Abstract

Triglycerides are transported in plasma by specific triglyceride-rich lipoproteins; in epidemiological studies, increased triglyceride levels correlate with higher risk for coronary artery disease (CAD). However, it is unclear whether this association reflects causal processes. We used 185 common variants recently mapped for plasma lipids (P < 5 × 10(-8) for each) to examine the role of triglycerides in risk for CAD. First, we highlight loci associated with both low-density lipoprotein cholesterol (LDL-C) and triglyceride levels, and we show that the direction and magnitude of the associations with both traits are factors in determining CAD risk. Second, we consider loci with only a strong association with triglycerides and show that these loci are also associated with CAD. Finally, in a model accounting for effects on LDL-C and/or high-density lipoprotein cholesterol (HDL-C) levels, the strength of a polymorphism's effect on triglyceride levels is correlated with the magnitude of its effect on CAD risk. These results suggest that triglyceride-rich lipoproteins causally influence risk for CAD.

Published
2013

89. A meta-analysis identifies new loci associated with body mass index in individuals of African ancestry

Author

Monda, Keri L, Chen, Gary K, Taylor, Kira C, Palmer, Cameron, Edwards, Todd L, Lange, Leslie A, Ng, Maggie CY, Adeyemo, Adebowale A, Allison, Matthew A, Bielak, Lawrence F, Chen, Guanjie, Graff, Mariaelisa, Irvin, Marguerite R, Rhie, Suhn K, Li, Guo, Liu, Yongmei, Liu, Youfang, Lu, Yingchang, Nalls, Michael A, Sun, Yan V, Wojczynski, Mary K, Yanek, Lisa R, Aldrich, Melinda C, Ademola, Adeyinka, Amos, Christopher I, Bandera, Elisa V, Bock, Cathryn H, Britton, Angela, Broeckel, Ulrich, Cai, Quiyin, Caporaso, Neil E, Carlson, Chris S, Carpten, John, Casey, Graham, Chen, Wei-Min, Chen, Fang, Chen, Yii-Der I, Chiang, Charleston WK, Coetzee, Gerhard A, Demerath, Ellen, Deming-Halverson, Sandra L, Driver, Ryan W, Dubbert, Patricia, Feitosa, Mary F, Feng, Ye, Freedman, Barry I, Gillanders, Elizabeth M, Gottesman, Omri, Guo, Xiuqing, Haritunians, Talin, Harris, Tamara, Harris, Curtis C, Hennis, Anselm JM, Hernandez, Dena G, McNeill, Lorna H, Howard, Timothy D, Howard, Barbara V, Howard, Virginia J, Johnson, Karen C, Kang, Sun J, Keating, Brendan J, Kolb, Suzanne, Kuller, Lewis H, Kutlar, Abdullah, Langefeld, Carl D, Lettre, Guillaume, Lohman, Kurt, Lotay, Vaneet, Lyon, Helen, Manson, JoAnn E, Maixner, William, Meng, Yan A, Monroe, Kristine R, Morhason-Bello, Imran, Murphy, Adam B, Mychaleckyj, Josyf C, Nadukuru, Rajiv, Nathanson, Katherine L, Nayak, Uma, N'Diaye, Amidou, Nemesure, Barbara, Wu, Suh-Yuh, Leske, M Cristina, Neslund-Dudas, Christine, Neuhouser, Marian, Nyante, Sarah, Ochs-Balcom, Heather, Ogunniyi, Adesola, Ogundiran, Temidayo O, Ojengbede, Oladosu, Olopade, Olufunmilayo I, Palmer, Julie R, Ruiz-Narvaez, Edward A, Palmer, Nicholette D, Press, Michael F, Rampersaud, Evandine, Rasmussen-Torvik, Laura J, Rodriguez-Gil, Jorge L, Salako, Babatunde, and Schadt, Eric E

Subjects
Biological Sciences, Genetics, Human Genome, Black or African American, Body Mass Index, Case-Control Studies, Gene Frequency, Genetic Loci, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Linkage Disequilibrium, Obesity, Polymorphism, Single Nucleotide, NABEC Consortium, UKBEC Consortium, BioBank Japan Project, AGEN Consortium, Medical and Health Sciences, Developmental Biology, Agricultural biotechnology, Bioinformatics and computational biology
Abstract

Genome-wide association studies (GWAS) have identified 36 loci associated with body mass index (BMI), predominantly in populations of European ancestry. We conducted a meta-analysis to examine the association of >3.2 million SNPs with BMI in 39,144 men and women of African ancestry and followed up the most significant associations in an additional 32,268 individuals of African ancestry. We identified one new locus at 5q33 (GALNT10, rs7708584, P = 3.4 × 10(-11)) and another at 7p15 when we included data from the GIANT consortium (MIR148A-NFE2L3, rs10261878, P = 1.2 × 10(-10)). We also found suggestive evidence of an association at a third locus at 6q16 in the African-ancestry sample (KLHL32, rs974417, P = 6.9 × 10(-8)). Thirty-two of the 36 previously established BMI variants showed directionally consistent effect estimates in our GWAS (binomial P = 9.7 × 10(-7)), five of which reached genome-wide significance. These findings provide strong support for shared BMI loci across populations, as well as for the utility of studying ancestrally diverse populations.

Published
2013

90. A Bivariate Genome-Wide Approach to Metabolic Syndrome STAMPEED Consortium

Author

Kraja, Aldi T, Vaidya, Dhananjay, Pankow, James S, Goodarzi, Mark O, Assimes, Themistocles L, Kullo, Iftikhar J, Sovio, Ulla, Mathias, Rasika A, Sun, Yan V, Franceschini, Nora, Absher, Devin, Li, Guo, Zhang, Qunyuan, Feitosa, Mary F, Glazer, Nicole L, Haritunians, Talin, Hartikainen, Anna-Liisa, Knowles, Joshua W, North, Kari E, Iribarren, Carlos, Kral, Brian, Yanek, Lisa, O’Reilly, Paul F, McCarthy, Mark I, Jaquish, Cashell, Couper, David J, Chakravarti, Aravinda, Psaty, Bruce M, Becker, Lewis C, Province, Michael A, Boerwinkle, Eric, Quertermous, Thomas, Palotie, Leena, Jarvelin, Marjo-Riitta, Becker, Diane M, Kardia, Sharon LR, Rotter, Jerome I, Chen, Yii-Der Ida, and Borecki, Ingrid B

Subjects
Biomedical and Clinical Sciences, Nutrition, Human Genome, Clinical Research, Cardiovascular, Diabetes, Obesity, Genetics, 2.1 Biological and endogenous factors, Aetiology, Metabolic and endocrine, Adult, Aged, Female, Genetic Predisposition to Disease, Genome-Wide Association Study, Genotype, Humans, Male, Meta-Analysis as Topic, Metabolic Syndrome, Middle Aged, Phenotype, Polymorphism, Single Nucleotide, Medical and Health Sciences, Endocrinology & Metabolism, Biomedical and clinical sciences
Abstract

OBJECTIVE The metabolic syndrome (MetS) is defined as concomitant disorders of lipid and glucose metabolism, central obesity, and high blood pressure, with an increased risk of type 2 diabetes and cardiovascular disease. This study tests whether common genetic variants with pleiotropic effects account for some of the correlated architecture among five metabolic phenotypes that define MetS. RESEARCH DESIGN AND METHODS Seven studies of the STAMPEED consortium, comprising 22,161 participants of European ancestry, underwent genome-wide association analyses of metabolic traits using a panel of ∼2.5 million imputed single nucleotide polymorphisms (SNPs). Phenotypes were defined by the National Cholesterol Education Program (NCEP) criteria for MetS in pairwise combinations. Individuals exceeding the NCEP thresholds for both traits of a pair were considered affected. RESULTS Twenty-nine common variants were associated with MetS or a pair of traits. Variants in the genes LPL, CETP, APOA5 (and its cluster), GCKR (and its cluster), LIPC, TRIB1, LOC100128354/MTNR1B, ABCB11, and LOC100129150 were further tested for their association with individual qualitative and quantitative traits. None of the 16 top SNPs (one per gene) associated simultaneously with more than two individual traits. Of them 11 variants showed nominal associations with MetS per se. The effects of 16 top SNPs on the quantitative traits were relatively small, together explaining from ∼9% of the variance in triglycerides, 5.8% of high-density lipoprotein cholesterol, 3.6% of fasting glucose, and 1.4% of systolic blood pressure. CONCLUSIONS Qualitative and quantitative pleiotropic tests on pairs of traits indicate that a small portion of the covariation in these traits can be explained by the reported common genetic variants.

Published
2011

91. Genome-wide association analysis identifies variants associated with nonalcoholic fatty liver disease that have distinct effects on metabolic traits.

Author

Speliotes, Elizabeth K, Yerges-Armstrong, Laura M, Wu, Jun, Hernaez, Ruben, Kim, Lauren J, Palmer, Cameron D, Gudnason, Vilmundur, Eiriksdottir, Gudny, Garcia, Melissa E, Launer, Lenore J, Nalls, Michael A, Clark, Jeanne M, Mitchell, Braxton D, Shuldiner, Alan R, Butler, Johannah L, Tomas, Marta, Hoffmann, Udo, Hwang, Shih-Jen, Massaro, Joseph M, O'Donnell, Christopher J, Sahani, Dushyant V, Salomaa, Veikko, Schadt, Eric E, Schwartz, Stephen M, Siscovick, David S, NASH CRN, GIANT Consortium, MAGIC Investigators, Voight, Benjamin F, Carr, J Jeffrey, Feitosa, Mary F, Harris, Tamara B, Fox, Caroline S, Smith, Albert V, Kao, WH Linda, Hirschhorn, Joel N, Borecki, Ingrid B, and GOLD Consortium

Subjects
NASH CRN, GIANT Consortium, MAGIC Investigators, GOLD Consortium, Humans, Fatty Liver, Insulin, Lipase, Blood Glucose, Adaptor Proteins, Signal Transducing, Lectins, C-Type, Membrane Proteins, Nerve Tissue Proteins, Tomography, X-Ray Computed, Case-Control Studies, Cohort Studies, Mutation, Missense, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Adult, Aged, Aged, 80 and over, Middle Aged, Male, Genome-Wide Association Study, Chondroitin Sulfate Proteoglycans, Non-alcoholic Fatty Liver Disease, Prevention, Human Genome, Digestive Diseases, Liver Disease, Hepatitis, Chronic Liver Disease and Cirrhosis, Genetics, Clinical Research, 2.1 Biological and endogenous factors, Developmental Biology
Abstract

Nonalcoholic fatty liver disease (NAFLD) clusters in families, but the only known common genetic variants influencing risk are near PNPLA3. We sought to identify additional genetic variants influencing NAFLD using genome-wide association (GWA) analysis of computed tomography (CT) measured hepatic steatosis, a non-invasive measure of NAFLD, in large population based samples. Using variance components methods, we show that CT hepatic steatosis is heritable (∼26%-27%) in family-based Amish, Family Heart, and Framingham Heart Studies (n = 880 to 3,070). By carrying out a fixed-effects meta-analysis of genome-wide association (GWA) results between CT hepatic steatosis and ∼2.4 million imputed or genotyped SNPs in 7,176 individuals from the Old Order Amish, Age, Gene/Environment Susceptibility-Reykjavik study (AGES), Family Heart, and Framingham Heart Studies, we identify variants associated at genome-wide significant levels (p

Published
2011

92. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index

Author

Speliotes, Elizabeth K, Willer, Cristen J, Berndt, Sonja I, Monda, Keri L, Thorleifsson, Gudmar, Jackson, Anne U, Allen, Hana Lango, Lindgren, Cecilia M, Luan, Jian'an, Mägi, Reedik, Randall, Joshua C, Vedantam, Sailaja, Winkler, Thomas W, Qi, Lu, Workalemahu, Tsegaselassie, Heid, Iris M, Steinthorsdottir, Valgerdur, Stringham, Heather M, Weedon, Michael N, Wheeler, Eleanor, Wood, Andrew R, Ferreira, Teresa, Weyant, Robert J, Segrè, Ayellet V, Estrada, Karol, Liang, Liming, Nemesh, James, Park, Ju-Hyun, Gustafsson, Stefan, Kilpeläinen, Tuomas O, Yang, Jian, Bouatia-Naji, Nabila, Esko, Tõnu, Feitosa, Mary F, Kutalik, Zoltán, Mangino, Massimo, Raychaudhuri, Soumya, Scherag, Andre, Smith, Albert Vernon, Welch, Ryan, Zhao, Jing Hua, Aben, Katja K, Absher, Devin M, Amin, Najaf, Dixon, Anna L, Fisher, Eva, Glazer, Nicole L, Goddard, Michael E, Heard-Costa, Nancy L, Hoesel, Volker, Hottenga, Jouke-Jan, Johansson, Åsa, Johnson, Toby, Ketkar, Shamika, Lamina, Claudia, Li, Shengxu, Moffatt, Miriam F, Myers, Richard H, Narisu, Narisu, Perry, John RB, Peters, Marjolein J, Preuss, Michael, Ripatti, Samuli, Rivadeneira, Fernando, Sandholt, Camilla, Scott, Laura J, Timpson, Nicholas J, Tyrer, Jonathan P, van Wingerden, Sophie, Watanabe, Richard M, White, Charles C, Wiklund, Fredrik, Barlassina, Christina, Chasman, Daniel I, Cooper, Matthew N, Jansson, John-Olov, Lawrence, Robert W, Pellikka, Niina, Prokopenko, Inga, Shi, Jianxin, Thiering, Elisabeth, Alavere, Helene, Alibrandi, Maria TS, Almgren, Peter, Arnold, Alice M, Aspelund, Thor, Atwood, Larry D, Balkau, Beverley, Balmforth, Anthony J, Bennett, Amanda J, Ben-Shlomo, Yoav, Bergman, Richard N, Bergmann, Sven, Biebermann, Heike, Blakemore, Alexandra IF, Boes, Tanja, Bonnycastle, Lori L, Bornstein, Stefan R, Brown, Morris J, and Buchanan, Thomas A

Subjects
Biological Sciences, Genetics, Prevention, Human Genome, Body Height, Body Mass Index, Body Size, Body Weight, Chromosome Mapping, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Obesity, Polymorphism, Single Nucleotide, White People, MAGIC, Procardis Consortium, Medical and Health Sciences, Developmental Biology, Agricultural biotechnology, Bioinformatics and computational biology
Abstract

Obesity is globally prevalent and highly heritable, but its underlying genetic factors remain largely elusive. To identify genetic loci for obesity susceptibility, we examined associations between body mass index and ∼ 2.8 million SNPs in up to 123,865 individuals with targeted follow up of 42 SNPs in up to 125,931 additional individuals. We confirmed 14 known obesity susceptibility loci and identified 18 new loci associated with body mass index (P < 5 × 10⁻⁸), one of which includes a copy number variant near GPRC5B. Some loci (at MC4R, POMC, SH2B1 and BDNF) map near key hypothalamic regulators of energy balance, and one of these loci is near GIPR, an incretin receptor. Furthermore, genes in other newly associated loci may provide new insights into human body weight regulation.

Published
2010

93. Hundreds of variants clustered in genomic loci and biological pathways affect human height

Author

Lango Allen, Hana, Estrada, Karol, Lettre, Guillaume, Berndt, Sonja I, Weedon, Michael N, Rivadeneira, Fernando, Willer, Cristen J, Jackson, Anne U, Vedantam, Sailaja, Raychaudhuri, Soumya, Ferreira, Teresa, Wood, Andrew R, Weyant, Robert J, Segrè, Ayellet V, Speliotes, Elizabeth K, Wheeler, Eleanor, Soranzo, Nicole, Park, Ju-Hyun, Yang, Jian, Gudbjartsson, Daniel, Heard-Costa, Nancy L, Randall, Joshua C, Qi, Lu, Vernon Smith, Albert, Mägi, Reedik, Pastinen, Tomi, Liang, Liming, Heid, Iris M, Luan, Jian’an, Thorleifsson, Gudmar, Winkler, Thomas W, Goddard, Michael E, Sin Lo, Ken, Palmer, Cameron, Workalemahu, Tsegaselassie, Aulchenko, Yurii S, Johansson, Åsa, Carola Zillikens, M, Feitosa, Mary F, Esko, Tõnu, Johnson, Toby, Ketkar, Shamika, Kraft, Peter, Mangino, Massimo, Prokopenko, Inga, Absher, Devin, Albrecht, Eva, Ernst, Florian, Glazer, Nicole L, Hayward, Caroline, Hottenga, Jouke-Jan, Jacobs, Kevin B, Knowles, Joshua W, Kutalik, Zoltán, Monda, Keri L, Polasek, Ozren, Preuss, Michael, Rayner, Nigel W, Robertson, Neil R, Steinthorsdottir, Valgerdur, Tyrer, Jonathan P, Voight, Benjamin F, Wiklund, Fredrik, Xu, Jianfeng, Hua Zhao, Jing, Nyholt, Dale R, Pellikka, Niina, Perola, Markus, Perry, John RB, Surakka, Ida, Tammesoo, Mari-Liis, Altmaier, Elizabeth L, Amin, Najaf, Aspelund, Thor, Bhangale, Tushar, Boucher, Gabrielle, Chasman, Daniel I, Chen, Constance, Coin, Lachlan, Cooper, Matthew N, Dixon, Anna L, Gibson, Quince, Grundberg, Elin, Hao, Ke, Juhani Junttila, M, Kaplan, Lee M, Kettunen, Johannes, König, Inke R, Kwan, Tony, Lawrence, Robert W, Levinson, Douglas F, Lorentzon, Mattias, McKnight, Barbara, Morris, Andrew P, Müller, Martina, Suh Ngwa, Julius, Purcell, Shaun, Rafelt, Suzanne, Salem, Rany M, and Salvi, Erika

Subjects
Biological Sciences, Genetics, Health Sciences, Mathematical Sciences, Statistics, Human Genome, Clinical Research, Biotechnology, Aetiology, 2.1 Biological and endogenous factors, Body Height, Chromosomes, Human, Pair 3, Genetic Loci, Genetic Predisposition to Disease, Genome, Human, Genome-Wide Association Study, Humans, Metabolic Networks and Pathways, Multifactorial Inheritance, Phenotype, Polymorphism, Single Nucleotide, General Science & Technology
Abstract

Most common human traits and diseases have a polygenic pattern of inheritance: DNA sequence variants at many genetic loci influence the phenotype. Genome-wide association (GWA) studies have identified more than 600 variants associated with human traits, but these typically explain small fractions of phenotypic variation, raising questions about the use of further studies. Here, using 183,727 individuals, we show that hundreds of genetic variants, in at least 180 loci, influence adult height, a highly heritable and classic polygenic trait. The large number of loci reveals patterns with important implications for genetic studies of common human diseases and traits. First, the 180 loci are not random, but instead are enriched for genes that are connected in biological pathways (P = 0.016) and that underlie skeletal growth defects (P

Published
2010

94. Biological, clinical and population relevance of 95 loci for blood lipids

Author

Teslovich, Tanya M, Musunuru, Kiran, Smith, Albert V, Edmondson, Andrew C, Stylianou, Ioannis M, Koseki, Masahiro, Pirruccello, James P, Ripatti, Samuli, Chasman, Daniel I, Willer, Cristen J, Johansen, Christopher T, Fouchier, Sigrid W, Isaacs, Aaron, Peloso, Gina M, Barbalic, Maja, Ricketts, Sally L, Bis, Joshua C, Aulchenko, Yurii S, Thorleifsson, Gudmar, Feitosa, Mary F, Chambers, John, Orho-Melander, Marju, Melander, Olle, Johnson, Toby, Li, Xiaohui, Guo, Xiuqing, Li, Mingyao, Shin Cho, Yoon, Jin Go, Min, Jin Kim, Young, Lee, Jong-Young, Park, Taesung, Kim, Kyunga, Sim, Xueling, Twee-Hee Ong, Rick, Croteau-Chonka, Damien C, Lange, Leslie A, Smith, Joshua D, Song, Kijoung, Hua Zhao, Jing, Yuan, Xin, Luan, Jian’an, Lamina, Claudia, Ziegler, Andreas, Zhang, Weihua, Zee, Robert YL, Wright, Alan F, Witteman, Jacqueline CM, Wilson, James F, Willemsen, Gonneke, Wichmann, H-Erich, Whitfield, John B, Waterworth, Dawn M, Wareham, Nicholas J, Waeber, Gérard, Vollenweider, Peter, Voight, Benjamin F, Vitart, Veronique, Uitterlinden, Andre G, Uda, Manuela, Tuomilehto, Jaakko, Thompson, John R, Tanaka, Toshiko, Surakka, Ida, Stringham, Heather M, Spector, Tim D, Soranzo, Nicole, Smit, Johannes H, Sinisalo, Juha, Silander, Kaisa, Sijbrands, Eric JG, Scuteri, Angelo, Scott, James, Schlessinger, David, Sanna, Serena, Salomaa, Veikko, Saharinen, Juha, Sabatti, Chiara, Ruokonen, Aimo, Rudan, Igor, Rose, Lynda M, Roberts, Robert, Rieder, Mark, Psaty, Bruce M, Pramstaller, Peter P, Pichler, Irene, Perola, Markus, Penninx, Brenda WJH, Pedersen, Nancy L, Pattaro, Cristian, Parker, Alex N, Pare, Guillaume, Oostra, Ben A, O’Donnell, Christopher J, Nieminen, Markku S, Nickerson, Deborah A, Montgomery, Grant W, Meitinger, Thomas, McPherson, Ruth, and McCarthy, Mark I

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Genetics, Heart Disease, Human Genome, Cardiovascular, Prevention, Atherosclerosis, Heart Disease - Coronary Heart Disease, 2.1 Biological and endogenous factors, Aetiology, Black or African American, Animals, Asian People, Cholesterol, HDL, Cholesterol, LDL, Coronary Artery Disease, Europe, Female, Genetic Loci, Genome-Wide Association Study, Genotype, Humans, Lipid Metabolism, Lipids, Liver, Male, Mice, N-Acetylgalactosaminyltransferases, Phenotype, Polymorphism, Single Nucleotide, Protein Phosphatase 1, Reproducibility of Results, Triglycerides, White People, Polypeptide N-acetylgalactosaminyltransferase, General Science & Technology
Abstract

Plasma concentrations of total cholesterol, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol and triglycerides are among the most important risk factors for coronary artery disease (CAD) and are targets for therapeutic intervention. We screened the genome for common variants associated with plasma lipids in >100,000 individuals of European ancestry. Here we report 95 significantly associated loci (P < 5 x 10(-8)), with 59 showing genome-wide significant association with lipid traits for the first time. The newly reported associations include single nucleotide polymorphisms (SNPs) near known lipid regulators (for example, CYP7A1, NPC1L1 and SCARB1) as well as in scores of loci not previously implicated in lipoprotein metabolism. The 95 loci contribute not only to normal variation in lipid traits but also to extreme lipid phenotypes and have an impact on lipid traits in three non-European populations (East Asians, South Asians and African Americans). Our results identify several novel loci associated with plasma lipids that are also associated with CAD. Finally, we validated three of the novel genes-GALNT2, PPP1R3B and TTC39B-with experiments in mouse models. Taken together, our findings provide the foundation to develop a broader biological understanding of lipoprotein metabolism and to identify new therapeutic opportunities for the prevention of CAD.

Published
2010

95. Interactions between genes involved in physiological dysregulation and axon guidance: role in Alzheimer’s disease

Author

Arbeev, Konstantin G., primary, Ukraintseva, Svetlana, additional, Bagley, Olivia, additional, Duan, Hongzhe, additional, Wu, Deqing, additional, Akushevich, Igor, additional, Stallard, Eric, additional, Kulminski, Alexander, additional, Christensen, Kaare, additional, Feitosa, Mary F., additional, O’Connell, Jeffrey R., additional, Parker, Daniel, additional, Whitson, Heather, additional, and Yashin, Anatoliy I., additional

Published
2023
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97. The Protective Effect of Familial Longevity Persists After Age 100: Findings From the Danish National Registers.

Author

Galvin, Angéline, Pedersen, Jacob Krabbe, Wojczynski, Mary K, Ukraintseva, Svetlana, Arbeev, Konstantin, Feitosa, Mary, Province, Michael A, and Christensen, Kaare

Subjects
PROPORTIONAL hazards models, LONGEVITY
Abstract

Background A recent study suggested that the protective effect of familial longevity becomes negligible for centenarians. However, the authors assessed the dependence on familial longevity in centenarians by comparing centenarians with 1 parent surviving to age 80+ to centenarians whose same-sexed parent did not survive to age 80. Here we test whether the protective effect of familial longevity persists after age 100 using more restrictive definitions of long-lived families. Methods Long-lived sibships were identified through 3 nationwide, consecutive studies in Denmark, including families with either at least 2 siblings aged 90+ or a Family Longevity Selection Score (FLoSS) above 7. Long-lived siblings enrolled in these studies and who reached age 100 were included. For each sibling, 5 controls matched on sex and year of birth were randomly selected among centenarians in the Danish population. Survival time from age 100 was described with Kaplan–Meier curves for siblings and controls separately. Survival analyses were performed using stratified Cox proportional hazards models. Results A total of 340 individuals from long-lived sibships who survived to age 100 and 1 700 controls were included. Among the long-lived siblings and controls, 1 650 (81%) were women. The results showed that long-lived siblings presented better overall survival after age 100 than sporadic long-livers (hazard ratio [HR]  = 0.80, 95% confidence interval [CI]  = 0.71–0.91), with even lower estimate (HR = 0.65, 95% CI = 0.50–0.85) if familial longevity was defined by FLoSS. Conclusions The present study, with virtually no loss to follow-up, demonstrated a persistence of protective effect of familial longevity after age 100. [ABSTRACT FROM AUTHOR]

Published
2024
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99. Genetic insights into resting heart rate and its role in cardiovascular disease

Author

van de Vegte, Yordi, Eppinga, Ruben P., van der Ende, M. Yldau, Hagemeijer, Yanick, Mahendran, Yuvaraj V., Salfati, Elias Y., Smith, Albert E., Tan, Vanessa, Arking, Dan V., Ntalla, Ioanna, Appel, Emil A., Schurmann, Claudia, Brody, Jennifer, Rueedi, Rico, Polasek, Ozren, Sveinbjornsson, Gardar, Lecoeur, Cecile, Ladenvall, Claes, Zhao, Jing Hua, Isaacs, Aaron, Wang, Lihua, Luan, Jian'an, Hwang, Shih-Jen, Mononen, Nina U., Auro, Kirsi F., Jackson, Anne, Bielak, Lawrence, Zeng, Linyao, Shah, Nabi, Nethander, Maria, Campbell, Archie, Rankinen, Tuomo, Pechlivanis, Sonali, Qi, Lu, Zhao, Wei, Rizzi, Federica, Tanaka, Toshiko, Robino, Antonietta, Cocca, Massimiliano, Lange, Leslie, Mueller-Nurasyid, Martina, Roselli, Carolina E., Zhang, Weihua, Kleber, Marcus J., Guo, Xiuqing, Lin, Henry E., Pavani, Francesca, Galesloot, Tessel, Noordam, Raymond E., Milaneschi, Yuri, Schraut, Katharina, den Hoed, Marcel, Degenhardt, Frauke E., Trompet, Stella, van den Berg, Marten, Pistis, Giorgio, Tham, Yih-Chung S., Weiss, Stefan L., Sim, Xueling J., Li, Hengtong M., van der Most, Peter, Nolte, Ilja, Lyytikaeinen, Leo-Pekka R., Said, M. Abdullah, Witte, Daniel, Iribarren, Carlos M., Launer, Lenore S., Ring, Susan, de Vries, Paul, Sever, Peter P., Linneberg, Allan, Bottinger, Erwin M., Padmanabhan, Sandosh, Psaty, Bruce, Sotoodehnia, Nona, Kolcic, Ivana, Roshandel, Delnaz D., Paterson, Andrew O., Arnar, David F., Gudbjartsson, Daniel, Holm, Hilma, Balkau, Beverley T., Silva, Claudia H., Newton-Cheh, Christopher, Nikus, Kjell, Salo, Perttu L., Mohlke, Karen A., Peyser, Patricia, Schunkert, Heribert, Lorentzon, Mattias, Lahti, Jari C., Rao, Dabeeru C., Cornelis, Marilyn D., Faul, Jessica A., Smith, Jennifer, Stolarz-Skrzypek, Katarzyna, Bandinelli, Stefania, Concas, Maria Pina, Sinagra, Gianfranco, Meitinger, Thomas, Waldenberger, Melanie F., Sinner, Moritz, Strauch, Konstantin E., Delgado, Graciela D., Taylor, Kent, Yao, Jie, Foco, Luisa, Melander, Olle, de Graaf, Jacqueline, de Mutsert, Renee, de Geus, Eco J. C., Johansson, Åsa, Joshi, Peter K., Lind, Lars, Franke, Andre W., Macfarlane, Peter V., Tarasov, Kirill, Tan, Nicholas B., Felix, Stephan, Tai, E-Shyong Q., Quek, Debra, Snieder, Harold, Ormel, Johan, Ingelsson, Martin, Lindgren, Cecilia P., Morris, Andrew T., Raitakari, Olli, Hansen, Torben, Assimes, Themistocles, Gudnason, Vilmundur J., Timpson, Nicholas C., Morrison, Alanna B., Munroe, Patricia P., Strachan, David, Grarup, Niels, Loos, Ruth J. F. R., Heckbert, Susan, Vollenweider, Peter, Hayward, Caroline, Stefansson, Kari, Froguel, Philippe, Groop, Leif J., Wareham, Nicholas M., van Duijn, Cornelia F., Feitosa, Mary J., O'Donnell, Christopher, Kaehoenen, Mika, Perola, Markus, Boehnke, Michael, Kardia, Sharon L. R., Erdmann, Jeanette, Palmer, Colin N. A., Ohlsson, Claes J., Porteous, David G., Eriksson, Johan, Bouchard, Claude, Moebus, Susanne, Kraft, Peter R., Weir, David, Cusi, Daniele, Ferrucci, Luigi, Ulivi, Sheila, Girotto, Giorgia, Correa, Adolfo, Kaeaeb, Stefan, Peters, Annette C., Chambers, John S., Kooner, Jaspal, Maerz, Winfried I., Rotter, Jerome A., Hicks, Andrew, Smith, J. Gustav, Kiemeney, Lambertus A. L. M. O., Mook-Kanamori, Dennis, Penninx, Brenda W. J. H., Gyllensten, Ulf, Wilson, James, Burgess, Stephen, Sundström, Johan, Lieb, Wolfgang, Jukema, J. Wouter, Eijgelsheim, Mark, Lakatta, Edward L. M., Cheng, Ching-Yu, Doerr, Marcus, Wong, Tien-Yin, Sabanayagam, Charumathi J., Oldehinkel, Albertine, Riese, Harriette, Lehtimaeki, Terho, Verweij, Niek, van der Harst, Pim, van de Vegte, Yordi, Eppinga, Ruben P., van der Ende, M. Yldau, Hagemeijer, Yanick, Mahendran, Yuvaraj V., Salfati, Elias Y., Smith, Albert E., Tan, Vanessa, Arking, Dan V., Ntalla, Ioanna, Appel, Emil A., Schurmann, Claudia, Brody, Jennifer, Rueedi, Rico, Polasek, Ozren, Sveinbjornsson, Gardar, Lecoeur, Cecile, Ladenvall, Claes, Zhao, Jing Hua, Isaacs, Aaron, Wang, Lihua, Luan, Jian'an, Hwang, Shih-Jen, Mononen, Nina U., Auro, Kirsi F., Jackson, Anne, Bielak, Lawrence, Zeng, Linyao, Shah, Nabi, Nethander, Maria, Campbell, Archie, Rankinen, Tuomo, Pechlivanis, Sonali, Qi, Lu, Zhao, Wei, Rizzi, Federica, Tanaka, Toshiko, Robino, Antonietta, Cocca, Massimiliano, Lange, Leslie, Mueller-Nurasyid, Martina, Roselli, Carolina E., Zhang, Weihua, Kleber, Marcus J., Guo, Xiuqing, Lin, Henry E., Pavani, Francesca, Galesloot, Tessel, Noordam, Raymond E., Milaneschi, Yuri, Schraut, Katharina, den Hoed, Marcel, Degenhardt, Frauke E., Trompet, Stella, van den Berg, Marten, Pistis, Giorgio, Tham, Yih-Chung S., Weiss, Stefan L., Sim, Xueling J., Li, Hengtong M., van der Most, Peter, Nolte, Ilja, Lyytikaeinen, Leo-Pekka R., Said, M. Abdullah, Witte, Daniel, Iribarren, Carlos M., Launer, Lenore S., Ring, Susan, de Vries, Paul, Sever, Peter P., Linneberg, Allan, Bottinger, Erwin M., Padmanabhan, Sandosh, Psaty, Bruce, Sotoodehnia, Nona, Kolcic, Ivana, Roshandel, Delnaz D., Paterson, Andrew O., Arnar, David F., Gudbjartsson, Daniel, Holm, Hilma, Balkau, Beverley T., Silva, Claudia H., Newton-Cheh, Christopher, Nikus, Kjell, Salo, Perttu L., Mohlke, Karen A., Peyser, Patricia, Schunkert, Heribert, Lorentzon, Mattias, Lahti, Jari C., Rao, Dabeeru C., Cornelis, Marilyn D., Faul, Jessica A., Smith, Jennifer, Stolarz-Skrzypek, Katarzyna, Bandinelli, Stefania, Concas, Maria Pina, Sinagra, Gianfranco, Meitinger, Thomas, Waldenberger, Melanie F., Sinner, Moritz, Strauch, Konstantin E., Delgado, Graciela D., Taylor, Kent, Yao, Jie, Foco, Luisa, Melander, Olle, de Graaf, Jacqueline, de Mutsert, Renee, de Geus, Eco J. C., Johansson, Åsa, Joshi, Peter K., Lind, Lars, Franke, Andre W., Macfarlane, Peter V., Tarasov, Kirill, Tan, Nicholas B., Felix, Stephan, Tai, E-Shyong Q., Quek, Debra, Snieder, Harold, Ormel, Johan, Ingelsson, Martin, Lindgren, Cecilia P., Morris, Andrew T., Raitakari, Olli, Hansen, Torben, Assimes, Themistocles, Gudnason, Vilmundur J., Timpson, Nicholas C., Morrison, Alanna B., Munroe, Patricia P., Strachan, David, Grarup, Niels, Loos, Ruth J. F. R., Heckbert, Susan, Vollenweider, Peter, Hayward, Caroline, Stefansson, Kari, Froguel, Philippe, Groop, Leif J., Wareham, Nicholas M., van Duijn, Cornelia F., Feitosa, Mary J., O'Donnell, Christopher, Kaehoenen, Mika, Perola, Markus, Boehnke, Michael, Kardia, Sharon L. R., Erdmann, Jeanette, Palmer, Colin N. A., Ohlsson, Claes J., Porteous, David G., Eriksson, Johan, Bouchard, Claude, Moebus, Susanne, Kraft, Peter R., Weir, David, Cusi, Daniele, Ferrucci, Luigi, Ulivi, Sheila, Girotto, Giorgia, Correa, Adolfo, Kaeaeb, Stefan, Peters, Annette C., Chambers, John S., Kooner, Jaspal, Maerz, Winfried I., Rotter, Jerome A., Hicks, Andrew, Smith, J. Gustav, Kiemeney, Lambertus A. L. M. O., Mook-Kanamori, Dennis, Penninx, Brenda W. J. H., Gyllensten, Ulf, Wilson, James, Burgess, Stephen, Sundström, Johan, Lieb, Wolfgang, Jukema, J. Wouter, Eijgelsheim, Mark, Lakatta, Edward L. M., Cheng, Ching-Yu, Doerr, Marcus, Wong, Tien-Yin, Sabanayagam, Charumathi J., Oldehinkel, Albertine, Riese, Harriette, Lehtimaeki, Terho, Verweij, Niek, and van der Harst, Pim

Abstract

The genetics and clinical consequences of resting heart rate (RHR) remain incompletely understood. Here, the authors discover new genetic variants associated with RHR and find that higher genetically predicted RHR decreases risk of atrial fibrillation and ischemic stroke. Resting heart rate is associated with cardiovascular diseases and mortality in observational and Mendelian randomization studies. The aims of this study are to extend the number of resting heart rate associated genetic variants and to obtain further insights in resting heart rate biology and its clinical consequences. A genome-wide meta-analysis of 100 studies in up to 835,465 individuals reveals 493 independent genetic variants in 352 loci, including 68 genetic variants outside previously identified resting heart rate associated loci. We prioritize 670 genes and in silico annotations point to their enrichment in cardiomyocytes and provide insights in their ECG signature. Two-sample Mendelian randomization analyses indicate that higher genetically predicted resting heart rate increases risk of dilated cardiomyopathy, but decreases risk of developing atrial fibrillation, ischemic stroke, and cardio-embolic stroke. We do not find evidence for a linear or non-linear genetic association between resting heart rate and all-cause mortality in contrast to our previous Mendelian randomization study. Systematic alteration of key differences between the current and previous Mendelian randomization study indicates that the most likely cause of the discrepancy between these studies arises from false positive findings in previous one-sample MR analyses caused by weak-instrument bias at lower P-value thresholds. The results extend our understanding of resting heart rate biology and give additional insights in its role in cardiovascular disease development.

Published
2023
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100. Gene-educational attainment interactions in a multi-population genome-wide meta-analysis identify novel lipid loci

Author

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

Abstract

Introduction: Educational attainment, widely used in epidemiologic studies as a surrogate for socioeconomic status, is a predictor of cardiovascular health outcomes. Methods: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 (p < 5 × 10−8) and suggestive (p < 1 × 10−6) 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). Results: 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 (FOXP1, MBOAT4, SKP2, STIM1, STX4), brain (BRI3, FILIP1, FOXP1, LINC00290, LMTK2, MBOAT4, MYO6, SENP6, SRGAP3, STIM1, TMEM167A, TMEM30A), and liver (BRI3, FOXP1) 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.Discussion: Genome-wide interaction analysis of educational a

Published
2023

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