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11 results on '"Peters, Marjolein J."'

1. An integrative cross-omics analysis of DNA methylation sites of glucose and insulin homeostasis

Author

Liu, Jun, Carnero-Montoro, Elena, van Dongen, Jenny, Lent, Samantha, Nedeljkovic, Ivana, Ligthart, Symen, Tsai, Pei-Chien, Martin, Tiphaine C, Mandaviya, Pooja R, Jansen, Rick, Peters, Marjolein J, Duijts, Liesbeth, Jaddoe, Vincent WV, Tiemeier, Henning, Felix, Janine F, Willemsen, Gonneke, de Geus, Eco JC, Chu, Audrey Y, Levy, Daniel, Hwang, Shih-Jen, Bressler, Jan, Gondalia, Rahul, Salfati, Elias L, Herder, Christian, Hidalgo, Bertha A, Tanaka, Toshiko, Moore, Ann Zenobia, Lemaitre, Rozenn N, Jhun, Min A, Smith, Jennifer A, Sotoodehnia, Nona, Bandinelli, Stefania, Ferrucci, Luigi, Arnett, Donna K, Grallert, Harald, Assimes, Themistocles L, Hou, Lifang, Baccarelli, Andrea, Whitsel, Eric A, van Dijk, Ko Willems, Amin, Najaf, Uitterlinden, André G, Sijbrands, Eric JG, Franco, Oscar H, Dehghan, Abbas, Spector, Tim D, Dupuis, Josée, Hivert, Marie-France, Rotter, Jerome I, Meigs, James B, Pankow, James S, van Meurs, Joyce BJ, Isaacs, Aaron, Boomsma, Dorret I, Bell, Jordana T, Demirkan, Ayşe, and van Duijn, Cornelia M

Subjects
Biological Sciences, Genetics, Nutrition, Diabetes, Human Genome, Obesity, 2.1 Biological and endogenous factors, Aetiology, Metabolic and endocrine, Adult, Aged, Aged, 80 and over, Computer Simulation, CpG Islands, DNA Methylation, Diabetes Mellitus, Type 2, Epigenesis, Genetic, Epigenomics, Female, Gene Expression Profiling, Gene Expression Regulation, Genome-Wide Association Study, Glucose, Homeostasis, Humans, Insulin, Male, Metabolic Networks and Pathways, Middle Aged, Polymorphism, Single Nucleotide, Young Adult
Abstract

Despite existing reports on differential DNA methylation in type 2 diabetes (T2D) and obesity, our understanding of its functional relevance remains limited. Here we show the effect of differential methylation in the early phases of T2D pathology by a blood-based epigenome-wide association study of 4808 non-diabetic Europeans in the discovery phase and 11,750 individuals in the replication. We identify CpGs in LETM1, RBM20, IRS2, MAN2A2 and the 1q25.3 region associated with fasting insulin, and in FCRL6, SLAMF1, APOBEC3H and the 15q26.1 region with fasting glucose. In silico cross-omics analyses highlight the role of differential methylation in the crosstalk between the adaptive immune system and glucose homeostasis. The differential methylation explains at least 16.9% of the association between obesity and insulin. Our study sheds light on the biological interactions between genetic variants driving differential methylation and gene expression in the early pathogenesis of T2D.

Published
2019

2. DNA methylation-based measures of biological age: meta-analysis predicting time to death

Author

Chen, Brian H, Marioni, Riccardo E, Colicino, Elena, Peters, Marjolein J, Ward-Caviness, Cavin K, Tsai, Pei-Chien, Roetker, Nicholas S, Just, Allan C, Demerath, Ellen W, Guan, Weihua, Bressler, Jan, Fornage, Myriam, Studenski, Stephanie, Vandiver, Amy R, Moore, Ann Zenobia, Tanaka, Toshiko, Kiel, Douglas P, Liang, Liming, Vokonas, Pantel, Schwartz, Joel, Lunetta, Kathryn L, Murabito, Joanne M, Bandinelli, Stefania, Hernandez, Dena G, Melzer, David, Nalls, Michael, Pilling, Luke C, Price, Timothy R, Singleton, Andrew B, Gieger, Christian, Holle, Rolf, Kretschmer, Anja, Kronenberg, Florian, Kunze, Sonja, Linseisen, Jakob, Meisinger, Christine, Rathmann, Wolfgang, Waldenberger, Melanie, Visscher, Peter M, Shah, Sonia, Wray, Naomi R, McRae, Allan F, Franco, Oscar H, Hofman, Albert, Uitterlinden, André G, Absher, Devin, Assimes, Themistocles, Levine, Morgan E, Lu, Ake T, Tsao, Philip S, Hou, Lifang, Manson, JoAnn E, Carty, Cara L, LaCroix, Andrea Z, Reiner, Alexander P, Spector, Tim D, Feinberg, Andrew P, Levy, Daniel, Baccarelli, Andrea, van Meurs, Joyce, Bell, Jordana T, Peters, Annette, Deary, Ian J, Pankow, James S, Ferrucci, Luigi, and Horvath, Steve

Subjects
Biological Sciences, Genetics, Clinical Research, Prevention, Good Health and Well Being, Aging, DNA Methylation, Epigenesis, Genetic, Female, Humans, Logistic Models, Male, Mortality, Racial Groups, Risk Factors, Survival Analysis, T-Lymphocyte Subsets, all-cause mortality, lifespan, epigenetics, epigenetic clock, DNA methylation, mortality, Biochemistry and cell biology, Clinical sciences
Abstract

Estimates of biological age based on DNA methylation patterns, often referred to as "epigenetic age", "DNAm age", have been shown to be robust biomarkers of age in humans. We previously demonstrated that independent of chronological age, epigenetic age assessed in blood predicted all-cause mortality in four human cohorts. Here, we expanded our original observation to 13 different cohorts for a total sample size of 13,089 individuals, including three racial/ethnic groups. In addition, we examined whether incorporating information on blood cell composition into the epigenetic age metrics improves their predictive power for mortality. All considered measures of epigenetic age acceleration were predictive of mortality (p≤8.2x10-9), independent of chronological age, even after adjusting for additional risk factors (p

Published
2016

3. DNA Methylation in Newborns and Maternal Smoking in Pregnancy: Genome-wide Consortium Meta-analysis

Author

Joubert, Bonnie R, Felix, Janine F, Yousefi, Paul, Bakulski, Kelly M, Just, Allan C, Breton, Carrie, Reese, Sarah E, Markunas, Christina A, Richmond, Rebecca C, Xu, Cheng-Jian, Küpers, Leanne K, Oh, Sam S, Hoyo, Cathrine, Gruzieva, Olena, Söderhäll, Cilla, Salas, Lucas A, Baïz, Nour, Zhang, Hongmei, Lepeule, Johanna, Ruiz, Carlos, Ligthart, Symen, Wang, Tianyuan, Taylor, Jack A, Duijts, Liesbeth, Sharp, Gemma C, Jankipersadsing, Soesma A, Nilsen, Roy M, Vaez, Ahmad, Fallin, M Daniele, Hu, Donglei, Litonjua, Augusto A, Fuemmeler, Bernard F, Huen, Karen, Kere, Juha, Kull, Inger, Munthe-Kaas, Monica Cheng, Gehring, Ulrike, Bustamante, Mariona, Saurel-Coubizolles, Marie José, Quraishi, Bilal M, Ren, Jie, Tost, Jörg, Gonzalez, Juan R, Peters, Marjolein J, Håberg, Siri E, Xu, Zongli, van Meurs, Joyce B, Gaunt, Tom R, Kerkhof, Marjan, Corpeleijn, Eva, Feinberg, Andrew P, Eng, Celeste, Baccarelli, Andrea A, Neelon, Sara E Benjamin, Bradman, Asa, Merid, Simon Kebede, Bergström, Anna, Herceg, Zdenko, Hernandez-Vargas, Hector, Brunekreef, Bert, Pinart, Mariona, Heude, Barbara, Ewart, Susan, Yao, Jin, Lemonnier, Nathanaël, Franco, Oscar H, Wu, Michael C, Hofman, Albert, McArdle, Wendy, Van der Vlies, Pieter, Falahi, Fahimeh, Gillman, Matthew W, Barcellos, Lisa F, Kumar, Ashish, Wickman, Magnus, Guerra, Stefano, Charles, Marie-Aline, Holloway, John, Auffray, Charles, Tiemeier, Henning W, Smith, George Davey, Postma, Dirkje, Hivert, Marie-France, Eskenazi, Brenda, Vrijheid, Martine, Arshad, Hasan, Antó, Josep M, Dehghan, Abbas, Karmaus, Wilfried, Annesi-Maesano, Isabella, Sunyer, Jordi, Ghantous, Akram, Pershagen, Göran, Holland, Nina, Murphy, Susan K, DeMeo, Dawn L, Burchard, Esteban G, Ladd-Acosta, Christine, Snieder, Harold, and Nystad, Wenche

Subjects
Genetics, Pediatric, Human Genome, Tobacco, Tobacco Smoke and Health, Prevention, Aetiology, 2.1 Biological and endogenous factors, 2.2 Factors relating to the physical environment, Respiratory, Reproductive health and childbirth, Good Health and Well Being, Asthma, Child, Child, Preschool, Chromosome Mapping, Cleft Lip, Cleft Palate, DNA Methylation, Epigenesis, Genetic, Female, Genetic Association Studies, Humans, Infant, Infant, Newborn, Pregnancy, Smoking, White People, Biological Sciences, Medical and Health Sciences, Genetics & Heredity
Abstract

Epigenetic modifications, including DNA methylation, represent a potential mechanism for environmental impacts on human disease. Maternal smoking in pregnancy remains an important public health problem that impacts child health in a myriad of ways and has potential lifelong consequences. The mechanisms are largely unknown, but epigenetics most likely plays a role. We formed the Pregnancy And Childhood Epigenetics (PACE) consortium and meta-analyzed, across 13 cohorts (n = 6,685), the association between maternal smoking in pregnancy and newborn blood DNA methylation at over 450,000 CpG sites (CpGs) by using the Illumina 450K BeadChip. Over 6,000 CpGs were differentially methylated in relation to maternal smoking at genome-wide statistical significance (false discovery rate, 5%), including 2,965 CpGs corresponding to 2,017 genes not previously related to smoking and methylation in either newborns or adults. Several genes are relevant to diseases that can be caused by maternal smoking (e.g., orofacial clefts and asthma) or adult smoking (e.g., certain cancers). A number of differentially methylated CpGs were associated with gene expression. We observed enrichment in pathways and processes critical to development. In older children (5 cohorts, n = 3,187), 100% of CpGs gave at least nominal levels of significance, far more than expected by chance (p value < 2.2 × 10(-16)). Results were robust to different normalization methods used across studies and cell type adjustment. In this large scale meta-analysis of methylation data, we identified numerous loci involved in response to maternal smoking in pregnancy with persistence into later childhood and provide insights into mechanisms underlying effects of this important exposure.

Published
2016

4. The transcriptional landscape of age in human peripheral blood.

Author

Peters, Marjolein J, Joehanes, Roby, Pilling, Luke C, Schurmann, Claudia, Conneely, Karen N, Powell, Joseph, Reinmaa, Eva, Sutphin, George L, Zhernakova, Alexandra, Schramm, Katharina, Wilson, Yana A, Kobes, Sayuko, Tukiainen, Taru, NABEC/UKBEC Consortium, Ramos, Yolande F, Göring, Harald HH, Fornage, Myriam, Liu, Yongmei, Gharib, Sina A, Stranger, Barbara E, De Jager, Philip L, Aviv, Abraham, Levy, Daniel, Murabito, Joanne M, Munson, Peter J, Huan, Tianxiao, Hofman, Albert, Uitterlinden, André G, Rivadeneira, Fernando, van Rooij, Jeroen, Stolk, Lisette, Broer, Linda, Verbiest, Michael MPJ, Jhamai, Mila, Arp, Pascal, Metspalu, Andres, Tserel, Liina, Milani, Lili, Samani, Nilesh J, Peterson, Pärt, Kasela, Silva, Codd, Veryan, Peters, Annette, Ward-Caviness, Cavin K, Herder, Christian, Waldenberger, Melanie, Roden, Michael, Singmann, Paula, Zeilinger, Sonja, Illig, Thomas, Homuth, Georg, Grabe, Hans-Jörgen, Völzke, Henry, Steil, Leif, Kocher, Thomas, Murray, Anna, Melzer, David, Yaghootkar, Hanieh, Bandinelli, Stefania, Moses, Eric K, Kent, Jack W, Curran, Joanne E, Johnson, Matthew P, Williams-Blangero, Sarah, Westra, Harm-Jan, McRae, Allan F, Smith, Jennifer A, Kardia, Sharon LR, Hovatta, Iiris, Perola, Markus, Ripatti, Samuli, Salomaa, Veikko, Henders, Anjali K, Martin, Nicholas G, Smith, Alicia K, Mehta, Divya, Binder, Elisabeth B, Nylocks, K Maria, Kennedy, Elizabeth M, Klengel, Torsten, Ding, Jingzhong, Suchy-Dicey, Astrid M, Enquobahrie, Daniel A, Brody, Jennifer, Rotter, Jerome I, Chen, Yii-Der I, Houwing-Duistermaat, Jeanine, Kloppenburg, Margreet, Slagboom, P Eline, Helmer, Quinta, den Hollander, Wouter, Bean, Shannon, Raj, Towfique, Bakhshi, Noman, Wang, Qiao Ping, Oyston, Lisa J, Psaty, Bruce M, Tracy, Russell P, Montgomery, Grant W, and Turner, Stephen T

Subjects
NABEC/UKBEC Consortium, Humans, Gene Expression Profiling, DNA Methylation, Aging, European Continental Ancestry Group, Transcriptome, Biomarkers
Abstract

Disease incidences increase with age, but the molecular characteristics of ageing that lead to increased disease susceptibility remain inadequately understood. Here we perform a whole-blood gene expression meta-analysis in 14,983 individuals of European ancestry (including replication) and identify 1,497 genes that are differentially expressed with chronological age. The age-associated genes do not harbor more age-associated CpG-methylation sites than other genes, but are instead enriched for the presence of potentially functional CpG-methylation sites in enhancer and insulator regions that associate with both chronological age and gene expression levels. We further used the gene expression profiles to calculate the 'transcriptomic age' of an individual, and show that differences between transcriptomic age and chronological age are associated with biological features linked to ageing, such as blood pressure, cholesterol levels, fasting glucose, and body mass index. The transcriptomic prediction model adds biological relevance and complements existing epigenetic prediction models, and can be used by others to calculate transcriptomic age in external cohorts.

Published
2015

5. A meta-analysis of gene expression signatures of blood pressure and hypertension.

Author

Huan, Tianxiao, Esko, Tõnu, Peters, Marjolein J, Pilling, Luke C, Schramm, Katharina, Schurmann, Claudia, Chen, Brian H, Liu, Chunyu, Joehanes, Roby, Johnson, Andrew D, Yao, Chen, Ying, Sai-Xia, Courchesne, Paul, Milani, Lili, Raghavachari, Nalini, Wang, Richard, Liu, Poching, Reinmaa, Eva, Dehghan, Abbas, Hofman, Albert, Uitterlinden, André G, Hernandez, Dena G, Bandinelli, Stefania, Singleton, Andrew, Melzer, David, Metspalu, Andres, Carstensen, Maren, Grallert, Harald, Herder, Christian, Meitinger, Thomas, Peters, Annette, Roden, Michael, Waldenberger, Melanie, Dörr, Marcus, Felix, Stephan B, Zeller, Tanja, International Consortium for Blood Pressure GWAS (ICBP), Vasan, Ramachandran, O'Donnell, Christopher J, Munson, Peter J, Yang, Xia, Prokisch, Holger, Völker, Uwe, van Meurs, Joyce BJ, Ferrucci, Luigi, and Levy, Daniel

Subjects
International Consortium for Blood Pressure GWAS, Humans, Hypertension, Genetic Predisposition to Disease, Gene Expression Regulation, Blood Pressure, Genotype, Polymorphism, Single Nucleotide, Genome-Wide Association Study, Transcriptome, Cardiovascular, Aging, Genetics, Prevention, Human Genome, Biotechnology, 2.1 Biological and endogenous factors, Developmental Biology
Abstract

Genome-wide association studies (GWAS) have uncovered numerous genetic variants (SNPs) that are associated with blood pressure (BP). Genetic variants may lead to BP changes by acting on intermediate molecular phenotypes such as coded protein sequence or gene expression, which in turn affect BP variability. Therefore, characterizing genes whose expression is associated with BP may reveal cellular processes involved in BP regulation and uncover how transcripts mediate genetic and environmental effects on BP variability. A meta-analysis of results from six studies of global gene expression profiles of BP and hypertension in whole blood was performed in 7017 individuals who were not receiving antihypertensive drug treatment. We identified 34 genes that were differentially expressed in relation to BP (Bonferroni-corrected p

Published
2015

6. 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

7. 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

8. Low-frequency and rare exome chip variants associate with fasting glucose and type 2 diabetes susceptibility.

Author

Wessel, Jennifer, Chu, Audrey Y, Willems, Sara M, Wang, Shuai, Yaghootkar, Hanieh, Brody, Jennifer A, Dauriz, Marco, Hivert, Marie-France, Raghavan, Sridharan, Lipovich, Leonard, Hidalgo, Bertha, Fox, Keolu, Huffman, Jennifer E, An, Ping, Lu, Yingchang, Rasmussen-Torvik, Laura J, Grarup, Niels, Ehm, Margaret G, Li, Li, Baldridge, Abigail S, Stančáková, Alena, Abrol, Ravinder, Besse, Céline, Boland, Anne, Bork-Jensen, Jette, Fornage, Myriam, Freitag, Daniel F, Garcia, Melissa E, Guo, Xiuqing, Hara, Kazuo, Isaacs, Aaron, Jakobsdottir, Johanna, Lange, Leslie A, Layton, Jill C, Li, Man, Hua Zhao, Jing, Meidtner, Karina, Morrison, Alanna C, Nalls, Mike A, Peters, Marjolein J, Sabater-Lleal, Maria, Schurmann, Claudia, Silveira, Angela, Smith, Albert V, Southam, Lorraine, Stoiber, Marcus H, Strawbridge, Rona J, Taylor, Kent D, Varga, Tibor V, Allin, Kristine H, Amin, Najaf, Aponte, Jennifer L, Aung, Tin, Barbieri, Caterina, Bihlmeyer, Nathan A, Boehnke, Michael, Bombieri, Cristina, Bowden, Donald W, Burns, Sean M, Chen, Yuning, Chen, Yii-DerI, Cheng, Ching-Yu, Correa, Adolfo, Czajkowski, Jacek, Dehghan, Abbas, Ehret, Georg B, Eiriksdottir, Gudny, Escher, Stefan A, Farmaki, Aliki-Eleni, Frånberg, Mattias, Gambaro, Giovanni, Giulianini, Franco, Goddard, William A, Goel, Anuj, Gottesman, Omri, Grove, Megan L, Gustafsson, Stefan, Hai, Yang, Hallmans, Göran, Heo, Jiyoung, Hoffmann, Per, Ikram, Mohammad K, Jensen, Richard A, Jørgensen, Marit E, Jørgensen, Torben, Karaleftheri, Maria, Khor, Chiea C, Kirkpatrick, Andrea, Kraja, Aldi T, Kuusisto, Johanna, Lange, Ethan M, Lee, IT, Lee, Wen-Jane, Leong, Aaron, Liao, Jiemin, Liu, Chunyu, Liu, Yongmei, Lindgren, Cecilia M, Linneberg, Allan, and Malerba, Giovanni

Subjects
EPIC-InterAct Consortium, Humans, Diabetes Mellitus, Type 2, Genetic Predisposition to Disease, Insulin, Glucose-6-Phosphatase, Blood Glucose, Oligonucleotide Array Sequence Analysis, Fasting, Polymorphism, Single Nucleotide, African Continental Ancestry Group, European Continental Ancestry Group, Genetic Variation, Genetic Loci, Genetic Association Studies, Mutation Rate, Exome, Glucagon-Like Peptide-1 Receptor, Diabetes Mellitus, Type 2, Polymorphism, Single Nucleotide, Diabetes, Genetics, Nutrition, Clinical Research, Prevention, Human Genome, 2.1 Biological and endogenous factors, Metabolic and Endocrine
Abstract

Fasting glucose and insulin are intermediate traits for type 2 diabetes. Here we explore the role of coding variation on these traits by analysis of variants on the HumanExome BeadChip in 60,564 non-diabetic individuals and in 16,491 T2D cases and 81,877 controls. We identify a novel association of a low-frequency nonsynonymous SNV in GLP1R (A316T; rs10305492; MAF=1.4%) with lower FG (β=-0.09±0.01 mmol l(-1), P=3.4 × 10(-12)), T2D risk (OR[95%CI]=0.86[0.76-0.96], P=0.010), early insulin secretion (β=-0.07±0.035 pmolinsulin mmolglucose(-1), P=0.048), but higher 2-h glucose (β=0.16±0.05 mmol l(-1), P=4.3 × 10(-4)). We identify a gene-based association with FG at G6PC2 (pSKAT=6.8 × 10(-6)) driven by four rare protein-coding SNVs (H177Y, Y207S, R283X and S324P). We identify rs651007 (MAF=20%) in the first intron of ABO at the putative promoter of an antisense lncRNA, associating with higher FG (β=0.02±0.004 mmol l(-1), P=1.3 × 10(-8)). Our approach identifies novel coding variant associations and extends the allelic spectrum of variation underlying diabetes-related quantitative traits and T2D susceptibility.

Published
2015

9. 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

10. DNA methylation signatures of chronic low-grade inflammation are associated with complex diseases

Author

Ligthart, Symen, Marzi, Carola, Aslibekyan, Stella, Mendelson, Michael M, Conneely, Karen N, Tanaka, Toshiko, Colicino, Elena, Waite, Lindsay L, Joehanes, Roby, Guan, Weihua, Brody, Jennifer A, Elks, Cathy, Marioni, Riccardo, Jhun, Min A, Agha, Golareh, Bressler, Jan, Ward-Caviness, Cavin K, Chen, Brian H, Huan, Tianxiao, Bakulski, Kelly, Salfati, Elias L, WHI-EMPC Investigators, Fiorito, Giovanni, CHARGE epigenetics of Coronary Heart Disease, Wahl, Simone, Schramm, Katharina, Sha, Jin, Hernandez, Dena G, Just, Allan C, Smith, Jennifer A, Sotoodehnia, Nona, Pilling, Luke C, Pankow, James S, Tsao, Phil S, Liu, Chunyu, Zhao, Wei, Guarrera, Simonetta, Michopoulos, Vasiliki J, Smith, Alicia K, Peters, Marjolein J, Melzer, David, Vokonas, Pantel, Fornage, Myriam, Prokisch, Holger, Bis, Joshua C, Chu, Audrey Y, Herder, Christian, Grallert, Harald, Yao, Chen, Shah, Sonia, McRae, Allan F, Lin, Honghuang, Horvath, Steve, Fallin, Daniele, Hofman, Albert, Wareham, Nicholas J, Wiggins, Kerri L, Feinberg, Andrew P, Starr, John M, Visscher, Peter M, Murabito, Joanne M, Kardia, Sharon LR, Absher, Devin M, Binder, Elisabeth B, Singleton, Andrew B, Bandinelli, Stefania, Peters, Annette, Waldenberger, Melanie, Matullo, Giuseppe, Schwartz, Joel D, Demerath, Ellen W, Uitterlinden, André G, van Meurs, Joyce BJ, Franco, Oscar H, Chen, Yii-Der Ida, Levy, Daniel, Turner, Stephen T, Deary, Ian J, Ressler, Kerry J, Dupuis, Josée, Ferrucci, Luigi, Ong, Ken K, Assimes, Themistocles L, Boerwinkle, Eric, Koenig, Wolfgang, Arnett, Donna K, Baccarelli, Andrea A, Benjamin, Emelia J, Dehghan, Abbas, Wareham, Nicholas [0000-0003-1422-2993], Ong, Kenneth [0000-0003-4689-7530], and Apollo - University of Cambridge Repository

Subjects
Male, Inflammation, DNA methylation, Quantitative Trait Loci, Diabetes, Gene Expression, Genetic Variation, White People, Epigenesis, Genetic, C-reactive protein, Black or African American, Coronary heart disease, Epigenome-wide association study, Humans, CpG Islands, Female, Nucleotide Motifs, Body mass index, Genome-Wide Association Study
Abstract

Background: Chronic low-grade inflammation reflects a subclinical immune response implicated in the pathogenesis of complex diseases. Identifying genetic loci where DNA methylation is associated with chronic low-grade inflammation may reveal novel pathways or therapeutic targets for inflammation.Results: We performed a meta-analysis of epigenome-wide association studies (EWAS) of serum C-reactive protein (CRP), which is a sensitive marker of low-grade inflammation, in a large European population (n = 8863) and trans-ethnic replication in African Americans (n = 4111). We found differential methylation at 218 CpG sites to be associated with CRP (P –7) in the discovery panel of European ancestry and replicated (P –4) 58 CpG sites (45 unique loci) among African Americans. To further characterize the molecular and clinical relevance of the findings, we examined the association with gene expression, genetic sequence variants, and clinical outcomes. DNA methylation at nine (16%) CpG sites was associated with whole blood gene expression in cis (P –5), ten (17%) CpG sites were associated with a nearby genetic variant (P –3), and 51 (88%) were also associated with at least one related cardiometabolic entity (P –5). An additive weighted score of replicated CpG sites accounted for up to 6% inter-individual variation (R2) of age-adjusted and sex-adjusted CRP, independent of known CRP-related genetic variants.Conclusion: We have completed an EWAS of chronic low-grade inflammation and identified many novel genetic loci underlying inflammation that may serve as targets for the development of novel therapeutic interventions for inflammation.

Published
2016

11. A meta-analysis of gene expression signatures of blood pressure and hypertension

Author

Huan, Tianxiao, Esko, Tõnu, Peters, Marjolein J, Pilling, Luke C, Schramm, Katharina, Schurmann, Claudia, Chen, Brian H, Liu, Chunyu, Joehanes, Roby, Johnson, Andrew D, Yao, Chen, Ying, Sai-Xia, Courchesne, Paul, Milani, Lili, Raghavachari, Nalini, Wang, Richard, Liu, Poching, Reinmaa, Eva, Dehghan, Abbas, Hofman, Albert, Uitterlinden, André G, Hernandez, Dena G, Bandinelli, Stefania, Singleton, Andrew, Melzer, David, Metspalu, Andres, Carstensen, Maren, Grallert, Harald, Herder, Christian, Meitinger, Thomas, Peters, Annette, Roden, Michael, Waldenberger, Melanie, Dörr, Marcus, Felix, Stephan B, Zeller, Tanja, International Consortium For Blood Pressure GWAS (ICBP), Vasan, Ramachandran, O'Donnell, Christopher J, Munson, Peter J, Yang, Xia, Prokisch, Holger, Völker, Uwe, Van Meurs, Joyce BJ, Ferrucci, Luigi, and Levy, Daniel

Subjects
Gene Expression Regulation, Genotype, Hypertension, Humans, Blood Pressure, Genetic Predisposition to Disease, Transcriptome, Polymorphism, Single Nucleotide, 3. Good health, Genome-Wide Association Study
Abstract

Genome-wide association studies (GWAS) have uncovered numerous genetic variants (SNPs) that are associated with blood pressure (BP). Genetic variants may lead to BP changes by acting on intermediate molecular phenotypes such as coded protein sequence or gene expression, which in turn affect BP variability. Therefore, characterizing genes whose expression is associated with BP may reveal cellular processes involved in BP regulation and uncover how transcripts mediate genetic and environmental effects on BP variability. A meta-analysis of results from six studies of global gene expression profiles of BP and hypertension in whole blood was performed in 7017 individuals who were not receiving antihypertensive drug treatment. We identified 34 genes that were differentially expressed in relation to BP (Bonferroni-corrected p

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