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2. Genome-wide association study and functional characterization identifies candidate genes for insulin-stimulated glucose uptake

Author

Williamson, Alice, Norris, Dougall M, Yin, Xianyong, Broadaway, K Alaine, Moxley, Anne H, Vadlamudi, Swarooparani, Wilson, Emma P, Jackson, Anne U, Ahuja, Vasudha, Andersen, Mette K, Arzumanyan, Zorayr, Bonnycastle, Lori L, Bornstein, Stefan R, Bretschneider, Maxi P, Buchanan, Thomas A, Chang, Yi-Cheng, Chuang, Lee-Ming, Chung, Ren-Hua, Clausen, Tine D, Damm, Peter, Delgado, Graciela E, de Mello, Vanessa D, Dupuis, Josée, Dwivedi, Om P, Erdos, Michael R, Silva, Lilian Fernandes, Frayling, Timothy M, Gieger, Christian, Goodarzi, Mark O, Guo, Xiuqing, Gustafsson, Stefan, Hakaste, Liisa, Hammar, Ulf, Hatem, Gad, Herrmann, Sandra, Højlund, Kurt, Horn, Katrin, Hsueh, Willa A, Hung, Yi-Jen, Hwu, Chii-Min, Jonsson, Anna, Kårhus, Line L, Kleber, Marcus E, Kovacs, Peter, Lakka, Timo A, Lauzon, Marie, Lee, I-Te, Lindgren, Cecilia M, Lindström, Jaana, Linneberg, Allan, Liu, Ching-Ti, Luan, Jian’an, Aly, Dina Mansour, Mathiesen, Elisabeth, Moissl, Angela P, Morris, Andrew P, Narisu, Narisu, Perakakis, Nikolaos, Peters, Annette, Prasad, Rashmi B, Rodionov, Roman N, Roll, Kathryn, Rundsten, Carsten F, Sarnowski, Chloé, Savonen, Kai, Scholz, Markus, Sharma, Sapna, Stinson, Sara E, Suleman, Sufyan, Tan, Jingyi, Taylor, Kent D, Uusitupa, Matti, Vistisen, Dorte, Witte, Daniel R, Walther, Romy, Wu, Peitao, Xiang, Anny H, Zethelius, Björn, Ahlqvist, Emma, Bergman, Richard N, Chen, Yii-Der Ida, Collins, Francis S, Fall, Tove, Florez, Jose C, Fritsche, Andreas, Grallert, Harald, Groop, Leif, Hansen, Torben, Koistinen, Heikki A, Komulainen, Pirjo, Laakso, Markku, Lind, Lars, Loeffler, Markus, März, Winfried, Meigs, James B, Raffel, Leslie J, Rauramaa, Rainer, Rotter, Jerome I, Schwarz, Peter EH, and Stumvoll, Michael

Subjects
Biochemistry and Cell Biology, Genetics, Biological Sciences, Diabetes, Clinical Research, Human Genome, Prevention, Nutrition, 2.1 Biological and endogenous factors, Aetiology, 5.1 Pharmaceuticals, Development of treatments and therapeutic interventions, Metabolic and endocrine, Humans, Insulin, Genome-Wide Association Study, Insulin Resistance, Diabetes Mellitus, Type 2, Glucose, Blood Glucose, Meta-Analysis of Glucose and Insulin-related Traits Consortium, Medical and Health Sciences, Developmental Biology, Agricultural biotechnology, Bioinformatics and computational biology
Abstract

Distinct tissue-specific mechanisms mediate insulin action in fasting and postprandial states. Previous genetic studies have largely focused on insulin resistance in the fasting state, where hepatic insulin action dominates. Here we studied genetic variants influencing insulin levels measured 2 h after a glucose challenge in >55,000 participants from three ancestry groups. We identified ten new loci (P

Published
2023

3. Integrating transcriptomics, metabolomics, and GWAS helps reveal molecular mechanisms for metabolite levels and disease risk

Author

Yin, Xianyong, Bose, Debraj, Kwon, Annie, Hanks, Sarah C, Jackson, Anne U, Stringham, Heather M, Welch, Ryan, Oravilahti, Anniina, Silva, Lilian Fernandes, FinnGen, Locke, Adam E, Fuchsberger, Christian, Service, Susan K, Erdos, Michael R, Bonnycastle, Lori L, Kuusisto, Johanna, Stitziel, Nathan O, Hall, Ira M, Morrison, Jean, Ripatti, Samuli, Palotie, Aarno, Freimer, Nelson B, Collins, Francis S, Mohlke, Karen L, Scott, Laura J, Fauman, Eric B, Burant, Charles, Boehnke, Michael, Laakso, Markku, and Wen, Xiaoquan

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Genetics, Epidemiology, Health Sciences, Medical Biochemistry and Metabolomics, Nutrition, Biotechnology, Human Genome, 2.1 Biological and endogenous factors, Aetiology, Good Health and Well Being, Bilirubin, Carnitine, Genome-Wide Association Study, Glycerophospholipids, Humans, Male, Metabolomics, Quantitative Trait Loci, Solute Carrier Family 22 Member 5, Transcriptome, FinnGen, colocalizataion, genome-wide association study, metabolomics, transcriptome-wide association study, transcriptomics, Medical and Health Sciences, Genetics & Heredity, Biological sciences, Biomedical and clinical sciences, Health sciences
Abstract

Transcriptomics data have been integrated with genome-wide association studies (GWASs) to help understand disease/trait molecular mechanisms. The utility of metabolomics, integrated with transcriptomics and disease GWASs, to understand molecular mechanisms for metabolite levels or diseases has not been thoroughly evaluated. We performed probabilistic transcriptome-wide association and locus-level colocalization analyses to integrate transcriptomics results for 49 tissues in 706 individuals from the GTEx project, metabolomics results for 1,391 plasma metabolites in 6,136 Finnish men from the METSIM study, and GWAS results for 2,861 disease traits in 260,405 Finnish individuals from the FinnGen study. We found that genetic variants that regulate metabolite levels were more likely to influence gene expression and disease risk compared to the ones that do not. Integrating transcriptomics with metabolomics results prioritized 397 genes for 521 metabolites, including 496 previously identified gene-metabolite pairs with strong functional connections and suggested 33.3% of such gene-metabolite pairs shared the same causal variants with genetic associations of gene expression. Integrating transcriptomics and metabolomics individually with FinnGen GWAS results identified 1,597 genes for 790 disease traits. Integrating transcriptomics and metabolomics jointly with FinnGen GWAS results helped pinpoint metabolic pathways from genes to diseases. We identified putative causal effects of UGT1A1/UGT1A4 expression on gallbladder disorders through regulating plasma (E,E)-bilirubin levels, of SLC22A5 expression on nasal polyps and plasma carnitine levels through distinct pathways, and of LIPC expression on age-related macular degeneration through glycerophospholipid metabolic pathways. Our study highlights the power of integrating multiple sets of molecular traits and GWAS results to deepen understanding of disease pathophysiology.

Published
2022

4. Genome-wide association studies of metabolites in Finnish men identify disease-relevant loci

Author

Yin, Xianyong, Chan, Lap Sum, Bose, Debraj, Jackson, Anne U, VandeHaar, Peter, Locke, Adam E, Fuchsberger, Christian, Stringham, Heather M, Welch, Ryan, Yu, Ketian, Fernandes Silva, Lilian, Service, Susan K, Zhang, Daiwei, Hector, Emily C, Young, Erica, Ganel, Liron, Das, Indraniel, Abel, Haley, Erdos, Michael R, Bonnycastle, Lori L, Kuusisto, Johanna, Stitziel, Nathan O, Hall, Ira M, Wagner, Gregory R, Kang, Jian, Morrison, Jean, Burant, Charles F, Collins, Francis S, Ripatti, Samuli, Palotie, Aarno, Freimer, Nelson B, Mohlke, Karen L, Scott, Laura J, Wen, Xiaoquan, Fauman, Eric B, Laakso, Markku, and Boehnke, Michael

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Genetics, Epidemiology, Health Sciences, Medical Biochemistry and Metabolomics, Human Genome, Clinical Research, Aetiology, 2.1 Biological and endogenous factors, Alleles, Finland, Gene Frequency, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Male, Phenotype, Polymorphism, Single Nucleotide, FinnGen
Abstract

Few studies have explored the impact of rare variants (minor allele frequency

Published
2022

5. Functional interrogation of twenty type 2 diabetes-associated genes using isogenic human embryonic stem cell-derived β-like cells

Author

Xue, Dongxiang, Narisu, Narisu, Taylor, D. Leland, Zhang, Meili, Grenko, Caleb, Taylor, Henry J., Yan, Tingfen, Tang, Xuming, Sinha, Neelam, Zhu, Jiajun, Vandana, J. Jeya, Nok Chong, Angie Chi, Lee, Angela, Mansell, Erin C., Swift, Amy J., Erdos, Michael R., Zhong, Aaron, Bonnycastle, Lori L., Zhou, Ting, Chen, Shuibing, and Collins, Francis S.

Published
2023
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6. ACE2 expression in adipose tissue is associated with cardio-metabolic risk factors and cell type composition—implications for COVID-19

Author

El-Sayed Moustafa, Julia S., Jackson, Anne U., Brotman, Sarah M., Guan, Li, Villicaña, Sergio, Roberts, Amy L., Zito, Antonino, Bonnycastle, Lori, Erdos, Michael R., Narisu, Narisu, Stringham, Heather M., Welch, Ryan, Yan, Tingfen, Lakka, Timo, Parker, Stephen, Tuomilehto, Jaakko, Seow, Jeffrey, Graham, Carl, Huettner, Isabella, Acors, Sam, Kouphou, Neophytos, Wadge, Samuel, Duncan, Emma L., Steves, Claire J., Doores, Katie J., Malim, Michael H., Collins, Francis S., Pajukanta, Päivi, Boehnke, Michael, Koistinen, Heikki A., Laakso, Markku, Falchi, Mario, Bell, Jordana T., Scott, Laura J., Mohlke, Karen L., and Small, Kerrin S.

Published
2022
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8. A targeted antisense therapeutic approach for Hutchinson-Gilford progeria syndrome

Author

Erdos, Michael R., Cabral, Wayne A., Tavarez, Urraca L., Cao, Kan, Gvozdenovic-Jeremic, Jelena, Narisu, Narisu, and Zerfas, Patricia M.

Subjects
Progeria -- Care and treatment -- Genetic aspects, Biological sciences, Health
Abstract

Hutchinson-Gilford progeria syndrome (HGPS) is a rare accelerated aging disorder characterized by premature death from myocardial infarction or stroke. It is caused by de novo single-nucleotide mutations in the LMNA gene that activate a cryptic splice donor site, resulting in the production of a toxic form of lamin A, which is termed progerin. Here we present a potential genetic therapeutic strategy that utilizes antisense peptide-conjugated phosphorodiamidate morpholino oligomers (PPMOs) to block pathogenic splicing of mutant transcripts. Of several candidates, PPMO SRP-2001 provided the most significant decrease in progerin transcripts in patient fibroblasts. Intravenous delivery of SRP-2001 to a transgenic mouse model of HGPS produced significant reduction of progerin transcripts in the aorta, a particularly critical target tissue in HGPS. Long-term continuous treatment with SRP-2001 yielded a 61.6% increase in lifespan and rescue of vascular smooth muscle cell loss in large arteries. These results provide a rationale for proceeding to human trials. A modified antisense oligonucleotide that blocks pathogenic splicing of progeria transcripts reduces the production of progerin in key target organs and extends animal lifespan in mouse model of progeria syndrome., Author(s): Michael R. Erdos [sup.1] , Wayne A. Cabral [sup.1] , Urraca L. Tavarez [sup.1] , Kan Cao [sup.2] , Jelena Gvozdenovic-Jeremic [sup.1] , Narisu Narisu [sup.1] , Patricia M. [...]

Published
2021
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9. In vivo base editing rescues Hutchinson-Gilford progeria syndrome in mice

Author

Koblan, Luke W., Erdos, Michael R., Wilson, Christopher, Cabral, Wayne A., Levy, Jonathan M., Xiong, Zheng-Mei, and Tavarez, Urraca L.

Subjects
Nucleotide sequence -- Analysis, Progeria -- Genetic aspects -- Models, Environmental issues, Science and technology, Zoology and wildlife conservation
Abstract

Hutchinson-Gilford progeria syndrome (HGPS or progeria) is typically caused by a dominant-negative C*G-to-T*A mutation (c.1824 C>T; p.G608G) in LMNA, the gene that encodes nuclear lamin A. This mutation causes RNA mis-splicing that produces progerin, a toxic protein that induces rapid ageing and shortens the lifespan of children with progeria to approximately 14 years.sup.1-4. Adenine base editors (ABEs) convert targeted A*T base pairs to G*C base pairs with minimal by-products and without requiring double-strand DNA breaks or donor DNA templates.sup.5,6. Here we describe the use of an ABE to directly correct the pathogenic HGPS mutation in cultured fibroblasts derived from children with progeria and in a mouse model of HGPS. Lentiviral delivery of the ABE to fibroblasts from children with HGPS resulted in 87-91% correction of the pathogenic allele, mitigation of RNA mis-splicing, reduced levels of progerin and correction of nuclear abnormalities. Unbiased off-target DNA and RNA editing analysis did not detect off-target editing in treated patient-derived fibroblasts. In transgenic mice that are homozygous for the human LMNA c.1824 C>T allele, a single retro-orbital injection of adeno-associated virus 9 (AAV9) encoding the ABE resulted in substantial, durable correction of the pathogenic mutation (around 20-60% across various organs six months after injection), restoration of normal RNA splicing and reduction of progerin protein levels. In vivo base editing rescued the vascular pathology of the mice, preserving vascular smooth muscle cell counts and preventing adventitial fibrosis. A single injection of ABE-expressing AAV9 at postnatal day 14 improved vitality and greatly extended the median lifespan of the mice from 215 to 510 days. These findings demonstrate the potential of in vivo base editing as a possible treatment for HGPS and other genetic diseases by directly correcting their root cause. In a mouse model of progeria, an adenine base editor delivered with adeno-associated virus corrects the pathogenic mutation in LMNA, rescues vascular pathology and markedly extends the lifespan of the mice., Author(s): Luke W. Koblan [sup.1] [sup.2] [sup.3] , Michael R. Erdos [sup.4] , Christopher Wilson [sup.1] [sup.2] [sup.3] , Wayne A. Cabral [sup.4] , Jonathan M. Levy [sup.1] [sup.2] [sup.3] [...]

Published
2021
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11. Integrative analysis of gene expression, DNA methylation, physiological traits, and genetic variation in human skeletal muscle

Author

Taylor, D. Leland, Jackson, Anne U., Narisu, Narisu, Hemani, Gibran, Erdos, Michael R., Chines, Peter S., Swift, Amy, Idol, Jackie, Didion, John P., Welch, Ryan P., Kinnunen, Leena, Saramies, Jouko, Lakka, Timo A., Laakso, Markku, Tuomilehto, Jaakko, Parker, Stephen C. J., Koistinen, Heikki A., Smith, George Davey, Boehnke, Michael, Scott, Laura J., Birney, Ewan, and Collins, Francis S.

Published
2019

12. 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

13. Impact of Type 2 Diabetes Susceptibility Variants on Quantitative Glycemic Traits Reveals Mechanistic Heterogeneity

Author

Dimas, Antigone S, Lagou, Vasiliki, Barker, Adam, Knowles, Joshua W, Mägi, Reedik, Hivert, Marie-France, Benazzo, Andrea, Rybin, Denis, Jackson, Anne U, Stringham, Heather M, Song, Ci, Fischer-Rosinsky, Antje, Boesgaard, Trine Welløv, Grarup, Niels, Abbasi, Fahim A, Assimes, Themistocles L, Hao, Ke, Yang, Xia, Lecoeur, Cécile, Barroso, Inês, Bonnycastle, Lori L, Böttcher, Yvonne, Bumpstead, Suzannah, Chines, Peter S, Erdos, Michael R, Graessler, Jurgen, Kovacs, Peter, Morken, Mario A, Narisu, Narisu, Payne, Felicity, Stancakova, Alena, Swift, Amy J, Tönjes, Anke, Bornstein, Stefan R, Cauchi, Stéphane, Froguel, Philippe, Meyre, David, Schwarz, Peter EH, Häring, Hans-Ulrich, Smith, Ulf, Boehnke, Michael, Bergman, Richard N, Collins, Francis S, Mohlke, Karen L, Tuomilehto, Jaakko, Quertemous, Thomas, Lind, Lars, Hansen, Torben, Pedersen, Oluf, Walker, Mark, Pfeiffer, Andreas FH, Spranger, Joachim, Stumvoll, Michael, Meigs, James B, Wareham, Nicholas J, Kuusisto, Johanna, Laakso, Markku, Langenberg, Claudia, Dupuis, Josée, Watanabe, Richard M, Florez, Jose C, Ingelsson, Erik, McCarthy, Mark I, Prokopenko, Inga, and Investigators, on behalf of the MAGIC

Subjects
Biomedical and Clinical Sciences, Genetics, Diabetes, Autoimmune Disease, Clinical Research, Prevention, Aetiology, 2.1 Biological and endogenous factors, Metabolic and endocrine, Alleles, Cluster Analysis, Diabetes Mellitus, Type 2, Female, Gene Frequency, Genetic Predisposition to Disease, Genetic Variation, Genome-Wide Association Study, Humans, Insulin, Insulin Resistance, Insulin Secretion, Insulin-Secreting Cells, Male, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Risk Factors, Transcription Factors, MAGIC Investigators, Medical and Health Sciences, Endocrinology & Metabolism, Biomedical and clinical sciences
Abstract

Patients with established type 2 diabetes display both β-cell dysfunction and insulin resistance. To define fundamental processes leading to the diabetic state, we examined the relationship between type 2 diabetes risk variants at 37 established susceptibility loci, and indices of proinsulin processing, insulin secretion, and insulin sensitivity. We included data from up to 58,614 nondiabetic subjects with basal measures and 17,327 with dynamic measures. We used additive genetic models with adjustment for sex, age, and BMI, followed by fixed-effects, inverse-variance meta-analyses. Cluster analyses grouped risk loci into five major categories based on their relationship to these continuous glycemic phenotypes. The first cluster (PPARG, KLF14, IRS1, GCKR) was characterized by primary effects on insulin sensitivity. The second cluster (MTNR1B, GCK) featured risk alleles associated with reduced insulin secretion and fasting hyperglycemia. ARAP1 constituted a third cluster characterized by defects in insulin processing. A fourth cluster (TCF7L2, SLC30A8, HHEX/IDE, CDKAL1, CDKN2A/2B) was defined by loci influencing insulin processing and secretion without a detectable change in fasting glucose levels. The final group contained 20 risk loci with no clear-cut associations to continuous glycemic traits. By assembling extensive data on continuous glycemic traits, we have exposed the diverse mechanisms whereby type 2 diabetes risk variants impact disease predisposition.

Published
2014

15. Chromatin stretch enhancer states drive cell-specific gene regulation and harbor human disease risk variants.

Author

Parker, Stephen CJ, Stitzel, Michael L, Taylor, D Leland, Orozco, Jose Miguel, Erdos, Michael R, Akiyama, Jennifer A, van Bueren, Kelly Lammerts, Chines, Peter S, Narisu, Narisu, NISC Comparative Sequencing Program, Black, Brian L, Visel, Axel, Pennacchio, Len A, Collins, Francis S, National Institutes of Health Intramural Sequencing Center Comparative Sequencing Program Authors, and NISC Comparative Sequencing Program Authors

Subjects
NISC Comparative Sequencing Program, National Institutes of Health Intramural Sequencing Center Comparative Sequencing Program Authors, NISC Comparative Sequencing Program Authors, Chromatin, Animals, Mice, Transgenic, Humans, Mice, Diabetes Mellitus, Type 2, Luciferases, Chromatin Immunoprecipitation, Gene Expression Profiling, Cell Differentiation, Gene Expression Regulation, Insulin-Secreting Cells, Enhancer Elements, Genetic, Genome-Wide Association Study, Epigenomics, High-Throughput Nucleotide Sequencing, Biotechnology, Diabetes, Human Genome, Genetics, Aetiology, 2.1 Biological and endogenous factors, Metabolic and endocrine
Abstract

Chromatin-based functional genomic analyses and genomewide association studies (GWASs) together implicate enhancers as critical elements influencing gene expression and risk for common diseases. Here, we performed systematic chromatin and transcriptome profiling in human pancreatic islets. Integrated analysis of islet data with those from nine cell types identified specific and significant enrichment of type 2 diabetes and related quantitative trait GWAS variants in islet enhancers. Our integrated chromatin maps reveal that most enhancers are short (median = 0.8 kb). Each cell type also contains a substantial number of more extended (≥ 3 kb) enhancers. Interestingly, these stretch enhancers are often tissue-specific and overlap locus control regions, suggesting that they are important chromatin regulatory beacons. Indeed, we show that (i) tissue specificity of enhancers and nearby gene expression increase with enhancer length; (ii) neighborhoods containing stretch enhancers are enriched for important cell type-specific genes; and (iii) GWAS variants associated with traits relevant to a particular cell type are more enriched in stretch enhancers compared with short enhancers. Reporter constructs containing stretch enhancer sequences exhibited tissue-specific activity in cell culture experiments and in transgenic mice. These results suggest that stretch enhancers are critical chromatin elements for coordinating cell type-specific regulatory programs and that sequence variation in stretch enhancers affects risk of major common human diseases.

Published
2013

16. No Interactions Between Previously Associated 2-Hour Glucose Gene Variants and Physical Activity or BMI on 2-Hour Glucose Levels

Author

Scott, Robert A, Chu, Audrey Y, Grarup, Niels, Manning, Alisa K, Hivert, Marie-France, Shungin, Dmitry, Tönjes, Anke, Yesupriya, Ajay, Barnes, Daniel, Bouatia-Naji, Nabila, Glazer, Nicole L, Jackson, Anne U, Kutalik, Zoltán, Lagou, Vasiliki, Marek, Diana, Rasmussen-Torvik, Laura J, Stringham, Heather M, Tanaka, Toshiko, Aadahl, Mette, Arking, Dan E, Bergmann, Sven, Boerwinkle, Eric, Bonnycastle, Lori L, Bornstein, Stefan R, Brunner, Eric, Bumpstead, Suzannah J, Brage, Soren, Carlson, Olga D, Chen, Han, Chen, Yii-Der Ida, Chines, Peter S, Collins, Francis S, Couper, David J, Dennison, Elaine M, Dowling, Nicole F, Egan, Josephine S, Ekelund, Ulf, Erdos, Michael R, Forouhi, Nita G, Fox, Caroline S, Goodarzi, Mark O, Grässler, Jürgen, Gustafsson, Stefan, Hallmans, Göran, Hansen, Torben, Hingorani, Aroon, Holloway, John W, Hu, Frank B, Isomaa, Bo, Jameson, Karen A, Johansson, Ingegerd, Jonsson, Anna, Jørgensen, Torben, Kivimaki, Mika, Kovacs, Peter, Kumari, Meena, Kuusisto, Johanna, Laakso, Markku, Lecoeur, Cécile, Lévy-Marchal, Claire, Li, Guo, Loos, Ruth JF, Lyssenko, Valeri, Marmot, Michael, Marques-Vidal, Pedro, Morken, Mario A, Müller, Gabriele, North, Kari E, Pankow, James S, Payne, Felicity, Prokopenko, Inga, Psaty, Bruce M, Renström, Frida, Rice, Ken, Rotter, Jerome I, Rybin, Denis, Sandholt, Camilla H, Sayer, Avan A, Shrader, Peter, Schwarz, Peter EH, Siscovick, David S, Stančáková, Alena, Stumvoll, Michael, Teslovich, Tanya M, Waeber, Gérard, Williams, Gordon H, Witte, Daniel R, Wood, Andrew R, Xie, Weijia, Boehnke, Michael, Cooper, Cyrus, Ferrucci, Luigi, Froguel, Philippe, Groop, Leif, Kao, WH Linda, Vollenweider, Peter, Walker, Mark, Watanabe, Richard M, Pedersen, Oluf, and Meigs, James B

Subjects
Biomedical and Clinical Sciences, Clinical Research, Genetics, Diabetes, Prevention, Obesity, Nutrition, 2.1 Biological and endogenous factors, Aetiology, 2.3 Psychological, social and economic factors, Metabolic and endocrine, Blood Glucose, Body Mass Index, Epigenesis, Genetic, Gene Expression Regulation, Genotype, Humans, Life Style, Motor Activity, Polymorphism, Single Nucleotide, Medical and Health Sciences, Endocrinology & Metabolism, Biomedical and clinical sciences
Abstract

Gene-lifestyle interactions have been suggested to contribute to the development of type 2 diabetes. Glucose levels 2 h after a standard 75-g glucose challenge are used to diagnose diabetes and are associated with both genetic and lifestyle factors. However, whether these factors interact to determine 2-h glucose levels is unknown. We meta-analyzed single nucleotide polymorphism (SNP) × BMI and SNP × physical activity (PA) interaction regression models for five SNPs previously associated with 2-h glucose levels from up to 22 studies comprising 54,884 individuals without diabetes. PA levels were dichotomized, with individuals below the first quintile classified as inactive (20%) and the remainder as active (80%). BMI was considered a continuous trait. Inactive individuals had higher 2-h glucose levels than active individuals (β = 0.22 mmol/L [95% CI 0.13-0.31], P = 1.63 × 10(-6)). All SNPs were associated with 2-h glucose (β = 0.06-0.12 mmol/allele, P ≤ 1.53 × 10(-7)), but no significant interactions were found with PA (P > 0.18) or BMI (P ≥ 0.04). In this large study of gene-lifestyle interaction, we observed no interactions between genetic and lifestyle factors, both of which were associated with 2-h glucose. It is perhaps unlikely that top loci from genome-wide association studies will exhibit strong subgroup-specific effects, and may not, therefore, make the best candidates for the study of interactions.

Published
2012

17. A genome-wide association search for type 2 diabetes genes in African Americans.

Author

Palmer, Nicholette D, McDonough, Caitrin W, Hicks, Pamela J, Roh, Bong H, Wing, Maria R, An, S Sandy, Hester, Jessica M, Cooke, Jessica N, Bostrom, Meredith A, Rudock, Megan E, Talbert, Matthew E, Lewis, Joshua P, DIAGRAM Consortium, MAGIC Investigators, Ferrara, Assiamira, Lu, Lingyi, Ziegler, Julie T, Sale, Michele M, Divers, Jasmin, Shriner, Daniel, Adeyemo, Adebowale, Rotimi, Charles N, Ng, Maggie CY, Langefeld, Carl D, Freedman, Barry I, Bowden, Donald W, Voight, Benjamin F, Scott, Laura J, Steinthorsdottir, Valgerdur, Morris, Andrew P, Dina, Christian, Welch, Ryan P, Zeggini, Eleftheria, Huth, Cornelia, Aulchenko, Yurii S, Thorleifsson, Gudmar, McCulloch, Laura J, Ferreira, Teresa, Grallert, Harald, Amin, Najaf, Wu, Guanming, Willer, Cristen J, Raychaudhuri, Soumya, McCarroll, Steve A, Langenberg, Claudia, Hofmann, Oliver M, Dupuis, Josée, Qi, Lu, Segrè, Ayellet V, van Hoek, Mandy, Navarro, Pau, Ardlie, Kristin, Balkau, Beverley, Benediktsson, Rafn, Bennett, Amanda J, Blagieva, Roza, Boerwinkle, Eric, Bonnycastle, Lori L, Boström, Kristina Bengtsson, Bravenboer, Bert, Bumpstead, Suzannah, Burtt, Noël P, Charpentier, Guillaume, Chines, Peter S, Cornelis, Marilyn, Couper, David J, Crawford, Gabe, Doney, Alex SF, Elliott, Katherine S, Elliott, Amanda L, Erdos, Michael R, Fox, Caroline S, Franklin, Christopher S, Ganser, Martha, Gieger, Christian, Grarup, Niels, Green, Todd, Griffin, Simon, Groves, Christopher J, Guiducci, Candace, Hadjadj, Samy, Hassanali, Neelam, Herder, Christian, Isomaa, Bo, Jackson, Anne U, Johnson, Paul RV, Jørgensen, Torben, Kao, Wen HL, Klopp, Norman, Kong, Augustine, Kraft, Peter, Kuusisto, Johanna, Lauritzen, Torsten, Li, Man, Lieverse, Aloysius, Lindgren, Cecilia M, Lyssenko, Valeriya, Marre, Michel, Meitinger, Thomas, and Midthjell, Kristian

Subjects
DIAGRAM Consortium, MAGIC Investigators, Humans, Diabetes Mellitus, Type 2, Genetic Predisposition to Disease, Case-Control Studies, Cohort Studies, Genotype, Polymorphism, Single Nucleotide, Adult, Aged, Middle Aged, African Americans, Female, Male, Meta-Analysis as Topic, Validation Studies as Topic, Genome-Wide Association Study, Diabetes Mellitus, Type 2, Polymorphism, Single Nucleotide, General Science & Technology
Abstract

African Americans are disproportionately affected by type 2 diabetes (T2DM) yet few studies have examined T2DM using genome-wide association approaches in this ethnicity. The aim of this study was to identify genes associated with T2DM in the African American population. We performed a Genome Wide Association Study (GWAS) using the Affymetrix 6.0 array in 965 African-American cases with T2DM and end-stage renal disease (T2DM-ESRD) and 1029 population-based controls. The most significant SNPs (n = 550 independent loci) were genotyped in a replication cohort and 122 SNPs (n = 98 independent loci) were further tested through genotyping three additional validation cohorts followed by meta-analysis in all five cohorts totaling 3,132 cases and 3,317 controls. Twelve SNPs had evidence of association in the GWAS (P

Published
2012

20. Genetic regulatory signatures underlying islet gene expression and type 2 diabetes

Author

NISC Comparative Sequencing Program, Varshney, Arushi, Scott, Laura J., Welch, Ryan P., Erdos, Michael R., Chines, Peter S., Narisu, Narisu, Albanus, Ricardo D’O., Orchard, Peter, Wolford, Brooke N., Kursawe, Romy, Vadlamudi, Swarooparani, Cannon, Maren E., Didion, John P., Hensley, John, Kirilusha, Anthony, Bonnycastle, Lori L., Taylor, D. Leland, Watanabe, Richard, Mohlke, Karen L., Boehnke, Michael, Collins, Francis S., Parker, Stephen C. J., and Stitzel, Michael L.

Published
2017

22. Genetic variant effects on gene expression in human pancreatic islets and their implications for T2D

Author

Viñuela, Ana, Varshney, Arushi, van de Bunt, Martijn, Prasad, Rashmi B., Asplund, Olof, Bennett, Amanda, Boehnke, Michael, Brown, Andrew A., Erdos, Michael R., Fadista, João, Hansson, Ola, Hatem, Gad, Howald, Cédric, Iyengar, Apoorva K., Johnson, Paul, Krus, Ulrika, MacDonald, Patrick E., Mahajan, Anubha, Manning Fox, Jocelyn E., Narisu, Narisu, Nylander, Vibe, Orchard, Peter, Oskolkov, Nikolay, Panousis, Nikolaos I., Payne, Anthony, Stitzel, Michael L., Vadlamudi, Swarooparani, Welch, Ryan, Collins, Francis S., Mohlke, Karen L., Gloyn, Anna L., Scott, Laura J., Dermitzakis, Emmanouil T., Groop, Leif, Parker, Stephen C. J., and McCarthy, Mark I.

Published
2020
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23. Creating Proper Occlusion With OccluSense® to Achieve Sound Function.

Author

Tawil, Isaac and Erdos, Michael

Subjects
OPERATIVE dentistry, PROSTHODONTICS, ORAL health, DENTISTS, ORTHODONTICS
Abstract

Proper occlusion is a foundational element of dentistry that affects various aspects of dental health, including a patient's oral function, structure, and esthetics. Among the tools dentists use to gauge a patient's occlusion are articulating paper and shimstock. A new system that combines both traditional and digital registration of the pressure distribution on occlusal surfaces can aid clinicians in further analyzing a patient's occlusion and more precisely diagnosing anomalies. In this article, three clinical cases are presented in which the OccluSense® system was used to address occlusion issues in restorative dentistry, orthodontics, and prosthodontics. [ABSTRACT FROM AUTHOR]

Published
2023

24. Type 2 Diabetes: Evidence for Linkage on Chromosome 20 in 716 Finnish Affected Sib Pairs

Author

Ghosh, Soumitra, Watanabe, Richard M., Hauser, Elizabeth R., Valle, Timo, Magnuson, Victoria L., Erdos, Michael R., Langefeld, Carl D., Balow,, James, Ally, Delphine S., Kohtamaki, Kimmo, Chines, Peter, Birznieks, Gunther, Kaleta, Hong-Shi, Musick, Anjene, Te, Catherine, Tannenbaum, Joyce, Eldridge, William, Shapiro, Shane, Martin, Colin, Witt, Alyson, So, Alistair, Chang, Jennie, Shurtleff, Ben, Porter, Rachel, Kudelko, Kristina, Unni, Arun, Segal, Leonid, Sharaf, Ravi, Blaschak-Harvan, Jillian, Eriksson, Johan, Tenkula, Tuula, Vidgren, Gabriele, Ehnholm, Christian, Tuomilehto-Wolf, Eva, Hagopian, William, Buchanan, Thomas A., Tuomilehto, Jaakko, Bergman, Richard N., Collins, Francis S., and Boehnke, Michael

Published
1999

26. Bone dysplasia in Hutchinson‐Gilford progeria syndrome is associated with dysregulated differentiation and function of bone cell populations.

Author

Cabral, Wayne A., Stephan, Chris, Terajima, Masahiko, Thaivalappil, Abhirami A., Blanchard, Owen, Tavarez, Urraca L., Narisu, Narisu, Yan, Tingfen, Wincovitch, Stephen M., Taga, Yuki, Yamauchi, Mitsuo, Kozloff, Kenneth M., Erdos, Michael R., and Collins, Francis S.

Subjects
PROGERIA, CELL populations, BONE cells, CELL physiology, DYSPLASIA, EXTRACELLULAR matrix
Abstract

Hutchinson‐Gilford progeria syndrome (HGPS) is a premature aging disorder affecting tissues of mesenchymal origin. Most individuals with HGPS harbor a de novo c.1824C > T (p.G608G) mutation in the gene encoding lamin A (LMNA), which activates a cryptic splice donor site resulting in production of the toxic "progerin" protein. Clinical manifestations include growth deficiency, lipodystrophy, sclerotic dermis, cardiovascular defects, and bone dysplasia. Here we utilized the LmnaG609G knock‐in (KI) mouse model of HGPS to further define mechanisms of bone loss associated with normal and premature aging disorders. Newborn skeletal staining of KI mice revealed altered rib cage shape and spinal curvature, and delayed calvarial mineralization with increased craniofacial and mandibular cartilage content. MicroCT analysis and mechanical testing of adult femurs indicated increased fragility associated with reduced bone mass, recapitulating the progressive bone deterioration that occurs in HGPS patients. We investigated mechanisms of bone loss in KI mice at the cellular level in bone cell populations. Formation of wild‐type and KI osteoclasts from marrow‐derived precursors was inhibited by KI osteoblast‐conditioned media in vitro, suggesting a secreted factor(s) responsible for decreased osteoclasts on KI trabecular surfaces in vivo. Cultured KI osteoblasts exhibited abnormal differentiation characterized by reduced deposition and mineralization of extracellular matrix with increased lipid accumulation compared to wild‐type, providing a mechanism for altered bone formation. Furthermore, quantitative analyses of KI transcripts confirmed upregulation of adipogenic genes both in vitro and in vivo. Thus, osteoblast phenotypic plasticity, inflammation and altered cellular cross‐talk contribute to abnormal bone formation in HGPS mice. [ABSTRACT FROM AUTHOR]

Published
2023
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27. Modeling islet enhancers using deep learning identifies candidate causal variants at loci associated with T2D and glycemic traits.

Author

Hudaiberdiev, Sanjarbek, Taylor, D. Leland, Wei Song, Narisu, Narisu, Bhuiyan, Redwan M., Taylor, Henry J., Xuming Tang, Tingfen Yan, Swift, Amy J., Bonnycastle, Lori L., Shuibing Chen, Stitzel, Michael L., Erdos, Michael R., Ovcharenko, Ivan, and Collins, Francis S.

Subjects
DEEP learning, PANCREATIC beta cells, ISLANDS, ISLANDS of Langerhans, TYPE 2 diabetes
Abstract

Genetic association studies have identified hundreds of independent signals associated with type 2 diabetes (T2D) and related traits. Despite these successes, the identification of specific causal variants underlying a genetic association signal remains challenging. In this study, we describe a deep learning (DL) method to analyze the impact of sequence variants on enhancers. Focusing on pancreatic islets, a T2D relevant tissue, we show that our model learns islet-specific transcription factor (TF) regulatory patterns and can be used to prioritize candidate causal variants. At 101 genetic signals associated with T2D and related glycemic traits where multiple variants occur in linkage disequilibrium, our method nominates a single causal variant for each association signal, including three variants previously shown to alter reporter activity in islet-relevant cell types. For another signal associated with blood glucose levels, we biochemically test all candidate causal variants from statistical fine-mapping using a pancreatic islet beta cell line and show biochemical evidence of allelic effects on TF binding for the model-prioritized variant. To aid in future research, we publicly distribute our model and islet enhancer perturbation scores across ~67 million genetic variants. We anticipate that DL methods like the one presented in this study will enhance the prioritization of candidate causal variants for functional studies. [ABSTRACT FROM AUTHOR]

Published
2023
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30. Super-enhancers delineate disease-associated regulatory nodes in T cells

Author

Vahedi, Golnaz, Kanno, Yuka, Furumoto, Yasuko, Jiang, Kan, Parker, Stephen C. J., Erdos, Michael R., Davis, Sean R., Roychoudhuri, Rahul, Restifo, Nicholas P., Gadina, Massimo, Tang, Zhonghui, Ruan, Yijun, Collins, Francis S., Sartorelli, Vittorio, and O'Shea, John J.

Subjects
T cells -- Physiological aspects, Transcription factors -- Physiological aspects, Environmental issues, Science and technology, Zoology and wildlife conservation
Abstract

A study of the super-enhancer landscape in three mouse T-helper lymphocyte subsets identifies nodes that have key roles in cell identity, with the locus encoding Bach2, a key negative regulator of effector differentiation, emerging as the most prominent T-cell super-enhancer. Super-enhancer interaction with T cells Super-enhancers are a group of transcriptional factors specialized for the regulation of cellular functions related to cell identity and genetic disease risk. This study of the super-enhancer landscape in three mouse T-helper lymphocyte subsets identifies nodes that have crucial roles in cell identity, with the Bach2 locus, encoding the transcription factor BACH2, which is an important negative regulator of effector differentiation, emerging as the most prominent T-cell super-enhancer. Perturbation of Bach2 leads to a preferential effect on the expression of super-enhancer-associated genes and non-coding RNAs. This work demonstrates a systematic approach by which the super-enhancer map of relevant cell types can be integrated with human genetics to discover drug target genes. Enhancers regulate spatiotemporal gene expression and impart cell-specific transcriptional outputs that drive cell identity.sup.1. Super-enhancers (SEs), also known as stretch-enhancers, are a subset of enhancers especially important for genes associated with cell identity and genetic risk of disease.sup.2,3,4,5,6. CD4.sup.+ T cells are critical for host defence and autoimmunity. Here we analysed maps of mouse T-cell SEs as a non-biased means of identifying key regulatory nodes involved in cell specification. We found that cytokines and cytokine receptors were the dominant class of genes exhibiting SE architecture in T cells. Nonetheless, the locus encoding Bach2, a key negative regulator of effector differentiation, emerged as the most prominent T-cell SE, revealing a network in which SE-associated genes critical for T-cell biology are repressed by BACH2. Disease-associated single-nucleotide polymorphisms for immune-mediated disorders, including rheumatoid arthritis, were highly enriched for T-cell SEs versus typical enhancers or SEs in other cell lineages.sup.7. Intriguingly, treatment of T cells with the Janus kinase (JAK) inhibitor tofacitinib disproportionately altered the expression of rheumatoid arthritis risk genes with SE structures. Together, these results indicate that genes with SE architecture in T cells encompass a variety of cytokines and cytokine receptors but are controlled by a 'guardian' transcription factor, itself endowed with an SE. Thus, enumeration of SEs allows the unbiased determination of key regulatory nodes in T cells, which are preferentially modulated by pharmacological intervention., Author(s): Golnaz Vahedi [sup.1] , Yuka Kanno [sup.1] , Yasuko Furumoto [sup.2] , Kan Jiang [sup.1] , Stephen C. J. Parker [sup.3] [sup.7] , Michael R. Erdos [sup.3] , Sean [...]

Published
2015
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31. Human pancreatic islet microRNAs implicated in diabetes and related traits by large-scale genetic analysis.

Author

Taylor, Henry J., Yu-Han Hung, Narisu, Narisu, Erdos, Michael R., Kanke, Matthew, Yan, Tingfen, Grenko, Caleb M., Swift, Amy J., Bonnycastle, Lori L., Sethupathy, Praveen, Collins, Francis S., and Taylor, D. Leland

Subjects
GENE expression, ISLANDS of Langerhans, GENETIC regulation, LOCUS (Genetics), GLYCOSYLATED hemoglobin
Abstract

Genetic studies have identified =240 loci associated with the risk of type 2 diabetes (T2D), yet most of these loci lie in non-coding regions, masking the underlying molecular mechanisms. Recent studies investigating mRNA expression in human pancreatic islets have yielded important insights into the molecular drivers of normal islet function and T2D pathophysiology. However, similar studies investigating microRNA (miRNA) expression remain limited. Here, we present data from 63 individuals, the largest sequencing-based analysis of miRNA expression in human islets to date. We characterized the genetic regulation of miRNA expression by decomposing the expression of highly heritable miRNAs into cis- and trans-acting genetic components and mapping cis-acting loci associated with miRNA expression [miRNA-expression quantitative trait loci (eQTLs)]. We found i) 84 heritable miRNAs, primarily regulated by trans-acting genetic effects, and ii) 5 miRNA-eQTLs. We also used several different strategies to identify T2D-associated miRNAs. First, we colocalized miRNA-eQTLs with genetic loci associated with T2D and multiple glycemic traits, identifying one miRNA, miR-1908, that shares genetic signals for blood glucose and glycated hemoglobin (HbA1c). Next, we intersected miRNA seed regions and predicted target sites with credible set SNPs associated with T2D and glycemic traits and found 32 miRNAs that may have altered binding and function due to disrupted seed regions. Finally, we performed differential expression analysis and identified 14 miRNAs associated with T2D status--including miR-187-3p, miR-21-5p, miR-668, and miR-199b-5p--and 4 miRNAs associated with a polygenic score for HbA1c levels--miR-216a, miR-25, miR-30a-3p, and miR-30a-5p. [ABSTRACT FROM AUTHOR]

Published
2023
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35. A Genome-Wide Association Study of Type 2 Diabetes in Finns Detects Multiple Susceptibility Variants

Author

Scott, Laura J., Mohlke, Karen L., Bonnycastle, Lori L., Willer, Cristen J., Li, Yun, Duren, William L., Erdos, Michael R., Stringham, Heather M., Chines, Peter S., Jackson, Anne U., Prokunina-Olsson, Ludmila, Ding, Chia-Jen, Switt, Amy J., Narisu, Narisu, Hu, Tianle, Pruim, Randall, Xiao, Rui, Li, Xiao-Yi, Conneely, Karen N., Riebow, Nancy L., Sprau, Andrew G., Tong, Maurine, White, Peggy P., Hetrick, Kurt N., Barnhart, Michael W., Bark, Craig W., Goldstein, Janet L., Watkins, Lee, Xiang, Fang, Saramies, Jouko, Buchanan, Thomas A., Watanabe, Richard M., Valle, Timo T., Kinnunen, Leena, Abecasis, Gonçalo R., Pugh, Elizabeth W., Doheny, Kimberly F., Bergman, Richard N., Tuomilehto, Jaakko, Collins, Francis S., and Boehnke, Michael

Published
2007
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39. SARS-CoV-2 infection induces beta cell transdifferentiation

Author

Tang, Xuming, Uhl, Skyler, Zhang, Tuo, Xue, Dongxiang, Li, Bo, Vandana, J. Jeya, Acklin, Joshua A., Bonnycastle, Lori L., Narisu, Narisu, Erdos, Michael R., Bram, Yaron, Chandar, Vasuretha, Chong, Angie Chi Nok, Lacko, Lauretta A., Min, Zaw, Lim, Jean K., Borczuk, Alain C., Xiang, Jenny, Naji, Ali, Collins, Francis S., Evans, Todd, Liu, Chengyang, tenOever, Benjamin R., Schwartz, Robert E., and Chen, Shuibing

Published
2021
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43. A genome-wide approach accounting for body mass index identifies genetic variants influencing fasting glycemic traits and insulin resistance

Author

Manning, Alisa K, Hivert, Marie-France, Scott, Robert A, Grimsby, Jonna L, Bouatia-Naji, Nabila, Chen, Han, Rybin, Denis, Liu, Ching-Ti, Bielak, Lawrence F, Prokopenko, Inga, Amin, Najaf, Barnes, Daniel, Cadby, Gemma, Hottenga, Jouke-Jan, Ingelsson, Erik, Jackson, Anne U, Johnson, Toby, Kanoni, Stavroula, Ladenvall, Claes, Lagou, Vasiliki, Lahti, Jari, Lecoeur, Cecile, Liu, Yongmei, Martinez-Larrad, Maria Teresa, Montasser, May E, Navarro, Pau, Perry, John R B, Rasmussen-Torvik, Laura J, Salo, Perttu, Sattar, Naveed, Shungin, Dmitry, Strawbridge, Rona J, Tanaka, Toshiko, van Duijn, Cornelia M, An, Ping, de Andrade, Mariza, Andrews, Jeanette S, Aspelund, Thor, Atalay, Mustafa, Aulchenko, Yurii, Balkau, Beverley, Bandinelli, Stefania, Beckmann, Jacques S, Beilby, John P, Bellis, Claire, Bergman, Richard N, Blangero, John, Boban, Mladen, Boehnke, Michael, Boerwinkle, Eric, Bonnycastle, Lori L, Boomsma, Dorret I, Borecki, Ingrid B, Böttcher, Yvonne, Bouchard, Claude, Brunner, Eric, Budimir, Danijela, Campbell, Harry, Carlson, Olga, Chines, Peter S, Clarke, Robert, Collins, Francis S, Corbatón-Anchuelo, Arturo, Couper, David, de Faire, Ulf, Dedoussis, George V, Deloukas, Panos, Dimitriou, Maria, Egan, Josephine M, Eiriksdottir, Gudny, Erdos, Michael R, Eriksson, Johan G, Eury, Elodie, Ferrucci, Luigi, Ford, Ian, Forouhi, Nita G, Fox, Caroline S, Franzosi, Maria Grazia, Franks, Paul W, Frayling, Timothy M, Froguel, Philippe, Galan, Pilar, de Geus, Eco, Gigante, Bruna, Glazer, Nicole L, Goel, Anuj, Groop, Leif, Gudnason, Vilmundur, Hallmans, Göran, Hamsten, Anders, Hansson, Ola, Harris, Tamara B, Hayward, Caroline, Heath, Simon, Hercberg, Serge, Hicks, Andrew A, Hingorani, Aroon, Hofman, Albert, Hui, Jennie, Hung, Joseph, Jarvelin, Marjo-Riitta, Jhun, Min A, Johnson, Paul C D, Jukema, J Wouter, Jula, Antti, Kao, W H, Kaprio, Jaakko, Kardia, Sharon L R, Keinanen-Kiukaanniemi, Sirkka, Kivimaki, Mika, Kolcic, Ivana, Kovacs, Peter, Kumari, Meena, Kuusisto, Johanna, Kyvik, Kirsten Ohm, Laakso, Markku, Lakka, Timo, Lannfelt, Lars, Lathrop, G Mark, Launer, Lenore J, Leander, Karin, Li, Guo, Lind, Lars, Lindstrom, Jaana, Lobbens, Stéphane, Loos, Ruth J F, Luan, Jian'an, Lyssenko, Valeriya, Mägi, Reedik, Magnusson, Patrik K E, Marmot, Michael, Meneton, Pierre, Mohlke, Karen L, Mooser, Vincent, Morken, Mario A, Miljkovic, Iva, Narisu, Narisu, O'Connell, Jeff, Ong, Ken K, Oostra, Ben A, Palmer, Lyle J, Palotie, Aarno, Pankow, James S, Peden, John F, Pedersen, Nancy L, Pehlic, Marina, Peltonen, Leena, Penninx, Brenda, Pericic, Marijana, Perola, Markus, Perusse, Louis, Peyser, Patricia A, Polasek, Ozren, Pramstaller, Peter P, Province, Michael A, Räikkönen, Katri, Rauramaa, Rainer, Rehnberg, Emil, Rice, Ken, Rotter, Jerome I, Rudan, Igor, Ruokonen, Aimo, Saaristo, Timo, Sabater-Lleal, Maria, Salomaa, Veikko, Savage, David B, Saxena, Richa, Schwarz, Peter, Seedorf, Udo, Sennblad, Bengt, Serrano-Rios, Manuel, Shuldiner, Alan R, Sijbrands, Eric J G, Siscovick, David S, Smit, Johannes H, Small, Kerrin S, Smith, Nicholas L, Smith, Albert Vernon, Stančáková, Alena, Stirrups, Kathleen, Stumvoll, Michael, Sun, Yan V, Swift, Amy J, Tönjes, Anke, Tuomilehto, Jaakko, Trompet, Stella, Uitterlinden, Andre G, Uusitupa, Matti, Vikström, Max, Vitart, Veronique, Vohl, Marie-Claude, Voight, Benjamin F, Vollenweider, Peter, Waeber, Gerard, Waterworth, Dawn M, Watkins, Hugh, Wheeler, Eleanor, Widen, Elisabeth, Wild, Sarah H, Willems, Sara M, Willemsen, Gonneke, Wilson, James F, Witteman, Jacqueline C M, Wright, Alan F, Yaghootkar, Hanieh, Zelenika, Diana, Zemunik, Tatijana, Zgaga, Lina, Wareham, Nicholas J, McCarthy, Mark I, Barroso, Ines, Watanabe, Richard M, Florez, Jose C, Dupuis, Josée, Meigs, James B, and Langenberg, Claudia

Published
2012
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44. Genome-wide associated loci influencing interleukin (IL)-10, IL-1Ra, and IL-6 levels in African Americans

Author

Tekola Ayele, Fasil, Doumatey, Ayo, Huang, Hanxia, Zhou, Jie, Charles, Bashira, Erdos, Michael, Adeleye, Jokotade, Balogun, Williams, Fasanmade, Olufemi, Johnson, Thomas, Oli, Johnnie, Okafor, Godfrey, Amoah, Albert, Eghan, Jr., Benjamin A., Agyenim-Boateng, Kofi, Acheampong, Joseph, Adebamowo, Clement A., Herbert, Alan, Gerry, Norman, Christman, Michael, Chen, Guanjie, Shriner, Daniel, Adeyemo, Adebowale, and Rotimi, Charles N.

Published
2012
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45. Progerin and telomere dysfunction collaborate to trigger cellular senescence in normal human fibroblasts

Author

Cao, Kan, Blair, Cecilia D., Faddah, Dina A., Kieckhaefer, Julia E., Olive, Michelle, Erdos, Michael R., Nabel, Elizabeth G., and Collins, Francis S.

Subjects
Gene mutations -- Health aspects -- Research, Progeria -- Risk factors -- Genetic aspects -- Research, Fibroblasts -- Physiological aspects -- Genetic aspects -- Research, Telomeres -- Physiological aspects -- Research, Health care industry
Abstract

Hutchinson-Gilford progeria syndrome (HGPS), a devastating premature aging disease, is caused by a point mutation in the lamin A gene (LMNA). This mutation constitutively activates a cryptic splice donor site, [...]

Published
2011
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48. Recurrent de novo point mutations in lamin A cause Hutchinson-Gilford progeria syndrome

Author

Eriksson, Maria, Brown, W. Ted, Gordon, Leslie B., Glynn, Michael W., Singer, Joel, Scott, Laura, Erdos, Michael R., Robbins, Christiane M., Moses, Tracy Y., Berglund, Peter, Dutra, Amalia, Pak, Evgenia, Durkin, Sandra, Csoka, Antonei B., Boehnke, Michael, Glover, Thomas W., and Collins, Francis S.

Subjects
Environmental issues, Science and technology, Zoology and wildlife conservation
Abstract

Author(s): Maria Eriksson [1]; W. Ted Brown [2]; Leslie B. Gordon [3]; Michael W. Glynn [4]; Joel Singer [5]; Laura Scott [5]; Michael R. Erdos [1]; Christiane M. Robbins [1]; [...]

Published
2003
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