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3. Differential CpG methylation at Nnat in the early establishment of beta cell heterogeneity

Author

Yu, Vanessa, Yong, Fiona, Marta, Angellica, Khadayate, Sanjay, Osakwe, Adrien, Bhattacharya, Supriyo, Varghese, Sneha S., Chabosseau, Pauline, Tabibi, Sayed M., Chen, Keran, Georgiadou, Eleni, Parveen, Nazia, Suleiman, Mara, Stamoulis, Zoe, Marselli, Lorella, De Luca, Carmela, Tesi, Marta, Ostinelli, Giada, Delgadillo-Silva, Luis, Wu, Xiwei, Hatanaka, Yuki, Montoya, Alex, Elliott, James, Patel, Bhavik, Demchenko, Nikita, Whilding, Chad, Hajkova, Petra, Shliaha, Pavel, Kramer, Holger, Ali, Yusuf, Marchetti, Piero, Sladek, Robert, Dhawan, Sangeeta, Withers, Dominic J., Rutter, Guy A., and Millership, Steven J.

Published
2024
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5. Case-Base Neural Networks: survival analysis with time-varying, higher-order interactions

Author

Islam, Jesse, Turgeon, Maxime, Sladek, Robert, and Bhatnagar, Sahir

Subjects
Statistics - Machine Learning, Computer Science - Machine Learning
Abstract

In the context of survival analysis, data-driven neural network-based methods have been developed to model complex covariate effects. While these methods may provide better predictive performance than regression-based approaches, not all can model time-varying interactions and complex baseline hazards. To address this, we propose Case-Base Neural Networks (CBNNs) as a new approach that combines the case-base sampling framework with flexible neural network architectures. Using a novel sampling scheme and data augmentation to naturally account for censoring, we construct a feed-forward neural network that includes time as an input. CBNNs predict the probability of an event occurring at a given moment to estimate the full hazard function. We compare the performance of CBNNs to regression and neural network-based survival methods in a simulation and three case studies using two time-dependent metrics. First, we examine performance on a simulation involving a complex baseline hazard and time-varying interactions to assess all methods, with CBNN outperforming competitors. Then, we apply all methods to three real data applications, with CBNNs outperforming the competing models in two studies and showing similar performance in the third. Our results highlight the benefit of combining case-base sampling with deep learning to provide a simple and flexible framework for data-driven modeling of single event survival outcomes that estimates time-varying effects and a complex baseline hazard by design. An R package is available at https://github.com/Jesse-Islam/cbnn.

Published
2023
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7. Discriminating protein tags on dsDNA constructs using a dual Nanopore device

Author

Seth, Swarnadeep, Rand, Arthur, Reisner, Walter, Dunbar, William B., Sladek, Robert, and Bhattacharya, Aniket

Subjects
Condensed Matter - Soft Condensed Matter
Abstract

We report a novel simulation strategy that enables us to identify key parameters controlling the experimentally measurable characteristics of structural protein tags on dsDNA construct translocating through a double nanopore setup. First, we validate the scheme in silico by reproducing and explaining the physical origin of the experimental dwell time distributions of the Streptavidin markers on a 48 kbp long dsDNA. These studies reveal the important differences in the characteristics of the protein tags compared to the dynamics of dsDNA segments, immediately providing clues on how to improve the measurement protocols to decipher the unknown genomic lengths accurately. Of particular importance is the in silico studies on the effect of electric field inside and beyond the pores which we find is critical to discriminate protein tags based on their effective charges and masses revealed through a generic power-law dependence of the average dwell time at each pore. The simulation protocols enable to monitor piecewise dynamics of the individual monomers at a sub-nanometer length scale and provide an explanation of the disparate velocity variation from one tag to the other using the nonequilibrium tension propagation theory, - a key element to decipher genomic lengths accurately. We further justify the model and the chosen simulation parameters by calculating the Peclet number which is in close agreement with the experiment. Analysis of our simulation results from the CG model has the capability to refine the accuracy of the experimentally obtained genomic lengths and carefully chosen simulation strategies can serve as a powerful tool to discriminate different types of neutral and charged tags of different origins on a dsDNA construct in terms of their physical characteristics and can provide insights to increase both the efficiency and accuracy of an experimental dual-nanopore setup.

Published
2022

8. Whole genome sequence association analysis of fasting glucose and fasting insulin levels in diverse cohorts from the NHLBI TOPMed program

Author

DiCorpo, Daniel, Gaynor, Sheila M, Russell, Emily M, Westerman, Kenneth E, Raffield, Laura M, Majarian, Timothy D, Wu, Peitao, Sarnowski, Chloé, Highland, Heather M, Jackson, Anne, Hasbani, Natalie R, de Vries, Paul S, Brody, Jennifer A, Hidalgo, Bertha, Guo, Xiuqing, Perry, James A, O’Connell, Jeffrey R, Lent, Samantha, Montasser, May E, Cade, Brian E, Jain, Deepti, Wang, Heming, D’Oliveira Albanus, Ricardo, Varshney, Arushi, Yanek, Lisa R, Lange, Leslie, Palmer, Nicholette D, Almeida, Marcio, Peralta, Juan M, Aslibekyan, Stella, Baldridge, Abigail S, Bertoni, Alain G, Bielak, Lawrence F, Chen, Chung-Shiuan, Chen, Yii-Der Ida, Choi, Won Jung, Goodarzi, Mark O, Floyd, James S, Irvin, Marguerite R, Kalyani, Rita R, Kelly, Tanika N, Lee, Seonwook, Liu, Ching-Ti, Loesch, Douglas, Manson, JoAnn E, Minster, Ryan L, Naseri, Take, Pankow, James S, Rasmussen-Torvik, Laura J, Reiner, Alexander P, Reupena, Muagututi’a Sefuiva, Selvin, Elizabeth, Smith, Jennifer A, Weeks, Daniel E, Xu, Huichun, Yao, Jie, Zhao, Wei, Parker, Stephen, Alonso, Alvaro, Arnett, Donna K, Blangero, John, Boerwinkle, Eric, Correa, Adolfo, Cupples, L Adrienne, Curran, Joanne E, Duggirala, Ravindranath, He, Jiang, Heckbert, Susan R, Kardia, Sharon LR, Kim, Ryan W, Kooperberg, Charles, Liu, Simin, Mathias, Rasika A, McGarvey, Stephen T, Mitchell, Braxton D, Morrison, Alanna C, Peyser, Patricia A, Psaty, Bruce M, Redline, Susan, Shuldiner, Alan R, Taylor, Kent D, Vasan, Ramachandran S, Viaud-Martinez, Karine A, Florez, Jose C, Wilson, James G, Sladek, Robert, Rich, Stephen S, Rotter, Jerome I, Lin, Xihong, Dupuis, Josée, Meigs, James B, Wessel, Jennifer, and Manning, Alisa K

Subjects
Biological Sciences, Genetics, Nutrition, Clinical Research, Human Genome, Precision Medicine, Biotechnology, Diabetes, Minority Health, 2.1 Biological and endogenous factors, Metabolic and endocrine, Good Health and Well Being, Diabetes Mellitus, Type 2, Fasting, Glucose, Humans, Insulin, National Heart, Lung, and Blood Institute (U.S.), Nerve Tissue Proteins, Polymorphism, Single Nucleotide, Receptors, Immunologic, United States, Biological sciences, Biomedical and clinical sciences
Abstract

The genetic determinants of fasting glucose (FG) and fasting insulin (FI) have been studied mostly through genome arrays, resulting in over 100 associated variants. We extended this work with high-coverage whole genome sequencing analyses from fifteen cohorts in NHLBI's Trans-Omics for Precision Medicine (TOPMed) program. Over 23,000 non-diabetic individuals from five race-ethnicities/populations (African, Asian, European, Hispanic and Samoan) were included. Eight variants were significantly associated with FG or FI across previously identified regions MTNR1B, G6PC2, GCK, GCKR and FOXA2. We additionally characterize suggestive associations with FG or FI near previously identified SLC30A8, TCF7L2, and ADCY5 regions as well as APOB, PTPRT, and ROBO1. Functional annotation resources including the Diabetes Epigenome Atlas were compiled for each signal (chromatin states, annotation principal components, and others) to elucidate variant-to-function hypotheses. We provide a catalog of nucleotide-resolution genomic variation spanning intergenic and intronic regions creating a foundation for future sequencing-based investigations of glycemic traits.

Published
2022

10. Comparative proximity biotinylation implicates the small GTPase RAB18 in sterol mobilization and biosynthesis

Author

Kiss, Robert S., Chicoine, Jarred, Khalil, Youssef, Sladek, Robert, Chen, He, Pisaturo, Alessandro, Martin, Cyril, Dale, Jessica D., Brudenell, Tegan A., Kamath, Archith, Kyei-Boahen, Jeffrey, Hafiane, Anouar, Daliah, Girija, Alecki, Célia, Hopes, Tayah S., Heier, Martin, Aligianis, Irene A., Lebrun, Jean-Jacques, Aspden, Julie, Paci, Emanuele, Kerksiek, Anja, Lütjohann, Dieter, Clayton, Peter, Wills, Jimi C., von Kriegsheim, Alex, Nilsson, Tommy, Sheridan, Eamonn, and Handley, Mark T.

Published
2023
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11. The Type 2 Diabetes Knowledge Portal: An open access genetic resource dedicated to type 2 diabetes and related traits

Author

Abecasis, Gonçalo, Akolkar, Beena, Alexander, Benjamin R., Allred, Nicholette D., Altshuler, David, Below, Jennifer E., Bergman, Richard, Beulens, Joline W.J., Blangero, John, Boehnke, Michael, Bokvist, Krister, Bottinger, Erwin, Boughton, Andrew P., Bowden, Donald, Brosnan, M. Julia, Brown, Christopher, Bruskiewicz, Kenneth, Burtt, Noël P., Carmichael, Mary, Caulkins, Lizz, Cebola, Inês, Chambers, John, Ida Chen, Yii-Der, Cherkas, Andriy, Chu, Audrey Y., Clark, Christopher, Claussnitzer, Melina, Costanzo, Maria C., Cox, Nancy J., Hoed, Marcel den, Dong, Duc, Duby, Marc, Duggirala, Ravindranath, Dupuis, Josée, Elders, Petra J.M., Engreitz, Jesse M., Fauman, Eric, Ferrer, Jorge, Flannick, Jason, Flicek, Paul, Flickinger, Matthew, Florez, Jose C., Fox, Caroline S., Frayling, Timothy M., Frazer, Kelly A., Gaulton, Kyle J., Gilbert, Clint, Gloyn, Anna L., Green, Todd, Hanis, Craig L., Hanson, Robert, Hattersley, Andrew T., Hoang, Quy, Im, Hae Kyung, Iqbal, Sidra, Jacobs, Suzanne B.R., Jang, Dong-Keun, Jordan, Tad, Kamphaus, Tania, Karpe, Fredrik, Keane, Thomas M., Kim, Seung K., Kluge, Alexandria, Koesterer, Ryan, Kudtarkar, Parul, Lage, Kasper, Lange, Leslie A., Lazar, Mitchell, Lehman, Donna, Liu, Ching-Ti, Loos, Ruth J.F., Ma, Ronald Ching-wan, MacDonald, Patrick, Massung, Jeffrey, Maurano, Matthew T., McCarthy, Mark I., McVean, Gil, Meigs, James B., Mercader, Josep M., Miller, Melissa R., Mitchell, Braxton, Mohlke, Karen L., Morabito, Samuel, Morgan, Claire, Mullican, Shannon, Narendra, Sharvari, Ng, Maggie C.Y., Nguyen, Lynette, Palmer, Colin N.A., Parker, Stephen C.J., Parrado, Antonio, Parsa, Afshin, Pawlyk, Aaron C., Pearson, Ewan R., Plump, Andrew, Province, Michael, Quertermous, Thomas, Redline, Susan, Reilly, Dermot F., Ren, Bing, Rich, Stephen S., Richards, J. Brent, Rotter, Jerome I., Ruebenacker, Oliver, Ruetten, Hartmut, Salem, Rany M., Sander, Maike, Sanders, Michael, Sanghera, Dharambir, Scott, Laura J., Sengupta, Sebanti, Siedzik, David, Sim, Xueling, Singh, Preeti, Sladek, Robert, Small, Kerrin, Smith, Philip, Stein, Peter, Spalding, Dylan, Stringham, Heather M., Sun, Ying, Susztak, Katalin, ’t Hart, Leen M., Taliun, Daniel, Taylor, Kent, Thomas, Melissa K., Todd, Jennifer A., Udler, Miriam S., Voight, Benjamin, von Grotthuss, Marcin, Wan, Andre, Welch, Ryan P., Wholley, David, Yuksel, Kaan, Zaghloul, Norann A., Jang, Dongkeun, Moriondo, Annie, Nguyen, Trang, Smadbeck, Patrick, Brandes, MacKenzie, Dornbos, Peter, Huellas-Bruskiewicz, Kenneth C., Ji, Yue, McMahon, Aoife C., Fauman, Eric B., Kamphaus, Tania Nayak, and Abecasis, Gonçalo R.

Published
2023
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13. A Long Non-coding RNA, LOC157273, Is an Effector Transcript at the Chromosome 8p23.1-PPP1R3B Metabolic Traits and Type 2 Diabetes Risk Locus

Author

Manning, Alisa K, Goustin, Anton Scott, Kleinbrink, Erica L, Thepsuwan, Pattaraporn, Cai, Juan, Ju, Donghong, Leong, Aaron, Udler, Miriam S, Brown, James Bentley, Goodarzi, Mark O, Rotter, Jerome I, Sladek, Robert, Meigs, James B, and Lipovich, Leonard

Subjects
Biological Sciences, Genetics, Digestive Diseases, Women's Health, Biotechnology, Human Genome, Obesity, Diabetes, 2.1 Biological and endogenous factors, Metabolic and endocrine, insulin resistance, hepatic glycogen storage, long non-coding RNA, metabolism, type 2 diabetes, regulatory mechanisms, Clinical Sciences, Law
Abstract

AimsCausal transcripts at genomic loci associated with type 2 diabetes (T2D) are mostly unknown. The chr8p23.1 variant rs4841132, associated with an insulin-resistant diabetes risk phenotype, lies in the second exon of a long non-coding RNA (lncRNA) gene, LOC157273, located 175 kilobases from PPP1R3B, which encodes a key protein regulating insulin-mediated hepatic glycogen storage in humans. We hypothesized that LOC157273 regulates expression of PPP1R3B in human hepatocytes.MethodsWe tested our hypothesis using Stellaris fluorescent in situ hybridization to assess subcellular localization of LOC157273; small interfering RNA (siRNA) knockdown of LOC157273, followed by RT-PCR to quantify LOC157273 and PPP1R3B expression; RNA-seq to quantify the whole-transcriptome gene expression response to LOC157273 knockdown; and an insulin-stimulated assay to measure hepatocyte glycogen deposition before and after knockdown.ResultsWe found that siRNA knockdown decreased LOC157273 transcript levels by approximately 80%, increased PPP1R3B mRNA levels by 1.7-fold, and increased glycogen deposition by >50% in primary human hepatocytes. An A/G heterozygous carrier (vs. three G/G carriers) had reduced LOC157273 abundance due to reduced transcription of the A allele and increased PPP1R3B expression and glycogen deposition.ConclusionWe show that the lncRNA LOC157273 is a negative regulator of PPP1R3B expression and glycogen deposition in human hepatocytes and a causal transcript at an insulin-resistant T2D risk locus.

Published
2020

17. Evaluating the contribution of rare variants to type 2 diabetes and related traits using pedigrees

Author

Jun, Goo, Manning, Alisa, Almeida, Marcio, Zawistowski, Matthew, Wood, Andrew R, Teslovich, Tanya M, Fuchsberger, Christian, Feng, Shuang, Cingolani, Pablo, Gaulton, Kyle J, Dyer, Thomas, Blackwell, Thomas W, Chen, Han, Chines, Peter S, Choi, Sungkyoung, Churchhouse, Claire, Fontanillas, Pierre, King, Ryan, Lee, SungYoung, Lincoln, Stephen E, Trubetskoy, Vasily, DePristo, Mark, Fingerlin, Tasha, Grossman, Robert, Grundstad, Jason, Heath, Alison, Kim, Jayoun, Kim, Young Jin, Laramie, Jason, Lee, Jaehoon, Li, Heng, Liu, Xuanyao, Livne, Oren, Locke, Adam E, Maller, Julian, Mazur, Alexander, Morris, Andrew P, Pollin, Toni I, Ragona, Derek, Reich, David, Rivas, Manuel A, Scott, Laura J, Sim, Xueling, Tearle, Rick G, Teo, Yik Ying, Williams, Amy L, Zöllner, Sebastian, Curran, Joanne E, Peralta, Juan, Akolkar, Beena, Bell, Graeme I, Burtt, Noël P, Cox, Nancy J, Florez, Jose C, Hanis, Craig L, McKeon, Catherine, Mohlke, Karen L, Seielstad, Mark, Wilson, James G, Atzmon, Gil, Below, Jennifer E, Dupuis, Josée, Nicolae, Dan L, Lehman, Donna, Park, Taesung, Won, Sungho, Sladek, Robert, Altshuler, David, McCarthy, Mark I, Duggirala, Ravindranath, Boehnke, Michael, Frayling, Timothy M, Abecasis, Gonçalo R, and Blangero, John

Subjects
Diabetes, Genetics, Human Genome, Clinical Research, Obesity, Genetic Testing, 2.1 Biological and endogenous factors, Aetiology, Metabolic and endocrine, Diabetes Mellitus, Type 2, Family Health, Female, Gene Frequency, Genetic Predisposition to Disease, Genetic Variation, Genome-Wide Association Study, Genotype, Humans, Male, Mexican Americans, Pedigree, Phenotype, Quantitative Trait Loci, Whole Genome Sequencing, genetics, sequencing, type 2 diabetes, eQTL, rare variants
Abstract

A major challenge in evaluating the contribution of rare variants to complex disease is identifying enough copies of the rare alleles to permit informative statistical analysis. To investigate the contribution of rare variants to the risk of type 2 diabetes (T2D) and related traits, we performed deep whole-genome analysis of 1,034 members of 20 large Mexican-American families with high prevalence of T2D. If rare variants of large effect accounted for much of the diabetes risk in these families, our experiment was powered to detect association. Using gene expression data on 21,677 transcripts for 643 pedigree members, we identified evidence for large-effect rare-variant cis-expression quantitative trait loci that could not be detected in population studies, validating our approach. However, we did not identify any rare variants of large effect associated with T2D, or the related traits of fasting glucose and insulin, suggesting that large-effect rare variants account for only a modest fraction of the genetic risk of these traits in this sample of families. Reliable identification of large-effect rare variants will require larger samples of extended pedigrees or different study designs that further enrich for such variants.

Published
2018

18. Time-to-Event Genome-Wide Association Study for Incident Cardiovascular Disease in People with Type 2 Diabetes Mellitus

Author

Heon Kwak, Soo, primary, B. Hernandez-Cancela, Ryan, primary, A DiCorpo, Daniel, primary, E. Condon, David, primary, Merino, Jordi, primary, Wu, Peitao, primary, A Brody, Jennifer, primary, Yao, Jie, primary, Guo, Xiuqing, primary, Ahmadizar, Fariba, primary, Meyer, Mariah, primary, Sincan, Murat, primary, M. Mercader, Josep, primary, Lee, Sujin, primary, Haessler, Jeffrey, primary, My T. Vy, Ha, primary, Lin, Zhaotong, primary, D. Armstrong, Nicole, primary, Gu, Shaopeng, primary, L. Tsao, Noah, primary, A. Lange, Leslie, primary, Wang, Ningyuan, primary, L. Wiggins, Kerri, primary, Trompet, Stella, primary, Liu, Simin, primary, J.F. Loos, Ruth, primary, Judy, Renae, primary, H. Schroeder, Philip, primary, Hasbani, Natalie R., primary, M. Bos, Maxime, primary, C. Morrison, Alanna, primary, D. Jackson, Rebecca, primary, P. Reiner, Alexander, primary, E. Manson, JoAnn, primary, S. Chaudhary, Ninad, primary, K. Carmichael, Lynn, primary, Ida Chen, Yii-Der, primary, D. Taylor, Kent, primary, Ghanbari, Mohsen, primary, van Meurs, Joyce, primary, N Pitsillides, Achilleas, primary, M. Psaty, Bruce, primary, Noordam, Raymond, primary, Do, Ron, primary, Soo Park, Kyong, primary, Wouter Jukema, J, primary, Kavousi, Maryam, primary, Correa, Adolfo, primary, S. Rich, Stephen, primary, M. Damrauer, Scott, primary, Hajek, Catherine, primary, H. Cho, Nam, primary, R. Irvin, Marguerite, primary, S. Pankow, James, primary, N. Nadkarni, Girish, primary, Sladek, Robert, primary, O. Goodarzi, Mark, primary, C. Florez, Jose, primary, I. Chasman, Daniel, primary, R. Heckbert, Susan, primary, Kooperberg, Charles, primary, Dupuis, Josée, primary, Malhotra, Rajeev, primary, S. de Vries, Paul, primary, Liu, Ching-Ti, primary, I. Rotter, Jerome, primary, and B. Meigs, James, primary

Published
2024
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21. Associations Between Attention-Deficit/Hyperactivity Disorder and Various Eating Disorders: A Swedish Nationwide Population Study Using Multiple Genetically Informative Approaches

Author

Adan, Roger, Ando, Tetsuya, Baker, Jessica, Bergen, Andrew, Berrettini, Wade, Birgegård, Andreas, Boni, Claudette, Boraska Perica, Vesna, Brandt, Harry, Burghardt, Roland, Cassina, Matteo, Cesta, Carolyn, Clementi, Maurizio, Coleman, Joni, Cone, Roger, Courtet, Philippe, Crawford, Steven, Crow, Scott, Crowley, James, Danner, Unna, Davis, Oliver, de Zwaan, Martina, Dedoussis, George, Degortes, Daniela, DeSocio, Janiece, Dick, Danielle, Dikeos, Dimitris, Dmitrzak-Weglarz, Monika, Docampo, Elisa, Egberts, Karin, Ehrlich, Stefan, Escaramís, Geòrgia, Esko, Tõnu, Estivill, Xavier, Favaro, Angela, Fernández-Aranda, Fernando, Fichter, Manfred, Finan, Chris, Fischer, Krista, Föcker, Manuel, Foretova, Lenka, Forzan, Monica, Franklin, Christopher, Gaspar, Héléna, Gonidakis, Fragiskos, Gorwood, Philip, Gratacos, Monica, Guillaume, Sébastien, Guo, Yiran, Hakonarson, Hakon, Halmi, Katherine, Hatzikotoulas, Konstantinos, Hauser, Joanna, Hebebrand, Johannes, Helder, Sietske, Hendriks, Judith, Herpertz-Dahlmann, Beate, Herzog, Wolfgang, Hilliard, Christopher, Hinney, Anke, Huckins, Laura, Hudson, James, Huemer, Julia, Imgart, Hartmut, Inoko, Hidetoshi, Jiménez-Murcia, Susana, Johnson, Craig, Jordan, Jenny, Juréus, Anders, Kalsi, Gursharan, Kaminska, Debora, Kaplan, Allan, Kaprio, Jaakko, Karhunen, Leila, Karwautz, Andreas, Kas, Martien, Kaye, Walter, Kennedy, James, Kennedy, Martin, Keski-Rahkonen, Anna, Kiezebrink, Kirsty, Kim, Youl-Ri, Klump, Kelly, Knudsen, Gun Peggy, Koeleman, Bobby, Koubek, Doris, La Via, Maria, Landén, Mikael, Levitan, Robert, Li, Dong, Lichtenstein, Paul, Lilenfeld, Lisa, Lissowska, Jolanta, Magistretti, Pierre, Maj, Mario, Mannik, Katrin, Martin, Nicholas, McDevitt, Sara, McGuffin, Peter, Merl, Elisabeth, Metspalu, Andres, Meulenbelt, Ingrid, Micali, Nadia, Mitchell, James, Mitchell, Karen, Monteleone, Palmiero, Monteleone, Alessio Maria, Mortensen, Preben, Munn-Chernoff, Melissa, Nacmias, Benedetta, Nilsson, Ida, Norring, Claes, Ntalla, Ioanna, O’Toole, Julie, Pantel, Jacques, Papezova, Hana, Parker, Richard, Rabionet, Raquel, Raevuori, Anu, Rajewski, Andrzej, Ramoz, Nicolas, Rayner, N. William, Reichborn-Kjennerud, Ted, Ricca, Valdo, Ripke, Stephan, Ritschel, Franziska, Roberts, Marion, Rotondo, Alessandro, Rybakowski, Filip, Santonastaso, Paolo, Scherag, André, Schmidt, Ulrike, Schork, Nicholas, Schosser, Alexandra, Seitz, Jochen, Slachtova, Lenka, Slagboom, P. Eline, Slof-Op’t Landt, Margarita, Slopien, Agnieszka, Smith, Tosha, Sorbi, Sandro, Strengman, Eric, Strober, Michael, Sullivan, Patrick, Szatkiewicz, Jin, Szeszenia-Dabrowska, Neonila, Tachmazidou, Ioanna, Tenconi, Elena, Thornton, Laura, Tortorella, Alfonso, Tozzi, Federica, Treasure, Janet, Tsitsika, Artemis, Tziouvas, Konstantinos, van Elburg, Annemarie, van Furth, Eric, Wade, Tracey, Wagner, Gudrun, Walton, Esther, Watson, Hunna, Woodside, D. Blake, Yao, Shuyang, Yilmaz, Zeynep, Zeggini, Eleftheria, Zerwas, Stephanie, Zipfel, Stephan, Alfredsson, Lars, Andreassen, Ole, Aschauer, Harald, Barrett, Jeffrey, Bencko, Vladimir, Carlberg, Laura, Cichon, Sven, Cohen-Woods, Sarah, Dina, Christian, Ding, Bo, Espeseth, Thomas, Floyd, James, Gallinger, Steven, Gambaro, Giovanni, Giegling, Ina, Herms, Stefan, Janout, Vladimir, Julià, Antonio, Klareskog, Lars, Le Hellard, Stephanie, Leboyer, Marion, Lundervold, Astri, Marsal, Sara, Mattingsdal, Morten, Navratilova, Marie, Ophoff, Roel, Palotie, Aarno, Pinto, Dalila, Ripatti, Samuli, Rujescu, Dan, Scherer, Stephen, Scott, Laura, Sladek, Robert, Soranzo, Nicole, Southam, Lorraine, Steen, Vidar, Wichmann, H-Erich, Widen, Elisabeth, Breen, Gerome, Bulik, Cynthia, Kuja-Halkola, Ralf, Martin, Joanna, Lu, Yi, Hübel, Christopher, Almqvist, Catarina, Thornton, Laura M., Magnusson, Patrik K., Bulik, Cynthia M., and Larsson, Henrik

Published
2019
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23. Association of Levels of Fasting Glucose and Insulin With Rare Variants at the Chromosome 11p11.2-MADD Locus

Author

Cornes, Belinda K, Brody, Jennifer A, Nikpoor, Naghmeh, Morrison, Alanna C, Chu, Huan, Ahn, Byung Soo, Wang, Shuai, Dauriz, Marco, Barzilay, Joshua I, Dupuis, Josée, Florez, Jose C, Coresh, Josef, Gibbs, Richard A, Kao, WH Linda, Liu, Ching-Ti, McKnight, Barbara, Muzny, Donna, Pankow, James S, Reid, Jeffrey G, White, Charles C, Johnson, Andrew D, Wong, Tien Y, Psaty, Bruce M, Boerwinkle, Eric, Rotter, Jerome I, Siscovick, David S, Sladek, Robert, and Meigs, James B

Subjects
Biomedical and Clinical Sciences, Cardiovascular Medicine and Haematology, Aging, Diabetes, Biotechnology, Human Genome, Genetics, Minority Health, 1.1 Normal biological development and functioning, Metabolic and endocrine, Aged, Aged, 80 and over, Blood Glucose, Chromosomes, Human, Pair 11, Cohort Studies, Death Domain Receptor Signaling Adaptor Proteins, Diabetes Mellitus, Type 2, Fasting, Female, Gene Frequency, Genetic Variation, Genome-Wide Association Study, Genomics, Guanine Nucleotide Exchange Factors, Heart Diseases, Humans, Insulin, Male, Middle Aged, Polymorphism, Single Nucleotide, Sequence Analysis, DNA, genetic epidemiology, glucose, human genetics, insulin, molecular genetics, Medical Biotechnology, Cardiorespiratory Medicine and Haematology, Cardiovascular System & Hematology, Cardiovascular medicine and haematology
Abstract

BackgroundCommon variation at the 11p11.2 locus, encompassing MADD, ACP2, NR1H3, MYBPC3, and SPI1, has been associated in genome-wide association studies with fasting glucose and insulin (FI). In the Cohorts for Heart and Aging Research in Genomic Epidemiology Targeted Sequencing Study, we sequenced 5 gene regions at 11p11.2 to identify rare, potentially functional variants influencing fasting glucose or FI levels.Methods and resultsSequencing (mean depth, 38×) across 16.1 kb in 3566 individuals without diabetes mellitus identified 653 variants, 79.9% of which were rare (minor allele frequency 2 independent signals at 11p11.2. One predicted regulatory variant, chr11:47227430 (hg18; minor allele frequency=0.00068), contributed 20.6% to the overall sequence kernel association test score at NR1H3, lies in intron 2 of NR1H3, and is a predicted binding site for forkhead box A1 (FOXA1), a transcription factor associated with insulin regulation. In human HepG2 hepatoma cells, the rare chr11:47227430 A allele disrupted FOXA1 binding and reduced FOXA1-dependent transcriptional activity.ConclusionsSequencing at 11p11.2-NR1H3 identified rare variation associated with FI. One variant, chr11:47227430, seems to be functional, with the rare A allele reducing transcription factor FOXA1 binding and FOXA1-dependent transcriptional activity.

Published
2014

24. Low copy number of the salivary amylase gene predisposes to obesity

Author

Falchi, Mario, El-Sayed Moustafa, Julia Sarah, Takousis, Petros, Pesce, Francesco, Bonnefond, Amélie, Andersson-Assarsson, Johanna C, Sudmant, Peter H, Dorajoo, Rajkumar, Al-Shafai, Mashael Nedham, Bottolo, Leonardo, Ozdemir, Erdal, So, Hon-Cheong, Davies, Robert W, Patrice, Alexandre, Dent, Robert, Mangino, Massimo, Hysi, Pirro G, Dechaume, Aurélie, Huyvaert, Marlène, Skinner, Jane, Pigeyre, Marie, Caiazzo, Robert, Raverdy, Violeta, Vaillant, Emmanuel, Field, Sarah, Balkau, Beverley, Marre, Michel, Visvikis-Siest, Sophie, Weill, Jacques, Poulain-Godefroy, Odile, Jacobson, Peter, Sjostrom, Lars, Hammond, Christopher J, Deloukas, Panos, Sham, Pak Chung, McPherson, Ruth, Lee, Jeannette, Tai, E Shyong, Sladek, Robert, Carlsson, Lena MS, Walley, Andrew, Eichler, Evan E, Pattou, Francois, Spector, Timothy D, and Froguel, Philippe

Subjects
Biological Sciences, Genetics, Cancer, Body Mass Index, Carbohydrate Metabolism, Gene Dosage, Genetic Predisposition to Disease, Genomics, Humans, Microarray Analysis, Obesity, Odds Ratio, Salivary alpha-Amylases, Medical and Health Sciences, Developmental Biology, Agricultural biotechnology, Bioinformatics and computational biology
Abstract

Common multi-allelic copy number variants (CNVs) appear enriched for phenotypic associations compared to their biallelic counterparts. Here we investigated the influence of gene dosage effects on adiposity through a CNV association study of gene expression levels in adipose tissue. We identified significant association of a multi-allelic CNV encompassing the salivary amylase gene (AMY1) with body mass index (BMI) and obesity, and we replicated this finding in 6,200 subjects. Increased AMY1 copy number was positively associated with both amylase gene expression (P = 2.31 × 10(-14)) and serum enzyme levels (P < 2.20 × 10(-16)), whereas reduced AMY1 copy number was associated with increased BMI (change in BMI per estimated copy = -0.15 (0.02) kg/m(2); P = 6.93 × 10(-10)) and obesity risk (odds ratio (OR) per estimated copy = 1.19, 95% confidence interval (CI) = 1.13-1.26; P = 1.46 × 10(-10)). The OR value of 1.19 per copy of AMY1 translates into about an eightfold difference in risk of obesity between subjects in the top (copy number > 9) and bottom (copy number < 4) 10% of the copy number distribution. Our study provides a first genetic link between carbohydrate metabolism and BMI and demonstrates the power of integrated genomic approaches beyond genome-wide association studies.

Published
2014

25. SharePro: an accurate and efficient genetic colocalization method accounting for multiple causal signals.

Author

Zhang, Wenmin, Lu, Tianyuan, Sladek, Robert, Li, Yue, Najafabadi, Hamed, and Dupuis, Josée

Subjects
ACCOUNTING methods, GENOME-wide association studies, BONE density, LINKAGE disequilibrium, DRUG target
Abstract

Motivation Colocalization analysis is commonly used to assess whether two or more traits share the same genetic signals identified in genome-wide association studies (GWAS), and is important for prioritizing targets for functional follow-up of GWAS results. Existing colocalization methods can have suboptimal performance when there are multiple causal variants in one genomic locus. Results We propose SharePro to extend the COLOC framework for colocalization analysis. SharePro integrates linkage disequilibrium (LD) modeling and colocalization assessment by grouping correlated variants into effect groups. With an efficient variational inference algorithm, posterior colocalization probabilities can be accurately estimated. In simulation studies, SharePro demonstrated increased power with a well-controlled false positive rate at a low computational cost. Compared to existing methods, SharePro provided stronger and more consistent colocalization evidence for known lipid-lowering drug target proteins and their corresponding lipid traits. Through an additional challenging case of the colocalization analysis of the circulating abundance of R-spondin 3 GWAS and estimated bone mineral density GWAS, we demonstrated the utility of SharePro in identifying biologically plausible colocalized signals. Availability and implementation SharePro for colocalization analysis is written in Python and openly available at https://github.com/zhwm/SharePro_coloc. [ABSTRACT FROM AUTHOR]

Published
2024
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27. Time-to-Event Genome-Wide Association Study for Incident Cardiovascular Disease in People with Type 2 Diabetes Mellitus

Author

Kwak, Soo Heon, primary, Hernandez-Cancela, Ryan B., additional, DiCorpo, Daniel A, additional, Condon, David E., additional, Merino, Jordi, additional, Wu, Peitao, additional, Brody, Jennifer A, additional, Yao, Jie, additional, Guo, Xiuqing, additional, Ahmadizar, Fariba, additional, Meyer, Mariah, additional, Sincan, Murat, additional, Mercader, Josep M., additional, Lee, Sujin, additional, Haessler, Jeffrey, additional, Vy, Ha My T., additional, Lin, Zhaotong, additional, Armstrong, Nicole D., additional, Gu, Shaopeng, additional, Tsao, Noah L., additional, Lange, Leslie A., additional, Wang, Ningyuan, additional, Wiggins, Kerri L., additional, Trompet, Stella, additional, Liu, Simin, additional, Loos, Ruth J.F., additional, Judy, Renae, additional, Schroeder, Philip H., additional, Hasbani, Natalie R., additional, Bos, Maxime M., additional, Morrison, Alanna C., additional, Jackson, Rebecca D., additional, Reiner, Alexander P., additional, Manson, JoAnn E., additional, Chaudhary, Ninad S., additional, Carmichael, Lynn K., additional, Chen, Yii-Der Ida, additional, Taylor, Kent D., additional, Ghanbari, Mohsen, additional, van Meurs, Joyce, additional, Pitsillides, Achilleas N, additional, Psaty, Bruce M., additional, Noordam, Raymond, additional, Do, Ron, additional, Park, Kyong Soo, additional, Jukema, J Wouter, additional, Kavousi, Maryam, additional, Correa, Adolfo, additional, Rich, Stephen S., additional, Damrauer, Scott M., additional, Hajek, Catherine, additional, Cho, Nam H., additional, Irvin, Marguerite R., additional, Pankow, James S., additional, Nadkarni, Girish N., additional, Sladek, Robert, additional, Goodarzi, Mark O., additional, Florez, Jose C., additional, Chasman, Daniel I., additional, Heckbert, Susan R., additional, Kooperberg, Charles, additional, Dupuis, Josée, additional, Malhotra, Rajeev, additional, de Vries, Paul S., additional, Liu, Ching-Ti, additional, Rotter, Jerome I., additional, and Meigs, James B., additional

Published
2023
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29. Time-to-Event Genome-Wide Association Study for Incident Cardiovascular Disease in People with Type 2 Diabetes Mellitus

Author

RWE/Causal inference, Child Health, Kwak, Soo Heon, Hernandez-Cancela, Ryan B, DiCorpo, Daniel A, Condon, David E, Merino, Jordi, Wu, Peitao, Brody, Jennifer A, Yao, Jie, Guo, Xiuqing, Ahmadizar, Fariba, Meyer, Mariah, Sincan, Murat, Mercader, Josep M, Lee, Sujin, Haessler, Jeffrey, Vy, Ha My T, Lin, Zhaotong, Armstrong, Nicole D, Gu, Shaopeng, Tsao, Noah L, Lange, Leslie A, Wang, Ningyuan, Wiggins, Kerri L, Trompet, Stella, Liu, Simin, Loos, Ruth J F, Judy, Renae, Schroeder, Philip H, Hasbani, Natalie R, Bos, Maxime M, Morrison, Alanna C, Jackson, Rebecca D, Reiner, Alexander P, Manson, JoAnn E, Chaudhary, Ninad S, Carmichael, Lynn K, Chen, Yii-Der Ida, Taylor, Kent D, Ghanbari, Mohsen, van Meurs, Joyce, Pitsillides, Achilleas N, Psaty, Bruce M, Noordam, Raymond, Do, Ron, Park, Kyong Soo, Jukema, J Wouter, Kavousi, Maryam, Correa, Adolfo, Rich, Stephen S, Damrauer, Scott M, Hajek, Catherine, Cho, Nam H, Irvin, Marguerite R, Pankow, James S, Nadkarni, Girish N, Sladek, Robert, Goodarzi, Mark O, Florez, Jose C, Chasman, Daniel I, Heckbert, Susan R, Kooperberg, Charles, Dupuis, Josée, Malhotra, Rajeev, de Vries, Paul S, Liu, Ching-Ti, Rotter, Jerome I, Meigs, James B, RWE/Causal inference, Child Health, Kwak, Soo Heon, Hernandez-Cancela, Ryan B, DiCorpo, Daniel A, Condon, David E, Merino, Jordi, Wu, Peitao, Brody, Jennifer A, Yao, Jie, Guo, Xiuqing, Ahmadizar, Fariba, Meyer, Mariah, Sincan, Murat, Mercader, Josep M, Lee, Sujin, Haessler, Jeffrey, Vy, Ha My T, Lin, Zhaotong, Armstrong, Nicole D, Gu, Shaopeng, Tsao, Noah L, Lange, Leslie A, Wang, Ningyuan, Wiggins, Kerri L, Trompet, Stella, Liu, Simin, Loos, Ruth J F, Judy, Renae, Schroeder, Philip H, Hasbani, Natalie R, Bos, Maxime M, Morrison, Alanna C, Jackson, Rebecca D, Reiner, Alexander P, Manson, JoAnn E, Chaudhary, Ninad S, Carmichael, Lynn K, Chen, Yii-Der Ida, Taylor, Kent D, Ghanbari, Mohsen, van Meurs, Joyce, Pitsillides, Achilleas N, Psaty, Bruce M, Noordam, Raymond, Do, Ron, Park, Kyong Soo, Jukema, J Wouter, Kavousi, Maryam, Correa, Adolfo, Rich, Stephen S, Damrauer, Scott M, Hajek, Catherine, Cho, Nam H, Irvin, Marguerite R, Pankow, James S, Nadkarni, Girish N, Sladek, Robert, Goodarzi, Mark O, Florez, Jose C, Chasman, Daniel I, Heckbert, Susan R, Kooperberg, Charles, Dupuis, Josée, Malhotra, Rajeev, de Vries, Paul S, Liu, Ching-Ti, Rotter, Jerome I, and Meigs, James B

Published
2023

30. The Type 2 Diabetes Knowledge Portal: An open access genetic resource dedicated to type 2 diabetes and related traits

Author

Costanzo, Maria C., primary, von Grotthuss, Marcin, additional, Massung, Jeffrey, additional, Jang, Dongkeun, additional, Caulkins, Lizz, additional, Koesterer, Ryan, additional, Gilbert, Clint, additional, Welch, Ryan P., additional, Kudtarkar, Parul, additional, Hoang, Quy, additional, Boughton, Andrew P., additional, Singh, Preeti, additional, Sun, Ying, additional, Duby, Marc, additional, Moriondo, Annie, additional, Nguyen, Trang, additional, Smadbeck, Patrick, additional, Alexander, Benjamin R., additional, Brandes, MacKenzie, additional, Carmichael, Mary, additional, Dornbos, Peter, additional, Green, Todd, additional, Huellas-Bruskiewicz, Kenneth C., additional, Ji, Yue, additional, Kluge, Alexandria, additional, McMahon, Aoife C., additional, Mercader, Josep M., additional, Ruebenacker, Oliver, additional, Sengupta, Sebanti, additional, Spalding, Dylan, additional, Taliun, Daniel, additional, Smith, Philip, additional, Thomas, Melissa K., additional, Akolkar, Beena, additional, Brosnan, M. Julia, additional, Cherkas, Andriy, additional, Chu, Audrey Y., additional, Fauman, Eric B., additional, Fox, Caroline S., additional, Kamphaus, Tania Nayak, additional, Miller, Melissa R., additional, Nguyen, Lynette, additional, Parsa, Afshin, additional, Reilly, Dermot F., additional, Ruetten, Hartmut, additional, Wholley, David, additional, Zaghloul, Norann A., additional, Abecasis, Gonçalo R., additional, Altshuler, David, additional, Keane, Thomas M., additional, McCarthy, Mark I., additional, Gaulton, Kyle J., additional, Florez, Jose C., additional, Boehnke, Michael, additional, Burtt, Noël P., additional, Flannick, Jason, additional, Abecasis, Gonçalo, additional, Allred, Nicholette D., additional, Below, Jennifer E., additional, Bergman, Richard, additional, Beulens, Joline W.J., additional, Blangero, John, additional, Bokvist, Krister, additional, Bottinger, Erwin, additional, Bowden, Donald, additional, Brown, Christopher, additional, Bruskiewicz, Kenneth, additional, Cebola, Inês, additional, Chambers, John, additional, Ida Chen, Yii-Der, additional, Clark, Christopher, additional, Claussnitzer, Melina, additional, Costanzo, Maria C., additional, Cox, Nancy J., additional, Hoed, Marcel den, additional, Dong, Duc, additional, Duggirala, Ravindranath, additional, Dupuis, Josée, additional, Elders, Petra J.M., additional, Engreitz, Jesse M., additional, Fauman, Eric, additional, Ferrer, Jorge, additional, Flicek, Paul, additional, Flickinger, Matthew, additional, Frayling, Timothy M., additional, Frazer, Kelly A., additional, Gloyn, Anna L., additional, Hanis, Craig L., additional, Hanson, Robert, additional, Hattersley, Andrew T., additional, Im, Hae Kyung, additional, Iqbal, Sidra, additional, Jacobs, Suzanne B.R., additional, Jang, Dong-Keun, additional, Jordan, Tad, additional, Kamphaus, Tania, additional, Karpe, Fredrik, additional, Kim, Seung K., additional, Lage, Kasper, additional, Lange, Leslie A., additional, Lazar, Mitchell, additional, Lehman, Donna, additional, Liu, Ching-Ti, additional, Loos, Ruth J.F., additional, Ma, Ronald Ching-wan, additional, MacDonald, Patrick, additional, Maurano, Matthew T., additional, McVean, Gil, additional, Meigs, James B., additional, Mitchell, Braxton, additional, Mohlke, Karen L., additional, Morabito, Samuel, additional, Morgan, Claire, additional, Mullican, Shannon, additional, Narendra, Sharvari, additional, Ng, Maggie C.Y., additional, Palmer, Colin N.A., additional, Parker, Stephen C.J., additional, Parrado, Antonio, additional, Pawlyk, Aaron C., additional, Pearson, Ewan R., additional, Plump, Andrew, additional, Province, Michael, additional, Quertermous, Thomas, additional, Redline, Susan, additional, Ren, Bing, additional, Rich, Stephen S., additional, Richards, J. Brent, additional, Rotter, Jerome I., additional, Salem, Rany M., additional, Sander, Maike, additional, Sanders, Michael, additional, Sanghera, Dharambir, additional, Scott, Laura J., additional, Siedzik, David, additional, Sim, Xueling, additional, Sladek, Robert, additional, Small, Kerrin, additional, Stein, Peter, additional, Stringham, Heather M., additional, Susztak, Katalin, additional, ’t Hart, Leen M., additional, Taylor, Kent, additional, Todd, Jennifer A., additional, Udler, Miriam S., additional, Voight, Benjamin, additional, Wan, Andre, additional, and Yuksel, Kaan, additional

Published
2023
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32. Differential CpG methylation atNnatin the early establishment of beta cell heterogeneity

Author

Yu, Vanessa, primary, Yong, Fiona, additional, Marta, Angellica, additional, Khadayate, Sanjay, additional, Osakwe, Adrien, additional, Bhattacharya, Supriyo, additional, Varghese, Sneha S., additional, Chabosseau, Pauline, additional, Tabibi, Sayed M., additional, Chen, Keran, additional, Georgiadou, Eleni, additional, Parveen, Nazia, additional, Suleiman, Mara, additional, Stamoulis, Zoe, additional, Marselli, Lorella, additional, De Luca, Carmela, additional, Tesi, Marta, additional, Ostinelli, Giada, additional, Delgadillo-Silva, Luis, additional, Wu, Xiwei, additional, Hatanaka, Yuki, additional, Montoya, Alex, additional, Elliott, James, additional, Patel, Bhavik, additional, Demchenko, Nikita, additional, Whilding, Chad, additional, Hajkova, Petra, additional, Shliaha, Pavel, additional, Kramer, Holger, additional, Ali, Yusuf, additional, Marchetti, Piero, additional, Sladek, Robert, additional, Dhawan, Sangeeta, additional, Withers, Dominic J., additional, Rutter, Guy A., additional, and Millership, Steven J., additional

Published
2023
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37. Molecular phenotyping of single pancreatic islet leader beta cells by “Flash-Seq”

Author

Chabosseau, Pauline, primary, Yong, Fiona, additional, Delgadillo-Silva, Luis F., additional, Lee, Eun Young, additional, Li, Shiying, additional, Gandhi, Nidhi, additional, Wastin, Jules, additional, Noriega, Livia Lopez, additional, Leclerc, Isabelle, additional, Ali, Yusuf, additional, Hughes, Jing W., additional, Sladek, Robert, additional, Martinez-Sanchez, Aida, additional, and Rutter, Guy A., additional

Published
2022
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38. A Polymorphism within the G6PC2 Gene Is Associated with Fasting Plasma Glucose Levels

Author

Bouatia-Naji, Nabila, Rocheleau, Ghislain, Van Lommel, Leentje, Lemaire, Katleen, Schuit, Frans, Cavalcanti-Proença, Christine, Marchand, Marion, Hartikainen, Anna-Liisa, Sovio, Ulla, De Graeve, Franck, Rung, Johan, Vaxillaire, Martine, Tichet, Jean, Marre, Michel, Balkau, Beverley, Weill, Jacques, Elliott, Paul, Jarvelin, Marjo-Riitta, Meyre, David, Polychronakos, Constantin, Dina, Christian, Sladek, Robert, and Froguel, Philippe

Published
2008
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40. A glycerol shunt functions as a glucose excess security valve in pancreatic β-cells

Author

Al-Mass, Anfal, primary, Poursharifi, Pegah, additional, Peyot, Marie-Line, additional, Lussier, Roxane, additional, Levens, Emily, additional, Guida, Julian, additional, Mugabo, Yves, additional, Possik, Elite, additional, Ahmad, Rasheed, additional, Al-Mulla, Fahd, additional, Sladek, Robert, additional, Madiraju, S.R.Murthy, additional, and Prentki, Marc, additional

Published
2021
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41. Single-Cell RNA Sequencing Reveals a Role for Reactive Oxygen Species and Peroxiredoxins in Fatty Acid–Induced Rat β-Cell Proliferation.

Author

Vivoli, Alexis, Ghislain, Julien, Filali-Mouhim, Ali, Angeles, Zuraya Elisa, Castell, Anne-Laure, Sladek, Robert, and Poitout, Vincent

Subjects
REACTIVE oxygen species, RNA sequencing, PEROXIREDOXINS, TYPE 2 diabetes, FATTY acids, FATTY acid analysis
Abstract

The functional mass of insulin-secreting pancreatic β-cells expands to maintain glucose homeostasis in the face of nutrient excess, in part via replication of existing β-cells. Type 2 diabetes appears when these compensatory mechanisms fail. Nutrients including glucose and fatty acids are important contributors to the β-cell compensatory response, but their underlying mechanisms of action remain poorly understood. We investigated the transcriptional mechanisms of β-cell proliferation in response to fatty acids. Isolated rat islets were exposed to 16.7 mmol/L glucose with or without 0.5 mmol/L oleate (C18:1) or palmitate (C16:0) for 48 h. The islet transcriptome was assessed by single-cell RNA sequencing. β-Cell proliferation was measured by flow cytometry. Unsupervised clustering of pooled β-cells identified different subclusters, including proliferating β-cells. β-Cell proliferation increased in response to oleate but not palmitate. Both fatty acids enhanced the expression of genes involved in energy metabolism and mitochondrial activity. Comparison of proliferating versus nonproliferating β-cells and pseudotime ordering suggested the involvement of reactive oxygen species (ROS) and peroxiredoxin signaling. Accordingly, N-acetyl cysteine and the peroxiredoxin inhibitor conoidin A both blocked oleate-induced β-cell proliferation. Our study reveals a key role for ROS signaling through peroxiredoxin activation in oleate-induced β-cell proliferation. [ABSTRACT FROM AUTHOR]

Published
2023
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46. Glycerol‐3‐phosphate phosphatase/ Pgp in Pancreatic ß‐cells Functions as a Glucose Excess Security Valve Preventing Oversecretion of Insulin Secretion and Glucotoxicity

Author

Almass, Anfal, primary, Poursharifi, Pegah, additional, Peyot, Marie‐Line, additional, Lussier, Roxane, additional, Levens, Emily, additional, Guida, Julian, additional, Mugabo, Yves, additional, Possik, Elite, additional, Erb, Heidi, additional, Joly, Erik, additional, Madiraju, S.R.Murthy, additional, Sladek, Robert, additional, and Prentki, Marc, additional

Published
2021
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47. Impact of common genetic determinants of Hemoglobin A1c on type 2 diabetes risk and diagnosis in ancestrally diverse populations: A transethnic genome-wide meta-analysis

Author

Wheeler, Eleanor, Leong, Aaron, Liu, Ching-Ti, Hivert, Marie-France, Strawbridge, Rona J., Podmore, Clara, Li, Man, Yao, Jie, Sim, Xueling, Hong, Jaeyoung, Chu, Audrey Y., Zhang, Weihua, Wang, Xu, Chen, Peng, Maruthur, Nisa M., Porneala, Bianca C., Sharp, Stephen J., Jia, Yucheng, Kabagambe, Edmond K., Chang, Li-Ching, Chen, Wei-Min, Elks, Cathy E., Evans, Daniel S., Fan, Qiao, Giulianini, Franco, Go, Min Jin, Hottenga, Jouke-Jan, Hu, Yao, Jackson, Anne U., Kanoni, Stavroula, Kim, Young Jin, Kleber, Marcus E., Ladenvall, Claes, Lecoeur, Cecile, Lim, Sing-Hui, Lu, Yingchang, Mahajan, Anubha, Marzi, Carola, Nalls, Mike A., Navarro, Pau, Nolte, Ilja M., Rose, Lynda M., Rybin, Denis V., Sanna, Serena, Shi, Yuan, Stram, Daniel O., Takeuchi, Fumihiko, Tan, Shu Pei, van der Most, Peter J., Van Vliet-Ostaptchouk, Jana V., Wong, Andrew, Yengo, Loic, Zhao, Wanting, Goel, Anuj, Martinez Larrad, Maria Teresa, Radke, Dörte, Salo, Perttu, Tanaka, Toshiko, van Iperen, Erik P. A., Abecasis, Goncalo, Afaq, Saima, Alizadeh, Behrooz Z., Bertoni, Alain G., Bonnefond, Amelie, Böttcher, Yvonne, Bottinger, Erwin P., Campbell, Harry, Carlson, Olga D., Chen, Chien-Hsiun, Cho, Yoon Shin, Garvey, W. Timothy, Gieger, Christian, Goodarzi, Mark O., Grallert, Harald, Hamsten, Anders, Hartman, Catharina A., Herder, Christian, Hsiung, Chao Agnes, Huang, Jie, Igase, Michiya, Isono, Masato, Katsuya, Tomohiro, Khor, Chiea-Chuen, Kiess, Wieland, Kohara, Katsuhiko, Kovacs, Peter, Lee, Juyoung, Lee, Wen-Jane, Lehne, Benjamin, Li, Huaixing, Liu, Jianjun, Lobbens, Stephane, Luan, Jian'an, Lyssenko, Valeriya, Meitinger, Thomas, Miki, Tetsuro, Miljkovic, Iva, Moon, Sanghoon, Mulas, Antonella, Müller, Gabriele, Müller-Nurasyid, Martina, Nagaraja, Ramaiah, Nauck, Matthias, Pankow, James S., Polasek, Ozren, Prokopenko, Inga, Ramos, Paula S., Rasmussen-Torvik, Laura, Rathmann, Wolfgang, Rich, Stephen S., Robertson, Neil R., Roden, Michael, Roussel, Ronan, Rudan, Igor, Scott, Robert A., Scott, William R., Sennblad, Bengt, Siscovick, David S., Strauch, Konstantin, Sun, Liang, Swertz, Morris, Tajuddin, Salman M., Taylor, Kent D., Teo, Yik-Ying, Tham, Yih Chung, Tönjes, Anke, Wareham, Nicholas J., Willemsen, Gonneke, Wilsgaard, Tom, Hingorani, Aroon D., Egan, Josephine, Ferrucci, Luigi, Hovingh, G. Kees, Jula, Antti, Kivimaki, Mika, Kumari, Meena, Njølstad, Inger, Palmer, Colin N. A., Serrano Ríos, Manuel, Stumvoll, Michael, Watkins, Hugh, Aung, Tin, Blüher, Matthias, Boehnke, Michael, Boomsma, Dorret I., Bornstein, Stefan R., Chambers, John C., Chasman, Daniel I., Chen, Yii-Der Ida, Chen, Yduan-Tsong, Cheng, Ching-Yu, Cucca, Francesco, de Geus, Eco J. C., Deloukas, Panos, Evans, Michele K., Fornage, Myriam, Friedlander, Yechiel, Froguel, Philippe, Groop, Leif, Gross, Myron D., Harris, Tamara B., Hayward, Caroline, Heng, Chew-Kiat, Ingelsson, Erik, Kato, Norihiro, Kim, Bong-Jo, Koh, Woon-Puay, Kooner, Jaspal S., Körner, Antje, Kuh, Diana, Kuusisto, Johanna, Laakso, Markku, Lin, Xu, Liu, Yongmei, Loos, Ruth J. F., Magnusson, Patrik K. E., März, Winfried, McCarthy, Mark I., Oldehinkel, Albertine J., Ong, Ken K., Pedersen, Nancy L., Pereira, Mark A., Peters, Annette, Ridker, Paul M., Sabanayagam, Charumathi, Sale, Michele, Saleheen, Danish, Saltevo, Juha, Schwarz, Peter EH., Sheu, Wayne H. H., Snieder, Harold, Spector, Timothy D., Tabara, Yasuharu, Tuomilehto, Jaakko, van Dam, Rob M., Wilson, James G., Wilson, James F., Wolffenbuttel, Bruce H. R., Wong, Tien Yin, Wu, Jer-Yuarn, Yuan, Jian-Min, Zonderman, Alan B., Soranzo, Nicole, Guo, Xiuqing, Roberts, David J., Florez, Jose C., Sladek, Robert, Dupuis, Josée, Morris, Andrew P., Tai, E-Shyong, Selvin, Elizabeth, Rotter, Jerome I., Langenberg, Claudia, Barroso, Inês, and Meigs, James B.

Subjects
Genotypes -- Research, African Americans -- Analysis -- Health aspects, Type 2 diabetes -- Risk factors -- Diagnosis, Biological sciences
Abstract

Background Glycated hemoglobin (HbA1c) is used to diagnose type 2 diabetes (T2D) and assess glycemic control in patients with diabetes. Previous genome-wide association studies (GWAS) have identified 18 HbA1c-associated genetic variants. These variants proved to be classifiable by their likely biological action as erythrocytic (also associated with erythrocyte traits) or glycemic (associated with other glucose-related traits). In this study, we tested the hypotheses that, in a very large scale GWAS, we would identify more genetic variants associated with HbA1c and that HbA1c variants implicated in erythrocytic biology would affect the diagnostic accuracy of HbA1c. We therefore expanded the number of HbA1c-associated loci and tested the effect of genetic risk-scores comprised of erythrocytic or glycemic variants on incident diabetes prediction and on prevalent diabetes screening performance. Throughout this multiancestry study, we kept a focus on interancestry differences in HbA1c genetics performance that might influence race-ancestry differences in health outcomes. Methods & findings Using genome-wide association meta-analyses in up to 159,940 individuals from 82 cohorts of European, African, East Asian, and South Asian ancestry, we identified 60 common genetic variants associated with HbA1c. We classified variants as implicated in glycemic, erythrocytic, or unclassified biology and tested whether additive genetic scores of erythrocytic variants (GS-E) or glycemic variants (GS-G) were associated with higher T2D incidence in multiethnic longitudinal cohorts (N = 33,241). Nineteen glycemic and 22 erythrocytic variants were associated with HbA1c at genome-wide significance. GS-G was associated with higher T2D risk (incidence OR = 1.05, 95% CI 1.04-1.06, per HbA1c-raising allele, p = 3 x 10.sup.-29 ); whereas GS-E was not (OR = 1.00, 95% CI 0.99-1.01, p = 0.60). In Europeans and Asians, erythrocytic variants in aggregate had only modest effects on the diagnostic accuracy of HbA1c. Yet, in African Americans, the X-linked G6PD G202A variant (T-allele frequency 11%) was associated with an absolute decrease in HbA1c of 0.81%-units (95% CI 0.66-0.96) per allele in hemizygous men, and 0.68%-units (95% CI 0.38-0.97) in homozygous women. The G6PD variant may cause approximately 2% (N = 0.65 million, 95% CI 0.55-0.74) of African American adults with T2D to remain undiagnosed when screened with HbA1c. Limitations include the smaller sample sizes for non-European ancestries and the inability to classify approximately one-third of the variants. Further studies in large multiethnic cohorts with HbA1c, glycemic, and erythrocytic traits are required to better determine the biological action of the unclassified variants. Conclusions As G6PD deficiency can be clinically silent until illness strikes, we recommend investigation of the possible benefits of screening for the G6PD genotype along with using HbA1c to diagnose T2D in populations of African ancestry or groups where G6PD deficiency is common. Screening with direct glucose measurements, or genetically-informed HbA1c diagnostic thresholds in people with G6PD deficiency, may be required to avoid missed or delayed diagnoses., Author(s): Eleanor Wheeler 1, Aaron Leong 2,3, Ching-Ti Liu 4, Marie-France Hivert 5,6, Rona J. Strawbridge 7,8, Clara Podmore 9,10, Man Li 11,12,13, Jie Yao 14, Xueling Sim 15, Jaeyoung [...]

Published
2017
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