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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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7. Generation of Human Isogenic Induced Pluripotent Stem Cell Lines with CRISPR Prime Editing

Author

Bonnycastle, Lori L., primary, Swift, Amy J., additional, Mansell, Erin C., additional, Lee, Angela, additional, Winnicki, Elizabeth, additional, Li, Elizabeth S., additional, Robertson, Catherine C., additional, Parsons, Victoria A., additional, Huynh, Trung, additional, Krilow, Chad, additional, Mohlke, Karen L., additional, Erdos, Michael R., additional, Narisu, Narisu, additional, and Collins, Francis S., additional

Published
2024
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9. 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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10. 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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12. Population-scale skeletal muscle single-nucleus multi-omic profiling reveals extensive context specific genetic regulation

Author

Varshney, Arushi, primary, Manickam, Nandini, additional, Orchard, Peter, additional, Tovar, Adelaide, additional, Zhang, Zhenhao, additional, Feng, Fan, additional, Erdos, Michael R, additional, Narisu, Narisu, additional, Ventresca, Christa, additional, Nishino, Kirsten, additional, Rai, Vivek, additional, Stringham, Heather M, additional, Jackson, Anne U, additional, Tamsen, Tricia, additional, Gao, Chao, additional, Yang, Mao, additional, Koues, Olivia I, additional, Welch, Joshua D, additional, Burant, Charles F, additional, Williams, L Keoki, additional, Jenkinson, Chris, additional, DeFronzo, Ralph A, additional, Norton, Luke, additional, Saramies, Jouko, additional, Lakka, Timo A, additional, Laakso, Markku, additional, Tuomilehto, Jaakko, additional, Mohlke, Karen L, additional, Kitzman, Jacob O, additional, Koistinen, Heikki A, additional, Liu, Jie, additional, Boehnke, Michael, additional, Collins, Francis S, additional, Scott, Laura J, additional, and Parker, Stephen C J, additional

Published
2023
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13. Adipose tissue eQTL meta-analysis reveals the contribution of allelic heterogeneity to gene expression regulation and cardiometabolic traits

Author

Brotman, Sarah M., primary, El-Sayed Moustafa, Julia S., additional, Guan, Li, additional, Broadaway, K. Alaine, additional, Wang, Dongmeng, additional, Jackson, Anne U., additional, Welch, Ryan, additional, Currin, Kevin W., additional, Tomlinson, Max, additional, Vadlamudi, Swarooparani, additional, Stringham, Heather M., additional, Roberts, Amy L., additional, Lakka, Timo A., additional, Oravilahti, Anniina, additional, Fernandes Silva, Lilian, additional, Narisu, Narisu, additional, Erdos, Michael R., additional, Yan, Tingfen, additional, Bonnycastle, Lori L., additional, Raulerson, Chelsea K., additional, Raza, Yasrab, additional, Yan, Xinyu, additional, Parker, Stephen C.J., additional, Kuusisto, Johanna, additional, Pajukanta, Paivi, additional, Tuomilehto, Jaakko, additional, Collins, Francis S., additional, Boehnke, Michael, additional, Love, Michael I., additional, Koistinen, Heikki A., additional, Laakso, Markku, additional, Mohlke, Karen L., additional, Small, Kerrin S., additional, and Scott, Laura J., additional

Published
2023
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14. 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

15. 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

16. 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

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

Author

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

Published
2023
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19. 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

20. 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

21. 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

24. 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

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

Author

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

Published
2023
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27. Functional interrogation of twenty type 2 diabetes-associated genes using isogenic hESC-derived β-like cells

Author

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

Published
2023
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29. 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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30. Single-cell transcriptomic profiling of human pancreatic islets reveals genes responsive to glucose exposure over 24 hours

Author

Grenko, Caleb, Bonnycastle, Lori L., Taylor, Henry J., Yan, Tingfen, Swift, Amy J., Robertson, Catherine C., Narisu, Narisu, Erdos, Michael R., Collins, Francis S., and Taylor, D. Leland

Subjects
Article
Abstract

Disruption of pancreatic islet function and glucose homeostasis can lead to the development of sustained hyperglycemia, beta cell glucotoxicity, and ultimately type 2 diabetes (T2D). In this study, we sought to explore the effects of hyperglycemia on human pancreatic islet (HPI) gene expression by exposing HPIs from two donors to low (2.8mM) and high (15.0mM) glucose concentrations over 24 hours, assaying the transcriptome at seven time points using single-cell RNA sequencing (scRNA-seq). We modeled time as both a discrete and continuous variable to determine momentary and longitudinal changes in transcription associated with islet time in culture or glucose exposure. Across all cell types, we identified 1,528 genes associated with time, 1,185 genes associated with glucose exposure, and 845 genes associated with interaction effects between time and glucose. We clustered differentially expressed genes across cell types and found 347 modules of genes with similar expression patterns across time and glucose conditions, including two beta cell modules enriched in genes associated with T2D. Finally, by integrating genomic features from this study and genetic summary statistics for T2D and related traits, we nominate 374 candidate effector genes that may underlie genetic associations for T2D and related traits.

Published
2023

31. Functional interrogation of twenty type 2 diabetes-associated genes using isogenic hESC-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, Chong, Angie Chi Nok, Lee, Angela, Mansell, Erin C., Swift, Amy J., Erdos, Michael R., Zhou, Ting, Bonnycastle, Lori L., Zhong, Aaron, Chen, Shuibing, and Collins, Francis S.

Subjects
Article
Abstract

SummaryGenetic studies have identified numerous loci associated with type 2 diabetes (T2D), but the functional role of many loci has remained unexplored. In this study, we engineered isogenic knockout human embryonic stem cell (hESC) lines for 20 genes associated with T2D risk. We systematically examined β-cell differentiation, insulin production and secretion, and survival. We performed RNA-seq and ATAC-seq on hESC-β cells from each knockout line. Analyses of T2D GWAS signals overlapping with HNF4A-dependent ATAC peaks identified a specific SNP as a likely causal variant. In addition, we performed integrative association analyses and identified four genes (CP, RNASE1, PCSK1NandGSTA2) associated with insulin production, and two genes (TAGLN3andDHRS2) associated with sensitivity to lipotoxicity. Finally, we leveraged deep ATAC-seq read coverage to assess allele-specific imbalance at variants heterozygous in the parental hESC line, to identify a single likely functional variant at each of 23 T2D GWAS signals.

Published
2023

32. 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

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

Author

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

Published
2023
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37. Human pancreatic islet microRNAs implicated in diabetes and related traits by large-scale genetic analysis

Author

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

Published
2022
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38. 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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42. 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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46. 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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49. Genetic effects on liver chromatin accessibility identify disease regulatory variants

Author

Currin, Kevin W., primary, Erdos, Michael R., additional, Narisu, Narisu, additional, Rai, Vivek, additional, Vadlamudi, Swarooparani, additional, Perrin, Hannah J., additional, Idol, Jacqueline R., additional, Yan, Tingfen, additional, Albanus, Ricardo D’Oliveira, additional, Broadaway, K. Alaine, additional, Etheridge, Amy S., additional, Bonnycastle, Lori L., additional, Orchard, Peter, additional, Didion, John P., additional, Chaudhry, Amarjit S., additional, Innocenti, Federico, additional, Schuetz, Erin G., additional, Scott, Laura J., additional, Parker, Stephen C.J., additional, Collins, Francis S., additional, Mohlke, Karen L., additional, Barnabas, Beatrice B., additional, Black, Sean, additional, Bouffard, Gerard G., additional, Brooks, Shelise Y., additional, Coleman, Holly, additional, Dekhtyar, Lyudmila, additional, Han, Joel, additional, Ho, Shi-ling, additional, Kim, Juyun, additional, Legaspi, Richelle, additional, Maduro, Quino L., additional, Masiello, Catherine A., additional, McDowell, Jennifer C., additional, Montemayor, Casandra, additional, Mullikin, James C., additional, Park, Morgan, additional, Riebow, Nancy L., additional, Schandler, Karen, additional, Schmidt, Brian, additional, Sison, Christina, additional, Stantripop, Sirintorn, additional, Thomas, James W., additional, Thomas, Pamela J., additional, Vemulapalli, Meghana, additional, and Young, Alice C., additional

Published
2021
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