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1,851 results on '"Florez, Jose"'

1. Rare variant analyses in 51,256 type 2 diabetes cases and 370,487 controls reveal the pathogenicity spectrum of monogenic diabetes genes

Author

Huerta-Chagoya, Alicia, Schroeder, Philip, Mandla, Ravi, Li, Jiang, Morris, Lowri, Vora, Maheak, Alkanaq, Ahmed, Nagy, Dorka, Szczerbinski, Lukasz, Madsen, Jesper G. S., Bonàs-Guarch, Silvia, Mollandin, Fanny, Cole, Joanne B., Porneala, Bianca, Westerman, Kenneth, Li, Josephine H., Pollin, Toni I., Florez, Jose C., Gloyn, Anna L., Carey, David J., Cebola, Inês, Mirshahi, Uyenlinh L., Manning, Alisa K., Leong, Aaron, Udler, Miriam, and Mercader, Josep M.

Published
2024
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2. Multi-ancestry polygenic mechanisms of type 2 diabetes.

Author

Smith, Kirk, Deutsch, Aaron, McGrail, Carolyn, Kim, Hyunkyung, Hsu, Sarah, Huerta-Chagoya, Alicia, Mandla, Ravi, Schroeder, Philip, Westerman, Kenneth, Szczerbinski, Lukasz, Majarian, Timothy, Kaur, Varinderpal, Williamson, Alice, Zaitlen, Noah, Claussnitzer, Melina, Florez, Jose, Manning, Alisa, Mercader, Josep, Gaulton, Kyle, and Udler, Miriam

Subjects
Humans, Diabetes Mellitus, Type 2, Genome-Wide Association Study, Risk Factors, Phenotype, Multifactorial Inheritance, Genetic Predisposition to Disease
Abstract

Type 2 diabetes (T2D) is a multifactorial disease with substantial genetic risk, for which the underlying biological mechanisms are not fully understood. In this study, we identified multi-ancestry T2D genetic clusters by analyzing genetic data from diverse populations in 37 published T2D genome-wide association studies representing more than 1.4 million individuals. We implemented soft clustering with 650 T2D-associated genetic variants and 110 T2D-related traits, capturing known and novel T2D clusters with distinct cardiometabolic trait associations across two independent biobanks representing diverse genetic ancestral populations (African, n = 21,906; Admixed American, n = 14,410; East Asian, n =2,422; European, n = 90,093; and South Asian, n = 1,262). The 12 genetic clusters were enriched for specific single-cell regulatory regions. Several of the polygenic scores derived from the clusters differed in distribution among ancestry groups, including a significantly higher proportion of lipodystrophy-related polygenic risk in East Asian ancestry. T2D risk was equivalent at a body mass index (BMI) of 30 kg m-2 in the European subpopulation and 24.2 (22.9-25.5) kg m-2 in the East Asian subpopulation; after adjusting for cluster-specific genetic risk, the equivalent BMI threshold increased to 28.5 (27.1-30.0) kg m-2 in the East Asian group. Thus, these multi-ancestry T2D genetic clusters encompass a broader range of biological mechanisms and provide preliminary insights to explain ancestry-associated differences in T2D risk profiles.

Published
2024

5. A Type 1 Diabetes Polygenic Score Is Not Associated With Prevalent Type 2 Diabetes in Large Population Studies

Author

Srinivasan, Shylaja, Wu, Peitao, Mercader, Josep M, Udler, Miriam S, Porneala, Bianca C, Bartz, Traci M, Floyd, James S, Sitlani, Colleen, Guo, Xiquing, Haessler, Jeffrey, Kooperberg, Charles, Liu, Jun, Ahmad, Shahzad, van Duijn, Cornelia, Liu, Ching-Ti, Goodarzi, Mark O, Florez, Jose C, Meigs, James B, Rotter, Jerome I, Rich, Stephen S, Dupuis, Josée, and Leong, Aaron

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Diabetes, Pediatric, Genetics, Autoimmune Disease, Human Genome, Aetiology, 2.1 Biological and endogenous factors, Metabolic and endocrine, type 1 diabetes, type 2 diabetes, genetics, polygenic score, Cardiovascular medicine and haematology
Abstract

ContextBoth type 1 diabetes (T1D) and type 2 diabetes (T2D) have significant genetic contributions to risk and understanding their overlap can offer clinical insight.ObjectiveWe examined whether a T1D polygenic score (PS) was associated with a diagnosis of T2D in the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium.MethodsWe constructed a T1D PS using 79 known single nucleotide polymorphisms associated with T1D risk. We analyzed 13 792 T2D cases and 14 169 controls from CHARGE cohorts to determine the association between the T1D PS and T2D prevalence. We validated findings in an independent sample of 2256 T2D cases and 27 052 controls from the Mass General Brigham Biobank (MGB Biobank). As secondary analyses in 5228 T2D cases from CHARGE, we used multivariable regression models to assess the association of the T1D PS with clinical outcomes associated with T1D.ResultsThe T1D PS was not associated with T2D both in CHARGE (P = .15) and in the MGB Biobank (P = .87). The partitioned human leukocyte antigens only PS was associated with T2D in CHARGE (OR 1.02 per 1 SD increase in PS, 95% CI 1.01-1.03, P = .006) but not in the MGB Biobank. The T1D PS was weakly associated with insulin use (OR 1.007, 95% CI 1.001-1.012, P = .03) in CHARGE T2D cases but not with other outcomes.ConclusionIn large biobank samples, a common variant PS for T1D was not consistently associated with prevalent T2D. However, possible heterogeneity in T2D cannot be ruled out and future studies are needed do subphenotyping.

Published
2023

7. Identification of Genetic Variation Influencing Metformin Response in a Multiancestry Genome-Wide Association Study in the Diabetes Prevention Program (DPP).

Author

Li, Josephine, Perry, James, Jablonski, Kathleen, Srinivasan, Shylaja, Chen, Ling, Todd, Jennifer, Harden, Maegan, Mercader, Josep, Pan, Qing, Dawed, Adem, Yee, Sook Wah, Pearson, Ewan, Giacomini, Kathleen, Giri, Ayush, Hung, Adriana, Xiao, Shujie, Williams, L, Franks, Paul, Hanson, Robert, Kahn, Steven, Knowler, William, Pollin, Toni, and Florez, Jose

Subjects
Humans, Metformin, Diabetes Mellitus, Type 2, Genome-Wide Association Study, Prediabetic State, Genetic Variation, Polymorphism, Single Nucleotide
Abstract

Genome-wide significant loci for metformin response in type 2 diabetes reported elsewhere have not been replicated in the Diabetes Prevention Program (DPP). To assess pharmacogenetic interactions in prediabetes, we conducted a genome-wide association study (GWAS) in the DPP. Cox proportional hazards models tested associations with diabetes incidence in the metformin (MET; n = 876) and placebo (PBO; n = 887) arms. Multiple linear regression assessed association with 1-year change in metformin-related quantitative traits, adjusted for baseline trait, age, sex, and 10 ancestry principal components. We tested for gene-by-treatment interaction. No significant associations emerged for diabetes incidence. We identified four genome-wide significant variants after correcting for correlated traits (P < 9 × 10-9). In the MET arm, rs144322333 near ENOSF1 (minor allele frequency [MAF]AFR = 0.07; MAFEUR = 0.002) was associated with an increase in percentage of glycated hemoglobin (per minor allele, β = 0.39 [95% CI 0.28, 0.50]; P = 2.8 × 10-12). rs145591055 near OMSR (MAF = 0.10 in American Indians) was associated with weight loss (kilograms) (per G allele, β = -7.55 [95% CI -9.88, -5.22]; P = 3.2 × 10-10) in the MET arm. Neither variant was significant in PBO; gene-by-treatment interaction was significant for both variants [P(G×T) < 1.0 × 10-4]. Replication in individuals with diabetes did not yield significant findings. A GWAS for metformin response in prediabetes revealed novel ethnic-specific associations that require further investigation but may have implications for tailored therapy.

Published
2023

8. Publisher Correction: Rare variant analyses in 51,256 type 2 diabetes cases and 370,487 controls reveal the pathogenicity spectrum of monogenic diabetes genes

Author

Huerta-Chagoya, Alicia, Schroeder, Philip, Mandla, Ravi, Li, Jiang, Morris, Lowri, Vora, Maheak, Alkanaq, Ahmed, Nagy, Dorka, Szczerbinski, Lukasz, Madsen, Jesper G. S., Bonàs-Guarch, Silvia, Mollandin, Fanny, Cole, Joanne B., Porneala, Bianca, Westerman, Kenneth, Li, Josephine H., Pollin, Toni I., Florez, Jose C., Gloyn, Anna L., Carey, David J., Cebola, Inês, Mirshahi, Uyenlinh L., Manning, Alisa K., Leong, Aaron, Udler, Miriam, and Mercader, Josep M.

Published
2024
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9. 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

10. Initial Insights into the Genetic Variation Associated with Metformin Treatment Failure in Youth with Type 2 Diabetes

Author

Srinivasan, Shylaja, Chen, Ling, Udler, Miriam, Todd, Jennifer, Kelsey, Megan M, Haymond, Morey W, Arslanian, Silva, Zeitler, Philip, Gubitosi-Klug, Rose, Nadeau, Kristen J, Kutney, Katherine, White, Neil H, Li, Josephine H, Perry, James A, Kaur, Varinderpal, Brenner, Laura, Mercader, Josep M, Dawed, Adem, Pearson, Ewan R, Yee, Sook-Wah, Giacomini, Kathleen M, Pollin, Toni, and Florez, Jose C

Subjects
Paediatrics, Biomedical and Clinical Sciences, Clinical Sciences, Diabetes, Pediatric, Genetics, Human Genome, Prevention, Clinical Research, 2.1 Biological and endogenous factors, Metabolic and endocrine, Adult, Humans, Adolescent, Metformin, Diabetes Mellitus, Type 2, C-Peptide, Treatment Failure, Genetic Variation, Blood Glucose, Hypoglycemic Agents, Paediatrics and Reproductive Medicine, Endocrinology & Metabolism, Clinical sciences
Abstract

Metformin is the first-line treatment for type 2 diabetes (T2D) in youth but with limited sustained glycemic response. To identify common variants associated with metformin response, we used a genome-wide approach in 506 youth from the Treatment Options for Type 2 Diabetes in Adolescents and Youth (TODAY) study and examined the relationship between T2D partitioned polygenic scores (pPS), glycemic traits, and metformin response in these youth. Several variants met a suggestive threshold (P < 1 × 10-6), though none including published adult variants reached genome-wide significance. We pursued replication of top nine variants in three cohorts, and rs76195229 in ATRNL1 was associated with worse metformin response in the Metformin Genetics Consortium (n = 7,812), though statistically not being significant after Bonferroni correction (P = 0.06). A higher β-cell pPS was associated with a lower insulinogenic index (P = 0.02) and C-peptide (P = 0.047) at baseline and higher pPS related to two insulin resistance processes were associated with increased C-peptide at baseline (P = 0.04,0.02). Although pPS were not associated with changes in glycemic traits or metformin response, our results indicate a trend in the association of the β-cell pPS with reduced β-cell function over time. Our data show initial evidence for genetic variation associated with metformin response in youth with T2D.

Published
2023

11. Investigating Gene-Diet Interactions Impacting the Association Between Macronutrient Intake and Glycemic Traits.

Author

Westerman, Kenneth E, Walker, Maura E, Gaynor, Sheila M, Wessel, Jennifer, DiCorpo, Daniel, Ma, Jiantao, Alonso, Alvaro, Aslibekyan, Stella, Baldridge, Abigail S, Bertoni, Alain G, Biggs, Mary L, Brody, Jennifer A, Chen, Yii-Der Ida, Dupuis, Joseé, Goodarzi, Mark O, Guo, Xiuqing, Hasbani, Natalie R, Heath, Adam, Hidalgo, Bertha, Irvin, Marguerite R, Johnson, W Craig, Kalyani, Rita R, Lange, Leslie, Lemaitre, Rozenn N, Liu, Ching-Ti, Liu, Simin, Moon, Jee-Young, Nassir, Rami, Pankow, James S, Pettinger, Mary, Raffield, Laura M, Rasmussen-Torvik, Laura J, Selvin, Elizabeth, Senn, Mackenzie K, Shadyab, Aladdin H, Smith, Albert V, Smith, Nicholas L, Steffen, Lyn, Talegakwar, Sameera, Taylor, Kent D, de Vries, Paul S, Wilson, James G, Wood, Alexis C, Yanek, Lisa R, Yao, Jie, Zheng, Yinan, Boerwinkle, Eric, Morrison, Alanna C, Fornage, Miriam, Russell, Tracy P, Psaty, Bruce M, Levy, Daniel, Heard-Costa, Nancy L, Ramachandran, Vasan S, Mathias, Rasika A, Arnett, Donna K, Kaplan, Robert, North, Kari E, Correa, Adolfo, Carson, April, Rotter, Jerome I, Rich, Stephen S, Manson, JoAnn E, Reiner, Alexander P, Kooperberg, Charles, Florez, Jose C, Meigs, James B, Merino, Jordi, Tobias, Deirdre K, Chen, Han, and Manning, Alisa K

Subjects
Biomedical and Clinical Sciences, Nutrition and Dietetics, Nutrition, Diabetes, Human Genome, Minority Health, Genetics, Cardiovascular, Health Disparities, Prevention, Precision Medicine, Clinical Research, Metabolic and endocrine, Good Health and Well Being, Humans, Glycated Hemoglobin, Diet, Diabetes Mellitus, Eating, Guanine Nucleotide Dissociation Inhibitors, Genome-Wide Association Study, Medical and Health Sciences, Endocrinology & Metabolism, Biomedical and clinical sciences
Abstract

Few studies have demonstrated reproducible gene-diet interactions (GDIs) impacting metabolic disease risk factors, likely due in part to measurement error in dietary intake estimation and insufficient capture of rare genetic variation. We aimed to identify GDIs across the genetic frequency spectrum impacting the macronutrient-glycemia relationship in genetically and culturally diverse cohorts. We analyzed 33,187 participants free of diabetes from 10 National Heart, Lung, and Blood Institute Trans-Omics for Precision Medicine program cohorts with whole-genome sequencing, self-reported diet, and glycemic trait data. We fit cohort-specific, multivariable-adjusted linear mixed models for the effect of diet, modeled as an isocaloric substitution of carbohydrate for fat, and its interactions with common and rare variants genome-wide. In main effect meta-analyses, participants consuming more carbohydrate had modestly lower glycemic trait values (e.g., for glycated hemoglobin [HbA1c], -0.013% HbA1c/250 kcal substitution). In GDI meta-analyses, a common African ancestry-enriched variant (rs79762542) reached study-wide significance and replicated in the UK Biobank cohort, indicating a negative carbohydrate-HbA1c association among major allele homozygotes only. Simulations revealed that >150,000 samples may be necessary to identify similar macronutrient GDIs under realistic assumptions about effect size and measurement error. These results generate hypotheses for further exploration of modifiable metabolic disease risk in additional cohorts with African ancestry.Article highlightsWe aimed to identify genetic modifiers of the dietary macronutrient-glycemia relationship using whole-genome sequence data from 10 Trans-Omics for Precision Medicine program cohorts. Substitution models indicated a modest reduction in glycemia associated with an increase in dietary carbohydrate at the expense of fat. Genome-wide interaction analysis identified one African ancestry-enriched variant near the FRAS1 gene that may interact with macronutrient intake to influence hemoglobin A1c. Simulation-based power calculations accounting for measurement error suggested that substantially larger sample sizes may be necessary to discover further gene-macronutrient interactions.

Published
2023

12. Review of databases for experimentally validated human microRNA–mRNA interactions

Author

Kariuki, Dorian, Asam, Kesava, Aouizerat, Bradley E, Lewis, Kimberly A, Florez, Jose C, and Flowers, Elena

Subjects
Genetics, Biotechnology, Humans, MicroRNAs, RNA, Messenger, Databases, Nucleic Acid, Computational Biology, PubMed, Data Format, Library and Information Studies
Abstract

MicroRNAs (miRs) may contribute to disease etiology by influencing gene expression. Numerous databases are available for miR target prediction and validation, but their functionality is varied, and outputs are not standardized. The purpose of this review is to identify and describe databases for cataloging validated miR targets. Using Tools4miRs and PubMed, we identified databases with experimentally validated targets, human data, and a focus on miR-messenger RNA (mRNA) interactions. Data were extracted about the number of times each database was cited, the number of miRs, the target genes, the interactions per database, experimental methodology and key features of each database. The search yielded 10 databases, which in order of most cited to least were: miRTarBase, starBase/The Encyclopedia of RNA Interactomes, DIANA-TarBase, miRWalk, miRecords, miRGator, miRSystem, miRGate, miRSel and targetHub. Findings from this review suggest that the information presented within miR target validation databases can be enhanced by adding features such as flexibility in performing queries in multiple ways, downloadable data, ongoing updates and integrating tools for further miR-mRNA target interaction analysis. This review is designed to aid researchers, especially those new to miR bioinformatics tools, in database selection and to offer considerations for future development and upkeep of validation tools. Database URL http://mirtarbase.cuhk.edu.cn/.

Published
2023

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

Author

Costanzo, Maria C, von Grotthuss, Marcin, Massung, Jeffrey, Jang, Dongkeun, Caulkins, Lizz, Koesterer, Ryan, Gilbert, Clint, Welch, Ryan P, Kudtarkar, Parul, Hoang, Quy, Boughton, Andrew P, Singh, Preeti, Sun, Ying, Duby, Marc, Moriondo, Annie, Nguyen, Trang, Smadbeck, Patrick, Alexander, Benjamin R, Brandes, MacKenzie, Carmichael, Mary, Dornbos, Peter, Green, Todd, Huellas-Bruskiewicz, Kenneth C, Ji, Yue, Kluge, Alexandria, McMahon, Aoife C, Mercader, Josep M, Ruebenacker, Oliver, Sengupta, Sebanti, Spalding, Dylan, Taliun, Daniel, Consortium, AMP-T2D, Abecasis, Gonçalo, Akolkar, Beena, Allred, Nicholette D, Altshuler, David, Below, Jennifer E, Bergman, Richard, Beulens, Joline WJ, Blangero, John, Boehnke, Michael, Bokvist, Krister, Bottinger, Erwin, Bowden, Donald, Brosnan, M Julia, Brown, Christopher, Bruskiewicz, Kenneth, Burtt, Noël P, Cebola, Inês, Chambers, John, Chen, Yii-Der Ida, Cherkas, Andriy, Chu, Audrey Y, Clark, Christopher, Claussnitzer, Melina, Cox, Nancy J, Hoed, Marcel den, Dong, Duc, Duggirala, Ravindranath, Dupuis, Josée, Elders, Petra JM, 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, Gloyn, Anna L, Hanis, Craig L, Hanson, Robert, Hattersley, Andrew T, Im, Hae Kyung, Iqbal, Sidra, Jacobs, Suzanne BR, Jang, Dong-Keun, Jordan, Tad, Kamphaus, Tania, Karpe, Fredrik, Keane, Thomas M, Kim, Seung K, Lage, Kasper, Lange, Leslie A, and Lazar, Mitchell

Subjects
Genetics, Diabetes, Human Genome, Metabolic and endocrine, Good Health and Well Being, Humans, Diabetes Mellitus, Type 2, Access to Information, Prospective Studies, Genomics, Phenotype, AMP-T2D Consortium, CMDKP, GWAS, T2DKP, data sharing, diabetes, effector genes, genetic associations, genetic support, genomics, portal, Biochemistry and Cell Biology, Medical Biochemistry and Metabolomics, Endocrinology & Metabolism
Abstract

Associations between human genetic variation and clinical phenotypes have become a foundation of biomedical research. Most repositories of these data seek to be disease-agnostic and therefore lack disease-focused views. The Type 2 Diabetes Knowledge Portal (T2DKP) is a public resource of genetic datasets and genomic annotations dedicated to type 2 diabetes (T2D) and related traits. Here, we seek to make the T2DKP more accessible to prospective users and more useful to existing users. First, we evaluate the T2DKP's comprehensiveness by comparing its datasets with those of other repositories. Second, we describe how researchers unfamiliar with human genetic data can begin using and correctly interpreting them via the T2DKP. Third, we describe how existing users can extend their current workflows to use the full suite of tools offered by the T2DKP. We finally discuss the lessons offered by the T2DKP toward the goal of democratizing access to complex disease genetic results.

Published
2023

14. Genetic drivers of heterogeneity in type 2 diabetes pathophysiology

Author

Suzuki, Ken, Hatzikotoulas, Konstantinos, Southam, Lorraine, Taylor, Henry J., Yin, Xianyong, Lorenz, Kim M., Mandla, Ravi, Huerta-Chagoya, Alicia, Melloni, Giorgio E. M., Kanoni, Stavroula, Rayner, Nigel W., Bocher, Ozvan, Arruda, Ana Luiza, Sonehara, Kyuto, Namba, Shinichi, Lee, Simon S. K., Preuss, Michael H., Petty, Lauren E., Schroeder, Philip, Vanderwerff, Brett, Kals, Mart, Bragg, Fiona, Lin, Kuang, Guo, Xiuqing, Zhang, Weihua, Yao, Jie, Kim, Young Jin, Graff, Mariaelisa, Takeuchi, Fumihiko, Nano, Jana, Lamri, Amel, Nakatochi, Masahiro, Moon, Sanghoon, Scott, Robert A., Cook, James P., Lee, Jung-Jin, Pan, Ian, Taliun, Daniel, Parra, Esteban J., Chai, Jin-Fang, Bielak, Lawrence F., Tabara, Yasuharu, Hai, Yang, Thorleifsson, Gudmar, Grarup, Niels, Sofer, Tamar, Wuttke, Matthias, Sarnowski, Chloé, Gieger, Christian, Nousome, Darryl, Trompet, Stella, Kwak, Soo-Heon, Long, Jirong, Sun, Meng, Tong, Lin, Chen, Wei-Min, Nongmaithem, Suraj S., Noordam, Raymond, Lim, Victor J. Y., Tam, Claudia H. T., Joo, Yoonjung Yoonie, Chen, Chien-Hsiun, Raffield, Laura M., Prins, Bram Peter, Nicolas, Aude, Yanek, Lisa R., Chen, Guanjie, Brody, Jennifer A., Kabagambe, Edmond, An, Ping, Xiang, Anny H., Choi, Hyeok Sun, Cade, Brian E., Tan, Jingyi, Broadaway, K. Alaine, Williamson, Alice, Kamali, Zoha, Cui, Jinrui, Thangam, Manonanthini, Adair, Linda S., Adeyemo, Adebowale, Aguilar-Salinas, Carlos A., Ahluwalia, Tarunveer S., Anand, Sonia S., Bertoni, Alain, Bork-Jensen, Jette, Brandslund, Ivan, Buchanan, Thomas A., Burant, Charles F., Butterworth, Adam S., Canouil, Mickaël, Chan, Juliana C. N., Chang, Li-Ching, Chee, Miao-Li, Chen, Ji, Chen, Shyh-Huei, Chen, Yuan-Tsong, Chen, Zhengming, Chuang, Lee-Ming, Cushman, Mary, Danesh, John, Das, Swapan K., de Silva, H. Janaka, Dedoussis, George, Dimitrov, Latchezar, Doumatey, Ayo P., Du, Shufa, Duan, Qing, Eckardt, Kai-Uwe, Emery, Leslie S., Evans, Daniel S., Evans, Michele K., Fischer, Krista, Floyd, James S., Ford, Ian, Franco, Oscar H., Frayling, Timothy M., Freedman, Barry I., Genter, Pauline, Gerstein, Hertzel C., Giedraitis, Vilmantas, González-Villalpando, Clicerio, González-Villalpando, Maria Elena, Gordon-Larsen, Penny, Gross, Myron, Guare, Lindsay A., Hackinger, Sophie, Hakaste, Liisa, Han, Sohee, Hattersley, Andrew T., Herder, Christian, Horikoshi, Momoko, Howard, Annie-Green, Hsueh, Willa, Huang, Mengna, Huang, Wei, Hung, Yi-Jen, Hwang, Mi Yeong, Hwu, Chii-Min, Ichihara, Sahoko, Ikram, Mohammad Arfan, Ingelsson, Martin, Islam, Md. Tariqul, Isono, Masato, Jang, Hye-Mi, Jasmine, Farzana, Jiang, Guozhi, Jonas, Jost B., Jørgensen, Torben, Kamanu, Frederick K., Kandeel, Fouad R., Kasturiratne, Anuradhani, Katsuya, Tomohiro, Kaur, Varinderpal, Kawaguchi, Takahisa, Keaton, Jacob M., Kho, Abel N., Khor, Chiea-Chuen, Kibriya, Muhammad G., Kim, Duk-Hwan, Kronenberg, Florian, Kuusisto, Johanna, Läll, Kristi, Lange, Leslie A., Lee, Kyung Min, Lee, Myung-Shik, Lee, Nanette R., Leong, Aaron, Li, Liming, Li, Yun, Li-Gao, Ruifang, Ligthart, Symen, Lindgren, Cecilia M., Linneberg, Allan, Liu, Ching-Ti, Liu, Jianjun, Locke, Adam E., Louie, Tin, Luan, Jian’an, Luk, Andrea O., Luo, Xi, Lv, Jun, Lynch, Julie A., Lyssenko, Valeriya, Maeda, Shiro, Mamakou, Vasiliki, Mansuri, Sohail Rafik, Matsuda, Koichi, Meitinger, Thomas, Melander, Olle, Metspalu, Andres, Mo, Huan, Morris, Andrew D., Moura, Filipe A., Nadler, Jerry L., Nalls, Michael A., Nayak, Uma, Ntalla, Ioanna, Okada, Yukinori, Orozco, Lorena, Patel, Sanjay R., Patil, Snehal, Pei, Pei, Pereira, Mark A., Peters, Annette, Pirie, Fraser J., Polikowsky, Hannah G., Porneala, Bianca, Prasad, Gauri, Rasmussen-Torvik, Laura J., Reiner, Alexander P., Roden, Michael, Rohde, Rebecca, Roll, Katheryn, Sabanayagam, Charumathi, Sandow, Kevin, Sankareswaran, Alagu, Sattar, Naveed, Schönherr, Sebastian, Shahriar, Mohammad, Shen, Botong, Shi, Jinxiu, Shin, Dong Mun, Shojima, Nobuhiro, Smith, Jennifer A., So, Wing Yee, Stančáková, Alena, Steinthorsdottir, Valgerdur, Stilp, Adrienne M., Strauch, Konstantin, Taylor, Kent D., Thorand, Barbara, Thorsteinsdottir, Unnur, Tomlinson, Brian, Tran, Tam C., Tsai, Fuu-Jen, Tuomilehto, Jaakko, Tusie-Luna, Teresa, Udler, Miriam S., Valladares-Salgado, Adan, van Dam, Rob M., van Klinken, Jan B., Varma, Rohit, Wacher-Rodarte, Niels, Wheeler, Eleanor, Wickremasinghe, Ananda R., van Dijk, Ko Willems, Witte, Daniel R., Yajnik, Chittaranjan S., Yamamoto, Ken, Yamamoto, Kenichi, Yoon, Kyungheon, Yu, Canqing, Yuan, Jian-Min, Yusuf, Salim, Zawistowski, Matthew, Zhang, Liang, Zheng, Wei, Raffel, Leslie J., Igase, Michiya, Ipp, Eli, Redline, Susan, Cho, Yoon Shin, Lind, Lars, Province, Michael A., Fornage, Myriam, Hanis, Craig L., Ingelsson, Erik, Zonderman, Alan B., Psaty, Bruce M., Wang, Ya-Xing, Rotimi, Charles N., Becker, Diane M., Matsuda, Fumihiko, Liu, Yongmei, Yokota, Mitsuhiro, Kardia, Sharon L. R., Peyser, Patricia A., Pankow, James S., Engert, James C., Bonnefond, Amélie, Froguel, Philippe, Wilson, James G., Sheu, Wayne H. H., Wu, Jer-Yuarn, Hayes, M. Geoffrey, Ma, Ronald C. W., Wong, Tien-Yin, Mook-Kanamori, Dennis O., Tuomi, Tiinamaija, Chandak, Giriraj R., Collins, Francis S., Bharadwaj, Dwaipayan, Paré, Guillaume, Sale, Michèle M., Ahsan, Habibul, Motala, Ayesha A., Shu, Xiao-Ou, Park, Kyong-Soo, Jukema, J. Wouter, Cruz, Miguel, Chen, Yii-Der Ida, Rich, Stephen S., McKean-Cowdin, Roberta, Grallert, Harald, Cheng, Ching-Yu, Ghanbari, Mohsen, Tai, E-Shyong, Dupuis, Josee, Kato, Norihiro, Laakso, Markku, Köttgen, Anna, Koh, Woon-Puay, Bowden, Donald W., Palmer, Colin N. A., Kooner, Jaspal S., Kooperberg, Charles, Liu, Simin, North, Kari E., Saleheen, Danish, Hansen, Torben, Pedersen, Oluf, Wareham, Nicholas J., Lee, Juyoung, Kim, Bong-Jo, Millwood, Iona Y., Walters, Robin G., Stefansson, Kari, Ahlqvist, Emma, Goodarzi, Mark O., Mohlke, Karen L., Langenberg, Claudia, Haiman, Christopher A., Loos, Ruth J. F., Florez, Jose C., Rader, Daniel J., Ritchie, Marylyn D., Zöllner, Sebastian, Mägi, Reedik, Marston, Nicholas A., Ruff, Christian T., van Heel, David A., Finer, Sarah, Denny, Joshua C., Yamauchi, Toshimasa, Kadowaki, Takashi, Chambers, John C., Ng, Maggie C. Y., Sim, Xueling, Below, Jennifer E., Tsao, Philip S., Chang, Kyong-Mi, McCarthy, Mark I., Meigs, James B., Mahajan, Anubha, Spracklen, Cassandra N., Mercader, Josep M., Boehnke, Michael, Rotter, Jerome I., Vujkovic, Marijana, Voight, Benjamin F., Morris, Andrew P., and Zeggini, Eleftheria

Published
2024
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15. Genetic architecture and biology of youth-onset type 2 diabetes

Author

Kwak, Soo Heon, Srinivasan, Shylaja, Chen, Ling, Todd, Jennifer, Mercader, Josep M., Jensen, Elizabeth T., Divers, Jasmin, Mottl, Amy K., Pihoker, Catherine, Gandica, Rachelle G., Laffel, Lori M., Isganaitis, Elvira, Haymond, Morey W., Levitsky, Lynne L., Pollin, Toni I., Florez, Jose C., and Flannick, Jason

Published
2024
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16. High-throughput genetic clustering of type 2 diabetes loci reveals heterogeneous mechanistic pathways of metabolic disease.

Author

Kim, Hyunkyung, Westerman, Kenneth, Smith, Kirk, Chiou, Joshua, Cole, Joanne, Majarian, Timothy, von Grotthuss, Marcin, Kwak, Soo, Kim, Jaegil, Mercader, Josep, Florez, Jose, Manning, Alisa, Udler, Miriam, and Gaulton, Kyle

Subjects
Bayesian non-negative matrix factorisation, Clustering, Disease pathways, GWAS, Genetics, NMF, Polygenic risk scores, Subtypes, Type 2 diabetes, bNMF, Humans, Diabetes Mellitus, Type 2, Genome-Wide Association Study, Genetic Predisposition to Disease, Bayes Theorem, Cluster Analysis, Polymorphism, Single Nucleotide
Abstract

AIMS/HYPOTHESIS: Type 2 diabetes is highly polygenic and influenced by multiple biological pathways. Rapid expansion in the number of type 2 diabetes loci can be leveraged to identify such pathways. METHODS: We developed a high-throughput pipeline to enable clustering of type 2 diabetes loci based on variant-trait associations. Our pipeline extracted summary statistics from genome-wide association studies (GWAS) for type 2 diabetes and related traits to generate a matrix of 323 variants × 64 trait associations and applied Bayesian non-negative matrix factorisation (bNMF) to identify genetic components of type 2 diabetes. Epigenomic enrichment analysis was performed in 28 cell types and single pancreatic cells. We generated cluster-specific polygenic scores and performed regression analysis in an independent cohort (N=25,419) to assess for clinical relevance. RESULTS: We identified ten clusters of genetic loci, recapturing the five from our prior analysis as well as novel clusters related to beta cell dysfunction, pronounced insulin secretion, and levels of alkaline phosphatase, lipoprotein A and sex hormone-binding globulin. Four clusters related to mechanisms of insulin deficiency, five to insulin resistance and one had an unclear mechanism. The clusters displayed tissue-specific epigenomic enrichment, notably with the two beta cell clusters differentially enriched in functional and stressed pancreatic beta cell states. Additionally, cluster-specific polygenic scores were differentially associated with patient clinical characteristics and outcomes. The pipeline was applied to coronary artery disease and chronic kidney disease, identifying multiple overlapping clusters with type 2 diabetes. CONCLUSIONS/INTERPRETATION: Our approach stratifies type 2 diabetes loci into physiologically interpretable genetic clusters associated with distinct tissues and clinical outcomes. The pipeline allows for efficient updating as additional GWAS become available and can be readily applied to other conditions, facilitating clinical translation of GWAS findings. Software to perform this clustering pipeline is freely available.

Published
2023

17. Multi-tissue epigenetic analysis identifies distinct associations underlying insulin resistance and Alzheimer’s disease at CPT1A locus

Author

Sarnowski, Chloé, Huan, Tianxiao, Ma, Yiyi, Joehanes, Roby, Beiser, Alexa, DeCarli, Charles S, Heard-Costa, Nancy L, Levy, Daniel, Lin, Honghuang, Liu, Ching-Ti, Liu, Chunyu, Meigs, James B, Satizabal, Claudia L, Florez, Jose C, Hivert, Marie-France, Dupuis, Josée, De Jager, Philip L, Bennett, David A, Seshadri, Sudha, and Morrison, Alanna C

Subjects
Biological Sciences, Genetics, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Dementia, Neurodegenerative, Alzheimer's Disease, Human Genome, Aging, Brain Disorders, Acquired Cognitive Impairment, Neurosciences, 2.1 Biological and endogenous factors, Aetiology, Metabolic and endocrine, Neurological, Humans, Alzheimer Disease, Diabetes Mellitus, Type 2, DNA Methylation, Epigenesis, Genetic, Genetic Markers, Genome-Wide Association Study, Insulin Resistance, Epigenetics, Insulin resistance, Alzheimer's disease, FHS, ROSMAP, DNA methylation, Alzheimer’s disease, Clinical Sciences, Paediatrics and Reproductive Medicine
Abstract

BackgroundInsulin resistance (IR) is a major risk factor for Alzheimer's disease (AD) dementia. The mechanisms by which IR predisposes to AD are not well-understood. Epigenetic studies may help identify molecular signatures of IR associated with AD, thus improving our understanding of the biological and regulatory mechanisms linking IR and AD.MethodsWe conducted an epigenome-wide association study of IR, quantified using the homeostatic model assessment of IR (HOMA-IR) and adjusted for body mass index, in 3,167 participants from the Framingham Heart Study (FHS) without type 2 diabetes at the time of blood draw used for methylation measurement. We identified DNA methylation markers associated with IR at the genome-wide level accounting for multiple testing (P

Published
2023

18. Ancestral diversity improves discovery and fine-mapping of genetic loci for anthropometric traits—The Hispanic/Latino Anthropometry Consortium

Author

Fernández-Rhodes, Lindsay, Graff, Mariaelisa, Buchanan, Victoria L, Justice, Anne E, Highland, Heather M, Guo, Xiuqing, Zhu, Wanying, Chen, Hung-Hsin, Young, Kristin L, Adhikari, Kaustubh, Palmer, Nicholette D, Below, Jennifer E, Bradfield, Jonathan, Pereira, Alexandre C, Glover, LáShauntá, Kim, Daeeun, Lilly, Adam G, Shrestha, Poojan, Thomas, Alvin G, Zhang, Xinruo, Chen, Minhui, Chiang, Charleston WK, Pulit, Sara, Horimoto, Andrea, Krieger, Jose E, Guindo-Martínez, Marta, Preuss, Michael, Schumann, Claudia, Smit, Roelof AJ, Torres-Mejía, Gabriela, Acuña-Alonzo, Victor, Bedoya, Gabriel, Bortolini, Maria-Cátira, Canizales-Quinteros, Samuel, Gallo, Carla, González-José, Rolando, Poletti, Giovanni, Rothhammer, Francisco, Hakonarson, Hakon, Igo, Robert, Adler, Sharon G, Iyengar, Sudha K, Nicholas, Susanne B, Gogarten, Stephanie M, Isasi, Carmen R, Papnicolaou, George, Stilp, Adrienne M, Qi, Qibin, Kho, Minjung, Smith, Jennifer A, Langefeld, Carl D, Wagenknecht, Lynne, Mckean-Cowdin, Roberta, Gao, Xiaoyi Raymond, Nousome, Darryl, Conti, David V, Feng, Ye, Allison, Matthew A, Arzumanyan, Zorayr, Buchanan, Thomas A, Chen, Yii-Der Ida, Genter, Pauline M, Goodarzi, Mark O, Hai, Yang, Hsueh, Willa, Ipp, Eli, Kandeel, Fouad R, Lam, Kelvin, Li, Xiaohui, Nadler, Jerry L, Raffel, Leslie J, Roll, Kathryn, Sandow, Kevin, Tan, Jingyi, Taylor, Kent D, Xiang, Anny H, Yao, Jie, Audirac-Chalifour, Astride, Peralta Romero, Jose de Jesus, Hartwig, Fernando, Horta, Bernando, Blangero, John, Curran, Joanne E, Duggirala, Ravindranath, Lehman, Donna E, Puppala, Sobha, Fejerman, Laura, John, Esther M, Aguilar-Salinas, Carlos, Burtt, Noël P, Florez, Jose C, García-Ortíz, Humberto, González-Villalpando, Clicerio, Mercader, Josep, Orozco, Lorena, Tusié-Luna, Teresa, Blanco, Estela, Gahagan, Sheila, Cox, Nancy J, and Hanis, Craig

Abstract

[This corrects the article DOI: 10.1016/j.xhgg.2022.100099.].

Published
2023

19. A combined polygenic score of 21,293 rare and 22 common variants improves diabetes diagnosis based on hemoglobin A1C levels

Author

Dornbos, Peter, Koesterer, Ryan, Ruttenburg, Andrew, Nguyen, Trang, Cole, Joanne B, Leong, Aaron, Meigs, James B, Florez, Jose C, Rotter, Jerome I, Udler, Miriam S, and Flannick, Jason

Subjects
Biological Sciences, Genetics, Precision Medicine, Diabetes, 2.1 Biological and endogenous factors, Metabolic and endocrine, Good Health and Well Being, Humans, Multifactorial Inheritance, Glycated Hemoglobin, Diabetes Mellitus, Type 2, Genome-Wide Association Study, AMP-T2D-GENES Consortium, Medical and Health Sciences, Developmental Biology, Agricultural biotechnology, Bioinformatics and computational biology
Abstract

Polygenic scores (PGSs) combine the effects of common genetic variants1,2 to predict risk or treatment strategies for complex diseases3-7. Adding rare variation to PGSs has largely unknown benefits and is methodically challenging. Here, we developed a method for constructing rare variant PGSs and applied it to calculate genetically modified hemoglobin A1C thresholds for type 2 diabetes (T2D) diagnosis7-10. The resultant rare variant PGS is highly polygenic (21,293 variants across 154 genes), depends on ultra-rare variants (72.7% observed in fewer than three people) and identifies significantly more undiagnosed T2D cases than expected by chance (odds ratio = 2.71; P = 1.51 × 10-6). A PGS combining common and rare variants is expected to identify 4.9 million misdiagnosed T2D cases in the United States-nearly 1.5-fold more than the common variant PGS alone. These results provide a method for constructing complex trait PGSs from rare variants and suggest that rare variants will augment common variants in precision medicine approaches for common disease.

Published
2022

20. Second international consensus report on gaps and opportunities for the clinical translation of precision diabetes medicine

Author

Tobias, Deirdre K., Merino, Jordi, Ahmad, Abrar, Aiken, Catherine, Benham, Jamie L., Bodhini, Dhanasekaran, Clark, Amy L., Colclough, Kevin, Corcoy, Rosa, Cromer, Sara J., Duan, Daisy, Felton, Jamie L., Francis, Ellen C., Gillard, Pieter, Gingras, Véronique, Gaillard, Romy, Haider, Eram, Hughes, Alice, Ikle, Jennifer M., Jacobsen, Laura M., Kahkoska, Anna R., Kettunen, Jarno L. T., Kreienkamp, Raymond J., Lim, Lee-Ling, Männistö, Jonna M. E., Massey, Robert, Mclennan, Niamh-Maire, Miller, Rachel G., Morieri, Mario Luca, Most, Jasper, Naylor, Rochelle N., Ozkan, Bige, Patel, Kashyap Amratlal, Pilla, Scott J., Prystupa, Katsiaryna, Raghavan, Sridharan, Rooney, Mary R., Schön, Martin, Semnani-Azad, Zhila, Sevilla-Gonzalez, Magdalena, Svalastoga, Pernille, Takele, Wubet Worku, Tam, Claudia Ha-ting, Thuesen, Anne Cathrine B., Tosur, Mustafa, Wallace, Amelia S., Wang, Caroline C., Wong, Jessie J., Yamamoto, Jennifer M., Young, Katherine, Amouyal, Chloé, Andersen, Mette K., Bonham, Maxine P., Chen, Mingling, Cheng, Feifei, Chikowore, Tinashe, Chivers, Sian C., Clemmensen, Christoffer, Dabelea, Dana, Dawed, Adem Y., Deutsch, Aaron J., Dickens, Laura T., DiMeglio, Linda A., Dudenhöffer-Pfeifer, Monika, Evans-Molina, Carmella, Fernández-Balsells, María Mercè, Fitipaldi, Hugo, Fitzpatrick, Stephanie L., Gitelman, Stephen E., Goodarzi, Mark O., Grieger, Jessica A., Guasch-Ferré, Marta, Habibi, Nahal, Hansen, Torben, Huang, Chuiguo, Harris-Kawano, Arianna, Ismail, Heba M., Hoag, Benjamin, Johnson, Randi K., Jones, Angus G., Koivula, Robert W., Leong, Aaron, Leung, Gloria K. W., Libman, Ingrid M., Liu, Kai, Long, S. Alice, Lowe, Jr, William L., Morton, Robert W., Motala, Ayesha A., Onengut-Gumuscu, Suna, Pankow, James S., Pathirana, Maleesa, Pazmino, Sofia, Perez, Dianna, Petrie, John R., Powe, Camille E., Quinteros, Alejandra, Jain, Rashmi, Ray, Debashree, Ried-Larsen, Mathias, Saeed, Zeb, Santhakumar, Vanessa, Kanbour, Sarah, Sarkar, Sudipa, Monaco, Gabriela S. F., Scholtens, Denise M., Selvin, Elizabeth, Sheu, Wayne Huey-Herng, Speake, Cate, Stanislawski, Maggie A., Steenackers, Nele, Steck, Andrea K., Stefan, Norbert, Støy, Julie, Taylor, Rachael, Tye, Sok Cin, Ukke, Gebresilasea Gendisha, Urazbayeva, Marzhan, Van der Schueren, Bart, Vatier, Camille, Wentworth, John M., Hannah, Wesley, White, Sara L., Yu, Gechang, Zhang, Yingchai, Zhou, Shao J., Beltrand, Jacques, Polak, Michel, Aukrust, Ingvild, de Franco, Elisa, Flanagan, Sarah E., Maloney, Kristin A., McGovern, Andrew, Molnes, Janne, Nakabuye, Mariam, Njølstad, Pål Rasmus, Pomares-Millan, Hugo, Provenzano, Michele, Saint-Martin, Cécile, Zhang, Cuilin, Zhu, Yeyi, Auh, Sungyoung, de Souza, Russell, Fawcett, Andrea J., Gruber, Chandra, Mekonnen, Eskedar Getie, Mixter, Emily, Sherifali, Diana, Eckel, Robert H., Nolan, John J., Philipson, Louis H., Brown, Rebecca J., Billings, Liana K., Boyle, Kristen, Costacou, Tina, Dennis, John M., Florez, Jose C., Gloyn, Anna L., Gomez, Maria F., Gottlieb, Peter A., Greeley, Siri Atma W., Griffin, Kurt, Hattersley, Andrew T., Hirsch, Irl B., Hivert, Marie-France, Hood, Korey K., Josefson, Jami L., Kwak, Soo Heon, Laffel, Lori M., Lim, Siew S., Loos, Ruth J. F., Ma, Ronald C. W., Mathieu, Chantal, Mathioudakis, Nestoras, Meigs, James B., Misra, Shivani, Mohan, Viswanathan, Murphy, Rinki, Oram, Richard, Owen, Katharine R., Ozanne, Susan E., Pearson, Ewan R., Perng, Wei, Pollin, Toni I., Pop-Busui, Rodica, Pratley, Richard E., Redman, Leanne M., Redondo, Maria J., Reynolds, Rebecca M., Semple, Robert K., Sherr, Jennifer L., Sims, Emily K., Sweeting, Arianne, Tuomi, Tiinamaija, Udler, Miriam S., Vesco, Kimberly K., Vilsbøll, Tina, Wagner, Robert, Rich, Stephen S., and Franks, Paul W.

Published
2023
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21. Genome-wide meta-analysis and omics integration identifies novel genes associated with diabetic kidney disease

Author

Sandholm, Niina, Cole, Joanne B, Nair, Viji, Sheng, Xin, Liu, Hongbo, Ahlqvist, Emma, van Zuydam, Natalie, Dahlström, Emma H, Fermin, Damian, Smyth, Laura J, Salem, Rany M, Forsblom, Carol, Valo, Erkka, Harjutsalo, Valma, Brennan, Eoin P, McKay, Gareth J, Andrews, Darrell, Doyle, Ross, Looker, Helen C, Nelson, Robert G, Palmer, Colin, McKnight, Amy Jayne, Godson, Catherine, Maxwell, Alexander P, Groop, Leif, McCarthy, Mark I, Kretzler, Matthias, Susztak, Katalin, Hirschhorn, Joel N, Florez, Jose C, and Groop, Per-Henrik

Subjects
Human Genome, Kidney Disease, Prevention, Genetics, Diabetes, Biotechnology, 2.1 Biological and endogenous factors, Aetiology, Renal and urogenital, Metabolic and endocrine, Good Health and Well Being, Diabetes Mellitus, Type 2, Diabetic Nephropathies, Doublecortin-Like Kinases, Fibrosis, Genome-Wide Association Study, Humans, Intracellular Signaling Peptides and Proteins, Kidney, Polymorphism, Single Nucleotide, Protein Serine-Threonine Kinases, Diabetes complications, Diabetic kidney disease, Genome-wide association study, Meta-analysis, Transcriptomics, GENIE Consortium, Genome-wide association study, Meta-analysis, Transcriptomics, Clinical Sciences, Paediatrics and Reproductive Medicine, Public Health and Health Services, Endocrinology & Metabolism
Abstract

Aims/hypothesisDiabetic kidney disease (DKD) is the leading cause of kidney failure and has a substantial genetic component. Our aim was to identify novel genetic factors and genes contributing to DKD by performing meta-analysis of previous genome-wide association studies (GWAS) on DKD and by integrating the results with renal transcriptomics datasets.MethodsWe performed GWAS meta-analyses using ten phenotypic definitions of DKD, including nearly 27,000 individuals with diabetes. Meta-analysis results were integrated with estimated quantitative trait locus data from human glomerular (N=119) and tubular (N=121) samples to perform transcriptome-wide association study. We also performed gene aggregate tests to jointly test all available common genetic markers within a gene, and combined the results with various kidney omics datasets.ResultsThe meta-analysis identified a novel intronic variant (rs72831309) in the TENM2 gene associated with a lower risk of the combined chronic kidney disease (eGFR9.3×10-9). Gene-level analysis identified ten genes associated with DKD (COL20A1, DCLK1, EIF4E, PTPRN-RESP18, GPR158, INIP-SNX30, LSM14A and MFF; p

Published
2022

22. Genome-Wide Meta-analysis Identifies Genetic Variants Associated With Glycemic Response to Sulfonylureas

Author

Dawed, Adem Y, Yee, Sook Wah, Zhou, Kaixin, van Leeuwen, Nienke, Zhang, Yanfei, Siddiqui, Moneeza K, Etheridge, Amy, Innocenti, Federico, Xu, Fei, Li, Josephine H, Beulens, Joline W, van der Heijden, Amber A, Slieker, Roderick C, Chang, Yu-Chuan, Mercader, Josep M, Kaur, Varinderpal, Witte, John S, Lee, Ming Ta Michael, Kamatani, Yoichiro, Momozawa, Yukihide, Kubo, Michiaki, Palmer, Colin NA, Florez, Jose C, Hedderson, Monique M, Hart, Leen MT, Giacomini, Kathleen M, Pearson, Ewan R, investigators:, MetGen Plus, Pearson, Ewan, Dawed, Adem, Holman, Rury, Coleman, Ruth, Hart, Leen T, Slieker, Roderick, Beulens, Joline, van der Heijden, Amber, Nijpels, Giel, Elders, Petra, Rutters, Femke, Stricker, Bruno, Ahmadizar, Fariba, de Keyser, Catherine, Koov, Adriaan, Out, Mattijs, Kloviņš, Jānis, Zaharenko, Linda, Javorsky, Martin, Tkac, Ivan, Florez, Jose, Giacomini, Kathy, Hedderson, Monique, Motsinger-Reif, Alison, Wagner, Michael, Semiz, Sabina, Dujic, Tanja, Christensen, Mette, Brøsen, Kim, Waterworth, Dawn, Ehm, Meg, Ma, Ronald, Psaty, Bruce, and Floyd, James

Subjects
Pharmacology and Pharmaceutical Sciences, Biomedical and Clinical Sciences, Prevention, Diabetes, Human Genome, Genetics, Evaluation of treatments and therapeutic interventions, 6.1 Pharmaceuticals, Metabolic and endocrine, Blood Glucose, Diabetes Mellitus, Type 2, Genome-Wide Association Study, Glycated Hemoglobin, Humans, Hypoglycemic Agents, Likelihood Functions, Liver-Specific Organic Anion Transporter 1, Metformin, Sulfonylurea Compounds, for MetGen Plus, for the DIRECT Consortium, MetGen Plus investigators:
Abstract

ObjectiveSulfonylureas, the first available drugs for the management of type 2 diabetes, remain widely prescribed today. However, there exists significant variability in glycemic response to treatment. We aimed to establish heritability of sulfonylurea response and identify genetic variants and interacting treatments associated with HbA1c reduction.Research design and methodsAs an initiative of the Metformin Genetics Plus Consortium (MetGen Plus) and the DIabetes REsearCh on patient straTification (DIRECT) consortium, 5,485 White Europeans with type 2 diabetes treated with sulfonylureas were recruited from six referral centers in Europe and North America. We first estimated heritability using the generalized restricted maximum likelihood approach and then undertook genome-wide association studies of glycemic response to sulfonylureas measured as HbA1c reduction after 12 months of therapy followed by meta-analysis. These results were supported by acute glipizide challenge in humans who were naïve to type 2 diabetes medications, cis expression quantitative trait loci (eQTL), and functional validation in cellular models. Finally, we examined for possible drug-drug-gene interactions.ResultsAfter establishing that sulfonylurea response is heritable (mean ± SEM 37 ± 11%), we identified two independent loci near the GXYLT1 and SLCO1B1 genes associated with HbA1c reduction at a genome-wide scale (P < 5 × 10-8). The C allele at rs1234032, near GXYLT1, was associated with 0.14% (1.5 mmol/mol), P = 2.39 × 10-8), lower reduction in HbA1c. Similarly, the C allele was associated with higher glucose trough levels (β = 1.61, P = 0.005) in healthy volunteers in the SUGAR-MGH given glipizide (N = 857). In 3,029 human whole blood samples, the C allele is a cis eQTL for increased expression of GXYLT1 (β = 0.21, P = 2.04 × 10-58). The C allele of rs10770791, in an intronic region of SLCO1B1, was associated with 0.11% (1.2 mmol/mol) greater reduction in HbA1c (P = 4.80 × 10-8). In 1,183 human liver samples, the C allele at rs10770791 is a cis eQTL for reduced SLCO1B1 expression (P = 1.61 × 10-7), which, together with functional studies in cells expressing SLCO1B1, supports a key role for hepatic SLCO1B1 (encoding OATP1B1) in regulation of sulfonylurea transport. Further, a significant interaction between statin use and SLCO1B1 genotype was observed (P = 0.001). In statin nonusers, C allele homozygotes at rs10770791 had a large absolute reduction in HbA1c (0.48 ± 0.12% [5.2 ± 1.26 mmol/mol]), equivalent to that associated with initiation of a dipeptidyl peptidase 4 inhibitor.ConclusionsWe have identified clinically important genetic effects at genome-wide levels of significance, and important drug-drug-gene interactions, which include commonly prescribed statins. With increasing availability of genetic data embedded in clinical records these findings will be important in prescribing glucose-lowering drugs.

Published
2021

23. Ancestral diversity improves discovery and fine-mapping of genetic loci for anthropometric traits—The Hispanic/Latino Anthropometry Consortium

Author

Fernández-Rhodes, Lindsay, Graff, Mariaelisa, Buchanan, Victoria L, Justice, Anne E, Highland, Heather M, Guo, Xiuqing, Zhu, Wanying, Chen, Hung-Hsin, Young, Kristin L, Adhikari, Kaustubh, Palmer, Nicholette D, Below, Jennifer E, Bradfield, Jonathan, Pereira, Alexandre C, Glover, LáShauntá, Kim, Daeeun, Lilly, Adam G, Shrestha, Poojan, Thomas, Alvin G, Zhang, Xinruo, Chen, Minhui, Chiang, Charleston WK, Pulit, Sara, Horimoto, Andrea, Krieger, Jose E, Guindo-Martínez, Marta, Preuss, Michael, Schumann, Claudia, Smit, Roelof AJ, Torres-Mejía, Gabriela, Acuña-Alonzo, Victor, Bedoya, Gabriel, Bortolini, Maria-Cátira, Canizales-Quinteros, Samuel, Gallo, Carla, González-José, Rolando, Poletti, Giovanni, Rothhammer, Francisco, Hakonarson, Hakon, Igo, Robert, Adler, Sharon G, Iyengar, Sudha K, Nicholas, Susanne B, Gogarten, Stephanie M, Isasi, Carmen R, Papnicolaou, George, Stilp, Adrienne M, Qi, Qibin, Kho, Minjung, Smith, Jennifer A, Langefeld, Carl D, Wagenknecht, Lynne, Mckean-Cowdin, Roberta, Gao, Xiaoyi Raymond, Nousome, Darryl, Conti, David V, Feng, Ye, Allison, Matthew A, Arzumanyan, Zorayr, Buchanan, Thomas A, Chen, Yii-Der Ida, Genter, Pauline M, Goodarzi, Mark O, Hai, Yang, Hsueh, Willa, Ipp, Eli, Kandeel, Fouad R, Lam, Kelvin, Li, Xiaohui, Nadler, Jerry L, Raffel, Leslie J, Roll, Kathryn, Sandow, Kevin, Tan, Jingyi, Taylor, Kent D, Xiang, Anny H, Yao, Jie, Audirac-Chalifour, Astride, de Jesus Peralta Romero, Jose, Hartwig, Fernando, Horta, Bernando, Blangero, John, Curran, Joanne E, Duggirala, Ravindranath, Lehman, Donna E, Puppala, Sobha, Fejerman, Laura, John, Esther M, Aguilar-Salinas, Carlos, Burtt, Noël P, Florez, Jose C, García-Ortíz, Humberto, González-Villalpando, Clicerio, Mercader, Josep, Orozco, Lorena, Tusié-Luna, Teresa, Blanco, Estela, Gahagan, Sheila, Cox, Nancy J, and Hanis, Craig

Subjects
Biological Sciences, Genetics, Obesity, Human Genome, Hispanic/Latino, anthropometrics, diversity, fine-mapping, obesity, population stratification, trans-ancestral or trans-ethnic
Abstract

Hispanic/Latinos have been underrepresented in genome-wide association studies (GWAS) for anthropometric traits despite their notable anthropometric variability, ancestry proportions, and high burden of growth stunting and overweight/obesity. To address this knowledge gap, we analyzed densely imputed genetic data in a sample of Hispanic/Latino adults to identify and fine-map genetic variants associated with body mass index (BMI), height, and BMI-adjusted waist-to-hip ratio (WHRadjBMI). We conducted a GWAS of 18 studies/consortia as part of the Hispanic/Latino Anthropometry (HISLA) Consortium (stage 1, n = 59,771) and generalized our findings in 9 additional studies (stage 2, n = 10,538). We conducted a trans-ancestral GWAS with summary statistics from HISLA stage 1 and existing consortia of European and African ancestries. In our HISLA stage 1 + 2 analyses, we discovered one BMI locus, as well as two BMI signals and another height signal each within established anthropometric loci. In our trans-ancestral meta-analysis, we discovered three BMI loci, one height locus, and one WHRadjBMI locus. We also identified 3 secondary signals for BMI, 28 for height, and 2 for WHRadjBMI in established loci. We show that 336 known BMI, 1,177 known height, and 143 known WHRadjBMI (combined) SNPs demonstrated suggestive transferability (nominal significance and effect estimate directional consistency) in Hispanic/Latino adults. Of these, 36 BMI, 124 height, and 11 WHRadjBMI SNPs were significant after trait-specific Bonferroni correction. Trans-ancestral meta-analysis of the three ancestries showed a small-to-moderate impact of uncorrected population stratification on the resulting effect size estimates. Our findings demonstrate that future studies may also benefit from leveraging diverse ancestries and differences in linkage disequilibrium patterns to discover novel loci and additional signals with less residual population stratification.

Published
2022

24. GWAS of random glucose in 476,326 individuals provide insights into diabetes pathophysiology, complications and treatment stratification

Author

Lagou, Vasiliki, Jiang, Longda, Ulrich, Anna, Zudina, Liudmila, González, Karla Sofia Gutiérrez, Balkhiyarova, Zhanna, Faggian, Alessia, Maina, Jared G., Chen, Shiqian, Todorov, Petar V., Sharapov, Sodbo, David, Alessia, Marullo, Letizia, Mägi, Reedik, Rujan, Roxana-Maria, Ahlqvist, Emma, Thorleifsson, Gudmar, Gao, Ηe, Εvangelou, Εvangelos, Benyamin, Beben, Scott, Robert A., Isaacs, Aaron, Zhao, Jing Hua, Willems, Sara M., Johnson, Toby, Gieger, Christian, Grallert, Harald, Meisinger, Christa, Müller-Nurasyid, Martina, Strawbridge, Rona J., Goel, Anuj, Rybin, Denis, Albrecht, Eva, Jackson, Anne U., Stringham, Heather M., Corrêa, Jr., Ivan R., Farber-Eger, Eric, Steinthorsdottir, Valgerdur, Uitterlinden, André G., Munroe, Patricia B., Brown, Morris J., Schmidberger, Julian, Holmen, Oddgeir, Thorand, Barbara, Hveem, Kristian, Wilsgaard, Tom, Mohlke, Karen L., Wang, Zhe, Shmeliov, Aleksey, den Hoed, Marcel, Loos, Ruth J. F., Kratzer, Wolfgang, Haenle, Mark, Koenig, Wolfgang, Boehm, Bernhard O., Tan, Tricia M., Tomas, Alejandra, Salem, Victoria, Barroso, Inês, Tuomilehto, Jaakko, Boehnke, Michael, Florez, Jose C., Hamsten, Anders, Watkins, Hugh, Njølstad, Inger, Wichmann, H.-Erich, Caulfield, Mark J., Khaw, Kay-Tee, van Duijn, Cornelia M., Hofman, Albert, Wareham, Nicholas J., Langenberg, Claudia, Whitfield, John B., Martin, Nicholas G., Montgomery, Grant, Scapoli, Chiara, Tzoulaki, Ioanna, Elliott, Paul, Thorsteinsdottir, Unnur, Stefansson, Kari, Brittain, Evan L., McCarthy, Mark I., Froguel, Philippe, Sexton, Patrick M., Wootten, Denise, Groop, Leif, Dupuis, Josée, Meigs, James B., Deganutti, Giuseppe, Demirkan, Ayse, Pers, Tune H., Reynolds, Christopher A., Aulchenko, Yurii S., Kaakinen, Marika A., Jones, Ben, and Prokopenko, Inga

Published
2023
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25. Diabetes subgroups and sociodemographic inequalities in Mexico: a cross-sectional analysis of nationally representative surveys from 2016 to 2022

Author

Antonio-Villa, Neftali Eduardo, Bello-Chavolla, Omar Yaxmehen, Fermín-Martínez, Carlos A., Ramírez-García, Daniel, Vargas-Vázquez, Arsenio, Basile-Alvarez, Martín Roberto, Núñez-Luna, Alejandra, Sánchez-Castro, Paulina, Fernández-Chirino, Luisa, Díaz-Sánchez, Juan Pablo, Dávila-López, Gael, Posadas-Sánchez, Rosalinda, Vargas-Alarcón, Gilberto, Caballero, A. Enrique, Florez, Jose C., and Seiglie, Jacqueline A.

Published
2024
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26. Type 2 Diabetes Partitioned Polygenic Scores Associate With Disease Outcomes in 454,193 Individuals Across 13 Cohorts

Author

DiCorpo, Daniel, LeClair, Jessica, Cole, Joanne B, Sarnowski, Chloé, Ahmadizar, Fariba, Bielak, Lawrence F, Blokstra, Anneke, Bottinger, Erwin P, Chaker, Layal, Chen, Yii-Der I, Chen, Ye, de Vries, Paul S, Faquih, Tariq, Ghanbari, Mohsen, Gudmundsdottir, Valborg, Guo, Xiuqing, Hasbani, Natalie R, Ibi, Dorina, Ikram, M Arfan, Kavousi, Maryam, Leonard, Hampton L, Leong, Aaron, Mercader, Josep M, Morrison, Alanna C, Nadkarni, Girish N, Nalls, Mike A, Noordam, Raymond, Preuss, Michael, Smith, Jennifer A, Trompet, Stella, Vissink, Petra, Yao, Jie, Zhao, Wei, Boerwinkle, Eric, Goodarzi, Mark O, Gudnason, Vilmundur, Jukema, J Wouter, Kardia, Sharon LR, Loos, Ruth JF, Liu, Ching-Ti, Manning, Alisa K, Mook-Kanamori, Dennis, Pankow, James S, Picavet, H Susan J, Sattar, Naveed, Simonsick, Eleanor M, Verschuren, WM Monique, van Dijk, Ko Willems, Florez, Jose C, Rotter, Jerome I, Meigs, James B, Dupuis, Josée, and Udler, Miriam S

Subjects
Epidemiology, Biomedical and Clinical Sciences, Health Sciences, Genetics, Obesity, Heart Disease - Coronary Heart Disease, Liver Disease, Heart Disease, Cardiovascular, Digestive Diseases, Diabetes, Nutrition, Prevention, Aetiology, 2.1 Biological and endogenous factors, Metabolic and endocrine, Good Health and Well Being, Alleles, Cross-Sectional Studies, Diabetes Mellitus, Type 2, Genetic Loci, Humans, Pharmaceutical Preparations, Medical and Health Sciences, Endocrinology & Metabolism, Biomedical and clinical sciences, Health sciences
Abstract

ObjectiveType 2 diabetes (T2D) has heterogeneous patient clinical characteristics and outcomes. In previous work, we investigated the genetic basis of this heterogeneity by clustering 94 T2D genetic loci using their associations with 47 diabetes-related traits and identified five clusters, termed β-cell, proinsulin, obesity, lipodystrophy, and liver/lipid. The relationship between these clusters and individual-level metabolic disease outcomes has not been assessed.Research design and methodsHere we constructed individual-level partitioned polygenic scores (pPS) for these five clusters in 12 studies from the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium and the UK Biobank (n = 454,193) and tested for cross-sectional association with T2D-related outcomes, including blood pressure, renal function, insulin use, age at T2D diagnosis, and coronary artery disease (CAD).ResultsDespite all clusters containing T2D risk-increasing alleles, they had differential associations with metabolic outcomes. Increased obesity and lipodystrophy cluster pPS, which had opposite directions of association with measures of adiposity, were both significantly associated with increased blood pressure and hypertension. The lipodystrophy and liver/lipid cluster pPS were each associated with CAD, with increasing and decreasing effects, respectively. An increased liver/lipid cluster pPS was also significantly associated with reduced renal function. The liver/lipid cluster includes known loci linked to liver lipid metabolism (e.g., GCKR, PNPLA3, and TM6SF2), and these findings suggest that cardiovascular disease risk and renal function may be impacted by these loci through their shared disease pathway.ConclusionsOur findings support that genetically driven pathways leading to T2D also predispose differentially to clinical outcomes.

Published
2022

27. Obesity Partially Mediates the Diabetogenic Effect of Lowering LDL Cholesterol.

Author

Wu, Peitao, Moon, Jee-Young, Daghlas, Iyas, Franco, Giulianini, Porneala, Bianca, Ahmadizar, Fariba, Richardson, Tom, Isaksen, Jonas, Hindy, Georgy, Yao, Jie, Sitlani, Colleen, Raffield, Laura, Yanek, Lisa, Feitosa, Mary, Cuadrat, Rafael, Qi, Qibin, Arfan Ikram, M, Ellervik, Christina, Ericson, Ulrika, Goodarzi, Mark, Brody, Jennifer, Lange, Leslie, Mercader, Josep, Vaidya, Dhananjay, An, Ping, Schulze, Matthias, Masana, Lluis, Ghanbari, Mohsen, Olesen, Morten, Cai, Jianwen, Guo, Xiuqing, Floyd, James, Jäger, Susanne, Province, Michael, Kalyani, Rita, Psaty, Bruce, Orho-Melander, Marju, Ridker, Paul, Kanters, Jørgen, Uitterlinden, Andre, Davey Smith, George, Gill, Dipender, Kaplan, Robert, Kavousi, Maryam, Raghavan, Sridharan, Chasman, Daniel, Rotter, Jerome, Meigs, James, Florez, Jose, Dupuis, Josée, Liu, Ching-Ti, and Merino, Jordi

Subjects
Cholesterol, LDL, Diabetes Mellitus, Type 2, Genome-Wide Association Study, Humans, Mendelian Randomization Analysis, Obesity, Risk Factors
Abstract

OBJECTIVE: LDL cholesterol (LDLc)-lowering drugs modestly increase body weight and type 2 diabetes risk, but the extent to which the diabetogenic effect of lowering LDLc is mediated through increased BMI is unknown. RESEARCH DESIGN AND METHODS: We conducted summary-level univariable and multivariable Mendelian randomization (MR) analyses in 921,908 participants to investigate the effect of lowering LDLc on type 2 diabetes risk and the proportion of this effect mediated through BMI. We used data from 92,532 participants from 14 observational studies to replicate findings in individual-level MR analyses. RESULTS: A 1-SD decrease in genetically predicted LDLc was associated with increased type 2 diabetes odds (odds ratio [OR] 1.12 [95% CI 1.01, 1.24]) and BMI (β = 0.07 SD units [95% CI 0.02, 0.12]) in univariable MR analyses. The multivariable MR analysis showed evidence of an indirect effect of lowering LDLc on type 2 diabetes through BMI (OR 1.04 [95% CI 1.01, 1.08]) with a proportion mediated of 38% of the total effect (P = 0.03). Total and indirect effect estimates were similar across a number of sensitivity analyses. Individual-level MR analyses confirmed the indirect effect of lowering LDLc on type 2 diabetes through BMI with an estimated proportion mediated of 8% (P = 0.04). CONCLUSIONS: These findings suggest that the diabetogenic effect attributed to lowering LDLc is partially mediated through increased BMI. Our results could help advance understanding of adipose tissue and lipids in type 2 diabetes pathophysiology and inform strategies to reduce diabetes risk among individuals taking LDLc-lowering medications.

Published
2022

28. Epigenome-wide meta-analysis identifies DNA methylation biomarkers associated with diabetic kidney disease

Author

Smyth, Laura J, Dahlström, Emma H, Syreeni, Anna, Kerr, Katie, Kilner, Jill, Doyle, Ross, Brennan, Eoin, Nair, Viji, Fermin, Damian, Nelson, Robert G, Looker, Helen C, Wooster, Christopher, Andrews, Darrell, Anderson, Kerry, McKay, Gareth J, Cole, Joanne B, Salem, Rany M, Conlon, Peter J, Kretzler, Matthias, Hirschhorn, Joel N, Sadlier, Denise, Godson, Catherine, Florez, Jose C, Forsblom, Carol, Maxwell, Alexander P, Groop, Per-Henrik, Sandholm, Niina, and McKnight, Amy Jayne

Subjects
Biological Sciences, Genetics, Autoimmune Disease, Diabetes, Human Genome, Kidney Disease, Aetiology, 2.1 Biological and endogenous factors, Metabolic and endocrine, Renal and urogenital, Humans, DNA Methylation, Epigenome, Diabetic Nephropathies, Epigenesis, Genetic, Diabetes Mellitus, Type 1, Biomarkers, DNA, Genome-Wide Association Study, CpG Islands, GENIE consortium
Abstract

Type 1 diabetes affects over nine million individuals globally, with approximately 40% developing diabetic kidney disease. Emerging evidence suggests that epigenetic alterations, such as DNA methylation, are involved in diabetic kidney disease. Here we assess differences in blood-derived genome-wide DNA methylation associated with diabetic kidney disease in 1304 carefully characterised individuals with type 1 diabetes and known renal status from two cohorts in the United Kingdom-Republic of Ireland and Finland. In the meta-analysis, we identify 32 differentially methylated CpGs in diabetic kidney disease in type 1 diabetes, 18 of which are located within genes differentially expressed in kidneys or correlated with pathological traits in diabetic kidney disease. We show that methylation at 21 of the 32 CpGs predict the development of kidney failure, extending the knowledge and potentially identifying individuals at greater risk for diabetic kidney disease in type 1 diabetes.

Published
2022

29. 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, Human Genome, Biotechnology, Diabetes, 2.1 Biological and endogenous factors, Aetiology, 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, Precision Medicine, 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

31. The power of TOPMed imputation for the discovery of Latino-enriched rare variants associated with type 2 diabetes

Author

Huerta-Chagoya, Alicia, Schroeder, Philip, Mandla, Ravi, Deutsch, Aaron J., Zhu, Wanying, Petty, Lauren, Yi, Xiaoyan, Cole, Joanne B., Udler, Miriam S., Dornbos, Peter, Porneala, Bianca, DiCorpo, Daniel, Liu, Ching-Ti, Li, Josephine H., Szczerbiński, Lukasz, Kaur, Varinderpal, Kim, Joohyun, Lu, Yingchang, Martin, Alicia, Eizirik, Decio L., Marchetti, Piero, Marselli, Lorella, Chen, Ling, Srinivasan, Shylaja, Todd, Jennifer, Flannick, Jason, Gubitosi-Klug, Rose, Levitsky, Lynne, Shah, Rachana, Kelsey, Megan, Burke, Brian, Dabelea, Dana M., Divers, Jasmin, Marcovina, Santica, Stalbow, Lauren, Loos, Ruth J. F., Darst, Burcu F., Kooperberg, Charles, Raffield, Laura M., Haiman, Christopher, Sun, Quan, McCormick, Joseph B., Fisher-Hoch, Susan P., Ordoñez, Maria L., Meigs, James, Baier, Leslie J., González-Villalpando, Clicerio, González-Villalpando, Maria Elena, Orozco, Lorena, García-García, Lourdes, Moreno-Estrada, Andrés, Aguilar-Salinas, Carlos A., Tusié, Teresa, Dupuis, Josée, Ng, Maggie C. Y., Manning, Alisa, Highland, Heather M., Cnop, Miriam, Hanson, Robert, Below, Jennifer, Florez, Jose C., Leong, Aaron, and Mercader, Josep M.

Published
2023
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32. Precision subclassification of type 2 diabetes: a systematic review

Author

Misra, Shivani, Wagner, Robert, Ozkan, Bige, Schön, Martin, Sevilla-Gonzalez, Magdalena, Prystupa, Katsiaryna, Wang, Caroline C., Kreienkamp, Raymond J., Cromer, Sara J., Rooney, Mary R., Duan, Daisy, Thuesen, Anne Cathrine Baun, Wallace, Amelia S., Leong, Aaron, Deutsch, Aaron J., Andersen, Mette K., Billings, Liana K., Eckel, Robert H., Sheu, Wayne Huey-Herng, Hansen, Torben, Stefan, Norbert, Goodarzi, Mark O., Ray, Debashree, Selvin, Elizabeth, Florez, Jose C., Meigs, James B., and Udler, Miriam S.

Published
2023
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36. Discovering cellular programs of intrinsic and extrinsic drivers of metabolic traits using LipocyteProfiler

Author

Laber, Samantha, Strobel, Sophie, Mercader, Josep M., Dashti, Hesam, dos Santos, Felipe R.C., Kubitz, Phil, Jackson, Maya, Ainbinder, Alina, Honecker, Julius, Agrawal, Saaket, Garborcauskas, Garrett, Stirling, David R., Leong, Aaron, Figueroa, Katherine, Sinnott-Armstrong, Nasa, Kost-Alimova, Maria, Deodato, Giacomo, Harney, Alycen, Way, Gregory P., Saadat, Alham, Harken, Sierra, Reibe-Pal, Saskia, Ebert, Hannah, Zhang, Yixin, Calabuig-Navarro, Virtu, McGonagle, Elizabeth, Stefek, Adam, Dupuis, Josée, Cimini, Beth A., Hauner, Hans, Udler, Miriam S., Carpenter, Anne E., Florez, Jose C., Lindgren, Cecilia, Jacobs, Suzanne B.R., and Claussnitzer, Melina

Published
2023
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37. High Mannose N-Glycans Promote Migration of Bone-Marrow-Derived Mesenchymal Stromal Cells.

Author

Alonso-Garcia, Vivian, Chaboya, Cutter, Li, Qiongyu, Le, Bryan, Congleton, Timothy J, Florez, Jose, Tran, Victoria, Liu, Gang-Yu, Yao, Wei, Lebrilla, Carlito B, and Fierro, Fernando A

Subjects
Kifunensine, MAN1A1, amino-linked glycans, bone fracture, mesenchymal stromal cells, migration, Chemical Physics, Other Chemical Sciences, Genetics, Other Biological Sciences
Abstract

For hundreds of indications, mesenchymal stromal cells (MSCs) have not achieved the expected therapeutic efficacy due to an inability of the cells to reach target tissues. We show that inducing high mannose N-glycans either chemically, using the mannosidase I inhibitor Kifunensine, or genetically, using an shRNA to silence the expression of mannosidase I A1 (MAN1A1), strongly increases the motility of MSCs. We show that treatment of MSCs with Kifunensine increases cell migration toward bone fracture sites after percutaneous injection, and toward lungs after intravenous injection. Mechanistically, high mannose N-glycans reduce the contact area of cells with its substrate. Silencing MAN1A1 also makes cells softer, suggesting that an increase of high mannose N-glycoforms may change the physical properties of the cell membrane. To determine if treatment with Kifunensine is feasible for future clinical studies, we used mass spectrometry to analyze the N-glycan profile of MSCs over time and demonstrate that the effect of Kifunensine is both transitory and at the expense of specific N-glycoforms, including fucosylations. Finally, we also investigated the effect of Kifunensine on cell proliferation, differentiation, and the secretion profile of MSCs. Our results support the notion of inducing high mannose N-glycans in MSCs in order to enhance their migration potential.

Published
2020

38. Smoking-by-genotype interaction in type 2 diabetes risk and fasting glucose

Author

Wu, Peitao, Rybin, Denis, Bielak, Lawrence F, Feitosa, Mary F, Franceschini, Nora, Li, Yize, Lu, Yingchang, Marten, Jonathan, Musani, Solomon K, Noordam, Raymond, Raghavan, Sridharan, Rose, Lynda M, Schwander, Karen, Smith, Albert V, Tajuddin, Salman M, Vojinovic, Dina, Amin, Najaf, Arnett, Donna K, Bottinger, Erwin P, Demirkan, Ayse, Florez, Jose C, Ghanbari, Mohsen, Harris, Tamara B, Launer, Lenore J, Liu, Jingmin, Liu, Jun, Mook-Kanamori, Dennis O, Murray, Alison D, Nalls, Mike A, Peyser, Patricia A, Uitterlinden, André G, Voortman, Trudy, Bouchard, Claude, Chasman, Daniel, Correa, Adolfo, de Mutsert, Renée, Evans, Michele K, Gudnason, Vilmundur, Hayward, Caroline, Kao, Linda, Kardia, Sharon LR, Kooperberg, Charles, Loos, Ruth JF, Province, Michael M, Rankinen, Tuomo, Redline, Susan, Ridker, Paul M, Rotter, Jerome I, Siscovick, David, Smith, Blair H, van Duijn, Cornelia, Zonderman, Alan B, Rao, DC, Wilson, James G, Dupuis, Josée, Meigs, James B, Liu, Ching-Ti, and Vassy, Jason L

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Genetics, Epidemiology, Health Sciences, Pharmacology and Pharmaceutical Sciences, Tobacco, Aging, Tobacco Smoke and Health, Human Genome, Nutrition, Diabetes, Clinical Research, Prevention, Aetiology, 2.1 Biological and endogenous factors, Good Health and Well Being, Adult, Aged, Black People, Blood Glucose, Cigarette Smoking, Cohort Studies, Diabetes Mellitus, Type 2, Fasting, Feasibility Studies, Female, Genetic Loci, Genome-Wide Association Study, Genotype, Humans, Incidence, Male, Middle Aged, Polymorphism, Single Nucleotide, Risk, White People, General Science & Technology
Abstract

Smoking is a potentially causal behavioral risk factor for type 2 diabetes (T2D), but not all smokers develop T2D. It is unknown whether genetic factors partially explain this variation. We performed genome-environment-wide interaction studies to identify loci exhibiting potential interaction with baseline smoking status (ever vs. never) on incident T2D and fasting glucose (FG). Analyses were performed in participants of European (EA) and African ancestry (AA) separately. Discovery analyses were conducted using genotype data from the 50,000-single-nucleotide polymorphism (SNP) ITMAT-Broad-CARe (IBC) array in 5 cohorts from from the Candidate Gene Association Resource Consortium (n = 23,189). Replication was performed in up to 16 studies from the Cohorts for Heart Aging Research in Genomic Epidemiology Consortium (n = 74,584). In meta-analysis of discovery and replication estimates, 5 SNPs met at least one criterion for potential interaction with smoking on incident T2D at p

Published
2020

39. Interaction Between Type 2 Diabetes Prevention Strategies and Genetic Determinants of Coronary Artery Disease on Cardiometabolic Risk Factors

Author

Merino, Jordi, Jablonski, Kathleen A, Mercader, Josep M, Kahn, Steven E, Chen, Ling, Harden, Maegan, Delahanty, Linda M, Araneta, Maria Rosario G, Walford, Geoffrey A, Jacobs, Suzanne BR, Ibebuogu, Uzoma N, Franks, Paul W, Knowler, William C, Florez, Jose C, Bray, George A, Gadde, Kishore, Chatellier, Annie, Arceneaux, Jennifer, Dragg, Amber, Duncan, Crystal, Greenway, Frank L, Hsia, Daniel, Levy, Erma, Lockett, Monica, Ryan, Donna H, Ehrmann, David, Matulik, Margaret J, Czech, Kirsten, DeSandre, Catherine, Goldstein, Barry J, Furlong, Kevin, Smith, Kellie A, Wildman, Wendi, Pepe, Constance, Goldberg, Ronald B, Calles, Jeanette, Ojito, Juliet, Castillo-Florez, Sumaya, Florez, Hermes J, Giannella, Anna, Lara, Olga, Veciana, Beth, Haffner, Steven M, Hazuda, Helen P, Montez, Maria G, Hattaway, Kathy, Lorenzo, Carlos, Martinez, Arlene, Walker, Tatiana, Hamman, Richard F, Dabelea, Dana, Testaverde, Lisa, Anderson, Denise, Bouffard, Alexis, Jenkins, Tonya, Lenz, Dione, Perreault, Leigh, Price, David W, Steinke, Sheila C, Horton, Edward S, Poirier, Catherine S, Swift, Kati, Caballero, Enrique, Fargnoli, Barbara, Guidi, Ashley, Guido, Mathew, Jackson, Sharon D, Lambert, Lori, Lawton, Kathleen E, Ledbury, Sarah, Sansoucy, Jessica, Spellman, Jeanne, Montgomery, Brenda K, Fujimoto, Wilfred, Knopp, Robert H, Lipkin, Edward W, Morgan-Taggart, Ivy, Murillo, Anne, Taylor, Lonnese, Thomas, April, Tsai, Elaine C, Trence, Dace, Kitabchi, Abbas E, Dagogo-Jack, Samuel, Murphy, Mary E, Taylor, Laura, Dolgoff, Jennifer, Clark, Debra, Ibebuogu, Uzoma, Lambeth, Helen, Ricks, Harriet, Rutledge, Lily MK, Soberman, Judith E, Molitch, Mark E, Metzger, Boyd E, Johnson, Mariana K, Giles, Mimi M, Larsen, Diane, and Pen, Samsam C

Subjects
Biomedical and Clinical Sciences, Diabetes, Clinical Research, Atherosclerosis, Clinical Trials and Supportive Activities, Cardiovascular, Heart Disease, Prevention, Heart Disease - Coronary Heart Disease, Nutrition, Obesity, Metabolic and endocrine, Good Health and Well Being, Adult, Cardiovascular Diseases, Coronary Artery Disease, Diabetes Mellitus, Type 2, Exercise, Exercise Therapy, Female, Gene-Environment Interaction, Genetic Predisposition to Disease, Humans, Life Style, Male, Metabolic Syndrome, Metformin, Middle Aged, Prediabetic State, Preventive Health Services, Risk Factors, United States, Diabetes Prevention Program Research Group, Medical and Health Sciences, Endocrinology & Metabolism, Biomedical and clinical sciences
Abstract

Coronary artery disease (CAD) is more frequent among individuals with dysglycemia. Preventive interventions for diabetes can improve cardiometabolic risk factors (CRFs), but it is unclear whether the benefits on CRFs are similar for individuals at different genetic risk for CAD. We built a 201-variant polygenic risk score (PRS) for CAD and tested for interaction with diabetes prevention strategies on 1-year changes in CRFs in 2,658 Diabetes Prevention Program (DPP) participants. We also examined whether separate lifestyle behaviors interact with PRS and affect changes in CRFs in each intervention group. Participants in both the lifestyle and metformin interventions had greater improvement in the majority of recognized CRFs compared with placebo (P < 0.001) irrespective of CAD genetic risk (P interaction > 0.05). We detected nominal significant interactions between PRS and dietary quality and physical activity on 1-year change in BMI, fasting glucose, triglycerides, and HDL cholesterol in individuals randomized to metformin or placebo, but none of them achieved the multiple-testing correction for significance. This study confirms that diabetes preventive interventions improve CRFs regardless of CAD genetic risk and delivers hypothesis-generating data on the varying benefit of increasing physical activity and improving diet on intermediate cardiovascular risk factors depending on individual CAD genetic risk profile.

Published
2020

40. 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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45. Symmetry Field Breaking Effects in Sr$_2$RuO$_4$

Author

Contreras, Pedro, Florez, Jose, and Almeida, Rafael

Subjects
Condensed Matter - Superconductivity
Abstract

In this work, after reviewing the theory of the elastic properties of Strontium ruthenate, an extension suitable to explain the sound speed experiments of Lupien et. al. \protect\cite{lup2} and Clifford et. al. \protect\cite{clif1} is carried out. It is found that the discontinuity in the elastic constant C$_{66}$ gives unambiguous experimental evidence that the \sr superconducting order parameter $\Psi$ has two components and shows a broken time-reversal symmetry state. A detailed study of the elastic behavior is performed by means of a phenomenological theory employing the Ginzburg-Landau formalism., Comment: 19 pages, 4 figures

Published
2018

46. Protein-coding variants implicate novel genes related to lipid homeostasis contributing to body-fat distribution

Author

Justice, Anne E, Karaderi, Tugce, Highland, Heather M, Young, Kristin L, Graff, Mariaelisa, Lu, Yingchang, Turcot, Valérie, Auer, Paul L, Fine, Rebecca S, Guo, Xiuqing, Schurmann, Claudia, Lempradl, Adelheid, Marouli, Eirini, Mahajan, Anubha, Winkler, Thomas W, Locke, Adam E, Medina-Gomez, Carolina, Esko, Tõnu, Vedantam, Sailaja, Giri, Ayush, Lo, Ken Sin, Alfred, Tamuno, Mudgal, Poorva, Ng, Maggie CY, Heard-Costa, Nancy L, Feitosa, Mary F, Manning, Alisa K, Willems, Sara M, Sivapalaratnam, Suthesh, Abecasis, Goncalo, Alam, Dewan S, Allison, Matthew, Amouyel, Philippe, Arzumanyan, Zorayr, Balkau, Beverley, Bastarache, Lisa, Bergmann, Sven, Bielak, Lawrence F, Blüher, Matthias, Boehnke, Michael, Boeing, Heiner, Boerwinkle, Eric, Böger, Carsten A, Bork-Jensen, Jette, Bottinger, Erwin P, Bowden, Donald W, Brandslund, Ivan, Broer, Linda, Burt, Amber A, Butterworth, Adam S, Caulfield, Mark J, Cesana, Giancarlo, Chambers, John C, Chasman, Daniel I, Chen, Yii-Der Ida, Chowdhury, Rajiv, Christensen, Cramer, Chu, Audrey Y, Collins, Francis S, Cook, James P, Cox, Amanda J, Crosslin, David S, Danesh, John, de Bakker, Paul IW, Denus, Simon de, Mutsert, Renée de, Dedoussis, George, Demerath, Ellen W, Dennis, Joe G, Denny, Josh C, Di Angelantonio, Emanuele, Dörr, Marcus, Drenos, Fotios, Dubé, Marie-Pierre, Dunning, Alison M, Easton, Douglas F, Elliott, Paul, Evangelou, Evangelos, Farmaki, Aliki-Eleni, Feng, Shuang, Ferrannini, Ele, Ferrieres, Jean, Florez, Jose C, Fornage, Myriam, Fox, Caroline S, Franks, Paul W, Friedrich, Nele, Gan, Wei, Gandin, Ilaria, Gasparini, Paolo, Giedraitis, Vilmantas, Girotto, Giorgia, Gorski, Mathias, Grallert, Harald, Grarup, Niels, Grove, Megan L, Gustafsson, Stefan, Haessler, Jeff, Hansen, Torben, and Hattersley, Andrew T

Subjects
Clinical Research, Obesity, Genetics, Nutrition, Prevention, Biotechnology, Human Genome, Aetiology, 2.1 Biological and endogenous factors, Metabolic and endocrine, Cardiovascular, Animals, Body Fat Distribution, Body Mass Index, Case-Control Studies, Drosophila, Exome, Female, Gene Frequency, Genetic Predisposition to Disease, Genetic Variation, Genome-Wide Association Study, Homeostasis, Humans, Lipids, Male, Proteins, Risk Factors, Waist-Hip Ratio, CHD Exome+ Consortium, Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium, EPIC-CVD Consortium, ExomeBP Consortium, Global Lipids Genetic Consortium, GoT2D Genes Consortium, InterAct, ReproGen Consortium, T2D-Genes Consortium, MAGIC Investigators, Biological Sciences, Medical and Health Sciences, Developmental Biology
Abstract

Body-fat distribution is a risk factor for adverse cardiovascular health consequences. We analyzed the association of body-fat distribution, assessed by waist-to-hip ratio adjusted for body mass index, with 228,985 predicted coding and splice site variants available on exome arrays in up to 344,369 individuals from five major ancestries (discovery) and 132,177 European-ancestry individuals (validation). We identified 15 common (minor allele frequency, MAF ≥5%) and nine low-frequency or rare (MAF

Published
2019

47. Modeling Snyder-Robinson Syndrome in multipotent stromal cells reveals impaired mitochondrial function as a potential cause for deficient osteogenesis

Author

Ramsay, Ashley L, Alonso-Garcia, Vivian, Chaboya, Cutter, Radut, Brian, Le, Bryan, Florez, Jose, Schumacher, Cameron, and Fierro, Fernando A

Subjects
Biomedical and Clinical Sciences, Engineering, Biomedical Engineering, Nutrition, Regenerative Medicine, Genetics, Osteoporosis, Aging, Rare Diseases, Aetiology, 2.1 Biological and endogenous factors, Musculoskeletal, Animals, Cells, Cultured, Glucose, Humans, Lactic Acid, Male, Mental Retardation, X-Linked, Mesenchymal Stem Cells, Mice, Mice, Inbred NOD, Mice, SCID, Mitochondria, Osteogenesis, Spermine Synthase, Transcriptome
Abstract

Patients with Snyder-Robinson Syndrome (SRS) exhibit deficient Spermidine Synthase (SMS) gene expression, which causes neurodevelopmental defects and osteoporosis, often leading to extremely fragile bones. To determine the underlying mechanism for impaired bone formation, we modelled the disease by silencing SMS in human bone marrow - derived multipotent stromal cells (MSCs) derived from healthy donors. We found that silencing SMS in MSCs led to reduced cell proliferation and deficient bone formation in vitro, as evidenced by reduced mineralization and decreased bone sialoprotein expression. Furthermore, transplantation of MSCs in osteoconductive scaffolds into immune deficient mice shows that silencing SMS also reduces ectopic bone formation in vivo. Tag-Seq Gene Expression Profiling shows that deficient SMS expression causes strong transcriptome changes, especially in genes related to cell proliferation and metabolic functions. Similarly, metabolome analysis by mass spectrometry, shows that silencing SMS strongly impacts glucose metabolism. This was consistent with observations using electron microscopy, where SMS deficient MSCs show high levels of mitochondrial fusion. In line with these findings, SMS deficiency causes a reduction in glucose consumption and increase in lactate secretion. Our data also suggests that SMS deficiency affects iron metabolism in the cells, which we hypothesize is linked to deficient mitochondrial function. Altogether, our studies suggest that SMS deficiency causes strong transcriptomic and metabolic changes in MSCs, which are likely associated with the observed impaired osteogenesis both in vitro and in vivo.

Published
2019

48. Author Correction: Modeling Snyder-Robinson Syndrome in multipotent stromal cells reveals impaired mitochondrial function as a potential cause for deficient osteogenesis

Author

Ramsay, Ashley L, Alonso-Garcia, Vivian, Chaboya, Cutter, Radut, Brian, Le, Bryan, Florez, Jose, Schumacher, Cameron, and Fierro, Fernando A

Subjects
Biochemistry and Cell Biology, Engineering, Biological Sciences
Abstract

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Published
2019

49. Multi-ancestry genetic study of type 2 diabetes highlights the power of diverse populations for discovery and translation

Author

Mahajan, Anubha, Spracklen, Cassandra N., Zhang, Weihua, Ng, Maggie C. Y., Petty, Lauren E., Kitajima, Hidetoshi, Yu, Grace Z., Rüeger, Sina, Speidel, Leo, Kim, Young Jin, Horikoshi, Momoko, Mercader, Josep M., Taliun, Daniel, Moon, Sanghoon, Kwak, Soo-Heon, Robertson, Neil R., Rayner, Nigel W., Loh, Marie, Kim, Bong-Jo, Chiou, Joshua, Miguel-Escalada, Irene, della Briotta Parolo, Pietro, Lin, Kuang, Bragg, Fiona, Preuss, Michael H., Takeuchi, Fumihiko, Nano, Jana, Guo, Xiuqing, Lamri, Amel, Nakatochi, Masahiro, Scott, Robert A., Lee, Jung-Jin, Huerta-Chagoya, Alicia, Graff, Mariaelisa, Chai, Jin-Fang, Parra, Esteban J., Yao, Jie, Bielak, Lawrence F., Tabara, Yasuharu, Hai, Yang, Steinthorsdottir, Valgerdur, Cook, James P., Kals, Mart, Grarup, Niels, Schmidt, Ellen M., Pan, Ian, Sofer, Tamar, Wuttke, Matthias, Sarnowski, Chloe, Gieger, Christian, Nousome, Darryl, Trompet, Stella, Long, Jirong, Sun, Meng, Tong, Lin, Chen, Wei-Min, Ahmad, Meraj, Noordam, Raymond, Lim, Victor J. Y., Tam, Claudia H. T., Joo, Yoonjung Yoonie, Chen, Chien-Hsiun, Raffield, Laura M., Lecoeur, Cécile, Prins, Bram Peter, Nicolas, Aude, Yanek, Lisa R., Chen, Guanjie, Jensen, Richard A., Tajuddin, Salman, Kabagambe, Edmond K., An, Ping, Xiang, Anny H., Choi, Hyeok Sun, Cade, Brian E., Tan, Jingyi, Flanagan, Jack, Abaitua, Fernando, Adair, Linda S., Adeyemo, Adebowale, Aguilar-Salinas, Carlos A., Akiyama, Masato, Anand, Sonia S., Bertoni, Alain, Bian, Zheng, Bork-Jensen, Jette, Brandslund, Ivan, Brody, Jennifer A., Brummett, Chad M., Buchanan, Thomas A., Canouil, Mickaël, Chan, Juliana C. N., Chang, Li-Ching, Chee, Miao-Li, Chen, Ji, Chen, Shyh-Huei, Chen, Yuan-Tsong, Chen, Zhengming, Chuang, Lee-Ming, Cushman, Mary, Das, Swapan K., de Silva, H. Janaka, Dedoussis, George, Dimitrov, Latchezar, Doumatey, Ayo P., Du, Shufa, Duan, Qing, Eckardt, Kai-Uwe, Emery, Leslie S., Evans, Daniel S., Evans, Michele K., Fischer, Krista, Floyd, James S., Ford, Ian, Fornage, Myriam, Franco, Oscar H., Frayling, Timothy M., Freedman, Barry I., Fuchsberger, Christian, Genter, Pauline, Gerstein, Hertzel C., Giedraitis, Vilmantas, González-Villalpando, Clicerio, González-Villalpando, Maria Elena, Goodarzi, Mark O., Gordon-Larsen, Penny, Gorkin, David, Gross, Myron, Guo, Yu, Hackinger, Sophie, Han, Sohee, Hattersley, Andrew T., Herder, Christian, Howard, Annie-Green, Hsueh, Willa, Huang, Mengna, Huang, Wei, Hung, Yi-Jen, Hwang, Mi Yeong, Hwu, Chii-Min, Ichihara, Sahoko, Ikram, Mohammad Arfan, Ingelsson, Martin, Islam, Md Tariqul, Isono, Masato, Jang, Hye-Mi, Jasmine, Farzana, Jiang, Guozhi, Jonas, Jost B., Jørgensen, Marit E., Jørgensen, Torben, Kamatani, Yoichiro, Kandeel, Fouad R., Kasturiratne, Anuradhani, Katsuya, Tomohiro, Kaur, Varinderpal, Kawaguchi, Takahisa, Keaton, Jacob M., Kho, Abel N., Khor, Chiea-Chuen, Kibriya, Muhammad G., Kim, Duk-Hwan, Kohara, Katsuhiko, Kriebel, Jennifer, Kronenberg, Florian, Kuusisto, Johanna, Läll, Kristi, Lange, Leslie A., Lee, Myung-Shik, Lee, Nanette R., Leong, Aaron, Li, Liming, Li, Yun, Li-Gao, Ruifang, Ligthart, Symen, Lindgren, Cecilia M., Linneberg, Allan, Liu, Ching-Ti, Liu, Jianjun, Locke, Adam E., Louie, Tin, Luan, Jian’an, Luk, Andrea O., Luo, Xi, Lv, Jun, Lyssenko, Valeriya, Mamakou, Vasiliki, Mani, K. Radha, Meitinger, Thomas, Metspalu, Andres, Morris, Andrew D., Nadkarni, Girish N., Nadler, Jerry L., Nalls, Michael A., Nayak, Uma, Nongmaithem, Suraj S., Ntalla, Ioanna, Okada, Yukinori, Orozco, Lorena, Patel, Sanjay R., Pereira, Mark A., Peters, Annette, Pirie, Fraser J., Porneala, Bianca, Prasad, Gauri, Preissl, Sebastian, Rasmussen-Torvik, Laura J., Reiner, Alexander P., Roden, Michael, Rohde, Rebecca, Roll, Kathryn, Sabanayagam, Charumathi, Sander, Maike, Sandow, Kevin, Sattar, Naveed, Schönherr, Sebastian, Schurmann, Claudia, Shahriar, Mohammad, Shi, Jinxiu, Shin, Dong Mun, Shriner, Daniel, Smith, Jennifer A., So, Wing Yee, Stančáková, Alena, Stilp, Adrienne M., Strauch, Konstantin, Suzuki, Ken, Takahashi, Atsushi, Taylor, Kent D., Thorand, Barbara, Thorleifsson, Gudmar, Thorsteinsdottir, Unnur, Tomlinson, Brian, Torres, Jason M., Tsai, Fuu-Jen, Tuomilehto, Jaakko, Tusie-Luna, Teresa, Udler, Miriam S., Valladares-Salgado, Adan, van Dam, Rob M., van Klinken, Jan B., Varma, Rohit, Vujkovic, Marijana, Wacher-Rodarte, Niels, Wheeler, Eleanor, Whitsel, Eric A., Wickremasinghe, Ananda R., van Dijk, Ko Willems, Witte, Daniel R., Yajnik, Chittaranjan S., Yamamoto, Ken, Yamauchi, Toshimasa, Yengo, Loïc, Yoon, Kyungheon, Yu, Canqing, Yuan, Jian-Min, Yusuf, Salim, Zhang, Liang, Zheng, Wei, Raffel, Leslie J., Igase, Michiya, Ipp, Eli, Redline, Susan, Cho, Yoon Shin, Lind, Lars, Province, Michael A., Hanis, Craig L., Peyser, Patricia A., Ingelsson, Erik, Zonderman, Alan B., Psaty, Bruce M., Wang, Ya-Xing, Rotimi, Charles N., Becker, Diane M., Matsuda, Fumihiko, Liu, Yongmei, Zeggini, Eleftheria, Yokota, Mitsuhiro, Rich, Stephen S., Kooperberg, Charles, Pankow, James S., Engert, James C., Chen, Yii-Der Ida, Froguel, Philippe, Wilson, James G., Sheu, Wayne H. H., Kardia, Sharon L. R., Wu, Jer-Yuarn, Hayes, M. Geoffrey, Ma, Ronald C. W., Wong, Tien-Yin, Groop, Leif, Mook-Kanamori, Dennis O., Chandak, Giriraj R., Collins, Francis S., Bharadwaj, Dwaipayan, Paré, Guillaume, Sale, Michèle M., Ahsan, Habibul, Motala, Ayesha A., Shu, Xiao-Ou, Park, Kyong-Soo, Jukema, J. Wouter, Cruz, Miguel, McKean-Cowdin, Roberta, Grallert, Harald, Cheng, Ching-Yu, Bottinger, Erwin P., Dehghan, Abbas, Tai, E-Shyong, Dupuis, Josée, Kato, Norihiro, Laakso, Markku, Köttgen, Anna, Koh, Woon-Puay, Palmer, Colin N. A., Liu, Simin, Abecasis, Goncalo, Kooner, Jaspal S., Loos, Ruth J. F., North, Kari E., Haiman, Christopher A., Florez, Jose C., Saleheen, Danish, Hansen, Torben, Pedersen, Oluf, Mägi, Reedik, Langenberg, Claudia, Wareham, Nicholas J., Maeda, Shiro, Kadowaki, Takashi, Lee, Juyoung, Millwood, Iona Y., Walters, Robin G., Stefansson, Kari, Myers, Simon R., Ferrer, Jorge, Gaulton, Kyle J., Meigs, James B., Mohlke, Karen L., Gloyn, Anna L., Bowden, Donald W., Below, Jennifer E., Chambers, John C., Sim, Xueling, Boehnke, Michael, Rotter, Jerome I., McCarthy, Mark I., and Morris, Andrew P.

Published
2022
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50. Genetics in chronic kidney disease: conclusions from a Kidney Disease: Improving Global Outcomes (KDIGO) Controversies Conference

Author

Köttgen, Anna, Cornec-Le Gall, Emilie, Halbritter, Jan, Kiryluk, Krzysztof, Mallett, Andrew J., Parekh, Rulan S., Rasouly, Hila Milo, Sampson, Matthew G., Tin, Adrienne, Antignac, Corinne, Ars, Elisabet, Bergmann, Carsten, Bleyer, Anthony J., Bockenhauer, Detlef, Devuyst, Olivier, Florez, Jose C., Fowler, Kevin J., Franceschini, Nora, Fukagawa, Masafumi, Gale, Daniel P., Gbadegesin, Rasheed A., Goldstein, David B., Grams, Morgan E., Greka, Anna, Gross, Oliver, Guay-Woodford, Lisa M., Harris, Peter C., Hoefele, Julia, Hung, Adriana M., Knoers, Nine V.A.M., Kopp, Jeffrey B., Kretzler, Matthias, Lanktree, Matthew B., Lipska-Ziętkiewicz, Beata S., Nicholls, Kathleen, Nozu, Kandai, Ojo, Akinlolu, Parsa, Afshin, Pattaro, Cristian, Pei, York, Pollak, Martin R., Rhee, Eugene P., Sanna-Cherchi, Simone, Savige, Judy, Sayer, John A., Scolari, Francesco, Sedor, John R., Sim, Xueling, Somlo, Stefan, Susztak, Katalin, Tayo, Bamidele O., Torra, Roser, van Eerde, Albertien M., Weinstock, André, Winkler, Cheryl A., Wuttke, Matthias, Zhang, Hong, King, Jennifer M., Cheung, Michael, Jadoul, Michel, Winkelmayer, Wolfgang C., and Gharavi, Ali G.

Published
2022
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