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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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3. 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

5. Multi-ancestry polygenic mechanisms of type 2 diabetes

Author

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

Published
2024
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6. 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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7. 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

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. 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

10. 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

11. 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

12. 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

13. 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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14. 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

15. 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

16. 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

17. 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

18. 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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19. 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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20. 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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21. 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

22. 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

23. 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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24. 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

25. 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

26. 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

28. 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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33. 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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34. 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

35. 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

36. 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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41. 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

42. 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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43. 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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46. Identification of Immune Checkpoint Inhibitor–Induced Diabetes.

Author

Ruiz-Esteves, Karina N., Shank, Kaitlyn R., Deutsch, Aaron J., Gunturi, Alekhya, Chamorro-Pareja, Natalia, Colling, Caitlin A., Zubiri, Leyre, Perlman, Katherine, Ouyang, Tianqi, Villani, Alexandra-Chloé, Florez, Jose C., Gusev, Alexander, Reynolds, Kerry L., Miller, Karen K., Udler, Miriam S., Sise, Meghan E., and Rengarajan, Michelle

Published
2024
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47. Hemoglobin A1c Genetics and Disparities in Risk of Diabetic Retinopathy in Individuals of Genetically Inferred African American/African British and European Ancestries.

Author

Mandla, Ravi, Schroeder, Philip H., Florez, Jose C., Mercader, Josep M., and Leong, Aaron

Subjects
TYPE 2 diabetes, HEMOGLOBIN polymorphisms, PEOPLE with diabetes, MISSENSE mutation, GENETIC variation, HYPERGLYCEMIA
Abstract

OBJECTIVE: Individuals with diabetes who carry genetic variants that lower hemoglobin A1c (HbA1c) independently of glycemia may have higher real, but undetected, hyperglycemia compared with those without these variants despite achieving similar HbA1c targets, potentially placing them at greater risk for diabetes-related complications. We sought to determine whether these genetic variants, aggregated in a polygenic score, and the large-effect African ancestry–specific missense variant in G6PD (rs1050828) that lower HbA1c were associated with higher retinopathy risk. RESEARCH DESIGN AND METHODS: Using data from 29,828 type 2 diabetes cases of genetically inferred African American/African British and European ancestries, we calculated ancestry-specific nonglycemic HbA1c polygenic scores (ngA1cPS) composed of 122 variants associated with HbA1c at genome-wide significance, but not with glucose. We tested the association of the ngA1cPS and the G6PD variant with retinopathy, adjusting for measured HbA1c and retinopathy risk factors. RESULTS: Participants in the bottom quintile of the ngA1cPS showed between 20% and 50% higher retinopathy prevalence, compared with those above this quintile, despite similar levels of measured HbA1c. The adjusted meta-analytic odds ratio for the bottom quintile was 1.31 (95% CI 1.0, 1.73; P = 0.05) in African ancestry and 1.31 (95% CI 1.15, 1.50; P = 6.5 × 10−5) in European ancestry. Among individuals of African ancestry with HbA1c below 7%, retinopathy prevalence was higher in individuals below, compared with above, the 50th percentile of the ngA1cPS regardless of sex or G6PD carrier status. CONCLUSIONS: Genetic effects need to be considered to personalize HbA1c targets and improve outcomes of people with diabetes from diverse ancestries. [ABSTRACT FROM AUTHOR]

Published
2024
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49. A Genome-Wide Association Study of Diabetic Kidney Disease in Subjects With Type 2 Diabetes

Author

van Zuydam, Natalie R, Ahlqvist, Emma, Sandholm, Niina, Deshmukh, Harshal, Rayner, N William, Abdalla, Moustafa, Ladenvall, Claes, Ziemek, Daniel, Fauman, Eric, Robertson, Neil R, McKeigue, Paul M, Valo, Erkka, Forsblom, Carol, Harjutsalo, Valma, Perna, Annalisa, Rurali, Erica, Marcovecchio, M Loredana, Igo, Robert P, Salem, Rany M, Perico, Norberto, Lajer, Maria, Käräjämäki, Annemari, Imamura, Minako, Kubo, Michiaki, Takahashi, Atsushi, Sim, Xueling, Liu, Jianjun, van Dam, Rob M, Jiang, Guozhi, Tam, Claudia HT, Luk, Andrea OY, Lee, Heung Man, Lim, Cadmon KP, Szeto, Cheuk Chun, So, Wing Yee, Chan, Juliana CN, Ang, Su Fen, Dorajoo, Rajkumar, Wang, Ling, Clara, Tan Si Hua, McKnight, Amy-Jayne, Duffy, Seamus, Pezzolesi, Marcus G, Marre, Michel, Gyorgy, Beata, Hadjadj, Samy, Hiraki, Linda T, Ahluwalia, Tarunveer S, Almgren, Peter, Schulz, Christina-Alexandra, Orho-Melander, Marju, Linneberg, Allan, Christensen, Cramer, Witte, Daniel R, Grarup, Niels, Brandslund, Ivan, Melander, Olle, Paterson, Andrew D, Tregouet, David, Maxwell, Alexander P, Lim, Su Chi, Ma, Ronald CW, Tai, E Shyong, Maeda, Shiro, Lyssenko, Valeriya, Tuomi, Tiinamaija, Krolewski, Andrzej S, Rich, Stephen S, Hirschhorn, Joel N, Florez, Jose C, Dunger, David, Pedersen, Oluf, Hansen, Torben, Rossing, Peter, Remuzzi, Giuseppe, Brosnan, Mary Julia, Palmer, Colin NA, Groop, Per-Henrik, Colhoun, Helen M, Groop, Leif C, McCarthy, Mark I, Koivula, S, Uggeldahl, T, Forslund, T, Halonen, A, Koistinen, A, Koskiaho, P, Laukkanen, M, Saltevo, J, Tiihonen, M, Forsen, M, Granlund, H, Jonsson, A-C, Nyroos, B, Kinnunen, P, Orvola, A, Salonen, T, Vähänen, A, Paldanius, Kotka R, and Riihelä, M

Subjects
Diabetes, Genetics, Human Genome, Aetiology, 2.1 Biological and endogenous factors, Metabolic and endocrine, Good Health and Well Being, Adult, Aged, Aged, 80 and over, Case-Control Studies, Diabetes Mellitus, Type 2, Diabetic Nephropathies, Female, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Kidney Failure, Chronic, Male, Middle Aged, Polymorphism, Single Nucleotide, Renal Insufficiency, Chronic, Finnish Diabetic Nephropathy Study, Hong Kong Diabetes Registry Theme-based Research Scheme Project Group, Warren 3 and Genetics of Kidneys in Diabetes (GoKinD) Study Group, GENIE (GEnetics of Nephropathy an International Effort) Consortium, Diabetes Control and Complications Trial (DCCT)/Epidemiology of Diabetes Interventions and Complications (EDIC) Research Group, SUrrogate markers for Micro- and Macrovascular hard endpoints for Innovative diabetes Tools (SUMMIT) Consortium, Medical and Health Sciences, Endocrinology & Metabolism
Abstract

Identification of sequence variants robustly associated with predisposition to diabetic kidney disease (DKD) has the potential to provide insights into the pathophysiological mechanisms responsible. We conducted a genome-wide association study (GWAS) of DKD in type 2 diabetes (T2D) using eight complementary dichotomous and quantitative DKD phenotypes: the principal dichotomous analysis involved 5,717 T2D subjects, 3,345 with DKD. Promising association signals were evaluated in up to 26,827 subjects with T2D (12,710 with DKD). A combined T1D+T2D GWAS was performed using complementary data available for subjects with T1D, which, with replication samples, involved up to 40,340 subjects with diabetes (18,582 with DKD). Analysis of specific DKD phenotypes identified a novel signal near GABRR1 (rs9942471, P = 4.5 × 10-8) associated with microalbuminuria in European T2D case subjects. However, no replication of this signal was observed in Asian subjects with T2D or in the equivalent T1D analysis. There was only limited support, in this substantially enlarged analysis, for association at previously reported DKD signals, except for those at UMOD and PRKAG2, both associated with estimated glomerular filtration rate. We conclude that, despite challenges in addressing phenotypic heterogeneity, access to increased sample sizes will continue to provide more robust inference regarding risk variant discovery for DKD.

Published
2018

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

Author

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

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

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

Published
2018

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