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1. Algorithms for the identification of prevalent diabetes in the All of Us Research Program validated using polygenic scores

Author: Lukasz Szczerbinski, Ravi Mandla, Philip Schroeder, Bianca C. Porneala, Josephine H. Li, Jose C. Florez, Josep M. Mercader, Miriam S. Udler, and Alisa K. Manning
Subjects: Medicine, Science
Abstract: Abstract The All of Us Research Program (AoU) is an initiative designed to gather a comprehensive and diverse dataset from at least one million individuals across the USA. This longitudinal cohort study aims to advance research by providing a rich resource of genetic and phenotypic information, enabling powerful studies on the epidemiology and genetics of human diseases. One critical challenge to maximizing its use is the development of accurate algorithms that can efficiently and accurately identify well-defined disease and disease-free participants for case-control studies. This study aimed to develop and validate type 1 (T1D) and type 2 diabetes (T2D) algorithms in the AoU cohort, using electronic health record (EHR) and survey data. Building on existing algorithms and using diagnosis codes, medications, laboratory results, and survey data, we developed and implemented algorithms for identifying prevalent cases of type 1 and type 2 diabetes. The first set of algorithms used only EHR data (EHR-only), and the second set used a combination of EHR and survey data (EHR+). A universal algorithm was also developed to identify individuals without diabetes. The performance of each algorithm was evaluated by testing its association with polygenic scores (PSs) for type 1 and type 2 diabetes. We demonstrated the feasibility and utility of using AoU EHR and survey data to employ diabetes algorithms. For T1D, the EHR-only algorithm showed a stronger association with T1D-PS compared to the EHR + algorithm (DeLong p-value = 3 × 10−5). For T2D, the EHR + algorithm outperformed both the EHR-only and the existing T2D definition provided in the AoU Phenotyping Library (DeLong p-values = 0.03 and 1 × 10−4, respectively), identifying 25.79% and 22.57% more cases, respectively, and providing an improved association with T2D PS. We provide a new validated type 1 diabetes definition and an improved type 2 diabetes definition in AoU, which are freely available for diabetes research in the AoU. These algorithms ensure consistency of diabetes definitions in the cohort, facilitating high-quality diabetes research.
Published: 2024
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2. Polygenic scores for longitudinal prediction of incident type 2 diabetes in an ancestrally and medically diverse primary care physician network: a patient cohort study

Author: Ravi Mandla, Philip Schroeder, Bianca Porneala, Jose C. Florez, James B. Meigs, Josep M. Mercader, and Aaron Leong
Subjects: Polygenic scores, Type 2 diabetes, Primary care, Electronic health records, Medicine, Genetics, QH426-470
Abstract: Abstract Background The clinical utility of genetic information for type 2 diabetes (T2D) prediction with polygenic scores (PGS) in ancestrally diverse, real-world US healthcare systems is unclear, especially for those at low clinical phenotypic risk for T2D. Methods We tested the association of PGS with T2D incidence in patients followed within a primary care practice network over 16 years in four hypothetical scenarios that varied by clinical data availability (N = 14,712): (1) age and sex; (2) age, sex, body mass index (BMI), systolic blood pressure, and family history of T2D; (3) all variables in (2) and random glucose; and (4) all variables in (3), HDL, total cholesterol, and triglycerides, combined in a clinical risk score (CRS). To determine whether genetic effects differed by baseline clinical risk, we tested for interaction with the CRS. Results PGS was associated with incident T2D in all models. Adjusting for age and sex only, the Hazard Ratio (HR) per PGS standard deviation (SD) was 1.76 (95% CI 1.68, 1.84) and the HR of top 5% of PGS vs interquartile range (IQR) was 2.80 (2.39, 3.28). Adjusting for the CRS, the HR per SD was 1.48 (1.40, 1.57) and HR of the top 5% of PGS vs IQR was 2.09 (1.72, 2.55). Genetic effects differed by baseline clinical risk ((PGS-CRS interaction p = 0.05; CRS below the median: HR 1.60 (1.43, 1.79); CRS above the median: HR 1.45 (1.35, 1.55)). Conclusions Genetic information can help identify high-risk patients even among those perceived to be low risk in a clinical evaluation.
Published: 2024
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3. Diabetes subgroups and sociodemographic inequalities in Mexico: a cross-sectional analysis of nationally representative surveys from 2016 to 2022Research in context

Author: Neftali Eduardo Antonio-Villa, Omar Yaxmehen Bello-Chavolla, Carlos A. Fermín-Martínez, Daniel Ramírez-García, Arsenio Vargas-Vázquez, Martín Roberto Basile-Alvarez, Alejandra Núñez-Luna, Paulina Sánchez-Castro, Luisa Fernández-Chirino, Juan Pablo Díaz-Sánchez, Gael Dávila-López, Rosalinda Posadas-Sánchez, Gilberto Vargas-Alarcón, A. Enrique Caballero, Jose C. Florez, and Jacqueline A. Seiglie
Subjects: Diabetes subgroups, Mexico, Inequalities, Insulin deficiency, Prevalence, Public aspects of medicine, RA1-1270
Abstract: Summary: Background: Differences in the prevalence of four diabetes subgroups have been reported in Mexico compared to other populations, but factors that may contribute to these differences are poorly understood. Here, we estimate the prevalence of diabetes subgroups in Mexico and evaluate their correlates with indicators of social disadvantage using data from national representative surveys. Methods: We analyzed serial, cross-sectional Mexican National Health and Nutrition Surveys spanning 2016, 2018, 2020, 2021, and 2022, including 23,354 adults (>20 years). Diabetes subgroups (obesity-related [MOD], severe insulin-deficient [SIDD], severe insulin-resistant [SIRD], and age-related [MARD]) were classified using self-normalizing neural networks based on a previously validated algorithm. We used the density-independent social lag index (DISLI) as a proxy of state-level social disadvantage. Findings: We identified 4204 adults (median age: 57, IQR: 47–66, women: 64%) living with diabetes, yielding a pooled prevalence of 16.04% [95% CI: 14.92–17.17]. When stratified by diabetes subgroup, prevalence was 6.62% (5.69–7.55) for SIDD, 5.25% (4.52–5.97) for MOD, 2.39% (1.95–2.83) for MARD, and 1.27% (1.00–1.54) for SIRD. SIDD and MOD clustered in Southern Mexico, whereas MARD and SIRD clustered in Northern Mexico and Mexico City. Each standard deviation increase in DISLI was associated with higher odds of SIDD (OR: 1.12, 95% CI: 1.06–1.12) and lower odds of MOD (OR: 0.93, 0.88–0.99). Speaking an indigenous language was associated with higher odds of SIDD (OR: 1.35, 1.16–1.57) and lower odds of MARD (OR 0.58, 0.45–0.74). Interpretation: Diabetes prevalence in Mexico is rising in the context of regional and sociodemographic inequalities across distinct diabetes subgroups. SIDD is a subgroup of concern that may be associated with inadequate diabetes management, mainly in marginalized states. Funding: This research was supported by Instituto Nacional de Geriatría in Mexico.
Published: 2024
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4. Multi-tissue epigenetic analysis identifies distinct associations underlying insulin resistance and Alzheimer’s disease at CPT1A locus

Author: Chloé Sarnowski, Tianxiao Huan, Yiyi Ma, Roby Joehanes, Alexa Beiser, Charles S. DeCarli, Nancy L. Heard-Costa, Daniel Levy, Honghuang Lin, Ching-Ti Liu, Chunyu Liu, James B. Meigs, Claudia L. Satizabal, Jose C. Florez, Marie-France Hivert, Josée Dupuis, Philip L. De Jager, David A. Bennett, Sudha Seshadri, and Alanna C. Morrison
Subjects: Epigenetics, Insulin resistance, Alzheimer’s disease, FHS, ROSMAP, DNA methylation, Medicine, Genetics, QH426-470
Abstract: Abstract Background Insulin 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. Methods We 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
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5. Precision subclassification of type 2 diabetes: a systematic review

Author: Shivani Misra, Robert Wagner, Bige Ozkan, Martin Schön, Magdalena Sevilla-Gonzalez, Katsiaryna Prystupa, Caroline C. Wang, Raymond J. Kreienkamp, Sara J. Cromer, Mary R. Rooney, Daisy Duan, Anne Cathrine Baun Thuesen, Amelia S. Wallace, Aaron Leong, Aaron J. Deutsch, Mette K. Andersen, Liana K. Billings, Robert H. Eckel, Wayne Huey-Herng Sheu, Torben Hansen, Norbert Stefan, Mark O. Goodarzi, Debashree Ray, Elizabeth Selvin, Jose C. Florez, ADA/EASD PMDI, James B. Meigs, and Miriam S. Udler
Subjects: Medicine
Abstract: Abstract Background Heterogeneity in type 2 diabetes presentation and progression suggests that precision medicine interventions could improve clinical outcomes. We undertook a systematic review to determine whether strategies to subclassify type 2 diabetes were associated with high quality evidence, reproducible results and improved outcomes for patients. Methods We searched PubMed and Embase for publications that used ‘simple subclassification’ approaches using simple categorisation of clinical characteristics, or ‘complex subclassification’ approaches which used machine learning or ‘omics approaches in people with established type 2 diabetes. We excluded other diabetes subtypes and those predicting incident type 2 diabetes. We assessed quality, reproducibility and clinical relevance of extracted full-text articles and qualitatively synthesised a summary of subclassification approaches. Results Here we show data from 51 studies that demonstrate many simple stratification approaches, but none have been replicated and many are not associated with meaningful clinical outcomes. Complex stratification was reviewed in 62 studies and produced reproducible subtypes of type 2 diabetes that are associated with outcomes. Both approaches require a higher grade of evidence but support the premise that type 2 diabetes can be subclassified into clinically meaningful subtypes. Conclusion Critical next steps toward clinical implementation are to test whether subtypes exist in more diverse ancestries and whether tailoring interventions to subtypes will improve outcomes.
Published: 2023
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6. Large-scale exome array summary statistics resources for glycemic traits to aid effector gene prioritization [version 1; peer review: 2 approved]

Author: Natasha H. J. Ng, Sara M. Willems, Jian'an Luan, Rebecca S. Fine, Juan Fernandez, Jennifer Wessel, Eleanor Wheeler, Gaelle Marenne, Hidetoshi Kitajima, Hanieh Yaghootkar, Xueling Sim, Ian J. Deary, Sai Chen, Shuai Wang, Yii-Der Ida Chen, Caroline Hayward, Yuning Chen, Jennifer L. Asimit, Claudia Langenberg, Tibor V. Varga, Archie Campbell, Rona J. Strawbridge, Shuang Feng, Tarunveer S. Ahluwalia, Erica L. Kleinbrink, Emil V. Appel, Ping An, Lawrence F. Bielak, Dan E. Arking, Jennifer A. Brody, Nathan A. Bihlmeyer, David Porteous, Ayse Demirkan, Audrey Y. Chu, Franco Giulianini, James S. Floyd, Stefan Gustafsson, Xiuqing Guo, Johanna Jakobsdottir, Anne U. Jackson, Stavroula Kanoni, Richard A. Jensen, Igor Rudan, Man Li, Sirkka Keinanen-Kiukaanniemi, Alisa K. Manning, Yingchang Lu, Karina Meidtner, Jonathan Marten, Giorgio Pistis, Taulant Muka, Kenneth M. Rice, Bram Prins, Albert Vernon Smith, Serena Sanna, Lorraine Southam, Jennifer A. Smith, Vinicius Tragante, Heather M. Stringham, Helen R. Warren, Sander W. van der Laan, Andrianos M. Yiorkas, Jie Yao, Wei Zhao, Weihua Zhang, Heather M. Highland, Mariaelisa Graff, Eirini Marouli, Anne E. Justice, Wesam A. Alhejily, Saima Afaq, Folkert W. Asselbergs, Najaf Amin, Michiel L. Bots, Lori L. Bonnycastle, Ji Chen, Ivan Brandslund, Abbas Dehghan, John Danesh, Tapani Ebeling, Jessica D. Faul, Aliki-Eleni Farmaki, Steve Franks, Paul W. Franks, Anette P. Gjesing, Andreas Fritsche, Göran Hallmans, Mark O. Goodarzi, Karl-Heinz Herzig, Tamara B. Harris, Min A Jhun, Marie-France Hivert, Marit E. Jørgensen, Torben Jørgensen, Eero Kajantie, Pekka Jousilahti, Sharon L.R. Kardia, Maria Karaleftheri, Heikki A. Koistinen, Leena Kinnunen, Peter Kovacs, Pirjo Komulainen, Markku Laakso, Johanna Kuusisto, Aaron Leong, Lenore J. Launer, Jocelyn E. Manning Fox, Jaana Lindström, Nisa M. Maruthur, Satu Männistö, Antonella Mulas, Leena Moilanen, Matthew Neville, Mike A. Nalls, Alison Pattie, James S. Pankow, Hannu Puolijoki, Eva R.B. Petersen, Paul Redmond, Asif Rasheed, Michael Roden, Frida Renström, Juha Saltevo, Danish Saleheen, Sylvain Sebert, Kai Savonen, Alena Stančáková, Kerrin S. Small, Konstantin Strauch, Jakob Stokholm, Betina H. Thuesen, Juha Auvinen, E-Shyong Tai, Emmanouil Tsafantakis, Anke Tönjes, Jaakko Tuomilehto, Tiinamaija Tuomi, Marja Vääräsmäki, Matti Uusitupa, Magdalena Zoledziewska, Ilonca Vaartjes, Beverley Balkau, Goncalo Abecasis, Alexandra I. Blakemore, Hans Bisgaard, Heiner Boeing, Ruth J.F. Loos, Matthias Blüher, Klaus Bønnelykke, Eric Boerwinkle, Mark J. Caulfield, Erwin P. Bottinger, Daniel I. Chasman, John C. Chambers, Francis S. Collins, Ching-Yu Cheng, Francesco Cucca, Josef Coresh, George Dedoussis, Gert J. de Borst, Hester M. den Ruijter, Panos Deloukas, Ele Ferrannini, Michele K. Evans, Harald Grallert, Oscar H. Franco, Arfan Ikram, Joel N. Hirschhorn, Fredrik Karpe, Erik Ingelsson, Wieland Kiess, Carolina Medina-Gomez, Kay-Tee Kaw, Antje Körner, Jaspal S. Kooner, Cecilia M. Lindgren, Timo Lakka, Ching-Ti Liu, Leonard Lipovich, Patrick E. MacDonald, Jun Liu, Andrew D. Morris, Karen L. Mohlke, Alison Murray, Patricia B. Munroe, Gerard Pasterkamp, Colin N. A . Palmer, Patricia A. Peyser, Oluf Pedersen, Paul M. Ridker, Rainer Rauramaa, Patrik Rorsman, Olov Rolandsson, Veikko Salomaa, Frits R. Rosendaal, Robert Sladek, Matthias B. Schulze, Michael Stumvoll, Timothy D. Spector, Mark Walker, Cornelia M. van Duijn, David R. Weir, Nick J. Wareham, Tien Yin Wong, James G. Wilson, Alan B. Zonderman, Eleftheria Zeggini, Andrew P. Morris, Jerome I. Rotter, Jose C. Florez, Michael Boehnke, James B. Meigs, Mark I. McCarthy, Robert A. Scott, Anubha Mahajan, Inês Barroso, Anna L. Gloyn, Michael A. Province, Niels Grarup, Ruifang Li-Gao, Jette Bork-Jensen, Yongmei Liu, Allan Linneberg, Leslie A. Lange, Sandosh Padmanabhan, Gail Davies, Lars Lind, Bruce M. Psaty, Tea Skaaby, Torben Hansen, Ozren Polasek, John M. Starr, Dennis O. Mook-Kanamori, Vilmundur Gudnason, Kent D. Taylor, Marjo-Riitta Järvelin, Renée de Mutsert, Paul Elliott, Josée Dupuis, Blair H. Smith, and Andrew T. Hattersley
Subjects: exome chip, glycaemic traits, genetic discovery, effector genes, summary statistics resources, eng, Medicine, Science
Abstract: Background Genome-wide association studies for glycemic traits have identified hundreds of loci associated with these biomarkers of glucose homeostasis. Despite this success, the challenge remains to link variant associations to genes, and underlying biological pathways. Methods To identify coding variant associations which may pinpoint effector genes at both novel and previously established genome-wide association loci, we performed meta-analyses of exome-array studies for four glycemic traits: glycated hemoglobin (HbA1c, up to 144,060 participants), fasting glucose (FG, up to 129,665 participants), fasting insulin (FI, up to 104,140) and 2hr glucose post-oral glucose challenge (2hGlu, up to 57,878). In addition, we performed network and pathway analyses. Results Single-variant and gene-based association analyses identified coding variant associations at more than 60 genes, which when combined with other datasets may be useful to nominate effector genes. Network and pathway analyses identified pathways related to insulin secretion, zinc transport and fatty acid metabolism. HbA1c associations were strongly enriched in pathways related to blood cell biology. Conclusions Our results provided novel glycemic trait associations and highlighted pathways implicated in glycemic regulation. Exome-array summary statistic results are being made available to the scientific community to enable further discoveries.
Published: 2023
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7. Epigenome-wide meta-analysis identifies DNA methylation biomarkers associated with diabetic kidney disease

Author: Laura J. Smyth, Emma H. Dahlström, Anna Syreeni, Katie Kerr, Jill Kilner, Ross Doyle, Eoin Brennan, Viji Nair, Damian Fermin, Robert G. Nelson, Helen C. Looker, Christopher Wooster, Darrell Andrews, Kerry Anderson, Gareth J. McKay, Joanne B. Cole, Rany M. Salem, Peter J. Conlon, Matthias Kretzler, Joel N. Hirschhorn, Denise Sadlier, Catherine Godson, Jose C. Florez, GENIE consortium, Carol Forsblom, Alexander P. Maxwell, Per-Henrik Groop, Niina Sandholm, and Amy Jayne McKnight
Subjects: Science
Abstract: Approximately 40 percent of people with type 1 diabetes develop kidney disease, but the risk factors are not well understood. Here, the authors identify DNA methylation signatures associated with diabetic kidney disease, of which 21 biomarkers predict the development of kidney failure.
Published: 2022
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8. Proteomic analysis of cardiometabolic biomarkers and predictive modeling of severe outcomes in patients hospitalized with COVID-19

Author: Philip H. Schroeder, Laura N. Brenner, Varinderpal Kaur, Sara J. Cromer, Katrina Armstrong, Regina C. LaRocque, Edward T. Ryan, James B. Meigs, Jose C. Florez, Richelle C. Charles, Josep M. Mercader, and Aaron Leong
Subjects: COVID-19, Predictive modeling, Cardiometabolic biomarkers, Proteomics, Diseases of the circulatory (Cardiovascular) system, RC666-701
Abstract: Abstract Background The high heterogeneity in the symptoms and severity of COVID-19 makes it challenging to identify high-risk patients early in the disease. Cardiometabolic comorbidities have shown strong associations with COVID-19 severity in epidemiologic studies. Cardiometabolic protein biomarkers, therefore, may provide predictive insight regarding which patients are most susceptible to severe illness from COVID-19. Methods In plasma samples collected from 343 patients hospitalized with COVID-19 during the first wave of the pandemic, we measured 92 circulating protein biomarkers previously implicated in cardiometabolic disease. We performed proteomic analysis and developed predictive models for severe outcomes. We then used these models to predict the outcomes of out-of-sample patients hospitalized with COVID-19 later in the surge (N = 194). Results We identified a set of seven protein biomarkers predictive of admission to the intensive care unit and/or death (ICU/death) within 28 days of presentation to care. Two of the biomarkers, ADAMTS13 and VEGFD, were associated with a lower risk of ICU/death. The remaining biomarkers, ACE2, IL-1RA, IL6, KIM1, and CTSL1, were associated with higher risk. When used to predict the outcomes of the future, out-of-sample patients, the predictive models built with these protein biomarkers outperformed all models built from standard clinical data, including known COVID-19 risk factors. Conclusions These findings suggest that proteomic profiling can inform the early clinical impression of a patient’s likelihood of developing severe COVID-19 outcomes and, ultimately, accelerate the recognition and treatment of high-risk patients.
Published: 2022
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9. Whole genome sequence association analysis of fasting glucose and fasting insulin levels in diverse cohorts from the NHLBI TOPMed program

Author: Daniel DiCorpo, Sheila M. Gaynor, Emily M. Russell, Kenneth E. Westerman, Laura M. Raffield, Timothy D. Majarian, Peitao Wu, Chloé Sarnowski, Heather M. Highland, Anne Jackson, Natalie R. Hasbani, Paul S. de Vries, Jennifer A. Brody, Bertha Hidalgo, Xiuqing Guo, James A. Perry, Jeffrey R. O’Connell, Samantha Lent, May E. Montasser, Brian E. Cade, Deepti Jain, Heming Wang, Ricardo D’Oliveira Albanus, Arushi Varshney, Lisa R. Yanek, Leslie Lange, Nicholette D. Palmer, Marcio Almeida, Juan M. Peralta, Stella Aslibekyan, Abigail S. Baldridge, Alain G. Bertoni, Lawrence F. Bielak, Chung-Shiuan Chen, Yii-Der Ida Chen, Won Jung Choi, Mark O. Goodarzi, James S. Floyd, Marguerite R. Irvin, Rita R. Kalyani, Tanika N. Kelly, Seonwook Lee, Ching-Ti Liu, Douglas Loesch, JoAnn E. Manson, Ryan L. Minster, Take Naseri, James S. Pankow, Laura J. Rasmussen-Torvik, Alexander P. Reiner, Muagututi’a Sefuiva Reupena, Elizabeth Selvin, Jennifer A. Smith, Daniel E. Weeks, Huichun Xu, Jie Yao, Wei Zhao, Stephen Parker, Alvaro Alonso, Donna K. Arnett, John Blangero, Eric Boerwinkle, Adolfo Correa, L. Adrienne Cupples, Joanne E. Curran, Ravindranath Duggirala, Jiang He, Susan R. Heckbert, Sharon L. R. Kardia, Ryan W. Kim, Charles Kooperberg, Simin Liu, Rasika A. Mathias, Stephen T. McGarvey, Braxton D. Mitchell, Alanna C. Morrison, Patricia A. Peyser, Bruce M. Psaty, Susan Redline, Alan R. Shuldiner, Kent D. Taylor, Ramachandran S. Vasan, Karine A. Viaud-Martinez, Jose C. Florez, James G. Wilson, Robert Sladek, Stephen S. Rich, Jerome I. Rotter, Xihong Lin, Josée Dupuis, James B. Meigs, Jennifer Wessel, and Alisa K. Manning
Subjects: Biology (General), QH301-705.5
Abstract: This study of 23,000 non-diabetic individuals highlights loci associated with fasting glucose and fasting insulin in diverse cohorts with whole genome sequence data.
Published: 2022
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10. Variance-quantitative trait loci enable systematic discovery of gene-environment interactions for cardiometabolic serum biomarkers

Author: Kenneth E. Westerman, Timothy D. Majarian, Franco Giulianini, Dong-Keun Jang, Jenkai Miao, Jose C. Florez, Han Chen, Daniel I. Chasman, Miriam S. Udler, Alisa K. Manning, and Joanne B. Cole
Subjects: Science
Abstract: Understanding how our genes interact with the environment is critical to improving health. Using a large-scale discovery pipeline, here the authors investigate synergies between genetic variants and a broad range of environmental factors impacting cardiometabolic health.
Published: 2022
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11. Corrigendum: Genetic heritability as a tool to evaluate the precision of 24-hour recall dietary questionnaire variables in UK Biobank

Author: Joanne B. Cole, Kenneth E. Westerman, Alisa K. Manning, Jose C. Florez, and Joel N. Hirschhorn
Subjects: heritability, nutrigenomics, nutritional epidemiology, 24-hour diet recall questionnaires, relative validity, phenotyping, Genetics, QH426-470
Published: 2023
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12. Genetic heritability as a tool to evaluate the precision of 24-hour recall dietary questionnaire variables in UK Biobank

Author: Joanne B. Cole, Kenneth E. Westerman, Alisa K. Manning, Jose C. Florez, and Joel N. Hirschhorn
Subjects: heritability, nutrigenomics, nutritional epidemiology, 24-hour diet recall questionnaires, relative validity, phenotyping, Genetics, QH426-470
Abstract: A variety of statistical approaches in nutritional epidemiology have been developed to enhance the precision of dietary variables derived from longitudinal questionnaires. Correlation with biomarkers is often used to assess the relative validity of these different approaches, however, validated biomarkers do not always exist and are costly and laborious to collect. We present a novel high-throughput approach which utilizes the modest but importantly non-zero influence of genetic variation on variation in dietary intake to compare different statistical transformations of dietary variables. Specifically, we compare the heritability of crude averages with Empirical Bayes weighted averages for 302 correlated dietary variables from multiple 24-hour recall questionnaires in 177 K individuals in UK Biobank. Overall, the crude averages for frequency of consumption are more heritable than their Empirical Bayes counterparts only when the reliability of that item across questionnaires is high (measured by intra-class correlation), otherwise, the Empirical Bayes approach (for both unreliably measured frequencies and for average quantities independent of reliability) leads to higher heritability estimates. We also find that the more heritable versions of each dietary variable lead to stronger underlying statistical associations with specific genetic loci, many of which have well-known mechanisms, further supporting heritability as an alternative metric for relative validity in nutritional epidemiology and beyond.
Published: 2023
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13. Ancestral diversity improves discovery and fine-mapping of genetic loci for anthropometric traits—The Hispanic/Latino Anthropometry Consortium

Author: Lindsay Fernández-Rhodes, Mariaelisa Graff, Victoria L. Buchanan, Anne E. Justice, Heather M. Highland, Xiuqing Guo, Wanying Zhu, Hung-Hsin Chen, Kristin L. Young, Kaustubh Adhikari, Nicholette D. Palmer, Jennifer E. Below, Jonathan Bradfield, Alexandre C. Pereira, LáShauntá Glover, Daeeun Kim, Adam G. Lilly, Poojan Shrestha, Alvin G. Thomas, Xinruo Zhang, Minhui Chen, Charleston W.K. Chiang, Sara Pulit, Andrea Horimoto, Jose E. Krieger, Marta Guindo-Martínez, Michael Preuss, Claudia Schumann, Roelof A.J. Smit, Gabriela Torres-Mejía, Victor Acuña-Alonzo, Gabriel Bedoya, Maria-Cátira Bortolini, Samuel Canizales-Quinteros, Carla Gallo, Rolando González-José, Giovanni Poletti, Francisco Rothhammer, Hakon Hakonarson, Robert Igo, Sharon G. Adler, Sudha K. Iyengar, Susanne B. Nicholas, Stephanie M. Gogarten, Carmen R. Isasi, George Papnicolaou, Adrienne M. Stilp, Qibin Qi, Minjung Kho, Jennifer A. Smith, Carl D. Langefeld, Lynne Wagenknecht, Roberta Mckean-Cowdin, Xiaoyi Raymond Gao, Darryl Nousome, David V. Conti, Ye Feng, Matthew A. Allison, Zorayr Arzumanyan, Thomas A. Buchanan, Yii-Der Ida Chen, Pauline M. Genter, Mark O. Goodarzi, Yang Hai, Willa Hsueh, Eli Ipp, Fouad R. Kandeel, Kelvin Lam, Xiaohui Li, Jerry L. Nadler, Leslie J. Raffel, Kathryn Roll, Kevin Sandow, Jingyi Tan, Kent D. Taylor, Anny H. Xiang, Jie Yao, Astride Audirac-Chalifour, Jose de Jesus Peralta Romero, Fernando Hartwig, Bernando Horta, John Blangero, Joanne E. Curran, Ravindranath Duggirala, Donna E. Lehman, Sobha Puppala, Laura Fejerman, Esther M. John, Carlos Aguilar-Salinas, Noël P. Burtt, Jose C. Florez, Humberto García-Ortíz, Clicerio González-Villalpando, Josep Mercader, Lorena Orozco, Teresa Tusié-Luna, Estela Blanco, Sheila Gahagan, Nancy J. Cox, Craig Hanis, Nancy F. Butte, Shelley A. Cole, Anthony G. Comuzzie, V. Saroja Voruganti, Rebecca Rohde, Yujie Wang, Tamar Sofer, Elad Ziv, Struan F.A. Grant, Andres Ruiz-Linares, Jerome I. Rotter, Christopher A. Haiman, Esteban J. Parra, Miguel Cruz, Ruth J.F. Loos, and Kari E. North
Subjects: Genetics, QH426-470
Published: 2023
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14. Determinants of penetrance and variable expressivity in monogenic metabolic conditions across 77,184 exomes

Author: Julia K. Goodrich, Moriel Singer-Berk, Rachel Son, Abigail Sveden, Jordan Wood, Eleina England, Joanne B. Cole, Ben Weisburd, Nick Watts, Lizz Caulkins, Peter Dornbos, Ryan Koesterer, Zachary Zappala, Haichen Zhang, Kristin A. Maloney, Andy Dahl, Carlos A. Aguilar-Salinas, Gil Atzmon, Francisco Barajas-Olmos, Nir Barzilai, John Blangero, Eric Boerwinkle, Lori L. Bonnycastle, Erwin Bottinger, Donald W. Bowden, Federico Centeno-Cruz, John C. Chambers, Nathalie Chami, Edmund Chan, Juliana Chan, Ching-Yu Cheng, Yoon Shin Cho, Cecilia Contreras-Cubas, Emilio Córdova, Adolfo Correa, Ralph A. DeFronzo, Ravindranath Duggirala, Josée Dupuis, Ma Eugenia Garay-Sevilla, Humberto García-Ortiz, Christian Gieger, Benjamin Glaser, Clicerio González-Villalpando, Ma Elena Gonzalez, Niels Grarup, Leif Groop, Myron Gross, Christopher Haiman, Sohee Han, Craig L. Hanis, Torben Hansen, Nancy L. Heard-Costa, Brian E. Henderson, Juan Manuel Malacara Hernandez, Mi Yeong Hwang, Sergio Islas-Andrade, Marit E. Jørgensen, Hyun Min Kang, Bong-Jo Kim, Young Jin Kim, Heikki A. Koistinen, Jaspal Singh Kooner, Johanna Kuusisto, Soo-Heon Kwak, Markku Laakso, Leslie Lange, Jong-Young Lee, Juyoung Lee, Donna M. Lehman, Allan Linneberg, Jianjun Liu, Ruth J. F. Loos, Valeriya Lyssenko, Ronald C. W. Ma, Angélica Martínez-Hernández, James B. Meigs, Thomas Meitinger, Elvia Mendoza-Caamal, Karen L. Mohlke, Andrew D. Morris, Alanna C. Morrison, Maggie C. Y. Ng, Peter M. Nilsson, Christopher J. O’Donnell, Lorena Orozco, Colin N. A. Palmer, Kyong Soo Park, Wendy S. Post, Oluf Pedersen, Michael Preuss, Bruce M. Psaty, Alexander P. Reiner, Cristina Revilla-Monsalve, Stephen S. Rich, Jerome I. Rotter, Danish Saleheen, Claudia Schurmann, Xueling Sim, Rob Sladek, Kerrin S. Small, Wing Yee So, Timothy D. Spector, Konstantin Strauch, Tim M. Strom, E. Shyong Tai, Claudia H. T. Tam, Yik Ying Teo, Farook Thameem, Brian Tomlinson, Russell P. Tracy, Tiinamaija Tuomi, Jaakko Tuomilehto, Teresa Tusié-Luna, Rob M. van Dam, Ramachandran S. Vasan, James G. Wilson, Daniel R. Witte, Tien-Yin Wong, AMP-T2D-GENES Consortia, Noël P. Burtt, Noah Zaitlen, Mark I. McCarthy, Michael Boehnke, Toni I. Pollin, Jason Flannick, Josep M. Mercader, Anne O’Donnell-Luria, Samantha Baxter, Jose C. Florez, Daniel G. MacArthur, and Miriam S. Udler
Subjects: Science
Abstract: Penetrance of variants in monogenic disease and clinical utility of common polygenic variation has not been well explored on a large-scale. Here, the authors use exome sequencing data from 77,184 individuals to generate penetrance estimates and assess the utility of polygenic variation in risk prediction of monogenic variants.
Published: 2021
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15. Genetic Architecture of Plasma Alpha‐Aminoadipic Acid Reveals a Relationship With High‐Density Lipoprotein Cholesterol

Author: Mingjian Shi, Chuan Wang, Hao Mei, Marinella Temprosa, Jose C. Florez, Mark Tripputi, Jordi Merino, Loren Lipworth, Xiao‐Ou Shu, Robert E. Gerszten, Thomas J. Wang, Joshua A. Beckman, Jorge L. Gamboa, Jonathan D. Mosley, and Jane F. Ferguson
Subjects: 2‐aminoadipic acid, genome‐wide association study, HDL cholesterol, Mendelian randomization analysis, Diseases of the circulatory (Cardiovascular) system, RC666-701
Abstract: Background Elevated plasma levels of alpha‐aminoadipic acid (2‐AAA) have been associated with the development of type 2 diabetes and atherosclerosis. However, the nature of the association remains unknown. Methods and Results We identified genetic determinants of plasma 2‐AAA through meta‐analysis of genome‐wide association study data in 5456 individuals of European, African, and Asian ancestry from the Framingham Heart Study, Diabetes Prevention Program, Jackson Heart Study, and Shanghai Women’s and Men’s Health Studies. No single nucleotide polymorphisms reached genome‐wide significance across all samples. However, the top associations from the meta‐analysis included single‐nucleotide polymorphisms in the known 2‐AAA pathway gene DHTKD1, and single‐nucleotide polymorphisms in genes involved in mitochondrial respiration (NDUFS4) and macrophage function (MSR1). We used a Mendelian randomization instrumental variable approach to evaluate relationships between 2‐AAA and cardiometabolic phenotypes in large disease genome‐wide association studies. Mendelian randomization identified a suggestive inverse association between increased 2‐AAA and lower high‐density lipoprotein cholesterol (P=0.005). We further characterized the genetically predicted relationship through measurement of plasma 2‐AAA and high‐density lipoprotein cholesterol in 2 separate samples of individuals with and without cardiometabolic disease (N=98), and confirmed a significant negative correlation between 2‐AAA and high‐density lipoprotein (rs=−0.53, P
Published: 2022
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16. The impact of non-additive genetic associations on age-related complex diseases

Author: Marta Guindo-Martínez, Ramon Amela, Silvia Bonàs-Guarch, Montserrat Puiggròs, Cecilia Salvoro, Irene Miguel-Escalada, Caitlin E. Carey, Joanne B. Cole, Sina Rüeger, Elizabeth Atkinson, Aaron Leong, Friman Sanchez, Cristian Ramon-Cortes, Jorge Ejarque, Duncan S. Palmer, Mitja Kurki, FinnGen Consortium, Krishna Aragam, Jose C. Florez, Rosa M. Badia, Josep M. Mercader, and David Torrents
Subjects: Science
Abstract: Most genome-wide association studies assume an additive model, exclude the X chromosome, and use one reference panel. Here, the authors implement a strategy including non-additive models and find that the number of loci for age-related traits increases as compared to the additive model alone.
Published: 2021
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17. The comparative effect of exposure to various risk factors on the risk of hyperuricaemia: diet has a weak causal effect

Author: Ruth K. G. Topless, Tanya J. Major, Jose C. Florez, Joel N. Hirschhorn, Murray Cadzow, Nicola Dalbeth, Lisa K. Stamp, Philip L. Wilcox, Richard J. Reynolds, Joanne B. Cole, and Tony R. Merriman
Subjects: Hyperuricemia, Gout, Risk factor, Population attributable fraction, Variance, Genetic polymorphism, Diseases of the musculoskeletal system, RC925-935
Abstract: Abstract Background Prevention of hyperuricaemia (HU) is critical to the prevention of gout. Understanding causal relationships and relative contributions of various risk factors to hyperuricemia is therefore important in the prevention of gout. Here, we use attributable fraction to compare the relative contribution of genetic, dietary, urate-lowering therapy (ULT) and other exposures to HU. We use Mendelian randomisation to test for the causality of diet in urate levels. Methods Four European-ancestry sample sets, three from the general population (n = 419,060) and one of people with gout (n = 6781) were derived from the Database of Genotypes and Phenotypes (ARIC, FHS, CARDIA, CHS) and UK Biobank. Dichotomised exposures to diet, genetic risk variants, BMI, alcohol, diuretic treatment, sex and age were used to calculate adjusted population and average attributable fractions (PAF/AAF) for HU (≥0.42 mmol/L [≥7 mg/dL]). Exposure to ULT was also assessed in the gout cohort. Two sample Mendelian randomisation was done in the UK Biobank using dietary pattern-associated genetic variants as exposure and serum urate levels as outcome. Results Adherence to dietary recommendations, BMI (
Published: 2021
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18. Sex-dimorphic genetic effects and novel loci for fasting glucose and insulin variability

Author: Vasiliki Lagou, Reedik Mägi, Jouke- Jan Hottenga, Harald Grallert, John R. B. Perry, Nabila Bouatia-Naji, Letizia Marullo, Denis Rybin, Rick Jansen, Josine L. Min, Antigone S. Dimas, Anna Ulrich, Liudmila Zudina, Jesper R. Gådin, Longda Jiang, Alessia Faggian, Amélie Bonnefond, Joao Fadista, Maria G. Stathopoulou, Aaron Isaacs, Sara M. Willems, Pau Navarro, Toshiko Tanaka, Anne U. Jackson, May E. Montasser, Jeff R. O’Connell, Lawrence F. Bielak, Rebecca J. Webster, Richa Saxena, Jeanette M. Stafford, Beate St Pourcain, Nicholas J. Timpson, Perttu Salo, So-Youn Shin, Najaf Amin, Albert V. Smith, Guo Li, Niek Verweij, Anuj Goel, Ian Ford, Paul C. D. Johnson, Toby Johnson, Karen Kapur, Gudmar Thorleifsson, Rona J. Strawbridge, Laura J. Rasmussen-Torvik, Tõnu Esko, Evelin Mihailov, Tove Fall, Ross M. Fraser, Anubha Mahajan, Stavroula Kanoni, Vilmantas Giedraitis, Marcus E. Kleber, Günther Silbernagel, Julia Meyer, Martina Müller-Nurasyid, Andrea Ganna, Antti-Pekka Sarin, Loic Yengo, Dmitry Shungin, Jian’an Luan, Momoko Horikoshi, Ping An, Serena Sanna, Yvonne Boettcher, N. William Rayner, Ilja M. Nolte, Tatijana Zemunik, Erik van Iperen, Peter Kovacs, Nicholas D. Hastie, Sarah H. Wild, Stela McLachlan, Susan Campbell, Ozren Polasek, Olga Carlson, Josephine Egan, Wieland Kiess, Gonneke Willemsen, Johanna Kuusisto, Markku Laakso, Maria Dimitriou, Andrew A. Hicks, Rainer Rauramaa, Stefania Bandinelli, Barbara Thorand, Yongmei Liu, Iva Miljkovic, Lars Lind, Alex Doney, Markus Perola, Aroon Hingorani, Mika Kivimaki, Meena Kumari, Amanda J. Bennett, Christopher J. Groves, Christian Herder, Heikki A. Koistinen, Leena Kinnunen, Ulf de Faire, Stephan J. L. Bakker, Matti Uusitupa, Colin N. A. Palmer, J. Wouter Jukema, Naveed Sattar, Anneli Pouta, Harold Snieder, Eric Boerwinkle, James S. Pankow, Patrik K. Magnusson, Ulrika Krus, Chiara Scapoli, Eco J. C. N. de Geus, Matthias Blüher, Bruce H. R. Wolffenbuttel, Michael A. Province, Goncalo R. Abecasis, James B. Meigs, G. Kees Hovingh, Jaana Lindström, James F. Wilson, Alan F. Wright, George V. Dedoussis, Stefan R. Bornstein, Peter E. H. Schwarz, Anke Tönjes, Bernhard R. Winkelmann, Bernhard O. Boehm, Winfried März, Andres Metspalu, Jackie F. Price, Panos Deloukas, Antje Körner, Timo A. Lakka, Sirkka M. Keinanen-Kiukaanniemi, Timo E. Saaristo, Richard N. Bergman, Jaakko Tuomilehto, Nicholas J. Wareham, Claudia Langenberg, Satu Männistö, Paul W. Franks, Caroline Hayward, Veronique Vitart, Jaakko Kaprio, Sophie Visvikis-Siest, Beverley Balkau, David Altshuler, Igor Rudan, Michael Stumvoll, Harry Campbell, Cornelia M. van Duijn, Christian Gieger, Thomas Illig, Luigi Ferrucci, Nancy L. Pedersen, Peter P. Pramstaller, Michael Boehnke, Timothy M. Frayling, Alan R. Shuldiner, Patricia A. Peyser, Sharon L. R. Kardia, Lyle J. Palmer, Brenda W. Penninx, Pierre Meneton, Tamara B. Harris, Gerjan Navis, Pim van der Harst, George Davey Smith, Nita G. Forouhi, Ruth J. F. Loos, Veikko Salomaa, Nicole Soranzo, Dorret I. Boomsma, Leif Groop, Tiinamaija Tuomi, Albert Hofman, Patricia B. Munroe, Vilmundur Gudnason, David S. Siscovick, Hugh Watkins, Cecile Lecoeur, Peter Vollenweider, Anders Franco-Cereceda, Per Eriksson, Marjo-Riitta Jarvelin, Kari Stefansson, Anders Hamsten, George Nicholson, Fredrik Karpe, Emmanouil T. Dermitzakis, Cecilia M. Lindgren, Mark I. McCarthy, Philippe Froguel, Marika A. Kaakinen, Valeriya Lyssenko, Richard M. Watanabe, Erik Ingelsson, Jose C. Florez, Josée Dupuis, Inês Barroso, Andrew P. Morris, Inga Prokopenko, and Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC)
Subjects: Science
Abstract: Sex differences in fasting glucose and insulin have been identified, but the genetic loci underlying these differences have not. Here, the authors perform a meta-analysis of genome-wide association studies to detect sex-specific and sex-dimorphic loci associated with fasting glucose and insulin.
Published: 2021
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19. Polygenic scores, diet quality, and type 2 diabetes risk: An observational study among 35,759 adults from 3 US cohorts

Author: Jordi Merino, Marta Guasch-Ferré, Jun Li, Wonil Chung, Yang Hu, Baoshan Ma, Yanping Li, Jae H. Kang, Peter Kraft, Liming Liang, Qi Sun, Paul W. Franks, JoAnn E. Manson, Walter C. Willet, Jose C. Florez, and Frank B. Hu
Subjects: Medicine
Abstract: Background Both genetic and lifestyle factors contribute to the risk of type 2 diabetes, but the extent to which there is a synergistic effect of the 2 factors is unclear. The aim of this study was to examine the joint associations of genetic risk and diet quality with incident type 2 diabetes. Methods and findings We analyzed data from 35,759 men and women in the United States participating in the Nurses’ Health Study (NHS) I (1986 to 2016) and II (1991 to 2017) and the Health Professionals Follow-up Study (HPFS; 1986 to 2016) with available genetic data and who did not have diabetes, cardiovascular disease, or cancer at baseline. Genetic risk was characterized using both a global polygenic score capturing overall genetic risk and pathway-specific polygenic scores denoting distinct pathophysiological mechanisms. Diet quality was assessed using the Alternate Healthy Eating Index (AHEI). Cox models were used to calculate hazard ratios (HRs) for type 2 diabetes after adjusting for potential confounders. With over 902,386 person-years of follow-up, 4,433 participants were diagnosed with type 2 diabetes. The relative risk of type 2 diabetes was 1.29 (95% confidence interval [CI] 1.25, 1.32; P < 0.001) per standard deviation (SD) increase in global polygenic score and 1.13 (1.09, 1.17; P < 0.001) per 10-unit decrease in AHEI. Irrespective of genetic risk, low diet quality, as compared to high diet quality, was associated with approximately 30% increased risk of type 2 diabetes (Pinteraction = 0.69). The joint association of low diet quality and increased genetic risk was similar to the sum of the risk associated with each factor alone (Pinteraction = 0.30). Limitations of this study include the self-report of diet information and possible bias resulting from inclusion of highly educated participants with available genetic data. Conclusions These data provide evidence for the independent associations of genetic risk and diet quality with incident type 2 diabetes and suggest that a healthy diet is associated with lower diabetes risk across all levels of genetic risk. In an observational study of 3 cohorts in the United States, Jordi Merino, Marta Guasch-Ferré, Jun Li, and colleagues investigate the individual and combined associations between genetic risk, diet quality, and risk of type 2 diabetes. Author summary Why was this study done? Both genetic and lifestyle factors contribute to individual-level risk of type 2 diabetes. While previous studies have shown that adherence to a healthy lifestyle is associated with reduced risk of type 2 diabetes regardless of genetic risk, the partial characterization of genetic risk and the predominant assessment of interactions on the multiplicative scale might have prevented previous studies from identifying genetic profiles interacting with dietary exposures. Therefore, understanding how genetic risk and diet quality contribute to the development of type 2 diabetes is important to support evidence-based preventive interventions. What did the researchers do and find? In 3 cohort studies involving 35,759 men and women in the US, we used novel polygenic scores for type 2 diabetes to systematically evaluate the presence of additive and multiplicative interactions between genetic risk and diet quality on the development of type 2 diabetes. We found that both low diet quality and increased overall or pathway-specific genetic risk were independently associated with higher risk of type 2 diabetes. We documented that within any genetic risk category, high diet quality, as compared to low diet quality, was associated with a nearly 30% lower risk of type 2 diabetes. Further, we showed that the risk of type 2 diabetes attributed to the combination of increased genetic risk and low diet quality was similar to the sum of the risks associated with each factor alone. What do these findings mean? Results from this study suggest that consuming a healthier diet is associated with a lower risk of type 2 diabetes regardless of genetic risk. Our results underscore the value of genetic risk assessment to identify individuals at increased disease risk and their potential for risk stratification and surveillance. Such knowledge can serve to inform and design future strategies to advance the prevention of type 2 diabetes.
Published: 2022

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

Author: Lindsay Fernández-Rhodes, Mariaelisa Graff, Victoria L. Buchanan, Anne E. Justice, Heather M. Highland, Xiuqing Guo, Wanying Zhu, Hung-Hsin Chen, Kristin L. Young, Kaustubh Adhikari, Nicholette D. Palmer, Jennifer E. Below, Jonathan Bradfield, Alexandre C. Pereira, LáShauntá Glover, Daeeun Kim, Adam G. Lilly, Poojan Shrestha, Alvin G. Thomas, Xinruo Zhang, Minhui Chen, Charleston W.K. Chiang, Sara Pulit, Andrea Horimoto, Jose E. Krieger, Marta Guindo-Martínez, Michael Preuss, Claudia Schumann, Roelof A.J. Smit, Gabriela Torres-Mejía, Victor Acuña-Alonzo, Gabriel Bedoya, Maria-Cátira Bortolini, Samuel Canizales-Quinteros, Carla Gallo, Rolando González-José, Giovanni Poletti, Francisco Rothhammer, Hakon Hakonarson, Robert Igo, Sharon G. Adler, Sudha K. Iyengar, Susanne B. Nicholas, Stephanie M. Gogarten, Carmen R. Isasi, George Papnicolaou, Adrienne M. Stilp, Qibin Qi, Minjung Kho, Jennifer A. Smith, Carl D. Langefeld, Lynne Wagenknecht, Roberta Mckean-Cowdin, Xiaoyi Raymond Gao, Darryl Nousome, David V. Conti, Ye Feng, Matthew A. Allison, Zorayr Arzumanyan, Thomas A. Buchanan, Yii-Der Ida Chen, Pauline M. Genter, Mark O. Goodarzi, Yang Hai, Willa Hsueh, Eli Ipp, Fouad R. Kandeel, Kelvin Lam, Xiaohui Li, Jerry L. Nadler, Leslie J. Raffel, Kathryn Roll, Kevin Sandow, Jingyi Tan, Kent D. Taylor, Anny H. Xiang, Jie Yao, Astride Audirac-Chalifour, Jose de Jesus Peralta Romero, Fernando Hartwig, Bernando Horta, John Blangero, Joanne E. Curran, Ravindranath Duggirala, Donna E. Lehman, Sobha Puppala, Laura Fejerman, Esther M. John, Carlos Aguilar-Salinas, Noël P. Burtt, Jose C. Florez, Humberto García-Ortíz, Clicerio González-Villalpando, Josep Mercader, Lorena Orozco, Teresa Tusié-Luna, Estela Blanco, Sheila Gahagan, Nancy J. Cox, Craig Hanis, Nancy F. Butte, Shelley A. Cole, Anthony G. Comuzzie, V. Saroja Voruganti, Rebecca Rohde, Yujie Wang, Tamar Sofer, Elad Ziv, Struan F.A. Grant, Andres Ruiz-Linares, Jerome I. Rotter, Christopher A. Haiman, Esteban J. Parra, Miguel Cruz, Ruth J.F. Loos, and Kari E. North
Subjects: Hispanic/Latino, anthropometrics, obesity, diversity, trans-ancestral or trans-ethnic, fine-mapping, Genetics, QH426-470
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
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21. Comprehensive genomic analysis of dietary habits in UK Biobank identifies hundreds of genetic associations

Author: Joanne B. Cole, Jose C. Florez, and Joel N. Hirschhorn
Subjects: Science
Abstract: The choice of food intake is at least partially influenced by genetics, even though the effect sizes appear rather modest. Here, Cole et al. perform GWAS for food intake (85 individual food items and 85 derived dietary patterns) and test potential causal relationships with cardiometabolic traits using Mendelian randomization.
Published: 2020
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22. Circulating sex hormone binding globulin levels are modified with intensive lifestyle intervention, but their changes did not independently predict diabetes risk in the Diabetes Prevention Program

Author: Mike Reidy, Christine Lee, Michael Brändle, Sherita Hill Golden, Samuel Dagogo-Jack, David Kessler, Elizabeth Barrett-Connor, Steve Jones, Ling Chen, Judith Wylie-Rosett, Ping Zhang, Paula Williamson, Carlos Lorenzo, Leigh Perreault, Dana Dabelea, Santica Marcovina, Rachel Williams, Marie Smith, Carmen Pal, Patricia Katz, William H. Herman, Sharon L Edelstein, Yong Ma, Vanita R Aroda, Costas A Christophi, Catherine Kim, Sherita H Golden, Edward Horton, Kieren J Mather, George A. Bray, Kishore Gadde, Iris W. Culbert, Jennifer Arceneaux, Annie Chatellier, Amber Dragg, Catherine M. Champagne, Crystal Duncan, Barbara Eberhardt, Frank Greenway, Fonda G. Guillory, April A. Herbert, Michael L. Jeffirs, Betty M. Kennedy, Erma Levy, Monica Lockett, Jennifer C. Lovejoy, Laura H. Morris, Lee E. Melancon, Donna H. Ryan, Deborah A. Sanford, Kenneth G. Smith, Lisa L. Smith, Julia A. St, Richard T. Tulley Amant, Paula C. Vicknair, Donald Williamson, Jeffery J. Zachwieja, Kenneth S. Polonsky, Janet Tobian, David A. Ehrmann, Margaret J. Matulik, Bart Clark, Kirsten Czech, Catherine DeSandre, Ruthanne Hilbrich, Wylie McNabb, Ann R. Semenske, Jose F. Caro, Kevin Furlong, Barry J. Goldstein, Pamela G. Watson, Kellie A. Smith, Jewel Mendoza, Wendi Wildman, Renee Liberoni, John Spandorfer, Constance Pepe, Richard P. Donahue, Ronald B. Goldberg, Ronald Prineas, Jeanette Calles, Juliet Ojito, Patricia Rowe, Paul Cassanova-Romero, Sumaya Castillo-Florez, Hermes J. Florez, Anna Giannella, Lascelles Kirby, Carmen Larreal, Olga Lara, Valerie McLymont, Jadell Mendez, Arlette Perry, Patrice Saab, Beth Veciana, Steven M. Haffner, Helen P. Hazuda, Maria G. Montez, Kathy Hattaway, Arlene Martinez, Tatiana Walker, Richard F. Hamman, Patricia V. Nash, Sheila C. Steinke, Lisa Testaverde, Denise R. Anderson, Larry B. Ballonoff, Alexis Bouffard, Brian Bucca, B. Ned Calonge, Lynne Delve, Martha Farago, James O. Hill, Shelley R. Hoyer, Tonya Jenkins, Bonnie T. Jortberg, Dione Lenz, Marsha Miller, David W. Price, Judith G. Regensteiner, Helen Seagle, Carissa M. Smith, Brent VanDorsten, Edward S. Horton, Kathleen E. Lawton, Catherine S. Poirier, Kati Swift, Ronald A. Arky, Marybeth Bryant, Jacqueline P. Burke, Enrique Caballero, Karen M. Callaphan, Barbara Fargnoli, Therese Franklin, Om P. Ganda, Ashley Guidi, Mathew Guido, Sharon D. Jackson, Alan M. Jacobsen, Lori Lambert, Sarah Ledbury, Margaret Kocal, Lyn M. Kula, Maureen A. Malloy, Maryanne Nicosia, Cathryn F. Oldmixon, Jocelyn Pan, Marizel Quitingon, Stacy Rubtchinsky, Jessica Sansoucy, Dana Schweizer, Ellen W. Seely, Donald Simonson, Fannie Smith, Caren G. Solomon, Jeanne Spellman, James Warram, Steven E. Kahn, Brenda K. Montgomery, Wilfred Fujimoto, Robert H. Knopp, Edward W. Lipkin, Michelle Marr, Ivy Morgan-Taggart, Anne Murillo, Dace Trence, Lonnese Taylor, April Thomas, Elaine C. Tsai, Abbas E. Kitabchi, Mary E. Murphy, Laura Taylor, Jennifer Dolgoff, William B. Applegate, Michael Bryer-Ash, Debra Clark, Sandra L. Frieson, Uzoma Ibebuogu, Raed Imseis, Helen Lambeth, Lynne C. Lichtermann, Hooman Oktaei, Harriet Ricks, Lily M.K. Rutledge, Amy R. Sherman, Clara M. Smith, Judith E. Soberman, Beverly Williams-Cleaves, Boyd E. Metzger, Mark E. Molitch, Mariana K. Johnson, Daphne T. Adelman, Catherine Behrends, Michelle Cook, Marian Fitzgibbon, Mimi M. Giles, Deloris Heard, Cheryl K.H. Johnson, Diane Larsen, Anne Lowe, Megan Lyman, David McPherson, Samsam C. Penn, Thomas Pitts, Renee Reinhart, Susan Roston, Pamela A. Schinleber, David M. Nathan, Charles McKitrick, Heather Turgeon, Mary Larkin, Kathy Abbott, Ellen Anderson, Laurie Bissett, Kristy Bondi, Enrico Cagliero, Jose C. Florez, Kali D’Anna, Linda Delahanty, Valerie Goldman, Peter Lou, Alexandra Poulos, Elyse Raymond, Christine Stevens, Beverly Tseng, Jerrold M. Olefsky, Mary Lou Carrion-Petersen, Madeline Beltran, Lauren N. Claravall, Jonalle M. Dowden, Steven V. Edelman, Robert R. Henry, Javiva Horne, Marycie Lamkin, Simona Szerdi Janesch, Diana Leos, Sunder Mudaliar, William Polonsky, Jean Smith, Jennifer Torio-Hurley, Karen Vejvoda, F. Xavier Pi-Sunyer, Jane E. Lee, David B. Allison, Nnenna Agharanya, Nancy J. Aronoff, Maria Baldo, Jill P. Crandall, Sandra T. Foo, Susan Hagamen, Jose A. Luchsinger, Kathy Parkes, Mary Beth Pena, Ellen S. Rooney, Gretchen E.H. Van Wye, Kristine A. Viscovich, David G. Marrero, Kieren J. Mather, Melvin J. Prince, Susie M. Kelly, Marcia A. Jackson, Gina McAtee, Paula Putenney, Ronald T. Ackermann, Carolyn M. Cantrell, Yolanda F. Dotson, Edwin S. Fineberg, Megan Fultz, John C. Guare, Angela Hadden, James M. Ignaut, Marion S. Kirkman, Erin O’Kelly Phillips, Beverly D. Porter, Paris J. Roach, Nancy D. Rowland, Madelyn L. Wheeler, Vanita Aroda, Robert E. Ratner, Gretchen Youssef, Sue Shapiro, Catherine Bavido-Arrage, Geraldine Boggs, Marjorie Bronsord, Ernestine Brown, Wayman W. Cheatham, Susan Cola, Cindy Evans, Peggy Gibbs, Tracy Kellum, Renee Wiggins, Milvia Lagarda, Lilia Leon, Claresa Levatan, Milajurine Lindsay, Asha K. Nair, Maureen Passaro, Angela Silverman, Gabriel Uwaifo, Debra Wells-Thayer, Mohammed F. Saad, Karol Watson, Maria Budget, Sujata Jinagouda, Medhat Botrous, Khan Akbar, Claudia Conzues, Perpetua Magpuri, Kathy Ngo, Amer Rassam, Debra Waters, Kathy Xapthalamous, Julio V. Santiago, Neil H. White, Angela L. Brown, Samia Das, Prajakta Khare-Ranade, Tamara Stich, Ana Santiago, Edwin Fisher, Emma Hurt, Tracy Jones, Michelle Kerr, Lucy Ryder, Cormarie Wernimont, Christopher D. Saudek, Vanessa Bradley, Emily Sullivan, Tracy Whittington, Caroline Abbas, Adrienne Allen, Frederick L. Brancati, Sharon Cappelli, Jeanne M. Clark, Jeanne B. Charleston, Janice Freel, Katherine Horak, Alicia Greene, Dawn Jiggetts, Deloris Johnson, Hope Joseph, Kimberly Loman, Henry Mosley, John Reusing, Richard R. Rubin, Alafia Samuels, Thomas Shields, Shawne Stephens, Kerry J. Stewart, LeeLana Thomas, Evonne Utsey, David S. Schade, Karwyn S. Adams, Janene L. Canady, Carolyn Johannes, Claire Hemphill, Penny Hyde, Leslie F. Atler, Patrick J. Boyle, Mark R. Burge, Lisa Chai, Kathleen Colleran, Ysela Gonzales, Doris A. Hernandez-McGinnis, Carolyn King, Sofya Rubinchik, Willette Senter, Jill Crandall, Harry Shamoon, Janet O. Brown, Gilda Trandafirescu, Elsie Adorno, Liane Cox, Helena Duffy, Samuel Engel, Allison Friedler, Angela Goldstein, Crystal J. Howard-Century, Jennifer Lukin, Stacey Kloiber, Nadege Longchamp, Helen Martinez, Dorothy Pompi, Jonathan Scheindlin, Elissa Violino, Elizabeth A. Walker, Elise Zimmerman, Joel Zonszein, Trevor Orchard, Rena R. Wing, Susan Jeffries, Gaye Koenning, M. Kaye Kramer, Susan Barr, Catherine Benchoff, Miriam Boraz, Lisa Clifford, Rebecca Culyba, Marlene Frazier, Ryan Gilligan, Stephanie Guimond, Susan Harrier, Louann Harris, Andrea Kriska, Bonny Rockette-Wagner, Qurashia Manjoo, Monica Mullen, Alicia Noel, Amy Otto, Jessica Pettigrew, Debra Rubinstein, Linda Semler, Cheryl F. Smith, Elizabeth Venditti, Valarie Weinzierl, Katherine V. Williams, Tara Wilson, Richard F. Arakaki, Renee W. Latimer, Narleen K. Baker-Ladao, Mae K. Isonaga, Ralph Beddow, Nina E. Bermudez, Lorna Dias, Jillian Inouye, Marjorie K. Mau, John S. Melish, Kathy Mikami, Pharis Mohideen, Sharon K. Odom, Raynette U. Perry, Robin E. Yamamoto, William C. Knowler, Norman Cooeyate, Mary A. Hoskin, Carol A. Percy, Alvera Enote, Camille Natewa, Kelly J. Acton, Vickie L. Andre, Rosalyn Barber, Shandiin Begay, Peter H. Bennett, Mary Beth Benson, Evelyn C. Bird, Brenda A. Broussard, Brian C. Bucca, Marcella Chavez, Sherron Cook, Jeff Curtis, Tara Dacawyma, Matthew S. Doughty, Roberta Duncan, Charlotte Dodge, Cyndy Edgerton, Jacqueline M. Ghahate, Justin Glass, Martia Glass, Dorothy Gohdes, Wendy Grant, Robert L. Hanson, Ellie Horse, Louise E. Ingraham, Merry Jackson, Priscilla Jay, Roylen S. Kaskalla, Kathleen M. Kobus, Jonathan Krakoff, Jason Kurland, Catherine Manus, Cherie McCabe, Sara Michaels, Tina Morgan, Yolanda Nashboo, Julie A. Nelson, Steven Poirier, Evette Polczynski, Christopher Piromalli, Jeanine Roumain, Debra Rowse, Robert J. Roy, Sandra Sangster, Janet Sewenemewa, Miranda Smart, Darryl Tonemah, Charlton Wilson, Michelle Yazzie, Raymond Bain, Sarah Fowler, Marinella Temprosa, Michael D. Larsen, Tina Brenneman, Sharon L. Edelstein, Solome Abebe, Julie Bamdad, Melanie Barkalow, Joel Bethepu, Tsedenia Bezabeh, Nicole Butler, Jackie Callaghan, Caitlin E. Carter, Costas Christophi, Gregory M. Dwyer, Mary Foulkes, Yuping Gao, Robert Gooding, Adrienne Gottlieb, Kristina L. Grimes, Nisha Grover-Fairchild, Lori Haffner, Heather Hoffman, Kathleen Jablonski, Tara L. Jones, Richard Katz, Preethy Kolinjivadi, John M. Lachin, Pamela Mucik, Robert Orlosky, Qing Pan, Susan Reamer, James Rochon, Alla Sapozhnikova, Hanna Sherif, Charlotte Stimpson, Ashley Hogan Tjaden, Fredricka Walker-Murray, Elizabeth M. Venditti, Andrea M. Kriska, Valerie Weinzierl, Jessica Harting, F. Alan Aldrich, John Albers, Greg Strylewicz, R. Eastman, Judith Fradkin, Sanford Garfield, Edward Gregg, Morton B. Brown, David Altshuler, Liana K. Billings, Maegan Harden, Toni I. Pollin, Alan R. Shuldiner, Paul W. Franks, and Marie-France Hivert
Subjects: Diseases of the endocrine glands. Clinical endocrinology, RC648-665
Abstract: Introduction Sex hormone binding globulin (SHBG) levels are reported to be inversely associated with diabetes risk. It is unknown whether diabetes prevention interventions increase SHBG and whether resultant changes in SHBG affect diabetes risk. The purpose of this analysis was to determine whether intensive lifestyle intervention (ILS) or metformin changed circulating SHBG and if resultant changes influenced diabetes risk in the Diabetes Prevention Program (DPP).Research design and methods This is a secondary analysis from the DPP (1996–2001), a randomized trial of ILS or metformin versus placebo on diabetes risk over a mean follow-up of 3.2 years. The DPP was conducted across 27 academic study centers in the USA. Men, premenopausal and postmenopausal women without hormone use in the DPP were evaluated. The DPP included overweight/obese persons with elevated fasting glucose and impaired glucose tolerance. Main outcomes measures were changes in SHBG levels at 1 year and risk of diabetes over 3 years.Results ILS resulted in significantly higher increases (postmenopausal women: p
Published: 2020
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23. Genetic inactivation of ANGPTL4 improves glucose homeostasis and is associated with reduced risk of diabetes

Author: Viktoria Gusarova, Colm O’Dushlaine, Tanya M. Teslovich, Peter N. Benotti, Tooraj Mirshahi, Omri Gottesman, Cristopher V. Van Hout, Michael F. Murray, Anubha Mahajan, Jonas B. Nielsen, Lars Fritsche, Anders Berg Wulff, Daniel F. Gudbjartsson, Marketa Sjögren, Connor A. Emdin, Robert A. Scott, Wen-Jane Lee, Aeron Small, Lydia C. Kwee, Om Prakash Dwivedi, Rashmi B. Prasad, Shannon Bruse, Alexander E. Lopez, John Penn, Anthony Marcketta, Joseph B. Leader, Christopher D. Still, H. Lester Kirchner, Uyenlinh L. Mirshahi, Amr H. Wardeh, Cassandra M. Hartle, Lukas Habegger, Samantha N. Fetterolf, Teresa Tusie-Luna, Andrew P. Morris, Hilma Holm, Valgerdur Steinthorsdottir, Patrick Sulem, Unnur Thorsteinsdottir, Jerome I. Rotter, Lee-Ming Chuang, Scott Damrauer, David Birtwell, Chad M. Brummett, Amit V. Khera, Pradeep Natarajan, Marju Orho-Melander, Jason Flannick, Luca A. Lotta, Cristen J. Willer, Oddgeir L. Holmen, Marylyn D. Ritchie, David H. Ledbetter, Andrew J. Murphy, Ingrid B. Borecki, Jeffrey G. Reid, John D. Overton, Ola Hansson, Leif Groop, Svati H. Shah, William E. Kraus, Daniel J. Rader, Yii-Der I. Chen, Kristian Hveem, Nicholas J. Wareham, Sekar Kathiresan, Olle Melander, Kari Stefansson, Børge G. Nordestgaard, Anne Tybjærg-Hansen, Goncalo R. Abecasis, David Altshuler, Jose C. Florez, Michael Boehnke, Mark I. McCarthy, George D. Yancopoulos, David J. Carey, Alan R. Shuldiner, Aris Baras, Frederick E. Dewey, and Jesper Gromada
Subjects: Science
Abstract: Genetic variation in ANGPTL4 is associated with lipid traits. Here, the authors find that predicted loss-of-function variants in ANGPTL4 are associated with glucose homeostasis and reduced risk of type 2 diabetes and that Angptl4 −/− mice on a high-fat diet show improved insulin sensitivity.
Published: 2018
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24. Gain-of-Function Claims for Type-2-Diabetes-Associated Coding Variants in SLC16A11 Are Not Supported by the Experimental Data

Author: Eitan Hoch, Jose C. Florez, Eric S. Lander, and Suzanne B.R. Jacobs
Subjects: Biology (General), QH301-705.5
Abstract: Summary: Human genetic variants in SLC16A11 are associated with increased risk of type 2 diabetes (T2D). We previously identified two distinct mechanisms through which co-inherited T2D-risk coding and non-coding variants disrupt SLC16A11 expression and activity, thus implicating reduced SLC16A11 function as the disease-relevant direction of effect. In a recent publication, Zhao et al. (2019a) argue that human SLC16A11 coding variants confer gain of function, basing their conclusions on phenotypic changes observed following overexpression of mutant murine Slc16a11. However, data necessary to demonstrate gain-of-function activity are not reported. Furthermore, several fundamental flaws in their experimental system—including inaccurate modeling of the human variant haplotype and expression conditions that are not physiologically relevant—prevent conclusions about T2D-risk variant effects on human physiology. This Matters Arising paper is in response to Zhao et al. (2019a), published in Cell Reports. See also the response by Zhao et al. (2019b) in this issue of Cell Reports. : Hoch et al. discuss the analysis of coding variants of SLC16A11 in mice and humans in light of a recent Cell Reports publication. This Matters Arising paper is in response to Zhao et al. (2019a), published in Cell Reports. See also the response by Zhao et al. (2019b), published in this issue. Keywords: SLC16A11, type 2 diabetes, T2D
Published: 2019
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25. Publisher Correction: Sex-dimorphic genetic effects and novel loci for fasting glucose and insulin variability

Author: Vasiliki Lagou, Reedik Mägi, Jouke- Jan Hottenga, Harald Grallert, John R. B. Perry, Nabila Bouatia-Naji, Letizia Marullo, Denis Rybin, Rick Jansen, Josine L. Min, Antigone S. Dimas, Anna Ulrich, Liudmila Zudina, Jesper R. Gådin, Longda Jiang, Alessia Faggian, Amélie Bonnefond, Joao Fadista, Maria G. Stathopoulou, Aaron Isaacs, Sara M. Willems, Pau Navarro, Toshiko Tanaka, Anne U. Jackson, May E. Montasser, Jeff R. O’Connell, Lawrence F. Bielak, Rebecca J. Webster, Richa Saxena, Jeanette M. Stafford, Beate St Pourcain, Nicholas J. Timpson, Perttu Salo, So-Youn Shin, Najaf Amin, Albert V. Smith, Guo Li, Niek Verweij, Anuj Goel, Ian Ford, Paul C. D. Johnson, Toby Johnson, Karen Kapur, Gudmar Thorleifsson, Rona J. Strawbridge, Laura J. Rasmussen-Torvik, Tõnu Esko, Evelin Mihailov, Tove Fall, Ross M. Fraser, Anubha Mahajan, Stavroula Kanoni, Vilmantas Giedraitis, Marcus E. Kleber, Günther Silbernagel, Julia Meyer, Martina Müller-Nurasyid, Andrea Ganna, Antti-Pekka Sarin, Loic Yengo, Dmitry Shungin, Jian’an Luan, Momoko Horikoshi, Ping An, Serena Sanna, Yvonne Boettcher, N. William Rayner, Ilja M. Nolte, Tatijana Zemunik, Erik van Iperen, Peter Kovacs, Nicholas D. Hastie, Sarah H. Wild, Stela McLachlan, Susan Campbell, Ozren Polasek, Olga Carlson, Josephine Egan, Wieland Kiess, Gonneke Willemsen, Johanna Kuusisto, Markku Laakso, Maria Dimitriou, Andrew A. Hicks, Rainer Rauramaa, Stefania Bandinelli, Barbara Thorand, Yongmei Liu, Iva Miljkovic, Lars Lind, Alex Doney, Markus Perola, Aroon Hingorani, Mika Kivimaki, Meena Kumari, Amanda J. Bennett, Christopher J. Groves, Christian Herder, Heikki A. Koistinen, Leena Kinnunen, Ulf de Faire, Stephan J. L. Bakker, Matti Uusitupa, Colin N. A. Palmer, J. Wouter Jukema, Naveed Sattar, Anneli Pouta, Harold Snieder, Eric Boerwinkle, James S. Pankow, Patrik K. Magnusson, Ulrika Krus, Chiara Scapoli, Eco J. C. N. de Geus, Matthias Blüher, Bruce H. R. Wolffenbuttel, Michael A. Province, Goncalo R. Abecasis, James B. Meigs, G. Kees Hovingh, Jaana Lindström, James F. Wilson, Alan F. Wright, George V. Dedoussis, Stefan R. Bornstein, Peter E. H. Schwarz, Anke Tönjes, Bernhard R. Winkelmann, Bernhard O. Boehm, Winfried März, Andres Metspalu, Jackie F. Price, Panos Deloukas, Antje Körner, Timo A. Lakka, Sirkka M. Keinanen-Kiukaanniemi, Timo E. Saaristo, Richard N. Bergman, Jaakko Tuomilehto, Nicholas J. Wareham, Claudia Langenberg, Satu Männistö, Paul W. Franks, Caroline Hayward, Veronique Vitart, Jaakko Kaprio, Sophie Visvikis-Siest, Beverley Balkau, David Altshuler, Igor Rudan, Michael Stumvoll, Harry Campbell, Cornelia M. van Duijn, Christian Gieger, Thomas Illig, Luigi Ferrucci, Nancy L. Pedersen, Peter P. Pramstaller, Michael Boehnke, Timothy M. Frayling, Alan R. Shuldiner, Patricia A. Peyser, Sharon L. R. Kardia, Lyle J. Palmer, Brenda W. Penninx, Pierre Meneton, Tamara B. Harris, Gerjan Navis, Pim van der Harst, George Davey Smith, Nita G. Forouhi, Ruth J. F. Loos, Veikko Salomaa, Nicole Soranzo, Dorret I. Boomsma, Leif Groop, Tiinamaija Tuomi, Albert Hofman, Patricia B. Munroe, Vilmundur Gudnason, David S. Siscovick, Hugh Watkins, Cecile Lecoeur, Peter Vollenweider, Anders Franco-Cereceda, Per Eriksson, Marjo-Riitta Jarvelin, Kari Stefansson, Anders Hamsten, George Nicholson, Fredrik Karpe, Emmanouil T. Dermitzakis, Cecilia M. Lindgren, Mark I. McCarthy, Philippe Froguel, Marika A. Kaakinen, Valeriya Lyssenko, Richard M. Watanabe, Erik Ingelsson, Jose C. Florez, Josée Dupuis, Inês Barroso, Andrew P. Morris, Inga Prokopenko, and Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC)
Subjects: Science
Abstract: A Correction to this paper has been published: https://doi.org/10.1038/s41467-021-21276-3
Published: 2021
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26. An exome array study of the plasma metabolome

Author: Eugene P. Rhee, Qiong Yang, Bing Yu, Xuan Liu, Susan Cheng, Amy Deik, Kerry A. Pierce, Kevin Bullock, Jennifer E. Ho, Daniel Levy, Jose C. Florez, Sek Kathiresan, Martin G. Larson, Ramachandran S. Vasan, Clary B. Clish, Thomas J. Wang, Eric Boerwinkle, Christopher J. O’Donnell, and Robert E. Gerszten
Subjects: Science
Abstract: Several GWAS have identified many common variants associated with blood metabolites. Here, the authors use an exome array to identify low frequency, potentially functional variants that impact human metabolism.
Published: 2016
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27. Genome-wide association studies in the Japanese population identify seven novel loci for type 2 diabetes

Author: Minako Imamura, Atsushi Takahashi, Toshimasa Yamauchi, Kazuo Hara, Kazuki Yasuda, Niels Grarup, Wei Zhao, Xu Wang, Alicia Huerta-Chagoya, Cheng Hu, Sanghoon Moon, Jirong Long, Soo Heon Kwak, Asif Rasheed, Richa Saxena, Ronald C. W. Ma, Yukinori Okada, Minoru Iwata, Jun Hosoe, Nobuhiro Shojima, Minaka Iwasaki, Hayato Fujita, Ken Suzuki, John Danesh, Torben Jørgensen, Marit E. Jørgensen, Daniel R. Witte, Ivan Brandslund, Cramer Christensen, Torben Hansen, Josep M. Mercader, Jason Flannick, Hortensia Moreno-Macías, Noël P. Burtt, Rong Zhang, Young Jin Kim, Wei Zheng, Jai Rup Singh, Claudia H. T. Tam, Hiroshi Hirose, Hiroshi Maegawa, Chikako Ito, Kohei Kaku, Hirotaka Watada, Yasushi Tanaka, Kazuyuki Tobe, Ryuzo Kawamori, Michiaki Kubo, Yoon Shin Cho, Juliana C. N. Chan, Dharambir Sanghera, Philippe Frossard, Kyong Soo Park, Xiao-Ou Shu, Bong-Jo Kim, Jose C. Florez, Teresa Tusié-Luna, Weiping Jia, E Shyong Tai, Oluf Pedersen, Danish Saleheen, Shiro Maeda, and Takashi Kadowaki
Subjects: Science
Abstract: Here, Imamuraet al. conduct meta-analysis of genome-wide association studies to identify novel susceptibility loci for type 2 diabetes (T2D) in the Japanese population. By doing so, this study shows that both ethnicity-specific and ethnically-shared genetic loci can contribute to T2D risk.
Published: 2016
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28. The Genetic Basis of Type 2 Diabetes in Hispanics and Latin Americans: Challenges and Opportunities

Author: Josep M. Mercader and Jose C. Florez
Subjects: genetic basis, type 2 diabetes, Hispanic, Latin Americans, heritability, Public aspects of medicine, RA1-1270
Abstract: Type 2 diabetes (T2D) affects 415 million people worldwide, and has a much higher prevalence in Hispanics (16.9%), compared to non-Hispanic whites (10.2%). Genome-wide association studies and whole-genome and whole-exome sequencing studies have discovered more than 100 genetic regions associated with modified risk for T2D. However, the identified genetic factors explain a very small fraction of the estimated heritability. Until recently, little attention has been put in studying other non European populations that suffer from a higher burden of T2D, such as Hispanics/Latinos. In the past few years, genetic studies in Hispanic populations have started to provide new insights into the genetic architecture of T2D in this ancestry group. Of note, several genetic variants that are absent or very rare in non-Hispanic populations but more common in Hispanics have shown from moderate to strong association with T2D and have provided new insights into the biology of T2D, which may be ultimately useful for developing novel therapeutic strategies applicable to all populations. Studying diverse populations can also improve the ability to find the causal variants in known T2D loci by a multi-ancestry fine-mapping approach, which leverages the different patterns of linkage disequilibrium between the causal and the ascertained genetic variants. In this mini-review, we summarize the main genetic findings discovered in Hispanics and discuss the limitations and challenges of performing genetic studies in these populations. Finally, we present possible next steps to make studies in Latino populations more valuable in providing a deeper understanding of T2D and anticipate their future application to the development of predictive and preventive medicine and personalized therapies.
Published: 2017
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29. Loci for insulin processing and secretion provide insight into type 2 diabetes risk

Author: K. Alaine Broadaway, Xianyong Yin, Alice Williamson, Victoria A. Parsons, Emma P. Wilson, Anne H. Moxley, Swarooparani Vadlamudi, Arushi Varshney, Anne U. Jackson, Vasudha Ahuja, Stefan R. Bornstein, Laura J. Corbin, Graciela E. Delgado, Om P. Dwivedi, Lilian Fernandes Silva, Timothy M. Frayling, Harald Grallert, Stefan Gustafsson, Liisa Hakaste, Ulf Hammar, Christian Herder, Sandra Herrmann, Kurt Højlund, David A. Hughes, Marcus E. Kleber, Cecilia M. Lindgren, Ching-Ti Liu, Jian’an Luan, Anni Malmberg, Angela P. Moissl, Andrew P. Morris, Nikolaos Perakakis, Annette Peters, John R. Petrie, Michael Roden, Peter E.H. Schwarz, Sapna Sharma, Angela Silveira, Rona J. Strawbridge, Tiinamaija Tuomi, Andrew R. Wood, Peitao Wu, Björn Zethelius, Damiano Baldassarre, Johan G. Eriksson, Tove Fall, Jose C. Florez, Andreas Fritsche, Bruna Gigante, Anders Hamsten, Eero Kajantie, Markku Laakso, Jari Lahti, Deborah A. Lawlor, Lars Lind, Winfried März, James B. Meigs, Johan Sundström, Nicholas J. Timpson, Robert Wagner, Mark Walker, Nicholas J. Wareham, Hugh Watkins, Inês Barroso, Stephen O’Rahilly, Niels Grarup, Stephen CJ. Parker, Michael Boehnke, Claudia Langenberg, Eleanor Wheeler, and Karen L. Mohlke
Subjects: Genetics, ALSPAC, Genetics (clinical)
Abstract: Insulin secretion is critical for glucose homeostasis, and increased levels of the precursor proinsulin relative to insulin indicate pancreatic islet beta-cell stress and insufficient insulin secretory capacity in the setting of insulin resistance. We conducted meta-analyses of genome-wide association results for fasting proinsulin from 16 European-ancestry studies in 45,861 individuals. We found 36 independent signals at 30 loci (p-value < 5x10-8), which validated 12 previously reported loci for proinsulin and 10 additional loci previously identified for another glycemic trait. Half of the alleles associated with higher proinsulin showed higher vs. lower effects on glucose levels, corresponding to different mechanisms. Proinsulin loci included genes that affect prohormone convertases, beta-cell dysfunction, vesicle trafficking, beta-cell transcriptional regulation, and lysosomes/autophagy processes. We colocalized 11 proinsulin signals with islet expression quantitative trait loci (eQTL) data, suggesting candidate genes including ARSG, WIPI1, SLC7A14, and SIX3. The NKX6-3/ANK1 proinsulin signal colocalized with a T2D signal and an adipose ANK1 eQTL signal, but not the islet NKX6-3 eQTL. Signals were enriched for islet enhancers, and we showed a plausible islet regulatory mechanism for the lead signal in the MADD locus. These results show how detailed genetic studies of an intermediate phenotype can elucidate mechanisms predisposing to disease.
Published: 2023

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

Author: Hyunkyung Kim, Kenneth E. Westerman, Kirk Smith, Joshua Chiou, Joanne B. Cole, Timothy Majarian, Marcin von Grotthuss, Josep M. Mercader, Soo Heon Kwak, Jaegil Kim, Jose C. Florez, Kyle Gaulton, Alisa K. Manning, and Miriam S. Udler
Subjects: Endocrinology, Diabetes and Metabolism, Internal Medicine
Abstract: Aims/hypothesisType 2 diabetes (T2D) is highly polygenic and influenced by multiple biological pathways. Rapid expansion in the number of T2D loci can be leveraged to identify such pathways, thus facilitating improved disease management.MethodsWe developed a high-throughput pipeline to enable clustering of T2D loci based on variant-trait associations. Our pipeline extracted summary statistics from genome-wide association studies (GWAS) for T2D and related traits to generate a matrix of 324 variant x 64 trait associations and applied Bayesian Non-negative Factorization (bNMF) to identify genetic components of T2D. We generated cluster-specific polygenic scores and performed regression analysis in an independent cohort (N=25,419) to assess for clinical relevance.ResultsWe identified ten clusters, replicating the five from our prior analysis as well as novel clusters related to beta-cell dysfunction, pronounced insulin secretion, and levels of alkaline phosphatase, lipoprotein-A, and sex hormone-binding globulin. Four clusters related to mechanisms of insulin deficiency, five to insulin resistance, and one had an unclear mechanism. The clusters displayed tissue-specific epigenomic enrichment, notably with the two beta-cell clusters differentially enriched in functional and stressed pancreatic beta-cell states. Additionally, cluster-specific polygenic scores were differentially associated with patient clinical characteristics and outcomes. The pipeline was applied to coronary artery disease and chronic kidney disease, identifying multiple shared genetic pathways with T2D.Conclusions/interpretationOur approach stratifies T2D loci into physiologically meaningful genetic clusters associated with distinct tissues and clinical outcomes. The pipeline allows for efficient updating as additional GWAS become available and can be readily applied to other conditions, facilitating clinical translation of GWAS findings. Software to perform this clustering pipeline is freely available.
Published: 2022

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

Author: Peter, Dornbos, Ryan, Koesterer, Andrew, Ruttenburg, Trang, Nguyen, Joanne B, Cole, Aaron, Leong, James B, Meigs, Jose C, Florez, Jerome I, Rotter, Miriam S, Udler, and Jason, Flannick
Subjects: Glycated Hemoglobin, Multifactorial Inheritance, Diabetes Mellitus, Type 2, Genetics, Humans, Precision Medicine, Article, Genome-Wide Association Study
Abstract: Polygenic scores (PGS) combine the effects of common genetic variants(1,2) to predict risk or treatment strategies for complex diseases(3–7). Adding rare variation to PGS has largely unknown benefit and is methodically challenging. Here we developed a method for constructing rare variant PGS and applied it to calculate genetically modified hemoglobin A1C (HbA1C) thresholds for type 2 diabetes (T2D) diagnosis(7–10). The resultant rare variant PGS is highly polygenic (21,293 variants across 154 genes), depends on ultra-rare variants (72.7% observed in
Published: 2022

32. On the Verge of Precision Medicine in Diabetes

Author: Josephine H. Li and Jose C. Florez
Subjects: Pharmacology (medical)
Published: 2022

33. Genome-Wide Association Study of the Modified Stumvoll Insulin Sensitivity Index Identifies BCL2 and FAM19A2 as Novel Insulin Sensitivity Loci

Author: Richard A. Jensen, Denis Rybin, Jaakko Tuomilehto, Reedik Mägi, Inga Prokopenko, Jeffrey R. O'Connell, Marcus E. Kleber, Han Chen, Geoffrey A. Walford, Allan Linnenberg, Anke Tönjes, Francis S. Collins, Sonsoles Morcillo, Gemma Rojo-Martínez, Kenneth Rice, Jose C. Florez, Leif Groop, Manuel Serrano-Ríos, Josée Dupuis, Alisa K. Manning, Peter Kovacs, Torben Jørgensen, Graciela E. Delgado, Alena Stančáková, Hans-Ulrich Häring, Claudia Langenberg, Joshua P. Lewis, Norbert Stefan, Markku Laakso, Torben Hansen, Bruce M. Psaty, Jian'an Luan, Michael Stumvoll, Mark O. Goodarzi, Robert A. Scott, Oluf Pedersen, Ching-Ti Liu, Michael N. Weedon, Michael Boehnke, Ulf Smith, David S. Siscovick, Aaron Leong, Weijia Xie, James B. Meigs, Claes Landenvall, Anne U. Jackson, Joseph M. Zmuda, Mary L. Biggs, Jerome I. Rotter, Federico Soriguer, Winfried März, Zhongyang Zhang, May E. Montasser, Arturo Corbatón-Anchuelo, J M Gómez-Zumaquero, Günther Silbernagel, Kristine Færch, Karen L. Mohlke, Heikki A. Koistinen, Yii-Der Ida Chen, Jaeyoung Hong, Gracia María Martín-Núñez, María Teresa Martínez-Larrad, Emil V. R. Appel, Niels Grarup, Harald Staiger, Johanna Kuusisto, Lars Lind, Nicholas J. Wareham, Andreas Fritsche, Stefan Gustafsson, Andrew P. Morris, Richard N. Bergman, Mark Walker, Cecilia M. Lindgren, Erik Ingelsson, Jorge R. Kizer, Timothy M. Frayling, and Fausto Machicao
Subjects: 0301 basic medicine, Male, Insulin Receptor Substrate Proteins, Endocrinology, Diabetes and Metabolism, Locus (genetics), Genome-wide association study, Polymorphism, Single Nucleotide, 03 medical and health sciences, Endocrinology & Metabolism, Insulin resistance, Genotype, Internal Medicine, medicine, Humans, Genetic Predisposition to Disease, Genetic association, Genetics, business.industry, Insulin sensitivity, Genetics/Genomes/Proteomics/Metabolomics, 11 Medical And Health Sciences, medicine.disease, IRS1, 030104 developmental biology, Proto-Oncogene Proteins c-bcl-2, Chemokines, CC, Female, Insulin Resistance, business, Genome-Wide Association Study
Abstract: Genome-wide association studies (GWAS) have found few common variants that influence fasting measures of insulin sensitivity. We hypothesized that a GWAS of an integrated assessment of fasting and dynamic measures of insulin sensitivity would detect novel common variants. We performed a GWAS of the modified Stumvoll Insulin Sensitivity Index (ISI) within the Meta-Analyses of Glucose and Insulin-Related Traits Consortium. Discovery for genetic association was performed in 16,753 individuals, and replication was attempted for the 23 most significant novel loci in 13,354 independent individuals. Association with ISI was tested in models adjusted for age, sex, and BMI and in a model analyzing the combined influence of the genotype effect adjusted for BMI and the interaction effect between the genotype and BMI on ISI (model 3). In model 3, three variants reached genome-wide significance: rs13422522 (NYAP2; P = 8.87 × 10−11), rs12454712 (BCL2; P = 2.7 × 10−8), and rs10506418 (FAM19A2; P = 1.9 × 10−8). The association at NYAP2 was eliminated by conditioning on the known IRS1 insulin sensitivity locus; the BCL2 and FAM19A2 associations were independent of known cardiometabolic loci. In conclusion, we identified two novel loci and replicated known variants associated with insulin sensitivity. Further studies are needed to clarify the causal variant and function at the BCL2 and FAM19A2 loci.
Published: 2023
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34. DISTINCT INSULIN PHYSIOLOGY TRAJECTORIES IN EUGLYCEMIC PREGNANCY AND GESTATIONAL DIABETES MELLITUS

Author: Tanayott Thaweethai, Zainab Soetan, Kaitlyn James, Jose C. Florez, and Camille E. Powe
Subjects: Advanced and Specialized Nursing, Endocrinology, Diabetes and Metabolism, Internal Medicine
Abstract: OBJECTIVE To evaluate changes in insulin physiology in euglycemic pregnancy and gestational diabetes mellitus (GDM). RESEARCH DESIGN AND METHODS Participants underwent oral glucose tolerance tests at ≤15 weeks’ gestation (early pregnancy), 24–32 weeks’ gestation (mid-late pregnancy), and 6–24 weeks postpartum. We evaluated longitudinal changes in insulin secretory response (log Stumvoll first-phase estimate) and insulin sensitivity (log Matsuda index) using linear mixed models. We then evaluated participants who met GDM criteria in early pregnancy (early GDM) and mid-late pregnancy (classic GDM) separately from those without GDM. We derived the pregnancy insulin physiology (PIP) index to quantify β-cell compensation for insulin resistance. RESULTS Among 166 participants, 21 had early GDM and 24 developed classic GDM. Insulin sensitivity was reduced slightly in early pregnancy (β = −0.20, P < 0.001) and substantially in mid-late pregnancy (β = −0.47, P < 0.001) compared with postpartum. Insulin secretory response (adjusted for insulin sensitivity) was augmented in early pregnancy (β = 0.16, P < 0.001) and mid-late pregnancy (β = 0.16, P = 0.001) compared with postpartum. Compared with postpartum, the PIP index was augmented in early pregnancy (β = 215, P = 0.04) but not mid-late pregnancy (β = 55, P = 0.64). Early GDM was distinguished by a substantial reduction in early pregnancy insulin sensitivity (β = −0.59, P < 0.001) compared with postpartum. Both early and classic GDM lacked evidence of early pregnancy augmentation of insulin secretory response (adjusted for insulin sensitivity) and the PIP index (P > 0.1 vs. postpartum). Early pregnancy PIP index predicted GDM independent of participant characteristics (area under the curve without PIP index 0.70 [95% CI 0.61–0.79], area under the curve with PIP index 0.87 [95% CI 0.80–0.93]). CONCLUSIONS β-Cell function is enhanced in early pregnancy. Deficient first-trimester β-cell function predicts GDM.
Published: 2023

35. Response to Comment on Dawed et al. Genome-Wide Meta-analysis Identifies Genetic Variants Associated With Glycemic Response to Sulfonylureas. Diabetes Care 2021;44:2673-2682

Author: Adem Y, Dawed, Sook Wah, Yee, Kaixin, Zhou, Nienke, van Leeuwen, Yanfei, Zhang, Moneeza K, Siddiqui, Amy, Etheridge, Federico, Innocenti, Fei, Xu, Josephine H, Li, Joline W, Beulens, Amber A, van der Heijden, Roderick C, Slieker, Yu-Chuan, Chang, Josep M, Mercader, Varinderpal, Kaur, John S, Witte, Ming Ta Michael, Lee, Yoichiro, Kamatani, Yukihide, Momozawa, Michiaki, Kubo, Colin N A, Palmer, Jose C, Florez, Monique M, Hedderson, Leen M, 't Hart, Kathleen M, Giacomini, and Ewan R, Pearson
Subjects: Advanced and Specialized Nursing, Endocrinology, Diabetes and Metabolism, Internal Medicine
Published: 2023

36. Genetic Predictors of Change in Waist Circumference and Waist-to-Hip Ratio With Lifestyle Intervention

Author: Jeanne M. McCaffery, Kathleen A. Jablonski, Qing Pan, Arne Astrup, Malene Revsbech Christiansen, Dolores Corella, Lauren M.L. Corso, Jose C. Florez, Paul W. Franks, Christopher Gardner, Torben Hansen, Tuomas O. Kilpeläinen, William C. Knowler, Jaana Lindström, Wim H.M. Saris, Thorkild I.A. Sørensen, Jaakko Tuomilehto, Matti Uusitupa, Rena R. Wing, Tanya Agurs-Collins, RS: NUTRIM - R1 - Obesity, diabetes and cardiovascular health, and Humane Biologie
Subjects: Male, Waist-Hip Ratio, Endocrinology, Diabetes and Metabolism, FAT DISTRIBUTION, PREVENTION, Body Mass Index, TYPE-2, DESIGN, ADIPOSE, CARDIOVASCULAR-DISEASE, OBESITY, Weight Loss, Internal Medicine, RISK-FACTORS, Humans, Female, TRIAL, Waist Circumference, Life Style, Obesity Studies, Adiposity, Genome-Wide Association Study, ALL-CAUSE MORTALITY
Abstract: Genome-wide association studies have identified single nucleotide polymorphisms (SNPs) associated with waist circumference and waist-to-hip ratio adjusted for body mass index (WCadjBMI, WHRadjBMI), but it remains unclear whether these SNPs relate to change in WCadjBMI or WHRadjBMI with lifestyle intervention. We hypothesized that polygenic scores (PS) comprised of 59 SNPs previously associated with central adiposity would predict less of a reduction in WCadjBMI or WHRadjBMI at eight-to-ten weeks in two lifestyle intervention trials, NUGENOB and DiOGenes, and at one year in 5 lifestyle intervention trials, Look AHEAD, Diabetes Prevention Program, Diabetes Prevention Study, DIETFITS, and PREDIMED-Plus. One standard deviation higher PS related to smaller one-year change in WCadjBMI in the lifestyle intervention arms at year 1, and thus predicted poorer response (beta=0.007, SE=0.003, p=0.03) among White participants overall and in White men (beta=0.01, SE=0.004, p=0.01). At average weight loss, this amounted to 0.20-0.28 cm per standard deviation. No significant findings emerged in White women, African American men, for the 8-10 week outcomes or for WHRadjBMI. Findings were heterogeneous in African American women. These results indicate that polygenic risk relates to change in WCadjBMI with lifestyle intervention but the effects are small and not of sufficient magnitude to be clinically significant.
Published: 2022

37. Oral Glucose Tolerance Test-based Measures of Insulin Secretory Response in Pregnancy

Author: Camille E Powe, Joseph J Locascio, Larraine Huston Gordesky, Jose C Florez, and Patrick M Catalano
Subjects: Endocrinology, Endocrinology, Diabetes and Metabolism, Biochemistry (medical), Clinical Biochemistry, Online Only Articles, Biochemistry
Abstract: Background Oral glucose tolerance test (OGTT)-based measures of insulin secretory response have not been validated in pregnancy. Methods In a secondary analysis of a longitudinal study, participants were studied prepregnancy (n = 40), in early pregnancy (n = 36; 12-14 weeks’ gestation), and in late pregnancy (n = 36; 34-36 weeks’ gestation). Participants underwent an OGTT, an intravenous glucose tolerance test (IVGTT), and a hyperinsulinemic-euglycemic clamp at each timepoint. We calculated homeostatic model assessment of beta-cell function (HOMA-2B), insulinogenic index (IGI), corrected insulin response (CIR), ratio of the area under the insulin curve and the area under the glucose curve (AUCins/AUCglu), and Stumvoll first-phase estimate (Stumvoll) from OGTT insulin and glucose levels. We used Pearson correlation to compare measures from OGTT and IVGTT. We used mixed effects models to examine longitudinal changes in insulin secretory response. Results Stumvoll was the only OGTT-based measure that was significantly correlated with first-phase insulin response prior to and across gestation (prepregnancy: r = 0.44, P = 0.01; early pregnancy: r = 0.67, P = 0.0001; late pregnancy: r = 0.67, P = 0.0001). In early and late pregnancy, AUCins/AUCglu had the strongest correlation with first-phase insulin response (early pregnancy: r = 0.79, P Conclusions Stumvoll and AUCins/AUCglu are valid OGTT-based insulin secretory response measures for pregnancy studies.
Published: 2022

38. Abstract P240: Higher Body Mass Index is Associated With Lower Placental Expression of EPYC: Results From a Genome-Wide Transcriptomic Study in the Gen3G Cohort

Author: Sana Majid, Joanne E Sordillo, Frédérique White, Francois Aguet, Kristin G Ardlie, Ananth A Karumanchi, Jose C Florez, Camille Powe, Andrea G Edlow, Luigi Bouchard, Pierre-Étienne Jacques, and Marie-France Hivert
Subjects: Physiology (medical), Cardiology and Cardiovascular Medicine
Abstract: Background: Elevated maternal body mass index (BMI) in pregnancy is associated with adverse maternal and fetal outcomes. Identification of alterations in the placental transcriptome may elucidate molecular markers of the biological pathways underlying these associations. Methods: We investigated the association of first trimester maternal BMI with the placental transcriptome in Gen3G, a large population-based pregnancy cohort. We completed RNA sequencing of 449 placental samples and performed differential gene expression analysis of over 15,000 genes (Limma Voom). We used maternal BMI as a continuous variable, with estimates reported as change in normalized gene expression per 1-unit increase in BMI (kg/m2), as well as a categorical variable (participants with normal weight (N=270) vs. obesity (N=82)), with RNA expression estimates reported as log2 fold changes. We adjusted models for maternal age, fetal sex, gestational age and 6 surrogate variables inferred from the expression data to adjust for batch effects and other unmeasured sources of variability. We accounted for multiple testing using False Discovery Rate (FDR). Sensitivity analyses were performed by removing cases of gestational diabetes and hypertensive disorders in pregnancy. Results: At enrollment in early pregnancy, participants had a mean ± SD age of 28.2 ± 4.4 years and measured BMI of 25.4 ± 5.5 kg/m 2 ; 57% of participants were categorized in normal weight, 22% in overweight and 18% in obesity BMI categories. Maternal BMI was significantly associated with decreased expression of EPYC , a gene that regulates fibrillogenesis (slope= -0.14, FDR-adjusted P=1.68x10 -5 for continuous BMI; log2 fold change= -1.71, FDR-adjusted P=1.06x10 -2 for obesity vs. normal weight). Both sensitivity analyses with the removal of gestational diabetes and hypertensive disorders revealed similar associations for EPYC . Conclusions: We identified an association between elevated maternal BMI and downregulation of EPYC expression in the placenta. Additional studies are required to examine the role of placental EPYC expression in maternal and fetal health.
Published: 2023

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

Author: Maria C. Costanzo, Marcin von Grotthuss, Jeffrey Massung, Dongkeun Jang, Lizz Caulkins, Ryan Koesterer, Clint Gilbert, Ryan P. Welch, Parul Kudtarkar, Quy Hoang, Andrew P. Boughton, Preeti Singh, Ying Sun, Marc Duby, Annie Moriondo, Trang Nguyen, Patrick Smadbeck, Benjamin R. Alexander, MacKenzie Brandes, Mary Carmichael, Peter Dornbos, Todd Green, Kenneth C. Huellas-Bruskiewicz, Yue Ji, Alexandria Kluge, Aoife C. McMahon, Josep M. Mercader, Oliver Ruebenacker, Sebanti Sengupta, Dylan Spalding, Daniel Taliun, Philip Smith, Melissa K. Thomas, Beena Akolkar, M. Julia Brosnan, Andriy Cherkas, Audrey Y. Chu, Eric B. Fauman, Caroline S. Fox, Tania Nayak Kamphaus, Melissa R. Miller, Lynette Nguyen, Afshin Parsa, Dermot F. Reilly, Hartmut Ruetten, David Wholley, Norann A. Zaghloul, Gonçalo R. Abecasis, David Altshuler, Thomas M. Keane, Mark I. McCarthy, Kyle J. Gaulton, Jose C. Florez, Michael Boehnke, Noël P. Burtt, Jason Flannick, Gonçalo Abecasis, Nicholette D. Allred, Jennifer E. Below, Richard Bergman, Joline W.J. Beulens, John Blangero, Krister Bokvist, Erwin Bottinger, Donald Bowden, Christopher Brown, Kenneth Bruskiewicz, Inês Cebola, John Chambers, Yii-Der Ida Chen, Christopher Clark, Melina Claussnitzer, Nancy J. Cox, Marcel den Hoed, Duc Dong, Ravindranath Duggirala, Josée Dupuis, Petra J.M. Elders, Jesse M. Engreitz, Eric Fauman, Jorge Ferrer, Paul Flicek, Matthew Flickinger, Timothy M. Frayling, Kelly A. Frazer, Anna L. Gloyn, Craig L. Hanis, Robert Hanson, Andrew T. Hattersley, Hae Kyung Im, Sidra Iqbal, Suzanne B.R. Jacobs, Dong-Keun Jang, Tad Jordan, Tania Kamphaus, Fredrik Karpe, Seung K. Kim, Kasper Lage, Leslie A. Lange, Mitchell Lazar, Donna Lehman, Ching-Ti Liu, Ruth J.F. Loos, Ronald Ching-wan Ma, Patrick MacDonald, Matthew T. Maurano, Gil McVean, James B. Meigs, Braxton Mitchell, Karen L. Mohlke, Samuel Morabito, Claire Morgan, Shannon Mullican, Sharvari Narendra, Maggie C.Y. Ng, Colin N.A. Palmer, Stephen C.J. Parker, Antonio Parrado, Aaron C. Pawlyk, Ewan R. Pearson, Andrew Plump, Michael Province, Thomas Quertermous, Susan Redline, Bing Ren, Stephen S. Rich, J. Brent Richards, Jerome I. Rotter, Rany M. Salem, Maike Sander, Michael Sanders, Dharambir Sanghera, Laura J. Scott, David Siedzik, Xueling Sim, Robert Sladek, Kerrin Small, Peter Stein, Heather M. Stringham, Katalin Susztak, Leen M. ’t Hart, Kent Taylor, Jennifer A. Todd, Miriam S. Udler, Benjamin Voight, Andre Wan, Kaan Yuksel, Epidemiology and Data Science, ACS - Diabetes & metabolism, ACS - Heart failure & arrhythmias, APH - Health Behaviors & Chronic Diseases, and General practice
Subjects: Physiology, data sharing, T2DKP, effector genes, Medical Biochemistry and Metabolomics, Article, Access to Information, Endocrinology & Metabolism, CMDKP, Diabetes Mellitus, genomics, Genetics, Humans, GWAS, Prospective Studies, Molecular Biology, Metabolic and endocrine, diabetes, Human Genome, Cell Biology, AMP-T2D Consortium, Phenotype, portal, Good Health and Well Being, genetic support, genetic associations, Biochemistry and Cell Biology, Type 2
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

40. Association and Interaction of Genetics and Area-level Socioeconomic Factors on the Prevalence of Type 2 Diabetes and Obesity

Author: Sara J. Cromer, Chirag M. Lakhani, Josep M. Mercader, Timothy D. Majarian, Philip Schroeder, Joanne B. Cole, Jose C. Florez, Chirag J. Patel, Alisa K. Manning, Sherri-Ann M. Burnett-Bowie, Jordi Merino, and Miriam S. Udler
Subjects: Advanced and Specialized Nursing, Endocrinology, Diabetes and Metabolism, Internal Medicine
Abstract: OBJECTIVE Quantify the impact of genetic and socioeconomic factors on risk of type 2 diabetes (T2D) and obesity. RESEARCH DESIGN AND METHODS Among participants in the Mass General Brigham Biobank (MGBB) and UK Biobank (UKB), we used logistic regression models to calculate cross-sectional odds of T2D and obesity using 1) polygenic risk scores for T2D and BMI and 2) area-level socioeconomic risk (educational attainment) measures. The primary analysis included 26,737 participants of European genetic ancestry in MGBB with replication in UKB (N = 223,843), as well as in participants of non-European ancestry (MGBB N = 3,468; UKB N = 7,459). RESULTS The area-level socioeconomic measure most strongly associated with both T2D and obesity was percent without a college degree, and associations with disease prevalence were independent of genetic risk (P < 0.001 for each). Moving from lowest to highest quintiles of combined genetic and socioeconomic burden more than tripled T2D (3.1% to 22.2%) and obesity (20.9% to 69.0%) prevalence. Favorable socioeconomic risk was associated with lower disease prevalence, even in those with highest genetic risk (T2D 13.0% vs. 22.2%, obesity 53.6% vs. 69.0% in lowest vs. highest socioeconomic risk quintiles). Additive effects of genetic and socioeconomic factors accounted for 13.2% and 16.7% of T2D and obesity prevalence, respectively, explained by these models. Findings were replicated in independent European and non-European ancestral populations. CONCLUSIONS Genetic and socioeconomic factors significantly interact to increase risk of T2D and obesity. Favorable area-level socioeconomic status was associated with an almost 50% lower T2D prevalence in those with high genetic risk.
Published: 2023

41. Identification of genetic variation influencing metformin response in a multi-ancestry genome-wide association study in the Diabetes Prevention Program (DPP)

Author: Diabetes Prevention Program Research Group, Jose C. Florez, Toni I. Pollin, William C. Knowler, Steven E. Kahn, Robert L. Hanson, Paul W. Franks, L. Keoki Williams, Shujie Xiao, Adriana M. Hung, Ayush Giri, Kathleen M. Giacomini, Ewan R. Pearson, Sook Wah Yee, Adem Y. Dawed, Qing Pan, Josep M. Mercader, Maegan Harden, Jennifer N. Todd, Ling Chen, Shylaja Srinivasan, Kathleen A. Jablonski, James A. Perry, and Josephine Li
Abstract: Genome-wide significant loci for metformin response in type 2 diabetes reported elsewhere have not replicated in the Diabetes Prevention Program (DPP). To assess pharmacogenetic interactions in pre-diabetes, we conducted a genome-wide association study (GWAS) in the DPP. Cox proportional hazards models tested associations with diabetes incidence in metformin (MET, n=876) and placebo (PBO, n=887) arms. Multiple linear regression assessed association with one-year change in metformin-related quantitative traits, adjusted for baseline trait, age, sex, and 10 ancestry principal components. We tested for gene-by-treatment interaction. No significant associations emerged for diabetes incidence. We identified four genome-wide significant variants after correcting for correlated traits (pENOSF1 (minor allele frequency [MAF]AFR=0.07, MAFEUR=0.002) was associated with an increase in % glycated hemoglobin (per minor allele β=0.39 [95% CI 0.28, 0.50], p=2.8×10-12). Rs145591055 near OMSR (MAF=0.10 in American Indians), was associated with weight loss (kg) (per G allele β=-7.55 [95% CI -9.88, -5.22], p=3.2×10-10) in MET. Neither variant was significant in PBO; gene-by-treatment interaction was significant for both variants (p(G×T)
Published: 2023

42. Precision medicine in diabetes - current trends and future directions. Is the future now?

Author: Lukasz Szczerbinski and Jose C. Florez
Published: 2023

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

Author: Dorian Kariuki, Kesava Asam, Bradley E Aouizerat, Kimberly A Lewis, Jose C Florez, and Elena Flowers
Subjects: General Agricultural and Biological Sciences, General Biochemistry, Genetics and Molecular Biology, Information Systems
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

44. 'The Times They Are A-Changin'' at Diabetes Care

Author: Steven E. Kahn, Cheryl A.M. Anderson, John B. Buse, Elizabeth Selvin, Sonia Y. Angell, Vanita R. Aroda, Jessica R. Castle, Alice Y.Y. Cheng, Thomas Danne, Justin B. Echouffo-Tcheugui, Jose C. Florez, Meghana D. Gadgil, Amalia Gastaldelli, Jennifer B. Green, Ania M. Jastreboff, Alka M. Kanaya, Namratha R. Kandula, Csaba P. Kovesdy, Neda Laiteerapong, Kristen J. Nadeau, Rodica Pop-Busui, Camille E. Powe, Casey M. Rebholz, Michael R. Rickels, Naveed Sattar, Jonathan E. Shaw, Emily K. Sims, Kristina M. Utzschneider, Adrian Vella, and Cuilin Zhang
Subjects: Advanced and Specialized Nursing, Endocrinology, Diabetes and Metabolism, Internal Medicine
Published: 2022

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

Author: Josephine H, Li, James A, Perry, Kathleen A, Jablonski, Shylaja, Srinivasan, Ling, Chen, Jennifer N, Todd, Maegan, Harden, Josep M, Mercader, Qing, Pan, Adem Y, Dawed, Sook Wah, Yee, Ewan R, Pearson, Kathleen M, Giacomini, Ayush, Giri, Adriana M, Hung, Shujie, Xiao, L Keoki, Williams, Paul W, Franks, Robert L, Hanson, Steven E, Kahn, William C, Knowler, Toni I, Pollin, and Jose C, Florez
Subjects: Endocrinology, Diabetes and Metabolism, Internal Medicine
Abstract: Genome-wide significant loci for metformin response in type 2 diabetes reported elsewhere have not replicated in the Diabetes Prevention Program (DPP). To assess pharmacogenetic interactions in pre-diabetes, we conducted a genome-wide association study (GWAS) in the DPP. Cox proportional hazards models tested associations with diabetes incidence in metformin (MET, n=876) and placebo (PBO, n=887) arms. Multiple linear regression assessed association with one-year change in metformin-related quantitative traits, adjusted for baseline trait, age, sex, and 10 ancestry principal components. We tested for gene-by-treatment interaction. No significant associations emerged for diabetes incidence. We identified four genome-wide significant variants after correcting for correlated traits (p
Published: 2022

46. Genome-wide association analyses highlight etiological differences underlying newly defined subtypes of diabetes

Author: Annemari Käräjämäki, Rashmi B. Prasad, Jose C. Florez, Leif Groop, Anubha Mahajan, Ola Hansson, Rebecka Hjort, Tiinamaija Tuomi, Olle Melander, Om Prakash Dwivedi, Mikael Åkerlund, Dina Mansour Aly, Manonanthini Thangam, Emma Ahlqvist, Miriam S. Udler, Julia Brosnan, Mark I. McCarthy, and Sofia Carlsson
Subjects: Genetics, fungi, Genome-wide association study, Locus (genetics), Type 2 diabetes, Biology, medicine.disease, Genetic architecture, Diabetes mellitus, medicine, SNP, Family history, TCF7L2
Abstract: Type 2 diabetes has been reproducibly clustered into five subtypes with different disease progression and risk of complications; however, etiological differences are unknown. We used genome-wide association and genetic risk score (GRS) analysis to compare the underlying genetic drivers. Individuals from the Swedish ANDIS (All New Diabetics In Scania) study were compared to individuals without diabetes; the Finnish DIREVA (Diabetes register in Vasa) and Botnia studies were used for replication. We show that subtypes differ with regard to family history of diabetes and association with GRS for diabetes-related traits. The severe insulin-resistant subtype was uniquely associated with GRS for fasting insulin but not with variants in the TCF7L2 locus or GRS reflecting insulin secretion. Further, an SNP (rs10824307) near LRMDA was uniquely associated with mild obesity-related diabetes. Therefore, we conclude that the subtypes have partially distinct genetic backgrounds indicating etiological differences. Genome-wide association and genetic risk score analyses highlight differences in genetic architecture across five subtypes of diabetes.
Published: 2021

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

Author: Alicia Huerta-Chagoya, Philip Schroeder, Ravi Mandla, Aaron J. Deutsch, Wanying Zhu, Lauren Petty, Xiaoyan Yi, Joanne B. Cole, Miriam S. Udler, Peter Dornbos, Bianca Porneala, Daniel DiCorpo, Ching-Ti Liu, Josephine H. Li, Lukasz Szczerbiński, Varinderpal Kaur, Joohyun Kim, Yingchang Lu, Alicia Martin, Decio L. Eizirik, Piero Marchetti, Lorella Marselli, Ling Chen, Shylaja Srinivasan, Jennifer Todd, Jason Flannick, Rose Gubitosi-Klug, Lynne Levitsky, Rachana Shah, Megan Kelsey, Brian Burke, Dana M. Dabelea, Jasmin Divers, Santica Marcovina, Lauren Stalbow, Ruth J.F. Loos, Burcu F. Darst, Charles Kooperberg, Laura M. Raffield, Christopher Haiman, Quan Sun, Joseph B. McCormick, Susan P. Fisher-Hoch, Maria L. Ordoñez, James Meigs, Leslie J. Baier, Clicerio González-Villalpando, Maria Elena González-Villalpando, Lorena Orozco, Andrés Moreno, Carlos A. Aguilar-Salinas, Teresa Tusié, Josée Dupuis, Maggie C.Y. Ng, Alisa Manning, Heather M. Highland, Miriam Cnop, Robert Hanson, Jennifer Below, Jose C. Florez, Aaron Leong, and Josep M. Mercader
Abstract: HypothesisThe prevalence of type 2 diabetes is higher in Latino populations compared with other major ancestry groups. Not only has the Latino population been systematically underrepresented in large-scale genetic analyses, but previous studies relied on the imputation of ungenotyped variants based on the 1000 Genomes (1000G) imputation reference panel, which results in suboptimal capture of low-frequency or Latino-enriched variants. The NHLBI Trans-Omics for Precision Medicine (TOPMed) reference panel represents a unique opportunity to analyze rare genetic variations in the Latino population.MethodsWe evaluate the TOPMed imputation performance using genotyping array and whole-exome sequence data in 6 Latino cohorts. To evaluate the ability of TOPMed imputation of increasing the identified loci, we performed a Latino type 2 diabetes GWAS meta-analysis in 8,150 type 2 diabetes cases and 10,735 controls and replicated the results in 6 additional cohorts including whole-genome sequence data from the All of Us cohort.ResultsWe show that, compared to imputation with 1000G, the TOPMed panel improves the identification of rare and low-frequency variants. We identified 26 distinct signals including a novel genome-wide significant variant (minor allele frequency 1.6%, OR=2.0, P=3.4×10−9) near ORC5. A Latino-tailored polygenic score constructed from our data and GWAS data from East Asian and European populations improves the prediction accuracy in a Latino target dataset, explaining up to 7.6% of the type 2 diabetes risk variance.ConclusionsOur results demonstrate the utility of TOPMed imputation for identifying low-frequency variation in understudied populations, leading to the discovery of novel disease associations and the improvement of polygenic scores.
Published: 2022

48. Genetic analysis of dietary intake identifies new loci and functional links with metabolic traits

Author: Toshiko Tanaka, Audrey Y. Chu, Daniel I. Chasman, Martin K. Rutter, Miriam S. Udler, Josée Dupuis, John N. Campbell, Jordi Merino, Tune H. Pers, Jacqueline M. Lane, Chandler Tucker, Jose C. Florez, Petar Todorov, Chloé Sarnowski, Linus T.-Y. Tsai, Richa Saxena, Heming Wang, Jaegil Kim, Hassan S. Dashti, and Yanwei Song
Subjects: medicine.medical_specialty, Genotype, Social Psychology, Genome-wide association study, Experimental and Cognitive Psychology, Type 2 diabetes, Biology, Polymorphism, Single Nucleotide, Genetic analysis, Article, Behavioral Neuroscience, Epidemiology, medicine, Humans, Obesity, Gene, Epigenomics, Genetic association, Genetics, Dietary intake, Neurogenesis, Dopaminergic, Nuclear Proteins, medicine.disease, Phenotype, Biobank, Diet, Genetic Loci, Genome-Wide Association Study
Abstract: Dietary intake is a major contributor to the global obesity epidemic and represents a complex behavioural phenotype that is partially affected by innate biological differences. Here, we present a multivariate genome-wide association analysis of overall variation in dietary intake to account for the correlation between dietary carbohydrate, fat and protein in 282,271 participants of European ancestry from the UK Biobank (n = 191,157) and Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium (n = 91,114), and identify 26 distinct genome-wide significant loci. Dietary intake signals map exclusively to specific brain regions and are enriched for genes expressed in specialized subtypes of GABAergic, dopaminergic and glutamatergic neurons. We identified two main clusters of genetic variants for overall variation in dietary intake that were differently associated with obesity and coronary artery disease. These results enhance the biological understanding of interindividual differences in dietary intake by highlighting neural mechanisms, supporting functional follow-up experiments and possibly providing new avenues for the prevention and treatment of prevalent complex metabolic diseases. In a multivariate genetic analysis including 282,271 adults, Merino et al. identified 26 genomic regions associated with carbohydrate, protein and fat intake. The identified loci implicate brain regions and neuronal subtypes in influencing eating behaviour.
Published: 2021

49. Monogenic Diabetes in Youth With Presumed Type 2 Diabetes: Results From the Progress in Diabetes Genetics in Youth (ProDiGY) Collaboration

Author: Jasmin Divers, Toni I. Pollin, Sherida E. Tollefsen, Dana Dabelea, Lynne L. Levitsky, Lorraine E. Levitt Katz, Jose C. Florez, Fida Bacha, Jennifer N. Todd, Haichen Zhang, Jean M. Lawrence, Jason Flannick, Jeffrey W. Kleinberger, Jeanie B. Tryggestad, Catherine Pihoker, Giuseppina Imperatore, and Shylaja Srinivasan
Subjects: Advanced and Specialized Nursing, Research design, Pediatrics, medicine.medical_specialty, Younger age, medicine.diagnostic_test, business.industry, Endocrinology, Diabetes and Metabolism, Type 2 diabetes, medicine.disease, HNF1A, Diabetes mellitus, Clinical diagnosis, Internal Medicine, medicine, business, Monogenic Diabetes, Genetic testing
Abstract: OBJECTIVE Maturity-onset diabetes of the young (MODY) is frequently misdiagnosed as type 1 or type 2 diabetes. Correct diagnosis may result in a change in clinical treatment and impacts prediction of complications and familial risk. In this study, we aimed to assess the prevalence of MODY in multiethnic youth under age 20 years with a clinical diagnosis of type 2 diabetes. RESEARCH DESIGN AND METHODS We evaluated whole-exome sequence data of youth with a clinical diagnosis of type 2 diabetes. We considered participants to have MODY if they carried a MODY gene variant classified as likely pathogenic (LP) or pathogenic (P) according to current guidelines. RESULTS Of 3,333 participants, 93 (2.8%) carried an LP/P variant in HNF4A (16 participants), GCK (23), HNF1A (44), PDX1 (5), INS (4), and CEL (1). Compared with those with no LP/P variants, youth with MODY had a younger age at diagnosis (12.9 ± 2.5 vs. 13.6 ± 2.3 years, P = 0.002) and lower fasting C-peptide levels (3.0 ± 1.7 vs. 4.7 ± 3.5 ng/mL, P < 0.0001). Youth with MODY were less likely to have hypertension (6.9% vs. 19.5%, P = 0.007) and had higher HDL cholesterol (43.8 vs. 39.7 mg/dL, P = 0.006). CONCLUSIONS By comprehensively sequencing the coding regions of all MODY genes, we identified MODY in 2.8% of youth with clinically diagnosed type 2 diabetes; importantly, in 89% (n = 83) the specific diagnosis would have changed clinical management. No clinical criterion reliably separated the two groups. New tools are needed to find ideal criteria for selection of individuals for genetic testing.
Published: 2021

50. Investigating gene-diet interactions impacting the association between macronutrient intake and glycemic traits

Author: Kenneth E. Westerman, Maura E. Walker, Sheila M. Gaynor, Jennifer Wessel, Daniel DiCorpo, Jiantao Ma, Alvaro Alonso, Stella Aslibekyan, Abigail S. Baldridge, Alain G. Bertoni, Mary L. Biggs, Jennifer A. Brody, Yii-Der Ida Chen, Joseé Dupuis, Mark O. Goodarzi, Xiuqing Guo, Natalie R. Hasbani, Adam Heath, Bertha Hidalgo, Marguerite R. Irvin, W. Craig Johnson, Rita R. Kalyani, Leslie Lange, Rozenn N. Lemaitre, Ching-Ti Liu, Simin Liu, Jee-Young Moon, Rami Nassir, James S. Pankow, Mary Pettinger, Laura M. Raffield, Laura J. Rasmussen-Torvik, Elizabeth Selvin, Mackenzie K. Senn, Aladdin H. Shadyab, Albert V. Smith, Nicholas L. Smith, Lyn Steffen, Sameera Talegakwar, Kent D. Taylor, Paul S. de Vries, James G. Wilson, Alexis C. Wood, Lisa R. Yanek, Jie Yao, Yinan Zheng, Eric Boerwinkle, Alanna C. Morrison, Miriam Fornage, Tracy P. Russell, Bruce M. Psaty, Daniel Levy, Nancy L. Heard-Costa, Vasan S. Ramachandran, Rasika A. Mathias, Donna K. Arnett, Robert Kaplan, Kari E. North, Adolfo Correa, April Carson, Jerome I. Rotter, Stephen S. Rich, JoAnn E. Manson, Alexander P. Reiner, Charles Kooperberg, Jose C. Florez, James B. Meigs, Jordi Merino, Deirdre K. Tobias, Han Chen, and Alisa K. Manning
Subjects: Endocrinology, Diabetes and Metabolism, Internal Medicine
Abstract: BackgroundHeterogeneity in the long-term metabolic response to dietary macronutrient composition can be partially explained by genetic factors. However, few studies have demonstrated reproducible gene-diet interactions (GDIs), likely due in part to measurement error in dietary intake estimation as well as insufficient capture of rare genetic variation. Discovery analyses in ancestry-diverse cohorts that include rare genetic variants from whole-genome sequencing (WGS) could help identify genetic variants modifying the effects of dietary macronutrient composition on glycemic phenotypes.ObjectiveWe aimed to identify macronutrient GDIs across the genetic frequency spectrum associated with continuous glycemic traits in genetically and culturally diverse cohorts.MethodsWe analyzed N=33,187 diabetes-free participants from 10 cohorts in the NHLBI Trans-Omics for Precision Medicine (TOPMed) program with WGS, self-reported diet, and glycemic traits (fasting glucose [FG], insulin [FI], and hemoglobin A1c [HbA1c]). We fit multivariable-adjusted linear mixed models for the main effect of diet, modeled as an isocaloric substitution of carbohydrate for fat, and for its interactions with genetic variants genome-wide. Tests were performed for both common variants and gene-based rare variant sets in each cohort followed by a combined cohort meta-analysis.ResultsIn main effect models, participants consuming more calories from carbohydrate at the expense of fat had modestly lower glycemic trait values (β per 250 kcal substitution for FG: −0.030 mmol/L, p=2.7×10−6; lnFI: −0.008 log(pmol/L), p=0.17; HbA1c: −0.013 %, p=0.025). In GDI analyses, a common African ancestry-enriched variant (rs79762542; 78 kb upstream of the FRAS1 gene) reached study-wide significance (p = 1.14×10−8) indicating a higher HbA1c with greater proportion of calories from carbohydrate vs. fat among minor allele carriers only. This interaction was replicated in the UK Biobank cohort. Simulations revealed that there is (1) a substantial impact of measurement error on statistical power for GDI discovery at these sample sizes, especially for rare genetic variants, and (2) over 150,000 samples may be necessary to identify similar macronutrient GDIs under realistic assumptions about effect size and measurement error.ConclusionsOur analysis identified a potential genetic interaction modifying the dietary macronutrient-HbA1c association while highlighting the importance of precise exposure measurement and significantly increased sample size to identify additional similar effects.
Published: 2022

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