351
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Song Y, Zhou X, Kang J, Aung MT, Zhang M, Zhao W, Needham BL, Kardia SLR, Liu Y, Meeker JD, Smith JA, Mukherjee B. Bayesian hierarchical models for high-dimensional mediation analysis with coordinated selection of correlated mediators. Stat Med 2021; 40:6038-6056. [PMID: 34404112 PMCID: PMC9257993 DOI: 10.1002/sim.9168] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2020] [Revised: 07/30/2021] [Accepted: 08/05/2021] [Indexed: 01/18/2023]
Abstract
We consider Bayesian high-dimensional mediation analysis to identify among a large set of correlated potential mediators the active ones that mediate the effect from an exposure variable to an outcome of interest. Correlations among mediators are commonly observed in modern data analysis; examples include the activated voxels within connected regions in brain image data, regulatory signals driven by gene networks in genome data, and correlated exposure data from the same source. When correlations are present among active mediators, mediation analysis that fails to account for such correlation can be suboptimal and may lead to a loss of power in identifying active mediators. Building upon a recent high-dimensional mediation analysis framework, we propose two Bayesian hierarchical models, one with a Gaussian mixture prior that enables correlated mediator selection and the other with a Potts mixture prior that accounts for the correlation among active mediators in mediation analysis. We develop efficient sampling algorithms for both methods. Various simulations demonstrate that our methods enable effective identification of correlated active mediators, which could be missed by using existing methods that assume prior independence among active mediators. The proposed methods are applied to the LIFECODES birth cohort and the Multi-Ethnic Study of Atherosclerosis (MESA) and identified new active mediators with important biological implications.
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Affiliation(s)
- Yanyi Song
- Department of Biostatistics, University of Michigan, Ann Arbor, Michigan USA
| | - Xiang Zhou
- Department of Biostatistics, University of Michigan, Ann Arbor, Michigan USA
| | - Jian Kang
- Department of Biostatistics, University of Michigan, Ann Arbor, Michigan USA
| | - Max T. Aung
- Department of Biostatistics, University of Michigan, Ann Arbor, Michigan USA
| | - Min Zhang
- Department of Biostatistics, University of Michigan, Ann Arbor, Michigan USA
| | - Wei Zhao
- Department of Epidemiology, University of Michigan, Ann Arbor, Michigan USA
| | - Belinda L. Needham
- Department of Epidemiology, University of Michigan, Ann Arbor, Michigan USA
| | | | - Yongmei Liu
- Division of Cardiology, Department of Medicine, Duke University School of Medicine, Durham, North Carolina USA
| | - John D. Meeker
- Department of Environmental Health Sciences, University of Michigan, Ann Arbor, Michigan USA
| | - Jennifer A. Smith
- Department of Epidemiology, University of Michigan, Ann Arbor, Michigan USA
| | - Bhramar Mukherjee
- Department of Biostatistics, University of Michigan, Ann Arbor, Michigan USA
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352
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Yuan C, Jian Z, Gao X, Jin X, Wang M, Xiang L, Li H, Wang K. Type 2 diabetes mellitus increases risk of erectile dysfunction independent of obesity and dyslipidemia: A Mendelian randomization study. Andrology 2021; 10:518-524. [PMID: 34842357 DOI: 10.1111/andr.13132] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Revised: 09/20/2021] [Accepted: 11/21/2021] [Indexed: 02/05/2023]
Abstract
BACKGROUND The causal effects of individual risk factors of metabolic syndrome on erectile dysfunction (ED) are still unclear. OBJECTIVES To evaluate the causal effect of risk factors of metabolic syndrome on ED through Mendelian randomization (MR). MATERIALS AND METHODS Data for risk factors were obtained from multiple databases with 173,082-757,601 individuals, and that for ED were collected from a genome-wide association study including 223,805 Europeans. We performed univariate MR analysis using inverse-variance weighted, MR-Egger, weighted-median, weighted mode methods and multivariable MR analysis to evaluate the total and direct causal effects. RESULTS The univariable MR supported that type 2 diabetes mellitus (odds ratios [OR] = 1.14, 95% confidence intervals [CI]: 1.08-1.21, p < 0.001) and body mass index (BMI) (OR = 1.27, 95% CI: 1.12-1.44, p < 0.001) were associated with ED. After excluding the SNPs associated with BMI and other risk factors, the results of multivariable MR for T2D (OR = 1.15, 95% CI: 1.05-1.25, p = 0.001) remained consistent. However, the results of multivariable MR provided limited evidence for the causality between BMI and ED (OR = 1.06, 95% CI: 0.88-1.29, p = 0.532). For systolic blood pressure and lipid components (low-density lipoprotein, high-density lipoprotein and triglycerides), both univariable and multivariable MR failed to offer sufficient evidence to confirm their causal effect on ED. CONCLUSION T2D showed a direct causal effect on ED independent of obesity and dyslipidemia.
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Affiliation(s)
- Chi Yuan
- Department of Urology, Institute of Urology (Laboratory of Reconstructive Urology), West China Hospital, Sichuan University, Chengdu, P.R. China
| | - Zhongyu Jian
- Department of Urology, Institute of Urology (Laboratory of Reconstructive Urology), West China Hospital, Sichuan University, Chengdu, P.R. China.,West China Biomedical Big Data Center, Sichuan University, Chengdu, P.R. China
| | - Xiaoshuai Gao
- Department of Urology, Institute of Urology (Laboratory of Reconstructive Urology), West China Hospital, Sichuan University, Chengdu, P.R. China
| | - Xi Jin
- Department of Urology, Institute of Urology (Laboratory of Reconstructive Urology), West China Hospital, Sichuan University, Chengdu, P.R. China
| | - Menghua Wang
- Department of Urology, Institute of Urology (Laboratory of Reconstructive Urology), West China Hospital, Sichuan University, Chengdu, P.R. China
| | - Liyuan Xiang
- Department of Urology, Institute of Urology (Laboratory of Reconstructive Urology), West China Hospital, Sichuan University, Chengdu, P.R. China
| | - Hong Li
- Department of Urology, Institute of Urology (Laboratory of Reconstructive Urology), West China Hospital, Sichuan University, Chengdu, P.R. China
| | - Kunjie Wang
- Department of Urology, Institute of Urology (Laboratory of Reconstructive Urology), West China Hospital, Sichuan University, Chengdu, P.R. China
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353
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Wang Z, Peng H, Gao W, Cao W, Lv J, Yu C, Huang T, Sun D, Wang B, Liao C, Pang Y, Pang Z, Cong L, Wang H, Wu X, Liu Y, Li L. Blood DNA methylation markers associated with type 2 diabetes, fasting glucose, and HbA1c levels: An epigenome-wide association study in 316 adult twin pairs. Genomics 2021; 113:4206-4213. [PMID: 34774679 DOI: 10.1016/j.ygeno.2021.11.005] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Revised: 09/26/2021] [Accepted: 11/06/2021] [Indexed: 11/26/2022]
Abstract
DNA methylation plays an important role in the development and etiology of type 2 diabetes; however, few epigenomic studies have been conducted on twins. Herein, a two-stage study was performed to explore the associations between DNA methylation and type 2 diabetes, fasting plasma glucose, and HbA1c. DNA methylation in 316 twin pairs from the Chinese National Twin Registry (CNTR) was measured using Illumina Infinium BeadChips. In the discovery sample, the results revealed that 63 CpG sites and 6 CpG sites were significantly associated with fasting plasma glucose and HbA1c, respectively. In the replication sample, cg19690313 in TXNIP was associated with both fasting plasma glucose (P = 1.23 × 10-17, FDR < 0.001) and HbA1c (P = 2.29 × 10-18, FDR < 0.001). Furthermore, cg04816311, cg08309687, and cg09249494 may provide new insight in the metabolic mechanism of HbA1c. Our study provides solid evidence that cg19690313 on TXNIP correlates with HbA1c and fasting plasma glucose levels.
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Affiliation(s)
- Zhaonian Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Hexiang Peng
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Wenjing Gao
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China.
| | - Weihua Cao
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Jun Lv
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Canqing Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Tao Huang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Dianjianyi Sun
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Biqi Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Chunxiao Liao
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Yuanjie Pang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Zengchang Pang
- Qingdao Center for Diseases Control and Prevention, Qingdao, China
| | - Liming Cong
- Zhejiang Center for Disease Control and Prevention, Hangzhou, China
| | - Hua Wang
- Jiangsu Center for Disease Control and Prevention, Nanjing, China
| | - Xianping Wu
- Sichuan Center for Disease Control and Prevention, Chengdu, China
| | - Yu Liu
- Heilongjiang Center for Disease Control and Prevention, Harbin, China
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China.
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354
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Hanscombe KB, Persyn E, Traylor M, Glanville KP, Hamer M, Coleman JRI, Lewis CM. The genetic case for cardiorespiratory fitness as a clinical vital sign and the routine prescription of physical activity in healthcare. Genome Med 2021; 13:180. [PMID: 34753499 PMCID: PMC8579601 DOI: 10.1186/s13073-021-00994-9] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Accepted: 10/19/2021] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Cardiorespiratory fitness (CRF) and physical activity (PA) are well-established predictors of morbidity and all-cause mortality. However, CRF is not routinely measured and PA not routinely prescribed as part of standard healthcare. The American Heart Association (AHA) recently presented a scientific case for the inclusion of CRF as a clinical vital sign based on epidemiological and clinical observation. Here, we leverage genetic data in the UK Biobank (UKB) to strengthen the case for CRF as a vital sign and make a case for the prescription of PA. METHODS We derived two CRF measures from the heart rate data collected during a submaximal cycle ramp test: CRF-vo2max, an estimate of the participants' maximum volume of oxygen uptake, per kilogram of body weight, per minute; and CRF-slope, an estimate of the rate of increase of heart rate during exercise. Average PA over a 7-day period was derived from a wrist-worn activity tracker. After quality control, 70,783 participants had data on the two derived CRF measures, and 89,683 had PA data. We performed genome-wide association study (GWAS) analyses by sex, and post-GWAS techniques to understand genetic architecture of the traits and prioritise functional genes for follow-up. RESULTS We found strong evidence that genetic variants associated with CRF and PA influenced genetic expression in a relatively small set of genes in the heart, artery, lung, skeletal muscle and adipose tissue. These functionally relevant genes were enriched among genes known to be associated with coronary artery disease (CAD), type 2 diabetes (T2D) and Alzheimer's disease (three of the top 10 causes of death in high-income countries) as well as Parkinson's disease, pulmonary fibrosis, and blood pressure, heart rate, and respiratory phenotypes. Genetic variation associated with lower CRF and PA was also correlated with several disease risk factors (including greater body mass index, body fat and multiple obesity phenotypes); a typical T2D profile (including higher insulin resistance, higher fasting glucose, impaired beta-cell function, hyperglycaemia, hypertriglyceridemia); increased risk for CAD and T2D; and a shorter lifespan. CONCLUSIONS Genetics supports three decades of evidence for the inclusion of CRF as a clinical vital sign. Given the genetic, clinical and epidemiological evidence linking CRF and PA to increased morbidity and mortality, regular measurement of CRF as a marker of health and routine prescription of PA could be a prudent strategy to support public health.
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Affiliation(s)
- Ken B Hanscombe
- Department of Medical & Molecular Genetics, King's College London, London, UK. .,Social, Genetic and Developmental Psychiatry Centre, King's College London, London, UK.
| | - Elodie Persyn
- Department of Medical & Molecular Genetics, King's College London, London, UK
| | | | - Kylie P Glanville
- Social, Genetic and Developmental Psychiatry Centre, King's College London, London, UK
| | - Mark Hamer
- Institute of Sport Exercise & Health, Division of Surgery and Interventional Science, University College London, London, UK
| | - Jonathan R I Coleman
- Social, Genetic and Developmental Psychiatry Centre, King's College London, London, UK
| | - Cathryn M Lewis
- Department of Medical & Molecular Genetics, King's College London, London, UK.,Social, Genetic and Developmental Psychiatry Centre, King's College London, London, UK
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355
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The Influence of CYP2D6 and CYP2C19 Genetic Variation on Diabetes Mellitus Risk in People Taking Antidepressants and Antipsychotics. Genes (Basel) 2021; 12:genes12111758. [PMID: 34828364 PMCID: PMC8620997 DOI: 10.3390/genes12111758] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 10/27/2021] [Accepted: 10/30/2021] [Indexed: 11/21/2022] Open
Abstract
CYP2D6 and CYP2C19 enzymes are essential in the metabolism of antidepressants and antipsychotics. Genetic variation in these genes may increase risk of adverse drug reactions. Antidepressants and antipsychotics have previously been associated with risk of diabetes. We examined whether individual genetic differences in CYP2D6 and CYP2C19 contribute to these effects. We identified 31,579 individuals taking antidepressants and 2699 taking antipsychotics within UK Biobank. Participants were classified as poor, intermediate, or normal metabolizers of CYP2D6, and as poor, intermediate, normal, rapid, or ultra-rapid metabolizers of CYP2C19. Risk of diabetes mellitus represented by HbA1c level was examined in relation to the metabolic phenotypes. CYP2D6 poor metabolizers taking paroxetine had higher Hb1Ac than normal metabolizers (mean difference: 2.29 mmol/mol; p < 0.001). Among participants with diabetes who were taking venlafaxine, CYP2D6 poor metabolizers had higher HbA1c levels compared to normal metabolizers (mean differences: 10.15 mmol/mol; p < 0.001. Among participants with diabetes who were taking fluoxetine, CYP2D6 intermediate metabolizers and decreased HbA1c, compared to normal metabolizers (mean difference -7.74 mmol/mol; p = 0.017). We did not observe any relationship between CYP2D6 or CYP2C19 metabolic status and HbA1c levels in participants taking antipsychotic medication. Our results indicate that the impact of genetic variation in CYP2D6 differs depending on diabetes status. Although our findings support existing clinical guidelines, further research is essential to inform pharmacogenetic testing for people taking antidepressants and antipsychotics.
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356
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Deaton AM, Parker MM, Ward LD, Flynn-Carroll AO, BonDurant L, Hinkle G, Akbari P, Lotta LA, Baras A, Nioi P. Gene-level analysis of rare variants in 379,066 whole exome sequences identifies an association of GIGYF1 loss of function with type 2 diabetes. Sci Rep 2021; 11:21565. [PMID: 34732801 PMCID: PMC8566487 DOI: 10.1038/s41598-021-99091-5] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Accepted: 09/15/2021] [Indexed: 11/15/2022] Open
Abstract
Sequencing of large cohorts offers an unprecedented opportunity to identify rare genetic variants and to find novel contributors to human disease. We used gene-based collapsing tests to identify genes associated with glucose, HbA1c and type 2 diabetes (T2D) diagnosis in 379,066 exome-sequenced participants in the UK Biobank. We identified associations for variants in GCK, HNF1A and PDX1, which are known to be involved in Mendelian forms of diabetes. Notably, we uncovered novel associations for GIGYF1, a gene not previously implicated by human genetics in diabetes. GIGYF1 predicted loss of function (pLOF) variants associated with increased levels of glucose (0.77 mmol/L increase, p = 4.42 × 10–12) and HbA1c (4.33 mmol/mol, p = 1.28 × 10–14) as well as T2D diagnosis (OR = 4.15, p = 6.14 × 10–11). Multiple rare variants contributed to these associations, including singleton variants. GIGYF1 pLOF also associated with decreased cholesterol levels as well as an increased risk of hypothyroidism. The association of GIGYF1 pLOF with T2D diagnosis replicated in an independent cohort from the Geisinger Health System. In addition, a common variant association for glucose and T2D was identified at the GIGYF1 locus. Our results highlight the role of GIGYF1 in regulating insulin signaling and protecting from diabetes.
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Affiliation(s)
| | | | | | | | | | | | - Parsa Akbari
- Regeneron Genetics Center, Regeneron Pharmaceuticals, Tarrytown, NY, USA
| | - Luca A Lotta
- Regeneron Genetics Center, Regeneron Pharmaceuticals, Tarrytown, NY, USA
| | | | | | - Aris Baras
- Regeneron Genetics Center, Regeneron Pharmaceuticals, Tarrytown, NY, USA
| | - Paul Nioi
- Alnylam Pharmaceuticals, Cambridge, MA, USA
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357
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Huang T, Zhuang Z, Heianza Y, Sun D, Ma W, Wang W, Gao M, Fang Z, Ros E, Del Gobbo LC, Salas-Salvadó J, Martínez-González MA, Polak J, Laakso M, Astrup A, Langin D, Hager J, Hul G, Hansen T, Pedersen O, Oppert JM, Saris WHM, Arner P, Cofán M, Rajaram S, Tuomilehto J, Lindström J, de Mello VD, Stancacova A, Uusitupa M, Svendstrup M, Sørensen TIA, Gardner CD, Sabaté J, Corella D, Martinez JA, Qi L. Interaction of Diet/Lifestyle Intervention and TCF7L2 Genotype on Glycemic Control and Adiposity among Overweight or Obese Adults: Big Data from Seven Randomized Controlled Trials Worldwide. HEALTH DATA SCIENCE 2021; 2021:9897048. [PMID: 38487510 PMCID: PMC10904069 DOI: 10.34133/2021/9897048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/15/2021] [Accepted: 09/19/2021] [Indexed: 11/06/2022]
Abstract
Objective. The strongest locus which associated with type 2 diabetes (T2D) by the common variant rs7903146 is the transcription factor 7-like 2 gene (TCF7L2). We aimed to quantify the interaction of diet/lifestyle interventions and the genetic effect of TCF7L2 rs7903146 on glycemic traits, body weight, or waist circumference in overweight or obese adults in several randomized controlled trials (RCTs).Methods. From October 2016 to May 2018, a large collaborative analysis was performed by pooling individual-participant data from 7 RCTs. These RCTs reported changes in glycemic control and adiposity of the variant rs7903146 after dietary/lifestyle-related interventions in overweight or obese adults. Gene treatment interaction models which used the genetic effect encoded by the allele dose and common covariates were applicable to individual participant data in all studies.Results. In the joint analysis, a total of 7 eligible RCTs were included (n = 4,114 ). Importantly, we observed a significant effect modification of diet/lifestyle-related interventions on the TCF7L2 variant rs7903146 and changes in fasting glucose. Compared with the control group, diet/lifestyle interventions were related to lower fasting glucose by -3.06 (95% CI, -5.77 to -0.36) mg/dL (test for heterogeneity and overall effect: I 2 = 45.1 % , p < 0.05 ; z = 2.20 , p = 0.028 ) per one copy of the TCF7L2 T risk allele. Furthermore, regardless of genetic risk, diet/lifestyle interventions were associated with lower waist circumference. However, there was no significant change for diet/lifestyle interventions in other glycemic control and adiposity traits per one copy of TCF7L2 risk allele.Conclusions. Our findings suggest that carrying the TCF7L2 T risk allele may have a modestly greater benefit for specific diet/lifestyle interventions to improve the control of fasting glucose in overweight or obese adults.
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Affiliation(s)
- Tao Huang
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, China
- Department of Global Health, School of Public Health, Peking University, China
- Key Laboratory of Molecular Cardiovascular Sciences Ministry of Education, China
- Global Health Institute Peking University, China
| | - Zhenhuang Zhuang
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, China
| | - Yoriko Heianza
- Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, USA
| | - Dianjianyi Sun
- Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, USA
| | - Wenjie Ma
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Wenxiu Wang
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, China
| | - Meng Gao
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, China
| | - Zhe Fang
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, China
| | - Emilio Ros
- Department of Endocrinology & Nutrition, Institut d’Investigacions Biomèdiques August Pi Sunyer, Hospital Clínic, Barcelona, Spain
- CIBER Fisiopatologia de la Obesidad y Nutrición (CIBERobn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - Liana C. Del Gobbo
- Stanford Prevention Research Center, Stanford University, Stanford CA, USA
| | - Jordi Salas-Salvadó
- CIBER Fisiopatologia de la Obesidad y Nutrición (CIBERobn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
- Human Nutrition Unit, Faculty of Medicine and Health Sciences, Pere Virgili Health Research Institute, Rovira i Virgili University, Reus, Spain
| | - Miguel A. Martínez-González
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- CIBER Fisiopatologia de la Obesidad y Nutrición (CIBERobn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
- University of Navarra, Department of Preventive Medicine and Public Health, Medical School & IDISNA, Pamplona, Spain
| | - Jan Polak
- Department for the Study of Obesity and Diabetes, Third Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Markku Laakso
- Department of Medicine, University of Eastern Finland, Kuopio, Finland
| | - Arne Astrup
- University of Copenhagen, Department of Nutrition, Exercise and Sports, Faculty of Science, Copenhagen, Denmark
| | - Dominique Langin
- Institut National de la Santé et de la Recherche Médicale (Inserm), UMR1048, Institute of Metabolic and Cardiovascular Diseases, University of Toulouse and Paul Sabatier University, Toulouse, France
| | - Jorg Hager
- Nestlé Institute of Health Sciences, Lausanne, Switzerland
| | - Gabby Hul
- Department of Human Biology, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Centre +, Maastricht, Netherlands
| | - Torben Hansen
- Section of Metabolic Genetics, Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Oluf Pedersen
- Section of Metabolic Genetics, Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Jean-Michel Oppert
- Sorbonne Université, Institute of Cardiometabolism and Nutrition (ICAN), Department of Nutrition, Pitié-Salpêtrière Hospital, Assistance Publique-Hôpitaux de Paris (AP-HP), Paris, France
| | - Wim H. M. Saris
- Department of Human Biology, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Centre +, Maastricht, Netherlands
| | - Peter Arner
- Department of Medicine, Unit for Endocrinology and Diabetes, Karolinska University Hospital, Stockholm, Sweden
| | - Montserrat Cofán
- Department of Endocrinology & Nutrition, Institut d’Investigacions Biomèdiques August Pi Sunyer, Hospital Clínic, Barcelona, Spain
- CIBER Fisiopatologia de la Obesidad y Nutrición (CIBERobn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - Sujatha Rajaram
- School of Public Health, Loma Linda University, Loma Linda, CA, USA
| | - Jaakko Tuomilehto
- Department of Chronic Disease Prevention, Finnish National Institute for Health and Welfare, HelsinkiFinland
- Department of Public Health, University of Helsinki, Helsinki, Finland
- Diabetes Research Group, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Jaana Lindström
- Department of Chronic Disease Prevention, Finnish National Institute for Health and Welfare, HelsinkiFinland
| | - Vanessa D. de Mello
- Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland
| | - Alena Stancacova
- Department of Medicine, University of Eastern Finland, Kuopio, Finland
| | - Matti Uusitupa
- Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland
| | - Mathilde Svendstrup
- Section of Metabolic Genetics, Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Danish Diabetes Academy Odense, Denmark
| | - Thorkild I. A. Sørensen
- Section of Metabolic Genetics, Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Public Health, Section of Epidemiology, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark
| | | | - Joan Sabaté
- School of Public Health, Loma Linda University, Loma Linda, CA, USA
| | - Dolores Corella
- CIBER Fisiopatologia de la Obesidad y Nutrición (CIBERobn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
- Department of Preventive Medicine, University of Valencia, Valencia, Spain
| | - J. Alfredo Martinez
- CIBER Fisiopatologia de la Obesidad y Nutrición (CIBERobn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
- Department of Nutrition Food Science and Physiology, University of Navarra, IDISNA, Pamplona and IMDEA, Madrid, Spain
| | - Lu Qi
- Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, USA
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
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358
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Baumeister S, Nolde M, Alayash Z, Leitzmann M, Baurecht H, Meisinger C. Cannabis use does not impact on type 2 diabetes: A two-sample Mendelian randomization study. Addict Biol 2021; 26:e13020. [PMID: 33580533 DOI: 10.1111/adb.13020] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Revised: 01/18/2021] [Accepted: 01/20/2021] [Indexed: 12/17/2022]
Abstract
Cannabis has effects on the insulin/glucose metabolism. As the use of cannabis and the prevalence of type 2 diabetes increase worldwide, it is important to examine the effect of cannabis on the risk of diabetes. We conducted a Mendelian randomization (MR) study by using 19 single-nucleotide polymorphisms (SNPs) as instrumental variables for lifetime cannabis use and 14 SNPs to instrument cannabis use disorder and linking these to type 2 diabetes risk using genome-wide association study data (lifetime cannabis use [N = 184,765]; cannabis use disorder [2387 cases/48,985 controls], type 2 diabetes [74,124 cases/824,006 controls]). The MR analysis suggested no effect of lifetime cannabis use (inverse-variance weighted odds ratio [95% confidence interval] = 1.00 [0.93-1.09], P value = 0.935) and cannabis use disorder (OR = 1.03 [0.99-1.08]) on type 2 diabetes. Sensitivity analysis to assess potential pleiotropy led to no substantive change in the estimates. This study adds to the evidence base that cannabis use does not play a causal role in type 2 diabetes.
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Affiliation(s)
- Sebastian‐Edgar Baumeister
- Chair of Epidemiology LMU München, UNIKA‐T Augsburg Augsburg Germany
- Independent Research Group Clinical Epidemiology Helmholtz Zentrum München, German Research Center for Environmental Health Munich Germany
- Institute of Health Services Research in Dentistry University of Münster Münster Germany
| | - Michael Nolde
- Chair of Epidemiology LMU München, UNIKA‐T Augsburg Augsburg Germany
- Independent Research Group Clinical Epidemiology Helmholtz Zentrum München, German Research Center for Environmental Health Munich Germany
| | - Zoheir Alayash
- Chair of Epidemiology LMU München, UNIKA‐T Augsburg Augsburg Germany
| | - Michael Leitzmann
- Department of Epidemiology and Preventive Medicine University of Regensburg Regensburg Germany
| | - Hansjörg Baurecht
- Department of Epidemiology and Preventive Medicine University of Regensburg Regensburg Germany
| | - Christa Meisinger
- Chair of Epidemiology LMU München, UNIKA‐T Augsburg Augsburg Germany
- Independent Research Group Clinical Epidemiology Helmholtz Zentrum München, German Research Center for Environmental Health Munich Germany
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359
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Giannakopoulou O, Lin K, Meng X, Su MH, Kuo PH, Peterson RE, Awasthi S, Moscati A, Coleman JRI, Bass N, Millwood IY, Chen Y, Chen Z, Chen HC, Lu ML, Huang MC, Chen CH, Stahl EA, Loos RJF, Mullins N, Ursano RJ, Kessler RC, Stein MB, Sen S, Scott LJ, Burmeister M, Fang Y, Tyrrell J, Jiang Y, Tian C, McIntosh AM, Ripke S, Dunn EC, Kendler KS, Walters RG, Lewis CM, Kuchenbaecker K. The Genetic Architecture of Depression in Individuals of East Asian Ancestry: A Genome-Wide Association Study. JAMA Psychiatry 2021; 78:1258-1269. [PMID: 34586374 PMCID: PMC8482304 DOI: 10.1001/jamapsychiatry.2021.2099] [Citation(s) in RCA: 113] [Impact Index Per Article: 28.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Accepted: 05/17/2021] [Indexed: 02/06/2023]
Abstract
Importance Most previous genome-wide association studies (GWAS) of depression have used data from individuals of European descent. This limits the understanding of the underlying biology of depression and raises questions about the transferability of findings between populations. Objective To investigate the genetics of depression among individuals of East Asian and European descent living in different geographic locations, and with different outcome definitions for depression. Design, Setting, and Participants Genome-wide association analyses followed by meta-analysis, which included data from 9 cohort and case-control data sets comprising individuals with depression and control individuals of East Asian descent. This study was conducted between January 2019 and May 2021. Exposures Associations of genetic variants with depression risk were assessed using generalized linear mixed models and logistic regression. The results were combined across studies using fixed-effects meta-analyses. These were subsequently also meta-analyzed with the largest published GWAS for depression among individuals of European descent. Additional meta-analyses were carried out separately by outcome definition (clinical depression vs symptom-based depression) and region (East Asian countries vs Western countries) for East Asian ancestry cohorts. Main Outcomes and Measures Depression status was defined based on health records and self-report questionnaires. Results There were a total of 194 548 study participants (approximate mean age, 51.3 years; 62.8% women). Participants included 15 771 individuals with depression and 178 777 control individuals of East Asian descent. Five novel associations were identified, including 1 in the meta-analysis for broad depression among those of East Asian descent: rs4656484 (β = -0.018, SE = 0.003, P = 4.43x10-8) at 1q24.1. Another locus at 7p21.2 was associated in a meta-analysis restricted to geographically East Asian studies (β = 0.028, SE = 0.005, P = 6.48x10-9 for rs10240457). The lead variants of these 2 novel loci were not associated with depression risk in European ancestry cohorts (β = -0.003, SE = 0.005, P = .53 for rs4656484 and β = -0.005, SE = 0.004, P = .28 for rs10240457). Only 11% of depression loci previously identified in individuals of European descent reached nominal significance levels in the individuals of East Asian descent. The transancestry genetic correlation between cohorts of East Asian and European descent for clinical depression was r = 0.413 (SE = 0.159). Clinical depression risk was negatively genetically correlated with body mass index in individuals of East Asian descent (r = -0.212, SE = 0.084), contrary to findings for individuals of European descent. Conclusions and Relevance These results support caution against generalizing findings about depression risk factors across populations and highlight the need to increase the ancestral and geographic diversity of samples with consistent phenotyping.
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Affiliation(s)
- Olga Giannakopoulou
- Division of Psychiatry, University College of London, London, United Kingdom
| | - Kuang Lin
- Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Xiangrui Meng
- Division of Psychiatry, University College of London, London, United Kingdom
| | - Mei-Hsin Su
- Institute of Epidemiology and Preventive Medicine, National Taiwan University College of Public Health, Taipei, Taiwan
| | - Po-Hsiu Kuo
- Institute of Epidemiology and Preventive Medicine, National Taiwan University College of Public Health, Taipei, Taiwan
- Department of Psychiatry, National Taiwan University Hospital, Taipei, Taiwan
| | - Roseann E. Peterson
- Virginia Institute for Psychiatric and Behavioral Genetics, Department of Psychiatry, Virginia Commonwealth University, Richmond, Virginia
| | - Swapnil Awasthi
- Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin, Berlin, Germany
| | - Arden Moscati
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Jonathan R. I. Coleman
- Social, Genetic, and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology, and Neuroscience, King’s College London, London, United Kingdom
- National Institute for Health Research Maudsley Biomedical Research Centre, King’s College London, London, United Kingdom
| | - Nick Bass
- Division of Psychiatry, University College of London, London, United Kingdom
| | - Iona Y. Millwood
- Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
- MRC Population Health Research Unit, University of Oxford, Oxford, United Kingdom
| | - Yiping Chen
- Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
- MRC Population Health Research Unit, University of Oxford, Oxford, United Kingdom
| | - Zhengming Chen
- Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
- MRC Population Health Research Unit, University of Oxford, Oxford, United Kingdom
| | - Hsi-Chung Chen
- Department of Psychiatry, National Taiwan University Hospital, Taipei, Taiwan
| | - Mong-Liang Lu
- Department of Psychiatry, Wan-Fang Hospital, Taipei, Taiwan
- School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Ming-Chyi Huang
- School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
- Department of Psychiatry, Taipei City Psychiatric Center, Taipei, Taiwan
| | - Chun-Hsin Chen
- Department of Psychiatry, Wan-Fang Hospital, Taipei, Taiwan
- School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Eli A. Stahl
- The Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Ruth J. F. Loos
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
- The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Niamh Mullins
- The Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Robert J. Ursano
- Uniformed Services University of the Health Sciences, Bethesda, Maryland
| | | | | | - Srijan Sen
- Michigan Neuroscience Institute, Department of Psychiatry, University of Michigan, Ann Arbor, Michigan
| | - Laura J. Scott
- Department of Biostatistics, University of Michigan, Ann Arbor, Michigan
| | - Margit Burmeister
- Molecular & Behavioral Neuroscience Institute, Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, Michigan
| | - Yu Fang
- Michigan Neuroscience Institute, University of Michigan, Ann Arbor, Michigan
| | - Jess Tyrrell
- University of Exeter Medical School, University of Exeter, The RILD Building, RD&E Hospital, Exeter, United Kingdom
| | | | | | - Andrew M. McIntosh
- Division of Psychiatry, University of Edinburgh, Edinburgh, United Kingdom
| | - Stephan Ripke
- Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin, Berlin, Germany
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | - Erin C. Dunn
- Harvard Medical School, Boston, Massachusetts
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts
| | - Kenneth S. Kendler
- Virginia Institute for Psychiatric and Behavioral Genetics, Department of Psychiatry, Virginia Commonwealth University, Richmond, Virginia
| | - Robin G. Walters
- Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
- MRC Population Health Research Unit, University of Oxford, Oxford, United Kingdom
| | - Cathryn M. Lewis
- Social, Genetic, and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology, and Neuroscience, King’s College London, London, United Kingdom
- National Institute for Health Research Maudsley Biomedical Research Centre, King’s College London, London, United Kingdom
| | - Karoline Kuchenbaecker
- Division of Psychiatry, University College of London, London, United Kingdom
- UCL Genetics Institute, University College of London, London, United Kingdom
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360
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An open approach to systematically prioritize causal variants and genes at all published human GWAS trait-associated loci. Nat Genet 2021; 53:1527-1533. [PMID: 34711957 PMCID: PMC7611956 DOI: 10.1038/s41588-021-00945-5] [Citation(s) in RCA: 298] [Impact Index Per Article: 74.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Accepted: 08/20/2021] [Indexed: 12/25/2022]
Abstract
Genome-wide association studies (GWASs) have identified many variants associated with complex traits, but identifying the causal gene(s) is a major challenge. In the present study, we present an open resource that provides systematic fine mapping and gene prioritization across 133,441 published human GWAS loci. We integrate genetics (GWAS Catalog and UK Biobank) with transcriptomic, proteomic and epigenomic data, including systematic disease-disease and disease-molecular trait colocalization results across 92 cell types and tissues. We identify 729 loci fine mapped to a single-coding causal variant and colocalized with a single gene. We trained a machine-learning model using the fine-mapped genetics and functional genomics data and 445 gold-standard curated GWAS loci to distinguish causal genes from neighboring genes, outperforming a naive distance-based model. Our prioritized genes were enriched for known approved drug targets (odds ratio = 8.1, 95% confidence interval = 5.7, 11.5). These results are publicly available through a web portal ( http://genetics.opentargets.org ), enabling users to easily prioritize genes at disease-associated loci and assess their potential as drug targets.
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361
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Sujana C, Salomaa V, Kee F, Costanzo S, Söderberg S, Jordan J, Jousilahti P, Neville C, Iacoviello L, Oskarsson V, Westermann D, Koenig W, Kuulasmaa K, Reinikainen J, Blankenberg S, Zeller T, Herder C, Mansmann U, Peters A, Thorand B. Natriuretic Peptides and Risk of Type 2 Diabetes: Results From the Biomarkers for Cardiovascular Risk Assessment in Europe (BiomarCaRE) Consortium. Diabetes Care 2021; 44:2527-2535. [PMID: 34521639 DOI: 10.2337/dc21-0811] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Accepted: 08/13/2021] [Indexed: 02/03/2023]
Abstract
OBJECTIVE Natriuretic peptide (NP) concentrations are increased in cardiovascular diseases (CVDs) but are associated with a lower diabetes risk. We investigated associations of N-terminal pro-B-type NP (NT-proBNP) and midregional proatrial NP (MR-proANP) with incident type 2 diabetes stratified by the presence of CVD. RESEARCH DESIGN AND METHODS Based on the Biomarkers for Cardiovascular Risk Assessment in Europe (BiomarCaRE) Consortium, we included 45,477 participants with NT-proBNP measurements (1,707 developed type 2 diabetes over 6.5 years of median follow-up; among these, 209 had CVD at baseline) and 11,537 participants with MR-proANP measurements (857 developed type 2 diabetes over 13.8 years of median follow-up; among these, 106 had CVD at baseline). The associations were estimated using multivariable Cox regression models. RESULTS Both NPs were inversely associated with incident type 2 diabetes (hazard ratios [95% CI] per 1-SD increase of log NP: 0.84 [0.79; 0.89] for NT-proBNP and 0.77 [0.71; 0.83] for MR-proANP). The inverse association between NT-proBNP and type 2 diabetes was significant in individuals without CVD but not in individuals with CVD (0.81 [0.76; 0.86] vs. 1.04 [0.90; 1.19]; P multiplicative interaction = 0.001). There was no significant difference in the association of MR-proANP with type 2 diabetes between individuals without and with CVD (0.75 [0.69; 0.82] vs. 0.81 [0.66; 0.99]; P multiplicative interaction = 0.236). CONCLUSIONS NT-proBNP and MR-proANP are inversely associated with incident type 2 diabetes. However, the inverse association of NT-proBNP seems to be modified by the presence of CVD. Further investigations are warranted to confirm our findings and to investigate the underlying mechanisms.
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Affiliation(s)
- Chaterina Sujana
- Institute of Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany.,Institute for Medical Information Processing, Biometry, and Epidemiology (IBE), Pettenkofer School of Public Health, Ludwig-Maximilians-Universität, Munich, Germany.,German Center for Diabetes Research (DZD), Partner Munich-Neuherberg, Munich-Neuherberg, Germany
| | - Veikko Salomaa
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Frank Kee
- Centre for Public Health, Queens University of Belfast, Belfast, Northern Ireland, U.K
| | - Simona Costanzo
- Department of Epidemiology and Prevention, IRCCS Neuromed, Pozzilli, Isernia, Italy
| | - Stefan Söderberg
- Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden
| | - Jens Jordan
- Institute of Aerospace Medicine, German Aerospace Center (DLR) and University of Cologne, Cologne, Germany
| | - Pekka Jousilahti
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Charlotte Neville
- Centre for Public Health, Queens University of Belfast, Belfast, Northern Ireland, U.K
| | - Licia Iacoviello
- Department of Epidemiology and Prevention, IRCCS Neuromed, Pozzilli, Isernia, Italy.,Research Center in Epidemiology and Preventive Medicine (EPIMED), Department of Medicine and Surgery, University of Insubria, Varese, Italy
| | - Viktor Oskarsson
- Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden
| | - Dirk Westermann
- Department for General and Interventional Cardiology, University Heart Center Hamburg, Hamburg, Germany
| | - Wolfgang Koenig
- Institute of Epidemiology and Medical Biometry, University of Ulm, Ulm, Germany.,Deutsches Herzzentrum München, Technische Universität München, Munich, Germany.,German Centre for Cardiovascular Research (DZHK e.V.), partner site Munich Heart Alliance, Munich, Germany
| | - Kari Kuulasmaa
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Jaakko Reinikainen
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Stefan Blankenberg
- Department for General and Interventional Cardiology, University Heart Center Hamburg, Hamburg, Germany.,German Centre for Cardiovascular Research (DZHK e.V.), partner site Hamburg, Lübeck, Kiel, Hamburg, Germany
| | - Tanja Zeller
- Department for General and Interventional Cardiology, University Heart Center Hamburg, Hamburg, Germany.,German Centre for Cardiovascular Research (DZHK e.V.), partner site Hamburg, Lübeck, Kiel, Hamburg, Germany
| | - Christian Herder
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany.,Division of Endocrinology and Diabetology, Medical Faculty and University Hospital Düsseldorf, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany.,German Center for Diabetes Research (DZD), Partner Düsseldorf, Munich-Neuherberg, Germany
| | - Ulrich Mansmann
- Institute for Medical Information Processing, Biometry, and Epidemiology (IBE), Pettenkofer School of Public Health, Ludwig-Maximilians-Universität, Munich, Germany
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany.,German Center for Diabetes Research (DZD), Partner Munich-Neuherberg, Munich-Neuherberg, Germany.,German Centre for Cardiovascular Research (DZHK e.V.), partner site Munich Heart Alliance, Munich, Germany
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362
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Dai W, Choubey M, Patel S, Singer HA, Ozcan L. Adipocyte CAMK2 deficiency improves obesity-associated glucose intolerance. Mol Metab 2021; 53:101300. [PMID: 34303021 PMCID: PMC8365526 DOI: 10.1016/j.molmet.2021.101300] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Accepted: 07/13/2021] [Indexed: 12/27/2022] Open
Abstract
OBJECTIVE Obesity-related adipose tissue dysfunction has been linked to the development of insulin resistance, type 2 diabetes, and cardiovascular disease. Impaired calcium homeostasis is associated with altered adipose tissue metabolism; however, the molecular mechanisms that link disrupted calcium signaling to metabolic regulation are largely unknown. Here, we investigated the contribution of a calcium-sensing enzyme, calcium/calmodulin-dependent protein kinase II (CAMK2), to adipocyte function, obesity-associated insulin resistance, and glucose intolerance. METHODS To determine the impact of adipocyte CAMK2 deficiency on metabolic regulation, we generated a conditional knockout mouse model and acutely deleted CAMK2 in mature adipocytes. We further used in vitro differentiated adipocytes to dissect the mechanisms by which CAMK2 regulates adipocyte function. RESULTS CAMK2 activity was increased in obese adipose tissue, and depletion of adipocyte CAMK2 in adult mice improved glucose intolerance and insulin resistance without an effect on body weight. Mechanistically, we found that activation of CAMK2 disrupted adipocyte insulin signaling and lowered the amount of insulin receptor. Further, our results revealed that CAMK2 contributed to adipocyte lipolysis, tumor necrosis factor alpha (TNFα)-induced inflammation, and insulin resistance. CONCLUSIONS These results identify a new link between adipocyte CAMK2 activity, metabolic regulation, and whole-body glucose homeostasis.
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Affiliation(s)
- Wen Dai
- Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA; Department of Cardiology, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Mayank Choubey
- Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA
| | - Sonal Patel
- Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA
| | - Harold A Singer
- Department of Molecular and Cellular Physiology, Albany Medical College, Albany, NY, USA
| | - Lale Ozcan
- Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA.
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363
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Nielsen MB, Çolak Y, Benn M, Nordestgaard BG. Low Plasma Adiponectin in Risk of Type 2 Diabetes: Observational Analysis and One- and Two-Sample Mendelian Randomization Analyses in 756,219 Individuals. Diabetes 2021; 70:2694-2705. [PMID: 34426507 DOI: 10.2337/db21-0131] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Accepted: 08/16/2021] [Indexed: 12/12/2022]
Abstract
We tested the hypothesis that low plasma adiponectin is associated observationally and causally with increased risk of type 2 diabetes. Observational analyses are prone to confounding and reverse causation, while genetic Mendelian randomization (MR) analyses are much less influenced by these biases. We examined 30,045 individuals from the Copenhagen General Population Study observationally (plasma adiponectin [1,751 individuals with type 2 diabetes]), 96,903 Copenhagen individuals using one-sample MR (5 genetic variants [5,012 individuals with type 2 diabetes]), and 659,316 Europeans (ADIPOGen, GERA, DIAGRAM, UK Biobank) using two-sample MR (10 genetic variants [62,892 individuals type 2 diabetes]). Observationally, and in comparisons with individuals with median plasma adiponectin of 28.9 μg/mL (4th quartile), multivariable adjusted hazard ratios (HRs) for type 2 diabetes were 1.42 (95% CI 1.18-1.72) for 19.2 μg/mL (3rd quartile), 2.21 (1.84-2.66) for 13.9 μg/mL (2nd quartile), and 4.05 (3.38-4.86) for 9.2 μg/mL (1st quartile). Corresponding cumulative incidence for type 2 diabetes at age 70 years was 3%, 7%, 11%, and 20%, respectively. A 1 μg/mL lower plasma adiponectin conferred an HR for type 2 diabetes of 1.07 (1.06-1.09), while genetic, causal risk ratio per 1 unit log-transformed lower plasma adiponectin was 1.13 (95% CI 0.83-1.53) in one-sample MR and 1.26 (1.01-1.57) in two-sample MR. In conclusion, low plasma adiponectin is associated with increased risk of type 2 diabetes, an association that could represent a causal relationship.
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Affiliation(s)
- Maria B Nielsen
- Department of Clinical Biochemistry, Copenhagen University Hospital - Herlev and Gentofte, Copenhagen, Denmark
- The Copenhagen General Population Study, Copenhagen University Hospital - Herlev and Gentofte, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Yunus Çolak
- Department of Clinical Biochemistry, Copenhagen University Hospital - Herlev and Gentofte, Copenhagen, Denmark
- The Copenhagen General Population Study, Copenhagen University Hospital - Herlev and Gentofte, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Section of Respiratory Medicine, Department of Internal Medicine, Copenhagen University Hospital - Herlev and Gentofte, Copenhagen, Denmark
| | - Marianne Benn
- The Copenhagen General Population Study, Copenhagen University Hospital - Herlev and Gentofte, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Clinical Biochemistry, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark
| | - Børge G Nordestgaard
- Department of Clinical Biochemistry, Copenhagen University Hospital - Herlev and Gentofte, Copenhagen, Denmark
- The Copenhagen General Population Study, Copenhagen University Hospital - Herlev and Gentofte, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
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364
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Rosoff DB, Yoo J, Lohoff FW. Smoking is significantly associated with increased risk of COVID-19 and other respiratory infections. Commun Biol 2021; 4:1230. [PMID: 34711921 PMCID: PMC8553923 DOI: 10.1038/s42003-021-02685-y] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Accepted: 09/01/2021] [Indexed: 12/11/2022] Open
Abstract
Observational studies suggest smoking, cannabis use, alcohol consumption, and substance use disorders (SUDs) may impact risk for respiratory infections, including coronavirus 2019 (COVID-2019). However, causal inference is challenging due to comorbid substance use. Using summary-level European ancestry data (>1.7 million participants), we performed single-variable and multivariable Mendelian randomization (MR) to evaluate relationships between substance use behaviors, COVID-19 and other respiratory infections. Genetic liability for smoking demonstrated the strongest associations with COVID-19 infection risk, including the risk for very severe respiratory confirmed COVID-19 (odds ratio (OR) = 2.69, 95% CI, 1.42, 5.10, P-value = 0.002), and COVID-19 infections requiring hospitalization (OR = 3.49, 95% CI, 2.23, 5.44, P-value = 3.74 × 10-8); these associations generally remained robust in models accounting for other substance use and cardiometabolic risk factors. Smoking was also strongly associated with increased risk of other respiratory infections, including asthma-related pneumonia/sepsis (OR = 3.64, 95% CI, 2.16, 6.11, P-value = 1.07 × 10-6), chronic lower respiratory diseases (OR = 2.29, 95% CI, 1.80, 2.91, P-value = 1.69 × 10-11), and bacterial pneumonia (OR = 2.14, 95% CI, 1.42, 3.24, P-value = 2.84 × 10-4). We provide strong genetic evidence showing smoking increases the risk for COVID-19 and other respiratory infections even after accounting for other substance use behaviors and cardiometabolic diseases, which suggests that prevention programs aimed at reducing smoking may be important for the COVID-19 pandemic and have substantial public health benefits.
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Affiliation(s)
- Daniel B Rosoff
- Section on Clinical Genomics and Experimental Therapeutics, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD, USA
- NIH-Oxford-Cambridge Scholars Program; Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Joyce Yoo
- Section on Clinical Genomics and Experimental Therapeutics, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD, USA
| | - Falk W Lohoff
- Section on Clinical Genomics and Experimental Therapeutics, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD, USA.
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365
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Perrin HJ, Currin KW, Vadlamudi S, Pandey GK, Ng KK, Wabitsch M, Laakso M, Love MI, Mohlke KL. Chromatin accessibility and gene expression during adipocyte differentiation identify context-dependent effects at cardiometabolic GWAS loci. PLoS Genet 2021; 17:e1009865. [PMID: 34699533 PMCID: PMC8570510 DOI: 10.1371/journal.pgen.1009865] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Revised: 11/05/2021] [Accepted: 10/07/2021] [Indexed: 12/15/2022] Open
Abstract
Chromatin accessibility and gene expression in relevant cell contexts can guide identification of regulatory elements and mechanisms at genome-wide association study (GWAS) loci. To identify regulatory elements that display differential activity across adipocyte differentiation, we performed ATAC-seq and RNA-seq in a human cell model of preadipocytes and adipocytes at days 4 and 14 of differentiation. For comparison, we created a consensus map of ATAC-seq peaks in 11 human subcutaneous adipose tissue samples. We identified 58,387 context-dependent chromatin accessibility peaks and 3,090 context-dependent genes between all timepoint comparisons (log2 fold change>1, FDR<5%) with 15,919 adipocyte- and 18,244 preadipocyte-dependent peaks. Adipocyte-dependent peaks showed increased overlap (60.1%) with Roadmap Epigenomics adipocyte nuclei enhancers compared to preadipocyte-dependent peaks (11.5%). We linked context-dependent peaks to genes based on adipocyte promoter capture Hi-C data, overlap with adipose eQTL variants, and context-dependent gene expression. Of 16,167 context-dependent peaks linked to a gene, 5,145 were linked by two or more strategies to 1,670 genes. Among GWAS loci for cardiometabolic traits, adipocyte-dependent peaks, but not preadipocyte-dependent peaks, showed significant enrichment (LD score regression P<0.005) for waist-to-hip ratio and modest enrichment (P < 0.05) for HDL-cholesterol. We identified 659 peaks linked to 503 genes by two or more approaches and overlapping a GWAS signal, suggesting a regulatory mechanism at these loci. To identify variants that may alter chromatin accessibility between timepoints, we identified 582 variants in 454 context-dependent peaks that demonstrated allelic imbalance in accessibility (FDR<5%), of which 55 peaks also overlapped GWAS variants. At one GWAS locus for palmitoleic acid, rs603424 was located in an adipocyte-dependent peak linked to SCD and exhibited allelic differences in transcriptional activity in adipocytes (P = 0.003) but not preadipocytes (P = 0.09). These results demonstrate that context-dependent peaks and genes can guide discovery of regulatory variants at GWAS loci and aid identification of regulatory mechanisms. Cardiovascular and metabolic diseases are widespread, and an increased understanding of genetic mechanisms behind these diseases could improve treatment. Chromatin accessibility and gene expression in relevant cell contexts can guide identification of regulatory elements and genetic mechanisms for disease traits. A relevant context for cardiovascular and metabolic disease traits is adipocyte differentiation. To identify regulatory elements and genes that display differences in activity during adipocyte differentiation, we profiled chromatin accessibility and gene expression in a human cell model of preadipocytes and adipocytes. We identified chromatin regions that change accessibility during differentiation and predicted genes they may affect. We also linked these chromatin regions to genetic variants associated with risk of disease. At one genomic region linked to fatty acids, a chromatin region more accessible in adipocytes linked to a fatty acid synthesis gene and exhibited allelic differences in transcriptional activity in adipocytes but not preadipocytes. These results demonstrate that chromatin regions and genes that change during cell context can guide discovery of regulatory variants and aid identification of disease mechanisms.
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Affiliation(s)
- Hannah J. Perrin
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Kevin W. Currin
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Swarooparani Vadlamudi
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Gautam K. Pandey
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Kenneth K. Ng
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Martin Wabitsch
- Department of Pediatrics and Adolescent Medicine, Ulm University Hospital, Ulm, Germany
| | - Markku Laakso
- Department of Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
| | - Michael I. Love
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina, United States of America
- Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Karen L. Mohlke
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina, United States of America
- * E-mail:
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366
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Zhong Z, Feng X, Su G, Du L, Liao W, Liu S, Li F, Zuo X, Yang P. HMG-Coenzyme A Reductase as a Drug Target for the Prevention of Ankylosing Spondylitis. Front Cell Dev Biol 2021; 9:731072. [PMID: 34692687 PMCID: PMC8526849 DOI: 10.3389/fcell.2021.731072] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2021] [Accepted: 09/16/2021] [Indexed: 11/14/2022] Open
Abstract
Statins are an inhibitor of 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGCR). Growing evidence indicates that statins may have an anti-inflammatory effect. Whether genetically proxied HMGCR inhibition can reduce the risk of ankylosing spondylitis is unknown. We constructed an HMGCR genetic score comprising nearly randomly inherited variants significantly associated with LDL cholesterol levels within ± 100 kb from HMGCR to proxy for inhibition of HMGCR. We also constructed PCSK9 and NPC1L1 scores as well as the LDL polygenetic score to proxy for the inhibition of these drug targets as well as serum LDL cholesterol levels, respectively. We then compared the associations of these genetic scores with the risk of ankylosing spondylitis. Of 33,998 participants in the primary cohort, 12,596 individuals had been diagnosed with ankylosing spondylitis. Genetically proxied inhibition of HMGCR scaled to per mmol/L decrease in LDL cholesterol levels by the HMGCR score was associated with a lower risk of ankylosing spondylitis (OR, 0.57; 95% CI, 0.38–0.85; P value = 5.7 × 10–3). No significant association with ankylosing spondylitis was observed for the PCSK9 score (OR, 0.89; 95% CI, 0.68–1.16) and the NPC1L1 score (OR, 1.50; 95% CI, 0.39–5.77). For the LDL score, genetically determined per mmol/L decrease in LDL cholesterol levels led to a reduced risk of ankylosing spondylitis (OR, 0.64; 95% CI, 0.43–0.94), with significant heterogeneity and pleiotropy in the estimate. Exploratory analyses showed that genetically proxied inhibition of HMGCR appeared to have a similar effect to long-term statin therapy in modifying the risk of coronary artery disease and type 2 diabetes, suggesting that the HMGCR score might be a reliable model to assess the effect of statin. Genetically proxied inhibition of HMGCR was associated with a decreased risk of ankylosing spondylitis. This mechanism-based estimate was in line with existing observations suggesting the clinical benefits of statin therapy for ankylosing spondylitis.
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Affiliation(s)
- Zhenyu Zhong
- The First Affiliated Hospital of Chongqing Medical University, Chongqing Key Laboratory of Ophthalmology and Chongqing Eye Institute, Chongqing, China
| | - Xiaojie Feng
- The First Affiliated Hospital of Chongqing Medical University, Chongqing Key Laboratory of Ophthalmology and Chongqing Eye Institute, Chongqing, China
| | - Guannan Su
- The First Affiliated Hospital of Chongqing Medical University, Chongqing Key Laboratory of Ophthalmology and Chongqing Eye Institute, Chongqing, China
| | - Liping Du
- The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Weiting Liao
- The First Affiliated Hospital of Chongqing Medical University, Chongqing Key Laboratory of Ophthalmology and Chongqing Eye Institute, Chongqing, China
| | - Shengyun Liu
- The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Fuzhen Li
- The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Xianbo Zuo
- The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Peizeng Yang
- The First Affiliated Hospital of Chongqing Medical University, Chongqing Key Laboratory of Ophthalmology and Chongqing Eye Institute, Chongqing, China
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367
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Goldberger O, Livny J, Bhattacharyya R, Amster-Choder O. Wisdom of the crowds: A suggested polygenic plan for small-RNA-mediated regulation in bacteria. iScience 2021; 24:103096. [PMID: 34622151 PMCID: PMC8479692 DOI: 10.1016/j.isci.2021.103096] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Revised: 02/18/2021] [Accepted: 09/02/2021] [Indexed: 12/04/2022] Open
Abstract
The omnigenic/polygenic theory, which states that complex traits are not shaped by single/few genes, but by situation-specific large networks, offers an explanation for a major enigma in microbiology: deletion of specific small RNAs (sRNAs) playing key roles in various aspects of bacterial physiology, including virulence and antibiotic resistance, results in surprisingly subtle phenotypes. A recent study uncovered polar accumulation of most sRNAs upon osmotic stress, the majority not known to be involved in the applied stress. Here we show that cells deleted for a handful of pole-enriched sRNAs exhibit fitness defect in several stress conditions, as opposed to single, double, or triple sRNA-knockouts, implying that regulation by sRNA relies on sets of genes. Moreover, analysis of RNA-seq data of Escherichia coli and Salmonella typhimurium exposed to antibiotics and/or infection-relevant conditions reveals the involvement of multiple sRNAs in all cases, in line with the existence of a polygenic plan for sRNA-mediated regulation.
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Affiliation(s)
- Omer Goldberger
- Department of Microbiology and Molecular Genetics, IMRIC, The Hebrew University Faculty of Medicine, P.O.Box 12272, Jerusalem 91120, Israel
| | - Jonathan Livny
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02140, USA
| | - Roby Bhattacharyya
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02140, USA
| | - Orna Amster-Choder
- Department of Microbiology and Molecular Genetics, IMRIC, The Hebrew University Faculty of Medicine, P.O.Box 12272, Jerusalem 91120, Israel
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368
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Szejko N, Dunalska A, Lombroso A, McGuire JF, Piacentini J. Genomics of Obsessive-Compulsive Disorder-Toward Personalized Medicine in the Era of Big Data. Front Pediatr 2021; 9:685660. [PMID: 34746045 PMCID: PMC8564378 DOI: 10.3389/fped.2021.685660] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Accepted: 09/20/2021] [Indexed: 01/11/2023] Open
Abstract
Pathogenesis of obsessive-compulsive disorder (OCD) mainly involves dysregulation of serotonergic neurotransmission, but a number of other factors are involved. Genetic underprints of OCD fall under the category of "common disease common variant hypothesis," that suggests that if a disease that is heritable is common in the population (a prevalence >1-5%), then the genetic contributors-specific variations in the genetic code-will also be common in the population. Therefore, the genetic contribution in OCD is believed to come from multiple genes simultaneously and it is considered a polygenic disorder. Genomics offers a number of advanced tools to determine causal relationship between the exposure and the outcome of interest. Particularly, methods such as polygenic risk score (PRS) or Mendelian Randomization (MR) enable investigation of new pathways involved in OCD pathogenesis. This premise is also facilitated by the existence of publicly available databases that include vast study samples. Examples include population-based studies such as UK Biobank, China Kadoorie Biobank, Qatar Biobank, All of US Program sponsored by National Institute of Health or Generations launched by Yale University, as well as disease-specific databases, that include patients with OCD and co-existing pathologies, with the following examples: Psychiatric Genomics Consortium (PGC), ENIGMA OCD, The International OCD Foundation Genetics Collaborative (IOCDF-GC) or OCD Collaborative Genetic Association Study. The aim of this review is to present a comprehensive overview of the available Big Data resources for the study of OCD pathogenesis in the context of genomics and demonstrate that OCD should be considered a disorder which requires the approaches offered by personalized medicine.
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Affiliation(s)
- Natalia Szejko
- Department of Neurology, Yale School of Medicine, New Haven, CT, United States
- Department of Neurology, Medical University of Warsaw, Warsaw, Poland
- Department of Bioethics, Medical University of Warsaw, Warsaw, Poland
| | - Anna Dunalska
- Department of Neurology, Medical University of Warsaw, Warsaw, Poland
| | - Adam Lombroso
- Child Study Center, Yale School of Medicine, New Haven, CT, United States
| | - Joseph F. McGuire
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MS, United States
- Semel Institute of Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, United States
| | - John Piacentini
- Semel Institute of Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, United States
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369
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El-Huneidi W, Anjum S, Mohammed AK, Unnikannan H, Saeed R, Bajbouj K, Abu-Gharbieh E, Taneera J. Copine 3 "CPNE3" is a novel regulator for insulin secretion and glucose uptake in pancreatic β-cells. Sci Rep 2021; 11:20692. [PMID: 34667273 PMCID: PMC8526566 DOI: 10.1038/s41598-021-00255-0] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Accepted: 10/01/2021] [Indexed: 12/18/2022] Open
Abstract
Copine 3 (CPNE3) is a calcium-dependent phospholipid-binding protein that has been found to play an essential role in cancer progression and stages. However, its role in pancreatic β-cell function has not been investigated. Therefore, we performed a serial of bioinformatics and functional experiments to explore the potential role of Cpne3 on insulin secretion and β-cell function in human islets and INS-1 (832/13) cells. RNA sequencing and microarray data revealed that CPNE3 is highly expressed in human islets compared to other CPNE genes. In addition, expression of CPNE3 was inversely correlated with HbA1c and reduced in human islets from hyperglycemic donors. Silencing of Cpne3 in INS-1 cells impaired glucose-stimulated insulin secretion (GSIS), insulin content and glucose uptake efficiency without affecting cell viability or inducing apoptosis. Moreover, mRNA and protein expression of the key regulators in glucose sensing and insulin secretion (Insulin, GLUT2, NeuroD1, and INSR) were downregulated in Cpne3-silenced cells. Taken together, data from the present study provides a new understanding of the role of CPNE3 in maintaining normal β-cell function, which might contribute to developing a novel target for future management of type 2 diabetes therapy.
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Affiliation(s)
- Waseem El-Huneidi
- grid.412789.10000 0004 4686 5317Department of Basic Medical Sciences, College of Medicine, University of Sharjah, Sharjah, United Arab Emirates ,grid.412789.10000 0004 4686 5317University of Sharjah, Sharjah Institute for Medical Research, Sharjah, United Arab Emirates
| | - Shabana Anjum
- grid.412789.10000 0004 4686 5317University of Sharjah, Sharjah Institute for Medical Research, Sharjah, United Arab Emirates
| | - Abdul Khader Mohammed
- grid.412789.10000 0004 4686 5317University of Sharjah, Sharjah Institute for Medical Research, Sharjah, United Arab Emirates
| | - Hema Unnikannan
- grid.412789.10000 0004 4686 5317University of Sharjah, Sharjah Institute for Medical Research, Sharjah, United Arab Emirates
| | - Rania Saeed
- grid.412789.10000 0004 4686 5317University of Sharjah, Sharjah Institute for Medical Research, Sharjah, United Arab Emirates
| | - Khuloud Bajbouj
- grid.412789.10000 0004 4686 5317University of Sharjah, Sharjah Institute for Medical Research, Sharjah, United Arab Emirates
| | - Eman Abu-Gharbieh
- grid.412789.10000 0004 4686 5317University of Sharjah, Sharjah Institute for Medical Research, Sharjah, United Arab Emirates ,grid.412789.10000 0004 4686 5317Department of Clinical Sciences, College of Medicine, University of Sharjah, Sharjah, United Arab Emirates
| | - Jalal Taneera
- grid.412789.10000 0004 4686 5317Department of Basic Medical Sciences, College of Medicine, University of Sharjah, Sharjah, United Arab Emirates ,grid.412789.10000 0004 4686 5317University of Sharjah, Sharjah Institute for Medical Research, Sharjah, United Arab Emirates
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370
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Schaid DJ, Dikilitas O, Sinnwell JP, Kullo IJ. Penalized mediation models for multivariate data. Genet Epidemiol 2021; 46:32-50. [PMID: 34664742 DOI: 10.1002/gepi.22433] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2021] [Revised: 08/04/2021] [Accepted: 10/04/2021] [Indexed: 11/11/2022]
Abstract
Statistical methods to integrate multiple layers of data, from exposures to intermediate traits to outcome variables, are needed to guide interpretation of complex data sets for which variables are likely contributing in a causal pathway from exposure to outcome. Statistical mediation analysis based on structural equation models provide a general modeling framework, yet they can be difficult to apply to high-dimensional data and they are not automated to select the best fitting model. To overcome these limitations, we developed novel algorithms and software to simultaneously evaluate multiple exposure variables, multiple intermediate traits, and multiple outcome variables. Our penalized mediation models are computationally efficient and simulations demonstrate that they produce reliable results for large data sets. Application of our methods to a study of vascular disease demonstrates their utility to identify novel direct effects of single-nucleotide polymorphisms (SNPs) on coronary heart disease and peripheral artery disease, while disentangling the effects of SNPs on the intermediate risk factors including lipids, cigarette smoking, systolic blood pressure, and type 2 diabetes.
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Affiliation(s)
- Daniel J Schaid
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
| | - Ozan Dikilitas
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Jason P Sinnwell
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
| | - Iftikhar J Kullo
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA
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371
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Kokkinopoulou I, Diakoumi A, Moutsatsou P. Glucocorticoid Receptor Signaling in Diabetes. Int J Mol Sci 2021; 22:ijms222011173. [PMID: 34681832 PMCID: PMC8537243 DOI: 10.3390/ijms222011173] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Revised: 10/04/2021] [Accepted: 10/12/2021] [Indexed: 12/20/2022] Open
Abstract
Stress and depression increase the risk of Type 2 Diabetes (T2D) development. Evidence demonstrates that the Glucocorticoid (GC) negative feedback is impaired (GC resistance) in T2D patients resulting in Hypothalamic-Pituitary-Adrenal (HPA) axis hyperactivity and hypercortisolism. High GCs, in turn, activate multiple aspects of glucose homeostasis in peripheral tissues leading to hyperglycemia. Elucidation of the underlying molecular mechanisms revealed that Glucocorticoid Receptor (GR) mediates the GC-induced dysregulation of glucose production, uptake and insulin signaling in GC-sensitive peripheral tissues, such as liver, skeletal muscle, adipose tissue, and pancreas. In contrast to increased GR peripheral sensitivity, an impaired GR signaling in Peripheral Blood Mononuclear Cells (PBMCs) of T2D patients, associated with hyperglycemia, hyperlipidemia, and increased inflammation, has been shown. Given that GR changes in immune cells parallel those in brain, the above data implicate that a reduced brain GR function may be the biological link among stress, HPA hyperactivity, hypercortisolism and hyperglycemia. GR polymorphisms have also been associated with metabolic disturbances in T2D while dysregulation of micro-RNAs—known to target GR mRNA—has been described. Collectively, GR has a crucial role in T2D, acting in a cell-type and context-specific manner, leading to either GC sensitivity or GC resistance. Selective modulation of GR signaling in T2D therapy warrants further investigation.
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372
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Meeks KAC, Bentley AR, Gouveia MH, Chen G, Zhou J, Lei L, Adeyemo AA, Doumatey AP, Rotimi CN. Genome-wide analyses of multiple obesity-related cytokines and hormones informs biology of cardiometabolic traits. Genome Med 2021; 13:156. [PMID: 34620218 PMCID: PMC8499470 DOI: 10.1186/s13073-021-00971-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Accepted: 09/16/2021] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND A complex set of perturbations occur in cytokines and hormones in the etiopathogenesis of obesity and related cardiometabolic conditions such as type 2 diabetes (T2D). Evidence for the genetic regulation of these cytokines and hormones is limited, particularly in African-ancestry populations. In order to improve our understanding of the biology of cardiometabolic traits, we investigated the genetic architecture of a large panel of obesity- related cytokines and hormones among Africans with replication analyses in African Americans. METHODS We performed genome-wide association studies (GWAS) in 4432 continental Africans, enrolled from Ghana, Kenya, and Nigeria as part of the Africa America Diabetes Mellitus (AADM) study, for 13 obesity-related cytokines and hormones, including adipsin, glucose-dependent insulinotropic peptide (GIP), glucagon-like peptide-1 (GLP-1), interleukin-1 receptor antagonist (IL1-RA), interleukin-6 (IL-6), interleukin-10 (IL-10), leptin, plasminogen activator inhibitor-1 (PAI-1), resistin, visfatin, insulin, glucagon, and ghrelin. Exact and local replication analyses were conducted in African Americans (n = 7990). The effects of sex, body mass index (BMI), and T2D on results were investigated through stratified analyses. RESULTS GWAS identified 39 significant (P value < 5 × 10-8) loci across all 13 traits. Notably, 14 loci were African-ancestry specific. In this first GWAS for adipsin and ghrelin, we detected 13 and 4 genome-wide significant loci respectively. Stratified analyses by sex, BMI, and T2D showed a strong effect of these variables on detected loci. Eight novel loci were successfully replicated: adipsin (3), GIP (1), GLP-1 (1), and insulin (3). Annotation of these loci revealed promising links between these adipocytokines and cardiometabolic outcomes as illustrated by rs201751833 for adipsin and blood pressure and locus rs759790 for insulin level and T2D in lean individuals. CONCLUSIONS Our study identified genetic variants underlying variation in multiple adipocytokines, including the first loci for adipsin and ghrelin. We identified population differences in variants associated with adipocytokines and highlight the importance of stratification for discovery of loci. The high number of African-specific loci detected emphasizes the need for GWAS in African-ancestry populations, as these loci could not have been detected in other populations. Overall, our work contributes to the understanding of the biology linking adipocytokines to cardiometabolic traits.
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Affiliation(s)
- Karlijn A C Meeks
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, 12 South Drive Bldg 12A rm 4047, Bethesda, MD, 20814, USA
| | - Amy R Bentley
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, 12 South Drive Bldg 12A rm 4047, Bethesda, MD, 20814, USA
| | - Mateus H Gouveia
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, 12 South Drive Bldg 12A rm 4047, Bethesda, MD, 20814, USA
| | - Guanjie Chen
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, 12 South Drive Bldg 12A rm 4047, Bethesda, MD, 20814, USA
| | - Jie Zhou
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, 12 South Drive Bldg 12A rm 4047, Bethesda, MD, 20814, USA
| | - Lin Lei
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, 12 South Drive Bldg 12A rm 4047, Bethesda, MD, 20814, USA
| | - Adebowale A Adeyemo
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, 12 South Drive Bldg 12A rm 4047, Bethesda, MD, 20814, USA
| | - Ayo P Doumatey
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, 12 South Drive Bldg 12A rm 4047, Bethesda, MD, 20814, USA.
| | - Charles N Rotimi
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, 12 South Drive Bldg 12A rm 4047, Bethesda, MD, 20814, USA.
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373
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Ying K, Zhai R, Pyrkov TV, Shindyapina AV, Mariotti M, Fedichev PO, Shen X, Gladyshev VN. Genetic and phenotypic analysis of the causal relationship between aging and COVID-19. COMMUNICATIONS MEDICINE 2021; 1:35. [PMID: 35602207 PMCID: PMC9053191 DOI: 10.1038/s43856-021-00033-z] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2021] [Accepted: 08/31/2021] [Indexed: 12/29/2022] Open
Abstract
Background Epidemiological studies revealed that the elderly and those with comorbidities are most affected by COVID-19, but it is important to investigate shared genetic mechanisms between COVID-19 risk and aging. Methods We conducted a multi-instrument Mendelian Randomization analysis of multiple lifespan-related traits and COVID-19. Aging clock models were applied to the subjects with different COVID-19 conditions in the UK-Biobank cohort. We performed a bivariate genomic scan for age-related COVID-19 and Mendelian Randomization analysis of 389 immune cell traits to investigate their effect on lifespan and COVID-19 risk. Results We show that the genetic variation that supports longer life is significantly associated with the lower risk of COVID-19 infection and hospitalization. The odds ratio is 0.31 (P = 9.7 × 10-6) and 0.46 (P = 3.3 × 10-4), respectively, per additional 10 years of life. We detect an association between biological age acceleration and future incidence and severity of COVID-19 infection. Genetic profiling of age-related COVID-19 infection indicates key contributions of Notch signaling and immune system development. We reveal a negative correlation between the effects of immune cell traits on lifespan and COVID-19 risk. We find that lower B-cell CD19 levels are indicative of an increased risk of COVID-19 and decreased life expectancy, which is further validated by COVID-19 clinical data. Conclusions Our analysis suggests that the factors that accelerate aging lead to an increased COVID-19 risk and point to the importance of Notch signaling and B cells in both. Interventions that target these factors to reduce biological age may reduce the risk of COVID-19.
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Affiliation(s)
- Kejun Ying
- Biostatistics Group, State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, China
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA USA
- T. H. Chan School of Public Health, Harvard University, Boston, MA USA
| | - Ranran Zhai
- Biostatistics Group, State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, China
| | | | - Anastasia V. Shindyapina
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA USA
| | - Marco Mariotti
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA USA
- Department of Genetics, Microbiology and Statistics, Universitat de Barcelona, Barcelona, Catalonia Spain
| | - Peter O. Fedichev
- Gero LLC PTE, Singapore City, Singapore
- Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region Russia
| | - Xia Shen
- Biostatistics Group, State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, China
- Greater Bay Area Institute of Precision Medicine (Guangzhou), Fudan University, Guangzhou, China
| | - Vadim N. Gladyshev
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA USA
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374
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Ben Salah H, Jelassi R, Zidi I, Ben Amor A, Bizid S, Ammi R, Guizani L, Bouratbine A, Aoun K, Chelbi H. Rapid high-resolution melting method to identify human leukocyte antigen-G (HLA-G) 3' untranslated region polymorphism +3142C/G (rs1063320). Mol Genet Genomic Med 2021; 9:e1817. [PMID: 34605219 PMCID: PMC8606219 DOI: 10.1002/mgg3.1817] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2019] [Revised: 01/30/2020] [Accepted: 03/05/2020] [Indexed: 01/05/2023] Open
Abstract
Background HLA‐G is a non‐classical class I gene of the human Major Histocompatibility encoding molecules with immune‐modulatory properties. Expression of HLA‐G is being largely studied in pathological conditions, such as tumors, viral infections, inflammation, and autoimmune diseases, grafted tissues, among others. HLA‐G +3142C/G (rs1063320: dbSNP database) polymorphism is located in 3′ UTR of HAL‐G and plays a key role in determining the magnitude of gene and protein expression. The detection of HLA‐G +3142C/G polymorphism in the most published report is done through polymerase chain reaction followed by enzymatic digestion. Therefore, it is so interesting to develop a rapid and sensitive assay to genotype HLA‐G +3142C/G polymorphism. High‐resolution melt analysis (HRM) is a technology that is based on the analysis of the melting profile of PCR products through gradual temperature increase. The aim of this work is to apply high‐resolution melt method for genotyping the HLA‐G +3142C/G polymorphism. Methods DNA from 118 individuals was extracted from whole blood with QIAamp® DNA blood mini kit (Qiagen, Germany). Primer couple was designed using Primer 3 online tools so as to have only one SNP in the target sequence for high HRM efficiency. Positive Controls were identified using DNA sequencing and used as reference when assigning genotypes for trial samples. Results We were able to recognize the three genotypes with similar accuracy than DNA sequencing using high resolution melting method. Hardy‐Weinberg equilibrium test shows that our population is in equilibrium for the studied SNP. Genotypes frequencies of +3142C/G polymorphism in Tunisian general population are 0.475 for heterozygote G/C, 0.186 for homozygote G/G and 0.339 for homozygote C/C. Conclusion HRM is a cost‐effective method suitable for SNP genotyping.
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Affiliation(s)
- Hamza Ben Salah
- Laboratoire de parasitologie médicale, biotechnologies et biomolécules, Institut Pasteur de Tunis LR11IPT06, Tunis, Tunisie.,Faculté des Sciences de Tunis, Université de Tunis El Manar, Tunis, Tunisie
| | - Refka Jelassi
- Laboratoire de parasitologie médicale, biotechnologies et biomolécules, Institut Pasteur de Tunis LR11IPT06, Tunis, Tunisie.,Faculté des Sciences de Bizerte, Université de Carthage, Tunis, Tunisie
| | - Ines Zidi
- Laboratoire des microorganismes et biomolécules actives, faculté des Sciences de Tunis, Université de Tunis El-Manar, Tunis, Tunisie
| | - Amor Ben Amor
- Laboratoire de parasitologie médicale, biotechnologies et biomolécules, Institut Pasteur de Tunis LR11IPT06, Tunis, Tunisie.,Emirates College of Technology, Abu Dhabi, UAE
| | - Sondes Bizid
- Service de gastroentérologie, Hôpital militaire de Tunis, Tunis, Tunisie
| | - Radhia Ammi
- Service des consultants externes, Institut Pasteur de Tunis, Tunis, Tunisie
| | - Lamia Guizani
- Laboratoire de parasitologie médicale, biotechnologies et biomolécules, Institut Pasteur de Tunis LR11IPT06, Tunis, Tunisie
| | - Aida Bouratbine
- Laboratoire de parasitologie médicale, biotechnologies et biomolécules, Institut Pasteur de Tunis LR11IPT06, Tunis, Tunisie
| | - Karim Aoun
- Laboratoire de parasitologie médicale, biotechnologies et biomolécules, Institut Pasteur de Tunis LR11IPT06, Tunis, Tunisie
| | - Hanen Chelbi
- Laboratoire de parasitologie médicale, biotechnologies et biomolécules, Institut Pasteur de Tunis LR11IPT06, Tunis, Tunisie
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375
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Reay WR, Cairns MJ. Advancing the use of genome-wide association studies for drug repurposing. Nat Rev Genet 2021; 22:658-671. [PMID: 34302145 DOI: 10.1038/s41576-021-00387-z] [Citation(s) in RCA: 154] [Impact Index Per Article: 38.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/14/2021] [Indexed: 02/07/2023]
Abstract
Genome-wide association studies (GWAS) have revealed important biological insights into complex diseases, which are broadly expected to lead to the identification of new drug targets and opportunities for treatment. Drug development, however, remains hampered by the time taken and costs expended to achieve regulatory approval, leading many clinicians and researchers to consider alternative paths to more immediate clinical outcomes. In this Review, we explore approaches that leverage common variant genetics to identify opportunities for repurposing existing drugs, also known as drug repositioning. These approaches include the identification of compounds by linking individual loci to genes and pathways that can be pharmacologically modulated, transcriptome-wide association studies, gene-set association, causal inference by Mendelian randomization, and polygenic scoring.
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Affiliation(s)
- William R Reay
- School of Biomedical Sciences and Pharmacy, The University of Newcastle, Callaghan, New South Wales, Australia.,Centre for Brain and Mental Health Research, Hunter Medical Research Institute, Newcastle, New South Wales, Australia
| | - Murray J Cairns
- School of Biomedical Sciences and Pharmacy, The University of Newcastle, Callaghan, New South Wales, Australia. .,Centre for Brain and Mental Health Research, Hunter Medical Research Institute, Newcastle, New South Wales, Australia.
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376
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Akther J, Das A, Rahman MA, Saha SK, Hosen MI, Ebihara A, Nakagawa T, Suzuki F, Nabi AHMN. Non-coding Single Nucleotide Variants of Renin and the (Pro)renin Receptor are Associated with Polygenic Diseases in a Bangladeshi Population. Biochem Genet 2021; 59:1116-1145. [PMID: 33677630 DOI: 10.1007/s10528-021-10049-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2020] [Accepted: 02/10/2021] [Indexed: 02/06/2023]
Abstract
Non-coding variants or single-nucleotide polymorphisms (SNPs) play pivotal roles in orchestrating pathogeneses of polygenic diseases, including hypertension (HTN) and diabetes. Renin-angiotensin system (RAS) components-renin and (pro)renin receptor [(P)RR]-maintain homeostasis of body fluids. Genetic variants of RAS components are associated with risk of HTN and type 2 diabetes (T2D) in different ethnic groups. We identified associations of SNPs within the renin and (P)RR genes with HTN, T2D, and T2D-associated hypertension in 911 unrelated Bangladeshi individuals. Five non-coding SNPs were involved in modulating regulatory elements in diverse cell types when tagged with other SNPs. rs61827960 was not associated with any disease; rs3730102 was associated with increased risk of HTN and T2D while under dominant model, it showed protective role against T2D-associated HTN. SNP rs11571079 was associated with increased risk of HTN and T2D-associated HTN and decreased risk of T2D, exerting a protective effect. Renin haplotypes GCA and GTG were related to increased risk of T2D and T2D-associated HTN, respectively. Heterogeneous linkage of genotypic and allelic frequencies of rs2968915 and rs3112298 of (P)RR was observed. The (P)RR haplotype GA was associated with increased risk of HTN and significantly decreased risk of T2D. These findings highlight important roles of non-coding variants of renin and (P)RR genes in the etiology of several polygenic diseases.
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Affiliation(s)
- Jobaida Akther
- Laboratory of Population Genetics, Department of Biochemistry and Molecular Biology, University of Dhaka, Dhaka, 1000, Bangladesh
| | - Ashish Das
- Laboratory of Population Genetics, Department of Biochemistry and Molecular Biology, University of Dhaka, Dhaka, 1000, Bangladesh
| | - Md Arifur Rahman
- Laboratory of Population Genetics, Department of Biochemistry and Molecular Biology, University of Dhaka, Dhaka, 1000, Bangladesh
- National Institute of Cardiovascular Diseases, Sher-e-Bangla Nagar, Dhaka, 1207, Bangladesh
| | - Sajoy Kanti Saha
- Laboratory of Population Genetics, Department of Biochemistry and Molecular Biology, University of Dhaka, Dhaka, 1000, Bangladesh
| | - Md Ismail Hosen
- Laboratory of Population Genetics, Department of Biochemistry and Molecular Biology, University of Dhaka, Dhaka, 1000, Bangladesh
| | - Akio Ebihara
- Laboratory of Applied Biochemistry, Faculty of Applied Biological Sciences, Gifu University, 1-1 Yanagido, Gifu, 501-1193, Japan
- United Graduate School of Agricultural Science, Gifu University, 1-1 Yanagido, Gifu, 501-1193, Japan
| | - Tsutomu Nakagawa
- Laboratory of Applied Biochemistry, Faculty of Applied Biological Sciences, Gifu University, 1-1 Yanagido, Gifu, 501-1193, Japan
- United Graduate School of Agricultural Science, Gifu University, 1-1 Yanagido, Gifu, 501-1193, Japan
| | - Fumiaki Suzuki
- Laboratory of Applied Biochemistry, Faculty of Applied Biological Sciences, Gifu University, 1-1 Yanagido, Gifu, 501-1193, Japan
| | - A H M Nurun Nabi
- Laboratory of Population Genetics, Department of Biochemistry and Molecular Biology, University of Dhaka, Dhaka, 1000, Bangladesh.
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377
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The joint effect of PPARG upstream genetic variation in association with long-term persistent obesity: Tehran cardio-metabolic genetic study (TCGS). Eat Weight Disord 2021; 26:2325-2332. [PMID: 33389720 DOI: 10.1007/s40519-020-01063-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Accepted: 10/22/2020] [Indexed: 12/11/2022] Open
Abstract
PURPOSE This study is the first study that aims to assess the association between SNPs located at the PPARG gene with long term persistent obesity. In this cohort association study, all adult individuals who had at least three consecutive phases of BMI (at least nine years) in Tehran genetic Cardio-metabolic Study (TCGS) were included. METHODS Individuals who always had 30 ≤ BMI < 35 and individuals who always had 20 < BMI ≤ 25 were assigned to the long-term persistent obese group and persistent normal weight group, respectively. Other individuals were excluded from the study. We used four gamete rules to make SNP sets from correlated nearby SNPs and kernel machine regression to analyze the association between SNP sets and persistent obesity or normal weight. RESULTS The normal group consisted of 1547 individuals with the mean age of 40 years, and the obese group consisted of 1676 individuals with mean age of 48 years. Two groups had a significant difference between all measured clinical characteristics at entry time. The kernel machine result shows that nine correlated SNPs located upstream of PPARG have a significant joint effect on persistence obesity. CONCLUSION This is the first study on the association between PPARG variants with persistent obesity. Three of the nine associated markers were reported in previous GWAS studies to be associated with related diseases. For the studied markers in the PPARG gene, the Iranian allele frequency was near the American and European populations. LEVEL III Case-control analytic study.
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378
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Kim H, Bae JH, Park KS, Sung J, Kwak SH. DNA Methylation Changes Associated With Type 2 Diabetes and Diabetic Kidney Disease in an East Asian Population. J Clin Endocrinol Metab 2021; 106:e3837-e3851. [PMID: 34214161 DOI: 10.1210/clinem/dgab488] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/09/2021] [Indexed: 01/13/2023]
Abstract
CONTEXT There is a growing body of evidence that epigenetic changes including DNA methylation influence the risk of type 2 diabetes (T2D) and its microvascular complications. OBJECTIVE We conducted a methylome-wide association study (MWAS) to identify differentially methylated sites (DMSs) of T2D and diabetic kidney disease (DKD) in a Korean population. METHODS We performed an MWAS in 232 participants with T2D and 197 nondiabetic controls with the Illumina EPIC bead chip using peripheral blood leukocytes. The T2D group was subdivided into 87 DKD patients and 80 non-DKD controls. An additional 819 individuals from 2 population-based cohorts were used to investigate the association of identified DMSs with quantitative metabolic phenotypes. A mendelian randomization (MR) approach was applied to evaluate the causal effect of metabolic phenotypes on identified DMSs. RESULTS We identified 8 DMSs (each at BMP8A, NBPF20, STX18, ZNF365, CPT1A, and TRIM37, and 2 at TXNIP) that were significantly associated with the risk of T2D (P < 9.0 × 10-8), including 3 that were previously known (DMSs in TXNIP and CPT1A). We also identified 3 DMSs (in COMMD1, TMOD1, and FHOD1) associated with DKD. With our limited sample size, we were not able to observe a significant overlap between DMSs of T2D and DKD. DMSs in TXNIP and CTP1A were associated with fasting glucose and glycated hemoglobin A1c. In MR analysis, fasting glucose was causally associated with DMS in CPT1A. CONCLUSION In an East Asian population, we identified 8 DMSs, including 5 novel CpG loci, associated with T2D and 3 DMSs associated with DKD at methylome-wide statistical significance.
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Affiliation(s)
- Hakyung Kim
- Genome & Health Big Data Branch, Department of Public Health Science, Graduate School of Public Health, Seoul National University, Seoul, Republic of Korea
| | - Jae Hyun Bae
- Department of Internal Medicine, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea
| | - Kyong Soo Park
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
- Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Republic of Korea
- Department of Internal Medicine, Seoul National University Hospital, Seoul, Republic of Korea
| | - Joohon Sung
- Genome & Health Big Data Branch, Department of Public Health Science, Graduate School of Public Health, Seoul National University, Seoul, Republic of Korea
- Institute of Health and Environment, Seoul National University, Seoul, Republic of Korea
| | - Soo Heon Kwak
- Department of Internal Medicine, Seoul National University Hospital, Seoul, Republic of Korea
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379
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Shah N, Abdalla MA, Deshmukh H, Sathyapalan T. Therapeutics for type-2 diabetes mellitus: a glance at the recent inclusions and novel agents under development for use in clinical practice. Ther Adv Endocrinol Metab 2021; 12:20420188211042145. [PMID: 34589201 PMCID: PMC8474306 DOI: 10.1177/20420188211042145] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Accepted: 08/06/2021] [Indexed: 12/18/2022] Open
Abstract
Diabetes mellitus (DM) is a chronic, progressive, and multifaceted illness resulting in significant physical and psychological detriment to patients. As of 2019, 463 million people are estimated to be living with DM worldwide, out of which 90% have type-2 diabetes mellitus (T2DM). Over the years, significant progress has been made in identifying the risk factors for developing T2DM, understanding its pathophysiology and uncovering various metabolic pathways implicated in the disease process. This has culminated in the implementation of robust prevention programmes and the development of effective pharmacological agents, which have had a favourable impact on the management of T2DM in recent times. Despite these advances, the incidence and prevalence of T2DM continue to rise. Continuing research in improving efficacy, potency, delivery and reducing the adverse effect profile of currently available formulations is required to keep pace with this growing health challenge. Moreover, new metabolic pathways need to be targeted to produce novel pharmacotherapy to restore glucose homeostasis and address metabolic sequelae in patients with T2DM. We searched PubMed, MEDLINE, and Google Scholar databases for recently included agents and novel medication under development for treatment of T2DM. We discuss the pathophysiology of T2DM and review how the emerging anti-diabetic agents target the metabolic pathways involved. We also look at some of the limiting factors to developing new medication and the introduction of unique methods, including facilitating drug delivery to bypass some of these obstacles. However, despite the advances in the therapeutic options for the treatment of T2DM in recent years, the industry still lacks a curative agent.
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Affiliation(s)
- Najeeb Shah
- Hull University Teaching Hospitals NHS Trust,
Hull, UK
- Department of Academic Diabetes, Endocrinology
& Metabolism, Hull York Medical School, University of Hull, Brocklehurst
Building, 220-236 Anlaby Road, Hull, HU3 2RW, UK
| | - Mohammed Altigani Abdalla
- Department of Academic Diabetes, Endocrinology
& Metabolism, Hull York Medical School, University of Hull, Hull,
UK
| | - Harshal Deshmukh
- University Teaching Hospitals NHS Trust and
Department of Academic Diabetes, Endocrinology & Metabolism, Hull York
Medical School, University of Hull, Hull, UK
| | - Thozhukat Sathyapalan
- University Teaching Hospitals NHS Trust and
Department of Academic Diabetes, Endocrinology & Metabolism, Hull York
Medical School, University of Hull, Hull, UK
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380
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Podboi ICR, Stephenson S, Pilic L, Graham CAM, King A, Mavrommatis Y. Dietary Intake and TCF7L2 rs7903146 T Allele Are Associated with Elevated Blood Glucose Levels in Healthy Individuals. Lifestyle Genom 2021; 14:117-123. [PMID: 34515148 DOI: 10.1159/000518523] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Accepted: 07/09/2021] [Indexed: 11/19/2022] Open
Abstract
INTRODUCTION Type 2 diabetes (T2D) is a leading cause of global mortality with diet and genetics being considered amongst the most significant risk factors. Recently, studies have identified a single polymorphism of the TCF7L2 gene (rs7903146) as the most important genetic contributor. However, no studies have explored this factor in a healthy population and using glycated haemoglobin (HbA1c), which is a reliable long-term indicator of glucose management. This study investigates the association of the genetic polymorphism rs7903146 and dietary intake with T2D risk in a population free of metabolic disease. METHODS T2D risk was assessed using HbA1c plasma concentrations and dietary intake via a validated Food Frequency Questionnaire in 70 healthy participants. RESULTS T allele carriers had higher HbA1c levels than the CC group (32.4 ± 7.2 mmol/mol vs. 30.3 ± 7.6 mmol/mol, p = 0.005). Multiple regression reported associations between diet, genotype and HbA1c levels accounting for 37.1% of the variance in HbA1c (adj. R2 = 0.371, p < 0.001). The following macronutrients, expressed as a median percentage of total energy intake (TEI) in the risk group, were positively associated with HbA1c concentration: carbohydrate (≥39% TEI, p < 0.005; 95% CI 0.030/0.130) protein (≥21% TEI, p < 0.005, 95% CI 0.034/0.141), monounsaturated (≥15% TEI p < 0.05, 95% CI 0.006/0.163) and saturated fatty acids (≥13% TEI; p < 0.05, 95% CI 0.036/0.188). CONCLUSION Carriers of the T allele showed significantly higher levels of HbA1c compared to non-carriers. Dietary intake affected T2D risk to a greater extent than genetic effects of TCF7L2rs7903146 genotype in a healthy population. The study focus on healthy individuals is beneficial due to the applicability of findings for T2D screening.
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Affiliation(s)
| | - Sophie Stephenson
- Faculty of Sport, Allied Health and Performance Sciences/St Mary's University, Twickenham, United Kingdom
| | - Leta Pilic
- Faculty of Sport, Allied Health and Performance Sciences/St Mary's University, Twickenham, United Kingdom
| | | | - Alexandra King
- Faculty of Sport, Allied Health and Performance Sciences/St Mary's University, Twickenham, United Kingdom
| | - Yiannis Mavrommatis
- Faculty of Sport, Allied Health and Performance Sciences/St Mary's University, Twickenham, United Kingdom
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381
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Chen D, Tashman K, Palmer DS, Neale B, Roeder K, Bloemendal A, Churchhouse C, Ke ZT. A data harmonization pipeline to leverage external controls and boost power in GWAS. Hum Mol Genet 2021; 31:481-489. [PMID: 34508597 PMCID: PMC8825237 DOI: 10.1093/hmg/ddab261] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Revised: 09/02/2021] [Accepted: 09/03/2021] [Indexed: 11/12/2022] Open
Abstract
The use of external controls in genome-wide association study (GWAS) can significantly increase the size and diversity of the control sample, enabling high-resolution ancestry matching and enhancing the power to detect association signals. However, the aggregation of controls from multiple sources is challenging due to batch effects, difficulty in identifying genotyping errors, and the use of different genotyping platforms. These obstacles have impeded the use of external controls in GWAS and can lead to spurious results if not carefully addressed. We propose a unified data harmonization pipeline that includes an iterative approach to quality control (QC) and imputation, implemented before and after merging cohorts and arrays. We apply this harmonization pipeline to aggregate 27 517 European control samples from 16 collections within dbGaP. We leverage these harmonized controls to conduct a GWAS of Crohn's disease. We demonstrate a boost in power over using the cohort samples alone, and that our procedure results in summary statistics free of any significant batch effects. This harmonization pipeline for aggregating genotype data from multiple sources can also serve other applications where individual level genotypes, rather than summary statistics, are required.
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Affiliation(s)
- Danfeng Chen
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, 08544, New Jersey, United States
| | - Katherine Tashman
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, 02114, Massachusetts, United States.,Stanley Center for Psychiatric Research, Broad Institute of of MIT and Harvard, Cambridge, 02142, Massachusetts, United States
| | - Duncan S Palmer
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, 02114, Massachusetts, United States.,Stanley Center for Psychiatric Research, Broad Institute of of MIT and Harvard, Cambridge, 02142, Massachusetts, United States
| | - Benjamin Neale
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, 02114, Massachusetts, United States.,Stanley Center for Psychiatric Research, Broad Institute of of MIT and Harvard, Cambridge, 02142, Massachusetts, United States.,Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, 02142, Massachusetts, United States
| | - Kathryn Roeder
- Department of Statistics, Carnegie Mellon University, Pittsburgh, 15213, Pennsylvania, United States
| | - Alex Bloemendal
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, 02114, Massachusetts, United States.,Stanley Center for Psychiatric Research, Broad Institute of of MIT and Harvard, Cambridge, 02142, Massachusetts, United States
| | - Claire Churchhouse
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, 02114, Massachusetts, United States.,Stanley Center for Psychiatric Research, Broad Institute of of MIT and Harvard, Cambridge, 02142, Massachusetts, United States.,Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, 02142, Massachusetts, United States
| | - Zheng Tracy Ke
- Department of Statistics, Harvard University, Cambridge, 02138, Massachusetts, United States
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382
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Zhao R, Lu J, Li Q, Xiong F, Zhang Y, Zhu J, Peng G, Yang J. Single-cell heterogeneity analysis and CRISPR screens in MIN6 cell line reveal transcriptional regulators of insulin. Cell Cycle 2021; 20:2053-2065. [PMID: 34494921 DOI: 10.1080/15384101.2021.1969204] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
Abstract
Diabetes mellitus is caused by either insulin resistance or insulin deficiency. The pancreatic β cells are the primary producers of insulin. Large-scale CRISPR screens combined with single-cell RNA sequencing (scRNA-seq) on β cells has identified novel insulin regulators and revealed the presence of a highly complex inner network. Here, we performed pooled CRISPR delivery with single-cell transcriptome analysis on the MIN6 cell line, a pancreatic β-cell line. We have presented the scRNA-seq readout and demonstrated that the MIN6 cell line might develop genetic heterogeneity with increasing passage number based on GO and KEGG pathway analysis. Both computational and biological analyses revealed that the function of MIN6 cell lines could be divided into five clusters, including endocrine cells, basal cells, epithelial cells, and neuroendocrine cells. The fifth cluster was different from the other four clusters due to the differentially expressed insulin transcription and was called the lncRNA-enriched cluster. The experiments also confirmed that uncharacterized lncRNAs GM26917 and Cenpw were associated with insulin transcription. This study provides information that can be used to systematically characterize insulin regulator genes and other genes that control protein folding and vesicle trafficking.
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Affiliation(s)
- Ruxuan Zhao
- Beijing Key Laboratory of Diabetes Research and Care, Beijing Tongren Hospital, Capital Medical University, Beijing China
| | - Jing Lu
- Beijing Key Laboratory of Diabetes Research and Care, Beijing Tongren Hospital, Capital Medical University, Beijing China
| | - Qi Li
- Beijing Key Laboratory of Diabetes Research and Care, Beijing Tongren Hospital, Capital Medical University, Beijing China
| | - Fengran Xiong
- Beijing Key Laboratory of Diabetes Research and Care, Beijing Tongren Hospital, Capital Medical University, Beijing China
| | - Yingchao Zhang
- Beijing Key Laboratory of Diabetes Research and Care, Beijing Tongren Hospital, Capital Medical University, Beijing China
| | - Juanjuan Zhu
- Beijing Key Laboratory of Diabetes Research and Care, Beijing Tongren Hospital, Capital Medical University, Beijing China
| | - Gongxin Peng
- Center for Bioinformatics, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & School of Basic Medicine, Peking Union Medical College, Beijing, China
| | - Jinkui Yang
- Beijing Key Laboratory of Diabetes Research and Care, Beijing Tongren Hospital, Capital Medical University, Beijing China
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383
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Ong JS, Gharahkhani P, Vaughan TL, Whiteman D, Kendall BJ, MacGregor S. Assessing the genetic relationship between gastro-esophageal reflux disease and risk of COVID-19 infection. Hum Mol Genet 2021; 31:471-480. [PMID: 34553760 PMCID: PMC8522419 DOI: 10.1093/hmg/ddab253] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2021] [Revised: 07/29/2021] [Accepted: 08/26/2021] [Indexed: 12/11/2022] Open
Abstract
Background Symptoms related with Gastro-esophageal reflux disease (GERD) were previously shown to be linked with increased risk for the 2019 coronavirus disease (COVID-19). We aim to interrogate the possibility of a shared genetic basis between GERD and COVID-19 outcomes. Methods Using published GWAS data for GERD (78 707 cases; 288 734 controls) and COVID-19 susceptibility (up to 32 494 cases; 1.5 million controls), we examined the genetic relationship between GERD and three COVID-19 outcomes: risk of developing severe COVID-19, COVID-19 hospitalization and overall COVID-19 risk. We estimated the genetic correlation between GERD and COVID-19 outcomes followed by Mendelian randomization (MR) analyses to assess genetic causality. Conditional analyses were conducted to examine whether known COVID-19 risk factors (obesity, smoking, type-II diabetes, coronary artery disease) can explain the relationship between GERD and COVID-19. Results We found small to moderate genetic correlations between GERD and COVID-19 outcomes (rg between 0.06–0.24). MR analyses revealed a OR of 1.15 (95% CI: 0.96–1.39) for severe COVID-19; 1.16 (1.01–1.34) for risk of COVID-19 hospitalization; 1.05 (0.97–1.13) for overall risk of COVID-19 per doubling of odds in developing GERD. The genetic correlation/associations between GERD and COVID-19 showed mild attenuation towards the null when obesity and smoking was adjusted for. Conclusions Susceptibility for GERD and risk of COVID-19 hospitalization were genetically correlated, with MR findings supporting a potential causal role between the two. The genetic association between GERD and COVID-19 was partially attenuated when obesity is accounted for, consistent with obesity being a major risk factor for both diseases.
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Affiliation(s)
- Jue-Sheng Ong
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, 300 Herston Road, Herston, QLD 4006, Brisbane, Australia
| | - Puya Gharahkhani
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, 300 Herston Road, Herston, QLD 4006, Brisbane, Australia
| | - Thomas L Vaughan
- Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA, USA
| | - David Whiteman
- Department of Population Health, QIMR Berghofer Medical Research Institute, 300 Herston Road, Herston, QLD 4006, Brisbane, Australia
| | - Bradley J Kendall
- Department of Medicine, The University of Queensland, , Herston, QLD 4006, Brisbane, Australia.,Department of Gastroenterology and Hepatology, Princess Alexandra Hospital, Woolloongabba, QLD 4102, Brisbane, Australia
| | - Stuart MacGregor
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, 300 Herston Road, Herston, QLD 4006, Brisbane, Australia
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384
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Boer CG, Hatzikotoulas K, Southam L, Stefánsdóttir L, Zhang Y, Coutinho de Almeida R, Wu TT, Zheng J, Hartley A, Teder-Laving M, Skogholt AH, Terao C, Zengini E, Alexiadis G, Barysenka A, Bjornsdottir G, Gabrielsen ME, Gilly A, Ingvarsson T, Johnsen MB, Jonsson H, Kloppenburg M, Luetge A, Lund SH, Mägi R, Mangino M, Nelissen RRGHH, Shivakumar M, Steinberg J, Takuwa H, Thomas LF, Tuerlings M, Babis GC, Cheung JPY, Kang JH, Kraft P, Lietman SA, Samartzis D, Slagboom PE, Stefansson K, Thorsteinsdottir U, Tobias JH, Uitterlinden AG, Winsvold B, Zwart JA, Davey Smith G, Sham PC, Thorleifsson G, Gaunt TR, Morris AP, Valdes AM, Tsezou A, Cheah KSE, Ikegawa S, Hveem K, Esko T, Wilkinson JM, Meulenbelt I, Lee MTM, van Meurs JBJ, Styrkársdóttir U, Zeggini E. Deciphering osteoarthritis genetics across 826,690 individuals from 9 populations. Cell 2021; 184:4784-4818.e17. [PMID: 34450027 PMCID: PMC8459317 DOI: 10.1016/j.cell.2021.07.038] [Citation(s) in RCA: 215] [Impact Index Per Article: 53.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Revised: 03/26/2021] [Accepted: 07/30/2021] [Indexed: 12/19/2022]
Abstract
Osteoarthritis affects over 300 million people worldwide. Here, we conduct a genome-wide association study meta-analysis across 826,690 individuals (177,517 with osteoarthritis) and identify 100 independently associated risk variants across 11 osteoarthritis phenotypes, 52 of which have not been associated with the disease before. We report thumb and spine osteoarthritis risk variants and identify differences in genetic effects between weight-bearing and non-weight-bearing joints. We identify sex-specific and early age-at-onset osteoarthritis risk loci. We integrate functional genomics data from primary patient tissues (including articular cartilage, subchondral bone, and osteophytic cartilage) and identify high-confidence effector genes. We provide evidence for genetic correlation with phenotypes related to pain, the main disease symptom, and identify likely causal genes linked to neuronal processes. Our results provide insights into key molecular players in disease processes and highlight attractive drug targets to accelerate translation.
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Affiliation(s)
- Cindy G Boer
- Department of Internal Medicine, Erasmus MC, Medical Center, 3015CN Rotterdam, the Netherlands
| | - Konstantinos Hatzikotoulas
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764 Neuherberg, Germany
| | - Lorraine Southam
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764 Neuherberg, Germany
| | | | - Yanfei Zhang
- Genomic Medicine Institute, Geisinger Health System, Danville, PA 17822, USA
| | - Rodrigo Coutinho de Almeida
- Department of Biomedical Data Sciences, Section Molecular Epidemiology, Postzone S05-P Leiden University Medical Center, 2333ZC Leiden, the Netherlands
| | - Tian T Wu
- Department of Psychiatry, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong, China
| | - Jie Zheng
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, UK
| | - April Hartley
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, UK; Musculoskeletal Research Unit, Translation Health Sciences, Bristol Medical School, University of Bristol, Southmead Hospital, Bristol BS10 5NB, UK
| | - Maris Teder-Laving
- Estonian Genome Center, Institute of Genomics, University of Tartu, 51010 Tartu, Estonia
| | - Anne Heidi Skogholt
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, 7491 Trondheim, Norway
| | - Chikashi Terao
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Kanagawa 230-0045, Japan
| | - Eleni Zengini
- 4(th) Psychiatric Department, Dromokaiteio Psychiatric Hospital, 12461 Athens, Greece
| | - George Alexiadis
- 1(st) Department of Orthopaedics, KAT General Hospital, 14561 Athens, Greece
| | - Andrei Barysenka
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764 Neuherberg, Germany
| | | | - Maiken E Gabrielsen
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, 7491 Trondheim, Norway
| | - Arthur Gilly
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764 Neuherberg, Germany
| | - Thorvaldur Ingvarsson
- Faculty of Medicine, University of Iceland, 101 Reykjavik, Iceland; Department of Orthopedic Surgery, Akureyri Hospital, 600 Akureyri, Iceland
| | - Marianne B Johnsen
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, 7491 Trondheim, Norway; Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, 0316 Oslo, Norway; Research and Communication Unit for Musculoskeletal Health (FORMI), Department of Research, Innovation and Education, Division of Clinical Neuroscience, Oslo University Hospital, 0424 Oslo, Norway
| | - Helgi Jonsson
- Department of Medicine, Landspitali The National University Hospital of Iceland, 108 Reykjavik, Iceland; Faculty of Medicine, University of Iceland, 101 Reykjavik, Iceland
| | - Margreet Kloppenburg
- Departments of Rheumatology and Clinical Epidemiology, Leiden University Medical Center, 9600, 23OORC Leiden, the Netherlands
| | - Almut Luetge
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, 7491 Trondheim, Norway
| | | | - Reedik Mägi
- Estonian Genome Center, Institute of Genomics, University of Tartu, 51010 Tartu, Estonia
| | - Massimo Mangino
- Department of Twin Research and Genetic Epidemiology, Kings College London, London SE1 7EH, UK
| | - Rob R G H H Nelissen
- Department of Orthopaedics, Leiden University Medical Center, 9600, 23OORC Leiden, the Netherlands
| | - Manu Shivakumar
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Julia Steinberg
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764 Neuherberg, Germany; Daffodil Centre, The University of Sydney, a joint venture with Cancer Council NSW, Sydney, NSW 1340, Australia
| | - Hiroshi Takuwa
- Laboratory for Bone and Joint Diseases, RIKEN Center for Integrative Medical Sciences, Tokyo 108-8639, Japan; Department of Orthopedic Surgery, Shimane University, Shimane 693-8501, Japan
| | - Laurent F Thomas
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, 7491 Trondheim, Norway; Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, 7491 Trondheim, Norway; BioCore-Bioinformatics Core Facility, Norwegian University of Science and Technology, 7491 Trondheim, Norway; Clinic of Laboratory Medicine, St. Olavs Hospital, Trondheim University Hospital, 7030 Trondheim, Norway
| | - Margo Tuerlings
- Department of Biomedical Data Sciences, Section Molecular Epidemiology, Postzone S05-P Leiden University Medical Center, 2333ZC Leiden, the Netherlands
| | - George C Babis
- 2(nd) Department of Orthopaedics, National and Kapodistrian University of Athens, Medical School, Nea Ionia General Hospital Konstantopouleio, 14233 Athens, Greece
| | - Jason Pui Yin Cheung
- Department of Orthopaedics and Traumatology, The University of Hong Kong, Pokfulam, Hong Kong, China
| | - Jae Hee Kang
- Department of Medicine, Brigham and Women's Hospital, 181 Longwood Ave, Boston, MA 02115, USA
| | - Peter Kraft
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA 02115, USA
| | - Steven A Lietman
- Musculoskeletal Institute, Geisinger Health System, Danville, PA 17822, USA
| | - Dino Samartzis
- Department of Orthopaedics and Traumatology, The University of Hong Kong, Pokfulam, Hong Kong, China; Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, IL 60612, USA
| | - P Eline Slagboom
- Department of Biomedical Data Sciences, Section Molecular Epidemiology, Postzone S05-P Leiden University Medical Center, 2333ZC Leiden, the Netherlands
| | - Kari Stefansson
- deCODE Genetics/Amgen Inc., 102 Reykjavik, Iceland; Faculty of Medicine, University of Iceland, 101 Reykjavik, Iceland
| | - Unnur Thorsteinsdottir
- deCODE Genetics/Amgen Inc., 102 Reykjavik, Iceland; Faculty of Medicine, University of Iceland, 101 Reykjavik, Iceland
| | - Jonathan H Tobias
- Musculoskeletal Research Unit, Translation Health Sciences, Bristol Medical School, University of Bristol, Southmead Hospital, Bristol BS10 5NB, UK; MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, UK
| | - André G Uitterlinden
- Department of Internal Medicine, Erasmus MC, Medical Center, 3015CN Rotterdam, the Netherlands
| | - Bendik Winsvold
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, 7491 Trondheim, Norway; Department of Research, Innovation and Education, Division of Clinical Neuroscience, Oslo University Hospital and University of Oslo, 0450 Oslo, Norway; Department of Neurology, Oslo University Hospital, 0424 Oslo, Norway
| | - John-Anker Zwart
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, 7491 Trondheim, Norway; Department of Research, Innovation and Education, Division of Clinical Neuroscience, Oslo University Hospital and University of Oslo, 0450 Oslo, Norway
| | - George Davey Smith
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, UK; Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 2BN, UK
| | - Pak Chung Sham
- Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong, China
| | | | - Tom R Gaunt
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, UK
| | - Andrew P Morris
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, University of Manchester, Manchester M13 9LJ, UK
| | - Ana M Valdes
- Faculty of Medicine and Health Sciences, School of Medicine, University of Nottingham, Nottingham, Nottinghamshire NG5 1PB, UK
| | - Aspasia Tsezou
- Laboratory of Cytogenetics and Molecular Genetics, Faculty of Medicine, University of Thessaly, Larissa 411 10, Greece
| | - Kathryn S E Cheah
- School of Biomedical Sciences, The University of Hong Kong, Pokfulam, Hong Kong, China
| | - Shiro Ikegawa
- Laboratory for Bone and Joint Diseases, RIKEN Center for Integrative Medical Sciences, Tokyo 108-8639, Japan
| | - Kristian Hveem
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, 7491 Trondheim, Norway; HUNT Research Center, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, 7600 Levanger, Norway
| | - Tõnu Esko
- Estonian Genome Center, Institute of Genomics, University of Tartu, 51010 Tartu, Estonia
| | - J Mark Wilkinson
- Department of Oncology and Metabolism and Healthy Lifespan Institute, University of Sheffield, Sheffield S10 2RX, UK
| | - Ingrid Meulenbelt
- Department of Biomedical Data Sciences, Section Molecular Epidemiology, Postzone S05-P Leiden University Medical Center, 2333ZC Leiden, the Netherlands
| | - Ming Ta Michael Lee
- Genomic Medicine Institute, Geisinger Health System, Danville, PA 17822, USA; Institute of Biomedical Sciences, Academia Sinica, 115 Taipei, Taiwan
| | - Joyce B J van Meurs
- Department of Internal Medicine, Erasmus MC, Medical Center, 3015CN Rotterdam, the Netherlands
| | | | - Eleftheria Zeggini
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764 Neuherberg, Germany; TUM School of Medicine, Technical University of Munich and Klinikum Rechts der Isar, 81675 Munich, Germany.
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385
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Liu C, Sun YV. Anticipation of Precision Diabetes and Promise of Integrative Multi-Omics. Endocrinol Metab Clin North Am 2021; 50:559-574. [PMID: 34399961 DOI: 10.1016/j.ecl.2021.05.011] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Precision diabetes is a concept of customizing delivery of health practices based on variability of diabetes. The authors reviewed recent research on type 2 diabetes heterogeneity and -omic biomarkers, including genomic, epigenomic, and metabolomic markers associated with type 2 diabetes. The emerging multiomics approach integrates complementary and interconnected molecular layers to provide systems level understanding of disease mechanisms and subtypes. Although the multiomic approach is not currently ready for routine clinical applications, future studies in the context of precision diabetes, particular in populations from diverse ethnic and demographic groups, may lead to improved diagnosis, treatment, and management of diabetes and diabetic complications.
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Affiliation(s)
- Chang Liu
- Department of Epidemiology, Emory University Rollins School of Public Health, 1518 Clifton Road Northeast, Atlanta, GA 30322, USA
| | - Yan V Sun
- Department of Epidemiology, Emory University Rollins School of Public Health, 1518 Clifton Road Northeast, Atlanta, GA 30322, USA; Atlanta VA Healthcare System, 1670 Clairmont Road, Decatur, GA 30033, USA.
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386
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Thayer TE, Huang S, Farber-Eger E, Beckman JA, Brittain EL, Mosley JD, Wells QS. Using genetics to detangle the relationships between red cell distribution width and cardiovascular diseases: a unique role for body mass index. Open Heart 2021; 8:e001713. [PMID: 34521746 PMCID: PMC8442102 DOI: 10.1136/openhrt-2021-001713] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Accepted: 08/27/2021] [Indexed: 11/03/2022] Open
Abstract
OBJECTIVE Red cell distribution width (RDW) is an enigmatic biomarker associated with the presence and severity of multiple cardiovascular diseases (CVDs). It is unclear whether elevated RDW contributes to, results from, or is pleiotropically related to CVDs. We used contemporary genetic techniques to probe for evidence of aetiological associations between RDW, CVDs, and CVD risk factors. METHODS Using an electronic health record (EHR)-based cohort, we built and deployed a genetic risk score (GRS) for RDW to test for shared genetic architecture between RDW and the cardiovascular phenome. We also created GRSs for common CVDs (coronary artery disease, heart failure, atrial fibrillation, peripheral arterial disease, venous thromboembolism) and CVD risk factors (body mass index (BMI), low-density lipoprotein, high-density lipoprotein, systolic blood pressure, diastolic blood pressure, serum triglycerides, estimated glomerular filtration rate, diabetes mellitus) to test each for association with RDW. Significant GRS associations were further interrogated by two-sample Mendelian randomisation (MR). In a separate EHR-based cohort, RDW values from 1-year pre-gastric bypass surgery and 1-2 years post-gastric bypass surgery were compared. RESULTS In a cohort of 17 937 subjects, there were no significant associations between the RDW GRS and CVDs. Of the CVDs and CVD risk factors, only genetically predicted BMI was associated with RDW. In subsequent analyses, BMI was associated with RDW by multiple MR methods. In subjects undergoing bariatric surgery, RDW decreased postsurgery and followed a linear relationship with BMI change. CONCLUSIONS RDW is unlikely to be aetiologically upstream or downstream of CVDs or CVD risk factors except for BMI. Genetic and clinical association analyses support an aetiological relationship between BMI and RDW.
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Affiliation(s)
- Timothy E Thayer
- Department of Medicine, Division of Cardiovascular Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Shi Huang
- Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Eric Farber-Eger
- VICTR, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Joshua A Beckman
- Department of Medicine, Division of Cardiovascular Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Evan L Brittain
- Department of Medicine, Division of Cardiovascular Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Jonathan D Mosley
- Department of Medicine, Division of Cardiovascular Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Quinn S Wells
- Department of Medicine, Division of Cardiovascular Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
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387
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Gupta R, Karczewski KJ, Howrigan D, Neale BM, Mootha VK. Human genetic analyses of organelles highlight the nucleus in age-related trait heritability. eLife 2021; 10:68610. [PMID: 34467851 PMCID: PMC8476128 DOI: 10.7554/elife.68610] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2021] [Accepted: 08/30/2021] [Indexed: 12/15/2022] Open
Abstract
Most age-related human diseases are accompanied by a decline in cellular organelle integrity, including impaired lysosomal proteostasis and defective mitochondrial oxidative phosphorylation. An open question, however, is the degree to which inherited variation in or near genes encoding each organelle contributes to age-related disease pathogenesis. Here, we evaluate if genetic loci encoding organelle proteomes confer greater-than-expected age-related disease risk. As mitochondrial dysfunction is a 'hallmark' of aging, we begin by assessing nuclear and mitochondrial DNA loci near genes encoding the mitochondrial proteome and surprisingly observe a lack of enrichment across 24 age-related traits. Within nine other organelles, we find no enrichment with one exception: the nucleus, where enrichment emanates from nuclear transcription factors. In agreement, we find that genes encoding several organelles tend to be 'haplosufficient,' while we observe strong purifying selection against heterozygous protein-truncating variants impacting the nucleus. Our work identifies common variation near transcription factors as having outsize influence on age-related trait risk, motivating future efforts to determine if and how this inherited variation then contributes to observed age-related organelle deterioration.
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Affiliation(s)
- Rahul Gupta
- Howard Hughes Medical Institute and Department of Molecular Biology, Massachusetts General Hospital, Boston, United States.,Broad Institute of MIT and Harvard, Cambridge, United States.,Analytic and Translational Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, United States
| | - Konrad J Karczewski
- Broad Institute of MIT and Harvard, Cambridge, United States.,Analytic and Translational Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, United States
| | - Daniel Howrigan
- Broad Institute of MIT and Harvard, Cambridge, United States.,Analytic and Translational Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, United States
| | - Benjamin M Neale
- Broad Institute of MIT and Harvard, Cambridge, United States.,Analytic and Translational Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, United States
| | - Vamsi K Mootha
- Howard Hughes Medical Institute and Department of Molecular Biology, Massachusetts General Hospital, Boston, United States.,Broad Institute of MIT and Harvard, Cambridge, United States
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388
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Kerimov N, Hayhurst JD, Peikova K, Manning JR, Walter P, Kolberg L, Samoviča M, Sakthivel MP, Kuzmin I, Trevanion SJ, Burdett T, Jupp S, Parkinson H, Papatheodorou I, Yates AD, Zerbino DR, Alasoo K. A compendium of uniformly processed human gene expression and splicing quantitative trait loci. Nat Genet 2021; 53:1290-1299. [PMID: 34493866 PMCID: PMC8423625 DOI: 10.1038/s41588-021-00924-w] [Citation(s) in RCA: 203] [Impact Index Per Article: 50.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Accepted: 07/26/2021] [Indexed: 12/15/2022]
Abstract
Many gene expression quantitative trait locus (eQTL) studies have published their summary statistics, which can be used to gain insight into complex human traits by downstream analyses, such as fine mapping and co-localization. However, technical differences between these datasets are a barrier to their widespread use. Consequently, target genes for most genome-wide association study (GWAS) signals have still not been identified. In the present study, we present the eQTL Catalogue ( https://www.ebi.ac.uk/eqtl ), a resource of quality-controlled, uniformly re-computed gene expression and splicing QTLs from 21 studies. We find that, for matching cell types and tissues, the eQTL effect sizes are highly reproducible between studies. Although most QTLs were shared between most bulk tissues, we identified a greater diversity of cell-type-specific QTLs from purified cell types, a subset of which also manifested as new disease co-localizations. Our summary statistics are freely available to enable the systematic interpretation of human GWAS associations across many cell types and tissues.
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Affiliation(s)
- Nurlan Kerimov
- Institute of Computer Science, University of Tartu, Tartu, Estonia
- Open Targets, Wellcome Genome Campus, Cambridge, UK
| | - James D Hayhurst
- Open Targets, Wellcome Genome Campus, Cambridge, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, UK
| | - Kateryna Peikova
- Institute of Computer Science, University of Tartu, Tartu, Estonia
| | - Jonathan R Manning
- Open Targets, Wellcome Genome Campus, Cambridge, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, UK
| | - Peter Walter
- European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, UK
| | - Liis Kolberg
- Institute of Computer Science, University of Tartu, Tartu, Estonia
| | - Marija Samoviča
- Institute of Computer Science, University of Tartu, Tartu, Estonia
| | - Manoj Pandian Sakthivel
- Open Targets, Wellcome Genome Campus, Cambridge, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, UK
| | - Ivan Kuzmin
- Institute of Computer Science, University of Tartu, Tartu, Estonia
| | - Stephen J Trevanion
- Open Targets, Wellcome Genome Campus, Cambridge, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, UK
| | - Tony Burdett
- Open Targets, Wellcome Genome Campus, Cambridge, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, UK
| | - Simon Jupp
- Open Targets, Wellcome Genome Campus, Cambridge, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, UK
| | - Helen Parkinson
- Open Targets, Wellcome Genome Campus, Cambridge, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, UK
| | - Irene Papatheodorou
- Open Targets, Wellcome Genome Campus, Cambridge, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, UK
| | - Andrew D Yates
- Open Targets, Wellcome Genome Campus, Cambridge, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, UK
| | - Daniel R Zerbino
- Open Targets, Wellcome Genome Campus, Cambridge, UK.
- European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, UK.
| | - Kaur Alasoo
- Institute of Computer Science, University of Tartu, Tartu, Estonia.
- Open Targets, Wellcome Genome Campus, Cambridge, UK.
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389
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Wang H, Guo Z, Zheng Y, Yu C, Hou H, Chen B. No Casual Relationship Between T2DM and the Risk of Infectious Diseases: A Two-Sample Mendelian Randomization Study. Front Genet 2021; 12:720874. [PMID: 34527023 PMCID: PMC8435717 DOI: 10.3389/fgene.2021.720874] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2021] [Accepted: 07/26/2021] [Indexed: 12/29/2022] Open
Abstract
BACKGROUND In epidemiological studies, it has been proven that the occurrence of type 2 diabetes mellitus (T2DM) is related to an increased risk of infectious diseases. However, it is still unclear whether the relationship is casual. METHODS We employed a two-sample Mendelian randomization (MR) to clarify the causal effect of T2DM on high-frequency infectious diseases: sepsis, skin and soft tissue infections (SSTIs), urinary tract infections (UTIs), pneumonia, and genito-urinary infection (GUI) in pregnancy. And then, we analyzed the genome-wide association study (GWAS) meta-analysis of European-descent individuals and conducted T2DM-related single-nucleotide polymorphisms (SNPs) as instrumental variables (IVs) that were associated with genome-wide significance (p < 5 × 10-8). MR estimates were obtained using the inverse variance-weighted (IVW), the MR-Egger regression, the simple mode (SM), weighted median, and weighted mode. RESULTS The UK Biobank (UKB) cohort (n > 500,000) provided data for GWASs on infectious diseases. MR analysis showed little evidence of a causal relationship of T2DM with five mentioned infections' (sepsis, SSTI, UTI, pneumonia, and GUI in pregnancy) susceptibility [odds ratio (OR) = 0.99999, p = 0.916; OR = 0.99986, p = 0.233; OR = 0.99973, p = 0.224; OR = 0.99997, p = 0.686; OR, 1.00002, p = 0.766]. Sensitivity analysis showed similar results, indicating the robustness of causality. There were no heterogeneity and pleiotropic bias. CONCLUSION T2DM would not be causally associated with high-frequency infectious diseases (including sepsis, SSTI, UTI, pneumonia, and GUI in pregnancy).
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Affiliation(s)
- Huachen Wang
- Intensive Care Unit, The Second Hospital of Tianjin Medical University, Tianjin, China
| | - Zheng Guo
- Centre for Precision Health, School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA, Australia
| | - Yulu Zheng
- Centre for Precision Health, School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA, Australia
| | - Chunyan Yu
- Medical Imaging Department, Longgang District Central Hospital of Shenzhen, Shenzhen, China
| | - Haifeng Hou
- Shandong First Medical University & Shandong Academy of Medical Sciences, Taian, China
| | - Bing Chen
- Intensive Care Unit, The Second Hospital of Tianjin Medical University, Tianjin, China
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390
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Mir R, Elfaki I, Duhier FMA, Alotaibi MA, AlAlawy AI, Barnawi J, Babakr AT, Mir MM, Mirghani H, Hamadi A, Dabla PK. Molecular Determination of mirRNA-126 rs4636297, Phosphoinositide-3-Kinase Regulatory Subunit 1-Gene Variability rs7713645, rs706713 (Tyr73Tyr), rs3730089 (Met326Ile) and Their Association with Susceptibility to T2D. J Pers Med 2021; 11:861. [PMID: 34575638 PMCID: PMC8469127 DOI: 10.3390/jpm11090861] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Revised: 08/26/2021] [Accepted: 08/27/2021] [Indexed: 12/12/2022] Open
Abstract
Type 2 diabetes is a metabolic disease characterized by elevated blood sugar. It has serious complications and socioeconomic impact. The MicroRNAs are short single-stranded and non-coding RNA molecules. They regulate gene expression at the post-transcriptional levels. They are important for many physiological processes including metabolism, growth, and others. The phosphoinositide 3-kinase (PI3K) is important for insulin signaling and glucose uptake. The genome wide association studies have identified the association of certain loci with diseases including T2D. In this study we have examined the association of miR126 rs4636297 and Phosphoinositide-3-kinase regulatory subunit 1 (PIK3R1) gene Variations rs7713645, rs706713 (Tyr73Tyr), and rs3730089 (Met326Ile) with T2D using the amplification refractory mutation system PCR. Results indicated that there was a significant different (p-value < 0.05) in the Mir126 rs4636297 genotypes distribution between cases and controls, and the minor allele of the rs4636297 was also associated with T2D with OR = 0.58, p-value < 0.05. In addition results showed that there were significant differences (p-value < 0.05) of rs4636297 genotype distribution of patients with normal and patient with abnormal lipid profile. Results also showed that the PIK3R1 rs7713645 and rs3730089 genotype distribution was significantly different between cases and controls with a p-values < 0.05. In addition, the minor allele of the rs7713645 and rs3730089 were associated with T2D with OR = 0.58, p-value < 0.05. We conclude that the Mir126 rs4636297 and PIK3R1 SNPs (rs7713645 and rs3730089) were associated with T2D. These results need verification in future studies with larger sample sizes and in different populations. Protein-protein interaction and enzyme assay studies are also required to uncover the effect of the SNPs on the PI3K regulatory subunit (PI3KR1) and PI3K catalytic activity.
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Affiliation(s)
- Rashid Mir
- Department of Medical Lab Technology, Faculty of Applied Medical Sciences, University of Tabuk, Tabuk 71491, Saudi Arabia; (J.B.); (A.H.)
| | - Imadeldin Elfaki
- Department of Biochemistry, Faculty of Science, University of Tabuk, Tabuk 71491, Saudi Arabia;
| | - Faisel M. Abu Duhier
- Department of Medical Lab Technology, Faculty of Applied Medical Sciences, University of Tabuk, Tabuk 71491, Saudi Arabia; (J.B.); (A.H.)
| | - Maeidh A. Alotaibi
- King Faisal Medical Complex Department of Training, Research and Academic Affairs, P.O. Box 2775, Taif 21944, Saudi Arabia;
| | - Adel Ibrahim AlAlawy
- Department of Biochemistry, Faculty of Science, University of Tabuk, Tabuk 71491, Saudi Arabia;
| | - Jameel Barnawi
- Department of Medical Lab Technology, Faculty of Applied Medical Sciences, University of Tabuk, Tabuk 71491, Saudi Arabia; (J.B.); (A.H.)
| | - Abdullatif Taha Babakr
- Department of Medical Biochemistry, Faculty of Medicine, Umm Al-Qura University, Makkah 57039, Saudi Arabia;
| | - Mohammad Muzaffar Mir
- Department of Basic Medical Sciences, College of Medicine, University of Bisha, Bisha 61992, Saudi Arabia;
| | - Hyder Mirghani
- Internal Medicine and Endocrine, Medical Department, Faculty of Medicine, University of Tabuk, Tabuk 71491, Saudi Arabia;
| | - Abdullah Hamadi
- Department of Medical Lab Technology, Faculty of Applied Medical Sciences, University of Tabuk, Tabuk 71491, Saudi Arabia; (J.B.); (A.H.)
| | - Pradeep Kumar Dabla
- Department of Biochemistry, Govind Ballabh Pant Institute of Postgraduate Medical Education & Research (GIPMER), Associated to Maulana Azad Medical College, Delhi 110002, India;
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391
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Flowers E, Allen IE, Kanaya AM, Aouizerat BE. Circulating MicroRNAs predict glycemic improvement and response to a behavioral intervention. Biomark Res 2021; 9:65. [PMID: 34425916 PMCID: PMC8383422 DOI: 10.1186/s40364-021-00317-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Accepted: 07/27/2021] [Indexed: 01/16/2023] Open
Abstract
Background MicroRNAs may be important regulators of risk for type 2 diabetes. The purpose of this longitudinal observational study was to assess whether circulating microRNAs predicted improvements in fasting blood glucose, a major risk factor for type 2 diabetes, over 12 months. Methods The study included participants (n = 82) from a previously completed trial that tested the effect of restorative yoga on individuals with prediabetes. Circulating microRNAs were measured using a flow cytometry miRNA assay. Linear models were used to determine the optimal sets of microRNA predictors overall and by intervention group. Results Subsets of microRNAs were significant predictors of final fasting blood glucose after 12-months (R2 = 0.754, p < 0.001) and changes in fasting blood glucose over 12-months (R2 = 0.731, p < 0.001). Three microRNAs (let-7c, miR-363, miR-374b) were significant for the control group only, however there was no significant interaction by intervention group. Conclusions Circulating microRNAs are significant predictors of fasting blood glucose in individuals with prediabetes. Among the identified microRNAs, several have previously been associated with risk for type 2 diabetes. This is one of the first studies to use a longitudinal design to assess whether microRNAs predict changes in fasting blood glucose over time. Further exploration of the function of the microRNAs included in these models may provide new insights about the complex etiology of type 2 diabetes and responses to behavioral risk reduction interventions. Trial registration This study was a secondary analysis of a previously completed clinical trial that is registered at clinicaltrials.gov (NCT01024816) on December 3, 2009. Supplementary Information The online version contains supplementary material available at 10.1186/s40364-021-00317-5.
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Affiliation(s)
- Elena Flowers
- Department of Physiological Nursing, University of California, San Francisco, 2 Koret Way, #605L, CA, 94143-0610, San Francisco, USA. .,Institute for Human Genetics, University of California, San Francisco, 2 Koret Way, #605L, CA , 94143-0610, San Francisco, USA.
| | - Isabel Elaine Allen
- Department of Epidemiology and Biostatistics, University of California, San Francisco, USA
| | - Alka M Kanaya
- Department of Epidemiology and Biostatistics, University of California, San Francisco, USA.,Department of Medicine, University of California, San Francisco, USA
| | - Bradley E Aouizerat
- Bluestone Center for Clinical Research, New York University, New York, USA.,Department of Oral and Maxillofacial Surgery, New York University, New York, USA
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392
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Virginia DM, Wahyuningsih MSH, Nugrahaningsih DAA. PRKAA2 variation and the clinical characteristics of patients newly diagnosed with type 2 diabetes mellitus in Yogyakarta, Indonesia. ASIAN BIOMED 2021; 15:161-170. [PMID: 37551330 PMCID: PMC10388783 DOI: 10.2478/abm-2021-0021] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
Background Adenosine monophosphate (AMP)-activated protein kinase (AMPK; EC 2.7.11.31) enzymes play a pivotal role in cell metabolism. They are involved in type 2 diabetes mellitus (T2DM) pathogenesis. Genetic variation of PRKAA2 coding for the AMPK α2 catalytic subunit (AMPKα2) is reported to be associated with susceptibility for T2DM. Objectives To determine the association between PRKAA2 genetic variations (rs2796498, rs9803799, and rs2746342) with clinical characteristics in patients newly diagnosed with T2DM. Methods We performed a cross-sectional study including 166 T2DM patients from 10 primary health care centers in Yogyakarta, Indonesia. We measured fasting plasma glucose, hemoglobin A1c, serum creatinine, glomerular filtration rate, blood pressure, and body mass index as clinical characteristics. PRKAA2 genetic variations were determined by TaqMan SNP genotyping assay. Hardy-Weinberg equilibrium was calculated using χ2 tests. Results There was no difference in clinical characteristics for genotypes rs2796498, rs9803799, or rs2746342 (P > 0.05). No significant association was found between PRKAA2 genetic variations and any clinical feature observed. Further subgroup analysis adjusting for age, sex, and waist circumference did not detect any significant association of PRKAA2 genetic variations with clinical characteristics (P > 0.05). Conclusion PRKAA2 genetic variation is not associated with the clinical characteristics of Indonesian patients with newly diagnosed T2DM.
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Affiliation(s)
- Dita Maria Virginia
- Department of Pharmacology and Therapy, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta55281, Indonesia
- Faculty of Pharmacy, Universitas Sanata Dharma, Yogyakarta552181, Indonesia
| | - Mae Sri Hartati Wahyuningsih
- Department of Pharmacology and Therapy, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta55281, Indonesia
| | - Dwi Aris Agung Nugrahaningsih
- Department of Pharmacology and Therapy, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta55281, Indonesia
- Center of Genetic Study, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta55281, Indonesia
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393
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Potente C, Harris KM, Chumbley J, Cole SW, Gaydosh L, Xu W, Levitt B, Shanahan MJ. The Early Life Course of Body Weight and Gene Expression Signatures for Disease. Am J Epidemiol 2021; 190:1533-1540. [PMID: 33675221 PMCID: PMC8489427 DOI: 10.1093/aje/kwab049] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2020] [Revised: 02/23/2021] [Accepted: 02/23/2021] [Indexed: 12/01/2022] Open
Abstract
We examined the way body-weight patterns through the first 4 decades of life relate to gene expression signatures of common forms of morbidity, including cardiovascular disease (CVD), type 2 diabetes (T2D), and inflammation. As part of wave V of the nationally representative National Longitudinal Study of Adolescent to Adult Health (1997–2018) in the United States, mRNA abundance data were collected from peripheral blood (n = 1,132). We used a Bayesian modeling strategy to examine the relative associations between body size at 5 life stages—birth, adolescence, early adulthood, young adulthood, and adulthood—and gene expression–based disease signatures. We compared life-course models that consider critical or sensitive periods, as well as accumulation over the entire period. Our results are consistent with a sensitive-period model when examining CVD and T2D gene expression signatures: Birth weight has a prominent role for the CVD and T2D signatures (explaining 33.1% and 22.1%, respectively, of the total association accounted for by body size), while the most recent adult obesity status (ages 33–39) is important for both of these gene expression signatures (24.3% and 35.1%, respectively). Body size in all life stages was associated with inflammation, consistent with the accumulation model.
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Affiliation(s)
- Cecilia Potente
- Correspondence to Dr. Cecilia Potente, Jacobs Center for Productive Youth Development, University of Zürich, Andreasstrasse 15, 8050 Zürich, Switzerland (e-mail: ); or Prof. Dr. Michael J. Shanahan, Jacobs Center for Productive Youth Development, University of Zürich, Andreasstrasse 15, 8050 Zürich, Switzerland (e-mail: )
| | | | | | | | | | | | | | - Michael J Shanahan
- Correspondence to Dr. Cecilia Potente, Jacobs Center for Productive Youth Development, University of Zürich, Andreasstrasse 15, 8050 Zürich, Switzerland (e-mail: ); or Prof. Dr. Michael J. Shanahan, Jacobs Center for Productive Youth Development, University of Zürich, Andreasstrasse 15, 8050 Zürich, Switzerland (e-mail: )
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394
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Luo Y, Li X, Wang X, Gazal S, Mercader JM, 23 and Me Research Team, SIGMA Type 2 Diabetes Consortium, Neale BM, Florez JC, Auton A, Price AL, Finucane HK, Raychaudhuri S. Estimating heritability and its enrichment in tissue-specific gene sets in admixed populations. Hum Mol Genet 2021; 30:1521-1534. [PMID: 33987664 PMCID: PMC8330913 DOI: 10.1093/hmg/ddab130] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 04/29/2021] [Accepted: 04/29/2021] [Indexed: 01/07/2023] Open
Abstract
It is important to study the genetics of complex traits in diverse populations. Here, we introduce covariate-adjusted linkage disequilibrium (LD) score regression (cov-LDSC), a method to estimate SNP-heritability (${\boldsymbol{h}}_{\boldsymbol{g}}^{\mathbf{2}})$ and its enrichment in homogenous and admixed populations with summary statistics and in-sample LD estimates. In-sample LD can be estimated from a subset of the genome-wide association studies samples, allowing our method to be applied efficiently to very large cohorts. In simulations, we show that unadjusted LDSC underestimates ${\boldsymbol{h}}_{\boldsymbol{g}}^{\mathbf{2}}$ by 10-60% in admixed populations; in contrast, cov-LDSC is robustly accurate. We apply cov-LDSC to genotyping data from 8124 individuals, mostly of admixed ancestry, from the Slim Initiative in Genomic Medicine for the Americas study, and to approximately 161 000 Latino-ancestry individuals, 47 000 African American-ancestry individuals and 135 000 European-ancestry individuals, as classified by 23andMe. We estimate ${\boldsymbol{h}}_{\boldsymbol{g}}^{\mathbf{2}}$ and detect heritability enrichment in three quantitative and five dichotomous phenotypes, making this, to our knowledge, the most comprehensive heritability-based analysis of admixed individuals to date. Most traits have high concordance of ${\boldsymbol{h}}_{\boldsymbol{g}}^{\mathbf{2}}$ and consistent tissue-specific heritability enrichment among different populations. However, for age at menarche, we observe population-specific heritability estimates of ${\boldsymbol{h}}_{\boldsymbol{g}}^{\mathbf{2}}$. We observe consistent patterns of tissue-specific heritability enrichment across populations; for example, in the limbic system for BMI, the per-standardized-annotation effect size $ \tau $* is 0.16 ± 0.04, 0.28 ± 0.11 and 0.18 ± 0.03 in the Latino-, African American- and European-ancestry populations, respectively. Our approach is a powerful way to analyze genetic data for complex traits from admixed populations.
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Affiliation(s)
- Yang Luo
- Center for Data Sciences, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Immunology, and Immunity, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Division of Genetics, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Xinyi Li
- Center for Data Sciences, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Immunology, and Immunity, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Division of Genetics, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Xin Wang
- 23andMe, Inc., Mountain View, California, USA
| | - Steven Gazal
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Josep Maria Mercader
- Division of Genetics, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA
| | | | | | - Benjamin M Neale
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Jose C Florez
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA
| | - Adam Auton
- 23andMe, Inc., Mountain View, California, USA
| | - Alkes L Price
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Hilary K Finucane
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Soumya Raychaudhuri
- Center for Data Sciences, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Immunology, and Immunity, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Division of Genetics, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Arthritis Research UK Centre for Genetics and Genomics, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK
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395
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Abstract
Tracing the early paths leading to developmental disorders is critical for prevention. In previous work, we detected an interaction between genomic risk scores for schizophrenia (GRSs) and early-life complications (ELCs), so that the liability of the disorder explained by genomic risk was higher in the presence of a history of ELCs, compared with its absence. This interaction was specifically driven by loci harboring genes highly expressed in placentae from normal and complicated pregnancies [G. Ursini et al., Nat. Med. 24, 792-801 (2018)]. Here, we analyze whether fractionated genomic risk scores for schizophrenia and other developmental disorders and traits, based on placental gene-expression loci (PlacGRSs), are linked with early neurodevelopmental outcomes in individuals with a history of ELCs. We found that schizophrenia's PlacGRSs are negatively associated with neonatal brain volume in singletons and offspring of multiple pregnancies and, in singletons, with cognitive development at 1 y and, less strongly, at 2 y, when cognitive scores become more sensitive to other factors. These negative associations are stronger in males, found only with GRSs fractionated by placental gene expression, and not found in PlacGRSs for other developmental disorders and traits. The relationship of PlacGRSs with brain volume persists as an anlage of placenta biology in adults with schizophrenia, again selectively in males. Higher placental genomic risk for schizophrenia, in the presence of ELCs and particularly in males, alters early brain growth and function, defining a potentially reversible neurodevelopmental path of risk that may be unique to schizophrenia.
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396
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Mi J, Liu Z. Obesity, Type 2 Diabetes, and the Risk of Carpal Tunnel Syndrome: A Two-Sample Mendelian Randomization Study. Front Genet 2021; 12:688849. [PMID: 34367246 PMCID: PMC8339995 DOI: 10.3389/fgene.2021.688849] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Accepted: 06/03/2021] [Indexed: 12/31/2022] Open
Abstract
Some previous observational studies have reported an increased risk of carpal tunnel syndrome (CTS) in patients with obesity or type 2 diabetes (T2D), which was however, not observed in some other studies. In this study we performed a two-sample Mendelian randomization to assess the causal effect of obesity, T2D on the risk of CTS. Single nucleotide polymorphisms associated with the body mass index (BMI) and T2D were extracted from genome-wide association studies. Summary-level results of CTS were available through FinnGen repository. Univariable Mendelian randomization (MR) with inverse-variance-weighted method indicated a positive correlation of BMI with CTS risk [odds ratio (OR) 1.66, 95% confidence interval (CI), 1.39–1.97]. Genetically proxied T2D also significantly increased the risk of CTS [OR 1.17, 95% CI (1.07–1.29)]. The causal effect of BMI and T2D on CTS remained consistent after adjusting for each other with multivariable MR. Our mediation analysis indicated that 34.4% of BMI’s effect of CTS was mediated by T2D. We also assessed the effects of several BMI and glycemic related traits on CTS. Waist circumference and arm fat-free mass were also causally associated with CTS. However, the associations disappeared after adjusting for the effect of BMI. Our findings indicate that obesity and T2D are independent risk factors of CTS.
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Affiliation(s)
- Jiarui Mi
- Master's Programme in Biomedicine, Karolinska Institutet, Stockholm, Sweden
| | - Zhengye Liu
- Department of Orthopedics, Zhongnan Hospital of Wuhan University, Wuhan, China
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397
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Welzenbach J, Hammond NL, Nikolić M, Thieme F, Ishorst N, Leslie EJ, Weinberg SM, Beaty TH, Marazita ML, Mangold E, Knapp M, Cotney J, Rada-Iglesias A, Dixon MJ, Ludwig KU. Integrative approaches generate insights into the architecture of non-syndromic cleft lip with or without cleft palate. HGG ADVANCES 2021; 2:100038. [PMID: 35047836 PMCID: PMC8756534 DOI: 10.1016/j.xhgg.2021.100038] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Accepted: 05/27/2021] [Indexed: 12/15/2022] Open
Abstract
Non-syndromic cleft lip with or without cleft palate (nsCL/P) is a common congenital facial malformation with a multifactorial etiology. Genome-wide association studies (GWASs) have identified multiple genetic risk loci. However, functional interpretation of these loci is hampered by the underrepresentation in public resources of systematic functional maps representative of human embryonic facial development. To generate novel insights into the etiology of nsCL/P, we leveraged published GWAS data on nsCL/P as well as available chromatin modification and expression data on mid-facial development. Our analyses identified five novel risk loci, prioritized candidate target genes within associated regions, and highlighted distinct pathways. Furthermore, the results suggest the presence of distinct regulatory effects of nsCL/P risk variants throughout mid-facial development and shed light on its regulatory architecture. Our integrated data provide a platform to advance hypothesis-driven molecular investigations of nsCL/P and other human facial defects.
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Affiliation(s)
- Julia Welzenbach
- Institute of Human Genetics, University Hospital Bonn, Medical Faculty, Venusberg-Campus 1, 53127 Bonn, Germany
| | - Nigel L. Hammond
- Faculty of Biology, Medicine, and Health, Manchester Academic Health Sciences Centre, University of Manchester, Manchester M13 9PT, UK
| | - Miloš Nikolić
- Center for Molecular Medicine Cologne (CMMC), University of Cologne, Cologne, Germany
| | - Frederic Thieme
- Institute of Human Genetics, University Hospital Bonn, Medical Faculty, Venusberg-Campus 1, 53127 Bonn, Germany
| | - Nina Ishorst
- Institute of Human Genetics, University Hospital Bonn, Medical Faculty, Venusberg-Campus 1, 53127 Bonn, Germany
| | - Elizabeth J. Leslie
- Department of Human Genetics, School of Medicine, Emory University, Atlanta, GA, USA
| | - Seth M. Weinberg
- Center for Craniofacial and Dental Genetics, Department of Oral Biology, School of Dental Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Terri H. Beaty
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Mary L. Marazita
- Center for Craniofacial and Dental Genetics, Department of Oral Biology, School of Dental Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Psychiatry and Clinical and Translational Science Institute, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15260, USA
| | - Elisabeth Mangold
- Institute of Human Genetics, University Hospital Bonn, Medical Faculty, Venusberg-Campus 1, 53127 Bonn, Germany
| | - Michael Knapp
- Institute of Medical Biometry, Informatics, and Epidemiology, University Hospital Bonn, Bonn, Germany
| | - Justin Cotney
- Department of Genetics and Genome Sciences, UConn Health, Farmington, CT, USA
- Institute for Systems Genomics, University of Connecticut, Storrs, CT, USA
| | - Alvaro Rada-Iglesias
- Center for Molecular Medicine Cologne (CMMC), University of Cologne, Cologne, Germany
- Cluster of Excellence Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, Cologne, Germany
- Institute of Biomedicine and Biotechnology of Cantabria (IBBTEC), University of Cantabria, Cantabria, Spain
| | - Michael J. Dixon
- Faculty of Biology, Medicine, and Health, Manchester Academic Health Sciences Centre, University of Manchester, Manchester M13 9PT, UK
| | - Kerstin U. Ludwig
- Institute of Human Genetics, University Hospital Bonn, Medical Faculty, Venusberg-Campus 1, 53127 Bonn, Germany
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398
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Ebrahim S. Cohort Profiles: what are they good for? Int J Epidemiol 2021; 50:367-370. [PMID: 33837386 DOI: 10.1093/ije/dyab054] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/01/2021] [Indexed: 01/12/2023] Open
Affiliation(s)
- Shah Ebrahim
- Department of Non-communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, Keppel Street, London WC1E 7HT, UK
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399
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Untangling the genetic link between type 1 and type 2 diabetes using functional genomics. Sci Rep 2021; 11:13871. [PMID: 34230558 PMCID: PMC8260770 DOI: 10.1038/s41598-021-93346-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Accepted: 06/16/2021] [Indexed: 02/06/2023] Open
Abstract
There is evidence pointing towards shared etiological features between type 1 diabetes (T1D) and type 2 diabetes (T2D) despite both phenotypes being considered genetically distinct. However, the existence of shared genetic features for T1D and T2D remains complex and poorly defined. To better understand the link between T1D and T2D, we employed an integrated functional genomics approach involving extensive chromatin interaction data (Hi-C) and expression quantitative trait loci (eQTL) data to characterize the tissue-specific impacts of single nucleotide polymorphisms associated with T1D and T2D. We identified 195 pleiotropic genes that are modulated by tissue-specific spatial eQTLs associated with both T1D and T2D. The pleiotropic genes are enriched in inflammatory and metabolic pathways that include mitogen-activated protein kinase activity, pertussis toxin signaling, and the Parkinson's disease pathway. We identified 8 regulatory elements within the TCF7L2 locus that modulate transcript levels of genes involved in immune regulation as well as genes important in the etiology of T2D. Despite the observed gene and pathway overlaps, there was no significant genetic correlation between variant effects on T1D and T2D risk using European ancestral summary data. Collectively, our findings support the hypothesis that T1D and T2D specific genetic variants act through genetic regulatory mechanisms to alter the regulation of common genes, and genes that co-locate in biological pathways, to mediate pleiotropic effects on disease development. Crucially, a high risk genetic profile for T1D alters biological pathways that increase the risk of developing both T1D and T2D. The same is not true for genetic profiles that increase the risk of developing T2D. The conversion of information on genetic susceptibility to the protein pathways that are altered provides an important resource for repurposing or designing novel therapies for the management of diabetes.
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400
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Zeng Y, Zheng Z, Liu F, Yi G. Circular RNAs in metabolism and metabolic disorders. Obes Rev 2021; 22:e13220. [PMID: 33580638 DOI: 10.1111/obr.13220] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Revised: 01/07/2021] [Accepted: 01/22/2021] [Indexed: 12/21/2022]
Abstract
Metabolic syndrome (MetS) is a serious health condition triggered by hyperglycemia, dyslipidemia, and abnormal adipose deposition. Recently, circular RNAs (circRNAs) have been proposed as key molecular players in metabolic homeostasis due to their regulatory effects on genes linked to the modulation of multiple aspects of metabolism, including glucose and lipid homeostasis. Dysregulation of circRNAs can lead to metabolic disorders, indicating that circRNAs represent plausible potential targets to alleviate metabolic abnormalities. More recently, a series of circulating circRNAs have been identified to act as both essential regulatory molecules and biomarkers for the progression of metabolism-related disorders, including type 2 diabetes mellitus (T2DM or T2D) and cardiovascular disease (CVD). The findings of this study highlight the function of circRNAs in signaling pathways implicated in metabolic diseases and their potential as future therapeutics and disease biomarkers.
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Affiliation(s)
- Yongzhi Zeng
- Institute of Cardiovascular Disease, Key Laboratory for Arteriosclerology of Hunan Province, University of South China, Hengyang, China
| | - Zhi Zheng
- Institute of Cardiovascular Disease, Key Laboratory for Arteriosclerology of Hunan Province, University of South China, Hengyang, China
| | - Fengtao Liu
- Institute of Cardiovascular Disease, Key Laboratory for Arteriosclerology of Hunan Province, University of South China, Hengyang, China
| | - Guanghui Yi
- Institute of Cardiovascular Disease, Key Laboratory for Arteriosclerology of Hunan Province, University of South China, Hengyang, China
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