1
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Li J, Ma X, Yin C. Proteome-wide Mendelian randomization identifies potential therapeutic targets for nonalcoholic fatty liver diseases. Sci Rep 2024; 14:11814. [PMID: 38782984 PMCID: PMC11116402 DOI: 10.1038/s41598-024-62742-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2024] [Accepted: 05/21/2024] [Indexed: 05/25/2024] Open
Abstract
Nonalcoholic fatty liver disease (NAFLD) is the predominant cause of liver pathology. Current evidence highlights plasma proteins as potential therapeutic targets. However, their mechanistic roles in NAFLD remain unclear. This study investigated the involvement of specific plasma proteins and intermediate risk factors in NAFLD progression. Two-sample Mendelian randomization (MR) analysis was conducted to examine the association between plasma proteins and NAFLD. Colocalization analysis determined the shared causal variants between the identified proteins and NAFLD. The MR analysis was applied separately to proteins, risk factors, and NAFLD. Mediator shares were computed by detecting the correlations among these elements. Phenome-wide association studies (phewas) were utilized to assess the safety implications of targeting these proteins. Among 1,834 cis-protein quantitative trait loci (cis-pQTLs), after-FDR correction revealed correlations between the plasma levels of four gene-predicted proteins (CSPG3, CILP2, Apo-E, and GCKR) and NAFLD. Colocalization analysis indicated shared causal variants for CSPG3 and GCKR in NAFLD (posterior probability > 0.8). Out of the 22 risk factors screened for MR analysis, only 8 showed associations with NAFLD (p ≤ 0.05), while 4 linked to CSPG3 and GCKR. The mediator shares for these associations were calculated separately. Additionally, reverse MR analysis was performed on the pQTLs, risk factors, and NAFLD, which exhibited a causal relationship with forward MR analysis. Finally, phewas summarized the potential side effects of associated-targeting proteins, including CSPG3 and GCKR. Our research emphasized the potential therapeutic targets for NAFLD and provided modifiable risk factors for preventing NAFLD.
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Affiliation(s)
- Junhang Li
- Department of Ultrasonography, Dali Prefecture Third People's Hospital, Dali Prefecture, Yunnan Province, China
| | - Xiang Ma
- Chongqing Medical University, Chongqing, China
| | - Cuihua Yin
- Department of Ultrasonography, Dali Prefecture Third People's Hospital, Dali Prefecture, Yunnan Province, China.
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2
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Shrestha P, Graff M, Gu Y, Wang Y, Avery CL, Ginnis J, Simancas-Pallares MA, Ferreira Zandoná AG, Ahn HS, Nguyen KN, Lin DY, Preisser JS, Slade GD, Marazita ML, North KE, Divaris K. Multi-ancestry Genome-Wide Association Study of Early Childhood Caries. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.03.12.24303742. [PMID: 38562815 PMCID: PMC10984042 DOI: 10.1101/2024.03.12.24303742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Early childhood caries (ECC) is the most common non-communicable childhood disease. It is an important health problem with known environmental and social/behavioral influences that lacks evidence for specific associated genetic risk loci. To address this knowledge gap, we conducted a genome-wide association study of ECC in a multi-ancestry population of U.S. preschool-age children (n=6,103) participating in a community-based epidemiologic study of early childhood oral health. Calibrated examiners used ICDAS criteria to measure ECC with the primary trait using the dmfs index with decay classified as macroscopic enamel loss (ICDAS ≥3). We estimated heritability, concordance rates, and conducted genome-wide association analyses to estimate overall genetic effects; the effects stratified by sex, household water fluoride, and dietary sugar; and leveraged the combined gene/gene-environment effects using the 2-degree-of-freedom (2df) joint test. The common genetic variants explained 24% of the phenotypic variance (heritability) of the primary ECC trait and the concordance rate was higher with a higher degree of relatedness. We identified 21 novel non-overlapping genome-wide significant loci for ECC. Two loci, namely RP11-856F16 . 2 (rs74606067) and SLC41A3 (rs71327750) showed evidence of association with dental caries in external cohorts, namely the GLIDE consortium adult cohort (n=∼487,000) and the GLIDE pediatric cohort (n=19,000), respectively. The gene-based tests identified TAAR6 as a genome-wide significant gene. Implicated genes have relevant biological functions including roles in tooth development and taste. These novel associations expand the genomics knowledge base for this common childhood disease and underscore the importance of accounting for sex and pertinent environmental exposures in genetic investigations of oral health.
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3
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Höglund A, Henriksen R, Churcher AM, Guerrero-Bosagna CM, Martinez-Barrio A, Johnsson M, Jensen P, Wright D. The regulation of methylation on the Z chromosome and the identification of multiple novel Male Hyper-Methylated regions in the chicken. PLoS Genet 2024; 20:e1010719. [PMID: 38457441 PMCID: PMC10954189 DOI: 10.1371/journal.pgen.1010719] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 03/20/2024] [Accepted: 01/31/2024] [Indexed: 03/10/2024] Open
Abstract
DNA methylation is a key regulator of eukaryote genomes, and is of particular relevance in the regulation of gene expression on the sex chromosomes, with a key role in dosage compensation in mammalian XY systems. In the case of birds, dosage compensation is largely absent, with it being restricted to two small Male Hyper-Methylated (MHM) regions on the Z chromosome. To investigate how variation in DNA methylation is regulated on the Z chromosome we utilised a wild x domestic advanced intercross in the chicken, with both hypothalamic methylomes and transcriptomes assayed in 124 individuals. The relatively large numbers of individuals allowed us to identify additional genomic MHM regions on the Z chromosome that were significantly differentially methylated between the sexes. These regions appear to down-regulate local gene expression in males, but not remove it entirely (unlike the lncRNAs identified in the initial MHM regions). These MHM regions were further tested and the most balanced genes appear to show decreased expression in males, whilst methylation appeared to be far more correlated with gene expression in the less balanced, as compared to the most balanced genes. In addition, quantitative trait loci (QTL) that regulate variation in methylation on the Z chromosome, and those loci that regulate methylation on the autosomes that derive from the Z chromosome were mapped. Trans-effect hotspots were also identified that were based on the autosomes but affected the Z, and also one that was based on the Z chromosome but that affected both autosomal and sex chromosome DNA methylation regulation. We show that both cis and trans loci that originate from the Z chromosome never exhibit an interaction with sex, whereas trans loci originating from the autosomes but affecting the Z chromosome always display such an interaction. Our results highlight how additional MHM regions are actually present on the Z chromosome, and they appear to have smaller-scale effects on gene expression in males. Quantitative variation in methylation is also regulated both from the autosomes to the Z chromosome, and from the Z chromosome to the autosomes.
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Affiliation(s)
- Andrey Höglund
- Science for Life Laboratory, Department of Environmental Science, Stockholm University, Stockholm, Sweden
| | - Rie Henriksen
- AVIAN Behavioural Genomics and Physiology Group, Linköping University, Linköping, Sweden
| | | | - Carlos M. Guerrero-Bosagna
- Physiology and Environmental Toxicology Program, Department of Organismal Biology, Uppsala University, Uppsala, Sweden
| | | | - Martin Johnsson
- Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, Uppsala, Sweden
| | - Per Jensen
- AVIAN Behavioural Genomics and Physiology Group, Linköping University, Linköping, Sweden
| | - Dominic Wright
- AVIAN Behavioural Genomics and Physiology Group, Linköping University, Linköping, Sweden
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4
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Casazza W, Inkster AM, Del Gobbo GF, Yuan V, Delahaye F, Marsit C, Park YP, Robinson WP, Mostafavi S, Dennis JK. Sex-dependent placental methylation quantitative trait loci provide insight into the prenatal origins of childhood onset traits and conditions. iScience 2024; 27:109047. [PMID: 38357671 PMCID: PMC10865402 DOI: 10.1016/j.isci.2024.109047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 06/19/2023] [Accepted: 01/23/2024] [Indexed: 02/16/2024] Open
Abstract
Molecular quantitative trait loci (QTLs) allow us to understand the biology captured in genome-wide association studies (GWASs). The placenta regulates fetal development and shows sex differences in DNA methylation. We therefore hypothesized that placental methylation QTL (mQTL) explain variation in genetic risk for childhood onset traits, and that effects differ by sex. We analyzed 411 term placentas from two studies and found 49,252 methylation (CpG) sites with mQTL and 2,489 CpG sites with sex-dependent mQTL. All mQTL were enriched in regions that typically affect gene expression in prenatal tissues. All mQTL were also enriched in GWAS results for growth- and immune-related traits, but male- and female-specific mQTL were more enriched than cross-sex mQTL. mQTL colocalized with trait loci at 777 CpG sites, with 216 (28%) specific to males or females. Overall, mQTL specific to male and female placenta capture otherwise overlooked variation in childhood traits.
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Affiliation(s)
- William Casazza
- Centre for Molecular Medicine and Therapeutics, BC Children’s Hospital, Vancouver, BC, Canada
- Bioinformatics Graduate Program, University of British Columbia, Vancouver, BC, Canada
- BC Children’s Hospital Research Institute, Vancouver, BC, Canada
| | - Amy M. Inkster
- BC Children’s Hospital Research Institute, Vancouver, BC, Canada
- Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada
| | - Giulia F. Del Gobbo
- BC Children’s Hospital Research Institute, Vancouver, BC, Canada
- Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada
- Children’s Hospital of Eastern Ontario Research Institute, University of Ottawa, Ottawa, ON, Canada
| | - Victor Yuan
- BC Children’s Hospital Research Institute, Vancouver, BC, Canada
- Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada
| | | | - Carmen Marsit
- Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Yongjin P. Park
- Department of Statistics, University of British Columbia, Vancouver, BC, Canada
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Wendy P. Robinson
- BC Children’s Hospital Research Institute, Vancouver, BC, Canada
- Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada
| | - Sara Mostafavi
- Centre for Molecular Medicine and Therapeutics, BC Children’s Hospital, Vancouver, BC, Canada
- Paul Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA
| | - Jessica K. Dennis
- Centre for Molecular Medicine and Therapeutics, BC Children’s Hospital, Vancouver, BC, Canada
- Bioinformatics Graduate Program, University of British Columbia, Vancouver, BC, Canada
- BC Children’s Hospital Research Institute, Vancouver, BC, Canada
- Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada
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5
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Cox SL, Nicklisch N, Francken M, Wahl J, Meller H, Haak W, Alt KW, Rosenstock E, Mathieson I. Socio-cultural practices may have affected sex differences in stature in Early Neolithic Europe. Nat Hum Behav 2024; 8:243-255. [PMID: 38081999 DOI: 10.1038/s41562-023-01756-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Accepted: 10/09/2023] [Indexed: 02/21/2024]
Abstract
The rules and structure of human culture impact health as much as genetics or environment. To study these relationships, we combine ancient DNA (n = 230), skeletal metrics (n = 391), palaeopathology (n = 606) and dietary stable isotopes (n = 873) to analyse stature variation in Early Neolithic Europeans from North Central, South Central, Balkan and Mediterranean regions. In North Central Europe, stable isotopes and linear enamel hypoplasias indicate high environmental stress across sexes, but female stature is low, despite polygenic scores identical to males, and suggests that cultural factors preferentially supported male recovery from stress. In Mediterranean populations, sexual dimorphism is reduced, indicating male vulnerability to stress and no strong cultural preference for males. Our analysis indicates that biological effects of sex-specific inequities can be linked to cultural influences at least as early as 7,000 yr ago, and culture, more than environment or genetics, drove height disparities in Early Neolithic Europe.
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Affiliation(s)
- Samantha L Cox
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Physical Anthropology Section, Penn Museum, University of Pennsylvania, Philadelphia, PA, USA.
| | - Nicole Nicklisch
- Center of Natural and Cultural Human History, Danube Private University, Krems-Stein, Austria
| | - Michael Francken
- State Office for Cultural Heritage Management Baden-Württemberg, Osteology, Konstanz, Germany
| | - Joachim Wahl
- Paleoanthropology Section, Institute of Archaeological Sciences, Eberhard Karls University, Tübingen, Germany
| | - Harald Meller
- State Office for Heritage Management and Archaeology Saxony-Anhalt, State Museum of Prehistory, Halle, Germany
| | - Wolfgang Haak
- Department of Archaeogenetics, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
| | - Kurt W Alt
- Center of Natural and Cultural Human History, Danube Private University, Krems-Stein, Austria
| | - Eva Rosenstock
- Bonn Center for ArchaeoSciences, Universität Bonn, Bonn, Germany
| | - Iain Mathieson
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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6
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Woolf B, Mason A, Zagkos L, Sallis H, Munafò MR, Gill D. MRSamePopTest: introducing a simple falsification test for the two-sample mendelian randomisation 'same population' assumption. BMC Res Notes 2024; 17:27. [PMID: 38233927 PMCID: PMC10795421 DOI: 10.1186/s13104-024-06684-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Accepted: 01/03/2024] [Indexed: 01/19/2024] Open
Abstract
Two-sample MR is an increasingly popular method for strengthening causal inference in epidemiological studies. For the effect estimates to be meaningful, variant-exposure and variant-outcome associations must come from comparable populations. A recent systematic review of two-sample MR studies found that, if assessed at all, MR studies evaluated this assumption by checking that the genetic association studies had similar demographics. However, it is unclear if this is sufficient because less easily accessible factors may also be important. Here we propose an easy-to-implement falsification test. Since recent theoretical developments in causal inference suggest that a causal effect estimate can generalise from one study to another if there is exchangeability of effect modifiers, we suggest testing the homogeneity of variant-phenotype associations for a phenotype which has been measured in both genetic association studies as a method of exploring the 'same-population' test. This test could be used to facilitate designing MR studies with diverse populations. We developed a simple R package to facilitate the implementation of our proposed test. We hope that this research note will result in increased attention to the same-population assumption, and the development of better sensitivity analyses.
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Affiliation(s)
- Benjamin Woolf
- School of Psychological Science, University of Bristol, Bristol, UK.
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK.
| | - Amy Mason
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Loukas Zagkos
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
| | - Hannah Sallis
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Centre for Academic Mental Health, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Marcus R Munafò
- School of Psychological Science, University of Bristol, Bristol, UK
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | - Dipender Gill
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
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7
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Christians JK, Reue K. The role of gonadal hormones and sex chromosomes in sex-dependent effects of early nutrition on metabolic health. Front Endocrinol (Lausanne) 2023; 14:1304050. [PMID: 38189044 PMCID: PMC10770830 DOI: 10.3389/fendo.2023.1304050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Accepted: 12/11/2023] [Indexed: 01/09/2024] Open
Abstract
Early-life conditions such as prenatal nutrition can have long-term effects on metabolic health, and these effects may differ between males and females. Understanding the biological mechanisms underlying sex differences in the response to early-life environment will improve interventions, but few such mechanisms have been identified, and there is no overall framework for understanding sex differences. Biological sex differences may be due to chromosomal sex, gonadal sex, or interactions between the two. This review describes approaches to distinguish between the roles of chromosomal and gonadal sex, and summarizes findings regarding sex differences in metabolism. The Four Core Genotypes (FCG) mouse model allows dissociation of the sex chromosome genotype from gonadal type, whereas the XY* mouse model can be used to distinguish effects of X chromosome dosage vs the presence of the Y chromosome. Gonadectomy can be used to distinguish between organizational (permanent) and activational (reversible) effects of sex hormones. Baseline sex differences in a variety of metabolic traits are influenced by both activational and organizational effects of gonadal hormones, as well as sex chromosome complement. Thus far, these approaches have not been widely applied to examine sex-dependent effects of prenatal conditions, although a number of studies have found activational effects of estradiol to be protective against the development of hypertension following early-life adversity. Genes that escape X chromosome inactivation (XCI), such as Kdm5c, contribute to baseline sex-differences in metabolism, while Ogt, another XCI escapee, leads to sex-dependent responses to prenatal maternal stress. Genome-wide approaches to the study of sex differences include mapping genetic loci influencing metabolic traits in a sex-dependent manner. Seeking enrichment for binding sites of hormone receptors among genes showing sexually-dimorphic expression can elucidate the relative roles of hormones. Using the approaches described herein to identify mechanisms underlying sex-dependent effects of early nutrition on metabolic health may enable the identification of fundamental mechanisms and potential interventions.
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Affiliation(s)
- Julian K. Christians
- Department of Biological Sciences, Simon Fraser University, Burnaby, BC, Canada
- Centre for Cell Biology, Development and Disease, Simon Fraser University, Burnaby, BC, Canada
- British Columbia Children’s Hospital Research Institute, Vancouver, BC, Canada
- Women’s Health Research Institute, BC Women’s Hospital and Health Centre, Vancouver, BC, Canada
| | - Karen Reue
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
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8
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Ramezankhani A, Azizi F, Hadaegh F. Sex differences in risk factors for coronary heart disease events: a prospective cohort study in Iran. Sci Rep 2023; 13:22398. [PMID: 38104178 PMCID: PMC10725458 DOI: 10.1038/s41598-023-50028-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Accepted: 12/14/2023] [Indexed: 12/19/2023] Open
Abstract
We investigated sex-specific associations and their differences between major cardiovascular risk factors and the risk of incident coronary heart disease (CHD) and hard CHD (defined as nonfatal myocardial infarction and CHD death). A total of 7518 (3377 men) participants from the Tehran Lipid and Glucose Study were included. Cox models were used to estimate the hazard ratios (HRs) and women-to-men ratios of HRs for CHD events associated with each risk factor. During 20 years of follow-up (1999-2018), 1068 (631 men) and 345 (238 men) new cases of CHD and hard CHD, respectively, were documented. In total population, the incidence rates per 1000 person-years were 9.5 (9.0-10.1) and 2.9 (2.6-3.2) for CHD and hard CHD, respectively. Hypertension, diabetes, pre-diabetes, and a high waist-to-hip ratio (WHR) were associated with a greater HR of hard CHD in women than men; the women-to-men HRs were 2.85 [1.36-5.98], 1.92 [1.11-3.31], 2.04 [1.09-3.80] and 1.42 [1.10-1.82], respectively. Diabetes was associated with a higher HR of CHD in women than men (ratio of HRs 1.49 (1.10-2.01). In conclusion, we found that hypertension, diabetes, pre-diabetes, and high WHR conferred a greater excess risk of CHD events in women than in men, suggesting that Iranian women may require greater attention for the prevention of CHD events.
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Affiliation(s)
- Azra Ramezankhani
- Prevention of Metabolic Disorders Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Fereidoun Azizi
- Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Farzad Hadaegh
- Prevention of Metabolic Disorders Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
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9
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Dow LF, Case AM, Paustian MP, Pinkerton BR, Simeon P, Trippier PC. The evolution of small molecule enzyme activators. RSC Med Chem 2023; 14:2206-2230. [PMID: 37974956 PMCID: PMC10650962 DOI: 10.1039/d3md00399j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Accepted: 09/20/2023] [Indexed: 11/19/2023] Open
Abstract
There is a myriad of enzymes within the body responsible for maintaining homeostasis by providing the means to convert substrates to products as and when required. Physiological enzymes are tightly controlled by many signaling pathways and their products subsequently control other pathways. Traditionally, most drug discovery efforts focus on identifying enzyme inhibitors, due to upregulation being prevalent in many diseases and the existence of endogenous substrates that can be modified to afford inhibitor compounds. As enzyme downregulation and reduction of endogenous activators are observed in multiple diseases, the identification of small molecules with the ability to activate enzymes has recently entered the medicinal chemistry toolbox to afford chemical probes and potential therapeutics as an alternative means to intervene in diseases. In this review we highlight the progress made in the identification and advancement of non-kinase enzyme activators and their potential in treating various disease states.
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Affiliation(s)
- Louise F Dow
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Nebraska Medical Center Omaha NE 68106 USA
| | - Alfie M Case
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Nebraska Medical Center Omaha NE 68106 USA
| | - Megan P Paustian
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Nebraska Medical Center Omaha NE 68106 USA
| | - Braeden R Pinkerton
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Nebraska Medical Center Omaha NE 68106 USA
| | - Princess Simeon
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Nebraska Medical Center Omaha NE 68106 USA
| | - Paul C Trippier
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Nebraska Medical Center Omaha NE 68106 USA
- Fred & Pamela Buffett Cancer Center, University of Nebraska Medical Center Omaha NE 68106 USA
- UNMC Center for Drug Discovery, University of Nebraska Medical Center Omaha NE 68106 USA
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10
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Cao YT, Zhang WH, Lou Y, Yan QH, Zhang YJ, Qi F, Xiang LL, Lv TS, Fang ZY, Yu JY, Zhou XQ. Sex- and reproductive status-specific relationships between body composition and non-alcoholic fatty liver disease. BMC Gastroenterol 2023; 23:364. [PMID: 37875811 PMCID: PMC10598923 DOI: 10.1186/s12876-023-02997-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/01/2023] [Accepted: 10/13/2023] [Indexed: 10/26/2023] Open
Abstract
BACKGROUND Sex and reproductive status differences exist in both non-alcoholic fatty liver disease (NAFLD) and body composition. Our purpose was to investigate the relationship between body composition and the severity of liver steatosis and fibrosis in NAFLD in different sex and reproductive status populations. METHODS This cross-sectional study included 880 patients (355 men, 417 pre-menopausal women, 108 post-menopausal women). Liver steatosis and fibrosis and body composition data were measured using FibroScan and a bioelectrical impedance body composition analyzer (BIA), respectively, and the following parameters were obtained: liver stiffness measurement (LSM), controlled attenuation parameter (CAP), waist circumference (WC), body mass index (BMI), percent body fat (PBF), visceral fat area (VFA), appendicular skeletal muscle mass (ASM), appendicular skeletal muscle mass index (ASMI), fat mass (FM), fat free mass (FFM), and FFM to FM ratio (FFM/FM). Multiple ordinal logistic regression (MOLR) was used to analyze the independent correlation between body composition indicators and liver steatosis grade and fibrosis stage in different sex and menopausal status populations. RESULTS Men had higher WC, ASM, ASMI, FFM, and FFM/FM than pre- or post-menopausal women, while pre-menopausal women had higher PBF, VFA, and FM than the other two groups (p < 0.001). Besides, men had greater CAP and LSM values (p < 0.001). For MOLR, after adjusting for confounding factors, WC (OR, 1.07; 95% CI, 1.02-1.12; P = 0.011) and FFM/FM (OR, 0.52; 95% CI, 0.31-0.89; P = 0.017) in men and visceral obesity (OR, 4.16; 95% CI, 1.09-15.90; P = 0.037) in post-menopausal women were independently associated with liver steatosis grade. WC and visceral obesity were independently associated with liver fibrosis stage in men (OR, 1.05; 95% CI, 1.01-1.09, P = 0.013; OR, 3.92; 95% CI, 1.97-7.81; P < 0.001, respectively). CONCLUSIONS Increased WC and low FFM/FM in men and visceral obesity in post-menopausal women were independent correlates of more severe liver steatosis. In addition, increased WC and visceral obesity were independent correlates of worse liver fibrosis in men. These data support the sex- and reproductive status-specific management of NAFLD.
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Affiliation(s)
- Yu-Tian Cao
- Department of Endocrinology, Affiliated Hospital of Nanjing, Jiangsu Province Hospital of Chinese Medicine, University of Chinese Medicine, Nanjing, China
- The First School of Clinical Medicine, Nanjing University of Chinese Medicine, Nanjing, China
| | - Wen-Hui Zhang
- Department of Endocrinology, Affiliated Hospital of Nanjing, Jiangsu Province Hospital of Chinese Medicine, University of Chinese Medicine, Nanjing, China
- The First School of Clinical Medicine, Nanjing University of Chinese Medicine, Nanjing, China
| | - Yan Lou
- Department of Endocrinology, Affiliated Hospital of Nanjing, Jiangsu Province Hospital of Chinese Medicine, University of Chinese Medicine, Nanjing, China
| | - Qian-Hua Yan
- Department of Endocrinology, Affiliated Hospital of Nanjing, Jiangsu Province Hospital of Chinese Medicine, University of Chinese Medicine, Nanjing, China
| | - Yu-Juan Zhang
- Department of Endocrinology, Affiliated Hospital of Nanjing, Jiangsu Province Hospital of Chinese Medicine, University of Chinese Medicine, Nanjing, China
- The First School of Clinical Medicine, Nanjing University of Chinese Medicine, Nanjing, China
| | - Fang Qi
- Department of Endocrinology, Affiliated Hospital of Nanjing, Jiangsu Province Hospital of Chinese Medicine, University of Chinese Medicine, Nanjing, China
- The First School of Clinical Medicine, Nanjing University of Chinese Medicine, Nanjing, China
| | - Liu-Lan Xiang
- Department of Endocrinology, Affiliated Hospital of Nanjing, Jiangsu Province Hospital of Chinese Medicine, University of Chinese Medicine, Nanjing, China
- The First School of Clinical Medicine, Nanjing University of Chinese Medicine, Nanjing, China
| | - Tian-Su Lv
- The First School of Clinical Medicine, Nanjing University of Chinese Medicine, Nanjing, China
| | - Zhu-Yuan Fang
- Institute of Hypertension, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Jiang-Yi Yu
- Department of Endocrinology, Affiliated Hospital of Nanjing, Jiangsu Province Hospital of Chinese Medicine, University of Chinese Medicine, Nanjing, China
| | - Xi-Qiao Zhou
- Department of Endocrinology, Affiliated Hospital of Nanjing, Jiangsu Province Hospital of Chinese Medicine, University of Chinese Medicine, Nanjing, China.
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11
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Abstract
Obesity is a common complex trait that elevates the risk for various diseases, including type 2 diabetes and cardiovascular disease. A combination of environmental and genetic factors influences the pathogenesis of obesity. Advances in genomic technologies have driven the identification of multiple genetic loci associated with this disease, ranging from studying severe onset cases to investigating common multifactorial polygenic forms. Additionally, findings from epigenetic analyses of modifications to the genome that do not involve changes to the underlying DNA sequence have emerged as key signatures in the development of obesity. Such modifications can mediate the effects of environmental factors, including diet and lifestyle, on gene expression and clinical presentation. This review outlines what is known about the genetic and epigenetic contributors to obesity susceptibility, along with the albeit limited therapeutic options currently available. Furthermore, we delineate the potential mechanisms of actions through which epigenetic changes can mediate environmental influences and the related opportunities they present for future interventions in the management of obesity.
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Affiliation(s)
- Khanh Trang
- Center for Spatial and Functional Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104 USA
- Division of Human Genetics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104 USA
| | - Struan F.A. Grant
- Center for Spatial and Functional Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104 USA
- Division of Human Genetics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104 USA
- Division of Diabetes and Endocrinology, Children’s Hospital of Philadelphia, Philadelphia, PA 19104 USA
- Department of Pediatrics, The University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104 USA
- Department of Genetics, University of Pennsylvania, Philadelphia, PA 19104 USA
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12
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Kavousi M, Bos MM, Barnes HJ, Lino Cardenas CL, Wong D, Lu H, Hodonsky CJ, Landsmeer LPL, Turner AW, Kho M, Hasbani NR, de Vries PS, Bowden DW, Chopade S, Deelen J, Benavente ED, Guo X, Hofer E, Hwang SJ, Lutz SM, Lyytikäinen LP, Slenders L, Smith AV, Stanislawski MA, van Setten J, Wong Q, Yanek LR, Becker DM, Beekman M, Budoff MJ, Feitosa MF, Finan C, Hilliard AT, Kardia SLR, Kovacic JC, Kral BG, Langefeld CD, Launer LJ, Malik S, Hoesein FAAM, Mokry M, Schmidt R, Smith JA, Taylor KD, Terry JG, van der Grond J, van Meurs J, Vliegenthart R, Xu J, Young KA, Zilhão NR, Zweiker R, Assimes TL, Becker LC, Bos D, Carr JJ, Cupples LA, de Kleijn DPV, de Winther M, den Ruijter HM, Fornage M, Freedman BI, Gudnason V, Hingorani AD, Hokanson JE, Ikram MA, Išgum I, Jacobs DR, Kähönen M, Lange LA, Lehtimäki T, Pasterkamp G, Raitakari OT, Schmidt H, Slagboom PE, Uitterlinden AG, Vernooij MW, Bis JC, Franceschini N, Psaty BM, Post WS, Rotter JI, Björkegren JLM, O'Donnell CJ, Bielak LF, Peyser PA, Malhotra R, van der Laan SW, Miller CL. Multi-ancestry genome-wide study identifies effector genes and druggable pathways for coronary artery calcification. Nat Genet 2023; 55:1651-1664. [PMID: 37770635 PMCID: PMC10601987 DOI: 10.1038/s41588-023-01518-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Accepted: 08/29/2023] [Indexed: 09/30/2023]
Abstract
Coronary artery calcification (CAC), a measure of subclinical atherosclerosis, predicts future symptomatic coronary artery disease (CAD). Identifying genetic risk factors for CAC may point to new therapeutic avenues for prevention. Currently, there are only four known risk loci for CAC identified from genome-wide association studies (GWAS) in the general population. Here we conducted the largest multi-ancestry GWAS meta-analysis of CAC to date, which comprised 26,909 individuals of European ancestry and 8,867 individuals of African ancestry. We identified 11 independent risk loci, of which eight were new for CAC and five had not been reported for CAD. These new CAC loci are related to bone mineralization, phosphate catabolism and hormone metabolic pathways. Several new loci harbor candidate causal genes supported by multiple lines of functional evidence and are regulators of smooth muscle cell-mediated calcification ex vivo and in vitro. Together, these findings help refine the genetic architecture of CAC and extend our understanding of the biological and potential druggable pathways underlying CAC.
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Affiliation(s)
- Maryam Kavousi
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands.
| | - Maxime M Bos
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Hanna J Barnes
- Cardiovascular Research Center, Cardiology Division, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Christian L Lino Cardenas
- Cardiovascular Research Center, Cardiology Division, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Doris Wong
- Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA, USA
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Haojie Lu
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- Department of Internal Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Chani J Hodonsky
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Lennart P L Landsmeer
- Central Diagnostics Laboratory, Division Laboratories, Pharmacy, and Biomedical Genetics, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Adam W Turner
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Minjung Kho
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
- Graduate School of Data Science, Seoul National University, Seoul, Republic of Korea
| | - Natalie R Hasbani
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Center at Houston, Houston, TX, USA
| | - Paul S de Vries
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Center at Houston, Houston, TX, USA
| | - Donald W Bowden
- Department of Biochemistry, Wake Forest University Health Sciences, Winston-Salem, NC, USA
| | - Sandesh Chopade
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, UK
- University College London British Heart Foundation Research Accelerator Centre, London, UK
| | - Joris Deelen
- Biomedical Data Sciences, Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
- Max Planck Institute for Biology of Aging, Cologne, Germany
| | - Ernest Diez Benavente
- Laboratory of Experimental Cardiology, Division of Heart and Lungs, University Medical Center Utrecht and Utrecht University, Utrecht, The Netherlands
| | - Xiuqing Guo
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation (formerly Los Angeles Biomedical Research Institute) at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Edith Hofer
- Department of Neurology, Clinical Division of Neurogeriatrics, Medical University of Graz, Graz, Austria
- Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Graz, Austria
| | | | - Sharon M Lutz
- Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care, Boston, MA, USA
| | - Leo-Pekka Lyytikäinen
- Department of Clinical Chemistry, Fimlab Laboratories and Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Lotte Slenders
- Central Diagnostics Laboratory, Division Laboratories, Pharmacy, and Biomedical Genetics, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Albert V Smith
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
- Icelandic Heart Association, Kopavogur, Iceland
| | - Maggie A Stanislawski
- Department of Biomedical Informatics, University of Colorado, Anschutz Medical Campus, Aurora, CO, USA
| | - Jessica van Setten
- Department of Cardiology, Division of Heart and Lungs, University Medical Center Utrecht and Utrecht University, Utrecht, The Netherlands
| | - Quenna Wong
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Lisa R Yanek
- GeneSTAR Research Program, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Diane M Becker
- GeneSTAR Research Program, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Marian Beekman
- Biomedical Data Sciences, Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Matthew J Budoff
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation (formerly Los Angeles Biomedical Research Institute) at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Mary F Feitosa
- Department of Genetics, Division of Statistical Genomics, Washington University School of Medicine, St. Louis, MO, USA
| | - Chris Finan
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, UK
- University College London British Heart Foundation Research Accelerator Centre, London, UK
- Department of Cardiology, Division of Heart and Lungs, University Medical Center Utrecht and Utrecht University, Utrecht, The Netherlands
| | | | - Sharon L R Kardia
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Jason C Kovacic
- Victor Chang Cardiac Research Institute, Darlinghurst, New South Wales, Australia
- St Vincent's Clinical School, University of NSW, Sydney, New South Wales, Australia
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
| | - Brian G Kral
- GeneSTAR Research Program, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Carl D Langefeld
- Department of Biostatistical Sciences and Data Science, Wake Forest University Health Sciences, Winston-Salem, NC, USA
| | - Lenore J Launer
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Shaista Malik
- Susan Samueli Integrative Health Institute, Department of Medicine, University of California Irvine, Irvine, CA, USA
| | | | - Michal Mokry
- Central Diagnostics Laboratory, Division Laboratories, Pharmacy, and Biomedical Genetics, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Laboratory of Experimental Cardiology, Division of Heart and Lungs, University Medical Center Utrecht and Utrecht University, Utrecht, The Netherlands
| | - Reinhold Schmidt
- Department of Neurology, Clinical Division of Neurogeriatrics, Medical University of Graz, Graz, Austria
| | - Jennifer A Smith
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - Kent D Taylor
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation (formerly Los Angeles Biomedical Research Institute) at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - James G Terry
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jeroen van der Grond
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Joyce van Meurs
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- Department of Internal Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Rozemarijn Vliegenthart
- Department of Radiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Jianzhao Xu
- Department of Biochemistry, Wake Forest University Health Sciences, Winston-Salem, NC, USA
| | - Kendra A Young
- Department of Epidemiology, University of Colorado, Anschutz Medical Campus, Denver, CO, USA
| | | | - Robert Zweiker
- Department of Internal Medicine, Division of Cardiology, Medical University of Graz, Graz, Austria
| | - Themistocles L Assimes
- VA Palo Alto Healthcare System, Palo Alto, CA, USA
- Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Lewis C Becker
- GeneSTAR Research Program, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Daniel Bos
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- Department of Radiology and Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - J Jeffrey Carr
- Department of Internal Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - L Adrienne Cupples
- Department of Biostatistics, School of Public Health, Boston University, Boston, MA, USA
| | - Dominique P V de Kleijn
- Department of Vascular Surgery, University Medical Center Utrecht and Utrecht University, Utrecht, The Netherlands
| | - Menno de Winther
- Department of Medical Biochemistry, Experimental Vascular Biology, Amsterdam Cardiovascular Sciences: Atherosclerosis and Ischemic syndromes, Amsterdam Infection and Immunity: Inflammatory diseases, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, The Netherlands
| | - Hester M den Ruijter
- Laboratory of Experimental Cardiology, Division of Heart and Lungs, University Medical Center Utrecht and Utrecht University, Utrecht, The Netherlands
| | - Myriam Fornage
- Institute of Molecular Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Barry I Freedman
- Department of Internal Medicine, Wake Forest University Health Sciences, Winston-Salem, NC, USA
| | - Vilmundur Gudnason
- Icelandic Heart Association, Kopavogur, Iceland
- Faculty of Medicine, School of Public Health, University of Iceland, Reykjavik, Iceland
| | - Aroon D Hingorani
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, UK
- University College London British Heart Foundation Research Accelerator Centre, London, UK
| | - John E Hokanson
- Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - M Arfan Ikram
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Ivana Išgum
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands
- Department of Biomedical Engineering and Physics, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, The Netherlands
| | - David R Jacobs
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - Mika Kähönen
- Department of Clinical Physiology, Tampere University Hospital and Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Leslie A Lange
- Department of Biomedical Informatics, University of Colorado, Anschutz Medical Campus, Aurora, CO, USA
| | - Terho Lehtimäki
- Department of Clinical Chemistry, Fimlab Laboratories and Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Gerard Pasterkamp
- Central Diagnostics Laboratory, Division Laboratories, Pharmacy, and Biomedical Genetics, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Olli T Raitakari
- Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland
- Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland
| | - Helena Schmidt
- Gottfried Schatz Research Center (for Cell Signaling, Metabolism and Aging), Medical University of Graz, Graz, Austria
| | - P Eline Slagboom
- Biomedical Data Sciences, Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
| | - André G Uitterlinden
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- Department of Internal Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Meike W Vernooij
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- Department of Vascular Surgery, University Medical Center Utrecht and Utrecht University, Utrecht, The Netherlands
| | - Joshua C Bis
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Nora Franceschini
- Department of Epidemiology, University of North Carolina, Chapel Hill, NC, USA
| | - Bruce M Psaty
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
- Departments of Epidemiology, and Health Systems and Population Health, University of Washington, Seattle, WA, USA
| | - Wendy S Post
- Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Jerome I Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation (formerly Los Angeles Biomedical Research Institute) at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Johan L M Björkegren
- Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
- Department of Medicine, Integrated Cardio Metabolic Centre, Karolinska Institutet, Huddinge, Sweden
| | - Christopher J O'Donnell
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Cardiology Section, Department of Medicine, Veterans Affairs Boston Healthcare System, Boston, MA, USA
| | - Lawrence F Bielak
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Patricia A Peyser
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Rajeev Malhotra
- Cardiovascular Research Center, Cardiology Division, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Sander W van der Laan
- Central Diagnostics Laboratory, Division Laboratories, Pharmacy, and Biomedical Genetics, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Clint L Miller
- Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA, USA.
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA.
- Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA.
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13
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Peters SAE, Woodward M. A roadmap for sex- and gender-disaggregated health research. BMC Med 2023; 21:354. [PMID: 37704983 PMCID: PMC10500779 DOI: 10.1186/s12916-023-03060-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 08/30/2023] [Indexed: 09/15/2023] Open
Abstract
Sex and gender are fundamental aspects of health and wellbeing. Yet many research studies fail to consider sex or gender differences, and even when they do this is often limited to merely cataloguing such differences in the makeup of study populations. The evidence on sex and gender differences is thus incomplete in most areas of medicine. This article presents a roadmap for the systematic conduct of sex- and gender-disaggregated health research. We distinguish three phases: the exploration of sex and gender differences in disease risk, presentation, diagnosis, treatment, and outcomes; explaining any found differences by revealing the underlying mechanisms; and translation of the implications of such differences to policy and practice. For each phase, we provide critical methodological considerations and practical examples are provided, taken primarily from the field of cardiovascular disease. We also discuss key overarching themes and terminology that are at the essence of any study evaluating the relevance of sex and gender in health. Here, we limit ourselves to binary sex and gender in order to produce a coherent, succinct narrative. Further disaggregation by sex and gender separately and which recognises intersex, non-binary, and gender-diverse identities, as well as other aspects of intersectionality, can build on this basic minimum level of disaggregation. We envision that uptake of this roadmap, together with wider policy and educational activities, will aid researchers to systematically explore and explain relevant sex and gender differences in health and will aid educators, clinicians, and policymakers to translate the outcomes of research in the most effective and meaningful way, for the benefit of all.
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Affiliation(s)
- Sanne A E Peters
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, The Netherlands.
- School of Public Health, The George Institute for Global Health, Imperial College London, London, UK.
- The George Institute for Global Health, University of New South Wales, Sydney, Australia.
| | - Mark Woodward
- School of Public Health, The George Institute for Global Health, Imperial College London, London, UK
- The George Institute for Global Health, University of New South Wales, Sydney, Australia
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14
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Zhou M, Henricks M, Loch V, Zhang G, Lu Y, Li X. Mendelian randomization analysis revealed potential metabolic causal factors for breast cancer. Sci Rep 2023; 13:14290. [PMID: 37652957 PMCID: PMC10471756 DOI: 10.1038/s41598-023-41130-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Accepted: 08/22/2023] [Indexed: 09/02/2023] Open
Abstract
Observational studies showed that metabolic phenotypes were associated with the risk of developing breast cancer (BC). However, those results are inconsistent regarding the magnitude of the association, particularly by subtypes of breast cancer. Furthermore, the mechanisms of the association remain unclear. We performed two-sample Mendelian randomization (MR) analyses to evaluate the causal effect of metabolic risk factors on breast cancer in the European population. Assessed individually using MR, body mass index (BMI) (odds ratio [OR] 0.94, 95% Confidence interval [CI] 0.90-0.98, P = 0.007), high-density lipoprotein cholesterol (HDL-C) (OR 1.10, 95% CI 1.07-1.13, P = 6.10 × 10-11) and triglycerides (TG) (OR 0.92, 95% CI 0.90-0.96, P = 1.58 × 10-6) were causally related to breast cancer risk. In multivariable MR, only HDL-C (OR 1.08; 95% CI 1.02-1.14; P = 0.02) retained a robust effect, suggesting that the genetic association between BMI, HDL-C and TG with breast cancer risk in univariable analysis was explained via HDL-C. These findings suggest a possible causal role of HDL-C in breast cancer etiology.
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Affiliation(s)
- Mengshi Zhou
- Department of Mathematics and Statistics, St. Cloud State University, 720 4th Ave S, St. Cloud, MN, 56301, USA
| | - Mason Henricks
- Department of Mathematics and Statistics, St. Cloud State University, 720 4th Ave S, St. Cloud, MN, 56301, USA
| | - Valerie Loch
- Department of Mathematics and Statistics, St. Cloud State University, 720 4th Ave S, St. Cloud, MN, 56301, USA
| | - Gloria Zhang
- Department of Pathology, Robert J. Tomsich Pathology and Laboratory Medicine Institute, Cleveland Clinic, Cleveland, OH, 44195, USA
| | - Yong Lu
- Houston Methodist Cancer Center/Weill Cornell Medicine, Houston, TX, 77030, USA
| | - Xiaoyin Li
- Department of Mathematics and Statistics, St. Cloud State University, 720 4th Ave S, St. Cloud, MN, 56301, USA.
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15
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Kaisinger LR, Kentistou KA, Stankovic S, Gardner EJ, Day FR, Zhao Y, Mörseburg A, Carnie CJ, Zagnoli-Vieira G, Puddu F, Jackson SP, O’Rahilly S, Farooqi IS, Dearden L, Pantaleão LC, Ozanne SE, Ong KK, Perry JR. Large-scale exome sequence analysis identifies sex- and age-specific determinants of obesity. CELL GENOMICS 2023; 3:100362. [PMID: 37601970 PMCID: PMC10435378 DOI: 10.1016/j.xgen.2023.100362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 03/15/2023] [Accepted: 07/03/2023] [Indexed: 08/22/2023]
Abstract
Obesity contributes substantially to the global burden of disease and has a significant heritable component. Recent large-scale exome sequencing studies identified several genes in which rare, protein-coding variants have large effects on adult body mass index (BMI). Here we extended such work by performing sex-stratified associations in the UK Biobank study (N∼420,000). We identified genes in which rare heterozygous loss-of-function increases adult BMI in women (DIDO1, PTPRG, and SLC12A5) and in men (SLTM), with effect sizes up to ∼8 kg/m2. This is complemented by analyses implicating rare variants in OBSCN and MADD for recalled childhood adiposity. The known functions of these genes, as well as findings of common variant genome-wide pathway enrichment analyses, suggest a role for neuron death, apoptosis, and DNA damage response mechanisms in the susceptibility to obesity across the life-course. These findings highlight the importance of considering sex-specific and life-course effects in the genetic regulation of obesity.
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Affiliation(s)
- Lena R. Kaisinger
- MRC Epidemiology Unit, Wellcome-MRC Institute of Metabolic Science, University of Cambridge, Cambridge CB2 0QQ, UK
| | - Katherine A. Kentistou
- MRC Epidemiology Unit, Wellcome-MRC Institute of Metabolic Science, University of Cambridge, Cambridge CB2 0QQ, UK
| | - Stasa Stankovic
- MRC Epidemiology Unit, Wellcome-MRC Institute of Metabolic Science, University of Cambridge, Cambridge CB2 0QQ, UK
| | - Eugene J. Gardner
- MRC Epidemiology Unit, Wellcome-MRC Institute of Metabolic Science, University of Cambridge, Cambridge CB2 0QQ, UK
| | - Felix R. Day
- MRC Epidemiology Unit, Wellcome-MRC Institute of Metabolic Science, University of Cambridge, Cambridge CB2 0QQ, UK
| | - Yajie Zhao
- MRC Epidemiology Unit, Wellcome-MRC Institute of Metabolic Science, University of Cambridge, Cambridge CB2 0QQ, UK
| | - Alexander Mörseburg
- MRC Epidemiology Unit, Wellcome-MRC Institute of Metabolic Science, University of Cambridge, Cambridge CB2 0QQ, UK
- MRC Metabolic Diseases Unit, Wellcome-MRC Institute of Metabolic Science, University of Cambridge, Cambridge CB2 0QQ, UK
| | - Christopher J. Carnie
- Wellcome Trust/Cancer Research UK Gurdon Institute, Department of Biochemistry, University of Cambridge, Tennis Court Road, Cambridge CB2 1QR, UK
- Cancer Research UK Cambridge Institute, Li Ka Shing Building, University of Cambridge, Robinson Way, Cambridge CB2 0RE, UK
| | - Guido Zagnoli-Vieira
- Wellcome Trust/Cancer Research UK Gurdon Institute, Department of Biochemistry, University of Cambridge, Tennis Court Road, Cambridge CB2 1QR, UK
| | - Fabio Puddu
- Wellcome Trust/Cancer Research UK Gurdon Institute, Department of Biochemistry, University of Cambridge, Tennis Court Road, Cambridge CB2 1QR, UK
| | - Stephen P. Jackson
- Wellcome Trust/Cancer Research UK Gurdon Institute, Department of Biochemistry, University of Cambridge, Tennis Court Road, Cambridge CB2 1QR, UK
- Cancer Research UK Cambridge Institute, Li Ka Shing Building, University of Cambridge, Robinson Way, Cambridge CB2 0RE, UK
| | - Stephen O’Rahilly
- MRC Metabolic Diseases Unit, Wellcome-MRC Institute of Metabolic Science, University of Cambridge, Cambridge CB2 0QQ, UK
| | - I. Sadaf Farooqi
- MRC Metabolic Diseases Unit, Wellcome-MRC Institute of Metabolic Science, University of Cambridge, Cambridge CB2 0QQ, UK
| | - Laura Dearden
- MRC Metabolic Diseases Unit, Wellcome-MRC Institute of Metabolic Science, University of Cambridge, Cambridge CB2 0QQ, UK
| | - Lucas C. Pantaleão
- MRC Metabolic Diseases Unit, Wellcome-MRC Institute of Metabolic Science, University of Cambridge, Cambridge CB2 0QQ, UK
| | - Susan E. Ozanne
- MRC Metabolic Diseases Unit, Wellcome-MRC Institute of Metabolic Science, University of Cambridge, Cambridge CB2 0QQ, UK
| | - Ken K. Ong
- MRC Epidemiology Unit, Wellcome-MRC Institute of Metabolic Science, University of Cambridge, Cambridge CB2 0QQ, UK
| | - John R.B. Perry
- MRC Epidemiology Unit, Wellcome-MRC Institute of Metabolic Science, University of Cambridge, Cambridge CB2 0QQ, UK
- MRC Metabolic Diseases Unit, Wellcome-MRC Institute of Metabolic Science, University of Cambridge, Cambridge CB2 0QQ, UK
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16
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de Ruiter SC, Schmidt AF, Grobbee DE, den Ruijter HM, Peters SAE. Sex-specific Mendelian randomisation to assess the causality of sex differences in the effects of risk factors and treatment: spotlight on hypertension. J Hum Hypertens 2023; 37:602-608. [PMID: 37024639 PMCID: PMC10403357 DOI: 10.1038/s41371-023-00821-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Revised: 02/24/2023] [Accepted: 03/16/2023] [Indexed: 04/08/2023]
Abstract
Hypertension is a key modifiable risk factor for cardiovascular disease. Several observational studies have found a stronger association of blood pressure and cardiovascular disease risk in women compared to men. Since observational studies can be affected by sex-specific residual confounding and reverse causation, it remains unclear whether these differences reflect actual differential effects. Other study designs are needed to uncover the causality of sex differences in the strength of risk factor and treatment effects. Mendelian randomisation (MR) uses genetic variants as instrumental variables to provide evidence about putative causal relations between risk factors and outcomes. By exploiting the random allocation of genes at gamete forming, MR is unaffected by confounding and results in more reliable causal effect estimates. In this review, we discuss why and how sex-specific MR and cis-MR could be used to study sex differences in risk factor and drug target effects. Sex-specific MR can be helpful to strengthen causal inferences in the field of sex differences, where it is often challenging to distinguish nature from nurture. The challenge of sex-specific (drug target) MR lays in leveraging robust genetic instruments from sex-specific GWAS studies which are not commonly available. Knowledge on sex-specific causal effects of hypertension, or other risk factors, could improve clinical practice and health policies by tailoring interventions based on personalised risk. Drug target MR can help to determine the anticipated on-target effects of a drug compound and to identify targets to pursue in drug development.
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Affiliation(s)
- Sophie C de Ruiter
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - A Floriaan Schmidt
- Department of Cardiology, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, the Netherlands
- Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, UK
- UCL British Heart Foundation Research Accelerator Centre, London, UK
| | - Diederick E Grobbee
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Hester M den Ruijter
- Laboratory of Experimental Cardiology, Division Heart and Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Sanne A E Peters
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands.
- The George Institute for Global Health, School of Public Health, Imperial College London, London, UK.
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17
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Wu JX, Deng FY, Lei SF. The Casual Association Inference for the Chain of Falls Risk Factors-Falls-Falls Outcomes: A Mendelian Randomization Study. Healthcare (Basel) 2023; 11:1889. [PMID: 37444723 DOI: 10.3390/healthcare11131889] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 06/20/2023] [Accepted: 06/28/2023] [Indexed: 07/15/2023] Open
Abstract
Previous associations have been observed not only between risk factors and falls but also between falls and their clinical outcomes based on some cross-sectional designs, but their causal associations were still largely unclear. We performed Mendelian randomization (MR), multivariate Mendelian randomization (MVMR), and mediation analyses to explore the effects of falls. Our study data are mainly based on White European individuals (40-69 years) downloaded from the UK Biobank. MR analyses showed that osteoporosis (p = 0.006), BMI (p = 0.003), sleeplessness (p < 0.001), rheumatoid arthritis (p = 0.001), waist circumference (p < 0.001), and hip circumference (p < 0.001) have causal effects on falls. In addition, for every one standard deviation increase in fall risk, the risk of fracture increased by 1.148 (p < 0.001), the risk of stroke increased by 2.908 (p = 0.003), and a 1.016-fold risk increase in epilepsy (p = 0.009). The MVMR found that sleeplessness is an important risk factor for falls. Finally, our mediation analyses estimated the mediation effects of falls on the hip circumference and fracture (p < 0.001), waist circumference and epilepsy (p < 0.001), and sleeplessness and fracture (p = 0.005). Our study inferred the causal effects between risk factors and falls, falls, and outcomes, and also constructed three causal chains from risk factors → falls → falls outcomes.
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Affiliation(s)
- Jia-Xin Wu
- Center for Genetic Epidemiology and Genomics, School of Public Health, Medical College of Soochow University, Suzhou 215123, China
- Collaborative Innovation Center of Bone and Immunology between Sihong Hospital and Soochow University, Suzhou 215123, China
- Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Soochow University, Suzhou 215123, China
| | - Fei-Yan Deng
- Center for Genetic Epidemiology and Genomics, School of Public Health, Medical College of Soochow University, Suzhou 215123, China
- Collaborative Innovation Center of Bone and Immunology between Sihong Hospital and Soochow University, Suzhou 215123, China
- Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Soochow University, Suzhou 215123, China
| | - Shu-Feng Lei
- Center for Genetic Epidemiology and Genomics, School of Public Health, Medical College of Soochow University, Suzhou 215123, China
- Collaborative Innovation Center of Bone and Immunology between Sihong Hospital and Soochow University, Suzhou 215123, China
- Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Soochow University, Suzhou 215123, China
- Changzhou Geriatric Hospital, Soochow University, Changzhou 213000, China
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18
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Lu Z, Zhang H, Yang Y, Zhao H. Sex differences of the shared genetic landscapes between type 2 diabetes and peripheral artery disease in East Asians and Europeans. Hum Genet 2023:10.1007/s00439-023-02573-x. [PMID: 37341850 DOI: 10.1007/s00439-023-02573-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 05/12/2023] [Indexed: 06/22/2023]
Abstract
Type 2 diabetes (T2D) is a critical risk factor for peripheral artery disease (PAD). However, the sex differences in genetic basis, causality, and underlying mechanisms of the two diseases are still unclear. Using sex-stratified and ethnic-based GWAS summary, we explored the genetic correlation and causal relationship between T2D and PAD in both ethnicities and sexes by linkage disequilibrium score regression, LAVA and six Mendelian Randomization approaches. We observed stronger genetic correlations between T2D and PAD in females than males in East Asians and Europeans. East Asian females exhibit higher causal effects of T2D on PAD than males. The gene-level analysis found KCNJ11 and ANK1 genes associated with the cross-trait of T2D and PAD in both sexes. Our study provides genetic evidence for the sex difference of genetic correlations and causal relationships between PAD and T2D, indicating the importance of using sex-specific strategies for monitoring PAD in T2D patients.
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Affiliation(s)
- Zhiya Lu
- Department of Medical Research Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University; Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangzhou, China
| | - Haoyang Zhang
- School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China
| | - Yuanhao Yang
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Huiying Zhao
- Department of Medical Research Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University; Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangzhou, China.
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19
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Wang X, Kho PF, Ramachandran D, Bafligil C, Amant F, Goode EL, Scott RJ, Tomlinson I, Evans DG, Crosbie EJ, Dörk T, Spurdle AB, Glubb DM, O'Mara TA. Multi-trait genome-wide association study identifies a novel endometrial cancer risk locus that associates with testosterone levels. iScience 2023; 26:106590. [PMID: 37168552 PMCID: PMC10165198 DOI: 10.1016/j.isci.2023.106590] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 02/02/2023] [Accepted: 03/31/2023] [Indexed: 05/13/2023] Open
Abstract
To detect novel endometrial cancer risk variants, we leveraged information from endometrial cancer risk factors in a multi-trait GWAS analysis. We first assessed causal relationships between established and suspected endometrial cancer risk factors, and endometrial cancer using Mendelian randomization. Following multivariable analysis, five independent risk factors (waist circumference, testosterone levels, sex hormone binding globulin levels, age at menarche, and age at natural menopause) were included in a multi-trait Bayesian GWAS analysis. We identified three potentially novel loci that associate with endometrial cancer risk, one of which (7q22.1) replicated in an independent endometrial cancer GWAS dataset and was genome-wide significant in a meta-analysis. This locus may affect endometrial cancer risk through altered testosterone levels. Consistent with this, we observed colocalization between the signals for endometrial cancer risk and expression of CYP3A7, a gene involved in testosterone metabolism. Thus, our findings suggest opportunities for hormone therapy to prevent or treat endometrial cancer.
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Affiliation(s)
- Xuemin Wang
- Cancer Research Program, QIMR Berghofer Medical Research Institute, Brisbane, QLD 4006, Australia
| | - Pik Fang Kho
- Cancer Research Program, QIMR Berghofer Medical Research Institute, Brisbane, QLD 4006, Australia
| | | | - Cemsel Bafligil
- Division of Cancer Sciences, Faculty of Biology, Medicine and Health, University of Manchester, St Mary’s Hospital, Manchester M13 9WL, UK
- Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester M13 9WL, UK
| | - Frederic Amant
- Department of Obstetrics and Gynecology, Division of Gynecologic Oncology, University Hospitals KU Leuven, University of Leuven, 3000 Leuven, Belgium
| | - Ellen L. Goode
- Department of Quantitative Health Sciences, Division of Epidemiology, Mayo Clinic, Rochester, MN 55905, USA
| | - Rodney J. Scott
- Division of Molecular Medicine, Pathology North, John Hunter Hospital, Newcastle, NSW 2305, Australia
- Discipline of Medical Genetics, School of Biomedical Sciences and Pharmacy, Faculty of Health, University of Newcastle, Callaghan, NSW 2308, Australia
- Hunter Medical Research Institute, John Hunter Hospital, Newcastle, NSW 2305, Australia
| | - Ian Tomlinson
- Cancer Genetics and Evolution Laboratory, Cancer Research UK Edinburgh Centre, MRC Institute of Genetics & Molecular Medicine, The University of Edinburgh, Western General Hospital, Crewe Road South, Edinburgh EH4 2XR, UK
| | - D. Gareth Evans
- Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester M13 9WL, UK
- North West Genomics Laboratory Hub, Manchester Centre for Genomic Medicine, St Mary’s Hospital, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester M13 9WL, UK
| | - Emma J. Crosbie
- Division of Cancer Sciences, Faculty of Biology, Medicine and Health, University of Manchester, St Mary’s Hospital, Manchester M13 9WL, UK
- Department of Obstetrics and Gynaecology, St Mary’s Hospital, Manchester Academic Health Science Centre, Manchester University NHS Foundation Trust, Manchester M13 9WL, UK
| | - Thilo Dörk
- Gynaecology Research Unit, Hannover Medical School, 30625 Hannover, Germany
| | - Amanda B. Spurdle
- Population Health Research Program, QIMR Berghofer Medical Research Institute, Brisbane, QLD 4006, Australia
| | - Dylan M. Glubb
- Cancer Research Program, QIMR Berghofer Medical Research Institute, Brisbane, QLD 4006, Australia
- School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, Brisbane, QLD 4072, Australia
- School of Biomedical Sciences, Faculty of Health, Queensland University of Technology, Brisbane, QLD 4000, Australia
| | - Tracy A. O'Mara
- Cancer Research Program, QIMR Berghofer Medical Research Institute, Brisbane, QLD 4006, Australia
- School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, Brisbane, QLD 4072, Australia
- School of Biomedical Sciences, Faculty of Health, Queensland University of Technology, Brisbane, QLD 4000, Australia
- Corresponding author
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20
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Khramtsova EA, Wilson MA, Martin J, Winham SJ, He KY, Davis LK, Stranger BE. Quality control and analytic best practices for testing genetic models of sex differences in large populations. Cell 2023; 186:2044-2061. [PMID: 37172561 PMCID: PMC10266536 DOI: 10.1016/j.cell.2023.04.014] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 01/31/2023] [Accepted: 04/07/2023] [Indexed: 05/15/2023]
Abstract
Phenotypic sex-based differences exist for many complex traits. In other cases, phenotypes may be similar, but underlying biology may vary. Thus, sex-aware genetic analyses are becoming increasingly important for understanding the mechanisms driving these differences. To this end, we provide a guide outlining the current best practices for testing various models of sex-dependent genetic effects in complex traits and disease conditions, noting that this is an evolving field. Insights from sex-aware analyses will not only teach us about the biology of complex traits but also aid in achieving the goals of precision medicine and health equity for all.
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Affiliation(s)
- Ekaterina A Khramtsova
- Population Analytics and Insights, Data Science Analytics & Insights, Janssen R&D, Lower Gwynedd Township, PA, USA.
| | - Melissa A Wilson
- School of Life Sciences, Center for Evolution and Medicine, Biodesign Center for Mechanisms of Evolution, Arizona State University, Tempe, AZ 85282, USA
| | - Joanna Martin
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
| | - Stacey J Winham
- Department of Quantitative Health Sciences, Division of Computational Biology, Mayo Clinic, Rochester, MN, USA
| | - Karen Y He
- Population Analytics and Insights, Data Science Analytics & Insights, Janssen R&D, Lower Gwynedd Township, PA, USA
| | - Lea K Davis
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Barbara E Stranger
- Center for Genetic Medicine, Department of Pharmacology, Northwestern University, Chicago, IL, USA.
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21
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Price PD, Parkus SM, Wright AE. Recent progress in understanding the genomic architecture of sexual conflict. Curr Opin Genet Dev 2023; 80:102047. [PMID: 37163877 DOI: 10.1016/j.gde.2023.102047] [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: 02/28/2023] [Revised: 04/02/2023] [Accepted: 04/02/2023] [Indexed: 05/12/2023]
Abstract
Genomic conflict between the sexes over shared traits is widely assumed to be resolved through the evolution of sex-biased expression and the subsequent emergence of sexually dimorphic phenotypes. However, while there is support for a broad relationship between genome-wide patterns of expression level and sexual conflict, recent studies suggest that sex differences in the nature and strength of interactions between loci are instead key to conflict resolution. Furthermore, the advent of new technologies for measuring and perturbing expression means we now have much more power to detect genomic signatures of sexual conflict. Here, we review our current understanding of the genomic architecture of sexual conflict in the light of these new studies and highlight the potential for novel approaches to address outstanding knowledge gaps.
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Affiliation(s)
- Peter D Price
- Ecology and Evolutionary Biology, School of Biosciences, University of Sheffield, United Kingdom. https://twitter.com/@PeterDPrice
| | - Sylvie M Parkus
- Ecology and Evolutionary Biology, School of Biosciences, University of Sheffield, United Kingdom
| | - Alison E Wright
- Ecology and Evolutionary Biology, School of Biosciences, University of Sheffield, United Kingdom.
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22
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Glunk V, Laber S, Sinnott-Armstrong N, Sobreira DR, Strobel SM, Batista TM, Kubitz P, Moud BN, Ebert H, Huang Y, Brandl B, Garbo G, Honecker J, Stirling DR, Abdennur N, Calabuig-Navarro V, Skurk T, Ocvirk S, Stemmer K, Cimini BA, Carpenter AE, Dankel SN, Lindgren CM, Hauner H, Nobrega MA, Claussnitzer M. A non-coding variant linked to metabolic obesity with normal weight affects actin remodelling in subcutaneous adipocytes. Nat Metab 2023; 5:861-879. [PMID: 37253881 DOI: 10.1038/s42255-023-00807-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Accepted: 04/12/2023] [Indexed: 06/01/2023]
Abstract
Recent large-scale genomic association studies found evidence for a genetic link between increased risk of type 2 diabetes and decreased risk for adiposity-related traits, reminiscent of metabolically obese normal weight (MONW) association signatures. However, the target genes and cellular mechanisms driving such MONW associations remain to be identified. Here, we systematically identify the cellular programmes of one of the top-scoring MONW risk loci, the 2q24.3 risk locus, in subcutaneous adipocytes. We identify a causal genetic variant, rs6712203, an intronic single-nucleotide polymorphism in the COBLL1 gene, which changes the conserved transcription factor motif of POU domain, class 2, transcription factor 2, and leads to differential COBLL1 gene expression by altering the enhancer activity at the locus in subcutaneous adipocytes. We then establish the cellular programme under the genetic control of the 2q24.3 MONW risk locus and the effector gene COBLL1, which is characterized by impaired actin cytoskeleton remodelling in differentiating subcutaneous adipocytes and subsequent failure of these cells to accumulate lipids and develop into metabolically active and insulin-sensitive adipocytes. Finally, we show that perturbations of the effector gene Cobll1 in a mouse model result in organismal phenotypes matching the MONW association signature, including decreased subcutaneous body fat mass and body weight along with impaired glucose tolerance. Taken together, our results provide a mechanistic link between the genetic risk for insulin resistance and low adiposity, providing a potential therapeutic hypothesis and a framework for future identification of causal relationships between genome associations and cellular programmes in other disorders.
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Affiliation(s)
- Viktoria Glunk
- Institute of Nutritional Medicine, School of Medicine, Technical University of Munich, Munich, Germany
- ZIEL Institute for Food & Health, Else Kröner-Fresenius-Center for Nutritional Medicine, School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Samantha Laber
- Broad Institute of MIT and Harvard, Medical and Population Genetics Program & Type 2 Diabetes Systems Genomics Initiative, Cambridge, MA, USA
| | - Nasa Sinnott-Armstrong
- Broad Institute of MIT and Harvard, Medical and Population Genetics Program & Type 2 Diabetes Systems Genomics Initiative, Cambridge, MA, USA
- Department of Genetics, Stanford University, Stanford, CA, USA
- Herbold Computational Biology Program, Publich Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Debora R Sobreira
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
| | - Sophie M Strobel
- Institute of Nutritional Medicine, School of Medicine, Technical University of Munich, Munich, Germany
- ZIEL Institute for Food & Health, Else Kröner-Fresenius-Center for Nutritional Medicine, School of Life Sciences, Technical University of Munich, Freising, Germany
- Broad Institute of MIT and Harvard, Medical and Population Genetics Program & Type 2 Diabetes Systems Genomics Initiative, Cambridge, MA, USA
| | - Thiago M Batista
- Broad Institute of MIT and Harvard, Medical and Population Genetics Program & Type 2 Diabetes Systems Genomics Initiative, Cambridge, MA, USA
- Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Phil Kubitz
- Institute of Nutritional Medicine, School of Medicine, Technical University of Munich, Munich, Germany
- ZIEL Institute for Food & Health, Else Kröner-Fresenius-Center for Nutritional Medicine, School of Life Sciences, Technical University of Munich, Freising, Germany
- Broad Institute of MIT and Harvard, Medical and Population Genetics Program & Type 2 Diabetes Systems Genomics Initiative, Cambridge, MA, USA
- Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Bahareh Nemati Moud
- Institute of Nutritional Medicine, School of Medicine, Technical University of Munich, Munich, Germany
- ZIEL Institute for Food & Health, Else Kröner-Fresenius-Center for Nutritional Medicine, School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Hannah Ebert
- Institute of Nutritional Sciences, University of Hohenheim, Stuttgart, Germany
| | - Yi Huang
- Broad Institute of MIT and Harvard, Medical and Population Genetics Program & Type 2 Diabetes Systems Genomics Initiative, Cambridge, MA, USA
- Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Beate Brandl
- Institute of Nutritional Medicine, School of Medicine, Technical University of Munich, Munich, Germany
- ZIEL Institute for Food & Health, Else Kröner-Fresenius-Center for Nutritional Medicine, School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Garrett Garbo
- Broad Institute of MIT and Harvard, Medical and Population Genetics Program & Type 2 Diabetes Systems Genomics Initiative, Cambridge, MA, USA
| | - Julius Honecker
- Institute of Nutritional Medicine, School of Medicine, Technical University of Munich, Munich, Germany
- ZIEL Institute for Food & Health, Else Kröner-Fresenius-Center for Nutritional Medicine, School of Life Sciences, Technical University of Munich, Freising, Germany
| | - David R Stirling
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Nezar Abdennur
- Institute for Medical Engineering and Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Virtu Calabuig-Navarro
- Broad Institute of MIT and Harvard, Medical and Population Genetics Program & Type 2 Diabetes Systems Genomics Initiative, Cambridge, MA, USA
- Institute of Nutritional Sciences, University of Hohenheim, Stuttgart, Germany
| | - Thomas Skurk
- Institute of Nutritional Medicine, School of Medicine, Technical University of Munich, Munich, Germany
- ZIEL Institute for Food & Health, Else Kröner-Fresenius-Center for Nutritional Medicine, School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Soeren Ocvirk
- Division of Gastroenterology, Hepatology and Nutrition, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Intestinal Microbiology Research Group, Department of Molecular Toxicology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany
| | - Kerstin Stemmer
- Molecular Cell Biology, Institute for Theoretical Medicine, University of Augsburg, Augsburg, Germany
- Institute for Diabetes and Obesity, Helmholtz Zentrum München, Neuherberg, Germany
- German Center for Diabetes Research, Neuherberg, Germany
| | - Beth A Cimini
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Anne E Carpenter
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Simon N Dankel
- Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Cecilia M Lindgren
- Broad Institute of MIT and Harvard, Medical and Population Genetics Program & Type 2 Diabetes Systems Genomics Initiative, Cambridge, MA, USA
- Big Data Institute at the Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Hans Hauner
- Institute of Nutritional Medicine, School of Medicine, Technical University of Munich, Munich, Germany
- ZIEL Institute for Food & Health, Else Kröner-Fresenius-Center for Nutritional Medicine, School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Marcelo A Nobrega
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
| | - Melina Claussnitzer
- Broad Institute of MIT and Harvard, Medical and Population Genetics Program & Type 2 Diabetes Systems Genomics Initiative, Cambridge, MA, USA.
- Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, 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, MA, USA.
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23
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Fernández-Rhodes L, McArdle CE, Rao H, Wang Y, Martinez-Miller EE, Ward JB, Cai J, Sofer T, Isasi CR, North KE. A Gene-Acculturation Study of Obesity Among US Hispanic/Latinos: The Hispanic Community Health Study/Study of Latinos. Psychosom Med 2023; 85:358-365. [PMID: 36917487 PMCID: PMC10159946 DOI: 10.1097/psy.0000000000001193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/16/2023]
Abstract
OBJECTIVE In the United States, Hispanic/Latino adults face a high burden of obesity; yet, not all individuals are equally affected, partly due in part to this ethnic group's marked sociocultural diversity. We sought to analyze the modification of body mass index (BMI) genetic effects in Hispanic/Latino adults by their level of acculturation, a complex biosocial phenomenon that remains understudied. METHODS Among 11,747 Hispanic/Latinos adults in the Hispanic Community Health Study/Study of Latinos aged 18 to 76 years from four urban communities (2008-2011), we a) tested our hypothesis that the effect of a genetic risk score (GRS) for increased BMI may be exacerbated by higher levels of acculturation and b) examined if GRS acculturation interactions varied by gender or Hispanic/Latino background group. All genetic modeling controlled for relatedness, age, gender, principal components of ancestry, center, and complex study design within a generalized estimated equation framework. RESULTS We observed a GRS increase of 0.34 kg/m 2 per risk allele in weighted mean BMI. The estimated main effect of GRS on BMI varied both across acculturation level and across gender. The difference between high and low acculturation ranged from 0.03 to 0.23 kg/m 2 per risk allele, but varied across acculturation measure and gender. CONCLUSIONS These results suggest the presence of effect modification by acculturation, with stronger effects on BMI among highly acculturated individuals and female immigrants. Future studies of obesity in the Hispanic/Latino community should account for sociocultural environments and consider their intersection with gender to better target obesity interventions.
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Affiliation(s)
- Lindsay Fernández-Rhodes
- Department of Biobehavioral Health, Pennsylvania State University, University Park, PA
- Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Cristin E. McArdle
- Department of Biobehavioral Health, Pennsylvania State University, University Park, PA
| | - Hridya Rao
- Department of Biobehavioral Health, Pennsylvania State University, University Park, PA
| | - Yujie Wang
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Erline E. Martinez-Miller
- Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC
- Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, TX
| | - Julia B. Ward
- Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC
- Social & Scientific Systems, a DLH Holdings Company, Durham, NC
| | - Jianwen Cai
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Tamar Sofer
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Carmen R. Isasi
- Departments of Epidemiology & Population Health and Pediatrics, Albert Einstein College of Medicine, Bronx, NY
| | - Kari E North
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC
- Carolina Center for Genome Sciences, University of North Carolina at Chapel Hill, Chapel Hill, NC
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24
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Ivanova T, Churnosova M, Abramova M, Plotnikov D, Ponomarenko I, Reshetnikov E, Aristova I, Sorokina I, Churnosov M. Sex-Specific Features of the Correlation between GWAS-Noticeable Polymorphisms and Hypertension in Europeans of Russia. Int J Mol Sci 2023; 24:ijms24097799. [PMID: 37175507 PMCID: PMC10178435 DOI: 10.3390/ijms24097799] [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: 03/25/2023] [Revised: 04/13/2023] [Accepted: 04/20/2023] [Indexed: 05/15/2023] Open
Abstract
The aim of the study was directed at studying the sex-specific features of the correlation between genome-wide association studies (GWAS)-noticeable polymorphisms and hypertension (HTN). In two groups of European subjects of Russia (n = 1405 in total), such as men (n = 821 in total: n = 564 HTN, n = 257 control) and women (n = 584 in total: n = 375 HTN, n = 209 control), the distribution of ten specially selected polymorphisms (they have confirmed associations of GWAS level with blood pressure (BP) parameters and/or HTN in Europeans) has been considered. The list of studied loci was as follows: (PLCE1) rs932764 A > G, (AC026703.1) rs1173771 G > A, (CERS5) rs7302981 G > A, (HFE) rs1799945 C > G, (OBFC1) rs4387287 C > A, (BAG6) rs805303 G > A, (RGL3) rs167479 T > G, (ARHGAP42) rs633185 C > G, (TBX2) rs8068318 T > C, and (ATP2B1) rs2681472 A > G. The contribution of individual loci and their inter-locus interactions to the HTN susceptibility with bioinformatic interpretation of associative links was evaluated separately in men's and women's cohorts. The men-women differences in involvement in the disease of the BP/HTN-associated GWAS SNPs were detected. Among women, the HTN risk has been associated with HFE rs1799945 C > G (genotype GG was risky; ORGG = 11.15 ppermGG = 0.014) and inter-locus interactions of all 10 examined SNPs as part of 26 intergenic interactions models. In men, the polymorphism BAG6 rs805303 G > A (genotype AA was protective; ORAA = 0.30 ppermAA = 0.0008) and inter-SNPs interactions of eight loci in only seven models have been founded as HTN-correlated. HTN-linked loci and strongly linked SNPs were characterized by pronounced polyvector functionality in both men and women, but at the same time, signaling pathways of HTN-linked genes/SNPs in women and men were similar and were represented mainly by immune mechanisms. As a result, the present study has demonstrated a more pronounced contribution of BP/HTN-associated GWAS SNPs to the HTN susceptibility (due to weightier intergenic interactions) in European women than in men.
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Affiliation(s)
- Tatiana Ivanova
- Department of Medical Biological Disciplines, Belgorod State National Research University, 308015 Belgorod, Russia
| | - Maria Churnosova
- Department of Medical Biological Disciplines, Belgorod State National Research University, 308015 Belgorod, Russia
| | - Maria Abramova
- Department of Medical Biological Disciplines, Belgorod State National Research University, 308015 Belgorod, Russia
| | - Denis Plotnikov
- Genetic Epidemiology Lab, Kazan State Medical University, 420012 Kazan, Russia
| | - Irina Ponomarenko
- Department of Medical Biological Disciplines, Belgorod State National Research University, 308015 Belgorod, Russia
| | - Evgeny Reshetnikov
- Department of Medical Biological Disciplines, Belgorod State National Research University, 308015 Belgorod, Russia
| | - Inna Aristova
- Department of Medical Biological Disciplines, Belgorod State National Research University, 308015 Belgorod, Russia
| | - Inna Sorokina
- Department of Medical Biological Disciplines, Belgorod State National Research University, 308015 Belgorod, Russia
| | - Mikhail Churnosov
- Department of Medical Biological Disciplines, Belgorod State National Research University, 308015 Belgorod, Russia
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Link V, Schraiber JG, Fan C, Dinh B, Mancuso N, Chiang CW, Edge MD. Tree-based QTL mapping with expected local genetic relatedness matrices. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.07.536093. [PMID: 37066144 PMCID: PMC10104234 DOI: 10.1101/2023.04.07.536093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/18/2023]
Abstract
Understanding the genetic basis of complex phenotypes is a central pursuit of genetics. Genome-wide Association Studies (GWAS) are a powerful way to find genetic loci associated with phenotypes. GWAS are widely and successfully used, but they face challenges related to the fact that variants are tested for association with a phenotype independently, whereas in reality variants at different sites are correlated because of their shared evolutionary history. One way to model this shared history is through the ancestral recombination graph (ARG), which encodes a series of local coalescent trees. Recent computational and methodological breakthroughs have made it feasible to estimate approximate ARGs from large-scale samples. Here, we explore the potential of an ARG-based approach to quantitative-trait locus (QTL) mapping, echoing existing variance-components approaches. We propose a framework that relies on the conditional expectation of a local genetic relatedness matrix given the ARG (local eGRM). Simulations show that our method is especially beneficial for finding QTLs in the presence of allelic heterogeneity. By framing QTL mapping in terms of the estimated ARG, we can also facilitate the detection of QTLs in understudied populations. We use local eGRM to identify a large-effect BMI locus, the CREBRF gene, in a sample of Native Hawaiians in which it was not previously detectable by GWAS because of a lack of population-specific imputation resources. Our investigations can provide intuition about the benefits of using estimated ARGs in population- and statistical-genetic methods in general.
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Affiliation(s)
- Vivian Link
- Department of Quantitative and Computational Biology, University of Southern California
| | - Joshua G. Schraiber
- Department of Quantitative and Computational Biology, University of Southern California
| | - Caoqi Fan
- Department of Quantitative and Computational Biology, University of Southern California
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California
| | - Bryan Dinh
- Department of Quantitative and Computational Biology, University of Southern California
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California
| | - Nicholas Mancuso
- Department of Quantitative and Computational Biology, University of Southern California
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California
| | - Charleston W.K. Chiang
- Department of Quantitative and Computational Biology, University of Southern California
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California
| | - Michael D. Edge
- Department of Quantitative and Computational Biology, University of Southern California
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Sofer T, Kurniansyah N, Murray M, Ho YL, Abner E, Esko T, Huffman JE, Cho K, Wilson PWF, Gottlieb DJ. Genome-wide association study of obstructive sleep apnoea in the Million Veteran Program uncovers genetic heterogeneity by sex. EBioMedicine 2023; 90:104536. [PMID: 36989840 PMCID: PMC10065974 DOI: 10.1016/j.ebiom.2023.104536] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 03/07/2023] [Accepted: 03/08/2023] [Indexed: 03/30/2023] Open
Abstract
BACKGROUND Genome-wide association studies (GWAS) for obstructive sleep apnoea (OSA) are limited due to the underdiagnosis of OSA, leading to misclassification of OSA, which consequently reduces statistical power. We performed a GWAS of OSA in the Million Veteran Program (MVP) of the U.S. Department of Veterans Affairs (VA) healthcare system, where OSA prevalence is close to its true population prevalence. METHODS We performed GWAS of 568,576 MVP participants, stratified by biological sex and by harmonized race/ethnicity and genetic ancestry (HARE) groups of White, Black, Hispanic, and Asian individuals. We considered both BMI adjusted (BMI-adj) and unadjusted (BMI-unadj) models. We replicated associations in independent datasets, and analysed the heterogeneity of OSA genetic associations across HARE and sex groups. We finally performed a larger meta-analysis GWAS of MVP, FinnGen, and the MGB Biobank, totalling 916,696 individuals. FINDINGS MVP participants are 91% male. OSA prevalence is 21%. In MVP there were 18 and 6 genome-wide significant loci in BMI-unadj and BMI-adj analyses, respectively, corresponding to 21 association regions. Of these, 17 were not previously reported in association with OSA, and 13 replicated in FinnGen (False Discovery Rate p-value < 0.05). There were widespread significant differences in genetic effects between men and women, but less so across HARE groups. Meta-analysis of MVP, FinnGen, and MGB biobank revealed 17 additional, previously unreported, genome-wide significant regions. INTERPRETATION Sex differences in genetic associations with OSA are widespread, likely associated with multiple OSA risk factors. OSA shares genetic underpinnings with several sleep phenotypes, suggesting shared aetiology and causal pathways. FUNDING Described in acknowledgements.
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Affiliation(s)
- Tamar Sofer
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
| | - Nuzulul Kurniansyah
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Michael Murray
- Massachusetts Veterans Epidemiology Research and Information Center, VA Healthcare System, Boston, MA, USA
| | - Yuk-Lam Ho
- Massachusetts Veterans Epidemiology Research and Information Center, VA Healthcare System, Boston, MA, USA
| | - Erik Abner
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Tõnu Esko
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Jennifer E Huffman
- Massachusetts Veterans Epidemiology Research and Information Center, VA Healthcare System, Boston, MA, USA; VA Palo Alto Health Care System, Palo Alto, CA, USA; Palo Alto Veterans Institute for Research, Palo Alto, CA, USA
| | - Kelly Cho
- Massachusetts Veterans Epidemiology Research and Information Center, VA Healthcare System, Boston, MA, USA; Division of Aging, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | | | - Daniel J Gottlieb
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA; Massachusetts Veterans Epidemiology Research and Information Center, VA Healthcare System, Boston, MA, USA
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27
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Azarova I, Klyosova E, Polonikov A. Single Nucleotide Polymorphisms of the RAC1 Gene as Novel Susceptibility Markers for Neuropathy and Microvascular Complications in Type 2 Diabetes. Biomedicines 2023; 11:biomedicines11030981. [PMID: 36979960 PMCID: PMC10046239 DOI: 10.3390/biomedicines11030981] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 03/12/2023] [Accepted: 03/17/2023] [Indexed: 03/30/2023] Open
Abstract
Single nucleotide polymorphisms (SNP) in the RAC1 (Rac family small GTPase 1) gene have recently been linked to type 2 diabetes (T2D) and hyperglycemia due to their contribution to impaired redox homeostasis. The present study was designed to determine whether the common SNPs of the RAC1 gene are associated with diabetic complications such as neuropathy (DN), retinopathy (DR), nephropathy, angiopathy of the lower extremities (DA), and diabetic foot syndrome. A total of 1470 DNA samples from T2D patients were genotyped for six common SNPs by the MassArray Analyzer-4 system. The genotype rs7784465-T/C of RAC1 was associated with an increased risk of DR (p = 0.016) and DA (p = 0.03) in males, as well as with DR in females (p = 0.01). Furthermore, the SNP rs836478 showed an association with DR (p = 0.005) and DN (p = 0.025) in males, whereas the SNP rs10238136 was associated with DA in females (p = 0.002). In total, three RAC1 haplotypes showed significant associations (FDR < 0.05) with T2D complications in a sex-specific manner. The study's findings demonstrate, for the first time, that the RAC1 gene's polymorphisms represent novel and sex-specific markers of neuropathy and microvascular complications in type 2 diabetes, and that the gene could be a new target for the pharmacological inhibition of oxidative stress as a means of preventing diabetic complications.
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Affiliation(s)
- Iuliia Azarova
- Department of Biological Chemistry, Kursk State Medical University, 3 Karl Marx Street, Kursk 305041, Russia
- Laboratory of Biochemical Genetics and Metabolomics, Research Institute for Genetic and Molecular Epidemiology, Kursk State Medical University, 18 Yamskaya St., Kursk 305041, Russia
| | - Elena Klyosova
- Laboratory of Biochemical Genetics and Metabolomics, Research Institute for Genetic and Molecular Epidemiology, Kursk State Medical University, 18 Yamskaya St., Kursk 305041, Russia
- Department of Biology, Medical Genetics and Ecology, Kursk State Medical University, 3 Karl Marx Street, Kursk 305041, Russia
| | - Alexey Polonikov
- Department of Biology, Medical Genetics and Ecology, Kursk State Medical University, 3 Karl Marx Street, Kursk 305041, Russia
- Laboratory of Statistical Genetics and Bioinformatics, Research Institute for Genetic and Molecular Epidemiology, Kursk State Medical University, 18 Yamskaya St., Kursk 305041, Russia
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Kwak J, Shin D. Gene-Nutrient Interactions in Obesity: COBLL1 Genetic Variants Interact with Dietary Fat Intake to Modulate the Incidence of Obesity. Int J Mol Sci 2023; 24:ijms24043758. [PMID: 36835164 PMCID: PMC9959357 DOI: 10.3390/ijms24043758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 01/26/2023] [Accepted: 02/02/2023] [Indexed: 02/16/2023] Open
Abstract
The COBLL1 gene is associated with leptin, a hormone important for appetite and weight maintenance. Dietary fat is a significant factor in obesity. This study aimed to determine the association between COBLL1 gene, dietary fat, and incidence of obesity. Data from the Korean Genome and Epidemiology Study were used, and 3055 Korean adults aged ≥ 40 years were included. Obesity was defined as a body mass index ≥ 25 kg/m2. Patients with obesity at baseline were excluded. The effects of the COBLL1 rs6717858 genotypes and dietary fat on incidence of obesity were evaluated using multivariable Cox proportional hazard models. During an average follow-up period of 9.2 years, 627 obesity cases were documented. In men, the hazard ratio (HR) for obesity was higher in CT, CC carriers (minor allele carriers) in the highest tertile of dietary fat intake than for men with TT carriers in the lowest tertile of dietary fat intake (Model 1: HR: 1.66, 95% confidence interval [CI]: 1.07-2.58; Model 2: HR: 1.63, 95% CI: 1.04-2.56). In women, the HR for obesity was higher in TT carriers in the highest tertile of dietary fat intake than for women with TT carriers in the lowest tertile of dietary fat intake (Model 1: HR: 1.49, 95% CI: 1.08-2.06; Model 2: HR: 1.53, 95% CI: 1.10-2.13). COBLL1 genetic variants and dietary fat intake had different sex-dependent effects in obesity. These results imply that a low-fat diet may protect against the effects of COBLL1 genetic variants on future obesity risk.
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29
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Yang Y, Zheng Z, Chen Y, Wang X, Wang H, Si Z, Meng R, Wu J. A case control study on the relationship between occupational stress and genetic polymorphism and dyslipidemia in coal miners. Sci Rep 2023; 13:2321. [PMID: 36759651 PMCID: PMC9911731 DOI: 10.1038/s41598-023-29491-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Accepted: 02/06/2023] [Indexed: 02/11/2023] Open
Abstract
Dyslipidemia is one of the known risk factors for cardiovascular disease, and its prevalence is increasing worldwide. At present, the study of dyslipidemia has gradually shifted from simple environmental or genetic factors to environment-gene interactions. In order to further explore the etiology and mechanism of dyslipidemia, we used occupational stress(OS) and LYPLAL1, APOC3 and SOD2 gene as research variables to explore their association with dyslipidemia.Here we used a case-control study to include Han workers from a coal mining enterprise in China to determine the association between study variables and dyslipidemia. Monofactor analysis showed that smoking, drinking, physical activity level, DASH diet score, sleep quality, BMI, hypertension, hyperuricemia, shift work, OS were significantly different between the two groups (P < 0.05). In the APOC3 rs2854116 dominant model, patients with CT/CC genotype had a higher risk of dyslipidemia than those with TT genotype. In SOD2 rs4880 recessive model, patients with GG genotype had a lower risk of dyslipidemia than those with AA/AG genotype, and the difference was statistically significant. We found that rs12137855 and OS, rs2854116 and OS, rs4880 and OS had joint effects, but no interaction based on the multiplication and addition model was found (Pinteraction > 0.05). GMDR model showed that the rs12137855-rs2854116-rs4880-OS four-factor model had the highest cross-validation consistency and training-validation accuracy (P < 0.05), suggesting that there was a high-order interaction between them associated with dyslipidemia. We found that dyslipidemia in coal miners was related to OS and genetic factors. Through this study, we revealed the dual regulation of environmental factors and genetic factors on dyslipidemia. At the same time, this study provides clues for understanding the etiology and mechanism of dyslipidemia.
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Affiliation(s)
- Yongzhong Yang
- School of Public Health, North China University of Science and Technology, No. 21 Bohai Avenue, Caofeidian New Town, Tangshan, Hebei, 063210, People's Republic of China
| | - Ziwei Zheng
- School of Public Health, North China University of Science and Technology, No. 21 Bohai Avenue, Caofeidian New Town, Tangshan, Hebei, 063210, People's Republic of China
| | - Yuanyu Chen
- School of Public Health, North China University of Science and Technology, No. 21 Bohai Avenue, Caofeidian New Town, Tangshan, Hebei, 063210, People's Republic of China
| | - Xuelin Wang
- School of Public Health, North China University of Science and Technology, No. 21 Bohai Avenue, Caofeidian New Town, Tangshan, Hebei, 063210, People's Republic of China
| | - Hui Wang
- School of Public Health, North China University of Science and Technology, No. 21 Bohai Avenue, Caofeidian New Town, Tangshan, Hebei, 063210, People's Republic of China
| | - Zhikang Si
- School of Public Health, North China University of Science and Technology, No. 21 Bohai Avenue, Caofeidian New Town, Tangshan, Hebei, 063210, People's Republic of China
| | - Rui Meng
- School of Public Health, North China University of Science and Technology, No. 21 Bohai Avenue, Caofeidian New Town, Tangshan, Hebei, 063210, People's Republic of China
| | - Jianhui Wu
- School of Public Health, North China University of Science and Technology, No. 21 Bohai Avenue, Caofeidian New Town, Tangshan, Hebei, 063210, People's Republic of China. .,Hebei Province Key Laboratory of Occupational Health and Safety for Coal Industry, North China University of Science and Technology, Tangshan, Hebei, People's Republic of China.
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30
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Harlow CE, Patel VV, Waterworth DM, Wood AR, Beaumont RN, Ruth KS, Tyrrell J, Oguro-Ando A, Chu AY, Frayling TM. Genetically proxied therapeutic prolyl-hydroxylase inhibition and cardiovascular risk. Hum Mol Genet 2023; 32:496-505. [PMID: 36048866 PMCID: PMC9851745 DOI: 10.1093/hmg/ddac215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Revised: 08/05/2022] [Accepted: 08/22/2022] [Indexed: 01/24/2023] Open
Abstract
Prolyl hydroxylase (PHD) inhibitors are in clinical development for anaemia in chronic kidney disease. Epidemiological studies have reported conflicting results regarding safety of long-term therapeutic haemoglobin (Hgb) rises through PHD inhibition on risk of cardiovascular disease. Genetic variation in genes encoding PHDs can be used as partial proxies to investigate the potential effects of long-term Hgb rises. We used Mendelian randomization to investigate the effect of long-term Hgb level rises through genetically proxied PHD inhibition on coronary artery disease (CAD: 60 801 cases; 123 504 controls), myocardial infarction (MI: 42 561 cases; 123 504 controls) or stroke (40 585 cases; 406 111 controls). To further characterize long-term effects of Hgb level rises, we performed a phenome-wide association study (PheWAS) in up to 451 099 UK Biobank individuals. Genetically proxied therapeutic PHD inhibition, equivalent to a 1.00 g/dl increase in Hgb levels, was not associated (at P < 0.05) with increased odds of CAD; odd ratio (OR) [95% confidence intervals (CI)] = 1.06 (0.84, 1.35), MI [OR (95% CI) = 1.02 (0.79, 1.33)] or stroke [OR (95% CI) = 0.91 (0.66, 1.24)]. PheWAS revealed associations with blood related phenotypes consistent with EGLN's role, relevant kidney- and liver-related biomarkers like estimated glomerular filtration rate and microalbuminuria, and non-alcoholic fatty liver disease (Bonferroni-adjusted P < 5.42E-05) but these were not clinically meaningful. These findings suggest that long-term alterations in Hgb through PHD inhibition are unlikely to substantially increase cardiovascular disease risk; using large disease genome-wide association study data, we could exclude ORs of 1.35 for cardiovascular risk with a 1.00 g/dl increase in Hgb.
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Affiliation(s)
- Charli E Harlow
- College of Medicine and Health, University of Exeter, Exeter, Devon EX2 5DW, UK
| | - Vickas V Patel
- GlaxoSmithKline, Collegeville, PA 19426, USA.,Spark Therapeutics, Inc., Philadelphia, PA 19104, USA
| | - Dawn M Waterworth
- GlaxoSmithKline, Collegeville, PA 19426, USA.,Immunology Translational Sciences, Janssen, Spring House, PA 19044, USA
| | - Andrew R Wood
- College of Medicine and Health, University of Exeter, Exeter, Devon EX2 5DW, UK
| | - Robin N Beaumont
- College of Medicine and Health, University of Exeter, Exeter, Devon EX2 5DW, UK
| | - Katherine S Ruth
- College of Medicine and Health, University of Exeter, Exeter, Devon EX2 5DW, UK
| | - Jessica Tyrrell
- College of Medicine and Health, University of Exeter, Exeter, Devon EX2 5DW, UK
| | - Asami Oguro-Ando
- College of Medicine and Health, University of Exeter, Exeter, Devon EX2 5DW, UK
| | | | - Timothy M Frayling
- College of Medicine and Health, University of Exeter, Exeter, Devon EX2 5DW, UK
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31
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Tesfaye M, Wu J, Biedrzycki RJ, Grantz KL, Joseph P, Tekola-Ayele F. Prenatal social support in low-risk pregnancy shapes placental epigenome. BMC Med 2023; 21:12. [PMID: 36617561 PMCID: PMC9827682 DOI: 10.1186/s12916-022-02701-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Accepted: 12/09/2022] [Indexed: 01/09/2023] Open
Abstract
BACKGROUND Poor social support during pregnancy has been linked to inflammation and adverse pregnancy and childhood health outcomes. Placental epigenetic alterations may underlie these links but are still unknown in humans. METHODS In a cohort of low-risk pregnant women (n = 301) from diverse ethnic backgrounds, social support was measured using the ENRICHD Social Support Inventory (ESSI) during the first trimester. Placental samples collected at delivery were analyzed for DNA methylation and gene expression using Illumina 450K Beadchip Array and RNA-seq, respectively. We examined association between maternal prenatal social support and DNA methylation in placenta. Associated cytosine-(phosphate)-guanine sites (CpGs) were further assessed for correlation with nearby gene expression in placenta. RESULTS The mean age (SD) of the women was 27.7 (5.3) years. The median (interquartile range) of ESSI scores was 24 (22-25). Prenatal social support was significantly associated with methylation level at seven CpGs (PFDR < 0.05). The methylation levels at two of the seven CpGs correlated with placental expression of VGF and ILVBL (PFDR < 0.05), genes known to be involved in neurodevelopment and energy metabolism. The genes annotated with the top 100 CpGs were enriched for pathways related to fetal growth, coagulation system, energy metabolism, and neurodevelopment. Sex-stratified analysis identified additional significant associations at nine CpGs in male-bearing pregnancies and 35 CpGs in female-bearing pregnancies. CONCLUSIONS The findings suggest that prenatal social support is linked to placental DNA methylation changes in a low-stress setting, including fetal sex-dependent epigenetic changes. Given the relevance of some of these changes in fetal neurodevelopmental outcomes, the findings signal important methylation targets for future research on molecular mechanisms of effect of the broader social environment on pregnancy and fetal outcomes. TRIAL REGISTRATION NCT00912132 ( ClinicalTrials.gov ).
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Affiliation(s)
- Markos Tesfaye
- Section of Sensory Science and Metabolism (SenSMet), National Institute on Alcohol Abuse and Alcoholism & National Institute of Nursing Research, National Institutes of Health, Bethesda, MD, USA.,Department of Psychiatry, St. Paul's Hospital Millennium Medical College, Addis Ababa, Ethiopia
| | - Jing Wu
- Glotech, Inc., contractor for Division of Population Health Research, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA
| | - Richard J Biedrzycki
- Glotech, Inc., contractor for Division of Population Health Research, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA
| | - Katherine L Grantz
- Epidemiology Branch, Division of Population Health Research, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, MD, Bethesda, USA
| | - Paule Joseph
- Section of Sensory Science and Metabolism (SenSMet), National Institute on Alcohol Abuse and Alcoholism & National Institute of Nursing Research, National Institutes of Health, Bethesda, MD, USA
| | - Fasil Tekola-Ayele
- Epidemiology Branch, Division of Population Health Research, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, MD, Bethesda, USA.
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32
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Hayden LP, Hobbs BD, Busch R, Cho MH, Liu M, Lopes-Ramos CM, Lomas DA, Bakke P, Gulsvik A, Silverman EK, Crapo JD, Beaty TH, Laird NM, Lange C, DeMeo DL. X chromosome associations with chronic obstructive pulmonary disease and related phenotypes: an X chromosome-wide association study. Respir Res 2023; 24:38. [PMID: 36726148 PMCID: PMC9891756 DOI: 10.1186/s12931-023-02337-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Accepted: 01/18/2023] [Indexed: 02/03/2023] Open
Abstract
BACKGROUND The association between genetic variants on the X chromosome to risk of COPD has not been fully explored. We hypothesize that the X chromosome harbors variants important in determining risk of COPD related phenotypes and may drive sex differences in COPD manifestations. METHODS Using X chromosome data from three COPD-enriched cohorts of adult smokers, we performed X chromosome specific quality control, imputation, and testing for association with COPD case-control status, lung function, and quantitative emphysema. Analyses were performed among all subjects, then stratified by sex, and subsequently combined in meta-analyses. RESULTS Among 10,193 subjects of non-Hispanic white or European ancestry, a variant near TMSB4X, rs5979771, reached genome-wide significance for association with lung function measured by FEV1/FVC ([Formula: see text] 0.020, SE 0.004, p 4.97 × 10-08), with suggestive evidence of association with FEV1 ([Formula: see text] 0.092, SE 0.018, p 3.40 × 10-07). Sex-stratified analyses revealed X chromosome variants that were differentially trending in one sex, with significantly different effect sizes or directions. CONCLUSIONS This investigation identified loci influencing lung function, COPD, and emphysema in a comprehensive genetic association meta-analysis of X chromosome genetic markers from multiple COPD-related datasets. Sex differences play an important role in the pathobiology of complex lung disease, including X chromosome variants that demonstrate differential effects by sex and variants that may be relevant through escape from X chromosome inactivation. Comprehensive interrogation of the X chromosome to better understand genetic control of COPD and lung function is important to further understanding of disease pathology. Trial registration Genetic Epidemiology of COPD Study (COPDGene) is registered at ClinicalTrials.gov, NCT00608764 (Active since January 28, 2008). Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints Study (ECLIPSE), GlaxoSmithKline study code SCO104960, is registered at ClinicalTrials.gov, NCT00292552 (Active since February 16, 2006). Genetics of COPD in Norway Study (GenKOLS) holds GlaxoSmithKline study code RES11080, Genetics of Chronic Obstructive Lung Disease.
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Affiliation(s)
- Lystra P. Hayden
- grid.38142.3c000000041936754XDivision of Pulmonary Medicine, Boston Children’s Hospital, Harvard Medical School, Boston, MA USA ,grid.38142.3c000000041936754XChanning Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, 181 Longwood Ave, Boston, MA 02115 USA
| | - Brian D. Hobbs
- grid.38142.3c000000041936754XChanning Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, 181 Longwood Ave, Boston, MA 02115 USA ,grid.38142.3c000000041936754XDivision of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA USA
| | - Robert Busch
- grid.417587.80000 0001 2243 3366Division of Pulmonology, Allergy, and Critical Care, U.S. Food and Drug Administration, Silver Spring, MD USA
| | - Michael H. Cho
- grid.38142.3c000000041936754XChanning Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, 181 Longwood Ave, Boston, MA 02115 USA ,grid.38142.3c000000041936754XDivision of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA USA
| | - Ming Liu
- grid.268323.e0000 0001 1957 0327Bioinformatics and Computational Biology Program, Worcester Polytechnic Institute, Worcester, MA USA
| | - Camila M. Lopes-Ramos
- grid.38142.3c000000041936754XChanning Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, 181 Longwood Ave, Boston, MA 02115 USA ,grid.38142.3c000000041936754XDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA USA
| | - David A. Lomas
- grid.83440.3b0000000121901201UCL Respiratory, University College London, London, UK
| | - Per Bakke
- grid.7914.b0000 0004 1936 7443Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Amund Gulsvik
- grid.7914.b0000 0004 1936 7443Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Edwin K. Silverman
- grid.38142.3c000000041936754XChanning Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, 181 Longwood Ave, Boston, MA 02115 USA ,grid.38142.3c000000041936754XDivision of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA USA
| | - James D. Crapo
- grid.240341.00000 0004 0396 0728Division of Pulmonary Sciences and Critical Care Medicine, National Jewish Health, Denver, CO USA
| | - Terri H. Beaty
- grid.21107.350000 0001 2171 9311Johns Hopkins School of Public Health, Baltimore, MD USA
| | - Nan M. Laird
- grid.38142.3c000000041936754XDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA USA
| | - Christoph Lange
- grid.38142.3c000000041936754XDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA USA
| | - Dawn L. DeMeo
- grid.38142.3c000000041936754XChanning Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, 181 Longwood Ave, Boston, MA 02115 USA ,grid.38142.3c000000041936754XDivision of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA USA
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Summers KM, Bush SJ, Davis MR, Hume DA, Keshvari S, West JA. Fibrillin-1 and asprosin, novel players in metabolic syndrome. Mol Genet Metab 2023; 138:106979. [PMID: 36630758 DOI: 10.1016/j.ymgme.2022.106979] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Revised: 12/13/2022] [Accepted: 12/14/2022] [Indexed: 12/23/2022]
Abstract
Fibrillin-1 is a major component of the extracellular microfibrils, where it interacts with other extracellular matrix proteins to provide elasticity to connective tissues, and regulates the bioavailability of TGFβ family members. A peptide consisting of the C-terminal 140 amino acids of fibrillin-1 has recently been identified as a glucogenic hormone, secreted from adipose tissue during fasting and targeting the liver to release glucose. This fragment, called asprosin, also signals in the hypothalamus to stimulate appetite. Asprosin levels are correlated with many of the pathologies indicative of metabolic syndrome, including insulin resistance and obesity. Previous studies and reviews have addressed the therapeutic potential of asprosin as a target in obesity, diabetes and related conditions without considering mechanisms underlying the relationship between generation of asprosin and expression of the much larger fibrillin-1 protein. Profibrillin-1 undergoes obligatory cleavage at the cell surface as part of its assembly into microfibrils, producing the asprosin peptide as well as mature fibrillin-1. Patterns of FBN1 mRNA expression are inconsistent with the necessity for regulated release of asprosin. The asprosin peptide may be protected from degradation in adipose tissue. We present evidence for an alternative possibility, that asprosin mRNA is generated independently from an internal promoter within the 3' end of the FBN1 gene, which would allow for regulation independent of fibrillin-synthesis and is more economical of cellular resources. The discovery of asprosin opened exciting possibilities for treatment of metabolic syndrome related conditions, but there is much to be understood before such therapies could be introduced into the clinic.
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Affiliation(s)
- Kim M Summers
- Mater Research Institute-University of Queensland, Translational Research Institute, 37 Kent St, Woolloongabba, Queensland 4102, Australia.
| | - Stephen J Bush
- Weatherall Institute of Molecular Medicine, University of Oxford, John Radcliffe Hospital, Headley Way, Oxford OX3 9DS, United Kingdom.
| | - Margaret R Davis
- The Roslin Institute, University of Edinburgh, Easter Bush, Midlothian EH25 9RG, United Kingdom
| | - David A Hume
- Mater Research Institute-University of Queensland, Translational Research Institute, 37 Kent St, Woolloongabba, Queensland 4102, Australia.
| | - Sahar Keshvari
- Mater Research Institute-University of Queensland, Translational Research Institute, 37 Kent St, Woolloongabba, Queensland 4102, Australia.
| | - Jennifer A West
- Faculty of Medicine, The University of Queensland, Mayne Medical Building, 288 Herston Road, Herston, Queensland 4006, Australia.
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34
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Littleton SH, Grant SFA. Strategies to identify causal common genetic variants and corresponding effector genes for paediatric obesity. Pediatr Obes 2022; 17:e12968. [PMID: 35971868 DOI: 10.1111/ijpo.12968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 07/24/2022] [Accepted: 08/01/2022] [Indexed: 11/30/2022]
Abstract
BACKGROUND Childhood obesity rates are on the rise, but there are currently no effective therapies available to slow or halt their progression. Although environmental and lifestyle factors have been implicated in its pathogenesis, childhood obesity is considered a complex disorder with a clear genetic component. Intense genome-wide association study (GWAS) efforts through large-scale collaborations have enabled the discovery of genetic loci robustly associated with childhood obesity beyond the classic FTO locus. That said, GWAS itself does not pinpoint the actual underlying causal effector genes, but rather just yields association signals in the genome. OBJECTIVE This review aims to outline what has been elucidated thus far on the genetic aetiology of commong childhood obesity and to describe strategies to identify and validate both causal common genetic variants and their corresponding effector genes. RESULTS Relevant cell types for molecular studies can be identified by gene set enrichment analysis and considering known biology of obesity-related physiological processes. Putatively causal single nucleotide polymorphisms (SNPs) can be identified by several methods including statistical fine mapping and 'assay for transposase accessible chromatin sequencing' (ATAC-seq). Variant to gene mapping can then nominate effector genes likely regulated by cis-regulatory elements harbouring putatively causal SNPs. A SNP's cis-regulatory activity can be functionally validated by several in vitro methods including luciferase assay and CRISPR approaches. These CRISPR approaches can also be used to investigate how dysregulatn of effector genes may confer obesity risk. CONCLUSION Uncovering the causative genes related to GWAS signals and elucidating their functional contributions to paediatric obesity with these strategies will deepen our understanding of this disease and serve better treatment outcomes.
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Affiliation(s)
- Sheridan H Littleton
- Center for Spatial and Functional Genomics, Children's Hospital of Philadelphia, Philadelphia, USA.,Cell and Molecular Biology Graduate Group, University of Pennsylvania Perelman School of Medicine, Philadelphia, USA.,Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, USA
| | - Struan F A Grant
- Center for Spatial and Functional Genomics, Children's Hospital of Philadelphia, Philadelphia, USA.,Divisons of Genetics and Endocrinology, Children's Hospital of Philadelphia, Philadelphia, USA.,Department of Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, USA
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35
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Exploring the Genetic Association between Obesity and Serum Lipid Levels Using Bivariate Methods. Twin Res Hum Genet 2022; 25:234-244. [PMID: 36606461 DOI: 10.1017/thg.2022.39] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
It is crucial to understand the genetic mechanisms and biological pathways underlying the relationship between obesity and serum lipid levels. Structural equation models (SEMs) were constructed to calculate heritability for body mass index (BMI), total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and the genetic connections between BMI and the four classes of lipids using 1197 pairs of twins from the Chinese National Twin Registry (CNTR). Bivariate genomewide association studies (GWAS) were performed to identify genetic variants associated with BMI and lipids using the records of 457 individuals, and the results were further validated in 289 individuals. The genetic background affecting BMI may differ by gender, and the heritability of males and females was 71% (95% CI [.66, .75]) and 39% (95% CI [.15, .71]) respectively. BMI was positively correlated with TC, TG and LDL-C in phenotypic and genetic correlation, while negatively correlated with HDL-C. There were gender differences in the correlation between BMI and lipids. Bivariate GWAS analysis and validation stage found 7 genes (LOC105378740, LINC02506, CSMD1, MELK, FAM81A, ERAL1 and MIR144) that were possibly related to BMI and lipid levels. The significant biological pathways were the regulation of cholesterol reverse transport and the regulation of high-density lipoprotein particle clearance (p < .001). BMI and blood lipid levels were affected by genetic factors, and they were genetically correlated. There might be gender differences in their genetic correlation. Bivariate GWAS analysis found MIR144 gene and its related biological pathways may influence obesity and lipid levels.
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36
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Lamri A, De Paoli M, De Souza R, Werstuck G, Anand S, Pigeyre M. Insight into genetic, biological, and environmental determinants of sexual-dimorphism in type 2 diabetes and glucose-related traits. Front Cardiovasc Med 2022; 9:964743. [PMID: 36505380 PMCID: PMC9729955 DOI: 10.3389/fcvm.2022.964743] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Accepted: 10/21/2022] [Indexed: 11/25/2022] Open
Abstract
There is growing evidence that sex and gender differences play an important role in risk and pathophysiology of type 2 diabetes (T2D). Men develop T2D earlier than women, even though there is more obesity in young women than men. This difference in T2D prevalence is attenuated after the menopause. However, not all women are equally protected against T2D before the menopause, and gestational diabetes represents an important risk factor for future T2D. Biological mechanisms underlying sex and gender differences on T2D physiopathology are not yet fully understood. Sex hormones affect behavior and biological changes, and can have implications on lifestyle; thus, both sex-specific environmental and biological risk factors interact within a complex network to explain the differences in T2D risk and physiopathology in men and women. In addition, lifetime hormone fluctuations and body changes due to reproductive factors are generally more dramatic in women than men (ovarian cycle, pregnancy, and menopause). Progress in genetic studies and rodent models have significantly advanced our understanding of the biological pathways involved in the physiopathology of T2D. However, evidence of the sex-specific effects on genetic factors involved in T2D is still limited, and this gap of knowledge is even more important when investigating sex-specific differences during the life course. In this narrative review, we will focus on the current state of knowledge on the sex-specific effects of genetic factors associated with T2D over a lifetime, as well as the biological effects of these different hormonal stages on T2D risk. We will also discuss how biological insights from rodent models complement the genetic insights into the sex-dimorphism effects on T2D. Finally, we will suggest future directions to cover the knowledge gaps.
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Affiliation(s)
- Amel Lamri
- Department of Medicine, McMaster University, Hamilton, ON, Canada,Population Health Research Institute (PHRI), Hamilton, ON, Canada
| | - Monica De Paoli
- Department of Medicine, McMaster University, Hamilton, ON, Canada,Thrombosis and Atherosclerosis Research Institute (TaARI), Hamilton, ON, Canada
| | - Russell De Souza
- Population Health Research Institute (PHRI), Hamilton, ON, Canada,Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada
| | - Geoff Werstuck
- Department of Medicine, McMaster University, Hamilton, ON, Canada,Thrombosis and Atherosclerosis Research Institute (TaARI), Hamilton, ON, Canada
| | - Sonia Anand
- Department of Medicine, McMaster University, Hamilton, ON, Canada,Population Health Research Institute (PHRI), Hamilton, ON, Canada,Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada
| | - Marie Pigeyre
- Department of Medicine, McMaster University, Hamilton, ON, Canada,Population Health Research Institute (PHRI), Hamilton, ON, Canada,*Correspondence: Marie Pigeyre
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Baek EJ, Jung HU, Chung JY, Jung HI, Kwon SY, Lim JE, Kim HK, Kang JO, Oh B. The effect of heteroscedasticity on the prediction efficiency of genome-wide polygenic score for body mass index. Front Genet 2022; 13:1025568. [PMID: 36419825 PMCID: PMC9676478 DOI: 10.3389/fgene.2022.1025568] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Accepted: 10/25/2022] [Indexed: 11/09/2022] Open
Abstract
Globally, more than 1.9 billion adults are overweight. Thus, obesity is a serious public health issue. Moreover, obesity is a major risk factor for diabetes mellitus, coronary heart disease, and cardiovascular disease. Recently, GWAS examining obesity and body mass index (BMI) have increasingly unveiled many aspects of the genetic architecture of obesity and BMI. Information on genome-wide genetic variants has been used to estimate the genome-wide polygenic score (GPS) for a personalized prediction of obesity. However, the prediction power of GPS is affected by various factors, including the unequal variance in the distribution of a phenotype, known as heteroscedasticity. Here, we calculated a GPS for BMI using LDpred2, which was based on the BMI GWAS summary statistics from a European meta-analysis. Then, we tested the GPS in 354,761 European samples from the UK Biobank and found an effective prediction power of the GPS on BMI. To study a change in the variance of BMI, we investigated the heteroscedasticity of BMI across the GPS via graphical and statistical methods. We also studied the homoscedastic samples for BMI compared to the heteroscedastic sample, randomly selecting samples with various standard deviations of BMI residuals. Further, we examined the effect of the genetic interaction of GPS with environment (GPS×E) on the heteroscedasticity of BMI. We observed the changing variance (i.e., heteroscedasticity) of BMI along the GPS. The heteroscedasticity of BMI was confirmed by both the Breusch-Pagan test and the Score test. Compared to the heteroscedastic sample, the homoscedastic samples from small standard deviation of BMI residuals showed a decreased heteroscedasticity and an improved prediction accuracy, suggesting a quantitatively negative correlation between the phenotypic heteroscedasticity and the prediction accuracy of GPS. To further test the effects of the GPS×E on heteroscedasticity, first we tested the genetic interactions of the GPS with 21 environments and found 8 significant GPS×E interactions on BMI. However, the heteroscedasticity of BMI was not ameliorated after adjusting for the GPS×E interactions. Taken together, our findings suggest that the heteroscedasticity of BMI exists along the GPS and is not affected by the GPS×E interaction.
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Affiliation(s)
- Eun Ju Baek
- Department of Biomedical Science, Graduate School, Kyung Hee University, Seoul, South Korea
| | - Hae-Un Jung
- Department of Biomedical Science, Graduate School, Kyung Hee University, Seoul, South Korea
| | - Ju Yeon Chung
- Department of Biomedical Science, Graduate School, Kyung Hee University, Seoul, South Korea
| | - Hye In Jung
- Department of Biomedical Science, Graduate School, Kyung Hee University, Seoul, South Korea
| | - Shin Young Kwon
- Department of Biomedical Science, Graduate School, Kyung Hee University, Seoul, South Korea
| | - Ji Eun Lim
- Department of Biochemistry and Molecular Biology, School of Medicine, Kyung Hee University, Seoul, South Korea
| | - Han Kyul Kim
- Department of Biochemistry and Molecular Biology, School of Medicine, Kyung Hee University, Seoul, South Korea
| | - Ji-One Kang
- Department of Biochemistry and Molecular Biology, School of Medicine, Kyung Hee University, Seoul, South Korea
- *Correspondence: Ji-One Kang, ; Bermseok Oh,
| | - Bermseok Oh
- Department of Biomedical Science, Graduate School, Kyung Hee University, Seoul, South Korea
- Department of Biochemistry and Molecular Biology, School of Medicine, Kyung Hee University, Seoul, South Korea
- *Correspondence: Ji-One Kang, ; Bermseok Oh,
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Polygenic signals of sex differences in selection in humans from the UK Biobank. PLoS Biol 2022; 20:e3001768. [PMID: 36067235 PMCID: PMC9481184 DOI: 10.1371/journal.pbio.3001768] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 09/16/2022] [Accepted: 07/27/2022] [Indexed: 11/19/2022] Open
Abstract
Sex differences in the fitness effects of genetic variants can influence the rate of adaptation and the maintenance of genetic variation. For example, "sexually antagonistic" (SA) variants, which are beneficial for one sex and harmful for the other, can both constrain adaptation and increase genetic variability for fitness components such as survival, fertility, and disease susceptibility. However, detecting variants with sex-differential fitness effects is difficult, requiring genome sequences and fitness measurements from large numbers of individuals. Here, we develop new theory for studying sex-differential selection across a complete life cycle and test our models with genotypic and reproductive success data from approximately 250,000 UK Biobank individuals. We uncover polygenic signals of sex-differential selection affecting survival, reproductive success, and overall fitness, with signals of sex-differential reproductive selection reflecting a combination of SA polymorphisms and sexually concordant polymorphisms in which the strength of selection differs between the sexes. Moreover, these signals hold up to rigorous controls that minimise the contributions of potential confounders, including sequence mapping errors, population structure, and ascertainment bias. Functional analyses reveal that sex-differentiated sites are enriched in phenotype-altering genomic regions, including coding regions and loci affecting a range of quantitative traits. Population genetic analyses show that sex-differentiated sites exhibit evolutionary histories dominated by genetic drift and/or transient balancing selection, but not long-term balancing selection, which is consistent with theoretical predictions of effectively weak SA balancing selection in historically small populations. Overall, our results are consistent with polygenic sex-differential-including SA-selection in humans. Evidence for sex-differential selection is particularly strong for variants affecting reproductive success, in which the potential contributions of nonrandom sampling to signals of sex differentiation can be excluded.
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39
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Li H, Konja D, Wang L, Wang Y. Sex Differences in Adiposity and Cardiovascular Diseases. Int J Mol Sci 2022; 23:ijms23169338. [PMID: 36012601 PMCID: PMC9409326 DOI: 10.3390/ijms23169338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 08/11/2022] [Accepted: 08/17/2022] [Indexed: 11/16/2022] Open
Abstract
Body fat distribution is a well-established predictor of adverse medical outcomes, independent of overall adiposity. Studying body fat distribution sheds insights into the causes of obesity and provides valuable information about the development of various comorbidities. Compared to total adiposity, body fat distribution is more closely associated with risks of cardiovascular diseases. The present review specifically focuses on the sexual dimorphism in body fat distribution, the biological clues, as well as the genetic traits that are distinct from overall obesity. Understanding the sex determinations on body fat distribution and adiposity will aid in the improvement of the prevention and treatment of cardiovascular diseases (CVD).
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40
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Porcu E, Claringbould A, Weihs A, Lepik K, Richardson TG, Völker U, Santoni FA, Teumer A, Franke L, Reymond A, Kutalik Z. Limited evidence for blood eQTLs in human sexual dimorphism. Genome Med 2022; 14:89. [PMID: 35953856 PMCID: PMC9373355 DOI: 10.1186/s13073-022-01088-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Accepted: 07/14/2022] [Indexed: 12/02/2022] Open
Abstract
BACKGROUND The genetic underpinning of sexual dimorphism is very poorly understood. The prevalence of many diseases differs between men and women, which could be in part caused by sex-specific genetic effects. Nevertheless, only a few published genome-wide association studies (GWAS) were performed separately in each sex. The reported enrichment of expression quantitative trait loci (eQTLs) among GWAS-associated SNPs suggests a potential role of sex-specific eQTLs in the sex-specific genetic mechanism underlying complex traits. METHODS To explore this scenario, we combined sex-specific whole blood RNA-seq eQTL data from 3447 European individuals included in BIOS Consortium and GWAS data from UK Biobank. Next, to test the presence of sex-biased causal effect of gene expression on complex traits, we performed sex-specific transcriptome-wide Mendelian randomization (TWMR) analyses on the two most sexually dimorphic traits, waist-to-hip ratio (WHR) and testosterone levels. Finally, we performed power analysis to calculate the GWAS sample size needed to observe sex-specific trait associations driven by sex-biased eQTLs. RESULTS Among 9 million SNP-gene pairs showing sex-combined associations, we found 18 genes with significant sex-biased cis-eQTLs (FDR 5%). Our phenome-wide association study of the 18 top sex-biased eQTLs on >700 traits unraveled that these eQTLs do not systematically translate into detectable sex-biased trait-associations. In addition, we observed that sex-specific causal effects of gene expression on complex traits are not driven by sex-specific eQTLs. Power analyses using real eQTL- and causal-effect sizes showed that millions of samples would be necessary to observe sex-biased trait associations that are fully driven by sex-biased cis-eQTLs. Compensatory effects may further hamper their detection. CONCLUSIONS Our results suggest that sex-specific eQTLs in whole blood do not translate to detectable sex-specific trait associations of complex diseases, and vice versa that the observed sex-specific trait associations cannot be explained by sex-specific eQTLs.
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Affiliation(s)
- Eleonora Porcu
- Center for Integrative Genomics, University of Lausanne, Lausanne, Switzerland. .,Swiss Institute of Bioinformatics, Lausanne, Switzerland. .,University Center for Primary Care and Public Health, Lausanne, Switzerland.
| | - Annique Claringbould
- University Medical Centre Groningen, Groningen, the Netherlands.,Structural and Computational Biology Unit, European Molecular Biology Laboratories (EMBL), Heidelberg, Germany
| | - Antoine Weihs
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
| | - Kaido Lepik
- Institute of Computer Science, University of Tartu, Tartu, Estonia.,Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | | | - Tom G Richardson
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.,Novo Nordisk Research Centre Oxford, Old Road Campus, Oxford, OX3 7DQ, UK
| | - Uwe Völker
- Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany.,DZHK (German Centre for Cardiovascular Research), partner site Greifswald, Greifswald, Germany
| | - Federico A Santoni
- Endocrine, Diabetes, and Metabolism Service, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland.,Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Alexander Teumer
- DZHK (German Centre for Cardiovascular Research), partner site Greifswald, Greifswald, Germany.,Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Lude Franke
- University Medical Centre Groningen, Groningen, the Netherlands
| | - Alexandre Reymond
- Center for Integrative Genomics, University of Lausanne, Lausanne, Switzerland.
| | - Zoltán Kutalik
- Swiss Institute of Bioinformatics, Lausanne, Switzerland. .,University Center for Primary Care and Public Health, Lausanne, Switzerland. .,Department of Computational Biology, University of Lausanne, Lausanne, Switzerland.
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41
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Adipose Tissue Dysfunction and Obesity-Related Male Hypogonadism. Int J Mol Sci 2022; 23:ijms23158194. [PMID: 35897769 PMCID: PMC9330735 DOI: 10.3390/ijms23158194] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 07/19/2022] [Accepted: 07/21/2022] [Indexed: 12/12/2022] Open
Abstract
Obesity is a chronic illness associated with several metabolic derangements and comorbidities (i.e., insulin resistance, leptin resistance, diabetes, etc.) and often leads to impaired testicular function and male subfertility. Several mechanisms may indeed negatively affect the hypothalamic–pituitary–gonadal health, such as higher testosterone conversion to estradiol by aromatase activity in the adipose tissue, increased ROS production, and the release of several endocrine molecules affecting the hypothalamus–pituitary–testis axis by both direct and indirect mechanisms. In addition, androgen deficiency could further accelerate adipose tissue expansion and therefore exacerbate obesity, which in turn enhances hypogonadism, thus inducing a vicious cycle. Based on these considerations, we propose an overview on the relationship of adipose tissue dysfunction and male hypogonadism, highlighting the main biological pathways involved and the current therapeutic options to counteract this condition.
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42
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Yang Q, Sanderson E, Tilling K, Borges MC, Lawlor DA. Exploring and mitigating potential bias when genetic instrumental variables are associated with multiple non-exposure traits in Mendelian randomization. Eur J Epidemiol 2022; 37:683-700. [PMID: 35622304 PMCID: PMC9329407 DOI: 10.1007/s10654-022-00874-5] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2020] [Accepted: 04/18/2022] [Indexed: 12/19/2022]
Abstract
With the increasing size and number of genome-wide association studies, individual single nucleotide polymorphisms are increasingly found to associate with multiple traits. Many different mechanisms could result in proposed genetic IVs for an exposure of interest being associated with multiple non-exposure traits, some of which could bias MR results. We describe and illustrate, through causal diagrams, a range of scenarios that could result in proposed IVs being related to non-exposure traits in MR studies. These associations could occur due to five scenarios: (i) confounding, (ii) vertical pleiotropy, (iii) horizontal pleiotropy, (iv) reverse causation and (v) selection bias. For each of these scenarios we outline steps that could be taken to explore the underlying mechanism and mitigate any resulting bias in the MR estimation. We recommend MR studies explore possible IV-non-exposure associations across a wider range of traits than is usually the case. We highlight the pros and cons of relying on sensitivity analyses without considering particular pleiotropic paths versus systematically exploring and controlling for potential pleiotropic or other biasing paths via known traits. We apply our recommendations to an illustrative example of the effect of maternal insomnia on offspring birthweight in UK Biobank.
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Affiliation(s)
- Qian Yang
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK.
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
| | - Eleanor Sanderson
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Kate Tilling
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- National Institute for Health Research Bristol Biomedical Centre, University Hospitals Bristol NHS Foundation Trust and University of Bristol, Bristol, UK
| | - Maria Carolina Borges
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Deborah A Lawlor
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- National Institute for Health Research Bristol Biomedical Centre, University Hospitals Bristol NHS Foundation Trust and University of Bristol, Bristol, UK
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43
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Agrawal S, Wang M, Klarqvist MDR, Smith K, Shin J, Dashti H, Diamant N, Choi SH, Jurgens SJ, Ellinor PT, Philippakis A, Claussnitzer M, Ng K, Udler MS, Batra P, Khera AV. Inherited basis of visceral, abdominal subcutaneous and gluteofemoral fat depots. Nat Commun 2022; 13:3771. [PMID: 35773277 PMCID: PMC9247093 DOI: 10.1038/s41467-022-30931-2] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 05/25/2022] [Indexed: 12/11/2022] Open
Abstract
For any given level of overall adiposity, individuals vary considerably in fat distribution. The inherited basis of fat distribution in the general population is not fully understood. Here, we study up to 38,965 UK Biobank participants with MRI-derived visceral (VAT), abdominal subcutaneous (ASAT), and gluteofemoral (GFAT) adipose tissue volumes. Because these fat depot volumes are highly correlated with BMI, we additionally study six local adiposity traits: VAT adjusted for BMI and height (VATadj), ASATadj, GFATadj, VAT/ASAT, VAT/GFAT, and ASAT/GFAT. We identify 250 independent common variants (39 newly-identified) associated with at least one trait, with many associations more pronounced in female participants. Rare variant association studies extend prior evidence for PDE3B as an important modulator of fat distribution. Local adiposity traits (1) highlight depot-specific genetic architecture and (2) enable construction of depot-specific polygenic scores that have divergent associations with type 2 diabetes and coronary artery disease. These results - using MRI-derived, BMI-independent measures of local adiposity - confirm fat distribution as a highly heritable trait with important implications for cardiometabolic health outcomes.
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Affiliation(s)
- Saaket Agrawal
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Minxian Wang
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China
| | | | - Kirk Smith
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Joseph Shin
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Hesam Dashti
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Nathaniel Diamant
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Seung Hoan Choi
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Sean J Jurgens
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Experimental Cardiology, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Patrick T Ellinor
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Anthony Philippakis
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Eric and Wendy Schmidt Center, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Melina Claussnitzer
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Kenney Ng
- Center for Computational Health, IBM Research, Cambridge, MA, USA
| | - Miriam S Udler
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Puneet Batra
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Amit V Khera
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA.
- Department of Medicine, Harvard Medical School, Boston, MA, USA.
- Verve Therapeutics, Cambridge, MA, USA.
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Winkler TW, Rasheed H, Teumer A, Gorski M, Rowan BX, Stanzick KJ, Thomas LF, Tin A, Hoppmann A, Chu AY, Tayo B, Thio CHL, Cusi D, Chai JF, Sieber KB, Horn K, Li M, Scholz M, Cocca M, Wuttke M, van der Most PJ, Yang Q, Ghasemi S, Nutile T, Li Y, Pontali G, Günther F, Dehghan A, Correa A, Parsa A, Feresin A, de Vries APJ, Zonderman AB, Smith AV, Oldehinkel AJ, De Grandi A, Rosenkranz AR, Franke A, Teren A, Metspalu A, Hicks AA, Morris AP, Tönjes A, Morgan A, Podgornaia AI, Peters A, Körner A, Mahajan A, Campbell A, Freedman BI, Spedicati B, Ponte B, Schöttker B, Brumpton B, Banas B, Krämer BK, Jung B, Åsvold BO, Smith BH, Ning B, Penninx BWJH, Vanderwerff BR, Psaty BM, Kammerer CM, Langefeld CD, Hayward C, Spracklen CN, Robinson-Cohen C, Hartman CA, Lindgren CM, Wang C, Sabanayagam C, Heng CK, Lanzani C, Khor CC, Cheng CY, Fuchsberger C, Gieger C, Shaffer CM, Schulz CA, Willer CJ, Chasman DI, Gudbjartsson DF, Ruggiero D, Toniolo D, Czamara D, Porteous DJ, Waterworth DM, Mascalzoni D, Mook-Kanamori DO, Reilly DF, Daw EW, Hofer E, Boerwinkle E, Salvi E, Bottinger EP, Tai ES, Catamo E, Rizzi F, Guo F, Rivadeneira F, Guilianini F, Sveinbjornsson G, Ehret G, Waeber G, Biino G, Girotto G, Pistis G, Nadkarni GN, Delgado GE, Montgomery GW, Snieder H, Campbell H, White HD, Gao H, Stringham HM, Schmidt H, Li H, Brenner H, Holm H, Kirsten H, Kramer H, Rudan I, Nolte IM, Tzoulaki I, Olafsson I, Martins J, Cook JP, Wilson JF, Halbritter J, Felix JF, Divers J, Kooner JS, Lee JJM, O'Connell J, Rotter JI, Liu J, Xu J, Thiery J, Ärnlöv J, Kuusisto J, Jakobsdottir J, Tremblay J, Chambers JC, Whitfield JB, Gaziano JM, Marten J, Coresh J, Jonas JB, Mychaleckyj JC, Christensen K, Eckardt KU, Mohlke KL, Endlich K, Dittrich K, Ryan KA, Rice KM, Taylor KD, Ho K, Nikus K, Matsuda K, Strauch K, Miliku K, Hveem K, Lind L, Wallentin L, Yerges-Armstrong LM, Raffield LM, Phillips LS, Launer LJ, Lyytikäinen LP, Lange LA, Citterio L, Klaric L, Ikram MA, Ising M, Kleber ME, Francescatto M, Concas MP, Ciullo M, Piratsu M, Orho-Melander M, Laakso M, Loeffler M, Perola M, de Borst MH, Gögele M, Bianca ML, Lukas MA, Feitosa MF, Biggs ML, Wojczynski MK, Kavousi M, Kanai M, Akiyama M, Yasuda M, Nauck M, Waldenberger M, Chee ML, Chee ML, Boehnke M, Preuss MH, Stumvoll M, Province MA, Evans MK, O'Donoghue ML, Kubo M, Kähönen M, Kastarinen M, Nalls MA, Kuokkanen M, Ghanbari M, Bochud M, Josyula NS, Martin NG, Tan NYQ, Palmer ND, Pirastu N, Schupf N, Verweij N, Hutri-Kähönen N, Mononen N, Bansal N, Devuyst O, Melander O, Raitakari OT, Polasek O, Manunta P, Gasparini P, Mishra PP, Sulem P, Magnusson PKE, Elliott P, Ridker PM, Hamet P, Svensson PO, Joshi PK, Kovacs P, Pramstaller PP, Rossing P, Vollenweider P, van der Harst P, Dorajoo R, Sim RZH, Burkhardt R, Tao R, Noordam R, Mägi R, Schmidt R, de Mutsert R, Rueedi R, van Dam RM, Carroll RJ, Gansevoort RT, Loos RJF, Felicita SC, Sedaghat S, Padmanabhan S, Freitag-Wolf S, Pendergrass SA, Graham SE, Gordon SD, Hwang SJ, Kerr SM, Vaccargiu S, Patil SB, Hallan S, Bakker SJL, Lim SC, Lucae S, Vogelezang S, Bergmann S, Corre T, Ahluwalia TS, Lehtimäki T, Boutin TS, Meitinger T, Wong TY, Bergler T, Rabelink TJ, Esko T, Haller T, Thorsteinsdottir U, Völker U, Foo VHX, Salomaa V, Vitart V, Giedraitis V, Gudnason V, Jaddoe VWV, Huang W, Zhang W, Wei WB, Kiess W, März W, Koenig W, Lieb W, Gao X, Sim X, Wang YX, Friedlander Y, Tham YC, Kamatani Y, Okada Y, Milaneschi Y, Yu Z, Stark KJ, Stefansson K, Böger CA, Hung AM, Kronenberg F, Köttgen A, Pattaro C, Heid IM. Differential and shared genetic effects on kidney function between diabetic and non-diabetic individuals. Commun Biol 2022; 5:580. [PMID: 35697829 PMCID: PMC9192715 DOI: 10.1038/s42003-022-03448-z] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Accepted: 05/04/2022] [Indexed: 01/14/2023] Open
Abstract
Reduced glomerular filtration rate (GFR) can progress to kidney failure. Risk factors include genetics and diabetes mellitus (DM), but little is known about their interaction. We conducted genome-wide association meta-analyses for estimated GFR based on serum creatinine (eGFR), separately for individuals with or without DM (nDM = 178,691, nnoDM = 1,296,113). Our genome-wide searches identified (i) seven eGFR loci with significant DM/noDM-difference, (ii) four additional novel loci with suggestive difference and (iii) 28 further novel loci (including CUBN) by allowing for potential difference. GWAS on eGFR among DM individuals identified 2 known and 27 potentially responsible loci for diabetic kidney disease. Gene prioritization highlighted 18 genes that may inform reno-protective drug development. We highlight the existence of DM-only and noDM-only effects, which can inform about the target group, if respective genes are advanced as drug targets. Largely shared effects suggest that most drug interventions to alter eGFR should be effective in DM and noDM.
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Affiliation(s)
- Thomas W Winkler
- Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany.
| | - Humaira Rasheed
- K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- MRC Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Division of Medicine and Laboratory Sciences, University of Oslo, Oslo, Norway
| | - Alexander Teumer
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
- DZHK (German Center for Cardiovascular Research), partner site Greifswald, Greifswald, Germany
- Department of Population Medicine and Lifestyle Diseases Prevention, Medical University of Bialystok, Bialystok, Poland
| | - Mathias Gorski
- Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany
- Department of Nephrology, University Hospital Regensburg, Regensburg, Germany
| | - Bryce X Rowan
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Veteran's Affairs, Tennessee Valley Healthcare System (626)/Vanderbilt University, Nashville, TN, USA
| | - Kira J Stanzick
- Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany
| | - Laurent F Thomas
- K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Clinical and Molecular Medicine, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- BioCore-Bioinformatics Core Facility, Norwegian University of Science and Technology, Trondheim, Norway
| | - Adrienne Tin
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Anselm Hoppmann
- Institute of Genetic Epidemiology, Department of Data Driven Medicine, Faculty of Medicine and Medical Center-University of Freiburg, Freiburg, Germany
| | | | - Bamidele Tayo
- Department of Public Health Sciences, Loyola University Chicago, Maywood, IL, USA
| | - Chris H L Thio
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Daniele Cusi
- Institute of Biomedical Technologies, National Research Council of Italy, Milan, Italy
- Bio4Dreams-Business Nursery for Life Sciences, Milan, Italy
| | - Jin-Fang Chai
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | - Karsten B Sieber
- Target Sciences-Genetics, GlaxoSmithKline, Collegeville, PA, USA
| | - Katrin Horn
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
- LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - Man Li
- Division of Nephrology and Hypertension, Department of Medicine, University of Utah, Salt Lake City, UT, USA
| | - Markus Scholz
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
- LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - Massimiliano Cocca
- Institute for Maternal and Child Health, IRCCS 'Burlo Garofolo', Trieste, Italy
| | - Matthias Wuttke
- Institute of Genetic Epidemiology, Department of Data Driven Medicine, Faculty of Medicine and Medical Center-University of Freiburg, Freiburg, Germany
- Renal Division, Department of Medicine IV, Faculty of Medicine and Medical Center-University of Freiburg, Freiburg, Germany
| | - Peter J van der Most
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Qiong Yang
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Sahar Ghasemi
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
- DZHK (German Center for Cardiovascular Research), partner site Greifswald, Greifswald, Germany
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
| | - Teresa Nutile
- Institute of Genetics and Biophysics 'Adriano Buzzati-Traverso'-CNR, Naples, Italy
| | - Yong Li
- Institute of Genetic Epidemiology, Department of Data Driven Medicine, Faculty of Medicine and Medical Center-University of Freiburg, Freiburg, Germany
| | - Giulia Pontali
- Eurac Research, Institute for Biomedicine (affiliated with the University of Lübeck), Bolzano, Italy
- University of Trento, Department of Cellular, Computational and Integrative Biology-CIBIO, Trento, Italy
| | - Felix Günther
- Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany
- Statistical Consulting Unit StaBLab, Department of Statistics, LMU Munich, Munich, Germany
| | - Abbas Dehghan
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Medicine, Imperial College London, London, UK
- Dementia Research Institute, Imperial College London, London, UK
| | - Adolfo Correa
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA
| | - Afshin Parsa
- Division of Kidney, Urologic and Hematologic Diseases, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, USA
- University of Maryland School of Medicine, Baltimore, MD, USA
| | - Agnese Feresin
- Department of Medicine, Surgery and Health Sciences, University of Trieste, Trieste, Italy
| | - Aiko P J de Vries
- Section of Nephrology, Department of Internal Medicine, Leiden University Medical Center, Leiden, The Netherlands
| | - Alan B Zonderman
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, Intramural Research Program, US National Institutes of Health, Baltimore, MD, USA
| | - Albert V Smith
- Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland
- CNRS UMR 8199, European Genomic Institute for Diabetes (EGID), Institut Pasteur de Lille, University of Lille, Lille, France
| | - Albertine J Oldehinkel
- Interdisciplinary Center of Psychopathology and Emotion Regulation (ICPE), University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Alessandro De Grandi
- Eurac Research, Institute for Biomedicine (affiliated with the University of Lübeck), Bolzano, Italy
| | - Alexander R Rosenkranz
- Department of Internal Medicine, Division of Nephrology, Medical University Graz, Graz, Austria
| | - Andre Franke
- Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Kiel, Germany
| | - Andrej Teren
- LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
- Heart Center Leipzig, Leipzig, Germany
| | - Andres Metspalu
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Andrew A Hicks
- Eurac Research, Institute for Biomedicine (affiliated with the University of Lübeck), Bolzano, Italy
| | - Andrew P Morris
- Department of Health Data Science, University of Liverpool, Liverpool, UK
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, UK
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, The University of Manchester, Manchester, UK
| | - Anke Tönjes
- Medical Department III-Endocrinology, Nephrology, Rheumatology, University of Leipzig Medical Center, Leipzig, Germany
| | - Anna Morgan
- Institute for Maternal and Child Health, IRCCS 'Burlo Garofolo', Trieste, Italy
| | | | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Chair of Epidemiology, IBE, Faculty of Medicine, Ludwig-Maximilians-Universität München, München, Germany
| | - Antje Körner
- LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
- Department of Women and Child Health, Hospital for Children and Adolescents, University of Leipzig, Leipzig, Germany
- Center for Pediatric Research, University of Leipzig, Leipzig, Germany
| | - Anubha Mahajan
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, UK
| | - Archie Campbell
- Center for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Barry I Freedman
- Section on Nephrology, Internal Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Beatrice Spedicati
- Department of Medicine, Surgery and Health Sciences, University of Trieste, Trieste, Italy
| | - Belen Ponte
- Service de Néphrologie et Hypertension, Medicine Department, Geneva University Hospitals, Geneva, Switzerland
| | - Ben Schöttker
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Network Aging Research, University of Heidelberg, Heidelberg, Germany
| | - Ben Brumpton
- K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- Clinic of Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, 7030, Norway
- HUNT Research Centre, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Levanger, 7600, Norway
| | - Bernhard Banas
- Department of Nephrology, University Hospital Regensburg, Regensburg, Germany
| | - Bernhard K Krämer
- Vth Department of Medicine (Nephrology, Hypertensiology, Endocrinology, Diabetology, Rheumatology, Pneumology), Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Bettina Jung
- Department of Population Medicine and Lifestyle Diseases Prevention, Medical University of Bialystok, Bialystok, Poland
- Department of Nephrology, University Hospital Regensburg, Regensburg, Germany
- Department of Nephrology and Rheumatology, Kliniken Südostbayern, Traunstein, Germany
| | - Bjørn Olav Åsvold
- K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Endocrinology, Clinic of Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Blair H Smith
- Division of Population Health and Genomics, Ninewells Hospital and Medical School, University of Dundee, Dundee, UK
| | - Boting Ning
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Brenda W J H Penninx
- Department of Psychiatry, VU University Medical Centre, Amsterdam, The Netherlands
| | - Brett R Vanderwerff
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, 48109, USA
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, 48109, USA
| | - Bruce M Psaty
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
- Department of Epidemiology, University of Washington, Seattle, WA, USA
- Department of Health Systems and Population Health, University of Washington, Seattle, WA, USA
| | - Candace M Kammerer
- Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Carl D Langefeld
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Caroline Hayward
- Medical Research Council Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Cassandra N Spracklen
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
- Department of Biostatistics and Epidemiology, University of Massachusetts Amherst, Amherst, MA, USA
| | - Cassianne Robinson-Cohen
- Department of Veteran's Affairs, Tennessee Valley Healthcare System (626)/Vanderbilt University, Nashville, TN, USA
- Vanderbilt University Medical Center, Division of Nephrology and Hypertension, Vanderbilt Center for Kidney Disease and Integrated Program for Acute Kidney Injury Research, and Vanderbilt Precision Nephrology Program Nashville, Nashville, TN, USA
| | - Catharina A Hartman
- Interdisciplinary Center of Psychopathology and Emotion Regulation (ICPE), University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Cecilia M Lindgren
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, OX3 7LF, UK
| | - Chaolong Wang
- Genome Institute of Singapore, Agency for Science Technology and Research, Singapore, Singapore
- School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Charumathi Sabanayagam
- Singapore Eye Research Institute, Singapore National Eye Center, Singapore, Singapore
- Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
| | - Chew-Kiat Heng
- Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Khoo Teck Puat-National University Children's Medical Institute, National University Health System, Singapore, Singapore
| | - Chiara Lanzani
- Nephrology and Dialysis Unit, Genomics of Renal Diseases and Hypertension Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Chiea-Chuen Khor
- Genome Institute of Singapore, Agency for Science Technology and Research, Singapore, Singapore
- Singapore Eye Research Institute, Singapore National Eye Center, Singapore, Singapore
| | - Ching-Yu Cheng
- Singapore Eye Research Institute, Singapore National Eye Center, Singapore, Singapore
- Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
| | - Christian Fuchsberger
- Eurac Research, Institute for Biomedicine (affiliated with the University of Lübeck), Bolzano, Italy
| | - Christian Gieger
- Institute of Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Research Unit Molecular Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
| | - Christian M Shaffer
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | | | - Cristen J Willer
- Department of Internal Medicine, Division of Cardiology, University of Michigan, Ann Arbor, MI, 48109, USA
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, 48109, USA
- Department of Human Genetics, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Daniel I Chasman
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Daniel F Gudbjartsson
- deCODE Genetics/Amgen, Inc., Reykjavik, Iceland
- Iceland School of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland
| | - Daniela Ruggiero
- Institute of Genetics and Biophysics 'Adriano Buzzati-Traverso'-CNR, Naples, Italy
- IRCCS Neuromed, Pozzilli, Italy
| | | | - Darina Czamara
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
| | - David J Porteous
- Center for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
- Center for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
| | | | - Deborah Mascalzoni
- Eurac Research, Institute for Biomedicine (affiliated with the University of Lübeck), Bolzano, Italy
- Centre for Research Ethics & Bioethics, Department of Public Health and Caring Sciences, Uppsala University, Uppsala, Sweden
| | - Dennis O Mook-Kanamori
- Department of Public Health and Primary Care, Leiden University Medical Center, Leiden, The Netherlands
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
| | | | - E Warwick Daw
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
| | - Edith Hofer
- Clinical Division of Neurogeriatrics, Department of Neurology, Medical University of Graz, Graz, Austria
- Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Graz, Austria
| | - Eric Boerwinkle
- Human Genetics Center, University of Texas Health Science Center, Houston, TX, USA
| | - Erika Salvi
- Neuroalgology Unit, Fondazione IRCCS Istituto Neurologico 'Carlo Besta', Milan, Italy
| | - Erwin P Bottinger
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Digital Health Center, Hasso Plattner Institute and University of Potsdam, Potsdam, Germany
| | - E-Shyong Tai
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
- Duke - NUS Medical School, Singapore, Singapore
| | - Eulalia Catamo
- Institute for Maternal and Child Health, IRCCS 'Burlo Garofolo', Trieste, Italy
| | - Federica Rizzi
- Bio4Dreams-Business Nursery for Life Sciences, Milan, Italy
- ePhood Scientific Unit, ePhood SRL, Milano, Italy
| | - Feng Guo
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Fernando Rivadeneira
- Department of Internal Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Franco Guilianini
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | | | - Georg Ehret
- Cardiology, Geneva University Hospitals, Geneva, Switzerland
| | - Gerard Waeber
- Department of Medicine, Internal Medicine, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Ginevra Biino
- Institute of Molecular Genetics "Luigi Luca Cavalli-Sforza", National Research Council of Italy, Pavia, Italy
| | - Giorgia Girotto
- Institute for Maternal and Child Health, IRCCS 'Burlo Garofolo', Trieste, Italy
- Department of Medicine, Surgery and Health Sciences, University of Trieste, Trieste, Italy
| | - Giorgio Pistis
- Department of Psychiatry, University Hospital of Lausanne, Lausanne, Switzerland
| | - Girish N Nadkarni
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Graciela E Delgado
- Vth Department of Medicine (Nephrology, Hypertensiology, Rheumatology, Endocrinology, Diabetology), Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Grant W Montgomery
- Institute for Molecular Bioscience, University of Queensland, St Lucia, QLD, Australia
| | - Harold Snieder
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Harry Campbell
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Harvey D White
- Green Lane Cardiovascular Service, Auckland City Hospital and University of Auckland, Auckland, New Zealand
| | - He Gao
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Medicine, Imperial College London, London, UK
| | - Heather M Stringham
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Helena Schmidt
- Research Unit Genetic Epidemiology, Gottfried Schatz Research Center for Cell Signaling, Metabolism and Aging, Medical University of Graz, Graz, Austria
| | - Hengtong Li
- Singapore Eye Research Institute, Singapore National Eye Center, Singapore, Singapore
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Network Aging Research, University of Heidelberg, Heidelberg, Germany
| | - Hilma Holm
- deCODE Genetics/Amgen, Inc., Reykjavik, Iceland
| | - Holgen Kirsten
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
- LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - Holly Kramer
- Department of Public Health Sciences, Loyola University Chicago, Maywood, IL, USA
- Division of Nephrology and Hypertension, Loyola University Chicago, Chicago, IL, USA
| | - Igor Rudan
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Ilja M Nolte
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Ioanna Tzoulaki
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Medicine, Imperial College London, London, UK
- Dementia Research Institute, Imperial College London, London, UK
- Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, Greece
| | - Isleifur Olafsson
- Department of Clinical Biochemistry, Landspitali University Hospital, Reykjavik, Iceland
| | - Jade Martins
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
| | - James P Cook
- Department of Health Data Science, University of Liverpool, Liverpool, UK
| | - James F Wilson
- Medical Research Council Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Jan Halbritter
- Medical Department III-Endocrinology, Nephrology, Rheumatology, University of Leipzig Medical Center, Leipzig, Germany
- Department of Nephrology and Medical Intensive Care, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Janine F Felix
- Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Jasmin Divers
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Jaspal S Kooner
- Department of Cardiology, Ealing Hospital, London North West University Healthcare NHS Trust, Middlesex, UK
- Imperial College Healthcare NHS Trust, Imperial College London, London, UK
- MRC-PHE Center for Environment and Health, School of Public Health, Imperial College London, London, UK
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Jeannette Jen-Mai Lee
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | | | - Jerome I Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institutefor Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Jianjun Liu
- Genome Institute of Singapore, Agency for Science Technology and Research, Singapore, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
| | - Jie Xu
- Beijing Institute of Ophthalmology, Beijing Key Laboratory of Ophthalmology and Visual Sciences, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Joachim Thiery
- LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
- Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, University of Leipzig, Leipzig, Germany
| | - Johan Ärnlöv
- Division of Family Medicine and Primary Care, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
- School of Health and Social Studies, Dalarna University, Stockholm, Sweden
| | - Johanna Kuusisto
- University of Eastern Finland, Kuopio, Finland
- Kuopio University Hospital, Kuopio, Finland
| | - Johanna Jakobsdottir
- Icelandic Heart Association, Kopavogur, Iceland
- The Center of Public Health Sciences, University of Iceland, Reykjavík, Iceland
| | - Johanne Tremblay
- Montreal University Hospital Research Center, CHUM, Montreal, QC, Canada
- CRCHUM, Montreal, QC, Canada
| | - John C Chambers
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Medicine, Imperial College London, London, UK
- Department of Cardiology, Ealing Hospital, London North West University Healthcare NHS Trust, Middlesex, UK
- Imperial College Healthcare NHS Trust, Imperial College London, London, UK
- MRC-PHE Center for Environment and Health, School of Public Health, Imperial College London, London, UK
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - John B Whitfield
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - John M Gaziano
- Department of Internal Medicine, Harvard Medical School, Boston, MA, USA
- VA Cooperative Studies Program, VA Boston Healthcare System, Boston, MA, USA
| | - Jonathan Marten
- Medical Research Council Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Josef Coresh
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Jost B Jonas
- Beijing Institute of Ophthalmology, Beijing Key Laboratory of Ophthalmology and Visual Sciences, Beijing Tongren Hospital, Capital Medical University, Beijing, China
- Department of Ophthalmology, Medical Faculty Mannheim, University Heidelberg, Mannheim, Germany
- Instituteof Molecular and Clinical Ophthalmology, Basel, Switzerland
- Privatpraxis Prof Jonas und Dr Panda-Jonas, Heidelberg, Germany
| | - Josyf C Mychaleckyj
- Center for Public Health Genomics, University of Virginia, Charlottesville, Charlottesville, VA, USA
| | - Kaare Christensen
- Danish Aging Research Center, University of Southern Denmark, Odense C, Denmark
| | - Kai-Uwe Eckardt
- Intensive Care Medicine, Charité, Berlin, Germany
- Department of Nephrology and Hypertension, Friedrich Alexander University Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Karen L Mohlke
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
| | - Karlhans Endlich
- DZHK (German Center for Cardiovascular Research), partner site Greifswald, Greifswald, Germany
- Department of Anatomy and Cell Biology, University Medicine Greifswald, Greifswald, Germany
| | - Katalin Dittrich
- Department of Women and Child Health, Hospital for Children and Adolescents, University of Leipzig, Leipzig, Germany
- Center for Pediatric Research, University of Leipzig, Leipzig, Germany
| | - Kathleen A Ryan
- Division of Endocrinology, Diabetes and Nutrition, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Kenneth M Rice
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Kent D Taylor
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institutefor Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Kevin Ho
- Geisinger Research, Biomedical and Translational Informatics Institute, Rockville, MD, USA
- Department of Nephrology, Geisinger, Danville, PA, USA
| | - Kjell Nikus
- Department of Cardiology, Heart Center, Tampere University Hospital, Tampere, Finland
- Department of Cardiology, Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Koichi Matsuda
- Laboratory of Clinical Genome Sequencing, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Konstantin Strauch
- Institute of Genetic Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
- Chair of Genetic Epidemiology, IBE, Faculty of Medicine, Ludwig-Maximilians-Universität München, München, Germany
- Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Center, Johannes Gutenberg University, Mainz, Germany
| | - Kozeta Miliku
- Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Kristian Hveem
- K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - Lars Lind
- Cardiovascular Epidemiology, Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | - Lars Wallentin
- Cardiology, Department of Medical Sciences, Uppsala University, Uppsala, Sweden
- Uppsala Clinical Research Center, Uppsala University, Uppsala, Sweden
| | | | - Laura M Raffield
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
| | - Lawrence S Phillips
- Atlanta VA Health Care System, Decatur, GA, USA
- Division of Endocrinology and Metabolism, Department of Medicine, Emory University School of Medicine, Atlanta, GA, USA
| | - Lenore J Launer
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, Intramural Research Program, US National Institutes of Health, Bethesda, MD, USA
| | - Leo-Pekka Lyytikäinen
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland
- Department of Clinical Chemistry, Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Leslie A Lange
- Division of Biomedical Informatics and Personalized Medicine, School of Medicine, University of Colorado Denver-Anschutz Medical Campus, Aurora, CO, USA
| | - Lorena Citterio
- Nephrology and Dialysis Unit, Genomics of Renal Diseases and Hypertension Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Lucija Klaric
- Medical Research Council Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - M Arfan Ikram
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Marcus Ising
- Max Planck Institute of Psychiatry, Munich, Germany
| | - Marcus E Kleber
- Vth Department of Medicine (Nephrology, Hypertensiology, Rheumatology, Endocrinology, Diabetology), Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
- SYNLAB MVZ Humangenetik Mannheim, Mannheim, Germany
| | | | - Maria Pina Concas
- Institute for Maternal and Child Health, IRCCS 'Burlo Garofolo', Trieste, Italy
| | - Marina Ciullo
- Institute of Genetics and Biophysics 'Adriano Buzzati-Traverso'-CNR, Naples, Italy
- IRCCS Neuromed, Pozzilli, Italy
| | - Mario Piratsu
- Institute of Genetic and Biomedical Research, National Research Council of Italy, Cagliari, Italy
| | | | - Markku Laakso
- University of Eastern Finland, Kuopio, Finland
- Kuopio University Hospital, Kuopio, Finland
| | - Markus Loeffler
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
- LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - Markus Perola
- Finnish Institute for Health and Welfare, Helsinki, Finland
- Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Martin H de Borst
- Division of Nephrology, Department of Internal Medicine, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Martin Gögele
- Eurac Research, Institute for Biomedicine (affiliated with the University of Lübeck), Bolzano, Italy
| | - Martina La Bianca
- Institute for Maternal and Child Health, IRCCS 'Burlo Garofolo', Trieste, Italy
| | - Mary Ann Lukas
- Target Sciences-Genetics, GlaxoSmithKline, Albuquerque, NM, USA
| | - Mary F Feitosa
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
| | - Mary L Biggs
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Mary K Wojczynski
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
| | - Maryam Kavousi
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Masahiro Kanai
- Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences (IMS), Yokohama, Japan
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Masato Akiyama
- Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences (IMS), Yokohama, Japan
- Department of Ophthalmology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Masayuki Yasuda
- Singapore Eye Research Institute, Singapore National Eye Center, Singapore, Singapore
- Department of Ophthalmology, Tohoku University Graduate School of Medicine, Miyagi, Japan
| | - Matthias Nauck
- DZHK (German Center for Cardiovascular Research), partner site Greifswald, Greifswald, Germany
- Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Melanie Waldenberger
- Institute of Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
- Research Unit Molecular Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
- DZHK (German Center for Cardiovascular Research), Partner Site Munich Heart Alliance, Munich, Germany
| | - Miao-Li Chee
- Singapore Eye Research Institute, Singapore National Eye Center, Singapore, Singapore
| | - Miao-Ling Chee
- Singapore Eye Research Institute, Singapore National Eye Center, Singapore, Singapore
| | - Michael Boehnke
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Michael H Preuss
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Michael Stumvoll
- Medical Department III-Endocrinology, Nephrology, Rheumatology, University of Leipzig Medical Center, Leipzig, Germany
| | - Michael A Province
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
| | - Michele K Evans
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, Intramural Research Program, US National Institutes of Health, Baltimore, MD, USA
| | - Michelle L O'Donoghue
- Cardiovascular Division, Brigham and Women's Hospital, Boston, MA, USA
- TIMI Study Group, Boston, MA, USA
| | - Michiaki Kubo
- RIKEN Center for Integrative Medical Sciences (IMS), Yokohama (Kanagawa), Japan
| | - Mika Kähönen
- Department of Clinical Physiology, Tampere University Hospital, Tampere, Finland
- Department of Clinical Physiology, Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | | | - Mike A Nalls
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
- Data Tecnica International, Glen Echo, MD, USA
| | - Mikko Kuokkanen
- Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- The Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
- South Texas Diabetes and Obesity Institute and Department of Human Genetics, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, USA
| | - Mohsen Ghanbari
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- Department of Genetics, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Murielle Bochud
- Center for Primary Care and Public Health (Unisanté), University of Lausanne, 1010, Lausanne, Switzerland
| | - Navya Shilpa Josyula
- Department of Population Health Sciences, Geisinger Health, 100 N. Academy Ave., Danville, PA, USA
| | | | - Nicholas Y Q Tan
- Singapore Eye Research Institute, Singapore National Eye Center, Singapore, Singapore
| | | | - Nicola Pirastu
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Nicole Schupf
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University Medical Center, New York, NY, USA
| | - Niek Verweij
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Nina Hutri-Kähönen
- Tampere Centre for Skills Training and Simulation, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Nina Mononen
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland
- Department of Clinical Chemistry, Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Nisha Bansal
- Division of Nephrology, University of Washington, Seattle, WA, USA
- Kidney Research Institute, University of Washington, Seattle, WA, USA
| | - Olivier Devuyst
- Institute of Physiology, University of Zurich, Zurich, Switzerland
| | - Olle Melander
- Department of Clincial Sciences in Malmö, Lund University, Malmö, Sweden
| | - Olli T Raitakari
- Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland
- Research Center of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland
- Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland
| | - Ozren Polasek
- Faculty of Medicine, University of Split, Split, Croatia
- Algebra University College, Ilica 242, Zagreb, Croatia
| | - Paolo Manunta
- Nephrology and Dialysis Unit, Genomics of Renal Diseases and Hypertension Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Paolo Gasparini
- Institute for Maternal and Child Health, IRCCS 'Burlo Garofolo', Trieste, Italy
- Department of Medicine, Surgery and Health Sciences, University of Trieste, Trieste, Italy
| | - Pashupati P Mishra
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland
- Department of Clinical Chemistry, Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | | | - Patrik K E Magnusson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Paul Elliott
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Medicine, Imperial College London, London, UK
- Dementia Research Institute, Imperial College London, London, UK
- Imperial College NIHR Biomedical Research Center, Imperial College London, London, UK
- Health Data Research UK-London, London, UK
| | - Paul M Ridker
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Pavel Hamet
- Montreal University Hospital Research Center, CHUM, Montreal, QC, Canada
- Medpharmgene, Montreal, QC, Canada
| | - Per O Svensson
- Department of Clinical Science and Education, Karolinska Institutet, Södersjukhuset, Stockholm, Sweden
- Department of Cardiology, Södersjukhuset, Stockholm, Sweden
| | - Peter K Joshi
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Peter Kovacs
- Medical Department III-Endocrinology, Nephrology, Rheumatology, University of Leipzig Medical Center, Leipzig, Germany
- Integrated Research and Treatment Center Adiposity Diseases, University of Leipzig, Leipzig, Germany
| | - Peter P Pramstaller
- Eurac Research, Institute for Biomedicine (affiliated with the University of Lübeck), Bolzano, Italy
| | - Peter Rossing
- Steno Diabetes Center Copenhagen, Herlev, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Peter Vollenweider
- Department of Medicine, Internal Medicine, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Pim van der Harst
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Rajkumar Dorajoo
- Genome Institute of Singapore, Agency for Science Technology and Research, Singapore, Singapore
| | - Ralene Z H Sim
- Singapore Eye Research Institute, Singapore National Eye Center, Singapore, Singapore
| | - Ralph Burkhardt
- LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
- Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, University of Leipzig, Leipzig, Germany
- Institute of Clinical Chemistry and Laboratory Medicine, University Hospital Regensburg, Regensburg, Germany
| | - Ran Tao
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Raymond Noordam
- Section of Gerontology and Geriatrics, Department of Internal Medicine, Leiden University Medical Center, Leiden, The Netherlands
| | - Reedik Mägi
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Reinhold Schmidt
- Clinical Division of Neurogeriatrics, Department of Neurology, Medical University of Graz, Graz, Austria
| | - Renée de Mutsert
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Rico Rueedi
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Rob M van Dam
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
- Department of Exercise and Nutrition Sciences, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA
| | - Robert J Carroll
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Ron T Gansevoort
- Division of Nephrology, Department of Internal Medicine, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Ruth J F Loos
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Novo Nordisk Foundation Center for Basic Metabolic Research, Department of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | | | - Sanaz Sedaghat
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Sandosh Padmanabhan
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK
| | - Sandra Freitag-Wolf
- Institute of Medical Informatics and Statistics, Kiel University, University Hospital Schleswig-Holstein, Kiel, Germany
| | - Sarah A Pendergrass
- Geisinger Research, Biomedical and Translational Informatics Institute, Danville, PA, USA
| | - Sarah E Graham
- Department of Internal Medicine, Division of Cardiology, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Scott D Gordon
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Shih-Jen Hwang
- NHLBI's Framingham Heart Study, Framingham, MA, USA
- The Center for Population Studies, NHLBI, Framingham, MA, USA
| | - Shona M Kerr
- Medical Research Council Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Simona Vaccargiu
- Institute of Genetic and Biomedical Research, National Research Council of Italy, Cagliari, Italy
| | - Snehal B Patil
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, 48109, USA
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, 48109, USA
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Stein Hallan
- Department of Clinical and Molecular Medicine, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Nephrology, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Stephan J L Bakker
- Division of Nephrology, Department of Internal Medicine, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Su-Chi Lim
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
- Diabetes Center, Khoo Teck Puat Hospital, Singapore, Singapore
| | | | - Suzanne Vogelezang
- Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Sven Bergmann
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Tanguy Corre
- Center for Primary Care and Public Health (Unisanté), University of Lausanne, 1010, Lausanne, Switzerland
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Tarunveer S Ahluwalia
- Steno Diabetes Center Copenhagen, Herlev, Denmark
- The Bioinformatics Center, Department of Biology, Faculty of Science, University of Copenhagen, Copenhagen, Denmark
| | - Terho Lehtimäki
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland
- Department of Clinical Chemistry, Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Thibaud S Boutin
- Medical Research Council Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Thomas Meitinger
- DZHK (German Center for Cardiovascular Research), Partner Site Munich Heart Alliance, Munich, Germany
- Institute of Human Genetics, Helmholtz Zentrum München, Neuherberg, Germany
- Institute of Human Genetics, Technische Universität München, Munich, Germany
| | - Tien-Yin Wong
- Singapore Eye Research Institute, Singapore National Eye Center, Singapore, Singapore
- Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
| | - Tobias Bergler
- Department of Nephrology, University Hospital Regensburg, Regensburg, Germany
| | - Ton J Rabelink
- Section of Nephrology, Department of Internal Medicine, Leiden University Medical Center, Leiden, The Netherlands
- Einthoven Laboratory of Experimental Vascular Research, Leiden University Medical Center, Leiden, The Netherlands
| | - Tõnu Esko
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
| | - Toomas Haller
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Unnur Thorsteinsdottir
- Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland
- deCODE Genetics/Amgen, Inc., Reykjavik, Iceland
| | - Uwe Völker
- DZHK (German Center for Cardiovascular Research), partner site Greifswald, Greifswald, Germany
- Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany
| | - Valencia Hui Xian Foo
- Singapore Eye Research Institute, Singapore National Eye Center, Singapore, Singapore
| | - Veikko Salomaa
- Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Veronique Vitart
- Medical Research Council Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Vilmantas Giedraitis
- Molecular Geriatrics, Department of Public Health and Caring Sciences, Uppsala University, Uppsala, Sweden
| | - Vilmundur Gudnason
- Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland
- Icelandic Heart Association, Kopavogur, Iceland
| | - Vincent W V Jaddoe
- Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Wei Huang
- Department of Genetics, Shanghai-MOST Key Laboratory of Health and Disease Genomics, Chinese National Human Genome Center, Shanghai, China
- Shanghai Industrial Technology Institute, Shanghai, China
| | - Weihua Zhang
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Medicine, Imperial College London, London, UK
- Department of Cardiology, Ealing Hospital, London North West University Healthcare NHS Trust, Middlesex, UK
| | - Wen Bin Wei
- Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Wieland Kiess
- LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
- Department of Women and Child Health, Hospital for Children and Adolescents, University of Leipzig, Leipzig, Germany
- Center for Pediatric Research, University of Leipzig, Leipzig, Germany
| | - Winfried März
- Vth Department of Medicine (Nephrology, Hypertensiology, Rheumatology, Endocrinology, Diabetology), Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
- Synlab Academy, Synlab Holding Deutschland GmbH, Mannheim, Germany
- Clinical Institute of Medical and Chemical Laboratory Diagnostics, Medical University of Graz, Graz, Austria
| | - Wolfgang Koenig
- DZHK (German Center for Cardiovascular Research), Partner Site Munich Heart Alliance, Munich, Germany
- Deutsches Herzzentrum München, Technische Universität München, Munich, Germany
- Institute of Epidemiology and Medical Biometry, University of Ulm, Ulm, Germany
| | - Wolfgang Lieb
- Institute of Epidemiology and Biobank Popgen, Kiel University, Kiel, Germany
| | - Xin Gao
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Xueling Sim
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | - Ya Xing Wang
- Beijing Institute of Ophthalmology, Beijing Key Laboratory of Ophthalmology and Visual Sciences, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Yechiel Friedlander
- School of Public Health and Community Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Yih-Chung Tham
- Singapore Eye Research Institute, Singapore National Eye Center, Singapore, Singapore
| | - Yoichiro Kamatani
- Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences (IMS), Yokohama, Japan
- Laboratory of Complex Trait Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Yukinori Okada
- Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences (IMS), Yokohama, Japan
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences (IMS), Osaka, Japan
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Yuri Milaneschi
- Department of Psychiatry, VU University Medical Centre, Amsterdam, The Netherlands
| | - Zhi Yu
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Massachusetts General Hospital, Boston, MA, USA
| | - Klaus J Stark
- Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany
| | - Kari Stefansson
- Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland
- deCODE Genetics/Amgen, Inc., Reykjavik, Iceland
| | - Carsten A Böger
- Department of Population Medicine and Lifestyle Diseases Prevention, Medical University of Bialystok, Bialystok, Poland
- Department of Nephrology, University Hospital Regensburg, Regensburg, Germany
- Department of Nephrology and Rheumatology, Kliniken Südostbayern, Traunstein, Germany
| | - Adriana M Hung
- Department of Veteran's Affairs, Tennessee Valley Healthcare System (626)/Vanderbilt University, Nashville, TN, USA
- Vanderbilt University Medical Center, Division of Nephrology and Hypertension, Vanderbilt Center for Kidney Disease and Integrated Program for Acute Kidney Injury Research, and Vanderbilt Precision Nephrology Program Nashville, Nashville, TN, USA
| | - Florian Kronenberg
- Department of Genetics and Pharmacology, Institute of Genetic Epidemiology, Medical University of Innsbruck, Innsbruck, Austria
| | - Anna Köttgen
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Institute of Genetic Epidemiology, Department of Data Driven Medicine, Faculty of Medicine and Medical Center-University of Freiburg, Freiburg, Germany
| | - Cristian Pattaro
- Eurac Research, Institute for Biomedicine (affiliated with the University of Lübeck), Bolzano, Italy
| | - Iris M Heid
- Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany.
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De Silva K, Demmer RT, Jönsson D, Mousa A, Teede H, Forbes A, Enticott J. Causality of anthropometric markers associated with polycystic ovarian syndrome: Findings of a Mendelian randomization study. PLoS One 2022; 17:e0269191. [PMID: 35679284 PMCID: PMC9182303 DOI: 10.1371/journal.pone.0269191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Accepted: 05/16/2022] [Indexed: 11/18/2022] Open
Abstract
Introduction Using body mass index (BMI) as a proxy, previous Mendelian randomization (MR) studies found total causal effects of general obesity on polycystic ovarian syndrome (PCOS). Hitherto, total and direct causal effects of general- and central obesity on PCOS have not been comprehensively analyzed. Objectives To investigate the causality of central- and general obesity on PCOS using surrogate anthropometric markers. Methods Summary GWAS data of female-only, large-sample cohorts of European ancestry were retrieved for anthropometric markers of central obesity (waist circumference (WC), hip circumference (HC), waist-to-hip ratio (WHR)) and general obesity (BMI and its constituent variables–weight and height), from the IEU Open GWAS Project. As the outcome, we acquired summary data from a large-sample GWAS (118870 samples; 642 cases and 118228 controls) within the FinnGen cohort. Total causal effects were assessed via univariable two-sample Mendelian randomization (2SMR). Genetic architectures underlying causal associations were explored. Direct causal effects were analyzed by multivariable MR modelling. Results Instrumental variables demonstrated no weak instrument bias (F > 10). Four anthropometric exposures, namely, weight (2.69–77.05), BMI (OR: 2.90–4.06), WC (OR: 6.22–20.27), and HC (OR: 6.22–20.27) demonstrated total causal effects as per univariable 2SMR models. We uncovered shared and non-shared genetic architectures underlying causal associations. Direct causal effects of WC and HC on PCOS were revealed by two multivariable MR models containing exclusively the anthropometric markers of central obesity. Other multivariable MR models containing anthropometric markers of both central- and general obesity showed no direct causal effects on PCOS. Conclusions Both and general- and central obesity yield total causal effects on PCOS. Findings also indicated potential direct causal effects of normal weight-central obesity and more complex causal mechanisms when both central- and general obesity are present. Results underscore the importance of addressing both central- and general obesity for optimizing PCOS care.
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Affiliation(s)
- Kushan De Silva
- Monash Centre for Health Research and Implementation, School of Public Health and Preventive Medicine, Faculty of Medicine, Nursing, and Health Sciences, Monash University, Clayton, Australia
- * E-mail:
| | - Ryan T. Demmer
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, Minnesota, United States of America
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, United States of America
| | - Daniel Jönsson
- Department of Clinical Sciences, Faculty of Medicine, Lund University, Malmö, Sweden
- Public Dental Service of Skane, Lund, Sweden
| | - Aya Mousa
- Monash Centre for Health Research and Implementation, School of Public Health and Preventive Medicine, Faculty of Medicine, Nursing, and Health Sciences, Monash University, Clayton, Australia
| | - Helena Teede
- Monash Centre for Health Research and Implementation, School of Public Health and Preventive Medicine, Faculty of Medicine, Nursing, and Health Sciences, Monash University, Clayton, Australia
| | - Andrew Forbes
- Biostatistics Unit, Division of Research Methodology, School of Public Health and Preventive Medicine, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia
| | - Joanne Enticott
- Monash Centre for Health Research and Implementation, School of Public Health and Preventive Medicine, Faculty of Medicine, Nursing, and Health Sciences, Monash University, Clayton, Australia
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46
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Abaj F, Rafiee M, Koohdani F. A Personalized Diet Approach Study: Interaction between PPAR-γ Pro12Ala and Dietary Insulin Indices on Metabolic Markers in Diabetic Patients. J Hum Nutr Diet 2022; 35:663-674. [PMID: 35560467 DOI: 10.1111/jhn.13033] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Accepted: 04/05/2022] [Indexed: 11/28/2022]
Abstract
BACKGROUND The objectives were to investigate the effect of the interaction between peroxisome proliferator-activated receptor gamma (PPAR-γ) Pro12Ala polymorphisms and dietary insulin load and insulin index (DIL and DII) on Cardio-metabolic Markers among diabetic patients. METHODS This cross-sectional study was conducted on 393 diabetic patients. Food-frequency questionnaire (FFQ) was used for DIL and DII calculation. PPAR-γ Pro12Ala was genotyped by the PCR-RFLP method. Biochemical markers including TC, LDL, HDL, TG, SOD, CRP, TAC, PTX3, PGF2α. IL18, leptin and ghrelin were measured by standard protocol. RESULT Risk-allele carriers (CG, GG) had higher obesity indices WC (P interaction =0.04), BMI (P interaction =0.006) and, WC (P interaction =0.04) compared with individuals with the CC genotype when they consumed a diet with higher DIL and DII respectively. Besides, carriers of the G allele who were in the highest tertile of DIL, had lower HDL (P interaction =0.04) and higher PGF2α (P interaction =0.03) and PTX3 (P interaction =0.03). Moreover, the highest tertile of the DII, showed an increase in IL18 (P interaction =0.01) and lower SOD (P interaction =0.03) for risk allele carriers compared to those with CC homozygotes. CONCLUSION We revealed PPAR-γ Pro12Ala polymorphism was able to intensify the effect of DIL and DII on CVD risk factors; risk-allele carriers who consumed a diet with high DIL and DII score have more likely to be obese and have higher inflammatory markers. Also, protective factor against CVD risk factors were reduced significantly in this group compared to CC homozygotes. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Faezeh Abaj
- Department of Community Nutrition, School of Nutritional Sciences and Dietetics, Tehran University of Medical Sciences (TUMS), Tehran, Iran
| | - Masoumeh Rafiee
- Department of Clinical Nutrition, School of Nutrition and Food Science, Isfahan University of Medical Sciences (IUMS), Isfahan, Iran
| | - Fariba Koohdani
- Department of Cellular, Molecular Nutrition, School of Nutritional Sciences and Dietetics, Tehran University of Medical Sciences (TUMS), Tehran, Iran
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Loh M, Zhang W, Ng HK, Schmid K, Lamri A, Tong L, Ahmad M, Lee JJ, Ng MCY, Petty LE, Spracklen CN, Takeuchi F, Islam MT, Jasmine F, Kasturiratne A, Kibriya M, Mohlke KL, Paré G, Prasad G, Shahriar M, Chee ML, de Silva HJ, Engert JC, Gerstein HC, Mani KR, Sabanayagam C, Vujkovic M, Wickremasinghe AR, Wong TY, Yajnik CS, Yusuf S, Ahsan H, Bharadwaj D, Anand SS, Below JE, Boehnke M, Bowden DW, Chandak GR, Cheng CY, Kato N, Mahajan A, Sim X, McCarthy MI, Morris AP, Kooner JS, Saleheen D, Chambers JC. Identification of genetic effects underlying type 2 diabetes in South Asian and European populations. Commun Biol 2022; 5:329. [PMID: 35393509 PMCID: PMC8991226 DOI: 10.1038/s42003-022-03248-5] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2020] [Accepted: 03/08/2022] [Indexed: 02/08/2023] Open
Abstract
South Asians are at high risk of developing type 2 diabetes (T2D). We carried out a genome-wide association meta-analysis with South Asian T2D cases (n = 16,677) and controls (n = 33,856), followed by combined analyses with Europeans (neff = 231,420). We identify 21 novel genetic loci for significant association with T2D (P = 4.7 × 10-8 to 5.2 × 10-12), to the best of our knowledge at the point of analysis. The loci are enriched for regulatory features, including DNA methylation and gene expression in relevant tissues, and highlight CHMP4B, PDHB, LRIG1 and other genes linked to adiposity and glucose metabolism. A polygenic risk score based on South Asian-derived summary statistics shows ~4-fold higher risk for T2D between the top and bottom quartile. Our results provide further insights into the genetic mechanisms underlying T2D, and highlight the opportunities for discovery from joint analysis of data from across ancestral populations.
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Affiliation(s)
- Marie Loh
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, 308232, Singapore
- Department of Epidemiology and Biostatistics, Imperial College London, London, W2 1PG, UK
| | - Weihua Zhang
- Department of Epidemiology and Biostatistics, Imperial College London, London, W2 1PG, UK
- Department of Cardiology, Ealing Hospital, London North West Healthcare NHS Trust, Middlesex, UB1 3HW, UK
| | - Hong Kiat Ng
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, 308232, Singapore
| | - Katharina Schmid
- Institute of Computational Biology, Deutsches Forschungszentrum für Gesundheit und Umwelt, Helmholtz Zentrum München, 85764, Neuherberg, Germany
- Department of Informatics, Technical University of Munich, 85748, Garching bei München, Neuherberg, Germany
| | - Amel Lamri
- Department of Medicine, McMaster University, Hamilton, ON, Canada
- Population Health Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, ON, Canada
| | - Lin Tong
- The University of Chicago, Biological Sciences Division, Public Health Sciences, 5841 South Maryland Avenue, MC2000, Chicago, IL, 60637, USA
| | - Meraj Ahmad
- Genomic Research on Complex diseases, CSIR-Centre for Cellular and Molecular Biology (CSIR-CCMB), Hyderabad, India
| | - Jung-Jin Lee
- Translational Medicine and Human Genetics, Department of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Medicine, Mayo Hospital, Lahore, Pakistan
| | - Maggie C Y Ng
- Center for Genomics and Personalized Medicine Research, Center for Diabetes Research, Wake Forest School of Medicine, Winston-Salem, NC, 37215, USA
- Vanderbilt Genetics Institute, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - Lauren E Petty
- Vanderbilt Genetics Institute, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Cassandra N Spracklen
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
- Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA, 01003, USA
| | - Fumihiko Takeuchi
- Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
| | - Md Tariqul Islam
- U Chicago Research Bangladesh, House#4, Road#2b, Sector#4, Uttara, Dhaka, 1230, Bangladesh
| | - Farzana Jasmine
- The University of Chicago, Biological Sciences Division, Public Health Sciences, 5841 South Maryland Avenue, MC2000, Chicago, IL, 60637, USA
| | - Anuradhani Kasturiratne
- Department of Public Health, Faculty of Medicine, University of Kelaniya, Kelaniya, Sri Lanka
| | - Muhammad Kibriya
- The University of Chicago, Biological Sciences Division, Public Health Sciences, 5841 South Maryland Avenue, MC2000, Chicago, IL, 60637, USA
| | - Karen L Mohlke
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Guillaume Paré
- Population Health Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, ON, Canada
- Department of Pathology and Molecular Medicine, McMaster University, Hamilton, ON, Canada
| | - Gauri Prasad
- Academy of Scientific and Innovative Research, CSIR-Institute of Genomics and Integrative Biology Campus, New Delhi, 110020, India
- Systems Genomics Laboratory, School of Biotechnology, Jawaharlal Nehru University, New Delhi, 110067, India
| | - Mohammad Shahriar
- The University of Chicago, Biological Sciences Division, Public Health Sciences, 5841 South Maryland Avenue, MC2000, Chicago, IL, 60637, USA
| | - Miao Ling Chee
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
| | - H Janaka de Silva
- Department of Medicine, Faculty of Medicine, University of Kelaniya, Kelaniya, Sri Lanka
| | - James C Engert
- Department of Medicine, McGill University, Montreal, QC, Canada
- Department of Human Genetics, McGill University, Montreal, QC, Canada
| | - Hertzel C Gerstein
- Department of Medicine, McMaster University, Hamilton, ON, Canada
- Population Health Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, ON, Canada
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada
| | - K Radha Mani
- Genomic Research on Complex diseases, CSIR-Centre for Cellular and Molecular Biology (CSIR-CCMB), Hyderabad, India
| | - Charumathi Sabanayagam
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Ophthalmology & Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
| | - Marijana Vujkovic
- Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
| | - Ananda R Wickremasinghe
- Department of Public Health, Faculty of Medicine, University of Kelaniya, Kelaniya, Sri Lanka
| | - Tien Yin Wong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Ophthalmology & Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | | | - Salim Yusuf
- Department of Medicine, McMaster University, Hamilton, ON, Canada
- Population Health Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, ON, Canada
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada
| | - Habibul Ahsan
- The University of Chicago, Biological Sciences Division, Public Health Sciences, 5841 South Maryland Avenue, MC2000, Chicago, IL, 60637, USA
| | - Dwaipayan Bharadwaj
- Academy of Scientific and Innovative Research, CSIR-Institute of Genomics and Integrative Biology Campus, New Delhi, 110020, India
- Systems Genomics Laboratory, School of Biotechnology, Jawaharlal Nehru University, New Delhi, 110067, India
| | - Sonia S Anand
- Department of Medicine, McMaster University, Hamilton, ON, Canada
- Population Health Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, ON, Canada
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada
| | - Jennifer E Below
- Vanderbilt Genetics Institute, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Michael Boehnke
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, 48109, USA
| | - Donald W Bowden
- Department of Medicine, Mayo Hospital, Lahore, Pakistan
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC, 37215, USA
| | - Giriraj R Chandak
- Genomic Research on Complex diseases, CSIR-Centre for Cellular and Molecular Biology (CSIR-CCMB), Hyderabad, India
- JSS Academy of Health Education of Research, Mysuru, India
- Science and Engineering Research Board, Department of Science and Technology, Ministry of Science and technology, Government of India, New Delhi, India
| | - Ching-Yu Cheng
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Ophthalmology & Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Norihiro Kato
- Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
| | - Anubha Mahajan
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, OX3 7BN, UK
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, OX3 7BN, UK
| | - Xueling Sim
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | - Mark I McCarthy
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, OX3 7BN, UK
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, OX3 7BN, UK
- Oxford NIHR Biomedical Research Centre, Churchill Hosptial, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 7LE, UK
| | - Andrew P Morris
- Department of Biostatistics, University of Liverpool, Liverpool, L69 3GL, UK
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, UK
| | - Jaspal S Kooner
- Department of Cardiology, Ealing Hospital, London North West Healthcare NHS Trust, Middlesex, UB1 3HW, UK.
- Imperial College Healthcare NHS Trust, Imperial College London, London, W12 0HS, UK.
- MRC-PHE Centre for Enviroment and Health, Imperial College London, London, W2 1PG, UK.
- National Heart and Lung Institute, Imperial College London, London, W12 0NN, UK.
| | - Danish Saleheen
- Center for Non-Communicable Diseases, Karachi, Pakistan.
- Department of Medicine, Columbia University Irving Medical Center, New York, NY, 10032, USA.
- Department of Cardiology, Columbia University Irving Medical Center, New York, NY, 10032, USA.
| | - John C Chambers
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, 308232, Singapore.
- Department of Epidemiology and Biostatistics, Imperial College London, London, W2 1PG, UK.
- Department of Cardiology, Ealing Hospital, London North West Healthcare NHS Trust, Middlesex, UB1 3HW, UK.
- Imperial College Healthcare NHS Trust, Imperial College London, London, W12 0HS, UK.
- MRC-PHE Centre for Enviroment and Health, Imperial College London, London, W2 1PG, UK.
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Wood AC, Arora A, Newell M, Bland VL, Zhou J, Pirastu N, Ordovas JM, Klimentidis YC. Identification of genetic loci simultaneously associated with multiple cardiometabolic traits. Nutr Metab Cardiovasc Dis 2022; 32:1027-1034. [PMID: 35168826 PMCID: PMC9275655 DOI: 10.1016/j.numecd.2022.01.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Revised: 12/09/2021] [Accepted: 01/04/2022] [Indexed: 11/30/2022]
Abstract
BACKGROUND AND AIMS Cardiometabolic disorders (CMD) arise from a constellation of features such as increased adiposity, hyperlipidemia, hypertension and compromised glucose control. Many genetic loci have shown associations with individual CMD-related traits, but no investigations have focused on simultaneously identifying loci showing associations across all domains. We therefore sought to identify loci associated with risk across seven continuous CMD-related traits. METHODS AND RESULTS We conducted separate genome-wide association studies (GWAS) for systolic and diastolic blood pressure (SBP/DBP), hemoglobin A1c (HbA1c), low- and high- density lipoprotein cholesterol (LDL-C/HDL-C), waist-to-hip-ratio (WHR), and triglycerides (TGs) in the UK Biobank (N = 356,574-456,823). Multiple loci reached genome-wide levels of significance (N = 145-333) for each trait, but only four loci (in/near VEGFA, GRB14-COBLL1, KLF14, and RGS19-OPRL1) were associated with risk across all seven traits (P < 5 × 10-8). We sought replication of these four loci in an independent set of seven trait-specific GWAS meta-analyses. GRB14-COBLL1 showed the most consistent replication, revealing nominally significant associations (P < 0.05) with all traits except DBP. CONCLUSIONS Our analyses suggest that very few loci are associated in the same direction of risk with traits representing the full spectrum of CMD features. We identified four such loci, and an understanding of the pathways between these loci and CMD risk may eventually identify factors that can be used to identify pathologic disturbances that represent broadly beneficial therapeutic targets.
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Affiliation(s)
- Alexis C Wood
- USDA/ARS Children's Nutrition Research Center, 1100 Bates Avenue, Houston, TX, USA.
| | - Amit Arora
- Department of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, USA
| | - Michelle Newell
- Department of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, USA
| | - Victoria L Bland
- Division of Geriatric Medicine, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Jin Zhou
- Department of Biostatistics, University of California, Los Angeles, CA, USA
| | - Nicola Pirastu
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, Scotland, UK
| | - Jose M Ordovas
- Nutrition and Genomics Laboratory, Jean Mayer U.S. Department of Agriculture Human Nutrition Research Center on Aging, Tufts University, Boston, MA, USA; IMDEA-Food, Madrid, Spain
| | - Yann C Klimentidis
- Department of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, USA; BIO5 Institute, University of Arizona, Tucson, AZ, USA
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Feitosa MF, Wojczynski MK, Anema JA, Daw EW, Wang L, Santanasto AJ, Nygaard M, Province MA. Genetic pleiotropy between pulmonary function and age-related traits: The Long Life Family Study. J Gerontol A Biol Sci Med Sci 2022; 79:glac046. [PMID: 35180297 PMCID: PMC10873520 DOI: 10.1093/gerona/glac046] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND Pulmonary function (PF) progressively declines with aging. Forced expiratory volume in the first second (FEV1) and forced vital capacity (FVC) are predictors of morbidity of pulmonary and cardiovascular diseases and all-cause mortality. In addition, reduced PF is associated with elevated chronic low-grade systemic inflammation, glucose metabolism, body fatness, and low muscle strength. It may suggest pleiotropic genetic effects between PF with these age-related factors. METHODS We evaluated whether FEV1 and FVC share common pleiotropic genetic effects factors with interleukin-6, high-sensitivity C-reactive protein, body mass index, muscle (grip) strength, plasma glucose, and glycosylated hemoglobin in 3,888 individuals (age range: 26-106). We employed sex-combined and sex-specific correlated meta-analyses to test whether combining genome-wide association p-values from two or more traits enhances the ability to detect variants sharing effects on these correlated traits. RESULTS We identified 32 loci for PF, including 29 novel pleiotropic loci associated with pulmonary function and (i) body fatness (CYP2U1/SGMS2), (ii) glucose metabolism (CBWD1/DOCK8 and MMUT/CENPQ), (iii) inflammatory markers (GLRA3/HPGD, TRIM9, CALN1, CTNNB1/ZNF621, GATA5/SLCO4A1/NTSR1, and NPVF/C7orf31/CYCS), and (iv) muscle strength (MAL2, AC008825.1/LINC02103, AL136418.1). CONCLUSIONS The identified genes/loci for PF and age-related traits suggest their underlying shared genetic effects, which can explain part of their phenotypic correlations. Integration of gene expression and genomic annotation data shows enrichment of our genetic variants in lung, blood, adipose, pancreas, and muscles, among others. Our findings highlight the critical roles of identified gene/locus in systemic inflammation, glucose metabolism, strength performance, PF, and pulmonary disease, which are involved in accelerated biological aging.
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Affiliation(s)
- Mary F Feitosa
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Mary K Wojczynski
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Jason A Anema
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, Missouri, USA
| | - E Warwick Daw
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Lihua Wang
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Adam J Santanasto
- Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Marianne Nygaard
- Epidemiology, Biostatistics, and Biodemography, Department of Public Health, University of Southern Denmark, Odense C, Denmark
| | - Michael A Province
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, Missouri, USA
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50
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Boua PR, Brandenburg JT, Choudhury A, Sorgho H, Nonterah EA, Agongo G, Asiki G, Micklesfield L, Choma S, Gómez-Olivé FX, Hazelhurst S, Tinto H, Crowther NJ, Mathew CG, Ramsay M. Genetic associations with carotid intima-media thickness link to atherosclerosis with sex-specific effects in sub-Saharan Africans. Nat Commun 2022; 13:855. [PMID: 35165267 PMCID: PMC8844072 DOI: 10.1038/s41467-022-28276-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Accepted: 01/13/2022] [Indexed: 12/15/2022] Open
Abstract
Atherosclerosis precedes the onset of clinical manifestations of cardiovascular diseases (CVDs). We used carotid intima-media thickness (cIMT) to investigate genetic susceptibility to atherosclerosis in 7894 unrelated adults (3963 women, 3931 men; 40 to 60 years) resident in four sub-Saharan African countries. cIMT was measured by ultrasound and genotyping was performed on the H3Africa SNP Array. Two new African-specific genome-wide significant loci for mean-max cIMT, SIRPA (p = 4.7E-08), and FBXL17 (p = 2.5E-08), were identified. Sex-stratified analysis revealed associations with one male-specific locus, SNX29 (p = 6.3E-09), and two female-specific loci, LARP6 (p = 2.4E-09) and PROK1 (p = 1.0E-08). We replicate previous cIMT associations with different lead SNPs in linkage disequilibrium with SNPs primarily identified in European populations. Our study find significant enrichment for genes involved in oestrogen response from female-specific signals. The genes identified show biological relevance to atherosclerosis and/or CVDs, sex-differences and transferability of signals from non-African studies.
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Affiliation(s)
- Palwende Romuald Boua
- Clinical Research Unit of Nanoro, Institut de Recherche en Sciences de la Santé, Centre national de la Recherche scientifique et technologique (CNRST), Nanoro, Burkina Faso.
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
- Division of Human Genetics, National Health Laboratory Service and School of Pathology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
| | - Jean-Tristan Brandenburg
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Ananyo Choudhury
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Hermann Sorgho
- Clinical Research Unit of Nanoro, Institut de Recherche en Sciences de la Santé, Centre national de la Recherche scientifique et technologique (CNRST), Nanoro, Burkina Faso
| | - Engelbert A Nonterah
- Navrongo Health Research Centre, Ghana Health Service, Navrongo, Ghana
- Julius Global Health, Julius Centre for Health Sciences and Primary Care, University Medical Centre, Utrecht University, Utrecht, The Netherlands
| | - Godfred Agongo
- Navrongo Health Research Centre, Ghana Health Service, Navrongo, Ghana
- C.K. Tedam University of Technology and Applied Sciences, Navrongo, Ghana
| | - Gershim Asiki
- African Population and Health Research Center, Nairobi, Kenya
| | - Lisa Micklesfield
- MRC/Wits Developmental Pathways for Health Research Unit, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Solomon Choma
- Department of Pathology and Medical Sciences, University of Limpopo, Polokwane, South Africa
| | - Francesc Xavier Gómez-Olivé
- MRC/Wits Rural Public Health and Health Transitions Research Unit (Agincourt), School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Scott Hazelhurst
- School of Electrical and Information Engineering, University of the Witwatersrand, Johannesburg, South Africa
| | - Halidou Tinto
- Clinical Research Unit of Nanoro, Institut de Recherche en Sciences de la Santé, Centre national de la Recherche scientifique et technologique (CNRST), Nanoro, Burkina Faso
| | - Nigel J Crowther
- Department of Chemical Pathology, National Health Laboratory Service, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Christopher G Mathew
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- Division of Human Genetics, National Health Laboratory Service and School of Pathology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- Department of Medical and Molecular Genetics, Faculty of Life Sciences and Medicine, King's College London, London, UK
| | - Michèle Ramsay
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
- Division of Human Genetics, National Health Laboratory Service and School of Pathology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
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