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Palatini P, Saladini F, Mos L, Vriz O, Ermolao A, Battista F, Berton G, Canevari M, Rattazzi M. Healthy overweight and obesity in the young: Prevalence and risk of major adverse cardiovascular events. Nutr Metab Cardiovasc Dis 2024; 34:783-791. [PMID: 38228410 DOI: 10.1016/j.numecd.2023.11.013] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Revised: 11/13/2023] [Accepted: 11/20/2023] [Indexed: 01/18/2024]
Abstract
AIMS To investigate the prevalence of metabolically healthy overweight/obesity and to study its longitudinal association with major adverse cardiovascular and renal events (MARCE). METHODS AND RESULTS The study was conducted in 1210 young-to-middle-age subjects grouped according to their BMI and metabolic status. The risk of MARCE was evaluated during 17.4 years of follow-up. Forty-eight-percent of the participants had normal weight, 41.9% had overweight, and 9.3% had obesity. Metabolically healthy status was found in 31.1% of subjects with normal weight and in 20.0% of those with overweight/obesity. During the follow-up, there were 108 MARCE. In multivariate Cox analysis adjusted for confounders and risk factors, no association was found between MARCE and overweight/obesity (p = 0.49). In contrast, metabolic status considered as a two-class variable (0 versus at least one metabolic abnormality) was a significant predictor of MARCE (HR, 2.11; 95%CI, 1.21-3.70, p = 0.009). Exclusion of atrial fibrillation from MARCE (N = 87) provided similar results (HR, 2.11; 95%CI, 1.07-4.16, p = 0.030). Inclusion of average 24 h BP in the regression model attenuated the strength of the associations. Compared to the group with healthy metabolic status, the metabolically unhealthy overweight/obesity participants had an increased risk of MARCE with an adjusted HR of 2.33 (95%CI, 1.05-5.19, p = 0.038). Among the metabolically healthy individuals, the CV risk did not differ according to BMI group (p = 0.53). CONCLUSION The present data show that the risk of MARCE is not increased in young metabolically healthy overweight/obesity suggesting that the clinical approach to people with high BMI should focus on parameters of metabolic health rather than on BMI.
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Affiliation(s)
- Paolo Palatini
- Department of Medicine - University of Padova, Padova, Italy.
| | | | - Lucio Mos
- San Antonio Hospital, San Daniele del Friuli, Italy
| | - Olga Vriz
- San Antonio Hospital, San Daniele del Friuli, Italy
| | - Andrea Ermolao
- Department of Medicine - University of Padova, Padova, Italy
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2
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Mascalzoni D, Melotti R, Pattaro C, Pramstaller PP, Gögele M, De Grandi A, Biasiotto R. Ten years of dynamic consent in the CHRIS study: informed consent as a dynamic process. Eur J Hum Genet 2022; 30:1391-1397. [PMID: 36064788 PMCID: PMC9441838 DOI: 10.1038/s41431-022-01160-4] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 06/13/2022] [Accepted: 07/14/2022] [Indexed: 11/30/2022] Open
Abstract
The Cooperative Health Research in South Tyrol (CHRIS) is a longitudinal study in Northern Italy, using dynamic consent since its inception in 2011. The CHRIS study collects health data and biosamples for research, and foresees regular follow-ups over time. We describe the experience with the CHRIS study dynamic consent, providing an overview of its conceptualization and implementation, and of the participant-centered strategies used to assess and improve the process, directly linked to participation and communication. In order to comply with high ethical standards and to allow broadness in the areas of research, CHRIS dynamic consent was conceived as an interactive process: based on a strong governance and an ongoing tailored communication with participants, it aims to promote autonomy and to develop a trust-based engaged relationship with participants, also relevant for retention. Built within an online platform, the consent allows granular choices, which can be changed over time. In a process of co-production, participants views have been investigated and kept into account in policy development. Participants showed a high degree of participation, thus enabling the consolidation of the CHRIS resources. Even though a low change rate was reported in the baseline, participants valued the possibility of changing their informed consent choices. Communication (language-tailored, ongoing, multimedia) was important for participants, and for participation and retention. In our experience, dynamic consent was proven to be a flexible consent model, which allowed to meet ethical and legal standards for participation in research, and to accommodate participants' and researchers' needs.
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Affiliation(s)
- Deborah Mascalzoni
- grid.511439.bInstitute for Biomedicine, Eurac Research, Affiliated Institute of the University of Lübeck, Bolzano, Italy ,grid.8993.b0000 0004 1936 9457Center for Research Ethics and Bioethics, Department of Public Health and Caring Sciences, Uppsala University, Uppsala, Sweden
| | - Roberto Melotti
- grid.511439.bInstitute for Biomedicine, Eurac Research, Affiliated Institute of the University of Lübeck, Bolzano, Italy
| | - Cristian Pattaro
- grid.511439.bInstitute for Biomedicine, Eurac Research, Affiliated Institute of the University of Lübeck, Bolzano, Italy
| | - Peter Paul Pramstaller
- grid.511439.bInstitute for Biomedicine, Eurac Research, Affiliated Institute of the University of Lübeck, Bolzano, Italy
| | - Martin Gögele
- grid.511439.bInstitute for Biomedicine, Eurac Research, Affiliated Institute of the University of Lübeck, Bolzano, Italy
| | - Alessandro De Grandi
- grid.511439.bInstitute for Biomedicine, Eurac Research, Affiliated Institute of the University of Lübeck, Bolzano, Italy
| | - Roberta Biasiotto
- Institute for Biomedicine, Eurac Research, Affiliated Institute of the University of Lübeck, Bolzano, Italy. .,Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy.
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Rashnuodi P, Afshari D, Shirali GA, Amiri A, Zadeh MR, Samani AS. Metabolic syndrome and its relationship with shift work in petrochemical workers. Work 2022; 71:1175-1182. [DOI: 10.3233/wor-205223] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND: The metabolic syndrome is a complex of interdependent risk factors for cardiovascular disease and diabetes. Shift work might have an impact on metabolic variables, and be a risk factor for type 2 diabetes. To date, only few studies have been done on the prevalence of MetS in industrial work environments in Iran, and most of them have been conducted on a small sample size. OBJECTIVE: The aim of this study was to evaluate the impact of shift work on prevalence of metabolic syndrome in one of the petrochemical companies in Iran. METHODS: This cross-sectional study was conducted among 692 male workers of a petrochemical company in south-west Iran. Metabolic syndrome was diagnosed according to criteria recommended by Adult Treatment Panel III. In order to determine correlation between MetS and its factors with shift work odds ratio (ORs) for the MetS, 95% confidence level (95% CL), chi-square test and logistic regression analysis were performed. RESULTS: Overall 15.1% of workers were diagnosed with metabolic syndrome and 80% of them were shift workers. A significant difference for prevalence of metabolic syndrome and mean values for body mass index, blood pressure, fast blood sugar, waist circumference among shift workers and non-shift workers were identified (p < 0.001). Compared with the day workers, shift workers had a significantly higher risk of MetS (odds ratio = 4.852; 95% CI 2.34–9.974). CONCLUSIONS: There is an association between metabolic syndrome and shift work in petrochemical workers. Promising intervention strategies are needed for prevention of metabolic disorders for shift workers.
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Affiliation(s)
- Payam Rashnuodi
- Occupational Health Engineering, Student Research Committee, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Davood Afshari
- Department of Occupational Health Engineering, Faculty of Health, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Gholam Abbas Shirali
- Department of Occupational Health Engineering, Faculty of Health, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Arman Amiri
- Occupational Health Engineering, Student Research Committee, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Marziye Raesi Zadeh
- Occupational Health Engineering, Student Research Committee, Shahid Sadoughi University of Medical Sciences, Yazd, Iran
| | - Ali Sahraneshin Samani
- Department of Occupational Health Engineering, Faculty of Health, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
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4
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A multi-omics study of circulating phospholipid markers of blood pressure. Sci Rep 2022; 12:574. [PMID: 35022422 PMCID: PMC8755711 DOI: 10.1038/s41598-021-04446-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Accepted: 12/13/2021] [Indexed: 01/02/2023] Open
Abstract
High-throughput techniques allow us to measure a wide-range of phospholipids which can provide insight into the mechanisms of hypertension. We aimed to conduct an in-depth multi-omics study of various phospholipids with systolic blood pressure (SBP) and diastolic blood pressure (DBP). The associations of blood pressure and 151 plasma phospholipids measured by electrospray ionization tandem mass spectrometry were performed by linear regression in five European cohorts (n = 2786 in discovery and n = 1185 in replication). We further explored the blood pressure-related phospholipids in Erasmus Rucphen Family (ERF) study by associating them with multiple cardiometabolic traits (linear regression) and predicting incident hypertension (Cox regression). Mendelian Randomization (MR) and phenome-wide association study (Phewas) were also explored to further investigate these association results. We identified six phosphatidylethanolamines (PE 38:3, PE 38:4, PE 38:6, PE 40:4, PE 40:5 and PE 40:6) and two phosphatidylcholines (PC 32:1 and PC 40:5) which together predicted incident hypertension with an area under the ROC curve (AUC) of 0.61. The identified eight phospholipids are strongly associated with triglycerides, obesity related traits (e.g. waist, waist-hip ratio, total fat percentage, body mass index, lipid-lowering medication, and leptin), diabetes related traits (e.g. glucose, insulin resistance and insulin) and prevalent type 2 diabetes. The genetic determinants of these phospholipids also associated with many lipoproteins, heart rate, pulse rate and blood cell counts. No significant association was identified by bi-directional MR approach. We identified eight blood pressure-related circulating phospholipids that have a predictive value for incident hypertension. Our cross-omics analyses show that phospholipid metabolites in the circulation may yield insight into blood pressure regulation and raise a number of testable hypothesis for future research.
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Pattaro C, Barbieri G, Foco L, Weichenberger CX, Biasiotto R, De Grandi A, Fuchsberger C, Egger C, Amon VSC, Hicks AA, Mian M, Mahlknecht A, Lombardo S, Meier H, Weiss H, Rainer R, Dejaco C, Weiss G, Lavezzo E, Crisanti A, Pizzato M, Domingues FS, Mascalzoni D, Gögele M, Melotti R, Pramstaller PP. Prospective epidemiological, molecular, and genetic characterization of a novel coronavirus disease in the Val Venosta/Vinschgau: the CHRIS COVID-19 study protocol. Pathog Glob Health 2021; 116:128-136. [PMID: 34637685 PMCID: PMC8515786 DOI: 10.1080/20477724.2021.1978225] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023] Open
Abstract
The COVID-19 pandemic has been threatening the healthcare and socioeconomic systems of entire nations. While population-based surveys to assess the distribution of SARS-CoV-2 infection have become a priority, pre-existing longitudinal studies are ideally suited to assess the determinants of COVID-19 onset and severity.The Cooperative Health Research In South Tyrol (CHRIS) study completed the baseline recruitment of 13,393 adults from the Venosta/Vinschgau rural district in 2018, collecting extensive phenotypic and biomarker data, metabolomic data, densely imputed genotype and whole-exome sequencing data.Based on CHRIS, we designed a prospective study, called CHRIS COVID-19, aimed at: 1) estimating the incidence of SARS-CoV-2 infections; 2) screening for and investigating the determinants of incident infection among CHRIS participants and their household members; 3) monitoring the immune response of infected participants prospectively.An online screening questionnaire was sent to all CHRIS participants and their household members. A random sample of 1450 participants representative of the district population was invited to assess active (nasopharyngeal swab) or past (serum antibody test) infections. We prospectively invited for complete SARS-CoV-2 testing all questionnaire completers gauged as possible cases of past infection and their household members. In positive tested individuals, antibody response is monitored quarterly for one year. Untested and negative participants receive the screening questionnaire every four weeks until gauged as possible incident cases or till the study end.Originated from a collaboration between researchers and community stakeholders, the CHRIS COVID-19 study aims at generating knowledge about the epidemiological, molecular, and genetic characterization of COVID-19 and its long-term sequelae.
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Affiliation(s)
- Cristian Pattaro
- Institute for Biomedicine (affiliated with the University of Lübeck), Eurac Research, Bolzano, Italy
| | - Giulia Barbieri
- Institute for Biomedicine (affiliated with the University of Lübeck), Eurac Research, Bolzano, Italy
| | - Luisa Foco
- Institute for Biomedicine (affiliated with the University of Lübeck), Eurac Research, Bolzano, Italy
| | - Christian X Weichenberger
- Institute for Biomedicine (affiliated with the University of Lübeck), Eurac Research, Bolzano, Italy
| | - Roberta Biasiotto
- Institute for Biomedicine (affiliated with the University of Lübeck), Eurac Research, Bolzano, Italy
| | - Alessandro De Grandi
- Institute for Biomedicine (affiliated with the University of Lübeck), Eurac Research, Bolzano, Italy
| | - Christian Fuchsberger
- Institute for Biomedicine (affiliated with the University of Lübeck), Eurac Research, Bolzano, Italy
| | - Clemens Egger
- Institute for Biomedicine (affiliated with the University of Lübeck), Eurac Research, Bolzano, Italy
| | - Vera S C Amon
- Institute for Biomedicine (affiliated with the University of Lübeck), Eurac Research, Bolzano, Italy
| | - Andrew A Hicks
- Institute for Biomedicine (affiliated with the University of Lübeck), Eurac Research, Bolzano, Italy
| | - Michael Mian
- Department of Haematology, Hospital of Bolzano SABES-ASDAA, Bolzano, Italy.,College of Health-Care Professions, Claudiana, Bolzano, Italy
| | - Angelika Mahlknecht
- Institute of General Medicine, Provincial School of Health (Claudiana), Bolzano, Italy.,Institute of General Practice, Family Medicine and Preventive Medicine, Paracelsus Medical University, Salzburg, Austria
| | - Stefano Lombardo
- Provincial Institute of Statistics (ASTAT), Administration of the Autonomous Province of Bolzano, Italy
| | - Horand Meier
- Clinical Governance Unit, Administration of the Autonomous Province of Bolzano, Italy
| | - Helmuth Weiss
- Hospital of Silandro, Healthcare System of the Autonomous Province of Bolzano, Italy
| | - Robert Rainer
- Hospital of Silandro, Healthcare System of the Autonomous Province of Bolzano, Italy
| | - Christian Dejaco
- Department of Rheumatology, Medical University Graz, Austria.,Department of Rheumatology, Hospital of Bruneck (SABES-ASDAA), Italy
| | - Günter Weiss
- Department of Internal Medicine II, Medical University of Innsbruck, Innsbruck, Austria
| | - Enrico Lavezzo
- Department of Molecular Medicine, University of Padova, Italy
| | - Andrea Crisanti
- Department of Molecular Medicine, University of Padova, Italy
| | - Massimo Pizzato
- Laboratory of Virus-Cell Interaction, Centre for Integrative Biology, University of Trento, Italy
| | - Francisco S Domingues
- Institute for Biomedicine (affiliated with the University of Lübeck), Eurac Research, Bolzano, Italy
| | - Deborah Mascalzoni
- Institute for Biomedicine (affiliated with the University of Lübeck), Eurac Research, Bolzano, Italy.,Center for Research Ethics and Bioethics, Department of Public Health and Caring Sciences, Uppsala University, Uppsala Sweden
| | - Martin Gögele
- Institute for Biomedicine (affiliated with the University of Lübeck), Eurac Research, Bolzano, Italy
| | - Roberto Melotti
- Institute for Biomedicine (affiliated with the University of Lübeck), Eurac Research, Bolzano, Italy
| | - Peter P Pramstaller
- Institute for Biomedicine (affiliated with the University of Lübeck), Eurac Research, Bolzano, Italy
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6
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Castelo Rueda MP, Raftopoulou A, Gögele M, Borsche M, Emmert D, Fuchsberger C, Hantikainen EM, Vukovic V, Klein C, Pramstaller PP, Pichler I, Hicks AA. Frequency of Heterozygous Parkin ( PRKN) Variants and Penetrance of Parkinson's Disease Risk Markers in the Population-Based CHRIS Cohort. Front Neurol 2021; 12:706145. [PMID: 34434164 PMCID: PMC8382284 DOI: 10.3389/fneur.2021.706145] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Accepted: 07/02/2021] [Indexed: 01/10/2023] Open
Abstract
Mutations in the Parkin (PRKN) gene are the most frequent cause of autosomal recessive early-onset Parkinson's disease (PD). Heterozygous PRKN mutation carriers might also be at increased risk for developing clinical symptoms of PD. Given the high frequency of heterozygous mutations in the general population, it is essential to have better estimates of the penetrance of these variants, and to investigate, which clinical and biochemical markers are present in carriers and thus potentially useful for identifying those individuals at greater risk of developing clinical symptoms later in life. In the present study, we ascertained the frequency of heterozygous PRKN mutation carriers in a large population sample of the Cooperative Health Research in South Tyrol (CHRIS) study, and screened for reported PD risk markers. 164 confirmed heterozygous PRKN mutation carriers were compared with 2,582 controls. A higher number of heterozygous mutation carriers reported a detectable increase in an akinesia-related phenotype, and a higher percentage of carriers had manifested diabetes. We also observed lower resting heart rate in the PRKN mutation carriers. Extending our risk analyses to a larger number of potential carriers and non-carriers using genotype imputation (n = 299 carriers and n = 7,127 non-carriers), from previously published biomarkers we also observed a higher neutrophil-to-lymphocyte ratio (NLR) and lower serum albumin and sodium levels in the heterozygous PRKN variant carriers. These results identify a set of biomarkers that might be useful either individually or as an ensemble to identify variant carriers at greater risk of health issues due to carrier status.
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Affiliation(s)
| | - Athina Raftopoulou
- Institute for Biomedicine, Eurac Research, Affiliated Institute of the University of Lübeck, Bolzano, Italy
- Department of Economics, University of Patras, Patras, Greece
| | - Martin Gögele
- Institute for Biomedicine, Eurac Research, Affiliated Institute of the University of Lübeck, Bolzano, Italy
| | - Max Borsche
- Institute of Neurogenetics, University of Lübeck, Lübeck, Germany
| | - David Emmert
- Institute for Biomedicine, Eurac Research, Affiliated Institute of the University of Lübeck, Bolzano, Italy
| | - Christian Fuchsberger
- Institute for Biomedicine, Eurac Research, Affiliated Institute of the University of Lübeck, Bolzano, Italy
| | - Essi M. Hantikainen
- Institute for Biomedicine, Eurac Research, Affiliated Institute of the University of Lübeck, Bolzano, Italy
| | - Vladimir Vukovic
- Institute for Biomedicine, Eurac Research, Affiliated Institute of the University of Lübeck, Bolzano, Italy
- Centre for Disease Control and Prevention, Institute of Public Health of Vojvodina, Novi Sad, Serbia
| | - Christine Klein
- Institute of Neurogenetics, University of Lübeck, Lübeck, Germany
| | - Peter P. Pramstaller
- Institute for Biomedicine, Eurac Research, Affiliated Institute of the University of Lübeck, Bolzano, Italy
- Department of Neurology, University Medical Center Schleswig-Holstein, Lübeck, Germany
| | - Irene Pichler
- Institute for Biomedicine, Eurac Research, Affiliated Institute of the University of Lübeck, Bolzano, Italy
| | - Andrew A. Hicks
- Institute for Biomedicine, Eurac Research, Affiliated Institute of the University of Lübeck, Bolzano, Italy
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7
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Elmén L, Volpato CB, Kervadec A, Pineda S, Kalvakuri S, Alayari NN, Foco L, Pramstaller PP, Ocorr K, Rossini A, Cammarato A, Colas AR, Hicks AA, Bodmer R. Silencing of CCR4-NOT complex subunits affects heart structure and function. Dis Model Mech 2020; 13:dmm044727. [PMID: 32471864 PMCID: PMC7390626 DOI: 10.1242/dmm.044727] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Accepted: 05/19/2020] [Indexed: 12/12/2022] Open
Abstract
The identification of genetic variants that predispose individuals to cardiovascular disease and a better understanding of their targets would be highly advantageous. Genome-wide association studies have identified variants that associate with QT-interval length (a measure of myocardial repolarization). Three of the strongest associating variants (single-nucleotide polymorphisms) are located in the putative promotor region of CNOT1, a gene encoding the central CNOT1 subunit of CCR4-NOT: a multifunctional, conserved complex regulating gene expression and mRNA stability and turnover. We isolated the minimum fragment of the CNOT1 promoter containing all three variants from individuals homozygous for the QT risk alleles and demonstrated that the haplotype associating with longer QT interval caused reduced reporter expression in a cardiac cell line, suggesting that reduced CNOT1 expression might contribute to abnormal QT intervals. Systematic siRNA-mediated knockdown of CCR4-NOT components in human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) revealed that silencing CNOT1 and other CCR4-NOT genes reduced their proliferative capacity. Silencing CNOT7 also shortened action potential duration. Furthermore, the cardiac-specific knockdown of Drosophila orthologs of CCR4-NOT genes in vivo (CNOT1/Not1 and CNOT7/8/Pop2) was either lethal or resulted in dilated cardiomyopathy, reduced contractility or a propensity for arrhythmia. Silencing CNOT2/Not2, CNOT4/Not4 and CNOT6/6L/twin also affected cardiac chamber size and contractility. Developmental studies suggested that CNOT1/Not1 and CNOT7/8/Pop2 are required during cardiac remodeling from larval to adult stages. To summarize, we have demonstrated how disease-associated genes identified by GWAS can be investigated by combining human cardiomyocyte cell-based and whole-organism in vivo heart models. Our results also suggest a potential link of CNOT1 and CNOT7/8 to QT alterations and further establish a crucial role of the CCR4-NOT complex in heart development and function.This article has an associated First Person interview with the first author of the paper.
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Affiliation(s)
- Lisa Elmén
- Development Aging and Regeneration Program, Sanford Burnham Prebys Medical Discovery Institute, 10901 N Torrey Pines Rd, La Jolla, CA 92037, USA
| | - Claudia B Volpato
- Institute for Biomedicine, Eurac Research, Affiliated Institute of the University of Lübeck, Via Galvani 31, 39100 Bolzano, Italy
| | - Anaïs Kervadec
- Development Aging and Regeneration Program, Sanford Burnham Prebys Medical Discovery Institute, 10901 N Torrey Pines Rd, La Jolla, CA 92037, USA
| | - Santiago Pineda
- Development Aging and Regeneration Program, Sanford Burnham Prebys Medical Discovery Institute, 10901 N Torrey Pines Rd, La Jolla, CA 92037, USA
| | - Sreehari Kalvakuri
- Development Aging and Regeneration Program, Sanford Burnham Prebys Medical Discovery Institute, 10901 N Torrey Pines Rd, La Jolla, CA 92037, USA
| | - Nakissa N Alayari
- Development Aging and Regeneration Program, Sanford Burnham Prebys Medical Discovery Institute, 10901 N Torrey Pines Rd, La Jolla, CA 92037, USA
| | - Luisa Foco
- Institute for Biomedicine, Eurac Research, Affiliated Institute of the University of Lübeck, Via Galvani 31, 39100 Bolzano, Italy
| | - Peter P Pramstaller
- Institute for Biomedicine, Eurac Research, Affiliated Institute of the University of Lübeck, Via Galvani 31, 39100 Bolzano, Italy
| | - Karen Ocorr
- Development Aging and Regeneration Program, Sanford Burnham Prebys Medical Discovery Institute, 10901 N Torrey Pines Rd, La Jolla, CA 92037, USA
| | - Alessandra Rossini
- Institute for Biomedicine, Eurac Research, Affiliated Institute of the University of Lübeck, Via Galvani 31, 39100 Bolzano, Italy
| | - Anthony Cammarato
- Johns Hopkins University, Division of Cardiology, 720 Rutland Ave., Baltimore, MD 21205, USA
| | - Alexandre R Colas
- Development Aging and Regeneration Program, Sanford Burnham Prebys Medical Discovery Institute, 10901 N Torrey Pines Rd, La Jolla, CA 92037, USA
| | - Andrew A Hicks
- Institute for Biomedicine, Eurac Research, Affiliated Institute of the University of Lübeck, Via Galvani 31, 39100 Bolzano, Italy
| | - Rolf Bodmer
- Development Aging and Regeneration Program, Sanford Burnham Prebys Medical Discovery Institute, 10901 N Torrey Pines Rd, La Jolla, CA 92037, USA
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Latifi SM, Karandish M, Shahbazian H, Taha JM, Cheraghian B, Moradi M. Prevalence of Metabolically Healthy Obesity (MHO) and its relation with incidence of metabolic syndrome, hypertension and type 2 Diabetes amongst individuals aged over 20 years in Ahvaz: A 5 Year cohort Study (2009-2014). Diabetes Metab Syndr 2017; 11 Suppl 2:S1037-S1040. [PMID: 28781161 DOI: 10.1016/j.dsx.2017.07.036] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/15/2017] [Accepted: 07/21/2017] [Indexed: 12/19/2022]
Abstract
BACKGROUND Today, obesity epidemic is one of the major health problems of the present century. One of the phenotypes of obesity is metabolically healthy obesity. It seems that these obese individuals suffer less from cardiovascular disease and metabolically unhealthy obesity. This study aimed to investigate the prevalence of metabolically healthy obesity (MHO) and its relationship with incidence of metabolic syndrome, diabetes and hypertension in individuals over 20 years in the city of Ahvaz. MATERIALS AND METHODS This study was a 5-year cohort study, which was conducted on adults between years 2009 to 2014.Participants who were randomly selected from individuals covered by the health centers in the city of Ahvaz in baseline population, were again recalled by these centers. The subjects completed the question aires, and anthropometric measurements and blood samples were prepared for performing tests based on Phase 1. RESULTS A total of 591 individuals Participated in this study, 281 (47.5%) were males and 310 (52.5%) females with mean age of 42.2±13.3 years. The prevalence of MHO was 19.5% in the baseline population. The cumulative incidence of metabolic syndrome, diabetes and hypertension in MHO individuals were 29.6%, 24.3% and 13%, respectively. CONCLUSION The prevalence of MHO was 19.5% in the baseline population. There was a specific relationship between MHO and incidence of metabolic syndrome and diabetes; however, there was a less significant relationship between MHO and hypertension.
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Affiliation(s)
- Seyed Mahmoud Latifi
- Diabetes Research Center, Health Research Institute,, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran.
| | - Majid Karandish
- Nutrition and Metabolic Diseases Research Centre, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran.
| | - Hajieh Shahbazian
- Diabetes Research Center, Health Research Institute Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran,.
| | - Jalaly Mohammad Taha
- Diabetes Research Center, Health Research Institute Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran,.
| | - Bahman Cheraghian
- Department of Biostatistic and Epidemiology, Faculty of Health Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran.
| | - Mitra Moradi
- Diabetes Research Center, Health Research Institute Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran,.
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9
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Ortega FB, Lavie CJ, Blair SN. Obesity and Cardiovascular Disease. Circ Res 2016; 118:1752-70. [DOI: 10.1161/circresaha.115.306883] [Citation(s) in RCA: 578] [Impact Index Per Article: 72.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/20/2015] [Accepted: 01/11/2016] [Indexed: 12/20/2022]
Abstract
The prevalence of obesity has increased worldwide over the past few decades. In 2013, the prevalence of obesity exceeded the 50% of the adult population in some countries from Oceania, North Africa, and Middle East. Lower but still alarmingly high prevalence was observed in North America (≈30%) and in Western Europe (≈20%). These figures are of serious concern because of the strong link between obesity and disease. In the present review, we summarize the current evidence on the relationship of obesity with cardiovascular disease (CVD), discussing how both the degree and the duration of obesity affect CVD. Although in the general population, obesity and, especially, severe obesity are consistently and strongly related with higher risk of CVD incidence and mortality, the one-size-fits-all approach should not be used with obesity. There are relevant factors largely affecting the CVD prognosis of obese individuals. In this context, we thoroughly discuss important concepts such as the fat-but-fit paradigm, the metabolically healthy but obese (MHO) phenotype and the obesity paradox in patients with CVD. About the MHO phenotype and its CVD prognosis, available data have provided mixed findings, what could be partially because of the adjustment or not for key confounders such as cardiorespiratory fitness, and to the lack of consensus on the MHO definition. In the present review, we propose a scientifically based harmonized definition of MHO, which will hopefully contribute to more comparable data in the future and a better understanding on the MHO subgroup and its CVD prognosis.
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Affiliation(s)
- Francisco B. Ortega
- From the PROFITH “PROmoting FITness and Health through physical activity” Research Group, Department of Physical Education and Sports, Faculty of Sport Sciences, University of Granada, Granada, Spain (F.B.O.); Department of Biosciences and Nutrition at NOVUM, Karolinska Institutet, Huddinge, Sweden (F.B.O.); Department of Cardiovascular Diseases, John Ochsner Heart and Vascular Institute, Ochsner Clinical School-the University of Queensland School of Medicine, New Orleans, LA (C.J.L.); and
| | - Carl J. Lavie
- From the PROFITH “PROmoting FITness and Health through physical activity” Research Group, Department of Physical Education and Sports, Faculty of Sport Sciences, University of Granada, Granada, Spain (F.B.O.); Department of Biosciences and Nutrition at NOVUM, Karolinska Institutet, Huddinge, Sweden (F.B.O.); Department of Cardiovascular Diseases, John Ochsner Heart and Vascular Institute, Ochsner Clinical School-the University of Queensland School of Medicine, New Orleans, LA (C.J.L.); and
| | - Steven N. Blair
- From the PROFITH “PROmoting FITness and Health through physical activity” Research Group, Department of Physical Education and Sports, Faculty of Sport Sciences, University of Granada, Granada, Spain (F.B.O.); Department of Biosciences and Nutrition at NOVUM, Karolinska Institutet, Huddinge, Sweden (F.B.O.); Department of Cardiovascular Diseases, John Ochsner Heart and Vascular Institute, Ochsner Clinical School-the University of Queensland School of Medicine, New Orleans, LA (C.J.L.); and
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10
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Pattaro C, Gögele M, Mascalzoni D, Melotti R, Schwienbacher C, De Grandi A, Foco L, D'Elia Y, Linder B, Fuchsberger C, Minelli C, Egger C, Kofink LS, Zanigni S, Schäfer T, Facheris MF, Smárason SV, Rossini A, Hicks AA, Weiss H, Pramstaller PP. The Cooperative Health Research in South Tyrol (CHRIS) study: rationale, objectives, and preliminary results. J Transl Med 2015; 13:348. [PMID: 26541195 PMCID: PMC4635524 DOI: 10.1186/s12967-015-0704-9] [Citation(s) in RCA: 57] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2015] [Accepted: 10/20/2015] [Indexed: 01/08/2023] Open
Abstract
The Cooperative Health Research In South Tyrol (CHRIS) study is a population-based study with a longitudinal lookout to investigate the genetic and molecular basis of age-related common chronic conditions and their interaction with life style and environment in the general population. All adults of the middle and upper Vinschgau/Val Venosta are invited, while 10,000 participants are anticipated by mid-2017. Family participation is encouraged for complete pedigree reconstruction and disease inheritance mapping. After a pilot study on the compliance with a paperless assessment mode, computer-assisted interviews have been implemented to screen for conditions of the cardiovascular, endocrine, metabolic, genitourinary, nervous, behavioral, and cognitive system. Fat intake, cardiac health, and tremor are assessed instrumentally. Nutrient intake, physical activity, and life-course smoking are measured semi-quantitatively. Participants are phenotyped for 73 blood and urine parameters and 60 aliquots per participant are biobanked (cryo-preserved urine, DNA, and whole and fractionated blood). Through liquid-chromatography mass-spectrometry analysis, metabolite profiling of the mitochondrial function is assessed. Samples are genotyped on 1 million variants with the Illumina HumanOmniExpressExome array and the first data release including 4570 fully phenotyped and genotyped samples is now available for analysis. Participants’ follow-up is foreseen 6 years after the first visit. The target population is characterized by long-term social stability and homogeneous environment which should both favor the identification of enriched genetic variants. The CHRIS cohort is a valuable resource to assess the contribution of genomics, metabolomics, and environmental factors to human health and disease. It is awaited that this will result in the identification of novel molecular targets for disease prevention and treatment.
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Affiliation(s)
- Cristian Pattaro
- Center for Biomedicine, European Academy of Bolzano/Bozen (EURAC) (Affiliated to the University of Lübeck, Lübeck, Germany), Via Galvani 31, 39100, Bolzano/Bozen, Italy.
| | - Martin Gögele
- Center for Biomedicine, European Academy of Bolzano/Bozen (EURAC) (Affiliated to the University of Lübeck, Lübeck, Germany), Via Galvani 31, 39100, Bolzano/Bozen, Italy
| | - Deborah Mascalzoni
- Center for Biomedicine, European Academy of Bolzano/Bozen (EURAC) (Affiliated to the University of Lübeck, Lübeck, Germany), Via Galvani 31, 39100, Bolzano/Bozen, Italy
| | - Roberto Melotti
- Center for Biomedicine, European Academy of Bolzano/Bozen (EURAC) (Affiliated to the University of Lübeck, Lübeck, Germany), Via Galvani 31, 39100, Bolzano/Bozen, Italy
| | - Christine Schwienbacher
- Center for Biomedicine, European Academy of Bolzano/Bozen (EURAC) (Affiliated to the University of Lübeck, Lübeck, Germany), Via Galvani 31, 39100, Bolzano/Bozen, Italy
| | - Alessandro De Grandi
- Center for Biomedicine, European Academy of Bolzano/Bozen (EURAC) (Affiliated to the University of Lübeck, Lübeck, Germany), Via Galvani 31, 39100, Bolzano/Bozen, Italy
| | - Luisa Foco
- Center for Biomedicine, European Academy of Bolzano/Bozen (EURAC) (Affiliated to the University of Lübeck, Lübeck, Germany), Via Galvani 31, 39100, Bolzano/Bozen, Italy
| | - Yuri D'Elia
- Center for Biomedicine, European Academy of Bolzano/Bozen (EURAC) (Affiliated to the University of Lübeck, Lübeck, Germany), Via Galvani 31, 39100, Bolzano/Bozen, Italy
| | - Barbara Linder
- Center for Biomedicine, European Academy of Bolzano/Bozen (EURAC) (Affiliated to the University of Lübeck, Lübeck, Germany), Via Galvani 31, 39100, Bolzano/Bozen, Italy
| | - Christian Fuchsberger
- Center for Biomedicine, European Academy of Bolzano/Bozen (EURAC) (Affiliated to the University of Lübeck, Lübeck, Germany), Via Galvani 31, 39100, Bolzano/Bozen, Italy
| | - Cosetta Minelli
- Respiratory Epidemiology, Occupational Medicine and Public Health, National Heart and Lung Institute, Imperial College, London, UK
| | - Clemens Egger
- Center for Biomedicine, European Academy of Bolzano/Bozen (EURAC) (Affiliated to the University of Lübeck, Lübeck, Germany), Via Galvani 31, 39100, Bolzano/Bozen, Italy
| | - Lisa S Kofink
- Center for Biomedicine, European Academy of Bolzano/Bozen (EURAC) (Affiliated to the University of Lübeck, Lübeck, Germany), Via Galvani 31, 39100, Bolzano/Bozen, Italy
| | - Stefano Zanigni
- Functional MR Unit, Policlinico S. Orsola, Malpighi Bologna, Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
| | - Torsten Schäfer
- Dermatological Practice, Kirchplatz 3, 87059, Immenstadt, Germany
| | | | - Sigurður V Smárason
- Center for Biomedicine, European Academy of Bolzano/Bozen (EURAC) (Affiliated to the University of Lübeck, Lübeck, Germany), Via Galvani 31, 39100, Bolzano/Bozen, Italy
| | - Alessandra Rossini
- Center for Biomedicine, European Academy of Bolzano/Bozen (EURAC) (Affiliated to the University of Lübeck, Lübeck, Germany), Via Galvani 31, 39100, Bolzano/Bozen, Italy
| | - Andrew A Hicks
- Center for Biomedicine, European Academy of Bolzano/Bozen (EURAC) (Affiliated to the University of Lübeck, Lübeck, Germany), Via Galvani 31, 39100, Bolzano/Bozen, Italy
| | - Helmuth Weiss
- Center for Biomedicine, European Academy of Bolzano/Bozen (EURAC) (Affiliated to the University of Lübeck, Lübeck, Germany), Via Galvani 31, 39100, Bolzano/Bozen, Italy.,Hospital of Schlanders/Silandro, Schlanders/Silandro, Italy
| | - Peter P Pramstaller
- Center for Biomedicine, European Academy of Bolzano/Bozen (EURAC) (Affiliated to the University of Lübeck, Lübeck, Germany), Via Galvani 31, 39100, Bolzano/Bozen, Italy. .,Department of Neurology, Central Hospital, Bolzano, Italy. .,Department of Neurology, University of Lübeck, Lübeck, Germany.
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11
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Sun M, Jobling MA, Taliun D, Pramstaller PP, Egeland T, Sheehan NA. On the use of dense SNP marker data for the identification of distant relative pairs. Theor Popul Biol 2015; 107:14-25. [PMID: 26474828 DOI: 10.1016/j.tpb.2015.10.002] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2015] [Revised: 10/02/2015] [Accepted: 10/05/2015] [Indexed: 01/05/2023]
Abstract
There has been recent interest in the exploitation of readily available dense genome scan marker data for the identification of relatives. However, there are conflicting findings on how informative these data are in practical situations and, in particular, sets of thinned markers are often used with no concrete justification for the chosen spacing. We explore the potential usefulness of dense single nucleotide polymorphism (SNP) arrays for this application with a focus on inferring distant relative pairs. We distinguish between relationship estimation, as defined by a pedigree connecting the two individuals of interest, and estimation of general relatedness as would be provided by a kinship coefficient or a coefficient of relatedness. Since our primary interest is in the former case, we adopt a pedigree likelihood approach. We consider the effect of additional SNPs and data on an additional typed relative, together with choice of that relative, on relationship inference. We also consider the effect of linkage disequilibrium. When overall relatedness, rather than the specific relationship, would suffice, we propose an approximate approach that is easy to implement and appears to compete well with a popular moment-based estimator and a recent maximum likelihood approach based on chromosomal sharing. We conclude that denser marker data are more informative for distant relatives. However, linkage disequilibrium cannot be ignored and will be the main limiting factor for applications to real data.
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Affiliation(s)
- M Sun
- Department of Health Sciences, University of Leicester, UK
| | - M A Jobling
- Department of Genetics, University of Leicester, UK
| | - D Taliun
- Center for Biomedicine, European Academy of Bolzano (EURAC), Bolzano, Italy; Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - P P Pramstaller
- Center for Biomedicine, European Academy of Bolzano (EURAC), Bolzano, Italy
| | - T Egeland
- IKBM Norwegian University of Life Sciences, Norway
| | - N A Sheehan
- Department of Health Sciences, University of Leicester, UK; Department of Genetics, University of Leicester, UK.
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12
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Ramos EM, Gillis T, Mysore JS, Lee J, Gögele M, D'Elia Y, Pichler I, Sequeiros J, Pramstaller PP, Gusella JF, MacDonald ME, Alonso I. Haplotype analysis of the 4p16.3 region in Portuguese families with Huntington's disease. Am J Med Genet B Neuropsychiatr Genet 2015; 168B:135-43. [PMID: 25656686 PMCID: PMC5006842 DOI: 10.1002/ajmg.b.32289] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/15/2014] [Accepted: 11/25/2014] [Indexed: 12/12/2022]
Abstract
Huntington's disease (HD) is a neurodegenerative disorder characterized by involuntary choreic movements, cognitive impairment, and behavioral changes, caused by the expansion of an unstable CAG repeat in HTT. We characterized the genetic diversity of the HD mutation by performing an extensive haplotype analysis of ∼1Mb region flanking HTT in over 300 HD families of Portuguese origin. We observed that haplotype A, marked by HTT delta2642, was enriched in HD chromosomes and carried the two largest expansions reported in the Portuguese population. However, the most frequent HD haplotype B carried one of the largest (+12 CAGs) expansions, which resulted in an allele class change to full penetrance. Despite having a normal CAG distribution skewed to the higher end of the range, these two core haplotypes had similar expanded CAG repeat sizes compared to the other major core haplotypes (C and D) and there was no statistical difference in transmitted repeat instability across haplotypes. We observed a diversity of HTT region haplotypes in both normal and expanded chromosomes, representative of more than one ancestral chromosome underlying HD in Portugal, where multiple independent events on distinct chromosome 4 haplotypes have given rise to expansion into the pathogenic range.
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Affiliation(s)
- Eliana Marisa Ramos
- Center for Human Genetic ResearchMassachusetts General HospitalBostonMassachusettsUSA,UnIGENeIBMC–Institute for Molecular and Cell BiologyUniversity of PortoPortoPortugal
| | - Tammy Gillis
- Center for Human Genetic ResearchMassachusetts General HospitalBostonMassachusettsUSA
| | - Jayalakshmi S. Mysore
- Center for Human Genetic ResearchMassachusetts General HospitalBostonMassachusettsUSA
| | - Jong‐Min Lee
- Center for Human Genetic ResearchMassachusetts General HospitalBostonMassachusettsUSA
| | - Martin Gögele
- Center for BiomedicineEuropean Academy of Bozen/Bolzano (EURAC)BolzanoItaly
| | - Yuri D'Elia
- Center for BiomedicineEuropean Academy of Bozen/Bolzano (EURAC)BolzanoItaly
| | - Irene Pichler
- Center for BiomedicineEuropean Academy of Bozen/Bolzano (EURAC)BolzanoItaly
| | - Jorge Sequeiros
- UnIGENeIBMC–Institute for Molecular and Cell BiologyUniversity of PortoPortoPortugal,CGPPIBMC–Institute for Molecular and Cell BiologyUniversity of PortoPortoPortugal,ICBAS–Instituto de Ciências Biomédicas Abel SalazarUniversity of PortoPortoPortugal
| | - Peter P. Pramstaller
- Center for BiomedicineEuropean Academy of Bozen/Bolzano (EURAC)BolzanoItaly,Department of NeurologyCentral HospitalBolzanoItaly,Department of NeurologyUniversity of LübeckLübeckGermany
| | - James F. Gusella
- Center for Human Genetic ResearchMassachusetts General HospitalBostonMassachusettsUSA
| | - Marcy E. MacDonald
- Center for Human Genetic ResearchMassachusetts General HospitalBostonMassachusettsUSA
| | - Isabel Alonso
- UnIGENeIBMC–Institute for Molecular and Cell BiologyUniversity of PortoPortoPortugal,CGPPIBMC–Institute for Molecular and Cell BiologyUniversity of PortoPortoPortugal,ICBAS–Instituto de Ciências Biomédicas Abel SalazarUniversity of PortoPortoPortugal
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13
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Napolitano M, Santoro F, Puopolo M, Donfancesco C, Galluzzo L, De Grandi A, Cevenini E, De Curtis A, Sevini F, Palmieri L, Mascalzon D, Roazzi P, Scafato E, Pramstaller P, Iacoviello L, Donati MB, Giampaoli S, Franceschi C, Belardelli F, Bravo E. Development of a pilot project on data sharing among partners of the Italian Hub of Population Biobanks (HIBP): association between lipid profile and socio-demographic variables. Biopreserv Biobank 2014; 12:225-33. [PMID: 25075723 DOI: 10.1089/bio.2014.0001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
The Italian Hub of Population Biobanks (HIBP) includes both ongoing and completed studies that are heterogeneous in both their purpose and in the specimens collected. The heterogeneity in starting conditions makes sharing study data very difficult because of technical, ethical, and collection rights issues that hamper collaboration and synergy. With the aim of overcoming these difficulties and establishing the "proof-of-concept" that sharing studies is achievable among Italian collections, a data-sharing pilot project has been agreed to by HIBP members. Participants agreed to the general methodology and signed a shared Data Transfer Agreement. The biobanks involved were: EURAC (Micros study), CIG (GEHA project), CNESPS (FINE, MATISS, MONICA, OEC1998, ITR (Italian Twin Register), and IPREA studies, and MOLIBANK (Moli-Sani project). Biobank data were uploaded into a common database using a dedicated informatics infrastructure. Demographic data, and anthropometric and hematochemical parameters were shared for each record. Each biobank uploaded into the common database a dataset with a minimum of 1000 subjects, for a total of 5071 records. After a harmonization process, the final dataset included 3882 records. Subjects were grouped into three main geographic areas of Italy (North, Center, and South) and separate analyses were performed for men and women. The 3882 records were analyzed through multivariate logistic regression analysis. Results were expressed as odds ratios with 95% confidence interval. Results show several geographical differences in the lipidemic pattern, mostly regarding cholesterol-HDL, which represents a strong basis for further, deeper sample-based studies. This HIBP pilot study aimed to prove the feasibility of such collaborations and it provides a methodological prototype for future studies based on the participation in the partnership of well-established quality collections.
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Affiliation(s)
- Mariarosaria Napolitano
- 1 Department of Haematology, Oncology and Molecular Medicine, Istituto Superiore di Sanità , Rome, Italy
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14
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Dipierri J, Rodríguez-Larralde A, Barrai I, Camelo JL, Redomero EG, Rodríguez CA, Ramallo V, Bronberg R, Alfaro E. Random inbreeding, isonymy, and population isolates in Argentina. J Community Genet 2014; 5:241-8. [PMID: 24500769 PMCID: PMC4059845 DOI: 10.1007/s12687-013-0181-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2013] [Accepted: 12/29/2013] [Indexed: 12/20/2022] Open
Abstract
Population isolates are an important tool in identifying and mapping genes of Mendelian diseases and complex traits. The geographical identification of isolates represents a priority from a genetic and health care standpoint. The purpose of this study is to analyze the spatial distribution of consanguinity by random isonymy (F ST) in Argentina and its relationship with the isolates previously identified in the country. F ST was estimated from the surname distribution of 22.6 million electors registered for the year 2001 in the 24 provinces, 5 geographical regions, and 510 departments of the country. Statistically significant spatial clustering of F ST was determined using the SaTScan V5.1 software. F ST exhibited a marked regional and departamental variation, showing the highest values towards the North and West of Argentina. The clusters of high consanguinity by random isonymy followed the same distribution. Recognized Argentinean genetic isolates are mainly localized at the north of the country, in clusters of high inbreeding. Given the availability of listings of surnames in high-capacity storage devices for different countries, estimating F ST from them can provide information on inbreeding for all levels of administrative subdivisions, to be used as a demographic variable for the identification of isolates within the country for public health purposes.
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Affiliation(s)
- José Dipierri
- Instituto de Biología de la Altura, Universidad Nacional de Jujuy, Avda. Bolivia 1661, 4600, San Salvador de Jujuy, Argentina,
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15
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van Vliet-Ostaptchouk JV, Nuotio ML, Slagter SN, Doiron D, Fischer K, Foco L, Gaye A, Gögele M, Heier M, Hiekkalinna T, Joensuu A, Newby C, Pang C, Partinen E, Reischl E, Schwienbacher C, Tammesoo ML, Swertz MA, Burton P, Ferretti V, Fortier I, Giepmans L, Harris JR, Hillege HL, Holmen J, Jula A, Kootstra-Ros JE, Kvaløy K, Holmen TL, Männistö S, Metspalu A, Midthjell K, Murtagh MJ, Peters A, Pramstaller PP, Saaristo T, Salomaa V, Stolk RP, Uusitupa M, van der Harst P, van der Klauw MM, Waldenberger M, Perola M, Wolffenbuttel BHR. The prevalence of metabolic syndrome and metabolically healthy obesity in Europe: a collaborative analysis of ten large cohort studies. BMC Endocr Disord 2014; 14:9. [PMID: 24484869 PMCID: PMC3923238 DOI: 10.1186/1472-6823-14-9] [Citation(s) in RCA: 379] [Impact Index Per Article: 37.9] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/26/2014] [Accepted: 01/27/2014] [Indexed: 12/03/2022] Open
Abstract
BACKGROUND Not all obese subjects have an adverse metabolic profile predisposing them to developing type 2 diabetes or cardiovascular disease. The BioSHaRE-EU Healthy Obese Project aims to gain insights into the consequences of (healthy) obesity using data on risk factors and phenotypes across several large-scale cohort studies. Aim of this study was to describe the prevalence of obesity, metabolic syndrome (MetS) and metabolically healthy obesity (MHO) in ten participating studies. METHODS Ten different cohorts in seven countries were combined, using data transformed into a harmonized format. All participants were of European origin, with age 18-80 years. They had participated in a clinical examination for anthropometric and blood pressure measurements. Blood samples had been drawn for analysis of lipids and glucose. Presence of MetS was assessed in those with obesity (BMI ≥ 30 kg/m2) based on the 2001 NCEP ATP III criteria, as well as an adapted set of less strict criteria. MHO was defined as obesity, having none of the MetS components, and no previous diagnosis of cardiovascular disease. RESULTS Data for 163,517 individuals were available; 17% were obese (11,465 men and 16,612 women). The prevalence of obesity varied from 11.6% in the Italian CHRIS cohort to 26.3% in the German KORA cohort. The age-standardized percentage of obese subjects with MetS ranged in women from 24% in CHRIS to 65% in the Finnish Health2000 cohort, and in men from 43% in CHRIS to 78% in the Finnish DILGOM cohort, with elevated blood pressure the most frequently occurring factor contributing to the prevalence of the metabolic syndrome. The age-standardized prevalence of MHO varied in women from 7% in Health2000 to 28% in NCDS, and in men from 2% in DILGOM to 19% in CHRIS. MHO was more prevalent in women than in men, and decreased with age in both sexes. CONCLUSIONS Through a rigorous harmonization process, the BioSHaRE-EU consortium was able to compare key characteristics defining the metabolically healthy obese phenotype across ten cohort studies. There is considerable variability in the prevalence of healthy obesity across the different European populations studied, even when unified criteria were used to classify this phenotype.
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Affiliation(s)
- Jana V van Vliet-Ostaptchouk
- Department of Endocrinology, University of Groningen, University Medical Center Groningen, HPC AA31, P.O. Box 30001, Groningen RB 9700, The Netherlands
| | - Marja-Liisa Nuotio
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- Unit of Public Health Genomics, National Institute for Health and Welfare, Helsinki, Finland
| | - Sandra N Slagter
- Department of Endocrinology, University of Groningen, University Medical Center Groningen, HPC AA31, P.O. Box 30001, Groningen RB 9700, The Netherlands
| | - Dany Doiron
- Research Institute of the McGill University of Health Centre, Montreal, Canada
| | - Krista Fischer
- University of Tartu, Estonian Genome Center, Tartu, Estonia
| | - Luisa Foco
- Center for Biomedicine, European Academy of Bolzano/Bozen (EURAC), Bolzano, Italy
| | - Amadou Gaye
- Data to Knowledge Research Group, School of Social and Community Medicine, University of Bristol, Bristol, UK
| | - Martin Gögele
- Center for Biomedicine, European Academy of Bolzano/Bozen (EURAC), Bolzano, Italy
| | - Margit Heier
- Institute of Epidemiology II, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Tero Hiekkalinna
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- Unit of Public Health Genomics, National Institute for Health and Welfare, Helsinki, Finland
| | - Anni Joensuu
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- Unit of Public Health Genomics, National Institute for Health and Welfare, Helsinki, Finland
| | - Christopher Newby
- Data to Knowledge Research Group, School of Social and Community Medicine, University of Bristol, Bristol, UK
| | - Chao Pang
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
- Genomics Coordination Center, University of Groningen, Groningen Bioinformatics Center, and University Medical Center Groningen, Groningen, The Netherlands
| | - Eemil Partinen
- University of Tartu, Estonian Genome Center, Tartu, Estonia
| | - Eva Reischl
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | | | | | - Morris A Swertz
- Genomics Coordination Center, University of Groningen, Groningen Bioinformatics Center, and University Medical Center Groningen, Groningen, The Netherlands
| | - Paul Burton
- Data to Knowledge Research Group, School of Social and Community Medicine, University of Bristol, Bristol, UK
| | | | - Isabel Fortier
- Research Institute of the McGill University of Health Centre, Montreal, Canada
| | - Lisette Giepmans
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Jennifer R Harris
- Department of Genes and Environment, Division of Epidemiology, The Norwegian Institute of Public Health, Oslo, Norway
| | - Hans L Hillege
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Jostein Holmen
- HUNT Research Center, Department of Public Health and General Practice, Faculty of Medicine, Norwegian University of Science and Technology, Trondheim, Norway
| | - Antti Jula
- THL-National Institute for Health and Welfare, Helsinki, Finland
| | - Jenny E Kootstra-Ros
- Department of Laboratory Medicine, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Kirsti Kvaløy
- HUNT Research Center, Department of Public Health and General Practice, Faculty of Medicine, Norwegian University of Science and Technology, Trondheim, Norway
| | - Turid Lingaas Holmen
- HUNT Research Center, Department of Public Health and General Practice, Faculty of Medicine, Norwegian University of Science and Technology, Trondheim, Norway
| | - Satu Männistö
- Department of Chronic Disease Prevention, National Institute for Health and Welfare, Helsinki, Finland
| | | | - Kristian Midthjell
- HUNT Research Center, Department of Public Health and General Practice, Faculty of Medicine, Norwegian University of Science and Technology, Trondheim, Norway
| | - Madeleine J Murtagh
- Data to Knowledge Research Group, School of Social and Community Medicine, University of Bristol, Bristol, UK
| | - Annette Peters
- Institute of Epidemiology II, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Peter P Pramstaller
- Center for Biomedicine, European Academy of Bolzano/Bozen (EURAC), Bolzano, Italy
- Department of Neurology, Central Hospital, Bolzano, Italy
- Department of Neurology, University of Lübeck, Lübeck, Germany
| | - Timo Saaristo
- Pirkanmaa hospital district and Finnish Diabetes Association, Tampere, Finland
| | - Veikko Salomaa
- THL-National Institute for Health and Welfare, Helsinki, Finland
| | - Ronald P Stolk
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
- University of Groningen, University Medical Center Groningen, LifeLines Cohort Study, Groningen, The Netherlands
| | - Matti Uusitupa
- Institute of Public Health and Clinical Nutrition, University of Eastern Finland, and Research Unit, Kuopio University Hospital, Kuopio, Finland
| | - Pim van der Harst
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Melanie M van der Klauw
- Department of Endocrinology, University of Groningen, University Medical Center Groningen, HPC AA31, P.O. Box 30001, Groningen RB 9700, The Netherlands
- University of Groningen, University Medical Center Groningen, LifeLines Cohort Study, Groningen, The Netherlands
| | - Melanie Waldenberger
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Markus Perola
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- Unit of Public Health Genomics, National Institute for Health and Welfare, Helsinki, Finland
- University of Tartu, Estonian Genome Center, Tartu, Estonia
| | - Bruce HR Wolffenbuttel
- Department of Endocrinology, University of Groningen, University Medical Center Groningen, HPC AA31, P.O. Box 30001, Groningen RB 9700, The Netherlands
- University of Groningen, University Medical Center Groningen, LifeLines Cohort Study, Groningen, The Netherlands
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16
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Pattaro C, Riegler P, Stifter G, Modenese M, Minelli C, Pramstaller PP. Estimating the glomerular filtration rate in the general population using different equations: effects on classification and association. Nephron Clin Pract 2013; 123:102-11. [PMID: 23797027 DOI: 10.1159/000351043] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2012] [Accepted: 04/03/2013] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND/AIMS Several formulas for glomerular filtration rate (GFR) estimation, based on serum creatinine or cystatin C, have been proposed. We assessed the impact of some of these equations on estimated GFR (eGFR) and chronic kidney disease (CKD) prevalence, and on the association with cardiovascular risk factors, in a general population sample characterized by a young mean age. METHODS We studied 1,199 individuals from three Alpine villages enrolled into the MICROS study. eGFR was obtained with the 4- and 6-parameter MDRD study equations, the Virga equation, and with the three CKD-EPI formulas for creatinine, cystatin C, and the combination of creatinine and cystatin C. We assessed the concordance between quantitative eGFR levels, CKD prevalence, and in terms of association with total, LDL, and HDL cholesterol. RESULTS The highest and lowest eGFR levels corresponded to the cystatin C-based and MDRD-4 equations, respectively. CKD prevalence varied from 1.8% (Virga) to 5.8% (MDRD-4). The CKD-EPI based on creatinine showed the highest agreement with all other equations. Agreement between methods was higher at lower eGFR levels, older age, and in the presence of diabetes and hypertension. Creatinine-based estimates of eGFR were associated with total and low-density lipoprotein but not high-density lipoprotein cholesterol. The opposite was observed for the cystatin C-based GFR. CONCLUSION GFR estimation is strongly affected by the chosen equation. Differences are more pronounced in healthy and younger individuals. To identify CKD risk factors, the choice of the equation is of secondary importance to the choice of the biomarker used in the formula. If eGFR is not calibrated to a gold standard GFR in the general population, reports about CKD prevalence should be considered with caution.
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Affiliation(s)
- Cristian Pattaro
- Center for Biomedicine, European Academy of Bolzano/Bozen (EURAC), Bolzano, Italy.
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17
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Biino G, Santimone I, Minelli C, Sorice R, Frongia B, Traglia M, Ulivi S, Di Castelnuovo A, Gögele M, Nutile T, Francavilla M, Sala C, Pirastu N, Cerletti C, Iacoviello L, Gasparini P, Toniolo D, Ciullo M, Pramstaller P, Pirastu M, de Gaetano G, Balduini CL. Age- and sex-related variations in platelet count in Italy: a proposal of reference ranges based on 40987 subjects' data. PLoS One 2013; 8:e54289. [PMID: 23382888 PMCID: PMC3561305 DOI: 10.1371/journal.pone.0054289] [Citation(s) in RCA: 160] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2012] [Accepted: 12/10/2012] [Indexed: 02/06/2023] Open
Abstract
Background and Objectives Although several studies demonstrated that platelet count is higher in women, decreases with age, and is influenced by genetic background, most clinical laboratories still use the reference interval 150–400×109 platelets/L for all subjects. The present study was to identify age- and sex-specific reference intervals for platelet count. Methods We analysed electronic records of subjects enrolled in three population-based studies that investigated inhabitants of seven Italian areas including six geographic isolates. After exclusion of patients with malignancies, liver diseases, or inherited thrombocytopenias, which could affect platelet count, reference intervals were estimated from 40,987 subjects with the non parametric method computing the 2.5° and 97.5° percentiles. Results Platelet count was similar in men and women until the age of 14, but subsequently women had steadily more platelets than men. The number of platelets decreases quickly in childhood, stabilizes in adulthood, and further decreases in oldness. The final result of this phenomenon is that platelet count in old age was reduced by 35% in men and by 25% in women compared with early infancy. Based on these findings, we estimated reference intervals for platelet count ×109/L in children (176–452), adult men (141–362), adult women (156–405), old men (122–350) and, old women (140–379). Moreover, we calculated an “extended” reference interval that takes into account the differences in platelet count observed in different geographic areas. Conclusions The age-, sex-, and origin-related variability of platelet count is very wide, and the patient-adapted reference intervals we propose change the thresholds for diagnosing both thrombocytopenia and thrombocytosis in Italy.
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Affiliation(s)
- Ginevra Biino
- Institute of Molecular Genetics, National Research Council of Italy, Pavia, Italy.
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18
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Pichler I, Schwienbacher C, Zanon A, Fuchsberger C, Serafin A, Facheris MF, Marroni F, Pattaro C, Shen Y, Tellgren-Roth C, Gyllensten U, Gusella JF, Hicks AA, Pramstaller PP. Fine-mapping of restless legs locus 4 (RLS4) identifies a haplotype over the SPATS2L and KCTD18 genes. J Mol Neurosci 2012; 49:600-5. [PMID: 23054586 DOI: 10.1007/s12031-012-9891-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2012] [Accepted: 09/17/2012] [Indexed: 10/27/2022]
Abstract
Restless legs syndrome (RLS) is a sleep-related movement disorder that affects up to 15 % of the population. Linkage studies have identified several genomic loci in single families (12q, 14q, 9p, 2q, 20p and 16p, respectively). However, confirmation of these loci has not always been achieved, and causative mutations have not yet been identified. The locus on chromosome 2q33 (RLS4) was identified in two South Tyrolean families who shared a haplotype of microsatellite marker alleles across an 8.2-cM region. To pinpoint the gene localisation within RLS4, additional families from the same geographic region were evaluated, and linkage was replicated in one family. Within the candidate region, we initially found a haplotype of 23 single nucleotide polymorphism markers spanning 131.6 Kb shared by all affected members of the three linked families. Using a next generation sequencing approach, we further restricted the shared candidate region to 46.9 Kb over the potassium channel-related gene KCTD18 and exons 10-13 of SPATS2L.
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Affiliation(s)
- Irene Pichler
- Center for Biomedicine, European Academy Bozen/Bolzano (EURAC) (Affiliated institute of the University of Lübeck), Drususallee 1, 39100, Bozen/Bolzano, Italy
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19
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Tang W, Schwienbacher C, Lopez L, Ben-Shlomo Y, Oudot-Mellakh T, Johnson A, Samani N, Basu S, Gögele M, Davies G, Lowe G, Tregouet DA, Tan A, Pankow J, Tenesa A, Levy D, Volpato C, Rumley A, Gow A, Minelli C, Yarnell J, Porteous D, Starr J, Gallacher J, Boerwinkle E, Visscher P, Pramstaller P, Cushman M, Emilsson V, Plump A, Matijevic N, Morange PE, Deary I, Hicks A, Folsom A. Genetic associations for activated partial thromboplastin time and prothrombin time, their gene expression profiles, and risk of coronary artery disease. Am J Hum Genet 2012; 91:152-62. [PMID: 22703881 DOI: 10.1016/j.ajhg.2012.05.009] [Citation(s) in RCA: 73] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2011] [Revised: 04/10/2012] [Accepted: 05/05/2012] [Indexed: 10/28/2022] Open
Abstract
Activated partial thromboplastin time (aPTT) and prothrombin time (PT) are clinical tests commonly used to screen for coagulation-factor deficiencies. One genome-wide association study (GWAS) has been reported previously for aPTT, but no GWAS has been reported for PT. We conducted a GWAS and meta-analysis to identify genetic loci for aPTT and PT. The GWAS for aPTT was conducted in 9,240 individuals of European ancestry from the Atherosclerosis Risk in Communities (ARIC) study, and the GWAS for PT was conducted in 2,583 participants from the Genetic Study of Three Population Microisolates in South Tyrol (MICROS) and the Lothian Birth Cohorts (LBC) of 1921 and 1936. Replication was assessed in 1,041 to 3,467 individuals. For aPTT, previously reported associations with KNG1, HRG, F11, F12, and ABO were confirmed. A second independent association in ABO was identified and replicated (rs8176704, p = 4.26 × 10(-24)). Pooling the ARIC and replication data yielded two additional loci in F5 (rs6028, p = 3.22 × 10(-9)) and AGBL1 (rs2469184, p = 3.61 × 10(-8)). For PT, significant associations were identified and confirmed in F7 (rs561241, p = 3.71 × 10(-56)) and PROCR/EDEM2 (rs2295888, p = 5.25 × 10(-13)). Assessment of existing gene expression and coronary artery disease (CAD) databases identified associations of five of the GWAS loci with altered gene expression and two with CAD. In summary, eight genetic loci that account for ∼29% of the variance in aPTT and two loci that account for ∼14% of the variance in PT were detected and supported by functional data.
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20
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Ameur A, Enroth S, Johansson Å, Zaboli G, Igl W, Johansson A, Rivas M, Daly M, Schmitz G, Hicks A, Meitinger T, Feuk L, van Duijn C, Oostra B, Pramstaller P, Rudan I, Wright A, Wilson J, Campbell H, Gyllensten U. Genetic adaptation of fatty-acid metabolism: a human-specific haplotype increasing the biosynthesis of long-chain omega-3 and omega-6 fatty acids. Am J Hum Genet 2012; 90:809-20. [PMID: 22503634 DOI: 10.1016/j.ajhg.2012.03.014] [Citation(s) in RCA: 164] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2011] [Revised: 02/03/2012] [Accepted: 03/15/2012] [Indexed: 10/28/2022] Open
Abstract
Omega-3 and omega-6 long-chain polyunsaturated fatty acids (LC-PUFAs) are essential for the development and function of the human brain. They can be obtained directly from food, e.g., fish, or synthesized from precursor molecules found in vegetable oils. To determine the importance of genetic variability to fatty-acid biosynthesis, we studied FADS1 and FADS2, which encode rate-limiting enzymes for fatty-acid conversion. We performed genome-wide genotyping (n = 5,652 individuals) and targeted resequencing (n = 960 individuals) of the FADS region in five European population cohorts. We also analyzed available genomic data from human populations, archaic hominins, and more distant primates. Our results show that present-day humans have two common FADS haplotypes-defined by 28 closely linked SNPs across 38.9 kb-that differ dramatically in their ability to generate LC-PUFAs. No independent effects on FADS activity were seen for rare SNPs detected by targeted resequencing. The more efficient, evolutionarily derived haplotype appeared after the lineage split leading to modern humans and Neanderthals and shows evidence of positive selection. This human-specific haplotype increases the efficiency of synthesizing essential long-chain fatty acids from precursors and thereby might have provided an advantage in environments with limited access to dietary LC-PUFAs. In the modern world, this haplotype has been associated with lifestyle-related diseases, such as coronary artery disease.
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21
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Wei WH, Hemani G, Gyenesei A, Vitart V, Navarro P, Hayward C, Cabrera CP, Huffman JE, Knott SA, Hicks AA, Rudan I, Pramstaller PP, Wild SH, Wilson JF, Campbell H, Hastie ND, Wright AF, Haley CS. Genome-wide analysis of epistasis in body mass index using multiple human populations. Eur J Hum Genet 2012; 20:857-62. [PMID: 22333899 PMCID: PMC3400731 DOI: 10.1038/ejhg.2012.17] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
We surveyed gene–gene interactions (epistasis) in human body mass index (BMI) in four European populations (n<1200) via exhaustive pair-wise genome scans where interactions were computed as F ratios by testing a linear regression model fitting two single-nucleotide polymorphisms (SNPs) with interactions against the one without. Before the association tests, BMI was corrected for sex and age, normalised and adjusted for relatedness. Neither single SNPs nor SNP interactions were genome-wide significant in either cohort based on the consensus threshold (P=5.0E−08) and a Bonferroni corrected threshold (P=1.1E−12), respectively. Next we compared sub genome-wide significant SNP interactions (P<5.0E−08) across cohorts to identify common epistatic signals, where SNPs were annotated to genes to test for gene ontology (GO) enrichment. Among the epistatic genes contributing to the commonly enriched GO terms, 19 were shared across study cohorts of which 15 are previously published genome-wide association loci, including CDH13 (cadherin 13) associated with height and SORCS2 (sortilin-related VPS10 domain containing receptor 2) associated with circulating insulin-like growth factor 1 and binding protein 3. Interactions between the 19 shared epistatic genes and those involving BMI candidate loci (P<5.0E−08) were tested across cohorts and found eight replicated at the SNP level (P<0.05) in at least one cohort, which were further tested and showed limited replication in a separate European population (n>5000). We conclude that genome-wide analysis of epistasis in multiple populations is an effective approach to provide new insights into the genetic regulation of BMI but requires additional efforts to confirm the findings.
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Affiliation(s)
- Wen-Hua Wei
- MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, UK.
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22
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Biino G, Gasparini P, D’Adamo P, Ciullo M, Nutile T, Toniolo D, Sala C, Minelli C, Gögele M, Balduini CL. Influence of age, sex and ethnicity on platelet count in five Italian geographic isolates: mild thrombocytopenia may be physiological. Br J Haematol 2011; 157:384-7. [DOI: 10.1111/j.1365-2141.2011.08981.x] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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23
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Wei W, Hemani G, Hicks AA, Vitart V, Cabrera-Cardenas C, Navarro P, Huffman J, Hayward C, Knott SA, Rudan I, Pramstaller PP, Wild SH, Wilson JF, Campbell H, Dunlop MG, Hastie N, Wright AF, Haley CS. Characterisation of genome-wide association epistasis signals for serum uric acid in human population isolates. PLoS One 2011; 6:e23836. [PMID: 21886828 PMCID: PMC3158795 DOI: 10.1371/journal.pone.0023836] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2011] [Accepted: 07/26/2011] [Indexed: 01/05/2023] Open
Abstract
Genome-wide association (GWA) studies have identified a number of loci underlying variation in human serum uric acid (SUA) levels with the SLC2A9 gene having the largest effect identified so far. Gene-gene interactions (epistasis) are largely unexplored in these GWA studies. We performed a full pair-wise genome scan in the Italian MICROS population (n = 1201) to characterise epistasis signals in SUA levels. In the resultant epistasis profile, no SNP pairs reached the Bonferroni adjusted threshold for the pair-wise genome-wide significance. However, SLC2A9 was found interacting with multiple loci across the genome, with NFIA-SLC2A9 and SLC2A9-ESRRAP2 being significant based on a threshold derived for interactions between GWA significant SNPs and the genome and jointly explaining 8.0% of the phenotypic variance in SUA levels (3.4% by interaction components). Epistasis signal replication in a CROATIAN population (n = 1772) was limited at the SNP level but improved dramatically at the gene ontology level. In addition, gene ontology terms enriched by the epistasis signals in each population support links between SUA levels and neurological disorders. We conclude that GWA epistasis analysis is useful despite relatively low power in small isolated populations.
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Affiliation(s)
- Wenhua Wei
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, Western General Hospital, Edinburgh, United Kingdom
| | - Gibran Hemani
- The Roslin Institute and R(D)SVS, University of Edinburgh, Roslin, Midlothian, United Kingdom
| | - Andrew A. Hicks
- Institute of Genetic Medicine, European Academy Bozen/Bolzano (EURAC), Bolzano, Italy - Affiliated Institute of the University of Lübeck, Lübeck, Germany
| | - Veronique Vitart
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, Western General Hospital, Edinburgh, United Kingdom
| | - Claudia Cabrera-Cardenas
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, Western General Hospital, Edinburgh, United Kingdom
| | - Pau Navarro
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, Western General Hospital, Edinburgh, United Kingdom
| | - Jennifer Huffman
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, Western General Hospital, Edinburgh, United Kingdom
| | - Caroline Hayward
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, Western General Hospital, Edinburgh, United Kingdom
| | - Sara A. Knott
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, United Kingdom
| | - Igor Rudan
- Centre for Population Health Sciences, University of Edinburgh, Edinburgh, United Kingdom
- Croatian Centre for Global Health, University of Split, Split, Croatia
| | - Peter P. Pramstaller
- Institute of Genetic Medicine, European Academy Bozen/Bolzano (EURAC), Bolzano, Italy - Affiliated Institute of the University of Lübeck, Lübeck, Germany
- Department of Neurology, General Central Hospital, Bolzano, Italy
- Department of Neurology, University of Lübeck, Lübeck, Germany
| | - Sarah H. Wild
- Centre for Population Health Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - James F. Wilson
- Centre for Population Health Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Harry Campbell
- Centre for Population Health Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Malcolm G. Dunlop
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, Western General Hospital, Edinburgh, United Kingdom
| | - Nicholas Hastie
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, Western General Hospital, Edinburgh, United Kingdom
| | - Alan F. Wright
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, Western General Hospital, Edinburgh, United Kingdom
| | - Chris S. Haley
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, Western General Hospital, Edinburgh, United Kingdom
- The Roslin Institute and R(D)SVS, University of Edinburgh, Roslin, Midlothian, United Kingdom
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24
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Chen W, Hayward C, Wright AF, Hicks AA, Vitart V, Knott S, Wild SH, Pramstaller PP, Wilson JF, Rudan I, Porteous DJ. Copy number variation across European populations. PLoS One 2011; 6:e23087. [PMID: 21829696 PMCID: PMC3150386 DOI: 10.1371/journal.pone.0023087] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2011] [Accepted: 07/12/2011] [Indexed: 12/13/2022] Open
Abstract
Genome analysis provides a powerful approach to test for evidence of genetic variation within and between geographical regions and local populations. Copy number variants which comprise insertions, deletions and duplications of genomic sequence provide one such convenient and informative source. Here, we investigate copy number variants from genome wide scans of single nucleotide polymorphisms in three European population isolates, the island of Vis in Croatia, the islands of Orkney in Scotland and the South Tyrol in Italy. We show that whereas the overall copy number variant frequencies are similar between populations, their distribution is highly specific to the population of origin, a finding which is supported by evidence for increased kinship correlation for specific copy number variants within populations.
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Affiliation(s)
- Wanting Chen
- Medical Genetics Section, Centre for Molecular Medicine, Institute of Genetics & Molecular Medicine, University of Edinburgh, Western General Hospital, Crewe Road South, Edinburgh, United Kingdom
| | - Caroline Hayward
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, Western General Hospital, Edinburgh, United Kingdom
| | - Alan F. Wright
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, Western General Hospital, Edinburgh, United Kingdom
| | - Andrew A. Hicks
- Institute of Genetic Medicine, European Academy Bozen/Bolzano (EURAC), Bolzano/Bozen, Italy - Affiliated Institute of the University of Lübeck, Lübeck, Germany
| | - Veronique Vitart
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, Western General Hospital, Edinburgh, United Kingdom
| | - Sara Knott
- Institute of Evolutionary Biology, University of Edinburgh, Ashworth Laboratories, King's Buildings, Edinburgh, United Kingdom
| | - Sarah H. Wild
- Centre for Population Health Sciences, The University of Edinburgh Medical School, Edinburgh, United Kingdom
| | - Peter P. Pramstaller
- Institute of Genetic Medicine, European Academy Bozen/Bolzano (EURAC), Bolzano/Bozen, Italy - Affiliated Institute of the University of Lübeck, Lübeck, Germany
- Department of Neurology, General Central Hospital, Bolzano, Italy
- Department of Neurology, University of Lübeck, Lübeck, Germany
| | - James F. Wilson
- Centre for Population Health Sciences, The University of Edinburgh Medical School, Edinburgh, United Kingdom
| | - Igor Rudan
- Centre for Population Health Sciences, The University of Edinburgh Medical School, Edinburgh, United Kingdom
- Croatian Centre for Global Health, University of Split Medical School, Split, Croatia
| | - David J. Porteous
- Medical Genetics Section, Centre for Molecular Medicine, Institute of Genetics & Molecular Medicine, University of Edinburgh, Western General Hospital, Crewe Road South, Edinburgh, United Kingdom
- * E-mail:
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25
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Zaboli G, Ameur A, Igl W, Johansson Å, Hayward C, Vitart V, Campbell S, Zgaga L, Polasek O, Schmitz G, van Duijn C, Oostra B, Pramstaller P, Hicks A, Meitinger T, Rudan I, Wright A, Wilson JF, Campbell H, Gyllensten U. Sequencing of high-complexity DNA pools for identification of nucleotide and structural variants in regions associated with complex traits. Eur J Hum Genet 2011; 20:77-83. [PMID: 21811304 DOI: 10.1038/ejhg.2011.138] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023] Open
Abstract
We have used targeted genomic sequencing of high-complexity DNA pools based on long-range PCR and deep DNA sequencing by the SOLiD technology. The method was used for sequencing of 286 kb from four chromosomal regions with quantitative trait loci (QTL) influencing blood plasma lipid and uric acid levels in DNA pools of 500 individuals from each of five European populations. The method shows very good precision in estimating allele frequencies as compared with individual genotyping of SNPs (r(2) = 0.95, P < 10(-16)). Validation shows that the method is able to identify novel SNPs and estimate their frequency in high-complexity DNA pools. In our five populations, 17% of all SNPs and 61% of structural variants are not available in the public databases. A large fraction of the novel variants show a limited geographic distribution, with 62% of the novel SNPs and 59% of novel structural variants being detected in only one of the populations. The large number of population-specific novel SNPs underscores the need for comprehensive sequencing of local populations in order to identify the causal variants of human traits.
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Affiliation(s)
- Ghazal Zaboli
- Department of Immunology, Genetics and Pathology, Rudbeck Laboratory, SciLifeLab Uppsala, Uppsala University, Uppsala, Sweden
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26
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Böger CA, Chen MH, Tin A, Olden M, Köttgen A, de Boer IH, Fuchsberger C, O'Seaghdha CM, Pattaro C, Teumer A, Liu CT, Glazer NL, Li M, O'Connell JR, Tanaka T, Peralta CA, Kutalik Z, Luan J, Zhao JH, Hwang SJ, Akylbekova E, Kramer H, van der Harst P, Smith AV, Lohman K, de Andrade M, Hayward C, Kollerits B, Tönjes A, Aspelund T, Ingelsson E, Eiriksdottir G, Launer LJ, Harris TB, Shuldiner AR, Mitchell BD, Arking DE, Franceschini N, Boerwinkle E, Egan J, Hernandez D, Reilly M, Townsend RR, Lumley T, Siscovick DS, Psaty BM, Kestenbaum B, Haritunians T, Bergmann S, Vollenweider P, Waeber G, Mooser V, Waterworth D, Johnson AD, Florez JC, Meigs JB, Lu X, Turner ST, Atkinson EJ, Leak TS, Aasarød K, Skorpen F, Syvänen AC, Illig T, Baumert J, Koenig W, Krämer BK, Devuyst O, Mychaleckyj JC, Minelli C, Bakker SJ, Kedenko L, Paulweber B, Coassin S, Endlich K, Kroemer HK, Biffar R, Stracke S, Völzke H, Stumvoll M, Mägi R, Campbell H, Vitart V, Hastie ND, Gudnason V, Kardia SL, Liu Y, Polasek O, Curhan G, Kronenberg F, Prokopenko I, Rudan I, Ärnlöv J, Hallan S, Navis G, Parsa A, Ferrucci L, Coresh J, Shlipak MG, Bull SB, Paterson AD, Wichmann HE, Wareham NJ, Loos RJ, Rotter JI, Pramstaller PP, Cupples LA, Beckmann JS, Yang Q, Heid IM, Rettig R, Dreisbach AW, Bochud M, Fox CS, Kao W. CUBN is a gene locus for albuminuria. J Am Soc Nephrol 2011; 22:555-70. [PMID: 21355061 PMCID: PMC3060449 DOI: 10.1681/asn.2010060598] [Citation(s) in RCA: 180] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2010] [Accepted: 10/19/2010] [Indexed: 11/03/2022] Open
Abstract
Identification of genetic risk factors for albuminuria may alter strategies for early prevention of CKD progression, particularly among patients with diabetes. Little is known about the influence of common genetic variants on albuminuria in both general and diabetic populations. We performed a meta-analysis of data from 63,153 individuals of European ancestry with genotype information from genome-wide association studies (CKDGen Consortium) and from a large candidate gene study (CARe Consortium) to identify susceptibility loci for the quantitative trait urinary albumin-to-creatinine ratio (UACR) and the clinical diagnosis microalbuminuria. We identified an association between a missense variant (I2984V) in the CUBN gene, which encodes cubilin, and both UACR (P = 1.1 × 10(-11)) and microalbuminuria (P = 0.001). We observed similar associations among 6981 African Americans in the CARe Consortium. The associations between this variant and both UACR and microalbuminuria were significant in individuals of European ancestry regardless of diabetes status. Finally, this variant associated with a 41% increased risk for the development of persistent microalbuminuria during 20 years of follow-up among 1304 participants with type 1 diabetes in the prospective DCCT/EDIC Study. In summary, we identified a missense CUBN variant that associates with levels of albuminuria in both the general population and in individuals with diabetes.
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Affiliation(s)
- Carsten A. Böger
- Department of Internal Medicine II, University Medical Center Regensburg, Regensburg, Germany
| | - Ming-Huei Chen
- Department of Neurology, Boston University School of Medicine, Boston, Massachusetts
| | - Adrienne Tin
- Department of Epidemiology and the Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, Maryland
| | - Matthias Olden
- Department of Internal Medicine II, University Medical Center Regensburg, Regensburg, Germany
- Department of Epidemiology and Preventive Medicine, Regensburg University Medical Center, Regensburg, Germany
| | - Anna Köttgen
- Department of Epidemiology and the Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, Maryland
- Renal Division, University Hospital of Freiburg, Freiburg, Germany
| | - Ian H. de Boer
- Division of Nephrology, University of Washington, Seattle, Washington
| | - Christian Fuchsberger
- Institute of Genetic Medicine, European Academy of Bolzano/Bozen (EURAC), Italy and Affiliated Institute of the University of Lübeck, Lübeck, Germany
| | - Conall M. O'Seaghdha
- Division of Nephrology, Brigham and Women's Hospital and Harvard Medical School, Boston Massachusetts
| | - Cristian Pattaro
- Institute of Genetic Medicine, European Academy of Bolzano/Bozen (EURAC), Italy and Affiliated Institute of the University of Lübeck, Lübeck, Germany
| | - Alexander Teumer
- Interfaculty Institute for Genetics and Functional Genomics, University of Greifswald, Greifswald, Germany
| | - Ching-Ti Liu
- Department of Biostatistics, Boston University School of Public Health and NHLBI's Framingham Heart Study, Boston Massachusetts
| | - Nicole L. Glazer
- Cardiovascular Health Research Unit and Department of Biostatistics, University of Washington, Seattle, Washington
| | - Man Li
- Department of Epidemiology and the Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, Maryland
| | | | - Toshiko Tanaka
- Medstar Research Institute, Baltimore, Maryland
- Clinical Research Branch, National Institute on Aging, Baltimore, Maryland
| | - Carmen A. Peralta
- Division of Nephrology, University of California, San Francisco Medical School and San Francisco VA Medical Center, San Francisco, California
| | - Zoltán Kutalik
- Department of Medical Genetics, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Jian'an Luan
- MRC Epidemiology Unit, Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, United Kingdom
| | - Jing Hua Zhao
- MRC Epidemiology Unit, Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, United Kingdom
| | - Shih-Jen Hwang
- NHLBI's Framingham Heart Study and the Center for Population Studies, Framingham, Massachusetts
| | | | | | - Pim van der Harst
- Department of Cardiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Albert V. Smith
- University of Iceland, Reykjavik, Iceland
- Icelandic Heart Association, Hjartavernd, Holtasmara, Kopavogur, Iceland
| | - Kurt Lohman
- Department of Biostatistical Sciences, Wake Forest University, Division of Public Health Sciences, Winston-Salem, North Carolina
| | - Mariza de Andrade
- Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota
| | - Caroline Hayward
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, Western General Hospital, Crewe Road, Edinburgh, Scotland
| | - Barbara Kollerits
- Innsbruck Medical University, Division of Genetic Epidemiology, Innsbruck, Austria
| | - Anke Tönjes
- Department of Medicine, University of Leipzig, Leipzig, Germany
| | - Thor Aspelund
- University of Iceland, Reykjavik, Iceland
- Icelandic Heart Association, Hjartavernd, Holtasmara, Kopavogur, Iceland
| | - Erik Ingelsson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Gudny Eiriksdottir
- Icelandic Heart Association, Hjartavernd, Holtasmara, Kopavogur, Iceland
| | - Lenore J. Launer
- Laboratory of Epidemiology, Demography, and Biometry, NIA, Bethesda, Maryland
| | - Tamara B. Harris
- Laboratory of Epidemiology, Demography, and Biometry, NIA, Bethesda, Maryland
| | - Alan R. Shuldiner
- University of Maryland School of Medicine, Geriatric Research and Education Clinical Center, Veterans Administration Medical Center, Baltimore, Maryland
| | | | - Dan E. Arking
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins Medical Institutions, Baltimore, Maryland
| | - Nora Franceschini
- University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Eric Boerwinkle
- Human Genetics Center, University of Texas Health Science Center, Houston, Texas
| | - Josephine Egan
- Laboratory of Clinical Investigation, National Institute on Aging, Baltimore, Maryland
| | - Dena Hernandez
- Laboratory of Neurogenetics, National Institute on Aging, Bethesda, Maryland
| | - Muredach Reilly
- University of Pennsylvania Division of Cardiology, Perelman Center for Advanced Medicine, Philadelphia, Pennsylvania
| | - Raymond R. Townsend
- University of Pennsylvania Renal Electrolyte and Hypertension Division, Philadelphia, Pennsylvania
| | - Thomas Lumley
- Cardiovascular Health Research Unit and Department of Biostatistics, University of Washington, Seattle, Washington
| | - David S. Siscovick
- Departments of Epidemiology and Medicine, University of Washington, Seattle, Washington
| | - Bruce M. Psaty
- Cardiovascular Health Research Unit, Departments of Medicine, Epidemiology, and Health Services and Group Health Research Institute, Group Health Cooperative, Seattle, Washington
| | - Bryan Kestenbaum
- Division of Nephrology, University of Washington, Seattle, Washington
| | - Talin Haritunians
- Medical Genetics Institute, Cedars-Sinai Medical Center, Los Angeles, California
| | - Sven Bergmann
- Department of Medical Genetics, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Peter Vollenweider
- Department of Internal Medicine, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
| | - Gerard Waeber
- Department of Internal Medicine, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
| | - Vincent Mooser
- Genetics Division, GlaxoSmithKline, King of Prussia, Pennsylvania
| | - Dawn Waterworth
- Genetics Division, GlaxoSmithKline, King of Prussia, Pennsylvania
| | - Andrew D. Johnson
- NHLBI's Framingham Heart Study and the Center for Population Studies, Framingham, Massachusetts
| | - Jose C. Florez
- Center for Human Genetic Research and Diabetes Research Center (Diabetes Unit), Massachusetts General Hospital, Boston, Massachusetts, Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachussetts, and Department of Medicine, Harvard Medical School, Boston, Massachusetts
| | - James B. Meigs
- Department of General Internal Medicine, Massachussetts General Hospital, Boston, Massachusetts
| | - Xiaoning Lu
- Department of Biostatistics, Boston University School of Public Health and NHLBI's Framingham Heart Study, Boston Massachusetts
| | - Stephen T. Turner
- Department of Internal Medicine, Division of Nephrology and Hypertension, Mayo Clinic, Rochester, Minnesota
| | - Elizabeth J. Atkinson
- Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota
| | - Tennille S. Leak
- Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Knut Aasarød
- St Olav University Hospital, Trondheim, Norway
- Faculty of Medicine, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Frank Skorpen
- Faculty of Medicine, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Ann-Christine Syvänen
- Molecular Medicine, Department of Medical Sciences, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Thomas Illig
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Jens Baumert
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Wolfgang Koenig
- Zentrum für Innere Medizin, Klinik für Innere Medizin II - Kardiologie, Universitätsklinikum Ulm, Ulm, Germany
| | - Bernhard K. Krämer
- University Medical Centre Mannheim, 5th Department of Medicine, Mannheim, Germany
| | - Olivier Devuyst
- NEFR Unit Université Catholique de Louvain Medical School, Brussels, Belgium
| | | | - Cosetta Minelli
- Institute of Genetic Medicine, European Academy of Bolzano/Bozen (EURAC), Italy and Affiliated Institute of the University of Lübeck, Lübeck, Germany
| | - Stephan J.L. Bakker
- Department of Internal Medicine, University Medical Center, Groningen, University of Groningen, Groningen, The Netherlands
| | - Lyudmyla Kedenko
- First Department of Internal Medicine, Paracelsus Medical University, Salzburg, Austria
| | - Bernhard Paulweber
- First Department of Internal Medicine, Paracelsus Medical University, Salzburg, Austria
| | - Stefan Coassin
- Innsbruck Medical University, Division of Genetic Epidemiology, Innsbruck, Austria
| | - Karlhans Endlich
- Institute of Anatomy and Cell Biology, University of Greifswald, Greifswald, Germany
| | - Heyo K. Kroemer
- Institute of Pharmacology, University of Greifswald, Greifswald, Germany
| | - Reiner Biffar
- Clinic for Prosthodontic Dentistry, Gerostomatology and Material Science, University of Greifswald, Greifswald, Germany
| | - Sylvia Stracke
- Nephrology Clinic for Internal Medicine A, University of Greifswald, Greifswald, Germany
| | - Henry Völzke
- Institute for Community Medicine, University of Greifswald, Greifswald, Germany
| | | | - Reedik Mägi
- Wellcome Trust Centre for Human Genetics, and Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, United Kingdom
| | - Harry Campbell
- Centre for Population Health Sciences, University of Edinburgh, Edinburgh, Scotland
| | - Veronique Vitart
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, Western General Hospital, Crewe Road, Edinburgh, Scotland
| | - Nicholas D. Hastie
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, Western General Hospital, Crewe Road, Edinburgh, Scotland
| | - Vilmundur Gudnason
- University of Iceland, Reykjavik, Iceland
- Icelandic Heart Association, Hjartavernd, Holtasmara, Kopavogur, Iceland
| | - Sharon L.R. Kardia
- University of Michigan School of Public Health, Department of Epidemiology, University of Michigan, Ann Arbor, Michigan
| | - Yongmei Liu
- Department of Biostatistical Sciences, Wake Forest University, Division of Public Health Sciences, Winston-Salem, North Carolina
| | | | - Gary Curhan
- Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Florian Kronenberg
- Innsbruck Medical University, Division of Genetic Epidemiology, Innsbruck, Austria
| | - Inga Prokopenko
- Wellcome Trust Centre for Human Genetics, and Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, United Kingdom
| | - Igor Rudan
- Center for Population Health Sciences, University of Edinburgh Medical School, Edinburgh, Scotland
| | - Johan Ärnlöv
- Department of Public Health and Caring Sciences, Uppsala University, Uppsala, Sweden
| | - Stein Hallan
- St Olav University Hospital, Trondheim, Norway
- Faculty of Medicine, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Gerjan Navis
- Department of Internal Medicine, University Medical Center, Groningen, University of Groningen, Groningen, The Netherlands
| | - the CKDGen Consortium
- Department of Internal Medicine II, University Medical Center Regensburg, Regensburg, Germany
- Department of Neurology, Boston University School of Medicine, Boston, Massachusetts
- Department of Epidemiology and the Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, Maryland
- Department of Epidemiology and Preventive Medicine, Regensburg University Medical Center, Regensburg, Germany
- Renal Division, University Hospital of Freiburg, Freiburg, Germany
- Division of Nephrology, University of Washington, Seattle, Washington
- Institute of Genetic Medicine, European Academy of Bolzano/Bozen (EURAC), Italy and Affiliated Institute of the University of Lübeck, Lübeck, Germany
- Division of Nephrology, Brigham and Women's Hospital and Harvard Medical School, Boston Massachusetts
- Interfaculty Institute for Genetics and Functional Genomics, University of Greifswald, Greifswald, Germany
- Department of Biostatistics, Boston University School of Public Health and NHLBI's Framingham Heart Study, Boston Massachusetts
- Cardiovascular Health Research Unit and Department of Biostatistics, University of Washington, Seattle, Washington
- University of Maryland School of Medicine, Baltimore, Maryland
- Medstar Research Institute, Baltimore, Maryland
- Clinical Research Branch, National Institute on Aging, Baltimore, Maryland
- Division of Nephrology, University of California, San Francisco Medical School and San Francisco VA Medical Center, San Francisco, California
- Department of Medical Genetics, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- MRC Epidemiology Unit, Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, United Kingdom
- NHLBI's Framingham Heart Study and the Center for Population Studies, Framingham, Massachusetts
- Jackson State University, Jackson, Mississippi
- Loyola University, Maywood, Illinois
- Department of Cardiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- University of Iceland, Reykjavik, Iceland
- Icelandic Heart Association, Hjartavernd, Holtasmara, Kopavogur, Iceland
- Department of Biostatistical Sciences, Wake Forest University, Division of Public Health Sciences, Winston-Salem, North Carolina
- Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, Western General Hospital, Crewe Road, Edinburgh, Scotland
- Innsbruck Medical University, Division of Genetic Epidemiology, Innsbruck, Austria
- Department of Medicine, University of Leipzig, Leipzig, Germany
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Laboratory of Epidemiology, Demography, and Biometry, NIA, Bethesda, Maryland
- University of Maryland School of Medicine, Geriatric Research and Education Clinical Center, Veterans Administration Medical Center, Baltimore, Maryland
- University of Maryland School of Medicine, Baltimore, Maryland
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins Medical Institutions, Baltimore, Maryland
- University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
- Human Genetics Center, University of Texas Health Science Center, Houston, Texas
- Laboratory of Clinical Investigation, National Institute on Aging, Baltimore, Maryland
- Laboratory of Neurogenetics, National Institute on Aging, Bethesda, Maryland
- University of Pennsylvania Division of Cardiology, Perelman Center for Advanced Medicine, Philadelphia, Pennsylvania
- University of Pennsylvania Renal Electrolyte and Hypertension Division, Philadelphia, Pennsylvania
- Departments of Epidemiology and Medicine, University of Washington, Seattle, Washington
- Cardiovascular Health Research Unit, Departments of Medicine, Epidemiology, and Health Services and Group Health Research Institute, Group Health Cooperative, Seattle, Washington
- Medical Genetics Institute, Cedars-Sinai Medical Center, Los Angeles, California
- Department of Internal Medicine, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
- Genetics Division, GlaxoSmithKline, King of Prussia, Pennsylvania
- Center for Human Genetic Research and Diabetes Research Center (Diabetes Unit), Massachusetts General Hospital, Boston, Massachusetts, Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachussetts, and Department of Medicine, Harvard Medical School, Boston, Massachusetts
- Department of General Internal Medicine, Massachussetts General Hospital, Boston, Massachusetts
- Department of Internal Medicine, Division of Nephrology and Hypertension, Mayo Clinic, Rochester, Minnesota
- Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania
- St Olav University Hospital, Trondheim, Norway
- Faculty of Medicine, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
- Molecular Medicine, Department of Medical Sciences, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Zentrum für Innere Medizin, Klinik für Innere Medizin II - Kardiologie, Universitätsklinikum Ulm, Ulm, Germany
- University Medical Centre Mannheim, 5th Department of Medicine, Mannheim, Germany
- NEFR Unit Université Catholique de Louvain Medical School, Brussels, Belgium
- Center for Public Health Genomics, Charlottesville, Virginia
- Department of Internal Medicine, University Medical Center, Groningen, University of Groningen, Groningen, The Netherlands
- First Department of Internal Medicine, Paracelsus Medical University, Salzburg, Austria
- Institute of Anatomy and Cell Biology, University of Greifswald, Greifswald, Germany
- Institute of Pharmacology, University of Greifswald, Greifswald, Germany
- Clinic for Prosthodontic Dentistry, Gerostomatology and Material Science, University of Greifswald, Greifswald, Germany
- Nephrology Clinic for Internal Medicine A, University of Greifswald, Greifswald, Germany
- Institute for Community Medicine, University of Greifswald, Greifswald, Germany
- Wellcome Trust Centre for Human Genetics, and Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, United Kingdom
- Centre for Population Health Sciences, University of Edinburgh, Edinburgh, Scotland
- University of Michigan School of Public Health, Department of Epidemiology, University of Michigan, Ann Arbor, Michigan
- Gen-Info Ltd., Zagreb, Croatia
- Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
- Center for Population Health Sciences, University of Edinburgh Medical School, Edinburgh, Scotland
- Department of Public Health and Caring Sciences, Uppsala University, Uppsala, Sweden
- University of Maryland School of Medicine, Baltimore, Maryland
- Welch Center for Prevention, Epidemiology & Clinical Research, Johns Hopkins University, Baltimore, Maryland
- General Internal Medicine, University of California, San Francisco, San Francisco, California
- Samuel Lunenfeld Research Institute of Mount Sinai Hospital, Prosserman Centre for Health Research, Toronto, Ontario, Canada
- The Hospital for Sick Children, Toronto, Ontario, Canada
- Institute of Medical Informatics, Biometry and Epidemiology, Ludwig-Maximilians-Universität, Munich, Germany
- Klinikum Grosshadern, Munich, Germany
- Service of Medical Genetics, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts
- Institute of Physiology, University of Greifswald, Greifswald, Germany
- University of Mississippi Division of Nephrology, University of Mississippi, Jackson, Mississippi
- University Institute of Social and Preventive Medicine, Centre Hospitalier Universitaire Vaudois and University of Lausanne, IUMSP, Lausanne, Switzerland; and
- Division of Endocrinology, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
| | - Afshin Parsa
- University of Maryland School of Medicine, Baltimore, Maryland
| | - Luigi Ferrucci
- Clinical Research Branch, National Institute on Aging, Baltimore, Maryland
| | - Josef Coresh
- Welch Center for Prevention, Epidemiology & Clinical Research, Johns Hopkins University, Baltimore, Maryland
| | - Michael G. Shlipak
- General Internal Medicine, University of California, San Francisco, San Francisco, California
| | - Shelley B. Bull
- Samuel Lunenfeld Research Institute of Mount Sinai Hospital, Prosserman Centre for Health Research, Toronto, Ontario, Canada
| | | | - on behalf of DCCT/EDIC
- Department of Internal Medicine II, University Medical Center Regensburg, Regensburg, Germany
- Department of Neurology, Boston University School of Medicine, Boston, Massachusetts
- Department of Epidemiology and the Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, Maryland
- Department of Epidemiology and Preventive Medicine, Regensburg University Medical Center, Regensburg, Germany
- Renal Division, University Hospital of Freiburg, Freiburg, Germany
- Division of Nephrology, University of Washington, Seattle, Washington
- Institute of Genetic Medicine, European Academy of Bolzano/Bozen (EURAC), Italy and Affiliated Institute of the University of Lübeck, Lübeck, Germany
- Division of Nephrology, Brigham and Women's Hospital and Harvard Medical School, Boston Massachusetts
- Interfaculty Institute for Genetics and Functional Genomics, University of Greifswald, Greifswald, Germany
- Department of Biostatistics, Boston University School of Public Health and NHLBI's Framingham Heart Study, Boston Massachusetts
- Cardiovascular Health Research Unit and Department of Biostatistics, University of Washington, Seattle, Washington
- University of Maryland School of Medicine, Baltimore, Maryland
- Medstar Research Institute, Baltimore, Maryland
- Clinical Research Branch, National Institute on Aging, Baltimore, Maryland
- Division of Nephrology, University of California, San Francisco Medical School and San Francisco VA Medical Center, San Francisco, California
- Department of Medical Genetics, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- MRC Epidemiology Unit, Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, United Kingdom
- NHLBI's Framingham Heart Study and the Center for Population Studies, Framingham, Massachusetts
- Jackson State University, Jackson, Mississippi
- Loyola University, Maywood, Illinois
- Department of Cardiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- University of Iceland, Reykjavik, Iceland
- Icelandic Heart Association, Hjartavernd, Holtasmara, Kopavogur, Iceland
- Department of Biostatistical Sciences, Wake Forest University, Division of Public Health Sciences, Winston-Salem, North Carolina
- Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, Western General Hospital, Crewe Road, Edinburgh, Scotland
- Innsbruck Medical University, Division of Genetic Epidemiology, Innsbruck, Austria
- Department of Medicine, University of Leipzig, Leipzig, Germany
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Laboratory of Epidemiology, Demography, and Biometry, NIA, Bethesda, Maryland
- University of Maryland School of Medicine, Geriatric Research and Education Clinical Center, Veterans Administration Medical Center, Baltimore, Maryland
- University of Maryland School of Medicine, Baltimore, Maryland
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins Medical Institutions, Baltimore, Maryland
- University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
- Human Genetics Center, University of Texas Health Science Center, Houston, Texas
- Laboratory of Clinical Investigation, National Institute on Aging, Baltimore, Maryland
- Laboratory of Neurogenetics, National Institute on Aging, Bethesda, Maryland
- University of Pennsylvania Division of Cardiology, Perelman Center for Advanced Medicine, Philadelphia, Pennsylvania
- University of Pennsylvania Renal Electrolyte and Hypertension Division, Philadelphia, Pennsylvania
- Departments of Epidemiology and Medicine, University of Washington, Seattle, Washington
- Cardiovascular Health Research Unit, Departments of Medicine, Epidemiology, and Health Services and Group Health Research Institute, Group Health Cooperative, Seattle, Washington
- Medical Genetics Institute, Cedars-Sinai Medical Center, Los Angeles, California
- Department of Internal Medicine, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
- Genetics Division, GlaxoSmithKline, King of Prussia, Pennsylvania
- Center for Human Genetic Research and Diabetes Research Center (Diabetes Unit), Massachusetts General Hospital, Boston, Massachusetts, Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachussetts, and Department of Medicine, Harvard Medical School, Boston, Massachusetts
- Department of General Internal Medicine, Massachussetts General Hospital, Boston, Massachusetts
- Department of Internal Medicine, Division of Nephrology and Hypertension, Mayo Clinic, Rochester, Minnesota
- Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania
- St Olav University Hospital, Trondheim, Norway
- Faculty of Medicine, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
- Molecular Medicine, Department of Medical Sciences, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Zentrum für Innere Medizin, Klinik für Innere Medizin II - Kardiologie, Universitätsklinikum Ulm, Ulm, Germany
- University Medical Centre Mannheim, 5th Department of Medicine, Mannheim, Germany
- NEFR Unit Université Catholique de Louvain Medical School, Brussels, Belgium
- Center for Public Health Genomics, Charlottesville, Virginia
- Department of Internal Medicine, University Medical Center, Groningen, University of Groningen, Groningen, The Netherlands
- First Department of Internal Medicine, Paracelsus Medical University, Salzburg, Austria
- Institute of Anatomy and Cell Biology, University of Greifswald, Greifswald, Germany
- Institute of Pharmacology, University of Greifswald, Greifswald, Germany
- Clinic for Prosthodontic Dentistry, Gerostomatology and Material Science, University of Greifswald, Greifswald, Germany
- Nephrology Clinic for Internal Medicine A, University of Greifswald, Greifswald, Germany
- Institute for Community Medicine, University of Greifswald, Greifswald, Germany
- Wellcome Trust Centre for Human Genetics, and Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, United Kingdom
- Centre for Population Health Sciences, University of Edinburgh, Edinburgh, Scotland
- University of Michigan School of Public Health, Department of Epidemiology, University of Michigan, Ann Arbor, Michigan
- Gen-Info Ltd., Zagreb, Croatia
- Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
- Center for Population Health Sciences, University of Edinburgh Medical School, Edinburgh, Scotland
- Department of Public Health and Caring Sciences, Uppsala University, Uppsala, Sweden
- University of Maryland School of Medicine, Baltimore, Maryland
- Welch Center for Prevention, Epidemiology & Clinical Research, Johns Hopkins University, Baltimore, Maryland
- General Internal Medicine, University of California, San Francisco, San Francisco, California
- Samuel Lunenfeld Research Institute of Mount Sinai Hospital, Prosserman Centre for Health Research, Toronto, Ontario, Canada
- The Hospital for Sick Children, Toronto, Ontario, Canada
- Institute of Medical Informatics, Biometry and Epidemiology, Ludwig-Maximilians-Universität, Munich, Germany
- Klinikum Grosshadern, Munich, Germany
- Service of Medical Genetics, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts
- Institute of Physiology, University of Greifswald, Greifswald, Germany
- University of Mississippi Division of Nephrology, University of Mississippi, Jackson, Mississippi
- University Institute of Social and Preventive Medicine, Centre Hospitalier Universitaire Vaudois and University of Lausanne, IUMSP, Lausanne, Switzerland; and
- Division of Endocrinology, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
| | - H.-Erich Wichmann
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Medical Informatics, Biometry and Epidemiology, Ludwig-Maximilians-Universität, Munich, Germany
- Klinikum Grosshadern, Munich, Germany
| | - Nicholas J. Wareham
- MRC Epidemiology Unit, Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, United Kingdom
| | - Ruth J.F. Loos
- MRC Epidemiology Unit, Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, United Kingdom
| | - Jerome I. Rotter
- Medical Genetics Institute, Cedars-Sinai Medical Center, Los Angeles, California
| | - Peter P. Pramstaller
- Institute of Genetic Medicine, European Academy of Bolzano/Bozen (EURAC), Italy and Affiliated Institute of the University of Lübeck, Lübeck, Germany
| | - L. Adrienne Cupples
- Department of Biostatistics, Boston University School of Public Health and NHLBI's Framingham Heart Study, Boston Massachusetts
| | - Jacques S. Beckmann
- Service of Medical Genetics, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland
| | - Qiong Yang
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts
| | - Iris M. Heid
- Department of Epidemiology and Preventive Medicine, Regensburg University Medical Center, Regensburg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Rainer Rettig
- Institute of Physiology, University of Greifswald, Greifswald, Germany
| | - Albert W. Dreisbach
- University of Mississippi Division of Nephrology, University of Mississippi, Jackson, Mississippi
| | - Murielle Bochud
- University Institute of Social and Preventive Medicine, Centre Hospitalier Universitaire Vaudois and University of Lausanne, IUMSP, Lausanne, Switzerland; and
| | - Caroline S. Fox
- NHLBI's Framingham Heart Study and the Center for Population Studies, Framingham, Massachusetts
- Division of Endocrinology, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
| | - W.H.L. Kao
- Department of Epidemiology and the Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, Maryland
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27
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Del Greco M F, Pattaro C, Luchner A, Pichler I, Winkler T, Hicks AA, Fuchsberger C, Franke A, Melville SA, Peters A, Wichmann HE, Schreiber S, Heid IM, Krawczak M, Minelli C, Wiedermann CJ, Pramstaller PP. Genome-wide association analysis and fine mapping of NT-proBNP level provide novel insight into the role of the MTHFR-CLCN6-NPPA-NPPB gene cluster. Hum Mol Genet 2011; 20:1660-71. [PMID: 21273288 PMCID: PMC3063986 DOI: 10.1093/hmg/ddr035] [Citation(s) in RCA: 42] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023] Open
Abstract
High blood concentration of the N-terminal cleavage product of the B-type natriuretic peptide (NT-proBNP) is strongly associated with cardiac dysfunction and is increasingly used for heart failure diagnosis. To identify genetic variants associated with NT-proBNP level, we performed a genome-wide association analysis in 1325 individuals from South Tyrol, Italy, and followed up the most significant results in 1746 individuals from two German population-based studies. A genome-wide significant signal in the MTHFR-CLCN6-NPPA-NPPB gene cluster was replicated, after correction for multiple testing (replication one-sided P-value = 8.4 × 10−10). A conditional regression analysis of 128 single-nucleotide polymorphisms in the region of interest identified novel variants in the CLCN6 gene as independently associated with NT-proBNP. In this locus, four haplotypes were associated with increased NT-proBNP levels (haplotype-specific combined P-values from 8.3 × 10−03 to 9.3 × 10−11). The observed increase in the NT-proBNP level was proportional to the number of haplotype copies present (i.e. dosage effect), with an increase associated with two copies that varied between 20 and 100 pg/ml across populations. The identification of novel variants in the MTHFR-CLCN6-NPPA-NPPB cluster provides new insights into the biological mechanisms of cardiac dysfunction.
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Affiliation(s)
- Fabiola Del Greco M
- Institute of Genetic Medicine, European Academy Bozen/Bolzano (EURAC), Bolzano, Italy
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28
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Pichler I, Minelli C, Sanna S, Tanaka T, Schwienbacher C, Naitza S, Porcu E, Pattaro C, Busonero F, Zanon A, Maschio A, Melville SA, Grazia Piras M, Longo DL, Guralnik J, Hernandez D, Bandinelli S, Aigner E, Murphy AT, Wroblewski V, Marroni F, Theurl I, Gnewuch C, Schadt E, Mitterer M, Schlessinger D, Ferrucci L, Witcher DR, Hicks AA, Weiss G, Uda M, Pramstaller PP. Identification of a common variant in the TFR2 gene implicated in the physiological regulation of serum iron levels. Hum Mol Genet 2010; 20:1232-40. [PMID: 21208937 DOI: 10.1093/hmg/ddq552] [Citation(s) in RCA: 58] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023] Open
Abstract
The genetic determinants of variation in iron status are actively sought, but remain incompletely understood. Meta-analysis of two genome-wide association (GWA) studies and replication in three independent cohorts was performed to identify genetic loci associated in the general population with serum levels of iron and markers of iron status, including transferrin, ferritin, soluble transferrin receptor (sTfR) and sTfR-ferritin index. We identified and replicated a novel association of a common variant in the type-2 transferrin receptor (TFR2) gene with iron levels, with effect sizes highly consistent across samples. In addition, we identified and replicated an association between the HFE locus and ferritin and confirmed previously reported associations with the TF, TMPRSS6 and HFE genes. The five replicated variants were tested for association with expression levels of the corresponding genes in a publicly available data set of human liver samples, and nominally statistically significant expression differences by genotype were observed for all genes, although only rs3811647 in the TF gene survived the Bonferroni correction for multiple testing. In addition, we measured for the first time the effects of the common variant in TMPRSS6, rs4820268, on hepcidin mRNA in peripheral blood (n = 83 individuals) and on hepcidin levels in urine (n = 529) and observed an association in the same direction, though only borderline significant. These functional findings require confirmation in further studies with larger sample sizes, but they suggest that common variants in TMPRSS6 could modify the hepcidin-iron feedback loop in clinically unaffected individuals, thus making them more susceptible to imbalances of iron homeostasis.
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Affiliation(s)
- Irene Pichler
- Institute of Genetic Medicine, European Academy Bozen/Bolzano, 39100 Bolzano, Italy
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29
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Gögele M, Pattaro C, Fuchsberger C, Minelli C, Pramstaller PP, Wjst M. Heritability analysis of life span in a semi-isolated population followed across four centuries reveals the presence of pleiotropy between life span and reproduction. J Gerontol A Biol Sci Med Sci 2010; 66:26-37. [PMID: 20884848 DOI: 10.1093/gerona/glq163] [Citation(s) in RCA: 37] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Although genetic factors are known to influence the human aging process, the proportion of life span and longevity variation explained by them is still controversial. We evaluated the genetic contribution to life span using historical data from three Alpine communities in South Tyrol, Italy. We estimated the heritability of life span and survival to old age (longevity), and we assessed the hypothesis of a common genetic background between life span and reproduction. The heritability of life span was 0.15 (SE = 0.02), whereas the heritability of longevity increased from 0.20 to 0.35 as the longevity threshold increased. Heritability estimates were little influenced by shared environment, most likely due to the homogeneity of lifestyle and environmental factors in our study population. Life span showed both positive association and genetic correlation with reproductive history factors. Our study demonstrates a general low inheritance of human life span, but which increases substantially when considering long-living individuals, and a common genetic background of life span and reproduction, in agreement with evolutionary theories of aging.
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Affiliation(s)
- Martin Gögele
- Institute of Genetic Medicine, European Academy Bozen/Bolzano Research, Viale Druso/Drususallee, 1 39100 Bolzano/Bozen, Italy.
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30
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Schwienbacher C, De Grandi A, Fuchsberger C, Facheris MF, Svaldi M, Wjst M, Pramstaller PP, Hicks AA. Copy number variation and association over T-cell receptor genes--influence of DNA source. Immunogenetics 2010; 62:561-7. [PMID: 20582410 DOI: 10.1007/s00251-010-0459-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2010] [Accepted: 06/04/2010] [Indexed: 11/30/2022]
Abstract
Genomic copy number variants (CNVs) are a common, heritable source of inter-individual differences in genomic sequence. Their influence on phenotypic variability and their involvement in the pathogenesis of several common diseases is well established and the object of many current studies. In the course of examining CNV association to various quantitative traits in a general population, we have detected a strong association of CNVs over the four TCR genes to lymphocyte and neutrophil numbers in blood. In a small replication series, we have further characterized the nature of these CNVs and found them not to be germline, but dependent on the origin of analysed DNA. Germline deletion and rearrangement around the T-cell receptor (TCR) genes naturally occurs in white blood cells. Blood DNA derived from persons with high lymphocyte counts generates variable intensity signals which behave like germline CNVs over these genes. As DNA containing a relative high proportion of these CNV-like events involving the TCR genes has the ability to influence genotype counts of SNPs in the regions of these genes, care should be taken in interpreting and replicating association signals on variants within these genes when blood-derived DNA is the only source of data.
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31
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CLOCK gene variants associate with sleep duration in two independent populations. Biol Psychiatry 2010; 67:1040-7. [PMID: 20149345 DOI: 10.1016/j.biopsych.2009.12.026] [Citation(s) in RCA: 113] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/12/2009] [Revised: 11/05/2009] [Accepted: 12/11/2009] [Indexed: 01/22/2023]
Abstract
BACKGROUND Sleep is an active and complex behavior, yet it has two straightforward properties-timing and duration. Clock genes are associated with dysfunctional timing of sleep, mood, and obesity disorders, which are commonly associated with sleep duration. METHODS Sleep duration was assessed in Central Europe, Estonia, and South Tyrol (n approximately 77,000) with the Munich ChronoType Questionnaire. It showed a Gaussian distribution in all investigated populations after averaging over a standard workweek and normalization according to age and gender. A follow-up, two-stage design, linkage disequilibrium-based association study was conducted with subjects from South Tyrol (discovery sample; n = 283) and with short (< 7 hours) and long (> 8.5 hours) sleepers from Estonia (confirmation sample; n = 1011). One hundred ninety-four single nucleotide polymorphism markers covering 19 candidate clock genes were genotyped in the discovery sample, and two of the best association signals (analyzed by a linear regression model) were investigated in the confirmation sample. RESULTS Single and multi-marker associations were found within a CLOCK gene intronic region (rs12649507 and rs11932595). In a meta-analysis between South Tyrol and Estonia association signals, rs12649507 (p = .0087) remained significant. Significance persisted only for the multiple-marker association signal of the rs12649507/rs11932595 haplotype GGAA with long sleep (p = .0015). CONCLUSIONS We report an association between variants of the human CLOCK gene and sleep duration in two independent populations. This adds another putative function for CLOCK besides its possible involvement in circadian timing, depression, obesity, and personality.
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Johansson A, Marroni F, Hayward C, Franklin CS, Kirichenko AV, Jonasson I, Hicks AA, Vitart V, Isaacs A, Axenovich T, Campbell S, Floyd J, Hastie N, Knott S, Lauc G, Pichler I, Rotim K, Wild SH, Zorkoltseva IV, Wilson JF, Rudan I, Campbell H, Pattaro C, Pramstaller P, Oostra BA, Wright AF, van Duijn CM, Aulchenko YS, Gyllensten U. Linkage and genome-wide association analysis of obesity-related phenotypes: association of weight with the MGAT1 gene. Obesity (Silver Spring) 2010; 18:803-8. [PMID: 19851299 DOI: 10.1038/oby.2009.359] [Citation(s) in RCA: 38] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
As major risk-factors for diabetes and cardiovascular diseases, the genetic contribution to obesity-related traits has been of interest for decades. Recently, a limited number of common genetic variants, which have replicated in different populations, have been identified. One approach to increase the statistical power in genetic mapping studies is to focus on populations with increased levels of linkage disequilibrium (LD) and reduced genetic diversity. We have performed joint linkage and genome-wide association analyses for weight and BMI in 3,448 (linkage) and 3,925 (association) partly overlapping healthy individuals from five European populations. A total of four chromosomal regions (two for weight and two for BMI) showed suggestive linkage (lod >2.69) either in one of the populations or in the joint data. At the genome-wide level (nominal P < 1.6 x 10(-7), Bonferroni-adjusted P < 0.05) one single-nucleotide polymorphism (SNP) (rs12517906) (nominal P = 7.3 x 10(-8)) was associated with weight, whereas none with BMI. The SNP associated with weight is located close to MGAT1. The monoacylglycerol acyltransferase (MGAT) enzyme family is known to be involved in dietary fat absorption. There was no overlap between the linkage regions and the associated SNPs. Our results show that genetic effects influencing weight and BMI are shared across diverse European populations, even though some of these populations have experienced recent population bottlenecks and/or been affected by genetic drift. The analysis enabled us to identify a new candidate gene, MGAT1, associated with weight in women.
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Affiliation(s)
- Asa Johansson
- Department of Genetics and Pathology, Rudbeck laboratory, Uppsala University, Uppsala, Sweden
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33
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Pattaro C, De Grandi A, Vitart V, Hayward C, Franke A, Aulchenko YS, Johansson A, Wild SH, Melville SA, Isaacs A, Polasek O, Ellinghaus D, Kolcic I, Nöthlings U, Zgaga L, Zemunik T, Gnewuch C, Schreiber S, Campbell S, Hastie N, Boban M, Meitinger T, Oostra BA, Riegler P, Minelli C, Wright AF, Campbell H, van Duijn CM, Gyllensten U, Wilson JF, Krawczak M, Rudan I, Pramstaller PP. A meta-analysis of genome-wide data from five European isolates reveals an association of COL22A1, SYT1, and GABRR2 with serum creatinine level. BMC MEDICAL GENETICS 2010; 11:41. [PMID: 20222955 PMCID: PMC2848223 DOI: 10.1186/1471-2350-11-41] [Citation(s) in RCA: 41] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/30/2009] [Accepted: 03/11/2010] [Indexed: 11/10/2022]
Abstract
BACKGROUND Serum creatinine (S CR) is the most important biomarker for a quick and non-invasive assessment of kidney function in population-based surveys. A substantial proportion of the inter-individual variability in S CR level is explicable by genetic factors. METHODS We performed a meta-analysis of genome-wide association studies of S CR undertaken in five population isolates ('discovery cohorts'), all of which are part of the European Special Population Network (EUROSPAN) project. Genes showing the strongest evidence for an association with SCR (candidate loci) were replicated in two additional population-based samples ('replication cohorts'). RESULTS After the discovery meta-analysis, 29 loci were selected for replication. Association between SCR level and polymorphisms in the collagen type XXII alpha 1 (COL22A1) gene, on chromosome 8, and in the synaptotagmin-1 (SYT1) gene, on chromosome 12, were successfully replicated in the replication cohorts (p value = 1.0 x 10(-6) and 1.7 x 10(-4), respectively). Evidence of association was also found for polymorphisms in a locus including the gamma-aminobutyric acid receptor rho-2 (GABRR2) gene and the ubiquitin-conjugating enzyme E2-J1 (UBE2J1) gene (replication p value = 3.6 x 10(-3)). Previously reported findings, associating glomerular filtration rate with SNPs in the uromodulin (UMOD) gene and in the schroom family member 3 (SCHROOM3) gene were also replicated. CONCLUSIONS While confirming earlier results, our study provides new insights in the understanding of the genetic basis of serum creatinine regulatory processes. In particular, the association with the genes SYT1 and GABRR2 corroborate previous findings that highlighted a possible role of the neurotransmitters GABAA receptors in the regulation of the glomerular basement membrane and a possible interaction between GABAA receptors and synaptotagmin-I at the podocyte level.
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Affiliation(s)
- Cristian Pattaro
- Institute of Genetic Medicine, European Academy Bozen/Bolzano (EURAC), Bolzano, Italy - Affiliated Institute of the University Lübeck, Lübeck, Germany
| | - Alessandro De Grandi
- Institute of Genetic Medicine, European Academy Bozen/Bolzano (EURAC), Bolzano, Italy - Affiliated Institute of the University Lübeck, Lübeck, Germany
| | - Veronique Vitart
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, Edinburgh, UK
| | - Caroline Hayward
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, Edinburgh, UK
| | - Andre Franke
- Institute for Clinical Molecular Biology, Christian-Albrechts-University Kiel, Kiel, Germany
| | - Yurii S Aulchenko
- Genetic Epidemiology Unit, Departments of Epidemiology and Clinical Genetics, Erasmus MC, 3000 CA Rotterdam, the Netherlands
| | - Asa Johansson
- Department of Genetics and Pathology, Rudbeck laboratory, Uppsala University, SE-751 85, Uppsala, Sweden
| | - Sarah H Wild
- Centre for Population Health Sciences, University of Edinburgh Medical School, Teviot Place, Edinburgh EH8 9AG, UK
| | - Scott A Melville
- Institute of Genetic Medicine, European Academy Bozen/Bolzano (EURAC), Bolzano, Italy - Affiliated Institute of the University Lübeck, Lübeck, Germany
| | - Aaron Isaacs
- Genetic Epidemiology Unit, Departments of Epidemiology and Clinical Genetics, Erasmus MC, 3000 CA Rotterdam, the Netherlands
| | - Ozren Polasek
- Andrija Stampar School of Public Health, University of Zagreb Medical School, Rockefellerova 4, 10000 Zagreb, Croatia
- Gen-info Ltd, Ruzmarinka 17, 10000 Zagreb, Croatia
| | - David Ellinghaus
- Institute for Clinical Molecular Biology, Christian-Albrechts-University Kiel, Kiel, Germany
| | - Ivana Kolcic
- Andrija Stampar School of Public Health, University of Zagreb Medical School, Rockefellerova 4, 10000 Zagreb, Croatia
| | - Ute Nöthlings
- Popgen biobank, Christian-Albrechts-University Kiel, Kiel, Germany
- Institute for Experimental Medicine, Christian-Albrechts University Kiel, 24105 Kiel, Germany
| | - Lina Zgaga
- Andrija Stampar School of Public Health, University of Zagreb Medical School, Rockefellerova 4, 10000 Zagreb, Croatia
| | - Tatijana Zemunik
- Croatian Centre for Global Health, University of Split Medical School, Soltanska 2, 21000 Split, Croatia
| | - Carsten Gnewuch
- Institute for Clinical Chemistry and Laboratory Medicine, Regensburg University Medical Center, D-93053 Regensburg, Germany
| | - Stefan Schreiber
- Institute for Clinical Molecular Biology, Christian-Albrechts-University Kiel, Kiel, Germany
| | - Susan Campbell
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, Edinburgh, UK
| | - Nick Hastie
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, Edinburgh, UK
| | - Mladen Boban
- Croatian Centre for Global Health, University of Split Medical School, Soltanska 2, 21000 Split, Croatia
| | - Thomas Meitinger
- Institute of Human Genetics, Technical University of Munich, Munich, Germany
- Institute of Human Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstaedter Landstr 1, D-85764 Neuherberg, Germany
| | - Ben A Oostra
- Genetic Epidemiology Unit, Departments of Epidemiology and Clinical Genetics, Erasmus MC, 3000 CA Rotterdam, the Netherlands
| | - Peter Riegler
- Hemodialysis Unit, Hospital of Merano, Merano, Italy
| | - Cosetta Minelli
- Institute of Genetic Medicine, European Academy Bozen/Bolzano (EURAC), Bolzano, Italy - Affiliated Institute of the University Lübeck, Lübeck, Germany
| | - Alan F Wright
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, Edinburgh, UK
| | - Harry Campbell
- Centre for Population Health Sciences, University of Edinburgh Medical School, Teviot Place, Edinburgh EH8 9AG, UK
| | - Cornelia M van Duijn
- Genetic Epidemiology Unit, Departments of Epidemiology and Clinical Genetics, Erasmus MC, 3000 CA Rotterdam, the Netherlands
| | - Ulf Gyllensten
- Department of Genetics and Pathology, Rudbeck laboratory, Uppsala University, SE-751 85, Uppsala, Sweden
| | - James F Wilson
- Centre for Population Health Sciences, University of Edinburgh Medical School, Teviot Place, Edinburgh EH8 9AG, UK
| | - Michael Krawczak
- Popgen biobank, Christian-Albrechts-University Kiel, Kiel, Germany
- Institute of Medical Informatics and Statistics, Christian-Albrechts-University, Kiel, Germany
| | - Igor Rudan
- Centre for Population Health Sciences, University of Edinburgh Medical School, Teviot Place, Edinburgh EH8 9AG, UK
- Gen-info Ltd, Ruzmarinka 17, 10000 Zagreb, Croatia
- Croatian Centre for Global Health, University of Split Medical School, Soltanska 2, 21000 Split, Croatia
| | - Peter P Pramstaller
- Institute of Genetic Medicine, European Academy Bozen/Bolzano (EURAC), Bolzano, Italy - Affiliated Institute of the University Lübeck, Lübeck, Germany
- Department of Neurology, University of Lübeck, Lübeck, Germany
- Department of Neurology, Central Hospital, Bolzano, Italy
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Mascalzoni D, Janssens ACJW, Stewart A, Pramstaller P, Gyllensten U, Rudan I, van Duijn CM, Wilson JF, Campbell H, Quillan RMC. Comparison of participant information and informed consent forms of five European studies in genetic isolated populations. Eur J Hum Genet 2010; 18:296-302. [PMID: 19826451 PMCID: PMC2987217 DOI: 10.1038/ejhg.2009.155] [Citation(s) in RCA: 27] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2008] [Revised: 08/24/2009] [Accepted: 08/28/2009] [Indexed: 11/09/2022] Open
Abstract
Family-based research in genetically isolated populations is an effective approach for identifying loci influencing variation in disease traits. In common with all studies in humans, those in genetically isolated populations need ethical approval; however, existing ethical frameworks may be inadequate to protect participant privacy and confidentiality and to address participants' information needs in such populations. Using the ethical-legal guidelines of the Council for International Organizations of Medical Sciences (CIOMS) as a template, we compared the participant information leaflets and consent forms of studies in five European genetically isolated populations to identify additional information that should be incorporated into information leaflets and consent forms to guarantee satisfactorily informed consent. We highlight the additional information that participants require on the research purpose and the reasons why their population was chosen; on the potential risks and benefits of participation; on the opportunities for benefit sharing; on privacy; on the withdrawal of consent and on the disclosure of genetic data. This research raises some important issues that should be addressed properly and identifies relevant types of information that should be incorporated into information leaflets for this type of study.
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Affiliation(s)
- Deborah Mascalzoni
- Institute of Genetic Medicine, European Academy Bozen/Bolzano (EURAC), Bolzano, Italy.
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Volpato CB, De Grandi A, Buffone E, Facheris M, Gebert U, Schifferle G, Schönhuber R, Hicks A, Pramstaller PP. 2q37 as a susceptibility locus for idiopathic basal ganglia calcification (IBGC) in a large South Tyrolean family. J Mol Neurosci 2010; 39:346-53. [PMID: 19757205 DOI: 10.1007/s12031-009-9287-3] [Citation(s) in RCA: 37] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2009] [Accepted: 08/12/2009] [Indexed: 01/12/2023]
Abstract
Familial idiopathic basal ganglia calcification (FIBGC) is an inherited neurodegenerative disorder characterized by the accumulation of calcium deposits in different brain regions, particularly in the basal ganglia. FIBGC usually follows an autosomal dominant pattern of inheritance. Despite the mapping to chromosome 14q of a susceptibility locus for IBGC (IBCG1) in one family, this locus has been excluded in several others, demonstrating genetic heterogeneity in this disorder. The etiology of this disorder thus remains largely unknown. Using a large extended multigenerational Italian family from South Tyrol with 17 affected in a total of 56 members, we performed a genome-wide linkage analysis in which we were able to exclude linkage to the IBCG1 locus on chromosome 14q and obtain evidence of a novel locus on chromosome 2q37. Electronic supplementary material. The online version of this article (doi:10.1007/s12031-009-9287-3) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Claudia Béu Volpato
- Institute of Genetic Medicine, European Academy Bozen/Bolzano (EURAC), 39100 Bolzano, Bozen, Italy
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36
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Igl W, Johansson A, Wilson JF, Wild SH, Polasek O, Hayward C, Vitart V, Hastie N, Rudan P, Gnewuch C, Schmitz G, Meitinger T, Pramstaller PP, Hicks AA, Oostra BA, van Duijn CM, Rudan I, Wright A, Campbell H, Gyllensten U. Modeling of environmental effects in genome-wide association studies identifies SLC2A2 and HP as novel loci influencing serum cholesterol levels. PLoS Genet 2010; 6:e1000798. [PMID: 20066028 PMCID: PMC2792712 DOI: 10.1371/journal.pgen.1000798] [Citation(s) in RCA: 46] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2009] [Accepted: 12/03/2009] [Indexed: 11/25/2022] Open
Abstract
Genome-wide association studies (GWAS) have identified 38 larger genetic regions affecting classical blood lipid levels without adjusting for important environmental influences. We modeled diet and physical activity in a GWAS in order to identify novel loci affecting total cholesterol, LDL cholesterol, HDL cholesterol, and triglyceride levels. The Swedish (SE) EUROSPAN cohort (N(SE) = 656) was screened for candidate genes and the non-Swedish (NS) EUROSPAN cohorts (N(NS) = 3,282) were used for replication. In total, 3 SNPs were associated in the Swedish sample and were replicated in the non-Swedish cohorts. While SNP rs1532624 was a replication of the previously published association between CETP and HDL cholesterol, the other two were novel findings. For the latter SNPs, the p-value for association was substantially improved by inclusion of environmental covariates: SNP rs5400 (p(SE,unadjusted) = 3.6 x 10(-5), p(SE,adjusted) = 2.2 x 10(-6), p(NS,unadjusted) = 0.047) in the SLC2A2 (Glucose transporter type 2) and rs2000999 (p(SE,unadjusted) = 1.1 x 10(-3), p(SE,adjusted) = 3.8 x 10(-4), p(NS,unadjusted) = 0.035) in the HP gene (Haptoglobin-related protein precursor). Both showed evidence of association with total cholesterol. These results demonstrate that inclusion of important environmental factors in the analysis model can reveal new genetic susceptibility loci.
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Affiliation(s)
- Wilmar Igl
- Department of Genetics and Pathology, Rudbeck Laboratory, University of Uppsala, Uppsala, Sweden.
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37
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Hicks AA, Pramstaller PP, Johansson Å, Vitart V, Rudan I, Ugocsai P, Aulchenko Y, Franklin CS, Liebisch G, Erdmann J, Jonasson I, Zorkoltseva IV, Pattaro C, Hayward C, Isaacs A, Hengstenberg C, Campbell S, Gnewuch C, Janssens AC, Kirichenko AV, König IR, Marroni F, Polasek O, Demirkan A, Kolcic I, Schwienbacher C, Igl W, Biloglav Z, Witteman JCM, Pichler I, Zaboli G, Axenovich TI, Peters A, Schreiber S, Wichmann HE, Schunkert H, Hastie N, Oostra BA, Wild SH, Meitinger T, Gyllensten U, van Duijn CM, Wilson JF, Wright A, Schmitz G, Campbell H. Genetic determinants of circulating sphingolipid concentrations in European populations. PLoS Genet 2009; 5:e1000672. [PMID: 19798445 PMCID: PMC2745562 DOI: 10.1371/journal.pgen.1000672] [Citation(s) in RCA: 160] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2009] [Accepted: 09/02/2009] [Indexed: 01/01/2023] Open
Abstract
Sphingolipids have essential roles as structural components of cell membranes and in cell signalling, and disruption of their metabolism causes several diseases, with diverse neurological, psychiatric, and metabolic consequences. Increasingly, variants within a few of the genes that encode enzymes involved in sphingolipid metabolism are being associated with complex disease phenotypes. Direct experimental evidence supports a role of specific sphingolipid species in several common complex chronic disease processes including atherosclerotic plaque formation, myocardial infarction (MI), cardiomyopathy, pancreatic beta-cell failure, insulin resistance, and type 2 diabetes mellitus. Therefore, sphingolipids represent novel and important intermediate phenotypes for genetic analysis, yet little is known about the major genetic variants that influence their circulating levels in the general population. We performed a genome-wide association study (GWAS) between 318,237 single-nucleotide polymorphisms (SNPs) and levels of circulating sphingomyelin (SM), dihydrosphingomyelin (Dih-SM), ceramide (Cer), and glucosylceramide (GluCer) single lipid species (33 traits); and 43 matched metabolite ratios measured in 4,400 subjects from five diverse European populations. Associated variants (32) in five genomic regions were identified with genome-wide significant corrected p-values ranging down to 9.08x10(-66). The strongest associations were observed in or near 7 genes functionally involved in ceramide biosynthesis and trafficking: SPTLC3, LASS4, SGPP1, ATP10D, and FADS1-3. Variants in 3 loci (ATP10D, FADS3, and SPTLC3) associate with MI in a series of three German MI studies. An additional 70 variants across 23 candidate genes involved in sphingolipid-metabolizing pathways also demonstrate association (p = 10(-4) or less). Circulating concentrations of several key components in sphingolipid metabolism are thus under strong genetic control, and variants in these loci can be tested for a role in the development of common cardiovascular, metabolic, neurological, and psychiatric diseases.
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Affiliation(s)
- Andrew A. Hicks
- Institute of Genetic Medicine, European Academy Bozen/Bolzano (EURAC), Bolzano, Italy, Affiliated Institute of the University of Lübeck, Lübeck, Germany
| | - Peter P. Pramstaller
- Institute of Genetic Medicine, European Academy Bozen/Bolzano (EURAC), Bolzano, Italy, Affiliated Institute of the University of Lübeck, Lübeck, Germany
- Department of Neurology, General Central Hospital, Bolzano, Italy
- Department of Neurology, University of Lübeck, Lübeck, Germany
- * E-mail: (PPP); (HC)
| | - Åsa Johansson
- Department of Genetics and Pathology, Rudbeck Laboratory, Uppsala University, Uppsala, Sweden
| | - Veronique Vitart
- MRC Human Genetics Unit, IGMM, Western General Hospital, Edinburgh, United Kingdom
| | - Igor Rudan
- Centre for Population Health Sciences, University of Edinburgh, Edinburgh, United Kingdom
- Croatian Centre for Global Health, Faculty of Medicine, University of Split, Split, Croatia
- Gen-info Ltd, Zagreb, Croatia
| | - Peter Ugocsai
- Institute for Clinical Chemistry and Laboratory Medicine, University Hospital Regensburg, Regensburg, Germany
| | - Yurii Aulchenko
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | | | - Gerhard Liebisch
- Institute for Clinical Chemistry and Laboratory Medicine, University Hospital Regensburg, Regensburg, Germany
| | | | - Inger Jonasson
- Department of Genetics and Pathology, Rudbeck Laboratory, Uppsala University, Uppsala, Sweden
| | | | - Cristian Pattaro
- Institute of Genetic Medicine, European Academy Bozen/Bolzano (EURAC), Bolzano, Italy, Affiliated Institute of the University of Lübeck, Lübeck, Germany
| | - Caroline Hayward
- MRC Human Genetics Unit, IGMM, Western General Hospital, Edinburgh, United Kingdom
| | - Aaron Isaacs
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Christian Hengstenberg
- Klinik und Poliklinik für Innere Medizin II, Universität Regensburg, Regensburg, Germany
| | - Susan Campbell
- MRC Human Genetics Unit, IGMM, Western General Hospital, Edinburgh, United Kingdom
| | - Carsten Gnewuch
- Institute for Clinical Chemistry and Laboratory Medicine, University Hospital Regensburg, Regensburg, Germany
| | - A. CecileJ.W. Janssens
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | | | - Inke R. König
- Institut für Medizinische Biometrie und Statistik, University of Lübeck, Lübeck, Germany
| | - Fabio Marroni
- Institute of Genetic Medicine, European Academy Bozen/Bolzano (EURAC), Bolzano, Italy, Affiliated Institute of the University of Lübeck, Lübeck, Germany
| | - Ozren Polasek
- Gen-info Ltd, Zagreb, Croatia
- Andrija Stampar School of Public Health, Faculty of Medicine, University of Zagreb, Zagreb, Croatia
| | - Ayse Demirkan
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Ivana Kolcic
- Andrija Stampar School of Public Health, Faculty of Medicine, University of Zagreb, Zagreb, Croatia
| | - Christine Schwienbacher
- Institute of Genetic Medicine, European Academy Bozen/Bolzano (EURAC), Bolzano, Italy, Affiliated Institute of the University of Lübeck, Lübeck, Germany
- Department of Experimental and Diagnostic Medicine, University of Ferrara, Ferrara, Italy
| | - Wilmar Igl
- Department of Genetics and Pathology, Rudbeck Laboratory, Uppsala University, Uppsala, Sweden
| | - Zrinka Biloglav
- Andrija Stampar School of Public Health, Faculty of Medicine, University of Zagreb, Zagreb, Croatia
| | | | - Irene Pichler
- Institute of Genetic Medicine, European Academy Bozen/Bolzano (EURAC), Bolzano, Italy, Affiliated Institute of the University of Lübeck, Lübeck, Germany
| | - Ghazal Zaboli
- Department of Genetics and Pathology, Rudbeck Laboratory, Uppsala University, Uppsala, Sweden
| | | | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München – German Research Center for Environmental Health, Neuherberg, Germany
| | - Stefan Schreiber
- Institut für Klinische Molekularbiologie, Christian-Albrechts Universität, Kiel, Germany
| | - H.-Erich Wichmann
- Institute of Epidemiology, Helmholtz Zentrum München – German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Medical Information Science, Biometry and Epidemiology, Chair of Epidemiology, LMU Munich, Germany
| | | | - Nick Hastie
- MRC Human Genetics Unit, IGMM, Western General Hospital, Edinburgh, United Kingdom
| | - Ben A. Oostra
- Department of Clinical Genetics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Sarah H. Wild
- Centre for Population Health Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | | | - Ulf Gyllensten
- Department of Genetics and Pathology, Rudbeck Laboratory, Uppsala University, Uppsala, Sweden
| | - Cornelia M. van Duijn
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - James F. Wilson
- Centre for Population Health Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Alan Wright
- MRC Human Genetics Unit, IGMM, Western General Hospital, Edinburgh, United Kingdom
| | - Gerd Schmitz
- Institute for Clinical Chemistry and Laboratory Medicine, University Hospital Regensburg, Regensburg, Germany
| | - Harry Campbell
- Centre for Population Health Sciences, University of Edinburgh, Edinburgh, United Kingdom
- * E-mail: (PPP); (HC)
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38
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Genome-wide linkage analysis of serum creatinine in three isolated European populations. Kidney Int 2009; 76:297-306. [DOI: 10.1038/ki.2009.135] [Citation(s) in RCA: 60] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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FERTILITY PATTERN AND FAMILY STRUCTURE IN THREE ALPINE SETTLEMENTS IN SOUTH TYROL (ITALY): MARRIAGE COHORTS FROM 1750 TO 1949. J Biosoc Sci 2009; 41:697-701. [DOI: 10.1017/s0021932009003423] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
SummaryStelvio, Martello and Curon, three villages of the Venosta Valley, South Tyrol (Italy), were recently included in a large genetic survey because of their isolation. This study focuses on the long-term reproductive behaviour of these villages. Family size, age at marriage and marital fertility were estimated based on a genealogy going back in the 17th century. Marriage behaviour was characterized by an elevated age at marriage and a large proportion of adults never getting married. Marital fertility was among the highest worldwide, because couples tried to use the short time at their disposal to have the largest possible number of children. Together with the already known null population expansion and high geographic endogamy rates, the reduced number of siblings who had the opportunity to get married could have favoured an increased genetic homogeneity.
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Marroni F, Pfeufer A, Aulchenko YS, Franklin CS, Isaacs A, Pichler I, Wild SH, Oostra BA, Wright AF, Campbell H, Witteman JC, Kääb S, Hicks AA, Gyllensten U, Rudan I, Meitinger T, Pattaro C, van Duijn CM, Wilson JF, Pramstaller PP. A genome-wide association scan of RR and QT interval duration in 3 European genetically isolated populations: the EUROSPAN project. ACTA ACUST UNITED AC 2009; 2:322-8. [PMID: 20031603 DOI: 10.1161/circgenetics.108.833806] [Citation(s) in RCA: 55] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
BACKGROUND We set out to identify common genetic determinants of the length of the RR and QT intervals in 2325 individuals from isolated European populations. METHODS AND RESULTS We analyzed the heart rate at rest, measured as the RR interval, and the length of the corrected QT interval for association with 318 237 single-nucleotide polymorphisms. The RR interval was associated with common variants within GPR133, a G-protein-coupled receptor (rs885389, P=3.9 x 10(-8)). The QT interval was associated with the earlier reported NOS1AP gene (rs2880058, P=2.00 x 10(-10)) and with a region on chromosome 13 (rs2478333, P=4.34 x 10(-8)), which is 100 kb from the closest known transcript LOC730174 and has previously not been associated with the length of the QT interval. CONCLUSIONS Our results suggested an association between the RR interval and GPR133 and confirmed an association between the QT interval and NOS1AP.
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Affiliation(s)
- Fabio Marroni
- Institute of Genetic Medicine, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Munich, Germany
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41
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Common variants at ten loci modulate the QT interval duration in the QTSCD Study. Nat Genet 2009; 41:407-14. [PMID: 19305409 DOI: 10.1038/ng.362] [Citation(s) in RCA: 303] [Impact Index Per Article: 20.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2008] [Accepted: 01/19/2009] [Indexed: 12/19/2022]
Abstract
The QT interval, a measure of cardiac repolarization, predisposes to ventricular arrhythmias and sudden cardiac death (SCD) when prolonged or shortened. A common variant in NOS1AP is known to influence repolarization. We analyze genome-wide data from five population-based cohorts (ARIC, KORA, SardiNIA, GenNOVA and HNR) with a total of 15,842 individuals of European ancestry, to confirm the NOS1AP association and identify nine additional loci at P < 5 x 10(-8). Four loci map near the monogenic long-QT syndrome genes KCNQ1, KCNH2, SCN5A and KCNJ2. Two other loci include ATP1B1 and PLN, genes with established electrophysiological function, whereas three map to RNF207, near LITAF and within NDRG4-GINS3-SETD6-CNOT1, respectively, all of which have not previously been implicated in cardiac electrophysiology. These results, together with an accompanying paper from the QTGEN consortium, identify new candidate genes for ventricular arrhythmias and SCD.
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42
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Aulchenko YS, Ripatti S, Lindqvist I, Boomsma D, Heid IM, Pramstaller PP, Penninx BWJH, Janssens ACJW, Wilson JF, Spector T, Martin NG, Pedersen NL, Kyvik KO, Kaprio J, Hofman A, Freimer NB, Jarvelin MR, Gyllensten U, Campbell H, Rudan I, Johansson A, Marroni F, Hayward C, Vitart V, Jonasson I, Pattaro C, Wright A, Hastie N, Pichler I, Hicks AA, Falchi M, Willemsen G, Hottenga JJ, de Geus EJC, Montgomery GW, Whitfield J, Magnusson P, Saharinen J, Perola M, Silander K, Isaacs A, Sijbrands EJG, Uitterlinden AG, Witteman JCM, Oostra BA, Elliott P, Ruokonen A, Sabatti C, Gieger C, Meitinger T, Kronenberg F, Döring A, Wichmann HE, Smit JH, McCarthy MI, van Duijn CM, Peltonen L. Loci influencing lipid levels and coronary heart disease risk in 16 European population cohorts. Nat Genet 2009; 41:47-55. [PMID: 19060911 PMCID: PMC2687074 DOI: 10.1038/ng.269] [Citation(s) in RCA: 674] [Impact Index Per Article: 44.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2008] [Accepted: 10/01/2008] [Indexed: 12/11/2022]
Abstract
Recent genome-wide association (GWA) studies of lipids have been conducted in samples ascertained for other phenotypes, particularly diabetes. Here we report the first GWA analysis of loci affecting total cholesterol (TC), low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterol and triglycerides sampled randomly from 16 population-based cohorts and genotyped using mainly the Illumina HumanHap300-Duo platform. Our study included a total of 17,797-22,562 persons, aged 18-104 years and from geographic regions spanning from the Nordic countries to Southern Europe. We established 22 loci associated with serum lipid levels at a genome-wide significance level (P < 5 x 10(-8)), including 16 loci that were identified by previous GWA studies. The six newly identified loci in our cohort samples are ABCG5 (TC, P = 1.5 x 10(-11); LDL, P = 2.6 x 10(-10)), TMEM57 (TC, P = 5.4 x 10(-10)), CTCF-PRMT8 region (HDL, P = 8.3 x 10(-16)), DNAH11 (LDL, P = 6.1 x 10(-9)), FADS3-FADS2 (TC, P = 1.5 x 10(-10); LDL, P = 4.4 x 10(-13)) and MADD-FOLH1 region (HDL, P = 6 x 10(-11)). For three loci, effect sizes differed significantly by sex. Genetic risk scores based on lipid loci explain up to 4.8% of variation in lipids and were also associated with increased intima media thickness (P = 0.001) and coronary heart disease incidence (P = 0.04). The genetic risk score improves the screening of high-risk groups of dyslipidemia over classical risk factors.
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Affiliation(s)
- Yurii S Aulchenko
- [1] Department of Epidemiology and Biostatistics, Erasmus University Medical Center, P.O. Box 2040, 3000 CA Rotterdam, The Netherlands. [2] These authors contributed equally to this work
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43
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Johansson A, Marroni F, Hayward C, Franklin CS, Kirichenko AV, Jonasson I, Hicks AA, Vitart V, Isaacs A, Axenovich T, Campbell S, Dunlop MG, Floyd J, Hastie N, Hofman A, Knott S, Kolcic I, Pichler I, Polasek O, Rivadeneira F, Tenesa A, Uitterlinden AG, Wild SH, Zorkoltseva IV, Meitinger T, Wilson JF, Rudan I, Campbell H, Pattaro C, Pramstaller P, Oostra BA, Wright AF, van Duijn CM, Aulchenko YS, Gyllensten U. Common variants in the JAZF1 gene associated with height identified by linkage and genome-wide association analysis. Hum Mol Genet 2008; 18:373-80. [PMID: 18952825 DOI: 10.1093/hmg/ddn350] [Citation(s) in RCA: 80] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Abstract
Genes for height have gained interest for decades, but only recently have candidate genes started to be identified. We have performed linkage analysis and genome-wide association for height in approximately 4000 individuals from five European populations. A total of five chromosomal regions showed suggestive linkage and in one of these regions, two SNPs (rs849140 and rs1635852) were associated with height (nominal P = 7.0 x 10(-8) and P = 9.6 x 10(-7), respectively). In total, five SNPs across the genome showed an association with height that reached the threshold of genome-wide significance (nominal P < 1.6 x 10(-7)). The association with height was replicated for two SNPs (rs1635852 and rs849140) using three independent studies (n = 31 077, n=1268 and n = 5746) with overall meta P-values of 9.4 x 10(-10) and 5.3 x 10(-8). These SNPs are located in the JAZF1 gene, which has recently been associated with type II diabetes, prostate and endometrial cancer. JAZF1 is a transcriptional repressor of NR2C2, which results in low IGF1 serum concentrations, perinatal and early postnatal hypoglycemia and growth retardation when knocked out in mice. Both the linkage and association analyses independently identified the JAZF1 region affecting human height. We have demonstrated, through replication in additional independent populations, the consistency of the effect of the JAZF1 SNPs on height. Since this gene also has a key function in the metabolism of growth, JAZF1 represents one of the strongest candidates influencing human height identified so far.
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Affiliation(s)
- Asa Johansson
- Rudbeck Laboratory, Department of Genetics and Pathology, Uppsala University, SE-751 85 Uppsala, Sweden
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Volpato CB, De Grandi A, Buffone E, Pichler I, Gebert U, Schifferle G, Schönhuber R, Pramstaller PP. Exclusion of linkage to chromosome 14q in a large South Tyrolean family with Idiopathic Basal Ganglia Calcification (IBGC). Am J Med Genet B Neuropsychiatr Genet 2008; 147B:1319-22. [PMID: 18361429 DOI: 10.1002/ajmg.b.30748] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Familial Idiopathic Basal Ganglia Calcification (FIBGC) is a neurodegenerative syndrome that usually follows an autosomal dominant pattern of inheritance. Linkage to only one locus on chromosome 14q (IBCG1) has been described so far. We identified and characterized a large multigenerational Italian family from a population isolate with 14 FIBGC affected members. Linkage analysis excluded the IBCG1 locus, thus demonstrating further locus heterogeneity for this disease.
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45
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Facheris MF, Vogl FD, Hollmann S, Sixt G, Pattaro C, Schönhuber R, Pramstaller PP. Adapted Finnish Migraine-Specific Questionnaire for family studies (FMSQFS): a validation study in two languages. Eur J Neurol 2008; 15:1071-4. [DOI: 10.1111/j.1468-1331.2008.02248.x] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Fuchsberger C, Falchi M, Forer L, Pramstaller PP. PedVizApi: a Java API for the interactive, visual analysis of extended pedigrees. Bioinformatics 2007; 24:279-81. [PMID: 18033791 DOI: 10.1093/bioinformatics/btm577] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
UNLABELLED PedVizApi is a Java API (application program interface) for the visual analysis of large and complex pedigrees. It provides all the necessary functionality for the interactive exploration of extended genealogies. While available packages are mostly focused on a static representation or cannot be added to an existing application, PedVizApi is a highly flexible open source library for the efficient construction of visual-based applications for the analysis of family data. An extensive demo application and a R interface is provided. AVAILABILITY http://www.pedvizapi.org
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47
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Marroni F, Grazio D, Pattaro C, Devoto M, Pramstaller P. Estimates of genetic and environmental contribution to 43 quantitative traits support sharing of a homogeneous environment in an isolated population from South Tyrol, Italy. Hum Hered 2007; 65:175-82. [PMID: 17934319 DOI: 10.1159/000109734] [Citation(s) in RCA: 28] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2007] [Accepted: 06/20/2007] [Indexed: 11/19/2022] Open
Abstract
As part of the genomic health care program 'GenNova', we measured 43 quantitative traits in 1,136 subjects living in three isolated villages in South Tyrol (Italy), for which extended genealogical information was available. Thirty-seven of the studied traits had been previously investigated in other populations, while six of them are, to the best of our knowledge, studied here for the first time. For all 43 traits we estimated narrow-sense heritability, individual-specific environmental effects, and shared environmental effects. Estimates of narrow-sense heritability were in good agreement with previous findings. We found significant heritability for all traits; after correcting for multiple testing, all traits except serum concentration of glutamic oxaloacetic transaminase (GOT) and potassium still showed significant heritability. In contrast, the effect of living in the same sibship or village (the so-called sibship and household effects, respectively) was significant for a few traits only, and after correcting for multiple testing no trait showed significant shared environment effect. We suggest that the sharing of a highly similar environment by the subjects included in this study explains the low contribution of the household effects to the overall trait variation. This peculiarity should provide an advantage in gene-mapping projects by reducing environmental bias.
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