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      GCAT|Genomes for life: a prospective cohort study of the genomes of Catalonia

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          Abstract

          Purpose

          The prevalence of chronic non-communicable diseases (NCDs) is increasing worldwide. NCDs are the leading cause of both morbidity and mortality, and it is estimated that by 2030, they will be responsible for 80% of deaths across the world. The Genomes for Life (GCAT) project is a long-term prospective cohort study that was designed to integrate and assess the role of epidemiological, genomic and epigenomic factors in the development of major chronic diseases in Catalonia, a north-east region of Spain.

          Participants

          At the end of 2017, the GCAT Study will have recruited 20 000 participants aged 40–65 years. Participants who agreed to take part in the study completed a self-administered computer-driven questionnaire, and underwent blood pressure, cardiac frequency and anthropometry measurements. For each participant, blood plasma, blood serum and white blood cells are collected at baseline. The GCAT Study has access to the electronic health records of the Catalan Public Healthcare System. Participants will be followed biannually at least 20 years after recruitment.

          Findings to date

          Among all GCAT participants, 59.2% are women and 83.3% of the cohort identified themselves as Caucasian/white. More than half of the participants have higher education levels, 72.2% are current workers and 42.1% are classified as overweight (body mass index ≥25 and <30 kg/m 2). We have genotyped 5459 participants, of which 5000 have metabolome data. Further, the whole genome of 808 participants will be sequenced by the end of 2017.

          Future plans

          The first follow-up study started in December 2017 and will end by March 2018. Residences of all subjects will be geocoded during the following year. Several genomic analyses are ongoing, and metabolomic and genomic integrations will be performed to identify underlying genetic variants, as well as environmental factors that influence metabolites.

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          Most cited references24

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          Compendium of physical activities: an update of activity codes and MET intensities.

          We provide an updated version of the Compendium of Physical Activities, a coding scheme that classifies specific physical activity (PA) by rate of energy expenditure. It was developed to enhance the comparability of results across studies using self-reports of PA. The Compendium coding scheme links a five-digit code that describes physical activities by major headings (e.g., occupation, transportation, etc.) and specific activities within each major heading with its intensity, defined as the ratio of work metabolic rate to a standard resting metabolic rate (MET). Energy expenditure in MET-minutes, MET-hours, kcal, or kcal per kilogram body weight can be estimated for specific activities by type or MET intensity. Additions to the Compendium were obtained from studies describing daily PA patterns of adults and studies measuring the energy cost of specific physical activities in field settings. The updated version includes two new major headings of volunteer and religious activities, extends the number of specific activities from 477 to 605, and provides updated MET intensity levels for selected activities.
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            Ecologic studies in epidemiology: concepts, principles, and methods.

            An ecologic study focuses on the comparison of groups, rather than individuals; thus, individual-level data are missing on the joint distribution of variables within groups. Variables in an ecologic analysis may be aggregate measures, environmental measures, or global measures. The purpose of an ecologic analysis may be to make biologic inferences about effects on individual risks or to make ecologic inferences about effects on group rates. Ecologic study designs may be classified on two dimensions: (a) whether the primary group is measured (exploratory vs analytic study); and (b) whether subjects are grouped by place (multiple-group study), by time (time-trend study), or by place and time (mixed study). Despite several practical advantages of ecologic studies, there are many methodologic problems that severely limit causal inference, including ecologic and cross-level bias, problems of confounder control, within-group misclassification, lack of adequate data, temporal ambiguity, collinearity, and migration across groups.
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              Sample Size and Statistical Power Calculation in Genetic Association Studies

              A sample size with sufficient statistical power is critical to the success of genetic association studies to detect causal genes of human complex diseases. Genome-wide association studies require much larger sample sizes to achieve an adequate statistical power. We estimated the statistical power with increasing numbers of markers analyzed and compared the sample sizes that were required in case-control studies and case-parent studies. We computed the effective sample size and statistical power using Genetic Power Calculator. An analysis using a larger number of markers requires a larger sample size. Testing a single-nucleotide polymorphism (SNP) marker requires 248 cases, while testing 500,000 SNPs and 1 million markers requires 1,206 cases and 1,255 cases, respectively, under the assumption of an odds ratio of 2, 5% disease prevalence, 5% minor allele frequency, complete linkage disequilibrium (LD), 1:1 case/control ratio, and a 5% error rate in an allelic test. Under a dominant model, a smaller sample size is required to achieve 80% power than other genetic models. We found that a much lower sample size was required with a strong effect size, common SNP, and increased LD. In addition, studying a common disease in a case-control study of a 1:4 case-control ratio is one way to achieve higher statistical power. We also found that case-parent studies require more samples than case-control studies. Although we have not covered all plausible cases in study design, the estimates of sample size and statistical power computed under various assumptions in this study may be useful to determine the sample size in designing a population-based genetic association study.
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                Author and article information

                Journal
                BMJ Open
                BMJ Open
                bmjopen
                bmjopen
                BMJ Open
                BMJ Publishing Group (BMA House, Tavistock Square, London, WC1H 9JR )
                2044-6055
                2018
                27 March 2018
                : 8
                : 3
                : e018324
                Affiliations
                [1 ] departmentGenomes for Life -GCAT lab Group, Program of Predictive and Personalized Medicine of Cancer (PMPPC) , Institute for Health Science Research Germans Trias i Pujol (IGTP) , Badalona, Spain
                [2 ] departmentCancer Prevention and Control Program , Catalan Institute of Oncology (ICO-IDIBELL) , Hospitalet del Llobregat, Spain
                [3 ] Banc de Sang i Teixits (BST) , Barcelona, Spain
                [4 ] departmentProgram of Predictive and Personalized Medicine of Cancer (PMPPC) , Institute for Health Science Research Germans Trias i Pujol (IGTP) , Badalona, Spain
                [5 ] departmentUnit of Nutrition and Cancer, Cancer Epidemiology Research Program , Catalan Institute of Oncology (ICO-IDIBELL) , Hospitalet del Llobregat, Spain
                [6 ] CIBER Epidemiología y Salud Pública (CIBERESP) , Hospitalet del Llobregat, Madrid, Spain
                [7 ] departmentDepartment of Clinical Sciences, Faculty of Medicine , University of Barcelona , Barcelona, Spain
                Author notes
                [Correspondence to ] Dr Rafael de Cid; Rdecid@ 123456igtp.cat
                Author information
                http://orcid.org/0000-0003-4646-3513
                Article
                bmjopen-2017-018324
                10.1136/bmjopen-2017-018324
                5875652
                29593016
                da38aa41-0dcc-4881-8a00-33363fd9e0b8
                © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

                This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/

                History
                : 20 June 2017
                : 03 January 2018
                : 01 February 2018
                Funding
                Funded by: ’Ramón y Cajal' action from the Spanish Ministry of Economy and Competitiveness;
                Funded by: Agència de Gestió d’Ajuts Universitaris i de Recerca (AGAUR);
                Funded by: Acción de Dinamización del ISCIII-MINECO;
                Funded by: Ministry of Health of the Generalitat of Catalunya;
                Funded by: the Catalan Government DURSI;
                Categories
                Epidemiology
                Cohort Profile
                1506
                1692
                Custom metadata
                unlocked

                Medicine
                prospective cohort,non-communicable diseases,complex inheritance,genomics,follow-up,lifestyle,medical history,spanish cohort,catalan population,electronic health records,wgs,gwas

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