Suchergebnisse

Generation Scotland: Scottish Family Health Study (GS:SFHS) is a family-based genetic epidemiology study of ~24,000 volunteers from ~7000 families recruited across Scotland between 2006 and 2011 with the capacity for follow-up through record linkage and re-contact. Broad consent was obtained for linkage to "medical records" for 98% of the cohort.
Participants completed a questionnaire, provided samples, and underwent clinical assessment. The samples and data collected form a resource with consent for research on the genetics of health, becoming a longitudinal dataset by linkage to routine NHS hospital, maternity, lab test, prescribing, dentistry and mortality data.
Researchers can use the linked datasets to test research hypotheses on a stratified population and target recruitment to new studies. We have established and validated EHR linkage, overcoming technical and governance issues in the process. We plan to collaborate with UK Biobank, creating a combined cohort of over 50,000 people in Scotland, and using the SHARE register to obtain new research samples from routine NHS tests.
We will extend linkage to include primary care data and scanned images in the next year. The resources are available to academic and commercial researchers through a managed access process.

Background Generation Scotland is a family-based genetic epidemiology study of ~24,000 volunteers from ~7000 families recruited across Scotland with the capacity for follow-up through record linkage and re-contact. Broad consent was obtained for linkage to "medical records" for 98% of the cohort. This created a resource for investigation of the genetics of common conditions, available to researchers worldwide.
Methods Participants completed a demographic, health and lifestyle questionnaire, provided samples, and underwent detailed clinical assessment. The samples and data collected form a resource with broad consent for research on the genetics of conditions of current and projected public health importance. This has become a longitudinal dataset by linkage to routine NHS hospital, maternity, lab tests, prescribing, dentistry, and mortality data.
Results Researchers can use the linked datasets to find prevalent and incident disease cases, and healthy controls, in a stratified population. They can also do targeted recruitment of participants to new studies, including recall by genotype. We have established and validated EHR linkage, overcoming technical and governance issues in the process. Using consented data avoids some limitations of safe havens for analysis.
Genome-wide association studies (GWAS) have been done on a wide range of quantitative traits and biomarker measurements. Generation Scotland is a contributor to major international consortia and has collaborated with Dementia Platforms UK and Health Data Research UK to make the resources more widely known. There have been over 300 research collaborations, and GS data has contributed to 200 publications, with more in the pipeline.
Conclusions Generation Scotland has thoroughly tested the linkage process and is extending it to include primary care data and scanned images, with plans to collect more samples and data. The resources are available to academic and commercial researchers through a managed access process (www.generationscotland.org).

ObjectivesGeneration Scotland (GS) is a family-based genetic epidemiology study of around 24,000 volunteers from 7000 families recruited across Scotland between 2006 and 2011 with follow-up through record linkage and re-contact. Broad consent was obtained for linkage to medical records for 98% of the cohort. Recruitment has recommenced in 2022.
ApproachParticipants completed a demographic, health and lifestyle questionnaire, provided biological samples, and underwent detailed clinical assessment. The samples, phenotype and genotype data form a resource with broad consent for health-related research of current and projected public health importance.
This has become a longitudinal dataset by linkage to routine NHS hospital, maternity, lab test, prescriptions, dentistry, mortality, cancer screening, GP data records, Covid-19 testing and vaccinations. Genome-wide association studies (GWAS) have been done on quantitative traits and biomarkers, with DNA methylation data and proteomics available for most of the cohort. Our "CovidLife" surveys collected data on the effects of the pandemic.
ResultsResearchers can use the linked datasets to ascertain prevalent and incident disease cases and controls to test a wide range of research hypotheses. They can also recruit participants to new studies, including recall by genotype, utilising the NHS Scotland Community Health Index (CHI) Register for current contact details. We have established and validated electronic health record linkage, overcoming technical and governance issues in the process. Using consented data avoids some limitations of safe havens for analysis.
GS is a contributor to major international consortia, with collaborators from many institutions worldwide, both academic and commercial. New recruits are now asked to give consent to linkage to other administrative data, and reuse of samples from routine NHS tests for medical research.
ConclusionGS is now extending linkages to include radiology and health-relevant administrative data (e.g. education); and reopening recruitment to double the cohort size, including teenagers age 12+, collecting new data online and using remote sample collection.
GS resources are available to academic and commercial researchers through a managed access process (www.generationscotland.org).

ABSTRACT
BackgroundElectronic health records provides unprecedented opportunity for their re-use in genetic epidemiological research. However, electronic health records data from clinical settings, such as dental practices may be inaccurate or of insufficient granularity to be of use in research. In this study, we wish to determine the utility of National Health Service (NHS) electronic dental treatment records in genetic epidemiological research.
ObjectiveTo estimate the heritability of periodontal disease using NHS electronic dental treatment records linked to health and non-health data within the Generation Scotland: Scottish Family Health Study (GS:SFHS).
ApproachWe linked 852,355 NHS Scotland electronic dental treatment records from April 2000 to July 2015 to 20,626 participants within the GS:SFHS with pedigree, genomic, sociodemographic and clinical data. We then conducted a proof-of-principle genetic epidemiological analysis using periodontal (gum) disease treatment records. The data set analysed, consisted of 160,508 dental treatment records from 13,717 study participants; 3,387 of which were periodontal treatment records (from 2,192 study participants). We adjusted for the effects of previous treatment record, interval since last treatment, age, sex, treatment year, and treatment month, Scottish index of multiple deprivation, alcohol consumption, diabetes diagnosis, and smoking status in a linear model in the statistical software ASReml. We then calculated the mean risk of periodontal disease for each study participant based on residuals extracted from the aforementioned model. Genome-complex trait analysis (GCTA; with correction for population stratification) was used to estimate the pedigree and genomic based heritability of periodontal disease.
ResultsWe estimate the familial heritability of periodontal (gum) disease at 10.42% (95% confidence interval 5.97-14.88%). The genomic component did not contribute significantly to the heritability estimate.
Conclusionwe have demonstrated the usefulness of electronic dental treatment records in population based genetic epidemiological research .This study has also, to the best of our knowledge provided the first population based estimates of the genetic parameters for periodontal disease; confirming its familial nature. This invaluable and unique data resource will allow the acceleration of oral health research in Scotland and the exploration of research questions that could not be considered previously.

IntroductionHeritability (proportion of trait variation attributable to genetic factors) is not a fixed property. It can vary across different social settings and environments. Exploration of gene-environment interaction has been limited by lack of large sample sizes. Biobanks linked to electronic health records pose a solution to this sample size problem.
Objectives and ApproachSocial inequalities in periodontal health have been well documented in the dental scientific literature. However, gene-socioeconomic status interaction has yet to be examined. We identified 2,192 cases and 11,525 controls from linked electronic periodontal treatment records within the Generation Scotland: Scottish Family Health Study (GS: SFHS) (www.generationscotland.org). The measure of socioeconomic status used was the Scottish Index of Multiple Deprivation. The objective of this study was to investigate the gene-socioeconomic status interaction within this data. A reaction norm model was used to evaluate the presence of a gene-socioeconomic status interaction in the statistical software ASReml.
ResultsWe estimated the heritability of periodontal disease at 10.42% (95% confidence interval 5.97-14.88%). Socioeconomic status modified the heritability of periodontal disease. The heritability of was 13.37%, 0.14% and 11.70% in areas of high, moderate and low deprivation respectively; indicating the occurrence of a gene-socioeconomic status interaction with periodontal disease. These results indicate that socioeconomic status explains a large portion of genetic variation in periodontal disease risk. This information suggests that effective intervention and prevention programs for periodontal disease should involve socioeconomic aspects in their planning, implementations and evaluation. For instance, interventions targeted to reduce smoking in more deprived subjects with a genetic predisposition to periodontal disease could enhance the effect of health promotion strategies in reducing risk.
Conclusion/ImplicationsThis study presents contemporary evidence in a large population based cohort that gene-socioeconomic interaction leads to the progression of periodontal disease. This information may lead to the development of better preventative strategies for clinical dentistry.

Anecdotal and biographical reports have long suggested that bipolar disorder is more common in people with exceptional cognitive or creative ability. Epidemiological evidence for such a link is sparse. We investigated the relationship between intelligence and subsequent risk of hospitalisation for bipolar disorder in a prospective cohort study of 1,049,607 Swedish men. Intelligence was measured on conscription for military service at a mean age of 18.3 years and data on psychiatric hospital admissions over a mean follow-up period of 22.6 years was obtained from national records. Risk of hospitalization with any form of bipolar disorder fell in a stepwise manner as intelligence increased (p for linear trend <0.0001). However, when we restricted analyses to men with no psychiatric comorbidity, there was a 'reversed-J' shaped association: men with the lowest intelligence had the greatest risk of being admitted with pure bipolar disorder, but risk was also elevated among men with the highest intelligence (p for quadratic trend = 0.03), primarily in those with the highest verbal (p for quadratic trend=0.009) or technical ability (p for quadratic trend <0.0001). At least in men, high intelligence may indeed be a risk factor for bipolar disorder, but only in the minority of cases who have the disorder in a pure form with no psychiatric comorbidity.

Copyright: © 2013 Tenesa et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: Genotyping of the ABC1936, LBC1921, and LBC1936 cohorts and the analyses conducted here were supported by the UK's Biotechnology and Biological Sciences Research Council (BBSRC). Phenotype collection in the Lothian Birth Cohort 1921 was supported by the BBSRC, The Royal Society, and The Chief Scientist Office of the Scottish Government. Phenotype collection in the Lothian Birth Cohort 1936 was supported by Research Into Ageing (continues as part of Age UK's The Disconnected Mind project). Phenotype collection in the Aberdeen Birth Cohort 1936 was supported by the BBSRC, the Wellcome Trust, and the Alzheimer's Research Trust. SJR, AR and AT are funded by the BBSRC through the Roslin Institute's strategic programme grant and project grant BB/K000195/1. The Brain data was provided by the Division of Aging Biology and the Division of Geriatrics and Clinical Gerontology (NIA) through the NIH GWAS Data Repository (dbGaP Accession Number: phs000249.v1.p1) and funded as part of the Intramural Research Program, NIA. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. ; Peer reviewed ; Publisher PDF

ACKNOWLEDGEMENTS We are grateful to the families who took part in GS:SFHS, the GPs and Scottish School of Primary Care for their help in recruiting them, and the whole GS team, which includes academic researchers, clinic staff, laboratory technicians, clerical workers, IT staff, statisticians and research managers. This work is supported by the Wellcome Trust through a Strategic Award, reference 104036/Z/14/Z. The Chief Scientist Office of the Scottish Government and the Scottish Funding Council provided core support for Generation Scotland. GS:SFHS was funded by a grant from the Scottish Government Health Department, Chief Scientist Office, number CZD/16/6. We acknowledge with gratitude the financial support received for this work from the Dr Mortimer and Theresa Sackler Foundation. PT, DJP, IJD, and AMM are members of The University of Edinburgh Centre for Cognitive Ageing and Cognitive Epidemiology, part of the cross council Lifelong Health and Wellbeing Initiative (MR/K026992/1). Funding from the Biotechnology and Biological Sciences Research Council and Medical Research Council is gratefully acknowledged. ; Peer reviewed ; Publisher PDF

Acknowledgements: The authors thank all LBC study participants and research team members who have contributed, and continue to contribute, to the ongoing LBC study. The LBC1936 is supported by Age UK (Disconnected Mind programme) and the Medical Research Council [MR/M01311/1]. The LBC1921 is supported by the Biotechnology and Biological Sciences Research Council [SR176], the Chief Scientist Office [CZB/4/505; ETM/55] and the Medical Research Council [R42550]. Methylation typing was supported by the Centre for Cognitive Ageing and Cognitive Epidemiology (Pilot Fund award), Age UK, The Wellcome Trust Institutional Strategic Support Fund, The University of Edinburgh, and The University of Queensland. This work was conducted in the Centre for Cognitive Ageing and Cognitive Epidemiology, which is supported by the Medical Research Council and Biotechnology and Biological Sciences Research Council [MR/K026992/1], and which supports Ian Deary. Generation Scotland received core support from the Chief Scientist Office of the Scottish Government Health Directorates [CZD/16/6] and the Scottish Funding Council [HR03006]. Genotyping of the GS:SFHS samples was carried out by the Genetics Core Laboratory at the Wellcome Trust Clinical Research Facility, Edinburgh, Scotland, and was funded by the Medical Research Council UK and the Wellcome Trust (Wellcome Trust Strategic Award "STratifying Resilience and Depression Longitudinally" [(STRADL) 104036/Z/14/Z]) ; Peer reviewed ; Publisher PDF

This work was supported by a Alzheimer's Research UK Major Project grant (ARUK-PG2017B-10). Generation Scotland received core funding from the Chief Scientist Office of the Scottish Government Health Directorates (CZD/16/6) and the Scottish Funding Council (HR03006). We are grateful to all the families who took part, the general practitioners and the Scottish School of Primary Care for their help in recruiting them, and the whole Generation Scotland team that includes interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists, health-care assistants and nurses. Genotyping of the GS:SFHS samples was carried out by the Genetics Core Laboratory at the Wellcome Trust Clinical Research Facility, Edinburgh, Scotland, and was funded by the Medical Research Council UK and the Wellcome Trust (Wellcome Trust Strategic Award "STratifying Resilience and Depression Longitudinally" [STRADL];104036/Z/14/Z). DNA methylation data collection was funded by the Wellcome Trust Strategic Award (10436/Z/14/Z). The research was conducted in the University of Edinburgh Centre for Cognitive Ageing and Cognitive Epidemiology (CCACE), part of the cross-council Lifelong Health and Wellbeing Initiative (MR/K026992/1); funding from the Biotechnology and Biological Sciences Research Council (BBSRC) and Medical Research Council (MRC) is gratefully acknowledged. CCACE supports I.J.D. with some additional support from the Dementias Platform UK (MR/L015382/1). A.M.M. and H.C.W. have received support from the Sackler Institute. ; Peer reviewed ; Publisher PDF