Electronic health record and genome-wide genetic data in Generation Scotland participants

Abstract

This article provides the first detailed demonstration of the research value of the Electronic Health Record (EHR) linked to research data in Generation Scotland Scottish Family Health Study (GS:SFHS) participants, together with how to access this data. The structured, coded variables in the routine biochemistry, prescribing and morbidity records, in particular, represent highly valuable phenotypic data for a genomics research resource. Access to a wealth of other specialized datasets, including cancer, mental health and maternity inpatient information, is also possible through the same straightforward and transparent application process. The EHR linked dataset is a key component of GS:SFHS, a biobank conceived in 1999 for the purpose of studying the genetics of health areas of current and projected public health importance. Over 24,000 adults were recruited from 2006 to 2011, with broad and enduring written informed consent for biomedical research. Consent was obtained from 23,603 participants for GS:SFHS study data to be linked to their Scottish National Health Service (NHS) records, using their Community Health Index number. This identifying number is used for NHS Scotland procedures (registrations, attendances, samples, prescribing and investigations) and allows healthcare records for individuals to be linked across time and location. Here, we describe the NHS EHR dataset on the sub-cohort of 20,032 GS:SFHS participants with consent and mechanism for record linkage plus extensive genetic data. Together with existing study phenotypes, including family history and environmental exposures, such as smoking, the EHR is a rich resource of real world data that can be used in research to characterise the health trajectory of participants, available at low cost and a high degree of timeliness, matched to DNA, urine and serum samples and genome-wide genetic information.

An actionable KCNH2 Long QT Syndrome variant detected by sequence and haplotype analysis in a population research cohort

“…Eligible participants (greater than 18 years of age and with two or more grandparents from Shetland) were recruited to the Viking Health Study Shetland, REC reference: 12/SS/0151 (South East Scotland Research Ethics Committee, NHS Lothian). VIKING participants gave written informed consent for research procedures including electronic health record linkage, with NHS datasets accessed using a process essentially as described for the Generation Scotland cohort 29 . The data linkage and access to NHS Scotland-originated data was approved by the Public Benefit and Privacy Panel for Health and Social Care (Ref 1718-0380).…”

Uncovering the Genetic Architecture of Major Depression

“…DNA can also be obtained at scale and at low cost by posting simple salivary DNA collection kits to people's home address for returning to laboratory facilities. The eHR may also detail clinical symptoms assessed at interview, data on treatment response, and the results of diagnostic tests and other investigations (Hafferty et al, 2018;Kerr et al, 2017) obtained blind The clinical translation of genetic associations from GWASs starts with a MD-variant association set of test statistics and downstream gene, gene set, pathway, and other enrichment analyses (top left). These analyses generate candidate mechanisms and drug targets that can be tested in in vitro and in vivo models that utilize experimental approaches (top right).…”

Comparison of physical examination and laboratory data between a clinical study and electronic health records

“…Since the National Institutes of Health made leveraging EHRs for biomedical research a priority, researchers have been eagerly identifying and developing efficient methods for data accrual, integration, and analysis. For example, a growing number of large-scale biobanks have begun connecting dense, longitudinal EHR data with biorepositories among enrolled patients, thereby generating a new repository for medical research [1,2].…”