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Electronic health records: the next wave of complex disease genetics.

ABSTRACT: The combination of electronic health records (EHRs) with genetic data has ushered in the next wave of complex disease genetics. Population-based biobanks and other large cohorts provide sufficient sample sizes to identify novel genetic associations across the hundreds to thousands of phenotypes gleaned from EHRs. In this review, we summarize the current state of these EHR-linked biobanks, explore ongoing methods development in the field and highlight recent discoveries of genetic associations. We enumerate the many existing biobanks with EHRs linked to genetic data, many of which are available to researchers via application and contain sample sizes >50 000. We also discuss the computational and statistical considerations for analysis of such large datasets including mixed models, phenotype curation and cloud computing. Finally, we demonstrate how genome-wide association studies and phenome-wide association studies have identified novel genetic findings for complex diseases, specifically cardiometabolic traits. As more researchers employ innovative hypotheses and analysis approaches to study EHR-linked biobanks, we anticipate a richer understanding of the genetic etiology of complex diseases.

SUBMITTER: Wolford BN

PROVIDER: S-EPMC5946915 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature

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Electronic health records: the next wave of complex disease genetics.

Wolford Brooke N BN Willer Cristen J CJ Surakka Ida I

Human molecular genetics 20180501 R1

The combination of electronic health records (EHRs) with genetic data has ushered in the next wave of complex disease genetics. Population-based biobanks and other large cohorts provide sufficient sample sizes to identify novel genetic associations across the hundreds to thousands of phenotypes gleaned from EHRs. In this review, we summarize the current state of these EHR-linked biobanks, explore ongoing methods development in the field and highlight recent discoveries of genetic associations. W ...[more]

PMID: 29547983

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Project description:BACKGROUND:Administrative health records (AHRs) and electronic medical records (EMRs) are two key sources of population-based data for disease surveillance, but misclassification errors in the data can bias disease estimates. Methods that combine information from error-prone data sources can build on the strengths of AHRs and EMRs. We compared bias and error for four data-combining methods and applied them to estimate hypertension prevalence. METHODS:Our study included rule-based OR and AND methods that identify disease cases from either or both data sources, respectively, rule-based sensitivity-specificity adjusted (RSSA) method that corrects for inaccuracies using a deterministic rule, and probabilistic-based sensitivity-specificity adjusted (PSSA) method that corrects for error using a statistical model. Computer simulation was used to estimate relative bias (RB) and mean square error (MSE) under varying conditions of population disease prevalence, correlation amongst data sources, and amount of misclassification error. AHRs and EMRs for Manitoba, Canada were used to estimate hypertension prevalence using validated case definitions and multiple disease markers. RESULTS:The OR method had the lowest RB and MSE when population disease prevalence was 10%, and the RSSA method had the lowest RB and MSE when population prevalence increased to 20%. As the correlation between data sources increased, the OR method resulted in the lowest RB and MSE. Estimates of hypertension prevalence for AHRs and EMRs alone were 30.9% (95% CI: 30.6-31.2) and 24.9% (95% CI: 24.6-25.2), respectively. The estimates were 21.4% (95% CI: 21.1-21.7), for the AND method, 34.4% (95% CI: 34.1-34.8) for the OR method, 32.2% (95% CI: 31.8-32.6) for the RSSA method, and ranged from 34.3% (95% CI: 34.1-34.5) to 35.9% (95% CI, 35.7-36.1) for the PSSA method, depending on the statistical model. CONCLUSIONS:The OR and AND methods are influenced by correlation amongst the data sources, while the RSSA method is dependent on the accuracy of prior sensitivity and specificity estimates. The PSSA method performed well when population prevalence was high and average correlations amongst disease markers was low. This study will guide researchers to select a data-combining method that best suits their data characteristics.

Project description:BackgroundInherited primary arrhythmia syndromes and arrhythmogenic cardiomyopathies can lead to sudden cardiac arrest in otherwise healthy individuals. The burden and expression of these diseases in a real-world, well-phenotyped cardiovascular population is not well understood.MethodsWhole exome sequencing was performed on 8574 individuals from the CATHGEN cohort (Catheterization Genetics). Variants in 55 arrhythmia-related genes (associated with 8 disorders) were identified and assessed for pathogenicity based on American College of Genetics and Genomics/Association for Molecular Pathology criteria. Individuals carrying pathogenic/likely pathogenic (P/LP) variants were grouped by arrhythmogenic disorder and matched 1:5 to noncarrier controls based on age, sex, and genetic ancestry. Long-term phenotypic data were annotated through deep electronic health record review.ResultsFifty-eight P/LP variants were found in 79 individuals in 12 genes associated with 5 arrhythmogenic disorders (arrhythmogenic right ventricular cardiomyopathy, Brugada syndrome, hypertrophic cardiomyopathy, LMNA-related cardiomyopathy, and long QT syndrome). The penetrance of these P/LP variants in this cardiovascular cohort was 33%, 0%, 28%, 83%, and 4%, respectively. Carriers of P/LP variants associated with arrhythmogenic disorders showed significant differences in ECG, imaging, and clinical phenotypes compared with noncarriers, but displayed no difference in survival. Carriers of novel truncating variants in FLNC, MYBPC3, and MYH7 also developed relevant arrhythmogenic cardiomyopathy phenotypes.ConclusionsIn a real-world cardiovascular cohort, P/LP variants in arrhythmia-related genes were relatively common (1:108 prevalence) and most penetrant in LMNA. While hypertrophic cardiomyopathy P/LP variant carriers showed significant differences in clinical outcomes compared with noncarriers, carriers of P/LP variants associated with other arrhythmogenic disorders displayed only ECG differences.

Project description:ObjectiveTo optimally leverage the scalability and unique features of the electronic health records (EHR) for research that would ultimately improve patient care, we need to accurately identify patients and extract clinically meaningful measures. Using multiple sclerosis (MS) as a proof of principle, we showcased how to leverage routinely collected EHR data to identify patients with a complex neurological disorder and derive an important surrogate measure of disease severity heretofore only available in research settings.MethodsIn a cross-sectional observational study, 5,495 MS patients were identified from the EHR systems of two major referral hospitals using an algorithm that includes codified and narrative information extracted using natural language processing. In the subset of patients who receive neurological care at a MS Center where disease measures have been collected, we used routinely collected EHR data to extract two aggregate indicators of MS severity of clinical relevance multiple sclerosis severity score (MSSS) and brain parenchymal fraction (BPF, a measure of whole brain volume).ResultsThe EHR algorithm that identifies MS patients has an area under the curve of 0.958, 83% sensitivity, 92% positive predictive value, and 89% negative predictive value when a 95% specificity threshold is used. The correlation between EHR-derived and true MSSS has a mean R(2) = 0.38±0.05, and that between EHR-derived and true BPF has a mean R(2) = 0.22±0.08. To illustrate its clinical relevance, derived MSSS captures the expected difference in disease severity between relapsing-remitting and progressive MS patients after adjusting for sex, age of symptom onset and disease duration (p = 1.56×10(-12)).ConclusionIncorporation of sophisticated codified and narrative EHR data accurately identifies MS patients and provides estimation of a well-accepted indicator of MS severity that is widely used in research settings but not part of the routine medical records. Similar approaches could be applied to other complex neurological disorders.

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Electronic health records: the next wave of complex disease genetics.

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Electronic health records: the next wave of complex disease genetics.

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