Citations

Abstract Objective Health care generated data have become an important source for clinical and genomic research. Often, investigators create and iteratively refine phenotype algorithms to achieve high positive predictive values (PPVs) or sensitivity, thereby identifying valid cases and controls. These algorithms achieve the greatest utility when validated and shared by multiple health care systems. Materials and Methods We report the current status and impact of the Phenotype KnowledgeBase (PheKB, http://phekb.org ), an online environment supporting the workflow of building, sharing, and validating electronic phenotype algorithms. We analyze the most frequent components used in algorithms and their performance at authoring institutions and secondary implementation sites. Results As of June 2015, PheKB contained 30 finalized phenotype algorithms and 62 algorithms in development spanning a range of traits and diseases. Phenotypes have had over 3500 unique views in a 6-month period and have been reused by other institutions. International Classification of Disease codes were the most frequently used component, followed by medications and natural language processing. Among algorithms with published performance data, the median PPV was nearly identical when evaluated at the authoring institutions (n = 44; case 96.0%, control 100%) compared to implementation sites (n = 40; case 97.5%, control 100%). Discussion These results demonstrate that a broad range of algorithms to mine electronic health record data from different health systems can be developed with high PPV, and algorithms developed at one site are generally transportable to others. Conclusion By providing a central repository, PheKB enables improved development, transportability, and validity of algorithms for research-grade phenotypes using health care generated data.

SummaryObjectiveThere is a complex bidirectional relationship between sleep and epilepsy. Sleep/wake timing of seizures has been investigated for several individual seizure types and syndromes, but few large‐scale studies of the timing of seizures exist in people with varied epilepsy types. In addition, the genetic contributions to seizure timing have not been well studied.MethodsSleep/wake timing of seizures was determined for 1,395 subjects in 546 families enrolled in the Epilepsy Phenome/Genome Project (EPGP). We examined seizure timing among subjects with different epilepsy types, seizure types, epilepsy syndromes, and localization. We also examined the familial aggregation of sleep/wake occurrence of seizures.ResultsSeizures in nonacquired focal epilepsy (NAFE) were more likely to occur during sleep than seizures in generalized epilepsy (GE), for both convulsive (odds ratio [OR] 5.2, 95% confidence interval [CI] 3.59–7.52) and nonconvulsive seizures (OR 4.2, 95% CI 2.48–7.21). Seizures occurring within 1 h of awakening were more likely to occur in patients with GE than with NAFE for both convulsive (OR 2.3, 95% CI 1.54–3.39) and nonconvulsive (OR 1.7, 95% CI 1.04–2.66) seizures. Frontal onset seizures were more likely than temporal onset seizures to occur during sleep. Sleep/wake timing of seizures in first‐degree relatives predicted timing of seizures in the proband.SignificanceWe found that sleep/wake timing of seizures is associated with both epilepsy syndrome and seizure type. In addition, we provide the first evidence for a genetic contribution to sleep/wake timing of seizures in a large group of individuals with common epilepsy syndromes.

The legacy of human-Neandertal interbreeding Non-African humans are estimated to have inherited on average 1.5 to 4% of their genomes from Neandertals. However, how this genetic legacy affects human traits is unknown. Simonti et al. combined genotyping data with electronic health records. Individual Neandertal alleles were correlated with clinically relevant phenotypes in individuals of European descent. These archaic genetic variants were associated with medical conditions affecting the skin, the blood, and the risk of depression. Science , this issue p. 737

Genetic variation can affect drug response in multiple ways, although it remains unclear how rare genetic variants affect drug response. The electronic Medical Records and Genomics (eMERGE) Network, collaborating with the Pharmacogenomics Research Network, began eMERGE‐PGx, a targeted sequencing study to assess genetic variation in 82 pharmacogenes critical for implementation of “precision medicine.” The February 2015 eMERGE‐PGx data release includes sequence‐derived data from ∼5,000 clinical subjects. We present the variant frequency spectrum categorized by variant type, ancestry, and predicted function. We found 95.12% of genes have variants with a scaled Combined Annotation‐Dependent Depletion score above 20, and 96.19% of all samples had one or more Clinical Pharmacogenetics Implementation Consortium Level A actionable variants. These data highlight the distribution and scope of genetic variation in relevant pharmacogenes, identifying challenges associated with implementing clinical sequencing for drug treatment at a broader level, underscoring the importance for multifaceted research in the execution of precision medicine.

ABSTRACTBackgroundPatients with sinus node dysfunction (SND) have increased risk of atrial tachyarrhythmias, including atrial fibrillation (AF). To date, treatment patterns and outcomes of patients with SND and AF have not been well described.HypothesisPatients with SND and AF have higher risk of adverse cardiovascular outcomes.MethodsSinus node dysfunction was defined clinically, based on treating physician. Treatment patterns were described and logistic regression analysis performed to assess outcomes.ResultsOverall, 1710 (17.7%) out of 9631 patients had SND at enrollment. Patients with SND and AF had increased comorbid medical illnesses, more severe symptoms (European Heart Rhythm Association class IV: 17.5% vs 13.9%; P = 0.0007), and poorer quality of life (median 12‐month Atrial Fibrillation Effect on Quality of Life score: 79.6 vs 85.2; P = 0.0008). There were no differences in AF management strategy between patients with SND and those without (rate control, 69.7% vs 67.7%; rhythm control, 30.0% vs 32.0%; P = 0.11). After adjustment, patients with SND were more likely than those without SND to progress from paroxysmal AF at baseline to persistent or permanent AF at any follow‐up, or persistent AF at baseline to permanent AF at any follow‐up (odds ratio: 1.23, 95% confidence interval: 1.01‐1.49, P = 0.035). However, there was no association between SND and major risk‐adjusted outcomes.ConclusionsSinus node dysfunction is present in 1 of 6 patients with AF and is associated with increased comorbidities and higher symptom burden. However, SND is not associated with an increase in major risk‐adjusted outcomes.

The strong male predominance in Barrett's esophagus (BE) and esophageal adenocarcinoma (EAC) remains inadequately explained, but sex hormones might be involved. We hypothesized that single nucleotide polymorphisms (SNPs) in the androgen pathway influence risk of developing BE and EAC. This genetic‐epidemiological analysis included 14 studies from Australia, Europe and North America. Polymorphisms in 16 genes coding for the androgen pathway were analyzed using a gene‐based approach: versatile gene‐based test association study. This method evaluates associations between a trait and all SNPs within a specific gene rather than each SNP marker individually as in a conventional GWAS. The data were stratified for sex, body‐mass index, waist‐to‐hip ratio, tobacco smoking and gastroesophageal reflux status. Included were data from 1,508 EAC patients, 2,383 BE patients and 2,170 control participants. SNPs within the gene CYP17A1 were associated with risk of BE in the sexes combined (p = 0.002) and in males (p = 0.003), but not in females separately (p = 0.3). This association was found in tobacco smokers (p = 0.003) and in BE patients without reflux (p = 0.004), but not in nonsmokers (p = 0.2) or those with reflux (p = 0.036). SNPs within JMJD1C were associated with risk of EAC in females (p = 0.001). However, none of these associations replicated in a subsequent sample. Fourteen other genes studied did not reach statistically significant levels of association with BE, EAC or the combination of BE and EAC, after correcting for the number of genes included in the analysis. In conclusion, genetic variants in the androgen‐related genes CYP17A1 and JMJD1C might be associated with risk of BE and EAC, respectively, but replication data with larger sample sizes are needed.

AbstractBackground Electronic health records (EHRs) are increasingly used for clinical and translational research through the creation of phenotype algorithms. Currently, phenotype algorithms are most commonly represented as noncomputable descriptive documents and knowledge artifacts that detail the protocols for querying diagnoses, symptoms, procedures, medications, and/or text-driven medical concepts, and are primarily meant for human comprehension. We present desiderata for developing a computable phenotype representation model (PheRM).Methods A team of clinicians and informaticians reviewed common features for multisite phenotype algorithms published in PheKB.org and existing phenotype representation platforms. We also evaluated well-known diagnostic criteria and clinical decision-making guidelines to encompass a broader category of algorithms.Results We propose 10 desired characteristics for a flexible, computable PheRM: (1) structure clinical data into queryable forms; (2) recommend use of a common data model, but also support customization for the variability and availability of EHR data among sites; (3) support both human-readable and computable representations of phenotype algorithms; (4) implement set operations and relational algebra for modeling phenotype algorithms; (5) represent phenotype criteria with structured rules; (6) support defining temporal relations between events; (7) use standardized terminologies and ontologies, and facilitate reuse of value sets; (8) define representations for text searching and natural language processing; (9) provide interfaces for external software algorithms; and (10) maintain backward compatibility.Conclusion A computable PheRM is needed for true phenotype portability and reliability across different EHR products and healthcare systems. These desiderata are a guide to inform the establishment and evolution of EHR phenotype algorithm authoring platforms and languages.