Key Points Question Can population-level genomic screening identify those at risk for disease? Findings In this cross-sectional study of an unselected population cohort of 50 726 adults who underwent exome sequencing, pathogenic and likely pathogenic BRCA1 and BRCA2 variants were found in a higher proportion of patients than was previously reported. Meaning Current methods to identify BRCA1/2 variant carriers may not be sufficient as a screening tool; population genomic screening for hereditary breast and ovarian cancer may better identify patients at high risk and provide an intervention opportunity to reduce mortality and morbidity.

Imatinib as a single agent has no clinical activity in PDGFR-overexpressing MBC and has potential immunosuppressive effects.

The calcium-sensing receptor (CaSR) regulates serum calcium concentrations. CASR loss-or gain-of-function mutations cause familial hypocalciuric hypercalcemia type 1 (FHH1) or autosomal-dominant hypocalcemia type 1 (ADH1), respectively, but the population prevalence of FHH1 or ADH1 is unknown. Rare CASR variants were identified in whole-exome sequences from 51,289 de-identified individuals in the DiscovEHR cohort derived from a single US healthcare system. We integrated bioinformatics pathogenicity triage, mean serum Ca concentrations, and mode of inheritance to identify potential FHH1 or ADH1 variants, and we used a Sequence Kernel Association Test (SKAT) to identify rare variant-associated diseases. We identified predicted heterozygous loss-of-function CASR variants (6 different nonsense/frameshift variants and 12 different missense variants) in 38 unrelated individuals, 21 of whom were hypercalcemic. Missense CASR variants were identified in two unrelated hypocalcemic individuals. Functional studies showed that all hypercalcemiaassociated missense variants impaired heterologous expression, plasma membrane targeting, and/or signaling, whereas hypocalcemiaassociated missense variants increased expression, plasma membrane targeting, and/or signaling. Thus, 38 individuals with a genetic diagnosis of FHH1 and two individuals with a genetic diagnosis of ADH1 were identified in the 51,289 cohort, giving a prevalence in this population of 74.1 per 100,000 for FHH1 and 3.9 per 100,000 for ADH1. SKAT combining all nonsense, frameshift, and missense loss-of-function variants revealed associations with cardiovascular, neurological, and other diseases. In conclusion, FHH1 is a common cause of hypercalcemia, with prevalence similar to that of primary hyperparathyroidism, and is associated with altered disease risks, whereas ADH1 is a major cause of non-surgical hypoparathyroidism.

Key Points Question Are medication monitoring programs within a hospital associated with more accurate identification of patients with opioid use disorder through the use of proxy Diagnostic and Statistical Manual of Mental Disorders (Fifth Edition) ( DSM-5) criteria for opioid use disorder extracted from electronic health records? Findings This cross-sectional study demonstrated that DSM-5 criteria for opioid use disorder can be extracted through review of electronic health records and that patients who are part of a drug monitoring program had a higher mean prevalence of opiod use disorder and a higher mean number of psychiatric comorbidities associated with opioid use disorder. Meaning Proxy measures that rely on multiple sources of data, including prescription drug history and notes in the electronic health record, may help identify patients with opioid use disorder who have not received a diagnosis.

Background Familial hypercholesterolemia ( FH ), is a historically underdiagnosed, undertreated, high‐risk condition that is associated with a high burden of cardiovascular morbidity and mortality. In this study, we use a population‐based approach using electronic health record ( EHR )‐based algorithms to identify FH . We report the major adverse cardiovascular events, mortality, and cost of medical care associated with this diagnosis. Methods and Results In our 1.18 million EHR‐ eligible cohort, International Classification of Diseases, Ninth Revision ( ICD ‐9 ) code‐defined hyperlipidemia was categorized into FH and non‐ FH groups using an EHR algorithm designed using the modified Dutch Lipid Clinic Network criteria. Major adverse cardiovascular events, mortality, and cost of medical care were analyzed. A priori associated variables/confounders were used for multivariate analyses using binary logistic regression and linear regression with propensity score–based weighted methods as appropriate. EHR FH was identified in 32 613 individuals, which was 2.7% of the 1.18 million EHR cohort and 13.7% of 237 903 patients with hyperlipidemia. FH had higher rates of myocardial infarction (14.77% versus 8.33%; P <0.0001), heart failure (11.82% versus 10.50%; P <0.0001), and, after adjusting for traditional risk factors, significantly correlated to a composite major adverse cardiovascular events variable (odds ratio, 4.02; 95% CI, 3.88–4.16; P <0.0001), mortality (odds ratio, 1.20; CI, 1.15–1.26; P <0.0001), and higher total revenue per‐year (incidence rate ratio, 1.30; 95% CI, 1.28–1.33; P <0.0001). Conclusions EHR ‐based algorithms discovered a disproportionately high prevalence of FH in our medical cohort, which was associated with worse outcomes and higher costs of medical care. This data‐driven approach allows for a more precise method to identify traditionally high‐risk groups within large populations allowing for targeted prevention and therapeutic strategies.

Mutations of the sigma subunit of the heterotetrameric adaptor-related protein complex 2 (AP2σ) impair signalling of the calcium-sensing receptor (CaSR), and cause familial hypocalciuric hypercalcaemia type 3 (FHH3). To date, FHH3-associated AP2σ mutations have only been identified at one residue, Arg15. We hypothesized that additional rare AP2σ variants may also be associated with altered CaSR function and hypercalcaemia, and sought for these by analysing >111 995 exomes (>60 706 from ExAc and dbSNP, and 51 289 from the Geisinger Health System-Regeneron DiscovEHR dataset, which also contains clinical data). This identified 11 individuals to have 9 non-synonymous AP2σ variants (Arg3His, Arg15His (x3), Ala44Thr, Phe52Tyr, Arg61His, Thr112Met, Met117Ile, Glu122Gly and Glu142Lys) with 3 of the 4 individuals who had Arg15His and Met117Ile AP2σ variants having mild hypercalcaemia, thereby indicating a prevalence of FHH3-associated AP2σ mutations of ∼7.8 per 100 000 individuals. Structural modelling of the novel eight AP2σ variants (Arg3His, Ala44Thr, Phe52Tyr, Arg61His, Thr112Met, Met117Ile, Glu122Gly and Glu142Lys) predicted that the Arg3His, Thr112Met, Glu122Gly and Glu142Lys AP2σ variants would disrupt polar contacts within the AP2σ subunit or affect the interface between the AP2σ and AP2α subunits. Functional analyses of all eight AP2σ variants in CaSR-expressing cells demonstrated that the Thr112Met, Met117Ile and Glu142Lys variants, located in the AP2σ α4-α5 helical region that forms an interface with AP2α, impaired CaSR-mediated intracellular calcium (Cai2+) signalling, consistent with a loss of function, and this was rectified by treatment with the CaSR positive allosteric modulator cinacalcet. Thus, our studies demonstrate another potential class of FHH3-causing AP2σ mutations located at the AP2σ-AP2α interface.

A wide range of patient health data is recorded in Electronic Health Records (EHR). This data includes diagnosis, surgical procedures, clinical laboratory measurements, and medication information. Together this information reflects the patient's medical history. Many studies have efficiently used this data from the EHR to find associations that are clinically relevant, either by utilizing International Classification of Diseases, version 9 (ICD-9) codes or laboratory measurements, or by designing phenotype algorithms to extract case and control status with accuracy from the EHR.Here we developed a strategy to utilize longitudinal quantitative trait data from the EHR at Geisinger Health System focusing on outpatient metabolic and complete blood panel data as a starting point. Comprehensive Metabolic Panel (CMP) as well as Complete Blood Counts (CBC) are parts of routine care and provide a comprehensive picture from high level screening of patients' overall health and disease. We randomly split our data into two datasets to allow for discovery and replication. We first conducted a genome-wide association study (GWAS) with median values of 25 different clinical laboratory measurements to identify variants from Human Omni Express Exome beadchip data that are associated with these measurements. We identified 687 variants that associated and replicated with the tested clinical measurements at p<5x10 -08 . Since longitudinal data from the EHR provides a record of a patient's medical history, we utilized this information to further investigate the ICD-9 codes that might be associated with differences in variability of the measurements in the longitudinal dataset. We identified low and high variance patients by looking at changes within their individual longitudinal EHR laboratory results for each of the 25 clinical lab values (thus creating 50 groups -a high variance and a low variance for each lab variable). We then performed a PheWAS analysis with ICD-9 diagnosis codes, separately in the high variance group and the low variance group for each lab variable. We found 717 PheWAS associations that replicated at a p-value less than 0.001. Next, we evaluated the results of this study by comparing the association results between the high and low variance groups. For example, we found 39 SNPs (in multiple genes) associated with ICD-9 250.01 (Type-I diabetes) in patients with high variance of plasma glucose levels, but not in patients with low variance in plasma glucose levels. Another example is the association of 4 SNPs in UMOD with chronic kidney disease in patients with high variance for aspartate aminotransferase (discovery p-value: 8.71x10 -09 and replication p-value: 2.03x10 -06 ). In general, we see a pattern of many more statistically significant associations from patients with high variance in the quantitative lab variables, in comparison with the low variance group across all of the 25 laboratory measurements. This study is one of the first of its kind to utilize quantitative trait variance from longitudinal laborat...

The pace of deorphanization of G protein-coupled receptors (GPCRs) has slowed, and new approaches are required. Small molecule targeting of orphan GPCRs can potentially be of clinical benefit even if the endogenous receptor ligand has not been identified. Many GPCRs lack common variants that lead to reproducible genome-wide disease associations, and rare-variant approaches have emerged as a viable alternative to identify disease associations for such genes. Therefore, our goal was to prioritize orphan GPCRs by determining their associations with human diseases in a large clinical population. We used sequence kernel association tests to assess the disease associations of 85 orphan or understudied GPCRs in an unselected cohort of 51,289 individuals. Using rare loss-of-function variants, missense variants predicted to be pathogenic or likely pathogenic, and a subset of rare synonymous variants that cause large changes in local codon bias as independent data sets, we found strong, phenome-wide disease associations shared by two or more variant categories for 39% of the GPCRs. To validate the bioinformatics and sequence kernel association test analyses, we functionally characterized rare missense and synonymous variants of GPR39, a family A GPCR, revealing altered expression or Zn 2؉-mediated signaling for members of both variant classes. These results support the utility of rare variant analyses for identifying disease associations for GPCRs that lack impactful common variants. We highlight the importance of rare synonymous variants in human physiology and argue for their routine inclusion in any comprehensive analysis of genomic variants as potential causes of disease. The superfamily of G protein-coupled receptors (GPCRs) 3 translates extracellular signals from light, metabolites, and hormones into cellular changes, which makes them the targets of a significant fraction of drugs currently on the market (1). Genome-wide association studies (GWASs) on common variants in GPCRs have begun to identify their contributions to various disease processes (2, 3). However, many GPCRs lack functionally important common variants, and alternate strategies are needed to understand their roles in human physiology. Recently, sequence kernel association tests (SKATs) have been applied to rare variants to assess disease associations. Initial approaches binned all rare variants in a genomic region or gene (4, 5), but current methods group the rare variants most likely to contribute to disease associations or aggregate variants based on domain or family structures (6, 7). Large-scale studies of orphan or understudied GPCRs to characterize natural genetic variation in the human population can provide insights into the biological function and/or potential causal contributions to disease processes (8). The Dis-covEHR collaboration represents a tremendous resource (9) because whole exome sequences can be linked to the electronic health record (EHR) in a deidentified manner. Here we used whole exome sequences and clinical information from 51...