Rare GPR37L1 Variants Reveal Potential Association between GPR37L1 and Disorders of Anxiety and Migraine.

GPR37L1 is an orphan receptor that couples through heterotrimeric G-proteins to regulate physiological functions. Since its role in humans is not fully defined, we used an unbiased computational approach to assess the clinical significance of rare G-protein-coupled receptor 37-like 1 (GPR37L1) genetic variants found among 51,289 whole-exome sequences from the DiscovEHR cohort. Rare GPR37L1 coding variants were binned according to predicted pathogenicity and analyzed by sequence kernel association testing to reveal significant associations with disease diagnostic codes for epilepsy and migraine, among others. Since associations do not prove causality, rare GPR37L1 variants were functionally analyzed in SK-N-MC cells to evaluate potential signaling differences and pathogenicity. Notably, receptor variants exhibited varying abilities to reduce cAMP levels, activate mitogen-activated protein kinase (MAPK) signaling, and/or upregulate receptor expression in response to the agonist prosaptide (TX14(A)), as compared with the wild-type receptor. In addition to signaling changes, knock-out (KO) of GPR37L1 or expression of certain rare variants altered cellular cholesterol levels, which were also acutely regulated by administration of the agonist TX14(A) via activation of the MAPK pathway. Finally, to simulate the impact of rare nonsense variants found in the large patient cohort, a KO mouse line lacking Gpr37l1 was generated. Although KO animals did not recapitulate an acute migraine phenotype, the loss of this receptor produced sex-specific changes in anxiety-related disorders often seen in chronic migraineurs. Collectively, these observations define the existence of rare GPR37L1 variants associated with neuropsychiatric conditions in the human population and identify the signaling changes contributing to pathological processes.

Placental microRNA methylome signatures may serve as biomarkers and therapeutic targets for prenatally opioid-exposed infants with neonatal opioid withdrawal syndrome.

Introduction: The neonate exposed to opioids in utero faces a constellation of withdrawal symptoms postpartum commonly called neonatal opioid withdrawal syndrome (NOWS). The incidence of NOWS has increased in recent years due to the opioid epidemic. MicroRNAs (miRNAs) are small non-coding RNA molecules that play a crucial role in gene regulation. Epigenetic variations in microRNAs (miRNAs) and their impact on addiction-related processes is a rapidly evolving area of research. Methods: The Illumina Infinium Methylation EPIC BeadChip was used to analyze DNA methylation levels of miRNA-encoding genes in 96 human placental tissues to identify miRNA gene methylation profiles as-sociated with NOWS: 32 from mothers whose prenatally opioid-exposed infants required pharmacologic management for NOWS, 32 from mothers whose prenatally opioid-exposed infants did not require treat-ment for NOWS, and 32 unexposed controls. Results: The study identified 46 significantly differentially methylated (FDR p-value ≤ 0.05) CpGs associated with 47 unique miRNAs, with a receiver operating characteristic (ROC) area under the curve (AUC) ≥0.75 including 28 hypomethylated and 18 hypermethylated CpGs as potentially associated with NOWS. These dysregulated microRNA methylation patterns may be a contributing factor to NOWS pathogenesis. Conclusion: This is the first study to analyze miRNA methylation profiles in NOWS infants and illustrates the unique role miRNAs might have in diagnosing and treating the disease. Furthermore, these data may provide a step toward feasible precision medicine for NOWS babies as well.

Rare GPR37L1 variants reveal potential roles in anxiety and migraine disorders.

GPR37L1 is an orphan receptor that couples through heterotrimeric G-proteins to regulate physiological functions. Since its role in humans is not fully defined, we used an unbiased computational approach to assess the clinical significance of rare GPR37L1 genetic variants found among 51,289 whole exome sequences from the DiscovEHR cohort. Briefly, rare GPR37L1 coding variants were binned according to predicted pathogenicity, and analyzed by Sequence Kernel Association testing to reveal significant associations with disease diagnostic codes for epilepsy and migraine, among others. Since associations do not prove causality, rare GPR37L1 variants were then functionally analyzed in SK-N-MC cells to evaluate potential signaling differences and pathogenicity. Notably, receptor variants exhibited varying abilities to reduce cAMP levels, activate MAPK signaling, and/or upregulate receptor expression in response to the agonist prosaptide (TX14(A)), as compared to the wild-type receptor. In addition to signaling changes, knockout of GPR37L1 or expression of certain rare variants altered cellular cholesterol levels, which were also acutely regulated by administration of the agonist TX14(A) via activation of the MAPK pathway. Finally, to simulate the impact of rare nonsense variants found in the large patient cohort, a knockout (KO) mouse line lacking Gpr37L1 was generated, revealing loss of this receptor produced sex-specific changes implicated in migraine-related disorders. Collectively, these observations define the existence of rare GPR37L1 variants in the human population that are associated with neuropsychiatric conditions and identify the underlying signaling changes that are implicated in the in vivo actions of this receptor in pathological processes leading to anxiety and migraine. SIGNIFICANCE STATEMENT: G-protein coupled receptors (GPCRs) represent a diverse group of membrane receptors that contribute to a wide range of diseases and serve as effective drug targets. However, a number of these receptors have no identified ligands or functions, i.e., orphan receptors. Over the past decade, advances have been made, but there is a need for identifying new strategies to reveal their roles in health and disease. Our results highlight the utility of rare variant analyses of orphan receptors for identifying human disease associations, coupled with functional analyses in relevant cellular and animal systems, to ultimately reveal their roles as novel drug targets for treatment of neurological disorders that lack wide-spread efficacy.

Placental cytochrome P450 methylomes in infants exposed to prenatal opioids: exploring the effects of neonatal opioid withdrawal syndrome on health horizons.

Background: Neonatal opioid withdrawal syndrome (NOWS), arises due to increased opioid use during pregnancy. Cytochrome P450 (CYP) enzymes play a pivotal role in metabolizing a wide range of substances in the human body, including opioids, other drugs, toxins, and endogenous compounds. The association between CYP gene methylation and opioid effects is unexplored and it could offer promising insights. Objective: To investigate the impact of prenatal opioid exposure on disrupted CYPs in infants and their anticipated long-term clinical implications. Study Design: DNA methylation levels of CYP genes were analyzed in a cohort of 96 placental tissues using Illumina Infinium MethylationEPIC (850 k) BeadChips. This involved three groups of placental tissues: 32 from mothers with infants exposed to opioids prenatally requiring pharmacologic treatment for NOWS, 32 from mothers with prenatally opioid-exposed infants not needing NOWS treatment, and 32 from unexposed control mothers. Results: The study identified 20 significantly differentially methylated CpG sites associated with 17 distinct CYP genes, with 14 CpGs showing reduced methylation across 14 genes (CYP19A1, CYP1A2, CYP4V2, CYP1B1, CYP24A1, CYP26B1, CYP26C1, CYP2C18, CYP2C9, CYP2U1, CYP39A1, CYP2R1, CYP4Z1, CYP2D7P1 and), while 8 exhibited hypermethylation (CYP51A1, CYP26B1, CYP2R1, CYP2U1, CYP4X1, CYP1A2, CYP2W1, and CYP4V2). Genes such as CYP1A2, CYP26B1, CYP2R1, CYP2U1, and CYP4V2 exhibited both increased and decreased methylation. These genes are crucial for metabolizing eicosanoids, fatty acids, drugs, and diverse substances. Conclusion: The study identified profound methylation changes in multiple CYP genes in the placental tissues relevant to NOWS. This suggests that disruption of DNA methylation patterns in CYP transcripts might play a role in NOWS and may serve as valuable biomarkers, suggesting a future pathway for personalized treatment. Further research is needed to confirm these findings and explore their potential for diagnosis and treatment.

Artificial Intelligence and Circulating Cell-Free DNA Methylation Profiling: Mechanism and Detection of Alzheimer's Disease.

Background: Despite extensive efforts, significant gaps remain in our understanding of Alzheimer's disease (AD) pathophysiology. Novel approaches using circulating cell-free DNA (cfDNA) have the potential to revolutionize our understanding of neurodegenerative disorders. Methods: We performed DNA methylation profiling of cfDNA from AD patients and compared them to cognitively normal controls. Six Artificial Intelligence (AI) platforms were utilized for the diagnosis of AD while enrichment analysis was used to elucidate the pathogenesis of AD. Results: A total of 3684 CpGs were significantly (adj. p-value < 0.05) differentially methylated in AD versus controls. All six AI algorithms achieved high predictive accuracy (AUC = 0.949−0.998) in an independent test group. As an example, Deep Learning (DL) achieved an AUC (95% CI) = 0.99 (0.95−1.0), with 94.5% sensitivity and specificity. Conclusion: We describe numerous epigenetically altered genes which were previously reported to be differentially expressed in the brain of AD sufferers. Genes identified by AI to be the best predictors of AD were either known to be expressed in the brain or have been previously linked to AD. We highlight enrichment in the Calcium signaling pathway, Glutamatergic synapse, Hedgehog signaling pathway, Axon guidance and Olfactory transduction in AD sufferers. To the best of our knowledge, this is the first reported genome-wide DNA methylation study using cfDNA to detect AD.

Maternal opioid use disorder: Placental transcriptome analysis for neonatal opioid withdrawal syndrome.

Excessive prenatal opioid exposure may lead to the development of Neonatal Opioid Withdrawal Syndrome (NOWS). RNA-seq was done on 64 formalin-fixed paraffin-embedded placental tissue samples from 32 mothers with opioid use disorder, with newborns with NOWS that required treatment, and 32 prenatally unexposed controls. We identified 93 differentially expressed genes in the placentas of infants with NOWS compared to unexposed controls. There were 4 up- and 89 downregulated genes. Among these, 7 genes CYP1A1, APOB, RPH3A, NRXN1, LINC01206, AL157396.1, UNC80 achieved an FDR p-value of <0.01. The remaining 87 genes were significant with FDR p-value <0.05. The 4 upregulated, CYP1A1, FP671120.3, RAD1, RN7SL856P, and the 10 most significantly downregulated genes were RNA5SP364, GRIN2A, UNC5D, DMBT1P1, MIR3976HG, LINC02199, LINC02822, PANTR1, AC012178.1, CTNNA2. Ingenuity Pathway Analysis identified the 7 most likely to play an important role in the etiology of NOWS. Our study expands insights into the genetic mechanisms of NOWS development.

Artificial intelligence and leukocyte epigenomics: Evaluation and prediction of late-onset Alzheimer's disease.

We evaluated the utility of leucocyte epigenomic-biomarkers for Alzheimer's Disease (AD) detection and elucidates its molecular pathogeneses. Genome-wide DNA methylation analysis was performed using the Infinium MethylationEPIC BeadChip array in 24 late-onset AD (LOAD) and 24 cognitively healthy subjects. Data were analyzed using six Artificial Intelligence (AI) methodologies including Deep Learning (DL) followed by Ingenuity Pathway Analysis (IPA) was used for AD prediction. We identified 152 significantly (FDR p<0.05) differentially methylated intragenic CpGs in 171 distinct genes in AD patients compared to controls. All AI platforms accurately predicted AD with AUCs ≥0.93 using 283,143 intragenic and 244,246 intergenic/extragenic CpGs. DL had an AUC = 0.99 using intragenic CpGs, with both sensitivity and specificity being 97%. High AD prediction was also achieved using intergenic/extragenic CpG sites (DL significance value being AUC = 0.99 with 97% sensitivity and specificity). Epigenetically altered genes included CR1L & CTSV (abnormal morphology of cerebral cortex), S1PR1 (CNS inflammation), and LTB4R (inflammatory response). These genes have been previously linked with AD and dementia. The differentially methylated genes CTSV & PRMT5 (ventricular hypertrophy and dilation) are linked to cardiovascular disease and of interest given the known association between impaired cerebral blood flow, cardiovascular disease, and AD. We report a novel, minimally invasive approach using peripheral blood leucocyte epigenomics, and AI analysis to detect AD and elucidate its pathogenesis.

Placental DNA methylation profiles in opioid-exposed pregnancies and associations with the neonatal opioid withdrawal syndrome.

Opioid abuse during pregnancy can result in Neonatal Opioid Withdrawal Syndrome (NOWS). We investigated genome-wide methylation analyses of 96 placental tissue samples, including 32 prenatally opioid-exposed infants with NOWS who needed therapy (+Opioids/+NOWS), 32 prenatally opioid-exposed infants with NOWS who did not require treatment (+Opioids/-NOWS), and 32 prenatally unexposed controls (-Opioids/-NOWS, control). Statistics, bioinformatics, Artificial Intelligence (AI), including Deep Learning (DL), and Ingenuity Pathway Analyses (IPA) were performed. We identified 17 dysregulated pathways thought to be important in the pathophysiology of NOWS and reported accurate AI prediction of NOWS diagnoses. The DL had an AUC (95% CI) =0.98 (0.95-1.0) with a sensitivity and specificity of 100% for distinguishing NOWS from the +Opioids/-NOWS group and AUCs (95% CI) =1.00 (1.0-1.0) with a sensitivity and specificity of 100% for distinguishing NOWS versus control and + Opioids/-NOWS group versus controls. This study provides strong evidence of methylation dysregulation of placental tissue in NOWS development.

Assessment of Probable Opioid Use Disorder Using Electronic Health Record Documentation.

Importance: Electronic health records are a potentially valuable source of information for identifying patients with opioid use disorder (OUD). Objective: To evaluate whether proxy measures from electronic health record data can be used reliably to identify patients with probable OUD based on Diagnostic and Statistical Manual of Mental Disorders (Fifth Edition) (DSM-5) criteria. Design, Setting, and Participants: This retrospective cross-sectional study analyzed individuals within the Geisinger health system who were prescribed opioids between December 31, 2000, and May 31, 2017, using a mixed-methods approach. The cohort was identified from 16 253 patients enrolled in a contract-based, Geisinger-specific medication monitoring program (GMMP) for opioid use, including patients who maintained or violated contract terms, as well as a demographically matched control group of 16 253 patients who were prescribed opioids but not enrolled in the GMMP. Substance use diagnoses and psychiatric comorbidities were assessed using automated electronic health record summaries. A manual medical record review procedure using DSM-5 criteria for OUD was completed for a subset of patients. The analysis was conducted beginning from June 5, 2017, until May 29, 2020. Main Outcomes and Measures: The primary outcome was the prevalence of OUD as defined by proxy measures for DSM-5 criteria for OUD as well as the prevalence of comorbidities among patients prescribed opioids within an integrated health system. Results: Among the 16 253 patients enrolled in the GMMP (9309 women [57%]; mean [SD] age, 52 [14] years), OUD diagnoses as defined by diagnostic codes were present at a much lower rate than expected (291 [2%]), indicating the necessity for alternative diagnostic strategies. The DSM-5 criteria for OUD can be assessed using manual medical record review; a manual review of 200 patients in the GMMP and 200 control patients identifed a larger percentage of patients with probable moderate to severe OUD (GMMP, 145 of 200 [73%]; and control, 27 of 200 [14%]) compared with the prevalence of OUD assessed using diagnostic codes. Conclusions and Relevance: These results suggest that patients with OUD may be identified using information available in the electronic health record, even when diagnostic codes do not reflect this diagnosis. Furthermore, the study demonstrates the utility of coding for DSM-5 criteria from medical records to generate a quantitative DSM-5 score that is associated with OUD severity.

Familial Hypocalciuric Hypercalcemia Type 1 and Autosomal-Dominant Hypocalcemia Type 1: Prevalence in a Large Healthcare Population.

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 hypercalcemia-associated missense variants impaired heterologous expression, plasma membrane targeting, and/or signaling, whereas hypocalcemia-associated 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.

Rare-variant pathogenicity triage and inclusion of synonymous variants improves analysis of disease associations of orphan G protein-coupled receptors.

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 Zn2+-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.

Hidden Burden of Electronic Health Record-Identified Familial Hypercholesterolemia: Clinical Outcomes and Cost of Medical Care.

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.

Systematic characterization of germline variants from the DiscovEHR study endometrial carcinoma population.

BACKGROUND: Endometrial cancer (EMCA) is the fifth most common cancer among women in the world. Identification of potentially pathogenic germline variants from individuals with EMCA will help characterize genetic features that underlie the disease and potentially predispose individuals to its pathogenesis. METHODS: The Geisinger Health System's (GHS) DiscovEHR cohort includes exome sequencing on over 50,000 consenting patients, 297 of whom have evidence of an EMCA diagnosis in their electronic health record. Here, rare variants were annotated as potentially pathogenic. RESULTS: Eight genes were identified as having increased burden in the EMCA cohort relative to the non-cancer control cohort. None of the eight genes had an increased burden in the other hormone related cancer cohort from GHS, suggesting they can help characterize the underlying genetic variation that gives rise to EMCA. Comparing GHS to the cancer genome atlas (TCGA) EMCA germline data illustrated 34 genes with potentially pathogenic variation and eight unique potentially pathogenic variants that were present in both studies. Thus, similar germline variation among genes can be observed in unique EMCA cohorts and could help prioritize genes to investigate for future work. CONCLUSION: In summary, this systematic characterization of potentially pathogenic germline variants describes the genetic underpinnings of EMCA through the use of data from a single hospital system.

Correction to: Systematic characterization of germline variants from the DiscovEHR study endometrial carcinoma population.

Following publication of the original article [1], the authors reported that Fig. 1 was not correctly processed during the production process. The correct Fig. 1 is given below.

Genetic susceptibility to cerebrovascular disease: A systematic review.

Investigation of genetic susceptibility to cerebrovascular disease has been of growing interest. A systematic review of human studies assessing neurogenomic aspects of cerebrovascular disease was performed according to the preferred reporting items for systematic reviews and meta-analyses (PRISMA) statement. Any association study exploring genetic variants located in the exome associated with one of the major cerebrovascular diseases with at least 500 subjects was eligible for inclusion. Of 6874 manuscripts identified, 35 studies met the inclusion criteria. Most studies of interest focused on ischemic stroke and cerebrovascular occlusive disease. Large cohort genetic association studies on hemorrhagic cerebrovascular disease were less common. In addition to rare, well-established monogenic conditions with significant risk for cerebrovascular disease, a number of genetic variants are also relevant to cerebrovascular pathogenesis as part of a multifactorial process. The 45 polymorphisms identified were located in genes involved in processes related to endothelial and vascular health (15 (33.4%) variants), plasma lipid metabolism (10 (22.2%) variants), inflammation (9 (20%) variants), coagulation (3 (6.7%) variants), and blood pressure modulation (2 (4.4%) variants), and other (6 (13.3%) variants). This work represents a comprehensive overview of genetic variants in the exome relevant to ischemic and hemorrhagic stroke pathophysiology.

Large-scale exome datasets reveal a new class of adaptor-related protein complex 2 sigma subunit (AP2σ) mutations, located at the interface with the AP2 alpha subunit, that impair calcium-sensing receptor signalling.

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.

Exome Sequencing-Based Screening for BRCA1/2 Expected Pathogenic Variants Among Adult Biobank Participants.

Importance: Detection of disease-associated variants in the BRCA1 and BRCA2 (BRCA1/2) genes allows for cancer prevention and early diagnosis in high-risk individuals. Objectives: To identify pathogenic and likely pathogenic (P/LP) BRCA1/2 variants in an unselected research cohort, and to characterize the features associated with P/LP variants. Design, Setting, and Participants: This is a cross-sectional study of adult volunteers (n = 50 726) who underwent exome sequencing at a single health care system (Geisinger Health System, Danville, Pennsylvania) from January 1, 2014, to March 1, 2016. Participants are part of the DiscovEHR cohort and were identified through the Geisinger MyCode Community Health Initiative. They consented to a research protocol that included sequencing and return of actionable test results. Clinical data from electronic health records and clinical visits were correlated with variants. Comparisons were made between those with (cases) and those without (controls) P/LP variants in BRCA1/2. Main Outcomes: Prevalence of P/LP BRCA1/2 variants in cohort, proportion of variant carriers not previously ascertained through clinical testing, and personal and family history of relevant cancers among BRCA1/2 variant carriers and noncarriers. Results: Of the 50 726 health system patients who underwent exome sequencing, 50 459 (99.5%) had no expected pathogenic BRCA1/2 variants and 267 (0.5%) were BRCA1/2 carriers. Of the 267 cases (148 [55.4%] were women and 119 [44.6%] were men with a mean [range] age of 58.9 [23-90] years), 183 (68.5%) received clinically confirmed results in their electronic health record. Among the 267 participants with P/LP BRCA1/2 variants, 219 (82.0%) had no prior clinical testing, 95 (35.6%) had BRCA1 variants, and 172 (64.4%) had BRCA2 variants. Syndromic cancer diagnoses were present in 11 (47.8%) of the 23 deceased BRCA1/2 carriers and in 56 (20.9%) of all 267 BRCA1/2 carriers. Among women, 31 (20.9%) of 148 variant carriers had a personal history of breast cancer, compared with 1554 (5.2%) of 29 880 noncarriers (odds ratio [OR], 5.95; 95% CI, 3.88-9.13; P < .001). Ovarian cancer history was present in 15 (10.1%) of 148 variant carriers and in 195 (0.6%) of 29 880 variant noncarriers (OR, 18.30; 95% CI, 10.48-31.4; P < .001). Among 89 BRCA1/2 carriers without prior testing but with comprehensive personal and family history data, 44 (49.4%) did not meet published guidelines for clinical testing. Conclusions and Relevance: This study found that compared with previous clinical care, exome sequencing-based screening identified 5 times as many individuals with P/LP BRCA1/2 variants. These findings suggest that genomic screening may identify BRCA1/2-associated cancer risk that might otherwise remain undetected within health care systems and may provide opportunities to reduce morbidity and mortality in patients.

IDENTIFYING GENETIC ASSOCIATIONS WITH VARIABILITY IN METABOLIC HEALTH AND BLOOD COUNT LABORATORY VALUES: DIVING INTO THE QUANTITATIVE TRAITS BY LEVERAGING LONGITUDINAL DATA FROM AN EHR.

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<5×10-08. 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: 8.71×10-09 and replication p-value: 2.03×10-06). 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 laboratory data to find associations among genetic variants and clinical phenotypes obtained from an EHR, integrating laboratory values and diagnosis codes to understand the genetic complexities of common diseases.

Distribution and clinical impact of functional variants in 50,726 whole-exome sequences from the DiscovEHR study.

The DiscovEHR collaboration between the Regeneron Genetics Center and Geisinger Health System couples high-throughput sequencing to an integrated health care system using longitudinal electronic health records (EHRs). We sequenced the exomes of 50,726 adult participants in the DiscovEHR study to identify ~4.2 million rare single-nucleotide variants and insertion/deletion events, of which ~176,000 are predicted to result in a loss of gene function. Linking these data to EHR-derived clinical phenotypes, we find clinical associations supporting therapeutic targets, including genes encoding drug targets for lipid lowering, and identify previously unidentified rare alleles associated with lipid levels and other blood level traits. About 3.5% of individuals harbor deleterious variants in 76 clinically actionable genes. The DiscovEHR data set provides a blueprint for large-scale precision medicine initiatives and genomics-guided therapeutic discovery.