Genome-Wide Approach to Measure Variant-Based Heritability of Drug Outcome Phenotypes

Pharmacogenomic studies have successfully identified variants-typically with large effect sizes in drug target and metabolism enzymes-that predict drug outcome phenotypes. However, these variants may account for a limited proportion of phenotype variability attributable to the genome. Using genome-wide common variation, we measured the narrow-sense heritability ( h SNP 2 ) of seven pharmacodynamic and five pharmacokinetic phenotypes across three cardiovascular drugs, two antibiotics, and three immunosuppressants. We used a Bayesian hierarchical mixed model, BayesR, to model the distribution of genome-wide variant effect sizes for each drug phenotype as a mixture of four normal distributions of fixed variance (0, 0.01%, 0.1%, and 1% of the total additive genetic variance). This model allowed us to parse h SNP 2 into bins representing contributions of no-effect, small-effect, moderate-effect, and large-effect variants, respectively. For the 12 phenotypes, a median of 969 (range 235-6,304) unique individuals of European ancestry and a median of 1,201,626 (range 777,427-1,514,275) variants were included in our analyses. The number of variants contributing to h SNP 2 ranged from 2,791 to 5,356 (median 3,347). Estimates for h SNP 2 ranged from 0.05 (angiotensin-converting enzyme inhibitor-induced cough) to 0.59 (gentamicin concentration). Small-effect and moderate-effect variants contributed a majority to h SNP 2 for every phenotype (range 61-95%). We conclude that drug outcome phenotypes are highly polygenic. Thus, larger genome-wide association studies of drug phenotypes are needed both to discover novel variants and to determine how genome-wide approaches may improve clinical prediction of drug outcomes.

Management of Congenital Long-QT Syndrome: Commentary From the Experts

While published guidelines are useful in the care of patients with long-QT syndrome, it can be difficult to decide how to apply the guidelines to individual patients, particularly those with intermediate risk. We explored the diversity of opinion among 24 clinicians with expertise in long-QT syndrome. Experts from various regions and institutions were presented with 4 challenging clinical scenarios and asked to provide commentary emphasizing why they would make their treatment recommendations. All 24 authors were asked to vote on case-specific questions so as to demonstrate the degree of consensus or divergence of opinion. Of 24 authors, 23 voted and 1 abstained. Details of voting results with commentary are presented. There was consensus on several key points, particularly on the importance of the diagnostic evaluation and of β-blocker use. There was diversity of opinion about the appropriate use of other therapeutic measures in intermediate-risk individuals. Significant gaps in knowledge were identified.

Pleiotropy of systemic lupus erythematosus risk alleles and cardiometabolic disorders: A phenome-wide association study and inverse-variance weighted meta-analysis

OBJECTIVES

To test the hypothesis that genetic predisposition to systemic lupus erythematosus (SLE) increases the risk of cardiometabolic disorders.

METHODS

Using 41 single nucleotide polymorphisms (SNPs) associated with SLE, we calculated a weighted genetic risk score (wGRS) for SLE. In a large biobank we tested the association between this wGRS and 9 cardiometabolic phenotypes previously associated with SLE: atrial fibrillation, ischemic stroke, coronary artery disease, type 1 and type 2 diabetes, obesity, chronic kidney disease, hypertension, and hypercholesterolemia. Additionally, we performed a phenome-wide association analysis (pheWAS) to discover novel clinical associations with a genetic predisposition to SLE. Findings were replicated in the Electronic Medical Records and Genomics (eMERGE) Network. To further define the association between SLE-related risk alleles and the selected cardiometabolic phenotypes, we performed an inverse variance weighted regression (IVWR) meta-analysis.

RESULTS

The wGRS for SLE was calculated in 74,759 individuals of European ancestry. Among the pre-selected phenotypes, the wGRS was significantly associated with type 1 diabetes (OR [95%CI] =1.11 [1.06, 1.17], P-value = 1.05x10-5). In the PheWAS, the wGRS was associated with several autoimmune phenotypes, kidney disorders, and skin neoplasm; but only the associations with autoimmune phenotypes were replicated. In the IVWR meta-analysis, SLE-related risk alleles were nominally associated with type 1 diabetes (P = 0.048) but the associations were heterogeneous and did not meet the adjusted significance threshold.

CONCLUSION

A weighted GRS for SLE was associated with an increased risk of several autoimmune-related phenotypes including type I diabetes but not with cardiometabolic disorders.

Quinidine in the Management of Recurrent Ventricular Arrhythmias: A Reappraisal

OBJECTIVE

This study aimed to review the utility of quinidine in patients presenting with recurrent sustained ventricular arrhythmia (VA) and limited antiarrhythmic drug (AAD) options.

BACKGROUND

Therapeutic options are often limited in patients with structural heart disease and recurrent VAs. Quinidine has an established role in rare arrhythmic syndromes, but its potential use in other difficult VAs has not been assessed in the present era.

METHODS

We performed a retrospective analysis of 37 patients who had in-hospital quinidine initiation after multiple other therapies failed for VA suppression at our tertiary referral center. Clinical data and outcomes were obtained from the medical record.

RESULTS

Of 30 patients with in-hospital quantifiable VA episodes, quinidine reduced acute VA from a median of 3 episodes (interquartile range [IQR]: 2 to 7.5) to 0 (IQR: 0 to 0.5) during medians of 3 days before and 4 days after quinidine initiation (p < 0.001). VA events decreased from a median of 10.5 episodes per day (IQR: 5 to 15) to 0.5 episodes (IQR: 0 to 4) after quinidine initiation in the 12 patients presenting with electrical storm (p = 0.004). Among the 24 patients discharged on quinidine, 13 (54.2%) had VA recurrence during a median of 138 days. Adverse effects in 9 of the 37 patients (24.3%) led to drug discontinuation, most commonly gastrointestinal intolerance.

CONCLUSIONS

In patients with recurrent VAs and structural heart disease who have limited treatment options, quinidine can be useful, particularly as a short-term therapy.

Genetic Thyrotropin Regulation of Atrial Fibrillation Risk Is Mediated Through an Effect on Height

CONTEXT

A genetic predisposition to lower thyrotropin (TSH) levels is associated with increased atrial fibrillation (AF) risk through undefined mechanisms.

OBJECTIVE

Defining the genetic mediating mechanisms could lead to improved targeted therapies to mitigate AF risk.

METHODS

We used 2-sample mendelian randomization (MR) to test associations between TSH-associated single-nucleotide variations and 16 candidate mediators. We then performed multivariable mendelian randomization (MVMR) to test for a significant attenuation of the genetic association between TSH and AF, after adjusting for each mediator significantly associated with TSH.

RESULTS

Four candidate mediators (free thyroxine, systolic blood pressure, heart rate, and height) were significantly inversely associated with genetically predicted TSH after adjusting for multiple testing. In MVMR analyses, adjusting for height significantly decreased the magnitude of the association between TSH and AF from -0.12 (SE 0.02) occurrences of AF per SD change in height to -0.06 (0.02) (P = .005). Adjusting for the other candidate mediators did not significantly attenuate the association.

CONCLUSION

The genetic association between TSH and increased AF risk is mediated, in part, by taller stature. Thus, some genetic mechanisms underlying TSH variability may contribute to AF risk through mechanisms determining height occurring early in life that differ from those driven by thyroid hormone-level elevations in later life.

High-throughput framework for genetic analyses of adverse drug reactions using electronic health records

Understanding the contribution of genetic variation to drug response can improve the delivery of precision medicine. However, genome-wide association studies (GWAS) for drug response are uncommon and are often hindered by small sample sizes. We present a high-throughput framework to efficiently identify eligible patients for genetic studies of adverse drug reactions (ADRs) using “drug allergy” labels from electronic health records (EHRs). As a proof-of-concept, we conducted GWAS for ADRs to 14 common drug/drug groups with 81,739 individuals from Vanderbilt University Medical Center’s BioVU DNA Biobank. We identified 7 genetic loci associated with ADRs at P < 5 × 10⁻⁸, including known genetic associations such as CYP2D6 and OPRM1 for CYP2D6-metabolized opioid ADR. Additional expression quantitative trait loci and phenome-wide association analyses added evidence to the observed associations. Our high-throughput framework is both scalable and portable, enabling impactful pharmacogenomic research to improve precision medicine.

Adverse drug reactions are a considerable burden on the healthcare system. Genetic studies can improve our understanding of the pathophysiological mechanisms of adverse drug reactions but have been hindered by small sample sizes. Drug responses are less often recorded than physiological traits and common diseases. Here, we present a high-throughput framework to efficiently identify eligible patients for genetic studies of adverse drug reactions from electronic health records. We validated our approach by conducting genome-wide association studies for adverse reactions to 14 common drug/drug groups with 81,739 individuals from Vanderbilt University Medical Centre’s BioVU DNA Biobank, identifying 7 genetic loci associated with adverse drug reactions. Our high-throughput framework can enable impactful pharmacogenomic research to help develop clinical guidelines for the delivery of the right drug to the right person.

Robust, flexible, and scalable tests for Hardy-Weinberg equilibrium across diverse ancestries

Traditional Hardy-Weinberg equilibrium (HWE) tests (the χ2 test and the exact test) have long been used as a metric for evaluating genotype quality, as technical artifacts leading to incorrect genotype calls often can be identified as deviations from HWE. However, in data sets composed of individuals from diverse ancestries, HWE can be violated even without genotyping error, complicating the use of HWE testing to assess genotype data quality. In this manuscript, we present the Robust Unified Test for HWE (RUTH) to test for HWE while accounting for population structure and genotype uncertainty, and to evaluate the impact of population heterogeneity and genotype uncertainty on the standard HWE tests and alternative methods using simulated and real sequence data sets. Our results demonstrate that ignoring population structure or genotype uncertainty in HWE tests can inflate false-positive rates by many orders of magnitude. Our evaluations demonstrate different tradeoffs between false positives and statistical power across the methods, with RUTH consistently among the best across all evaluations. RUTH is implemented as a practical and scalable software tool to rapidly perform HWE tests across millions of markers and hundreds of thousands of individuals while supporting standard VCF/BCF formats. RUTH is publicly available at https://www.github.com/statgen/ruth.

PheMap: a multi-resource knowledge base for high-throughput phenotyping within electronic health records

Objective

Developing algorithms to extract phenotypes from electronic health records (EHRs) can be challenging and time-consuming. We developed PheMap, a high-throughput phenotyping approach that leverages multiple independent, online resources to streamline the phenotyping process within EHRs.

Materials and Methods

PheMap is a knowledge base of medical concepts with quantified relationships to phenotypes that have been extracted by natural language processing from publicly available resources. PheMap searches EHRs for each phenotype’s quantified concepts and uses them to calculate an individual’s probability of having this phenotype. We compared PheMap to clinician-validated phenotyping algorithms from the Electronic Medical Records and Genomics (eMERGE) network for type 2 diabetes mellitus (T2DM), dementia, and hypothyroidism using 84 821 individuals from Vanderbilt Univeresity Medical Center's BioVU DNA Biobank. We implemented PheMap-based phenotypes for genome-wide association studies (GWAS) for T2DM, dementia, and hypothyroidism, and phenome-wide association studies (PheWAS) for variants in FTO, HLA-DRB1, and TCF7L2.

Results

In this initial iteration, the PheMap knowledge base contains quantified concepts for 841 disease phenotypes. For T2DM, dementia, and hypothyroidism, the accuracy of the PheMap phenotypes were >97% using a 50% threshold and eMERGE case-control status as a reference standard. In the GWAS analyses, PheMap-derived phenotype probabilities replicated 43 of 51 previously reported disease-associated variants for the 3 phenotypes. For 9 of the 11 top associations, PheMap provided an equivalent or more significant P value than eMERGE-based phenotypes. The PheMap-based PheWAS showed comparable or better performance to a traditional phecode-based PheWAS. PheMap is publicly available online.

Conclusions

PheMap significantly streamlines the process of extracting research-quality phenotype information from EHRs, with comparable or better performance to current phenotyping approaches.

Evidence for Pharmacogenomic Effects on Risperidone Outcomes in Pediatrics

OBJECTIVE

To determine the association between genetic variants reported to affect risperidone and adverse events (AEs) in children and adolescents.

METHODS

Individuals aged 18 years or younger with ≥4 weeks of risperidone exposure in a deidentified DNA biobank were included. The primary outcome was AE frequency as a function of genotype. Individuals were classified according to metabolizer status for CYP2D6, CYP3A4, and CYP3A5; wild type, heterozygote, or homozygote for specific single nucleotide variants for DRD2, DRD3, HTR2A, and HTR2C; and wild type versus nonwild type for multiple uncommon variants in ABCG2, ABCB1, and HTR2C. Tests of association of each classification to AEs were performed using a Fisher exact test and logistic regression, and statistically significant classifications were included in a final logistic regression.

RESULTS

The final cohort included 257 individuals. AEs were more common in CYP2D6 poor/intermediate metabolizers (PMs/IMs) than normal/rapid/ultrarapid metabolizers (NMs/RMs/UMs) in univariate and multivariate analysis. HTR2A-rs6311 heterozygotes and homozygotes had fewer AEs than wild types in logistic regression but not in univariate analysis. In the final multivariable model adjusting for age, race, sex, and risperidone dose, AEs were associated with CYP2D6 (adjusted odds ratio [AOR] 2.6, 95% CI 1.1-5.5, for PMs/IMs vs. NMs/RMs/UMs) and HTR2A-rs6311 (AOR 0.6, 95% CI 0.4-0.9, for each variant allele), both consistent with previous studies.

CONCLUSION

Children and adolescents who are CYP2D6 PMs/IMs may have an increased risk for risperidone AEs. Of the genes and variants studied, only CYP2D6 has consistent association and sufficient data for clinical use, whereas HTR2A-rs6311 has limited data and requires further study.

Loperamide Induced Recurrent Torsades de Pointes: A Case Report

PURPOSE

A case of loperamide-induced recurrent torsades de pointes is reported to raise awareness of an increasingly common phenomenon that could be encountered by medical providers during the current opioid epidemic.

SUMMARY

A 40 year-old-man with a prior history of opioid abuse who presented to the emergency department after taking up to 100 tablets of loperamide 2 mg daily for 5 years to blunt opioid withdrawal symptoms and was subsequently admitted to the intensive care unit for altered mental status and hyperthermia. The patient had prolonged QTc and 2 episodes of torsades de pointes (TdP) that resulted in cardiac arrest with return of spontaneous circulation. He was managed with isoproterenol, overdrive pacing, and methylnatrexone with no other events of TdP or cardiac arrest.

CONCLUSION

A 40-year-old male who developed torsades de pointes from loperamide overdose effectively treated with overdrive pacing, isoproterenol, and methylnatrexone.

DDIWAS: High-throughput electronic health record-based screening of drug-drug interactions

OBJECTIVE

We developed and evaluated Drug-Drug Interaction Wide Association Study (DDIWAS). This novel method detects potential drug-drug interactions (DDIs) by leveraging data from the electronic health record (EHR) allergy list.

MATERIALS AND METHODS

To identify potential DDIs, DDIWAS scans for drug pairs that are frequently documented together on the allergy list. Using deidentified medical records, we tested 616 drugs for potential DDIs with simvastatin (a common lipid-lowering drug) and amlodipine (a common blood-pressure lowering drug). We evaluated the performance to rediscover known DDIs using existing knowledge bases and domain expert review. To validate potential novel DDIs, we manually reviewed patient charts and searched the literature.

RESULTS

DDIWAS replicated 34 known DDIs. The positive predictive value to detect known DDIs was 0.85 and 0.86 for simvastatin and amlodipine, respectively. DDIWAS also discovered potential novel interactions between simvastatin-hydrochlorothiazide, amlodipine-omeprazole, and amlodipine-valacyclovir. A software package to conduct DDIWAS is publicly available.

CONCLUSIONS

In this proof-of-concept study, we demonstrate the value of incorporating information mined from existing allergy lists to detect DDIs in a real-world clinical setting. Since allergy lists are routinely collected in EHRs, DDIWAS has the potential to detect and validate DDI signals across institutions.

Predictive Accuracy of a Polygenic Risk Score for Postoperative Atrial Fibrillation After Cardiac Surgery

BACKGROUND

Postoperative atrial fibrillation (PoAF) remains a significant risk factor for increased morbidity and mortality after cardiac surgery. The ability to accurately identify patients at risk through clinical risk factors is limited. There is growing evidence that polygenic risk contributes significantly to PoAF and incorporating measures of genetic risk could enhance prediction.

METHODS

A retrospective cohort study of 1047 patients of White European ancestry who underwent either coronary artery bypass grafting or valve surgery at a tertiary academic center and were free from a history or persistent preoperative atrial fibrillation. The primary outcome was defined as PoAF based on postoperative ECG reports, medical record documentation, and changes in medication. The exposure was a polygenic risk score (PRS) comprising 2746 single-nucleotide polymorphisms previously associated with atrial fibrillation risk. The prediction of PoAF risk was assessed using measures of model discrimination, calibration, and net reclassification improvement.

RESULTS

A total of 259 patients (24.7%) developed PoAF. The PRS was significantly associated with a higher risk for PoAF (odds ratio, 1.63 per SD increase in PRS [95% CI, 1.41-1.90]). Addition of PRS to patient- and procedure-related predictors of PoAF significantly increased the C statistic from 0.742 to 0.782 (change in C statistic, 0.040 [95% CI, 0.021-0.060]) while maintaining good calibration. The addition of the PRS to patient- and procedure-related predictors of PoAF improved model fit (likelihood ratio test, P=2.8×10^-15) and significantly improved measures of reclassification (net reclassification improvement, 0.158 [95% CI, 0.066-0.274]).

CONCLUSIONS

The PRS for PoAF was associated with improved discrimination, calibration, and risk reclassification compared with conventional clinical predictors suggesting that a PoAF PRS may enhance risk prediction of PoAF in patients undergoing coronary artery bypass grafting or valve surgery.

Improving the phenotype risk score as a scalable approach to identifying patients with Mendelian disease

OBJECTIVE

The Phenotype Risk Score (PheRS) is a method to detect Mendelian disease patterns using phenotypes from the electronic health record (EHR). We compared the performance of different approaches mapping EHR phenotypes to Mendelian disease features.

MATERIALS AND METHODS

PheRS utilizes Mendelian diseases descriptions annotated with Human Phenotype Ontology (HPO) terms. In previous work, we presented a map linking phecodes (based on International Classification of Diseases [ICD]-Ninth Revision) to HPO terms. For this study, we integrated ICD-Tenth Revision codes and lab data. We also created a new map between HPO terms using customized groupings of ICD codes. We compared the performance with cases and controls for 16 Mendelian diseases using 2.5 million de-identified medical records.

RESULTS

PheRS effectively distinguished cases from controls for all 15 positive controls and all approaches tested (P < 4 × 1016). Adding lab data led to a statistically significant improvement for 4 of 14 diseases. The custom ICD groupings improved specificity, leading to an average 8% increase for precision at 100 (-2% to 22%). Eight of 10 adults with cystic fibrosis tested had PheRS in the 95th percentile prio to diagnosis.

DISCUSSION

Both phecodes and custom ICD groupings were able to detect differences between affected cases and controls at the population level. The ICD map showed better precision for the highest scoring individuals. Adding lab data improved performance at detecting population-level differences.

CONCLUSIONS

PheRS is a scalable method to study Mendelian disease at the population level using electronic health record data and can potentially be used to find patients with undiagnosed Mendelian disease.

A retrospective approach to evaluating potential adverse outcomes associated with delay of procedures for cardiovascular and cancer-related diagnoses in the context of COVID-19

OBJECTIVE

During the COVID-19 pandemic, health systems postponed non-essential medical procedures to accommodate surge of critically-ill patients. The long-term consequences of delaying procedures in response to COVID-19 remains unknown. We developed a high-throughput approach to understand the impact of delaying procedures on patient health outcomes using electronic health record (EHR) data.

MATERIALS AND METHODS

We used EHR data from Vanderbilt University Medical Center's (VUMC) Research and Synthetic Derivatives. Elective procedures and non-urgent visits were suspended at VUMC between March 18, 2020 and April 24, 2020. Surgical procedure data from this period were compared to a similar timeframe in 2019. Potential adverse impact of delay in cardiovascular and cancer-related procedures was evaluated using EHR data collected from January 1, 1993 to March 17, 2020. For surgical procedure delay, outcomes included length of hospitalization (days), mortality during hospitalization, and readmission within six months. For screening procedure delay, outcomes included 5-year survival and cancer stage at diagnosis.

RESULTS

We identified 416 surgical procedures that were negatively impacted during the COVID-19 pandemic compared to the same timeframe in 2019. Using retrospective data, we found 27 significant associations between procedure delay and adverse patient outcomes. Clinician review indicated that 88.9% of the significant associations were plausible and potentially clinically significant. Analytic pipelines for this study are available online.

CONCLUSION

Our approach enables health systems to identify medical procedures affected by the COVID-19 pandemic and evaluate the effect of delay, enabling them to communicate effectively with patients and prioritize rescheduling to minimize adverse patient outcomes.

Pharmacogenomic polygenic response score predicts ischaemic events and cardiovascular mortality in clopidogrel-treated patients

AIMS

Clopidogrel is prescribed for the prevention of atherothrombotic events. While investigations have identified genetic determinants of inter-individual variability in on-treatment platelet inhibition (e.g. CYP2C19*2), evidence that these variants have clinical utility to predict major adverse cardiovascular events (CVEs) remains controversial.

METHODS AND RESULTS

We assessed the impact of 31 candidate gene polymorphisms on adenosine diphosphate (ADP)-stimulated platelet reactivity in 3391 clopidogrel-treated coronary artery disease patients of the International Clopidogrel Pharmacogenomics Consortium (ICPC). The influence of these polymorphisms on CVEs was tested in 2134 ICPC patients (N = 129 events) in whom clinical event data were available. Several variants were associated with on-treatment ADP-stimulated platelet reactivity (CYP2C19*2, P = 8.8 × 10-54; CES1 G143E, P = 1.3 × 10-16; CYP2C19*17, P = 9.5 × 10-10; CYP2B6 1294 + 53 C > T, P = 3.0 × 10-4; CYP2B6 516 G > T, P = 1.0 × 10-3; CYP2C9*2, P = 1.2 × 10-3; and CYP2C9*3, P = 1.5 × 10-3). While no individual variant was associated with CVEs, generation of a pharmacogenomic polygenic response score (PgxRS) revealed that patients who carried a greater number of alleles that associated with increased on-treatment platelet reactivity were more likely to experience CVEs (β = 0.17, SE 0.06, P = 0.01) and cardiovascular-related death (β = 0.43, SE 0.16, P = 0.007). Patients who carried eight or more risk alleles were significantly more likely to experience CVEs [odds ratio (OR) = 1.78, 95% confidence interval (CI) 1.14-2.76, P = 0.01] and cardiovascular death (OR = 4.39, 95% CI 1.35-14.27, P = 0.01) compared to patients who carried six or fewer of these alleles.

CONCLUSION

Several polymorphisms impact clopidogrel response and PgxRS is a predictor of cardiovascular outcomes. Additional investigations that identify novel determinants of clopidogrel response and validating polygenic models may facilitate future precision medicine strategies.

Pharmacogenetics to Predict Adverse Events Associated With Antidepressants

OBJECTIVES

To determine the association between cytochrome P450 2C19 (CYP2C19) metabolizer status and risk for escitalopram and citalopram, collectively termed (es)citalopram, and sertraline adverse events (AEs) in children.

METHODS

In this retrospective cohort study, we used deidentified electronic health records linked to DNA. The cohort included children ≤18 years with ≥2 days of (es)citalopram or ≥7 days of sertraline exposure. The primary outcome was AEs assessed by manual chart review. CYP2C19 was genotyped for functional variants (*2, *3, *4, *6, *8, and *17), and individuals were assigned metabolizer status. Association between AEs and metabolizer status was determined by using Cox regression adjusting for age, race, ethnicity, dose, and concomitant CYP2C19-inhibiting medications.

RESULTS

The cohort included 249 sertraline-exposed and 458 (es)citalopram-exposed children, with a median age of 14.2 years (interquartile range 11.2-16.2) and 13.4 years (interquartile range 10.1-15.9), respectively. Sertraline AEs were more common in normal metabolizers (NMs) compared to poor metabolizers (PMs) or intermediate metabolizers (IMs) (hazard ratio [HR] 1.8; 95% confidence interval [CI] 1.01-3.2; P = .047) in unadjusted analysis and after adjustment (HR 1.9; CI 1.04-3.4; P = .04). For (es)citalopram, more AEs were observed in NMs than PMs and IMs without statistically significant differences (unadjusted HR 1.6; CI 0.95-2.6; P = .08; adjusted HR 1.6; CI 0.95-2.6; P = .08).

CONCLUSIONS

In contrast to adults, in our pediatric cohort, CYP2C19 NMs experienced increased sertraline AEs than PMs and IMs. (Es)citalopram AEs were not associated with CYP2C19 status in the primary analysis. The mechanism underlying this pediatric-specific finding is unknown but may be related to physiologic differences of adolescence. Further research is required to inform genotype-guided prescribing for these drugs in children.

SCN5A Mutation Type and a Genetic Risk Score Associate Variably With Brugada Syndrome Phenotype in SCN5A Families

Supplemental Digital Content is available in the text.

Brugada syndrome (BrS) is characterized by the type 1 Brugada ECG pattern. Pathogenic rare variants in SCN5A (mutations) are identified in 20% of BrS families in whom incomplete penetrance and genotype-negative phenotype-positive individuals are observed. E1784K-SCN5A is the most common SCN5A mutation identified. We determined the association of a BrS genetic risk score (BrS-GRS) and SCN5A mutation type on BrS phenotype in BrS families with SCN5A mutations.

Inherited causes of clonal haematopoiesis in 97,691 whole genomes

Age is the dominant risk factor for most chronic human diseases, but the mechanisms through which ageing confers this risk are largely unknown1. The age-related acquisition of somatic mutations that lead to clonal expansion in regenerating haematopoietic stem cell populations has recently been associated with both haematological cancer2-4 and coronary heart disease5-this phenomenon is termed clonal haematopoiesis of indeterminate potential (CHIP)6. Simultaneous analyses of germline and somatic whole-genome sequences provide the opportunity to identify root causes of CHIP. Here we analyse high-coverage whole-genome sequences from 97,691 participants of diverse ancestries in the National Heart, Lung, and Blood Institute Trans-omics for Precision Medicine (TOPMed) programme, and identify 4,229 individuals with CHIP. We identify associations with blood cell, lipid and inflammatory traits that are specific to different CHIP driver genes. Association of a genome-wide set of germline genetic variants enabled the identification of three genetic loci associated with CHIP status, including one locus at TET2 that was specific to individuals of African ancestry. In silico-informed in vitro evaluation of the TET2 germline locus enabled the identification of a causal variant that disrupts a TET2 distal enhancer, resulting in increased self-renewal of haematopoietic stem cells. Overall, we observe that germline genetic variation shapes haematopoietic stem cell function, leading to CHIP through mechanisms that are specific to clonal haematopoiesis as well as shared mechanisms that lead to somatic mutations across tissues.

Genome-wide Study Identifies Association between HLA-B∗55:01 and Self-Reported Penicillin Allergy

Hypersensitivity reactions to drugs are often unpredictable and can be life threatening, underscoring a need for understanding their underlying mechanisms and risk factors. The extent to which germline genetic variation influences the risk of commonly reported drug allergies such as penicillin allergy remains largely unknown. We extracted data from the electronic health records of more than 600,000 participants from the UK, Estonian, and Vanderbilt University Medical Center's BioVU biobanks to study the role of genetic variation in the occurrence of self-reported penicillin hypersensitivity reactions. We used imputed SNP to HLA typing data from these cohorts to further fine map the human leukocyte antigen (HLA) association and replicated our results in 23andMe's research cohort involving a total of 1.12 million individuals. Genome-wide meta-analysis of penicillin allergy revealed two loci, including one located in the HLA region on chromosome 6. This signal was further fine-mapped to the HLA-B∗55:01 allele (OR 1.41 95% CI 1.33-1.49, p value 2.04 × 10-31) and confirmed by independent replication in 23andMe's research cohort (OR 1.30 95% CI 1.25-1.34, p value 1.00 × 10-47). The lead SNP was also associated with lower lymphocyte counts and in silico follow-up suggests a potential effect on T-lymphocytes at HLA-B∗55:01. We also observed a significant hit in PTPN22 and the GWAS results correlated with the genetics of rheumatoid arthritis and psoriasis. We present robust evidence for the role of an allele of the major histocompatibility complex (MHC) I gene HLA-B in the occurrence of penicillin allergy.

Pleiotropy in the Genetic Predisposition to Rheumatoid Arthritis: A Phenome-Wide Association Study and Inverse Variance-Weighted Meta-Analysis

OBJECTIVE

This study was undertaken to investigate the hypothesis that a genetic predisposition toward rheumatoid arthritis (RA) increases the risk of 10 cardiometabolic and autoimmune disorders previously associated with RA in epidemiologic studies, and to define new genetic pleiotropy present in RA.

METHODS

Two approaches were used to test our hypothesis. First, we constructed a weighted genetic risk score (wGRS) and then examined its association with 10 prespecified disorders. Additionally, a phenome-wide association study (PheWAS) was carried out to identify potential new associations. Second, inverse variance-weighted regression (IVWR) meta-analysis was used to characterize the association between genetic susceptibility to RA and the prespecified disorders, with the results expressed as odds ratios (ORs) and 95% confidence intervals (95% CIs).

RESULTS

The wGRS for RA was significantly associated with type 1 diabetes mellitus (DM) (OR 1.10 [95% CI 1.04-1.16]; P = 9.82 × 10^-4 ) and multiple sclerosis (OR 0.82 [95% CI 0.77-0.88]; P = 1.73 × 10^-8 ), but not with other cardiometabolic phenotypes. In the PheWAS, wGRS was also associated with an increased risk of several autoimmune phenotypes including RA, thyroiditis, and systemic sclerosis, and with a decreased risk of demyelinating disorders. In the IVWR meta-analyses, RA was significantly associated with an increased risk of type 1 DM (P = 1.15 × 10^-14 ), with evidence of horizontal pleiotropy (Mendelian Randomization-Egger intercept estimate P = 0.001) likely driven by rs2476601, a PTPN22 variant. The association between type 1 DM and RA remained significant (P = 9.53 × 10^-9 ) after excluding rs2476601, with no evidence of horizontal pleiotropy (intercept estimate P = 0.939). RA was also significantly associated with type 2 DM and C-reactive protein levels. These associations were driven by variation in the major histocompatibility complex region.

CONCLUSION

This study presents evidence of pleiotropy between the genetic predisposition to RA and associated phenotypes found in other autoimmune and cardiometabolic disorders, including type 1 DM.