Genetic insights into resting heart rate and its role in cardiovascular disease

Resting heart rate is associated with cardiovascular diseases and mortality in observational and Mendelian randomization studies. The aims of this study are to extend the number of resting heart rate associated genetic variants and to obtain further insights in resting heart rate biology and its clinical consequences. A genome-wide meta-analysis of 100 studies in up to 835,465 individuals reveals 493 independent genetic variants in 352 loci, including 68 genetic variants outside previously identified resting heart rate associated loci. We prioritize 670 genes and in silico annotations point to their enrichment in cardiomyocytes and provide insights in their ECG signature. Two-sample Mendelian randomization analyses indicate that higher genetically predicted resting heart rate increases risk of dilated cardiomyopathy, but decreases risk of developing atrial fibrillation, ischemic stroke, and cardio-embolic stroke. We do not find evidence for a linear or non-linear genetic association between resting heart rate and all-cause mortality in contrast to our previous Mendelian randomization study. Systematic alteration of key differences between the current and previous Mendelian randomization study indicates that the most likely cause of the discrepancy between these studies arises from false positive findings in previous one-sample MR analyses caused by weak-instrument bias at lower P-value thresholds. The results extend our understanding of resting heart rate biology and give additional insights in its role in cardiovascular disease development.

A machine learning approach to determine resilience utilizing wearable device data: analysis of an observational cohort

Objective

To assess whether an individual's degree of psychological resilience can be determined from physiological metrics passively collected from a wearable device.

Materials and Methods

Data were analyzed in this secondary analysis of the Warrior Watch Study dataset, a prospective cohort of healthcare workers enrolled across 7 hospitals in New York City. Subjects wore an Apple Watch for the duration of their participation. Surveys were collected measuring resilience, optimism, and emotional support at baseline.

Results

We evaluated data from 329 subjects (mean age 37.4 years, 37.1% male). Across all testing sets, gradient-boosting machines (GBM) and extreme gradient-boosting models performed best for high- versus low-resilience prediction, stratified on a median Connor-Davidson Resilience Scale-2 score of 6 (interquartile range = 5-7), with an AUC of 0.60. When predicting resilience as a continuous variable, multivariate linear models had a correlation of 0.24 (P = .029) and RMSE of 1.37 in the testing data. A positive psychological construct, comprised of resilience, optimism, and emotional support was also evaluated. The oblique random forest method performed best in estimating high- versus low-composite scores stratified on a median of 32.5, with an AUC of 0.65, a sensitivity of 0.60, and a specificity of 0.70.

Discussion

In a post hoc analysis, machine learning models applied to physiological metrics collected from wearable devices had some predictive ability in identifying resilience states and a positive psychological construct.

Conclusions

These findings support the further assessment of psychological characteristics from passively collected wearable data in dedicated studies.

Prescriber Adoption of SLCO1B1 Genotype-Guided Simvastatin Clinical Decision Support in a Clinical Pharmacogenetics Program

Pharmacogenetic implementation programs are increasingly feasible due to the availability of clinical guidelines for implementation research. The utilization of these resources has been reported with selected drug-gene pairs; however, little is known about how prescribers respond to pharmacogenetic recommendations for statin therapy. We prospectively assessed prescriber interaction with point-of-care clinical decision support (CDS) to guide simvastatin therapy for a diverse cohort of primary care patients enrolled in a clinical pharmacogenetics program. Of the 1,639 preemptively genotyped patients, 298 (18.2%) had an intermediate function (IF) OATP1B1 phenotype and 25 (1.53%) had a poor function (PF) phenotype, predicted by a common single nucleotide variant in the SLCO1B1 gene (c.521T>C; rs4149056). Clinicians were presented with CDS when simvastatin was prescribed for patients with IF or PF through the electronic health record. Importantly, 64.2% of the CDS deployed at the point-of-care was accepted by the prescribers and resulted in prescription changes. Statin intensity was found to significantly influence prescriber adoption of the pharmacogenetic-guided CDS, whereas patient gender or race, prescriber type, or pharmacogenetic training status did not significantly influence adoption. This study demonstrates that primary care providers readily adopt pharmacogenetic information to guide statin therapy for the majority of patients with preemptive genotype data.

Quantifying the phenome-wide disease burden of obesity using electronic health records and genomics

OBJECTIVE

High BMI is associated with many comorbidities and mortality. This study aimed to elucidate the overall clinical risk of obesity using a genome- and phenome-wide approach.

METHODS

This study performed a phenome-wide association study of BMI using a clinical cohort of 736,726 adults. This was followed by genetic association studies using two separate cohorts: one consisting of 65,174 adults in the Electronic Medical Records and Genomics (eMERGE) Network and another with 405,432 participants in the UK Biobank.

RESULTS

Class 3 obesity was associated with 433 phenotypes, representing 59.3% of all billing codes in individuals with severe obesity. A genome-wide polygenic risk score for BMI, accounting for 7.5% of variance in BMI, was associated with 296 clinical diseases, including strong associations with type 2 diabetes, sleep apnea, hypertension, and chronic liver disease. In all three cohorts, 199 phenotypes were associated with class 3 obesity and polygenic risk for obesity, including novel associations such as increased risk of renal failure, venous insufficiency, and gastroesophageal reflux.

CONCLUSIONS

This combined genomic and phenomic systematic approach demonstrated that obesity has a strong genetic predisposition and is associated with a considerable burden of disease across all disease classes.

Genetic loci and prioritization of genes for kidney function decline derived from a meta-analysis of 62 longitudinal genome-wide association studies

Estimated glomerular filtration rate (eGFR) reflects kidney function. Progressive eGFR-decline can lead to kidney failure, necessitating dialysis or transplantation. Hundreds of loci from genome-wide association studies (GWAS) for eGFR help explain population cross section variability. Since the contribution of these or other loci to eGFR-decline remains largely unknown, we derived GWAS for annual eGFR-decline and meta-analyzed 62 longitudinal studies with eGFR assessed twice over time in all 343,339 individuals and in high-risk groups. We also explored different covariate adjustment. Twelve genome-wide significant independent variants for eGFR-decline unadjusted or adjusted for eGFR-baseline (11 novel, one known for this phenotype), including nine variants robustly associated across models were identified. All loci for eGFR-decline were known for cross-sectional eGFR and thus distinguished a subgroup of eGFR loci. Seven of the nine variants showed variant-by-age interaction on eGFR cross section (further about 350,000 individuals), which linked genetic associations for eGFR-decline with age-dependency of genetic cross-section associations. Clinically important were two to four-fold greater genetic effects on eGFR-decline in high-risk subgroups. Five variants associated also with chronic kidney disease progression mapped to genes with functional in-silico evidence (UMOD, SPATA7, GALNTL5, TPPP). An unfavorable versus favorable nine-variant genetic profile showed increased risk odds ratios of 1.35 for kidney failure (95% confidence intervals 1.03-1.77) and 1.27 for acute kidney injury (95% confidence intervals 1.08-1.50) in over 2000 cases each, with matched controls). Thus, we provide a large data resource, genetic loci, and prioritized genes for kidney function decline, which help inform drug development pipelines revealing important insights into the age-dependency of kidney function genetics.

Insights From a Large-Scale Whole-Genome Sequencing Study of Systolic Blood Pressure, Diastolic Blood Pressure, and Hypertension

BACKGROUND

The availability of whole-genome sequencing data in large studies has enabled the assessment of coding and noncoding variants across the allele frequency spectrum for their associations with blood pressure.

METHODS

We conducted a multiancestry whole-genome sequencing analysis of blood pressure among 51 456 Trans-Omics for Precision Medicine and Centers for Common Disease Genomics program participants (stage-1). Stage-2 analyses leveraged array data from UK Biobank (N=383 145), Million Veteran Program (N=318 891), and Reasons for Geographic and Racial Differences in Stroke (N=10 643) participants, along with whole-exome sequencing data from UK Biobank (N=199 631) participants.

RESULTS

Two blood pressure signals achieved genome-wide significance in meta-analyses of stage-1 and stage-2 single variant findings (P<5×10^-8). Among them, a rare intergenic variant at novel locus, LOC100506274, was associated with lower systolic blood pressure in stage-1 (beta [SE]=-32.6 [6.0]; P=4.99×10^-8) but not stage-2 analysis (P=0.11). Furthermore, a novel common variant at the known INSR locus was suggestively associated with diastolic blood pressure in stage-1 (beta [SE]=-0.36 [0.07]; P=4.18×10^-7) and attained genome-wide significance in stage-2 (beta [SE]=-0.29 [0.03]; P=7.28×10^-23). Nineteen additional signals suggestively associated with blood pressure in meta-analysis of single and aggregate rare variant findings (P<1×10^-6 and P<1×10^-4, respectively).

DISCUSSION

We report one promising but unconfirmed rare variant for blood pressure and, more importantly, contribute insights for future blood pressure sequencing studies. Our findings suggest promise of aggregate analyses to complement single variant analysis strategies and the need for larger, diverse samples, and family studies to enable robust rare variant identification.

Evaluation of a machine learning approach utilizing wearable data for prediction of SARS-CoV-2 infection in healthcare workers

Objective

To determine whether a machine learning model can detect SARS-CoV-2 infection from physiological metrics collected from wearable devices.

Materials and Methods

Health care workers from 7 hospitals were enrolled and prospectively followed in a multicenter observational study. Subjects downloaded a custom smart phone app and wore Apple Watches for the duration of the study period. Daily surveys related to symptoms and the diagnosis of Coronavirus Disease 2019 were answered in the app.

Results

We enrolled 407 participants with 49 (12%) having a positive nasal SARS-CoV-2 polymerase chain reaction test during follow-up. We examined 5 machine-learning approaches and found that gradient-boosting machines (GBM) had the most favorable validation performance. Across all testing sets, our GBM model predicted SARS-CoV-2 infection with an average area under the receiver operating characteristic (auROC) = 86.4% (confidence interval [CI] 84-89%). The model was calibrated to value sensitivity over specificity, achieving an average sensitivity of 82% (CI ±∼4%) and specificity of 77% (CI ±∼1%). The most important predictors included parameters describing the circadian heart rate variability mean (MESOR) and peak-timing (acrophase), and age.

Discussion

We show that a tree-based ML algorithm applied to physiological metrics passively collected from a wearable device can identify and predict SARS-CoV-2 infection.

Conclusion

Applying machine learning models to the passively collected physiological metrics from wearable devices may improve SARS-CoV-2 screening methods and infection tracking.

Differential and shared genetic effects on kidney function between diabetic and non-diabetic individuals

Reduced glomerular filtration rate (GFR) can progress to kidney failure. Risk factors include genetics and diabetes mellitus (DM), but little is known about their interaction. We conducted genome-wide association meta-analyses for estimated GFR based on serum creatinine (eGFR), separately for individuals with or without DM (nDM = 178,691, nnoDM = 1,296,113). Our genome-wide searches identified (i) seven eGFR loci with significant DM/noDM-difference, (ii) four additional novel loci with suggestive difference and (iii) 28 further novel loci (including CUBN) by allowing for potential difference. GWAS on eGFR among DM individuals identified 2 known and 27 potentially responsible loci for diabetic kidney disease. Gene prioritization highlighted 18 genes that may inform reno-protective drug development. We highlight the existence of DM-only and noDM-only effects, which can inform about the target group, if respective genes are advanced as drug targets. Largely shared effects suggest that most drug interventions to alter eGFR should be effective in DM and noDM.

Multi-ancestry genetic study of type 2 diabetes highlights the power of diverse populations for discovery and translation

We assembled an ancestrally diverse collection of genome-wide association studies (GWAS) of type 2 diabetes (T2D) in 180,834 affected individuals and 1,159,055 controls (48.9% non-European descent) through the Diabetes Meta-Analysis of Trans-Ethnic association studies (DIAMANTE) Consortium. Multi-ancestry GWAS meta-analysis identified 237 loci attaining stringent genome-wide significance (P < 5 × 10-9), which were delineated to 338 distinct association signals. Fine-mapping of these signals was enhanced by the increased sample size and expanded population diversity of the multi-ancestry meta-analysis, which localized 54.4% of T2D associations to a single variant with >50% posterior probability. This improved fine-mapping enabled systematic assessment of candidate causal genes and molecular mechanisms through which T2D associations are mediated, laying the foundations for functional investigations. Multi-ancestry genetic risk scores enhanced transferability of T2D prediction across diverse populations. Our study provides a step toward more effective clinical translation of T2D GWAS to improve global health for all, irrespective of genetic background.

Type 2 Diabetes Partitioned Polygenic Scores Associate With Disease Outcomes in 454,193 Individuals Across 13 Cohorts

OBJECTIVE

Type 2 diabetes (T2D) has heterogeneous patient clinical characteristics and outcomes. In previous work, we investigated the genetic basis of this heterogeneity by clustering 94 T2D genetic loci using their associations with 47 diabetes-related traits and identified five clusters, termed β-cell, proinsulin, obesity, lipodystrophy, and liver/lipid. The relationship between these clusters and individual-level metabolic disease outcomes has not been assessed.

RESEARCH DESIGN AND METHODS

Here we constructed individual-level partitioned polygenic scores (pPS) for these five clusters in 12 studies from the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium and the UK Biobank (n = 454,193) and tested for cross-sectional association with T2D-related outcomes, including blood pressure, renal function, insulin use, age at T2D diagnosis, and coronary artery disease (CAD).

RESULTS

Despite all clusters containing T2D risk-increasing alleles, they had differential associations with metabolic outcomes. Increased obesity and lipodystrophy cluster pPS, which had opposite directions of association with measures of adiposity, were both significantly associated with increased blood pressure and hypertension. The lipodystrophy and liver/lipid cluster pPS were each associated with CAD, with increasing and decreasing effects, respectively. An increased liver/lipid cluster pPS was also significantly associated with reduced renal function. The liver/lipid cluster includes known loci linked to liver lipid metabolism (e.g., GCKR, PNPLA3, and TM6SF2), and these findings suggest that cardiovascular disease risk and renal function may be impacted by these loci through their shared disease pathway.

CONCLUSIONS

Our findings support that genetically driven pathways leading to T2D also predispose differentially to clinical outcomes.

Effects of Testing and Disclosing Ancestry-Specific Genetic Risk for Kidney Failure on Patients and Health Care Professionals: A Randomized Clinical Trial

IMPORTANCE

Risk variants in the apolipoprotein L1 (APOL1 [OMIM 603743]) gene on chromosome 22 are common in individuals of West African ancestry and confer increased risk of kidney failure for people with African ancestry and hypertension. Whether disclosing APOL1 genetic testing results to patients of African ancestry and their clinicians affects blood pressure, kidney disease screening, or patient behaviors is unknown.

OBJECTIVE

To determine the effects of testing and disclosing APOL1 genetic results to patients of African ancestry with hypertension and their clinicians.

DESIGN, SETTING, AND PARTICIPANTS

This pragmatic randomized clinical trial randomly assigned 2050 adults of African ancestry with hypertension and without existing chronic kidney disease in 2 US health care systems from November 1, 2014, through November 28, 2016; the final date of follow-up was January 16, 2018. Patients were randomly assigned to undergo immediate (intervention) or delayed (waiting list control group) APOL1 testing in a 7:1 ratio. Statistical analysis was performed from May 1, 2018, to July 31, 2020.

INTERVENTIONS

Patients randomly assigned to the intervention group received APOL1 genetic testing results from trained staff; their clinicians received results through clinical decision support in electronic health records. Waiting list control patients received the results after their 12-month follow-up visit.

MAIN OUTCOMES AND MEASURES

Coprimary outcomes were the change in 3-month systolic blood pressure and 12-month urine kidney disease screening comparing intervention patients with high-risk APOL1 genotypes and those with low-risk APOL1 genotypes. Secondary outcomes compared these outcomes between intervention group patients with high-risk APOL1 genotypes and controls. Exploratory analyses included psychobehavioral factors.

RESULTS

Among 2050 randomly assigned patients (1360 women [66%]; mean [SD] age, 53 [10] years), the baseline mean (SD) systolic blood pressure was significantly higher in patients with high-risk APOL1 genotypes vs those with low-risk APOL1 genotypes and controls (137 [21] vs 134 [19] vs 133 [19] mm Hg; P = .003 for high-risk vs low-risk APOL1 genotypes; P = .001 for high-risk APOL1 genotypes vs controls). At 3 months, the mean (SD) change in systolic blood pressure was significantly greater in patients with high-risk APOL1 genotypes vs those with low-risk APOL1 genotypes (6 [18] vs 3 [18] mm Hg; P = .004) and controls (6 [18] vs 3 [19] mm Hg; P = .01). At 12 months, there was a 12% increase in urine kidney disease testing among patients with high-risk APOL1 genotypes (from 39 of 234 [17%] to 68 of 234 [29%]) vs a 6% increase among those with low-risk APOL1 genotypes (from 278 of 1561 [18%] to 377 of 1561 [24%]; P = .10) and a 7% increase among controls (from 33 of 255 [13%] to 50 of 255 [20%]; P = .01). In response to testing, patients with high-risk APOL1 genotypes reported more changes in lifestyle (a subjective measure that included better dietary and exercise habits; 129 of 218 [59%] vs 547 of 1468 [37%]; P < .001) and increased blood pressure medication use (21 of 218 [10%] vs 68 of 1468 [5%]; P = .005) vs those with low-risk APOL1 genotypes; 1631 of 1686 (97%) declared they would get tested again.

CONCLUSIONS AND RELEVANCE

In this randomized clinical trial, disclosing APOL1 genetic testing results to patients of African ancestry with hypertension and their clinicians was associated with a greater reduction in systolic blood pressure, increased kidney disease screening, and positive self-reported behavior changes in those with high-risk genotypes.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT02234063.

Phe2vec: Automated disease phenotyping based on unsupervised embeddings from electronic health records

Robust phenotyping of patients from electronic health records (EHRs) at scale is a challenge in clinical informatics. Here, we introduce Phe2vec, an automated framework for disease phenotyping from EHRs based on unsupervised learning and assess its effectiveness against standard rule-based algorithms from Phenotype KnowledgeBase (PheKB). Phe2vec is based on pre-computing embeddings of medical concepts and patients' clinical history. Disease phenotypes are then derived from a seed concept and its neighbors in the embedding space. Patients are linked to a disease if their embedded representation is close to the disease phenotype. Comparing Phe2vec and PheKB cohorts head-to-head using chart review, Phe2vec performed on par or better in nine out of ten diseases. Differently from other approaches, it can scale to any condition and was validated against widely adopted expert-based standards. Phe2vec aims to optimize clinical informatics research by augmenting current frameworks to characterize patients by condition and derive reliable disease cohorts.

Factors Associated with Longitudinal Psychological and Physiological Stress in Health Care Workers During the COVID-19 Pandemic: Observational Study Using Apple Watch Data

Background

The COVID-19 pandemic has resulted in a high degree of psychological distress among health care workers (HCWs). There is a need to characterize which HCWs are at an increased risk of developing psychological effects from the pandemic. Given the differences in the response of individuals to stress, an analysis of both the perceived and physiological consequences of stressors can provide a comprehensive evaluation of its impact.

Objective

This study aimed to determine characteristics associated with longitudinal perceived stress in HCWs and to assess whether changes in heart rate variability (HRV), a marker of autonomic nervous system function, are associated with features protective against longitudinal stress.

Methods

HCWs across 7 hospitals in New York City, NY, were prospectively followed in an ongoing observational digital study using the custom Warrior Watch Study app. Participants wore an Apple Watch for the duration of the study to measure HRV throughout the follow-up period. Surveys measuring perceived stress, resilience, emotional support, quality of life, and optimism were collected at baseline and longitudinally.

Results

A total of 361 participants (mean age 36.8, SD 10.1 years; female: n=246, 69.3%) were enrolled. Multivariate analysis found New York City’s COVID-19 case count to be associated with increased longitudinal stress (P=.008). Baseline emotional support, quality of life, and resilience were associated with decreased longitudinal stress (P<.001). A significant reduction in stress during the 4-week period after COVID-19 diagnosis was observed in the highest tertial of emotional support (P=.03) and resilience (P=.006). Participants in the highest tertial of baseline emotional support and resilience had a significantly different circadian pattern of longitudinally collected HRV compared to subjects in the low or medium tertial.

Conclusions

High resilience, emotional support, and quality of life place HCWs at reduced risk of longitudinal perceived stress and have a distinct physiological stress profile. Our findings support the use of these characteristics to identify HCWs at risk of the psychological and physiological stress effects of the pandemic.

Kidney disease genetic risk variants alter lysosomal beta-mannosidase (MANBA) expression and disease severity

More than 800 million people in the world suffer from chronic kidney disease (CKD). Genome-wide association studies (GWAS) have identified hundreds of loci where genetic variants are associated with kidney function; however, causal genes and pathways for CKD remain unknown. Here, we performed integration of kidney function GWAS and human kidney-specific expression quantitative trait analysis and identified that the expression of beta-mannosidase (MANBA) was lower in kidneys of subjects with CKD risk genotype. We also show an increased incidence of renal failure in subjects with rare heterozygous loss-of-function coding variants in MANBA using phenome-wide association analysis of 40,963 subjects with exome sequencing data. MANBA is a lysosomal gene highly expressed in kidney tubule cells. Deep phenotyping revealed structural and functional lysosomal alterations in human kidneys from subjects with CKD risk alleles and mice with genetic deletion of Manba Manba heterozygous and knockout mice developed more severe kidney fibrosis when subjected to toxic injury induced by cisplatin or folic acid. Manba loss altered multiple pathways, including endocytosis and autophagy. In the absence of Manba, toxic acute tubule injury induced inflammasome activation and fibrosis. Together, these results illustrate the convergence of common noncoding and rare coding variants in MANBA in kidney disease development and demonstrate the role of the endolysosomal system in kidney disease development.

The trans-ancestral genomic architecture of glycemic traits

Glycemic traits are used to diagnose and monitor type 2 diabetes and cardiometabolic health. To date, most genetic studies of glycemic traits have focused on individuals of European ancestry. Here we aggregated genome-wide association studies comprising up to 281,416 individuals without diabetes (30% non-European ancestry) for whom fasting glucose, 2-h glucose after an oral glucose challenge, glycated hemoglobin and fasting insulin data were available. Trans-ancestry and single-ancestry meta-analyses identified 242 loci (99 novel; P < 5 × 10-8), 80% of which had no significant evidence of between-ancestry heterogeneity. Analyses restricted to individuals of European ancestry with equivalent sample size would have led to 24 fewer new loci. Compared with single-ancestry analyses, equivalent-sized trans-ancestry fine-mapping reduced the number of estimated variants in 99% credible sets by a median of 37.5%. Genomic-feature, gene-expression and gene-set analyses revealed distinct biological signatures for each trait, highlighting different underlying biological pathways. Our results increase our understanding of diabetes pathophysiology by using trans-ancestry studies for improved power and resolution.

Toward a fine-scale population health monitoring system

Understanding population health disparities is an essential component of equitable precision health efforts. Epidemiology research often relies on definitions of race and ethnicity, but these population labels may not adequately capture disease burdens and environmental factors impacting specific sub-populations. Here, we propose a framework for repurposing data from electronic health records (EHRs) in concert with genomic data to explore the demographic ties that can impact disease burdens. Using data from a diverse biobank in New York City, we identified 17 communities sharing recent genetic ancestry. We observed 1,177 health outcomes that were statistically associated with a specific group and demonstrated significant differences in the segregation of genetic variants contributing to Mendelian diseases. We also demonstrated that fine-scale population structure can impact the prediction of complex disease risk within groups. This work reinforces the utility of linking genomic data to EHRs and provides a framework toward fine-scale monitoring of population health.

Rapid response to the alpha-1 adrenergic agent phenylephrine in the perioperative period is impacted by genomics and ancestry

The emergence of genomic data in biobanks and health systems offers new ways to derive medically important phenotypes, including acute phenotypes occurring during inpatient clinical care. Here we study the genetic underpinnings of the rapid response to phenylephrine, an α1-adrenergic receptor agonist commonly used to treat hypotension during anesthesia and surgery. We quantified this response by extracting blood pressure (BP) measurements 5 min before and after the administration of phenylephrine. Based on this derived phenotype, we show that systematic differences exist between self-reported ancestry groups: European-Americans (EA; n = 1387) have a significantly higher systolic response to phenylephrine than African-Americans (AA; n = 1217) and Hispanic/Latinos (HA; n = 1713) (31.3% increase, p value < 6e-08 and 22.9% increase, p value < 5e-05 respectively), after adjusting for genetic ancestry, demographics, and relevant clinical covariates. We performed a genome-wide association study to investigate genetic factors underlying individual differences in this derived phenotype. We discovered genome-wide significant association signals in loci and genes previously associated with BP measured in ambulatory settings, and a general enrichment of association in these genes. Finally, we discovered two low frequency variants, present at ~1% in EAs and AAs, respectively, where patients carrying one copy of these variants show no phenylephrine response. This work demonstrates our ability to derive a quantitative phenotype suited for comparative statistics and genome-wide association studies from dense clinical and physiological measures captured for managing patients during surgery. We identify genetic variants underlying non response to phenylephrine, with implications for preemptive pharmacogenomic screening to improve safety during surgery.

Meta-analysis uncovers genome-wide significant variants for rapid kidney function decline

Rapid decline of glomerular filtration rate estimated from creatinine (eGFRcrea) is associated with severe clinical endpoints. In contrast to cross-sectionally assessed eGFRcrea, the genetic basis for rapid eGFRcrea decline is largely unknown. To help define this, we meta-analyzed 42 genome-wide association studies from the Chronic Kidney Diseases Genetics Consortium and United Kingdom Biobank to identify genetic loci for rapid eGFRcrea decline. Two definitions of eGFRcrea decline were used: 3 mL/min/1.73m2/year or more ("Rapid3"; encompassing 34,874 cases, 107,090 controls) and eGFRcrea decline 25% or more and eGFRcrea under 60 mL/min/1.73m2 at follow-up among those with eGFRcrea 60 mL/min/1.73m2 or more at baseline ("CKDi25"; encompassing 19,901 cases, 175,244 controls). Seven independent variants were identified across six loci for Rapid3 and/or CKDi25: consisting of five variants at four loci with genome-wide significance (near UMOD-PDILT (2), PRKAG2, WDR72, OR2S2) and two variants among 265 known eGFRcrea variants (near GATM, LARP4B). All these loci were novel for Rapid3 and/or CKDi25 and our bioinformatic follow-up prioritized variants and genes underneath these loci. The OR2S2 locus is novel for any eGFRcrea trait including interesting candidates. For the five genome-wide significant lead variants, we found supporting effects for annual change in blood urea nitrogen or cystatin-based eGFR, but not for GATM or LARP4B. Individuals at high compared to those at low genetic risk (8-14 vs. 0-5 adverse alleles) had a 1.20-fold increased risk of acute kidney injury (95% confidence interval 1.08-1.33). Thus, our identified loci for rapid kidney function decline may help prioritize therapeutic targets and identify mechanisms and individuals at risk for sustained deterioration of kidney function.

Use of Physiological Data From a Wearable Device to Identify SARS-CoV-2 Infection and Symptoms and Predict COVID-19 Diagnosis: Observational Study

BACKGROUND

Changes in autonomic nervous system function, characterized by heart rate variability (HRV), have been associated with infection and observed prior to its clinical identification.

OBJECTIVE

We performed an evaluation of HRV collected by a wearable device to identify and predict COVID-19 and its related symptoms.

METHODS

Health care workers in the Mount Sinai Health System were prospectively followed in an ongoing observational study using the custom Warrior Watch Study app, which was downloaded to their smartphones. Participants wore an Apple Watch for the duration of the study, measuring HRV throughout the follow-up period. Surveys assessing infection and symptom-related questions were obtained daily.

RESULTS

Using a mixed-effect cosinor model, the mean amplitude of the circadian pattern of the standard deviation of the interbeat interval of normal sinus beats (SDNN), an HRV metric, differed between subjects with and without COVID-19 (P=.006). The mean amplitude of this circadian pattern differed between individuals during the 7 days before and the 7 days after a COVID-19 diagnosis compared to this metric during uninfected time periods (P=.01). Significant changes in the mean and amplitude of the circadian pattern of the SDNN was observed between the first day of reporting a COVID-19-related symptom compared to all other symptom-free days (P=.01).

CONCLUSIONS

Longitudinally collected HRV metrics from a commonly worn commercial wearable device (Apple Watch) can predict the diagnosis of COVID-19 and identify COVID-19-related symptoms. Prior to the diagnosis of COVID-19 by nasal swab polymerase chain reaction testing, significant changes in HRV were observed, demonstrating the predictive ability of this metric to identify COVID-19 infection.

Discovery of rare variants associated with blood pressure regulation through meta-analysis of 1.3 million individuals

Genetic studies of blood pressure (BP) to date have mainly analyzed common variants (minor allele frequency > 0.05). In a meta-analysis of up to ~1.3 million participants, we discovered 106 new BP-associated genomic regions and 87 rare (minor allele frequency ≤ 0.01) variant BP associations (P < 5 × 10-8), of which 32 were in new BP-associated loci and 55 were independent BP-associated single-nucleotide variants within known BP-associated regions. Average effects of rare variants (44% coding) were ~8 times larger than common variant effects and indicate potential candidate causal genes at new and known loci (for example, GATA5 and PLCB3). BP-associated variants (including rare and common) were enriched in regions of active chromatin in fetal tissues, potentially linking fetal development with BP regulation in later life. Multivariable Mendelian randomization suggested possible inverse effects of elevated systolic and diastolic BP on large artery stroke. Our study demonstrates the utility of rare-variant analyses for identifying candidate genes and the results highlight potential therapeutic targets.

Retrospective cohort study of clinical characteristics of 2199 hospitalised patients with COVID-19 in New York City

OBJECTIVE

The COVID-19 pandemic is a global public health crisis, with over 33 million cases and 999 000 deaths worldwide. Data are needed regarding the clinical course of hospitalised patients, particularly in the USA. We aimed to compare clinical characteristic of patients with COVID-19 who had in-hospital mortality with those who were discharged alive.

DESIGN

Demographic, clinical and outcomes data for patients admitted to five Mount Sinai Health System hospitals with confirmed COVID-19 between 27 February and 2 April 2020 were identified through institutional electronic health records. We performed a retrospective comparative analysis of patients who had in-hospital mortality or were discharged alive.

SETTING

All patients were admitted to the Mount Sinai Health System, a large quaternary care urban hospital system.

PARTICIPANTS

Participants over the age of 18 years were included.

PRIMARY OUTCOMES

We investigated in-hospital mortality during the study period.

RESULTS

A total of 2199 patients with COVID-19 were hospitalised during the study period. As of 2 April, 1121 (51%) patients remained hospitalised, and 1078 (49%) completed their hospital course. Of the latter, the overall mortality was 29%, and 36% required intensive care. The median age was 65 years overall and 75 years in those who died. Pre-existing conditions were present in 65% of those who died and 46% of those discharged. In those who died, the admission median lymphocyte percentage was 11.7%, D-dimer was 2.4 μg/mL, C reactive protein was 162 mg/L and procalcitonin was 0.44 ng/mL. In those discharged, the admission median lymphocyte percentage was 16.6%, D-dimer was 0.93 μg/mL, C reactive protein was 79 mg/L and procalcitonin was 0.09 ng/mL.

CONCLUSIONS

In our cohort of hospitalised patients, requirement of intensive care and mortality were high. Patients who died typically had more pre-existing conditions and greater perturbations in inflammatory markers as compared with those who were discharged.

Machine Learning to Predict Mortality and Critical Events in a Cohort of Patients With COVID-19 in New York City: Model Development and Validation

BACKGROUND

COVID-19 has infected millions of people worldwide and is responsible for several hundred thousand fatalities. The COVID-19 pandemic has necessitated thoughtful resource allocation and early identification of high-risk patients. However, effective methods to meet these needs are lacking.

OBJECTIVE

The aims of this study were to analyze the electronic health records (EHRs) of patients who tested positive for COVID-19 and were admitted to hospitals in the Mount Sinai Health System in New York City; to develop machine learning models for making predictions about the hospital course of the patients over clinically meaningful time horizons based on patient characteristics at admission; and to assess the performance of these models at multiple hospitals and time points.

METHODS

We used Extreme Gradient Boosting (XGBoost) and baseline comparator models to predict in-hospital mortality and critical events at time windows of 3, 5, 7, and 10 days from admission. Our study population included harmonized EHR data from five hospitals in New York City for 4098 COVID-19-positive patients admitted from March 15 to May 22, 2020. The models were first trained on patients from a single hospital (n=1514) before or on May 1, externally validated on patients from four other hospitals (n=2201) before or on May 1, and prospectively validated on all patients after May 1 (n=383). Finally, we established model interpretability to identify and rank variables that drive model predictions.

RESULTS

Upon cross-validation, the XGBoost classifier outperformed baseline models, with an area under the receiver operating characteristic curve (AUC-ROC) for mortality of 0.89 at 3 days, 0.85 at 5 and 7 days, and 0.84 at 10 days. XGBoost also performed well for critical event prediction, with an AUC-ROC of 0.80 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. In external validation, XGBoost achieved an AUC-ROC of 0.88 at 3 days, 0.86 at 5 days, 0.86 at 7 days, and 0.84 at 10 days for mortality prediction. Similarly, the unimputed XGBoost model achieved an AUC-ROC of 0.78 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. Trends in performance on prospective validation sets were similar. At 7 days, acute kidney injury on admission, elevated LDH, tachypnea, and hyperglycemia were the strongest drivers of critical event prediction, while higher age, anion gap, and C-reactive protein were the strongest drivers of mortality prediction.

CONCLUSIONS

We externally and prospectively trained and validated machine learning models for mortality and critical events for patients with COVID-19 at different time horizons. These models identified at-risk patients and uncovered underlying relationships that predicted outcomes.

Utilization of Deep Learning for Subphenotype Identification in Sepsis-Associated Acute Kidney Injury

BACKGROUND AND OBJECTIVES

Sepsis-associated AKI is a heterogeneous clinical entity. We aimed to agnostically identify sepsis-associated AKI subphenotypes using deep learning on routinely collected data in electronic health records.

DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS

We used the Medical Information Mart for Intensive Care III database, which consists of electronic health record data from intensive care units in a tertiary care hospital in the United States. We included patients ≥18 years with sepsis who developed AKI within 48 hours of intensive care unit admission. We then used deep learning to utilize all available vital signs, laboratory measurements, and comorbidities to identify subphenotypes. Outcomes were mortality 28 days after AKI and dialysis requirement.

RESULTS

We identified 4001 patients with sepsis-associated AKI. We utilized 2546 combined features for K-means clustering, identifying three subphenotypes. Subphenotype 1 had 1443 patients, and subphenotype 2 had 1898 patients, whereas subphenotype 3 had 660 patients. Subphenotype 1 had the lowest proportion of liver disease and lowest Simplified Acute Physiology Score II scores compared with subphenotypes 2 and 3. The proportions of patients with CKD were similar between subphenotypes 1 and 3 (15%) but highest in subphenotype 2 (21%). Subphenotype 1 had lower median bilirubin levels, aspartate aminotransferase, and alanine aminotransferase compared with subphenotypes 2 and 3. Patients in subphenotype 1 also had lower median lactate, lactate dehydrogenase, and white blood cell count than patients in subphenotypes 2 and 3. Subphenotype 1 also had lower creatinine and BUN than subphenotypes 2 and 3. Dialysis requirement was lowest in subphenotype 1 (4% versus 7% [subphenotype 2] versus 26% [subphenotype 3]). The mortality 28 days after AKI was lowest in subphenotype 1 (23% versus 35% [subphenotype 2] versus 49% [subphenotype 3]). After adjustment, the adjusted odds ratio for mortality for subphenotype 3, with subphenotype 1 as a reference, was 1.9 (95% confidence interval, 1.5 to 2.4).

CONCLUSIONS

Utilizing routinely collected laboratory variables, vital signs, and comorbidities, we were able to identify three distinct subphenotypes of sepsis-associated AKI with differing outcomes.

Characterization of Patients Who Return to Hospital Following Discharge from Hospitalization for COVID-19

BACKGROUND

Data on patients with coronavirus disease 2019 (COVID-19) who return to hospital after discharge are scarce. Characterization of these patients may inform post-hospitalization care.

OBJECTIVE

To describe clinical characteristics of patients with COVID-19 who returned to the emergency department (ED) or required readmission within 14 days of discharge.

DESIGN

Retrospective cohort study of SARS-COV-2-positive patients with index hospitalization between February 27 and April 12, 2020, with ≥ 14-day follow-up. Significance was defined as P < 0.05 after multiplying P by 125 study-wide comparisons.

PARTICIPANTS

Hospitalized patients with confirmed SARS-CoV-2 discharged alive from five New York City hospitals.

MAIN MEASURES

Readmission or return to ED following discharge.

RESULTS

Of 2864 discharged patients, 103 (3.6%) returned for emergency care after a median of 4.5 days, with 56 requiring inpatient readmission. The most common reason for return was respiratory distress (50%). Compared with patients who did not return, there were higher proportions of COPD (6.8% vs 2.9%) and hypertension (36% vs 22.1%) among those who returned. Patients who returned also had a shorter median length of stay (LOS) during index hospitalization (4.5 [2.9,9.1] vs 6.7 [3.5, 11.5] days; P_adjusted = 0.006), and were less likely to have required intensive care on index hospitalization (5.8% vs 19%; P_adjusted = 0.001). A trend towards association between absence of in-hospital treatment-dose anticoagulation on index admission and return to hospital was also observed (20.9% vs 30.9%, P_adjusted = 0.06). On readmission, rates of intensive care and death were 5.8% and 3.6%, respectively.

CONCLUSIONS

Return to hospital after admission for COVID-19 was infrequent within 14 days of discharge. The most common cause for return was respiratory distress. Patients who returned more likely had COPD and hypertension, shorter LOS on index-hospitalization, and lower rates of in-hospital treatment-dose anticoagulation. Future studies should focus on whether these comorbid conditions, longer LOS, and anticoagulation are associated with reduced readmissions.

Trans-ethnic and Ancestry-Specific Blood-Cell Genetics in 746,667 Individuals from 5 Global Populations

Most loci identified by GWASs have been found in populations of European ancestry (EUR). In trans-ethnic meta-analyses for 15 hematological traits in 746,667 participants, including 184,535 non-EUR individuals, we identified 5,552 trait-variant associations at p < 5 × 10-9, including 71 novel associations not found in EUR populations. We also identified 28 additional novel variants in ancestry-specific, non-EUR meta-analyses, including an IL7 missense variant in South Asians associated with lymphocyte count in vivo and IL-7 secretion levels in vitro. Fine-mapping prioritized variants annotated as functional and generated 95% credible sets that were 30% smaller when using the trans-ethnic as opposed to the EUR-only results. We explored the clinical significance and predictive value of trans-ethnic variants in multiple populations and compared genetic architecture and the effect of natural selection on these blood phenotypes between populations. Altogether, our results for hematological traits highlight the value of a more global representation of populations in genetic studies.

The Polygenic and Monogenic Basis of Blood Traits and Diseases

Blood cells play essential roles in human health, underpinning physiological processes such as immunity, oxygen transport, and clotting, which when perturbed cause a significant global health burden. Here we integrate data from UK Biobank and a large-scale international collaborative effort, including data for 563,085 European ancestry participants, and discover 5,106 new genetic variants independently associated with 29 blood cell phenotypes covering a range of variation impacting hematopoiesis. We holistically characterize the genetic architecture of hematopoiesis, assess the relevance of the omnigenic model to blood cell phenotypes, delineate relevant hematopoietic cell states influenced by regulatory genetic variants and gene networks, identify novel splice-altering variants mediating the associations, and assess the polygenic prediction potential for blood traits and clinical disorders at the interface of complex and Mendelian genetics. These results show the power of large-scale blood cell trait GWAS to interrogate clinically meaningful variants across a wide allelic spectrum of human variation.

The Polygenic and Monogenic Basis of Blood Traits and Diseases

Blood cells play essential roles in human health, underpinning physiological processes such as immunity, oxygen transport, and clotting, which when perturbed cause a significant global health burden. Here we integrate data from UK Biobank and a large-scale international collaborative effort, including data for 563,085 European ancestry participants, and discover 5,106 new genetic variants independently associated with 29 blood cell phenotypes covering a range of variation impacting hematopoiesis. We holistically characterize the genetic architecture of hematopoiesis, assess the relevance of the omnigenic model to blood cell phenotypes, delineate relevant hematopoietic cell states influenced by regulatory genetic variants and gene networks, identify novel splice-altering variants mediating the associations, and assess the polygenic prediction potential for blood traits and clinical disorders at the interface of complex and Mendelian genetics. These results show the power of large-scale blood cell trait GWAS to interrogate clinically meaningful variants across a wide allelic spectrum of human variation.

Multi-ancestry GWAS of the electrocardiographic PR interval identifies 202 loci underlying cardiac conduction

The electrocardiographic PR interval reflects atrioventricular conduction, and is associated with conduction abnormalities, pacemaker implantation, atrial fibrillation (AF), and cardiovascular mortality. Here we report a multi-ancestry (N = 293,051) genome-wide association meta-analysis for the PR interval, discovering 202 loci of which 141 have not previously been reported. Variants at identified loci increase the percentage of heritability explained, from 33.5% to 62.6%. We observe enrichment for cardiac muscle developmental/contractile and cytoskeletal genes, highlighting key regulation processes for atrioventricular conduction. Additionally, 8 loci not previously reported harbor genes underlying inherited arrhythmic syndromes and/or cardiomyopathies suggesting a role for these genes in cardiovascular pathology in the general population. We show that polygenic predisposition to PR interval duration is an endophenotype for cardiovascular disease, including distal conduction disease, AF, and atrioventricular pre-excitation. These findings advance our understanding of the polygenic basis of cardiac conduction, and the genetic relationship between PR interval duration and cardiovascular disease.

Acute Kidney Injury in Hospitalized Patients with COVID-19

IMPORTANCE

Preliminary reports indicate that acute kidney injury (AKI) is common in coronavirus disease (COVID)-19 patients and is associated with worse outcomes. AKI in hospitalized COVID-19 patients in the United States is not well-described.

OBJECTIVE

To provide information about frequency, outcomes and recovery associated with AKI and dialysis in hospitalized COVID-19 patients.

DESIGN

Observational, retrospective study.

SETTING

Admitted to hospital between February 27 and April 15, 2020.

PARTICIPANTS

Patients aged ≥18 years with laboratory confirmed COVID-19 Exposures: AKI (peak serum creatinine increase of 0.3 mg/dL or 50% above baseline). Main Outcomes and Measures: Frequency of AKI and dialysis requirement, AKI recovery, and adjusted odds ratios (aOR) with mortality. We also trained and tested a machine learning model for predicting dialysis requirement with independent validation.

RESULTS

A total of 3,235 hospitalized patients were diagnosed with COVID-19. AKI occurred in 1406 (46%) patients overall and 280 (20%) with AKI required renal replacement therapy. The incidence of AKI (admission plus new cases) in patients admitted to the intensive care unit was 68% (553 of 815). In the entire cohort, the proportion with stages 1, 2, and 3 AKI were 35%, 20%, 45%, respectively. In those needing intensive care, the respective proportions were 20%, 17%, 63%, and 34% received acute renal replacement therapy. Independent predictors of severe AKI were chronic kidney disease, systolic blood pressure, and potassium at baseline. In-hospital mortality in patients with AKI was 41% overall and 52% in intensive care. The aOR for mortality associated with AKI was 9.6 (95% CI 7.4-12.3) overall and 20.9 (95% CI 11.7-37.3) in patients receiving intensive care. 56% of patients with AKI who were discharged alive recovered kidney function back to baseline. The area under the curve (AUC) for the machine learned predictive model using baseline features for dialysis requirement was 0.79 in a validation test.

CONCLUSIONS AND RELEVANCE

AKI is common in patients hospitalized with COVID-19, associated with worse mortality, and the majority of patients that survive do not recover kidney function. A machine-learned model using admission features had good performance for dialysis prediction and could be used for resource allocation.

Smoking-by-genotype interaction in type 2 diabetes risk and fasting glucose

Smoking is a potentially causal behavioral risk factor for type 2 diabetes (T2D), but not all smokers develop T2D. It is unknown whether genetic factors partially explain this variation. We performed genome-environment-wide interaction studies to identify loci exhibiting potential interaction with baseline smoking status (ever vs. never) on incident T2D and fasting glucose (FG). Analyses were performed in participants of European (EA) and African ancestry (AA) separately. Discovery analyses were conducted using genotype data from the 50,000-single-nucleotide polymorphism (SNP) ITMAT-Broad-CARe (IBC) array in 5 cohorts from from the Candidate Gene Association Resource Consortium (n = 23,189). Replication was performed in up to 16 studies from the Cohorts for Heart Aging Research in Genomic Epidemiology Consortium (n = 74,584). In meta-analysis of discovery and replication estimates, 5 SNPs met at least one criterion for potential interaction with smoking on incident T2D at p<1x10-7 (adjusted for multiple hypothesis-testing with the IBC array). Two SNPs had significant joint effects in the overall model and significant main effects only in one smoking stratum: rs140637 (FBN1) in AA individuals had a significant main effect only among smokers, and rs1444261 (closest gene C2orf63) in EA individuals had a significant main effect only among nonsmokers. Three additional SNPs were identified as having potential interaction by exhibiting a significant main effects only in smokers: rs1801232 (CUBN) in AA individuals, rs12243326 (TCF7L2) in EA individuals, and rs4132670 (TCF7L2) in EA individuals. No SNP met significance for potential interaction with smoking on baseline FG. The identification of these loci provides evidence for genetic interactions with smoking exposure that may explain some of the heterogeneity in the association between smoking and T2D.

Clinical Characteristics of Hospitalized Covid-19 Patients in New York City

Background

The coronavirus 2019 (Covid-19) pandemic is a global public health crisis, with over 1.6 million cases and 95,000 deaths worldwide. Data are needed regarding the clinical course of hospitalized patients, particularly in the United States.

Methods

Demographic, clinical, and outcomes data for patients admitted to five Mount Sinai Health System hospitals with confirmed Covid-19 between February 27 and April 2, 2020 were identified through institutional electronic health records. We conducted a descriptive study of patients who had in-hospital mortality or were discharged alive.

Results

A total of 2,199 patients with Covid-19 were hospitalized during the study period. As of April 2 ^nd , 1,121 (51%) patients remained hospitalized, and 1,078 (49%) completed their hospital course. Of the latter, the overall mortality was 29%, and 36% required intensive care. The median age was 65 years overall and 75 years in those who died. Pre-existing conditions were present in 65% of those who died and 46% of those discharged. In those who died, the admission median lymphocyte percentage was 11.7%, D-dimer was 2.4 ug/ml, C-reactive protein was 162 mg/L, and procalcitonin was 0.44 ng/mL. In those discharged, the admission median lymphocyte percentage was 16.6%, D-dimer was 0.93 ug/ml, C-reactive protein was 79 mg/L, and procalcitonin was 0.09 ng/mL.

Conclusions

This is the largest and most diverse case series of hospitalized patients with Covid-19 in the United States to date. Requirement of intensive care and mortality were high. Patients who died typically had pre-existing conditions and severe perturbations in inflammatory markers.

Association of Genetic Risk of Obesity with Postoperative Complications Using Mendelian Randomization

BACKGROUND

The extent to which obesity and genetics determine postoperative complications is incompletely understood.

METHODS

We performed a retrospective study using two population cohorts with electronic health record (EHR) data. The first included 736,726 adults with body mass index (BMI) recorded between 1990 and 2017 at Vanderbilt University Medical Center. The second cohort consisted of 65,174 individuals from 12 institutions contributing EHR and genome-wide genotyping data to the Electronic Medical Records and Genomics (eMERGE) Network. Pairwise logistic regression analyses were used to measure the association of BMI categories with postoperative complications derived from International Classification of Disease-9 codes, including postoperative infection, incisional hernia, and intestinal obstruction. A genetic risk score was constructed from 97 obesity-risk single-nucleotide polymorphisms for a Mendelian randomization study to determine the association of genetic risk of obesity on postoperative complications. Logistic regression analyses were adjusted for sex, age, site, and race/principal components.

RESULTS

Individuals with overweight or obese BMI (≥25 kg/m2) had increased risk of incisional hernia (odds ratio [OR] 1.7-5.5, p < 3.1 × 10-20), and people with obesity (BMI ≥ 30 kg/m2) had increased risk of postoperative infection (OR 1.2-2.3, p < 2.5 × 10-5). In the eMERGE cohort, genetically predicted BMI was associated with incisional hernia (OR 2.1 [95% CI 1.8-2.5], p = 1.4 × 10-6) and postoperative infection (OR 1.6 [95% CI 1.4-1.9], p = 3.1 × 10-6). Association findings were similar after limitation of the cohorts to those who underwent abdominal procedures.

CONCLUSIONS

Clinical and Mendelian randomization studies suggest that obesity, as measured by BMI, is associated with the development of postoperative incisional hernia and infection.

Associations of autozygosity with a broad range of human phenotypes

In many species, the offspring of related parents suffer reduced reproductive success, a phenomenon known as inbreeding depression. In humans, the importance of this effect has remained unclear, partly because reproduction between close relatives is both rare and frequently associated with confounding social factors. Here, using genomic inbreeding coefficients (FROH) for >1.4 million individuals, we show that FROH is significantly associated (p < 0.0005) with apparently deleterious changes in 32 out of 100 traits analysed. These changes are associated with runs of homozygosity (ROH), but not with common variant homozygosity, suggesting that genetic variants associated with inbreeding depression are predominantly rare. The effect on fertility is striking: FROH equivalent to the offspring of first cousins is associated with a 55% decrease [95% CI 44-66%] in the odds of having children. Finally, the effects of FROH are confirmed within full-sibling pairs, where the variation in FROH is independent of all environmental confounding.

Target genes, variants, tissues and transcriptional pathways influencing human serum urate levels

Elevated serum urate levels cause gout and correlate with cardiometabolic diseases via poorly understood mechanisms. We performed a trans-ancestry genome-wide association study of serum urate in 457,690 individuals, identifying 183 loci (147 previously unknown) that improve the prediction of gout in an independent cohort of 334,880 individuals. Serum urate showed significant genetic correlations with many cardiometabolic traits, with genetic causality analyses supporting a substantial role for pleiotropy. Enrichment analysis, fine-mapping of urate-associated loci and colocalization with gene expression in 47 tissues implicated the kidney and liver as the main target organs and prioritized potentially causal genes and variants, including the transcriptional master regulators in the liver and kidney, HNF1A and HNF4A. Experimental validation showed that HNF4A transactivated the promoter of ABCG2, encoding a major urate transporter, in kidney cells, and that HNF4A p.Thr139Ile is a functional variant. Transcriptional coregulation within and across organs may be a general mechanism underlying the observed pleiotropy between urate and cardiometabolic traits.

Harmonizing Clinical Sequencing and Interpretation for the eMERGE III Network

The advancement of precision medicine requires new methods to coordinate and deliver genetic data from heterogeneous sources to physicians and patients. The eMERGE III Network enrolled >25,000 participants from biobank and prospective cohorts of predominantly healthy individuals for clinical genetic testing to determine clinically actionable findings. The network developed protocols linking together the 11 participant collection sites and 2 clinical genetic testing laboratories. DNA capture panels targeting 109 genes were used for testing of DNA and sample collection, data generation, interpretation, reporting, delivery, and storage were each harmonized. A compliant and secure network enabled ongoing review and reconciliation of clinical interpretations, while maintaining communication and data sharing between clinicians and investigators. A total of 202 individuals had positive diagnostic findings relevant to the indication for testing and 1,294 had additional/secondary findings of medical significance deemed to be returnable, establishing data return rates for other testing endeavors. This study accomplished integration of structured genomic results into multiple electronic health record (EHR) systems, setting the stage for clinical decision support to enable genomic medicine. Further, the established processes enable different sequencing sites to harmonize technical and interpretive aspects of sequencing tests, a critical achievement toward global standardization of genomic testing. The eMERGE protocols and tools are available for widespread dissemination.

A Genome-Wide Association Study Identifies Blood Disorder-Related Variants Influencing Hemoglobin A1c With Implications for Glycemic Status in U.S. Hispanics/Latinos

OBJECTIVE

We aimed to identify hemoglobin A_1c (HbA_1c)-associated genetic variants and examine their implications for glycemic status evaluated by HbA_1c in U.S. Hispanics/Latinos with diverse genetic ancestries.

RESEARCH DESIGN AND METHODS

We conducted a genome-wide association study (GWAS) of HbA_1c in 9,636 U.S. Hispanics/Latinos without diabetes from the Hispanic Community Health Study/Study of Latinos, followed by a replication among 4,729 U.S. Hispanics/Latinos from three independent studies.

RESULTS

Our GWAS and replication analyses showed 10 previously known and novel loci associated with HbA_1c at genome-wide significance levels (P < 5.0 × 10^-8). In particular, two African ancestry-specific variants, HBB-rs334 and G6PD-rs1050828, which are causal mutations for sickle cell disease and G6PD deficiency, respectively, had ∼10 times larger effect sizes on HbA_1c levels (β = -0.31% [-3.4 mmol/mol]) and -0.35% [-3.8 mmol/mol] per minor allele, respectively) compared with other HbA_1c-associated variants (0.03-0.04% [0.3-0.4 mmol/mol] per allele). A novel Amerindian ancestry-specific variant, HBM-rs145546625, was associated with HbA_1c and hematologic traits but not with fasting glucose. The prevalence of hyperglycemia (prediabetes and diabetes) defined using fasting glucose or oral glucose tolerance test 2-h glucose was similar between carriers of HBB-rs334 or G6PD-rs1050828 HbA_1c-lowering alleles and noncarriers, whereas the prevalence of hyperglycemia defined using HbA_1c was significantly lower in carriers than in noncarriers (12.2% vs. 28.4%, P < 0.001). After recalibration of the HbA_1c level taking HBB-rs334 and G6PD-rs1050828 into account, the prevalence of hyperglycemia in carriers was similar to noncarriers (31.3% vs. 28.4%, P = 0.28).

CONCLUSIONS

This study in U.S. Hispanics/Latinos found several ancestry-specific alleles associated with HbA_1c through erythrocyte-related rather than glycemic-related pathways. The potential influences of these nonglycemic-related variants need to be considered when the HbA_1c test is performed.

A multi-ancestry genome-wide study incorporating gene-smoking interactions identifies multiple new loci for pulse pressure and mean arterial pressure

Elevated blood pressure (BP), a leading cause of global morbidity and mortality, is influenced by both genetic and lifestyle factors. Cigarette smoking is one such lifestyle factor. Across five ancestries, we performed a genome-wide gene-smoking interaction study of mean arterial pressure (MAP) and pulse pressure (PP) in 129 913 individuals in stage 1 and follow-up analysis in 480 178 additional individuals in stage 2. We report here 136 loci significantly associated with MAP and/or PP. Of these, 61 were previously published through main-effect analysis of BP traits, 37 were recently reported by us for systolic BP and/or diastolic BP through gene-smoking interaction analysis and 38 were newly identified (P < 5 × 10-8, false discovery rate < 0.05). We also identified nine new signals near known loci. Of the 136 loci, 8 showed significant interaction with smoking status. They include CSMD1 previously reported for insulin resistance and BP in the spontaneously hypertensive rats. Many of the 38 new loci show biologic plausibility for a role in BP regulation. SLC26A7 encodes a chloride/bicarbonate exchanger expressed in the renal outer medullary collecting duct. AVPR1A is widely expressed, including in vascular smooth muscle cells, kidney, myocardium and brain. FHAD1 is a long non-coding RNA overexpressed in heart failure. TMEM51 was associated with contractile function in cardiomyocytes. CASP9 plays a central role in cardiomyocyte apoptosis. Identified only in African ancestry were 30 novel loci. Our findings highlight the value of multi-ancestry investigations, particularly in studies of interaction with lifestyle factors, where genomic and lifestyle differences may contribute to novel findings.

Genome-Wide Association Study of Heavy Smoking and Daily/Nondaily Smoking in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL)

Introduction

Genetic variants associated with nicotine dependence have previously been identified, primarily in European-ancestry populations. No genome-wide association studies (GWAS) have been reported for smoking behaviors in Hispanics/Latinos in the United States and Latin America, who are of mixed ancestry with European, African, and American Indigenous components.

Methods

We examined genetic associations with smoking behaviors in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) (N = 12 741 with smoking data, 5119 ever-smokers), using ~2.3 million genotyped variants imputed to the 1000 Genomes Project phase 3. Mixed logistic regression models accounted for population structure, sampling, relatedness, sex, and age.

Results

The known region of CHRNA5, which encodes the α5 cholinergic nicotinic receptor subunit, was associated with heavy smoking at genome-wide significance (p ≤ 5 × 10-8) in a comparison of 1929 ever-smokers reporting cigarettes per day (CPD) > 10 versus 3156 reporting CPD ≤ 10. The functional variant rs16969968 in CHRNA5 had a p value of 2.20 × 10-7 and odds ratio (OR) of 1.32 for the minor allele (A); its minor allele frequency was 0.22 overall and similar across Hispanic/Latino background groups (Central American = 0.17; South American = 0.19; Mexican = 0.18; Puerto Rican = 0.22; Cuban = 0.29; Dominican = 0.19). CHRNA4 on chromosome 20 attained p < 10-4, supporting prior findings in non-Hispanics. For nondaily smoking, which is prevalent in Hispanic/Latino smokers, compared to daily smoking, loci on chromosomes 2 and 4 achieved genome-wide significance; replication attempts were limited by small Hispanic/Latino sample sizes.

Conclusions

Associations of nicotinic receptor gene variants with smoking, first reported in non-Hispanic European-ancestry populations, generalized to Hispanics/Latinos despite different patterns of smoking behavior.

Implications

We conducted the first large-scale genome-wide association study (GWAS) of smoking behavior in a US Hispanic/Latino cohort, and the first GWAS of daily/nondaily smoking in any population. Results show that the region of the nicotinic receptor subunit gene CHRNA5, which in non-Hispanic European-ancestry smokers has been associated with heavy smoking as well as cessation and treatment efficacy, is also significantly associated with heavy smoking in this Hispanic/Latino cohort. The results are an important addition to understanding the impact of genetic variants in understudied Hispanic/Latino smokers.

The role of country of birth, and genetic and self-identified ancestry, in obesity susceptibility among African and Hispanic Americans

BACKGROUND

African Americans (AAs) and Hispanic/Latinos (HLs) have higher risk of obesity than European Americans, possibly due to differences in environment and lifestyle, but also reflecting differences in genetic background.

OBJECTIVE

To gain insight into factors contributing to BMI (in kg/m2) and obesity risk (BMI ≥ 30) among ancestry groups, we investigate the role of self-reported ancestry, proportion of genetic African ancestry, and country of birth in 6368 self-identified AA and 7569 HL participants of the New York-based BioMe Biobank.

METHODS

AAs and HLs are admixed populations that trace their genetic ancestry to the Americas, Africa, and Europe. The proportion of African ancestry (PAA), quantified using ADMIXTURE, was higher among self-reported AA (median: 87%; IQR: 79-92%) than among HL (26%; 15-41%) participants. Approximately 18% of AA and 59% of HL participants were non-US-born.

RESULTS

Because of significant differences between sexes (PPAA*sex interaction = 4.8 × 10-22), we considered women and men separately. Among women, country of birth and genetic ancestry contributed independently to BMI. US-born women had a BMI 1.99 higher than those born abroad (P = 7.7 × 10-25). Every 10% increase in PAA was associated with a BMI 0.29 higher (P = 7.1 × 10-10). After accounting for PAA and country of birth, the contribution of self-reported ancestry was small (P = 0.046). The contribution of PAA to higher BMI was significantly more pronounced among US-born (0.35/10%PAA, P = 0.003) than among non-US-born (0.26/10%PAA, P = 0.01) women (PPAA*sex interaction = 0.004). In contrast, among men, only US-born status influenced BMI. US-born men had a BMI 1.33 higher than non-US-born men, whereas PAA and self-reported ancestry were not associated with BMI. Associations with obesity risk were similar to those observed for BMI.

CONCLUSIONS

Being US-born is associated with a substantially higher BMI and risk of obesity in both men and women. Genetic ancestry, but not self-reported ancestry, is associated with obesity susceptibility, but only among US-born women in this New York-based population.

Publisher Correction: Protein-altering variants associated with body mass index implicate pathways that control energy intake and expenditure in obesity

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Genetic analyses of diverse populations improves discovery for complex traits

Genome-wide association studies (GWAS) have laid the foundation for investigations into the biology of complex traits, drug development and clinical guidelines. However, the majority of discovery efforts are based on data from populations of European ancestry^1-3. In light of the differential genetic architecture that is known to exist between populations, bias in representation can exacerbate existing disease and healthcare disparities. Critical variants may be missed if they have a low frequency or are completely absent in European populations, especially as the field shifts its attention towards rare variants, which are more likely to be population-specific^4-10. Additionally, effect sizes and their derived risk prediction scores derived in one population may not accurately extrapolate to other populations^11,12. Here we demonstrate the value of diverse, multi-ethnic participants in large-scale genomic studies. The Population Architecture using Genomics and Epidemiology (PAGE) study conducted a GWAS of 26 clinical and behavioural phenotypes in 49,839 non-European individuals. Using strategies tailored for analysis of multi-ethnic and admixed populations, we describe a framework for analysing diverse populations, identify 27 novel loci and 38 secondary signals at known loci, as well as replicate 1,444 GWAS catalogue associations across these traits. Our data show evidence of effect-size heterogeneity across ancestries for published GWAS associations, substantial benefits for fine-mapping using diverse cohorts and insights into clinical implications. In the United States-where minority populations have a disproportionately higher burden of chronic conditions¹³-the lack of representation of diverse populations in genetic research will result in inequitable access to precision medicine for those with the highest burden of disease. We strongly advocate for continued, large genome-wide efforts in diverse populations to maximize genetic discovery and reduce health disparities.

A catalog of genetic loci associated with kidney function from analyses of a million individuals

Chronic kidney disease (CKD) is responsible for a public health burden with multi-systemic complications. Through trans-ancestry meta-analysis of genome-wide association studies of estimated glomerular filtration rate (eGFR) and independent replication (n = 1,046,070), we identified 264 associated loci (166 new). Of these, 147 were likely to be relevant for kidney function on the basis of associations with the alternative kidney function marker blood urea nitrogen (n = 416,178). Pathway and enrichment analyses, including mouse models with renal phenotypes, support the kidney as the main target organ. A genetic risk score for lower eGFR was associated with clinically diagnosed CKD in 452,264 independent individuals. Colocalization analyses of associations with eGFR among 783,978 European-ancestry individuals and gene expression across 46 human tissues, including tubulo-interstitial and glomerular kidney compartments, identified 17 genes differentially expressed in kidney. Fine-mapping highlighted missense driver variants in 11 genes and kidney-specific regulatory variants. These results provide a comprehensive priority list of molecular targets for translational research.

Multi-ancestry genome-wide gene-smoking interaction study of 387,272 individuals identifies new loci associated with serum lipids

The concentrations of high- and low-density-lipoprotein cholesterol and triglycerides are influenced by smoking, but it is unknown whether genetic associations with lipids may be modified by smoking. We conducted a multi-ancestry genome-wide gene-smoking interaction study in 133,805 individuals with follow-up in an additional 253,467 individuals. Combined meta-analyses identified 13 new loci associated with lipids, some of which were detected only because association differed by smoking status. Additionally, we demonstrate the importance of including diverse populations, particularly in studies of interactions with lifestyle factors, where genomic and lifestyle differences by ancestry may contribute to novel findings.

Protein-coding variants implicate novel genes related to lipid homeostasis contributing to body-fat distribution

Body-fat distribution is a risk factor for adverse cardiovascular health consequences. We analyzed the association of body-fat distribution, assessed by waist-to-hip ratio adjusted for body mass index, with 228,985 predicted coding and splice site variants available on exome arrays in up to 344,369 individuals from five major ancestries (discovery) and 132,177 European-ancestry individuals (validation). We identified 15 common (minor allele frequency, MAF ≥5%) and nine low-frequency or rare (MAF <5%) coding novel variants. Pathway/gene set enrichment analyses identified lipid particle, adiponectin, abnormal white adipose tissue physiology and bone development and morphology as important contributors to fat distribution, while cross-trait associations highlight cardiometabolic traits. In functional follow-up analyses, specifically in Drosophila RNAi-knockdowns, we observed a significant increase in the total body triglyceride levels for two genes (DNAH10 and PLXND1). We implicate novel genes in fat distribution, stressing the importance of interrogating low-frequency and protein-coding variants.

Trans-ethnic kidney function association study reveals putative causal genes and effects on kidney-specific disease aetiologies

Chronic kidney disease (CKD) affects ~10% of the global population, with considerable ethnic differences in prevalence and aetiology. We assemble genome-wide association studies of estimated glomerular filtration rate (eGFR), a measure of kidney function that defines CKD, in 312,468 individuals of diverse ancestry. We identify 127 distinct association signals with homogeneous effects on eGFR across ancestries and enrichment in genomic annotations including kidney-specific histone modifications. Fine-mapping reveals 40 high-confidence variants driving eGFR associations and highlights putative causal genes with cell-type specific expression in glomerulus, and in proximal and distal nephron. Mendelian randomisation supports causal effects of eGFR on overall and cause-specific CKD, kidney stone formation, diastolic blood pressure and hypertension. These results define novel molecular mechanisms and putative causal genes for eGFR, offering insight into clinical outcomes and routes to CKD treatment development.

Estimated glomerular filtration rate (eGFR) is a measure of kidney function used to define chronic kidney disease. Here, Morris et al. perform trans-ethnic genome-wide meta-analyses for eGFR in 312,468 individuals and identify novel loci and downstream putative causal genes.

Publisher Correction: Genetic analysis of over 1 million people identifies 535 new loci associated with blood pressure traits

In the version of this article originally published, the name of author Martin H. de Borst was coded incorrectly in the XML. The error has now been corrected in the HTML version of the paper.