Publications by authors named "Joshua C Denny"

314 Publications

Systemic inhibition of PTPN22 augments anticancer immunity.

J Clin Invest 2021 Jul 20. Epub 2021 Jul 20.

Sidney Kimmel Comprehensive Cancer Center, John Hopkins University, Baltimore, United States of America.

Both epidemiologic and cellular studies in the context of autoimmune diseases have established that protein tyrosine phosphatase non-receptor type 22 (PTPN22) is a key regulator of T cell receptor (TCR) signaling. However, its mechanism of action in tumors and its translatability as a target for cancer immunotherapy have not been established. Here we show that a germline variant of PTPN22, rs2476601, portended a lower likelihood of cancer in patients. PTPN22 expression was also associated with markers of immune regulation in multiple cancer types. In mice, lack of PTPN22 augmented antitumor activity with greater infiltration and activation of macrophages, natural killer (NK) cells, and T cells. Notably, we generated a novel small molecule inhibitor of PTPN22, named L-1, that phenocopied the antitumor effects seen in genotypic PTPN22 knockout. PTPN22 inhibition promoted activation of CD8+ T cells and macrophage subpopulations toward MHC-II expressing M1-like phenotypes, both of which were necessary for successful antitumor efficacy. Increased PD1-PDL1 axis in the setting of PTPN22 inhibition could be further leveraged with PD1 inhibition to augment antitumor effects. Similarly, cancer patients with the rs2476601 variant responded significantly better to checkpoint inhibitor immunotherapy. Our findings suggest that PTPN22 is a druggable systemic target for cancer immunotherapy.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1172/JCI146950DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8409589PMC
July 2021

Antibodies to SARS-CoV-2 in All of Us Research Program Participants, January 2-March 18, 2020.

Clin Infect Dis 2021 Jun 15. Epub 2021 Jun 15.

All of Us Research Program, National Institutes of Health, Bethesda, MD.

Background: With limited SARS-CoV-2 testing capacity in the US at the start of the epidemic (January - March), testing was focused on symptomatic patients with a travel history throughout February, obscuring the picture of SARS-CoV-2 seeding and community transmission. We sought to identify individuals with SARS-CoV-2 antibodies in the early weeks of the US epidemic.

Methods: All of Us study participants in all 50 US states provided blood specimens during study visits from January 2 to March 18, 2020. A participant was considered seropositive if they tested positive for SARS-CoV-2 immunoglobulin G (IgG) antibodies on the Abbott Architect SARS-CoV-2 IgG ELISA and the EUROIMMUN SARS-CoV-2 ELISA in a sequential testing algorithm. Sensitivity and specificity of the Abbott and EUROIMMUNE ELISAs and the net sensitivity and specificity of the sequential testing algorithm were estimated with 95% confidence intervals.

Results: The estimated sensitivity of Abbott and EUROIMMUN was 100% (107/107 [96.6%, 100%]) and 90.7% (97/107 [83.5%, 95.4%]), respectively. The estimated specificity of Abbott and EUROIMMUN was 99.5% (995/1,000 [98.8%, 99.8%]) and 99.7% (997/1,000 [99.1%, 99.9%), respectively. The net sensitivity and specificity of our sequential testing algorithm was 90.7% (97/107 [83.5%, 95.4%]) and 100.0% (1,000/1,000 [99.6%, 100%]), respectively. Of the 24,079 study participants with blood specimens from January 2 to March 18, 2020, 9 were seropositive, 7 of whom were seropositive prior to the first confirmed case in the states of Illinois, Massachusetts, Wisconsin, Pennsylvania, and Mississippi.

Conclusions: Our findings indicate SARS-CoV-2 infections weeks prior to the first recognized cases in 5 US states.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/cid/ciab519DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8384413PMC
June 2021

A Mendelian Randomization Approach Using 3-HMG-Coenzyme-A Reductase Gene Variation to Evaluate the Association of Statin-Induced Low-Density Lipoprotein Cholesterol Lowering With Noncardiovascular Disease Phenotypes.

JAMA Netw Open 2021 Jun 1;4(6):e2112820. Epub 2021 Jun 1.

Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee.

Importance: Observational studies suggest that statins, which inhibit 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase, may be associated with beneficial effects in many noncardiovascular diseases.

Objective: To construct a weighted HMG-CoA reductase (HMGCR) gene genetic risk score (GRS) using variants in the HMGCR gene affecting low-density lipoprotein cholesterol as an instrumental variable for mendelian randomization analyses to test associations with candidate noncardiovascular phenotypes previously associated with statin use in observational studies.

Design, Setting, And Participants: This cohort study included 53 385 unrelated adults of European ancestry with genome-wide genotypes available from BioVU (a practice-based biobank, used for discovery) and 30 444 unrelated adults with European ancestry available in the Electronic Medical Records and Genomics (eMERGE; a research consortium that conducts genetic research using electronic medical records, used for replication). The study was conducted from February 6, 2015, through April 31, 2019; data analysis was performed from August 26, 2019, through December 22, 2020.

Interventions: An HMGCR GRS was calculated.

Main Outcomes And Measures: The association between the HMGCR GRS and the presence or absence of 22 noncardiovascular phenotypes previously associated with statin use in clinical studies.

Results: Of the 53 385 individuals in BioVU, 29 958 (56.1%) were women; mean (SD) age was 59.9 (15.6) years. The finding between the HMGCR GRS and the noncardiovascular phenotypes of interest in this cohort was significant only for type 2 diabetes. An HMGCR GRS equivalent to a 10-mg/dL decrease in the low-density lipoprotein cholesterol level was associated with an increased risk of type 2 diabetes (odds ratio [OR], 1.09; 95% CI, 1.04-1.15; P = 5.58 × 10-4). The HMGCR GRS was not associated with other phenotypes; the closest were increased risk of Parkinson disease (OR, 1.30; 95% CI, 1.07-1.58; P = .007) and kidney failure (OR, 1.18; 95% CI, 1.05-1.34; P = .008). Of the 30 444 individuals in eMERGE, 16 736 (55.0%) were women; mean (SD) age was 68.7 (15.4) years. The association between the HMGCR GRS and type 2 diabetes was replicated in this cohort (OR, 1.09; 95% CI, 1.01-1.17; P = .02); however, the HMGCR GRS was not associated with Parkinson disease (OR, 0.93; 95% CI, 0.75-1.16; P = .53) and kidney failure (OR, 1.18; 95% CI, 0.98-1.41; P = .08) in the eMERGE cohort.

Conclusions And Relevance: A mendelian randomization approach using variants in the HMGCR gene replicated the association between statin use and increased type 2 diabetes risk but provided no strong evidence for pleiotropic effects of statin-induced decrease of the low-density lipoprotein cholesterol level on other diseases.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1001/jamanetworkopen.2021.12820DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8185593PMC
June 2021

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

PLoS Genet 2021 Jun 1;17(6):e1009593. Epub 2021 Jun 1.

Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America.

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.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1371/journal.pgen.1009593DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8195357PMC
June 2021

Medical Records-Based Genetic Studies of the Complement System.

J Am Soc Nephrol 2021 Aug 3;32(8):2031-2047. Epub 2021 May 3.

Division of Nephrology, Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University, New York, New York

Background: Genetic variants in complement genes have been associated with a wide range of human disease states, but well-powered genetic association studies of complement activation have not been performed in large multiethnic cohorts.

Methods: We performed medical records-based genome-wide and phenome-wide association studies for plasma C3 and C4 levels among participants of the Electronic Medical Records and Genomics (eMERGE) network.

Results: In a GWAS for C3 levels in 3949 individuals, we detected two genome-wide significant loci: chr.1q31.3 (CFH locus; rs3753396-A; =0.20; 95% CI, 0.14 to 0.25; =1.52x10) and chr.19p13.3 (C3 locus; rs11569470-G; =0.19; 95% CI, 0.13 to 0.24; =1.29x10). These two loci explained approximately 2% of variance in C3 levels. GWAS for C4 levels involved 3998 individuals and revealed a genome-wide significant locus at chr.6p21.32 (C4 locus; rs3135353-C; =0.40; 95% CI, 0.34 to 0.45; =4.58x10). This locus explained approximately 13% of variance in C4 levels. The multiallelic copy number variant analysis defined two structural genomic C4 variants with large effect on blood C4 levels: C4-BS (=-0.36; 95% CI, -0.42 to -0.30; =2.98x10) and C4-AL-BS (=0.25; 95% CI, 0.21 to 0.29; =8.11x10). Overall, C4 levels were strongly correlated with copy numbers of C4A and C4B genes. In comprehensive phenome-wide association studies involving 102,138 eMERGE participants, we cataloged a full spectrum of autoimmune, cardiometabolic, and kidney diseases genetically related to systemic complement activation.

Conclusions: We discovered genetic determinants of plasma C3 and C4 levels using eMERGE genomic data linked to electronic medical records. Genetic variants regulating C3 and C4 levels have large effects and multiple clinical correlations across the spectrum of complement-related diseases in humans.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1681/ASN.2020091371DOI Listing
August 2021

Medical records-based chronic kidney disease phenotype for clinical care and "big data" observational and genetic studies.

NPJ Digit Med 2021 Apr 13;4(1):70. Epub 2021 Apr 13.

Division of Nephrology, Department of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA.

Chronic Kidney Disease (CKD) represents a slowly progressive disorder that is typically silent until late stages, but early intervention can significantly delay its progression. We designed a portable and scalable electronic CKD phenotype to facilitate early disease recognition and empower large-scale observational and genetic studies of kidney traits. The algorithm uses a combination of rule-based and machine-learning methods to automatically place patients on the staging grid of albuminuria by glomerular filtration rate ("A-by-G" grid). We manually validated the algorithm by 451 chart reviews across three medical systems, demonstrating overall positive predictive value of 95% for CKD cases and 97% for healthy controls. Independent case-control validation using 2350 patient records demonstrated diagnostic specificity of 97% and sensitivity of 87%. Application of the phenotype to 1.3 million patients demonstrated that over 80% of CKD cases are undetected using ICD codes alone. We also demonstrated several large-scale applications of the phenotype, including identifying stage-specific kidney disease comorbidities, in silico estimation of kidney trait heritability in thousands of pedigrees reconstructed from medical records, and biobank-based multicenter genome-wide and phenome-wide association studies.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41746-021-00428-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8044136PMC
April 2021

Meeting the challenge: Health information technology's essential role in achieving precision medicine.

J Am Med Inform Assoc 2021 06;28(6):1345-1352

Veterans Affairs Salt Lake City Health Care System, Salt Lake City, Utah, USA.

Precision medicine can revolutionize health care by tailoring treatments to individual patient needs. Advancing precision medicine requires evidence development through research that combines needed data, including clinical data, at an unprecedented scale. Widespread adoption of health information technology (IT) has made digital clinical data broadly available. These data and information systems must evolve to support precision medicine research and delivery. Specifically, relevant health IT data, infrastructure, clinical integration, and policy needs must be addressed. This article outlines those needs and describes work the Office of the National Coordinator for Health Information Technology is leading to improve health IT through pilot projects and standards and policy development. The Office of the National Coordinator for Health Information Technology will build on these efforts and continue to coordinate with other key stakeholders to achieve the vision of precision medicine. Advancement of precision medicine will require ongoing, collaborative health IT policy and technical initiatives that advance discovery and transform healthcare delivery.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/jamia/ocab032DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8263078PMC
June 2021

Precision medicine in 2030-seven ways to transform healthcare.

Cell 2021 03;184(6):1415-1419

National Institutes of Health, Bethesda, MD, USA.

Precision medicine promises improved health by accounting for individual variability in genes, environment, and lifestyle. Precision medicine will continue to transform healthcare in the coming decade as it expands in key areas: huge cohorts, artificial intelligence (AI), routine clinical genomics, phenomics and environment, and returning value across diverse populations.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.cell.2021.01.015DOI Listing
March 2021

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

J Am Med Inform Assoc 2021 07;28(7):1421-1430

Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA.

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.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/jamia/ocab019DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8279788PMC
July 2021

CYP2C19 Loss-of-Function Associated with First-Time Ischemic Stroke in Non-surgical Asymptomatic Carotid Artery Stenosis During Clopidogrel Therapy.

Transl Stroke Res 2021 Feb 21. Epub 2021 Feb 21.

Surgical Outcomes Center for Kids, Monroe Carell Jr. Children's Hospital at Vanderbilt, Nashville, TN, USA.

This study measures effect of CYP2C19 genotype on ischemic stroke risk during clopidogrel therapy for asymptomatic, extracranial carotid stenosis patients. Using deidentified electronic health records, patients were selected for retrospective cohort using administrative code for carotid stenosis, availability of CYP2C19 genotype result, clopidogrel exposure, and established patient care. Patients with intracranial atherosclerosis, aneurysm, arteriovenous malformation, prior ischemic stroke, or observation time <1 month were excluded. Dual antiplatelet therapy patients were included. Patients with carotid endarterectomy or stenting were analyzed in a separate subgroup. Time-to-event analysis using Cox regression was conducted to model ischemic stroke events based on CYP2C19 loss-of-function allele and adjusted with the most predictive covariates from univariate analysis. Covariates included age, gender, race, length of aspirin, length of concurrent antiplatelet/anticoagulant treatment, diabetes, coagulopathy, hypertension, heart disease, atrial fibrillation, and lipid disorder. A total of 1110 patients met selection criteria for medical therapy cohort (median age 68 [interquartile range (IQR) 60-75] years, 64.9% male, 91.9% Caucasian). Median study period was 2.8 [0.8-5.3] years. A total of 47 patients (4.2%) had an ischemic stroke event during study period. CYP2C19 loss-of-function allele was strongly associated with ischemic stroke events (one allele: HR 2.3, 95% CI 1.1-4.7, p=0.020; two alleles: HR 10.2, 95% CI 2.8-36.8, p<0.001) after adjustment. For asymptomatic carotid stenosis patients receiving clopidogrel to prevent ischemic stroke, CYP2C19 loss-of-function allele is associated with 2- to 10-fold increased risk of ischemic stroke. CYP2C19 genotype may be considered when selecting antiplatelet therapy for stroke prophylaxis in non-procedural, asymptomatic carotid stenosis.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1007/s12975-021-00896-3DOI Listing
February 2021

Association between triglycerides, known risk SNVs and conserved rare variation in SLC25A40 in a multi-ancestry cohort.

BMC Med Genomics 2021 01 6;14(1):11. Epub 2021 Jan 6.

Division of Medical Genetics, School of Medicine, University of Washington Medical Center, 1705 NE Pacific St, Box 357720, Seattle, WA, 98195, USA.

Background: Elevated triglycerides (TG) are associated with, and may be causal for, cardiovascular disease (CVD), and co-morbidities such as type II diabetes and metabolic syndrome. Pathogenic variants in APOA5 and APOC3 as well as risk SNVs in other genes [APOE (rs429358, rs7412), APOA1/C3/A4/A5 gene cluster (rs964184), INSR (rs7248104), CETP (rs7205804), GCKR (rs1260326)] have been shown to affect TG levels. Knowledge of genetic causes for elevated TG may lead to early intervention and targeted treatment for CVD. We previously identified linkage and association of a rare, highly conserved missense variant in SLC25A40, rs762174003, with hypertriglyceridemia (HTG) in a single large family, and replicated this association with rare, highly conserved missense variants in a European American and African American sample.

Methods: Here, we analyzed a longitudinal mixed-ancestry cohort (European, African and Asian ancestry, N = 8966) from the Electronic Medical Record and Genomics (eMERGE) Network. We tested associations between median TG and the genes of interest, using linear regression, adjusting for sex, median age, median BMI, and the first two principal components of ancestry.

Results: We replicated the association between TG and APOC3, APOA5, and risk variation at APOE, APOA1/C3/A4/A5 gene cluster, and GCKR. We failed to replicate the association between rare, highly conserved variation at SLC25A40 and TG, as well as for risk variation at INSR and CETP.

Conclusions: Analysis using data from electronic health records presents challenges that need to be overcome. Although large amounts of genotype data is becoming increasingly accessible, usable phenotype data can be challenging to obtain. We were able to replicate known, strong associations, but were unable to replicate moderate associations due to the limited sample size and missing drug information.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12920-020-00854-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7789246PMC
January 2021

CYP2C19 Loss-of-Function is Associated with Increased Risk of Ischemic Stroke after Transient Ischemic Attack in Intracranial Atherosclerotic Disease.

J Stroke Cerebrovasc Dis 2021 Feb 24;30(2):105464. Epub 2020 Nov 24.

Department of Neurosurgery, Vanderbilt University Medical Center, Nashville, TN, United States.

Objectives: Intracranial atherosclerotic disease (ICAD) is responsible for 8-10% of acute ischemic strokes, and resistance to antiplatelet therapy is prevalent. CYP2C19 gene loss-of-function (up to 45% of patients) causes clopidogrel resistance. For patients with asymptomatic ICAD and ICAD characterized by transient ischemic attack (TIA), this study measures the effect of CYP2C19 loss-of-function on ischemic stroke risk during clopidogrel therapy.

Materials And Methods: From a deidentified database of medical records, patients were selected with ICD-9/10 code for ICAD, availability of CYP2C19 genotype, clopidogrel exposure, and established patient care. Dual-antiplatelet therapy patients were included. Patients with prior ischemic stroke, other neurovascular condition, intracranial angioplasty/stenting, or observation time <1 month were excluded. Time-to-event analysis using Cox regression was conducted to model first-time ischemic stroke events based on CYP2C19 loss-of-function allele and adjusted for age, gender, race, length of aspirin, length of concurrent antiplatelet/anticoagulant treatment, diabetes, coagulopathy, hypertension, heart disease, atrial fibrillation, and lipid disorder. Subset analyses were performed for asymptomatic and post-TIA subtypes of ICAD.

Results: A total of 337 patients were included (median age 68, 58% male, 88% Caucasian, 26% CYP2C19 loss-of-function). A total of 161 (47.8%) patients had TIA at time of ICAD diagnosis, while 176 (52.2%) were asymptomatic. First-time ischemic stroke was observed among 20 (12.4%) post-TIA ICAD patients and 17 (9.7%) asymptomatic ICAD patients. Median observation time was 2.82 [IQR 1.13-5.17] years. CYP2C19 loss-of-function allele was associated with ischemic stroke event (HR 2.2, 95% CI 1.1-4.3, p=0.020) after adjustment. Post-TIA ICAD patients had a higher risk of ischemic stroke from CYP2C19 loss-of-function (HR 3.4, 95% CI 1.4-8.2, p=0.006).

Conclusions: CYP2C19 loss-of-function was associated with 3-fold increased risk of first-time ischemic stroke for ICAD patients treated with clopidogrel after TIA. This effect was not observed for asymptomatic ICAD. CYP2C19-guided antiplatelet selection may improve stroke prevention in ICAD after TIA.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.jstrokecerebrovasdis.2020.105464DOI Listing
February 2021

LabWAS: Novel findings and study design recommendations from a meta-analysis of clinical labs in two independent biobanks.

PLoS Genet 2020 11 11;16(11):e1009077. Epub 2020 Nov 11.

Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, United States of America.

Phenotypes extracted from Electronic Health Records (EHRs) are increasingly prevalent in genetic studies. EHRs contain hundreds of distinct clinical laboratory test results, providing a trove of health data beyond diagnoses. Such lab data is complex and lacks a ubiquitous coding scheme, making it more challenging than diagnosis data. Here we describe the first large-scale cross-health system genome-wide association study (GWAS) of EHR-based quantitative laboratory-derived phenotypes. We meta-analyzed 70 lab traits matched between the BioVU cohort from the Vanderbilt University Health System and the Michigan Genomics Initiative (MGI) cohort from Michigan Medicine. We show high replication of known association for these traits, validating EHR-based measurements as high-quality phenotypes for genetic analysis. Notably, our analysis provides the first replication for 699 previous GWAS associations across 46 different traits. We discovered 31 novel associations at genome-wide significance for 22 distinct traits, including the first reported associations for two lab-based traits. We replicated 22 of these novel associations in an independent tranche of BioVU samples. The summary statistics for all association tests are freely available to benefit other researchers. Finally, we performed mirrored analyses in BioVU and MGI to assess competing analytic practices for EHR lab traits. We find that using the mean of all available lab measurements provides a robust summary value, but alternate summarizations can improve power in certain circumstances. This study provides a proof-of-principle for cross health system GWAS and is a framework for future studies of quantitative EHR lab traits.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1371/journal.pgen.1009077DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7682892PMC
November 2020

Real-time clinical note monitoring to detect conditions for rapid follow-up: A case study of clinical trial enrollment in drug-induced torsades de pointes and Stevens-Johnson syndrome.

J Am Med Inform Assoc 2021 01;28(1):126-131

Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA.

Identifying acute events as they occur is challenging in large hospital systems. Here, we describe an automated method to detect 2 rare adverse drug events (ADEs), drug-induced torsades de pointes and Stevens-Johnson syndrome and toxic epidermal necrolysis, in near real time for participant recruitment into prospective clinical studies. A text processing system searched clinical notes from the electronic health record (EHR) for relevant keywords and alerted study personnel via email of potential patients for chart review or in-person evaluation. Between 2016 and 2018, the automated recruitment system resulted in capture of 138 true cases of drug-induced rare events, improving recall from 43% to 93%. Our focused electronic alert system maintained 2-year enrollment, including across an EHR migration from a bespoke system to Epic. Real-time monitoring of EHR notes may accelerate research for certain conditions less amenable to conventional study recruitment paradigms.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/jamia/ocaa213DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7810433PMC
January 2021

PheWAS-ME: a web-app for interactive exploration of multimorbidity patterns in PheWAS.

Bioinformatics 2021 07;37(12):1778-1780

Department of Biostatistics, Vanderbilt University, Nashville, TN 37203, USA.

Summary: Electronic health records (EHRs) linked with a DNA biobank provide unprecedented opportunities for biomedical research in precision medicine. The Phenome-wide association study (PheWAS) is a widely used technique for the evaluation of relationships between genetic variants and a large collection of clinical phenotypes recorded in EHRs. PheWAS analyses are typically presented as static tables and charts of summary statistics obtained from statistical tests of association between a genetic variant and individual phenotypes. Comorbidities are common and typically lead to complex, multivariate gene-disease association signals that are challenging to interpret. Discovering and interrogating multimorbidity patterns and their influence in PheWAS is difficult and time-consuming. We present PheWAS-ME: an interactive dashboard to visualize individual-level genotype and phenotype data side-by-side with PheWAS analysis results, allowing researchers to explore multimorbidity patterns and their associations with a genetic variant of interest. We expect this application to enrich PheWAS analyses by illuminating clinical multimorbidity patterns present in the data.

Availability And Implementation: A demo PheWAS-ME application is publicly available at https://prod.tbilab.org/phewas_me/. Sample datasets are provided for exploration with the option to upload custom PheWAS results and corresponding individual-level data. Online versions of the appendices are available at https://prod.tbilab.org/phewas_me_info/. The source code is available as an R package on GitHub (https://github.com/tbilab/multimorbidity_explorer).

Supplementary Information: Supplementary data are available at Bioinformatics online.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/bioinformatics/btaa870DOI Listing
July 2021

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

J Am Med Inform Assoc 2020 11;27(11):1675-1687

Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA.

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.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/jamia/ocaa104DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7751140PMC
November 2020

Evaluation of the MC4R gene across eMERGE network identifies many unreported obesity-associated variants.

Int J Obes (Lond) 2021 01 20;45(1):155-169. Epub 2020 Sep 20.

Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center (CCHMC), Cincinnati, OH, USA.

Background/objectives: Melanocortin-4 receptor (MC4R) plays an essential role in food intake and energy homeostasis. More than 170 MC4R variants have been described over the past two decades, with conflicting reports regarding the prevalence and phenotypic effects of these variants in diverse cohorts. To determine the frequency of MC4R variants in large cohort of different ancestries, we evaluated the MC4R coding region for 20,537 eMERGE participants with sequencing data plus additional 77,454 independent individuals with genome-wide genotyping data at this locus.

Subjects/methods: The sequencing data were obtained from the eMERGE phase III study, in which multisample variant call format calls have been generated, curated, and annotated. In addition to penetrance estimation using body mass index (BMI) as a binary outcome, GWAS and PheWAS were performed using median BMI in linear regression analyses. All results were adjusted for principal components, age, sex, and sites of genotyping.

Results: Targeted sequencing data of MC4R revealed 125 coding variants in 1839 eMERGE participants including 30 unreported coding variants that were predicted to be functionally damaging. Highly penetrant unreported variants included (L325I, E308K, D298N, S270F, F261L, T248A, D111V, and Y80F) in which seven participants had obesity class III defined as BMI ≥ 40 kg/m. In GWAS analysis, in addition to known risk haplotype upstream of MC4R (best variant rs6567160 (P = 5.36 × 10, Beta = 0.37), a novel rare haplotype was detected which was protective against obesity and encompassed the V103I variant with known gain-of-function properties (P = 6.23 × 10, Beta = -0.62). PheWAS analyses extended this protective effect of V103I to type 2 diabetes, diabetic nephropathy, and chronic renal failure independent of BMI.

Conclusions: MC4R screening in a large eMERGE cohort confirmed many previous findings, extend the MC4R pleotropic effects, and discovered additional MC4R rare alleles that probably contribute to obesity.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41366-020-00675-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7752751PMC
January 2021

Diversity and inclusion for the All of Us research program: A scoping review.

PLoS One 2020 1;15(7):e0234962. Epub 2020 Jul 1.

National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, Maryland, United States of America.

The All of Us Research Program (All of Us) is a national effort to accelerate health research by exploring the relationship between lifestyle, environment, and genetics. It is set to become one of the largest research efforts in U.S. history, aiming to build a national resource of data from at least one million participants. All of Us aims to address the need for more diversity in research and set the stage for that diversity to be leveraged in precision medicine research to come. This paper describes how the program assessed demographic characteristics of participants who have enrolled in other U.S. biomedical research cohorts to better understand which groups are traditionally represented or underrepresented in biomedical research. We 1) reviewed the enrollment characteristics of national cohort studies like All of Us, and 2) surveyed the literature, focusing on key diversity categories essential to the program's enrollment aims. Based on these efforts, All of Us emphasizes enrollment of racial and ethnic minorities, and has formally designated the following additional groups as historically underrepresented: individuals-with inadequate access to medical care; under the age of 18 or over 65; with an annual household income at or below 200% of the federal poverty level; who have a cognitive or physical disability; have less than a high school education or equivalent; are intersex; identify as a sexual or gender minority; or live in rural or non-metropolitan areas. Research accounting for wider demographic variability is critical. Only by ensuring diversity and by addressing the very barriers that limit it, can we position All of Us to better understand and tackle health disparities.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0234962PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7329113PMC
September 2020

Evaluating the Utility of Polygenic Risk Scores in Identifying High-Risk Individuals for Eight Common Cancers.

JNCI Cancer Spectr 2020 Jun 12;4(3):pkaa021. Epub 2020 Mar 12.

Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA.

Background: Genome-wide association studies have identified common genetic risk variants in many loci associated with multiple cancers. We sought to systematically evaluate the utility of these risk variants in identifying high-risk individuals for eight common cancers.

Methods: We constructed polygenic risk scores (PRS) using genome-wide association studies-identified risk variants for each cancer. Using data from 400 812 participants of European descent in a population-based cohort study, UK Biobank, we estimated hazard ratios associated with PRS using Cox proportional hazard models and evaluated the performance of the PRS in cancer risk prediction and their ability to identify individuals at more than a twofold elevated risk, a risk level comparable to a moderate-penetrance mutation in known cancer predisposition genes.

Results: During a median follow-up of 5.8 years, 14 584 incident case patients of cancers were identified (ranging from 358 epithelial ovarian cancer case patients to 4430 prostate cancer case patients). Compared with those at an average risk, individuals among the highest 5% of the PRS had a two- to threefold elevated risk for cancer of the prostate, breast, pancreas, colorectal, or ovary, and an approximately 1.5-fold elevated risk of cancer of the lung, bladder, or kidney. The areas under the curve ranged from 0.567 to 0.662. Using PRS, 40.4% of the study participants can be classified as having more than a twofold elevated risk for at least one site-specific cancer.

Conclusions: A large proportion of the general population can be identified at an elevated cancer risk by PRS, supporting the potential clinical utility of PRS for personalized cancer risk prediction.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/jncics/pkaa021DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7306192PMC
June 2020

The polygenic architecture of left ventricular mass mirrors the clinical epidemiology.

Sci Rep 2020 05 5;10(1):7561. Epub 2020 May 5.

Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.

Left ventricular (LV) mass is a prognostic biomarker for incident heart disease and all-cause mortality. Large-scale genome-wide association studies have identified few SNPs associated with LV mass. We hypothesized that a polygenic discovery approach using LV mass measurements made in a clinical population would identify risk factors and diseases associated with adverse LV remodeling. We developed a polygenic single nucleotide polymorphism-based predictor of LV mass in 7,601 individuals with LV mass measurements made during routine clinical care. We tested for associations between this predictor and 894 clinical diagnoses measured in 58,838 unrelated genotyped individuals. There were 29 clinical phenotypes associated with the LV mass genetic predictor at FDR q < 0.05. Genetically predicted higher LV mass was associated with modifiable cardiac risk factors, diagnoses related to organ dysfunction and conditions associated with abnormal cardiac structure including heart failure and atrial fibrillation. Secondary analyses using polygenic predictors confirmed a significant association between higher LV mass and body mass index and, in men, associations with coronary atherosclerosis and systolic blood pressure. In summary, these analyses show that LV mass-associated genetic variability associates with diagnoses of cardiac diseases and with modifiable risk factors which contribute to these diseases.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41598-020-64525-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7200691PMC
May 2020

Phenome-wide association analysis suggests the APOL1 linked disease spectrum primarily drives kidney-specific pathways.

Kidney Int 2020 05 17;97(5):1032-1041. Epub 2020 Feb 17.

Department of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA; Renal Electrolyte and Hypertension Division, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA; Cardiovascular Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA. Electronic address:

The relationship between commonly occurring genetic variants (G1 and G2) in the APOL1 gene in African Americans and different disease traits, such as kidney disease, cardiovascular disease, and pre-eclampsia, remains the subject of controversy. Here we took a genotype-first approach, a phenome-wide association study, to define the spectrum of phenotypes associated with APOL1 high-risk variants in 1,837 African American participants of Penn Medicine Biobank and 4,742 African American participants of Vanderbilt BioVU. In the Penn Medicine Biobank, outpatient creatinine measurement-based estimated glomerular filtration rate and multivariable regression models were used to evaluate the association between high-risk APOL1 status and renal outcomes. In meta-analysis of both cohorts, the strongest phenome-wide association study associations were for the high-risk APOL1 variants and diagnoses codes were highly significant for "kidney dialysis" (odds ratio 3.75) and "end stage kidney disease" (odds ratio 3.42). A number of phenotypes were associated with APOL1 high-risk genotypes in an analysis adjusted only for demographic variables. However, no associations were detected with non-renal phenotypes after controlling for chronic/end stage kidney disease status. Using calculated estimated glomerular filtration rate -based phenotype analysis in the Penn Medicine Biobank, APOL1 high-risk status was associated with prevalent chronic/end stage kidney disease /kidney transplant (odds ratio 2.27, 95% confidence interval 1.67-3.08). In high-risk participants, the estimated glomerular filtration rate was 15.4 mL/min/1.73m; significantly lower than in low-risk participants. Thus, although APOL1 high-risk variants are associated with a range of phenotypes, the risks for other associated phenotypes appear much lower and in our dataset are driven by a primary effect on renal disease.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.kint.2020.01.027DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7265573PMC
May 2020

Leveraging Human Genetics to Identify Safety Signals Prior to Drug Marketing Approval and Clinical Use.

Drug Saf 2020 06;43(6):567-582

Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, TN, USA.

Introduction: When a new drug or biologic product enters the market, its full spectrum of side effects is not yet fully understood, as use in the real world often uncovers nuances not suggested within the relatively narrow confines of preapproval preclinical and trial work.

Objective: We describe a new, phenome-wide association study (PheWAS)- and evidence-based approach for detection of potential adverse drug effects.

Methods: We leveraged our established platform, which integrates human genetic data with associated phenotypes in electronic health records from 29,722 patients of European ancestry, to identify gene-phenotype associations that may represent known safety issues. We examined PheWAS data and the published literature for 16 genes, each of which encodes a protein targeted by at least one drug or biologic product.

Results: Initial data demonstrated that our novel approach (safety ascertainment using PheWAS [SA-PheWAS]) can replicate published safety information across multiple drug classes, with validated findings for 13 of 16 gene-drug class pairs.

Conclusions: By connecting and integrating in vivo and in silico data, SA-PheWAS offers an opportunity to supplement current methods for predicting or confirming safety signals associated with therapeutic agents.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1007/s40264-020-00915-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7398579PMC
June 2020

Mendelian Randomization of Circulating Polyunsaturated Fatty Acids and Colorectal Cancer Risk.

Cancer Epidemiol Biomarkers Prev 2020 04 12;29(4):860-870. Epub 2020 Feb 12.

Department of Clinical Genetics, Karolinska University Hospital, Stockholm, Sweden.

Background: Results from epidemiologic studies examining polyunsaturated fatty acids (PUFA) and colorectal cancer risk are inconsistent. Mendelian randomization may strengthen causal inference from observational studies. Given their shared metabolic pathway, examining the combined effects of aspirin/NSAID use with PUFAs could help elucidate an association between PUFAs and colorectal cancer risk.

Methods: Information was leveraged from genome-wide association studies (GWAS) regarding PUFA-associated SNPs to create weighted genetic scores (wGS) representing genetically predicted circulating blood PUFAs for 11,016 non-Hispanic white colorectal cancer cases and 13,732 controls in the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO). Associations per SD increase in the wGS were estimated using unconditional logistic regression. Interactions between PUFA wGSs and aspirin/NSAID use on colorectal cancer risk were also examined.

Results: Modest colorectal cancer risk reductions were observed per SD increase in circulating linoleic acid [OR = 0.96; 95% confidence interval (CI) = 0.93-0.98; = 5.2 × 10] and α-linolenic acid (OR = 0.95; 95% CI = 0.92-0.97; = 5.4 × 10), whereas modest increased risks were observed for arachidonic (OR = 1.06; 95% CI = 1.03-1.08; = 3.3 × 10), eicosapentaenoic (OR = 1.04; 95% CI = 1.01-1.07; = 2.5 × 10), and docosapentaenoic acids (OR = 1.03; 95% CI = 1.01-1.06; = 1.2 × 10). Each of these effects was stronger among aspirin/NSAID nonusers in the stratified analyses.

Conclusions: Our study suggests that higher circulating shorter-chain PUFAs (i.e., LA and ALA) were associated with reduced colorectal cancer risk, whereas longer-chain PUFAs (i.e., AA, EPA, and DPA) were associated with an increased colorectal cancer risk.

Impact: The interaction of PUFAs with aspirin/NSAID use indicates a shared colorectal cancer inflammatory pathway. Future research should continue to improve PUFA genetic instruments to elucidate the independent effects of PUFAs on colorectal cancer.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1158/1055-9965.EPI-19-0891DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7125012PMC
April 2020

Development of a System for Postmarketing Population Pharmacokinetic and Pharmacodynamic Studies Using Real-World Data From Electronic Health Records.

Clin Pharmacol Ther 2020 04 11;107(4):934-943. Epub 2020 Feb 11.

Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA.

Postmarketing population pharmacokinetic (PK) and pharmacodynamic (PD) studies can be useful to capture patient characteristics affecting PK or PD in real-world settings. These studies require longitudinally measured dose, outcomes, and covariates in large numbers of patients; however, prospective data collection is cost-prohibitive. Electronic health records (EHRs) can be an excellent source for such data, but there are challenges, including accurate ascertainment of drug dose. We developed a standardized system to prepare datasets from EHRs for population PK/PD studies. Our system handles a variety of tasks involving data extraction from clinical text using a natural language processing algorithm, data processing, and data building. Applying this system, we performed a fentanyl population PK analysis, resulting in comparable parameter estimates to a prior study. This new system makes the EHR data extraction and preparation process more efficient and accurate and provides a powerful tool to facilitate postmarketing population PK/PD studies using information available in EHRs.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1002/cpt.1787DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7093250PMC
April 2020

medExtractR: A targeted, customizable approach to medication extraction from electronic health records.

J Am Med Inform Assoc 2020 03;27(3):407-418

Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, USA.

Objective: We developed medExtractR, a natural language processing system to extract medication information from clinical notes. Using a targeted approach, medExtractR focuses on individual drugs to facilitate creation of medication-specific research datasets from electronic health records.

Materials And Methods: Written using the R programming language, medExtractR combines lexicon dictionaries and regular expressions to identify relevant medication entities (eg, drug name, strength, frequency). MedExtractR was developed on notes from Vanderbilt University Medical Center, using medications prescribed with varying complexity. We evaluated medExtractR and compared it with 3 existing systems: MedEx, MedXN, and CLAMP (Clinical Language Annotation, Modeling, and Processing). We also demonstrated how medExtractR can be easily tuned for better performance on an outside dataset using the MIMIC-III (Medical Information Mart for Intensive Care III) database.

Results: On 50 test notes per development drug and 110 test notes for an additional drug, medExtractR achieved high overall performance (F-measures >0.95), exceeding performance of the 3 existing systems across all drugs. MedExtractR achieved the highest F-measure for each individual entity, except drug name and dose amount for allopurinol. With tuning and customization, medExtractR achieved F-measures >0.90 in the MIMIC-III dataset.

Discussion: The medExtractR system successfully extracted entities for medications of interest. High performance in entity-level extraction provides a strong foundation for developing robust research datasets for pharmacological research. When working with new datasets, medExtractR should be tuned on a small sample of notes before being broadly applied.

Conclusions: The medExtractR system achieved high performance extracting specific medications from clinical text, leading to higher-quality research datasets for drug-related studies than some existing general-purpose medication extraction tools.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/jamia/ocz207DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7025369PMC
March 2020

A Polygenic and Phenotypic Risk Prediction for Polycystic Ovary Syndrome Evaluated by Phenome-Wide Association Studies.

J Clin Endocrinol Metab 2020 06;105(6)

Division of Endocrinology, Metabolism, and Molecular Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois.

Context: As many as 75% of patients with polycystic ovary syndrome (PCOS) are estimated to be unidentified in clinical practice.

Objective: Utilizing polygenic risk prediction, we aim to identify the phenome-wide comorbidity patterns characteristic of PCOS to improve accurate diagnosis and preventive treatment.

Design, Patients, And Methods: Leveraging the electronic health records (EHRs) of 124 852 individuals, we developed a PCOS risk prediction algorithm by combining polygenic risk scores (PRS) with PCOS component phenotypes into a polygenic and phenotypic risk score (PPRS). We evaluated its predictive capability across different ancestries and perform a PRS-based phenome-wide association study (PheWAS) to assess the phenomic expression of the heightened risk of PCOS.

Results: The integrated polygenic prediction improved the average performance (pseudo-R2) for PCOS detection by 0.228 (61.5-fold), 0.224 (58.8-fold), 0.211 (57.0-fold) over the null model across European, African, and multi-ancestry participants respectively. The subsequent PRS-powered PheWAS identified a high level of shared biology between PCOS and a range of metabolic and endocrine outcomes, especially with obesity and diabetes: "morbid obesity", "type 2 diabetes", "hypercholesterolemia", "disorders of lipid metabolism", "hypertension", and "sleep apnea" reaching phenome-wide significance.

Conclusions: Our study has expanded the methodological utility of PRS in patient stratification and risk prediction, especially in a multifactorial condition like PCOS, across different genetic origins. By utilizing the individual genome-phenome data available from the EHR, our approach also demonstrates that polygenic prediction by PRS can provide valuable opportunities to discover the pleiotropic phenomic network associated with PCOS pathogenesis.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1210/clinem/dgz326DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7453038PMC
June 2020

Translational Health Disparities Research in a Data-Rich World.

Health Equity 2019 8;3(1):588-600. Epub 2019 Nov 8.

Data Science Institute and Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia.

Despite decades of research and interventions, significant health disparities persist. Seventeen years is the estimated time to translate scientific discoveries into public health action. This Narrative Review argues that the translation process could be accelerated if representative data were gathered and used in more innovative and efficient ways. The National Institute on Minority Health and Health Disparities led a multiyear visioning process to identify research opportunities designed to frame the next decade of research and actions to improve minority health and reduce health disparities. "Big data" was identified as a research opportunity and experts collaborated on a systematic vision of how to use big data both to improve the granularity of information for place-based study and to efficiently translate health disparities research into improved population health. This Narrative Review is the result of that collaboration. Big data could enhance the process of translating scientific findings into reduced health disparities by contributing information at fine spatial and temporal scales suited to interventions. In addition, big data could fill pressing needs for health care system, genomic, and social determinant data to understand mechanisms. Finally, big data could lead to appropriately personalized health care for demographic groups. Rich new resources, including social media, electronic health records, sensor information from digital devices, and crowd-sourced and citizen-collected data, have the potential to complement more traditional data from health surveys, administrative data, and investigator-initiated registries or cohorts. This Narrative Review argues for a renewed focus on translational research cycles to accomplish this continual assessment. The promise of big data extends from etiology research to the evaluation of large-scale interventions and offers the opportunity to accelerate translation of health disparities studies. This data-rich world for health disparities research, however, will require continual assessment for efficacy, ethical rigor, and potential algorithmic or system bias.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1089/heq.2019.0042DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6844128PMC
November 2019

The "All of Us" Research Program. Reply.

N Engl J Med 2019 11;381(19):1884-1885

National Institutes of Health, Bethesda, MD.

View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1056/NEJMc1912496DOI Listing
November 2019

Cox regression increases power to detect genotype-phenotype associations in genomic studies using the electronic health record.

BMC Genomics 2019 Nov 4;20(1):805. Epub 2019 Nov 4.

Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA.

Background: The growth of DNA biobanks linked to data from electronic health records (EHRs) has enabled the discovery of numerous associations between genomic variants and clinical phenotypes. Nonetheless, although clinical data are generally longitudinal, standard approaches for detecting genotype-phenotype associations in such linked data, notably logistic regression, do not naturally account for variation in the period of follow-up or the time at which an event occurs. Here we explored the advantages of quantifying associations using Cox proportional hazards regression, which can account for the age at which a patient first visited the healthcare system (left truncation) and the age at which a patient either last visited the healthcare system or acquired a particular phenotype (right censoring).

Results: In comprehensive simulations, we found that, compared to logistic regression, Cox regression had greater power at equivalent Type I error. We then scanned for genotype-phenotype associations using logistic regression and Cox regression on 50 phenotypes derived from the EHRs of 49,792 genotyped individuals. Consistent with the findings from our simulations, Cox regression had approximately 10% greater relative sensitivity for detecting known associations from the NHGRI-EBI GWAS Catalog. In terms of effect sizes, the hazard ratios estimated by Cox regression were strongly correlated with the odds ratios estimated by logistic regression.

Conclusions: As longitudinal health-related data continue to grow, Cox regression may improve our ability to identify the genetic basis for a wide range of human phenotypes.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12864-019-6192-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6829851PMC
November 2019

Phenome-wide association analysis of LDL-cholesterol lowering genetic variants in PCSK9.

BMC Cardiovasc Disord 2019 10 29;19(1):240. Epub 2019 Oct 29.

Department Primary Care & Population Health, University College London, London, UK.

Background: We characterised the phenotypic consequence of genetic variation at the PCSK9 locus and compared findings with recent trials of pharmacological inhibitors of PCSK9.

Methods: Published and individual participant level data (300,000+ participants) were combined to construct a weighted PCSK9 gene-centric score (GS). Seventeen randomized placebo controlled PCSK9 inhibitor trials were included, providing data on 79,578 participants. Results were scaled to a one mmol/L lower LDL-C concentration.

Results: The PCSK9 GS (comprising 4 SNPs) associations with plasma lipid and apolipoprotein levels were consistent in direction with treatment effects. The GS odds ratio (OR) for myocardial infarction (MI) was 0.53 (95% CI 0.42; 0.68), compared to a PCSK9 inhibitor effect of 0.90 (95% CI 0.86; 0.93). For ischemic stroke ORs were 0.84 (95% CI 0.57; 1.22) for the GS, compared to 0.85 (95% CI 0.78; 0.93) in the drug trials. ORs with type 2 diabetes mellitus (T2DM) were 1.29 (95% CI 1.11; 1.50) for the GS, as compared to 1.00 (95% CI 0.96; 1.04) for incident T2DM in PCSK9 inhibitor trials. No genetic associations were observed for cancer, heart failure, atrial fibrillation, chronic obstructive pulmonary disease, or Alzheimer's disease - outcomes for which large-scale trial data were unavailable.

Conclusions: Genetic variation at the PCSK9 locus recapitulates the effects of therapeutic inhibition of PCSK9 on major blood lipid fractions and MI. While indicating an increased risk of T2DM, no other possible safety concerns were shown; although precision was moderate.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12872-019-1187-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6820948PMC
October 2019
-->