Biology subjects

Denny, J. C.

Publications and source records attributed to Denny, J. C..

13 recordsLinked to original sources

bioRxiv2022

Cloud gazing: demonstrating paths for unlocking the value of cloud genomics through cross-cohort analysis

The rapid growth of genomic data has led to a new research paradigm where data are stored centrally in Trusted Research Environments (TREs) such as the All of Us Researcher Workbench (AoU RW) and the UK Biobank Research Analysis Platform (RAP). To characterize the advantages and drawbacks of different TRE attributes in facilitating cross-cohort analysis, we conducted a Genome-Wide Association Study (GWAS) of standard lipid measures on the UKB RAP and AoU RW using two approaches: meta-analysis and pooled analysis. We curated lipid measurements for 37,754 All of Us participants with whole genome sequence (WGS) data and 190,982 UK Biobank participants with whole exome sequence (WES) data. For the meta-analysis, we performed a GWAS of each cohort in their respective platform and meta-analyzed the results. We separately performed a pooled GWAS on both datasets combined. We identified 490 and 464 significant variants in meta-analysis and pooled analysis, respectively. Comparison of full summary data from both meta-analysis and pooled analysis with an external study showed strong correlation of known loci with lipid levels (R2[~]83-97%). Importantly, 90 variants met the significance threshold only in the meta-analysis and 64 variants were significant only in pooled analysis. These method-specific differences may be explained by differences in cohort size, ancestry, and phenotype distributions in All of Us and UK Biobank. We noted approximately 20% of variants significant in only the pooled analysis or significant in only the meta-analysis were most prevalent in non-European, non-Asian ancestry individuals. Pooled analyses included more variants than meta-analyses. Pooled analysis required about half as many computational steps as meta-analysis. These findings have important implications for both platform implementations and researchers undertaking large-scale cross-cohort analyses, as technical and policy choices lead to cross-cohort analyses generating similar, but not identical results, particularly for non-European ancestral populations.

genetics↗

bioRxiv2022

The Phenotype-Genotype Reference Map: Improving biobank data science through replication.

Population-scale biobanks linked to electronic health record data provide vast opportunity to extend our knowledge of human genetics. While biobanks have already proven their value to research, data quality remains an important concern. Here we introduce the phenotype-genotype reference map (PGRM), a set of 5,879 genetic associations from 523 GWAS publications that can be used for high-throughput replication experiments in biobank data. We tested the PGRM on five ancestry-specific cohorts drawn from four established, independent biobanks and found evidence of robust replications across a wide array of phenotypes. We defined simple replication measures and show how these can be applied to any EHR-linked biobank to detect data corruption and to empirically assess parameters for phenome-wide studies. Finally, we used the PGRM to determine factors associated with reproducibility of GWAS results.

genetics↗

bioRxiv2020

Multi-ancestry Genome- and Phenome-wide Association Studies of Diverticular Disease in Electronic Health Records with Natural Language Processing enriched phenotype algorithm

Background and aimsDiverticular disease is among the most prevalent conditions encountered by gastroenterologists, affecting [~]50% of Americans before the age of 60. Our aim was to identify genetic risk variants and clinical phenotypes associated with diverticular disease, utilizing the electronic health record (EHR) with Natural Language Processing (NLP). MethodsWe developed a NLP-enriched phenotype algorithm that incorporated colonoscopy or abdominal imaging reports to accurately identify patients with diverticulosis and diverticulitis from multicenter EHRs. We performed genome-wide association studies (GWAS) of diverticular disease in European, African and multi-ancestry participants, followed by phenome-wide association studies (PheWAS) of the risk variants to identify their potential comorbid/pleiotropic effects in the clinical phenome. For more in-depth investigation of associated clinical phenotypes, we also performed PheWAS with the previously reported 52 GWAS susceptibility variants for diverticular disease. ResultsAncestry-stratified GWAS analyses confirmed the well-established associations between ARHGAP15 loci with diverticular disease in European cohorts, and found similar positive effect sizes in African cohorts but with non-significant p-values. With overall intensified GWAS signals in diverticulitis patients compared to diverticulosis patients, we found substantial genetic correlations between diverticulosis and diverticulitis, up to 0.997 in European ancestry. PheWAS analyses identified associations between the diverticular disease GWAS variants and circulatory system, genitourinary, and neoplastic EHR phenotypes. ConclusionOur multiancestry GWAS-PheWAS study demonstrated an effective use of multidimensional EHR information in disease case/control classification with NLP for more comprehensive and scalable phenotyping, and implementation of an integrative analytical pipeline to facilitate etiological investigation of a disease from a clinical perspective.

bioinformatics↗

bioRxiv2019

A polygenic and phenotypic risk prediction for Polycystic Ovary Syndrome evaluated by Phenome-wide association studies

PurposeAs many as 75% of patients with Polycystic ovary syndrome (PCOS) are estimated to be unidentified in clinical practice. Utilizing polygenic risk prediction, we aim to identify the phenome-wide comorbidity patterns characteristic of PCOS to improve accurate diagnosis and preventive treatment.\n\nMethods and FindingsLeveraging 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. 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.\n\nConclusionsOur 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.

bioinformatics↗

bioRxiv2019

Temporal changes in genetic admixture are linked to heterozygosity and health diagnoses in humans

Reproduction between individuals from different ancestral populations creates genetically admixed offspring. Admixture can have positive and negative impacts on individual health, feeding back to population health. Historical and forced migrations, and recent mobility, have brought formerly disparate populations of humans together. Here we sought to better understand how temporal changes in genetic admixture influence levels of heterozygosity and health outcomes. We evaluated variation in ancestry over 100 birth years in 35,842 individuals from a genetic database linked to health records in a population in the Southeastern United States. Analysis of 2,678 ancestrally informative markers revealed increased admixture and heterozygosity for all clinically-defined race groups since 1990. Most groups also exhibited increasing long-range linkage disequilibrium over time. A phenome-wide association study of clinical outcomes detected protective associations with female reproductive disorders and increased risk for diseases with links to autoimmunity dysfunction. These mixed effects have important ramifications for human health.

genetics↗

bioRxiv2019

A Phenome-wide Mendelian Randomisation study on genetically determined serum urate levels in UK Biobank cohort

IntroductionThe role of serum urate level has been extensively investigated in observational studies. However, the extent of any causal effect remains unclear, making it difficult to evaluate its clinical relevance.\n\nObjectivesTo explore any causal or pleiotropic association between serum urate level and a broad spectrum of disease outcomes.\n\nMethodsPhenome-wide association study (PheWAS) together with a Bayesian analysis of tree-structured phenotypic models (TreeWAS) was performed to examine disease outcomes related to genetically determined serum urate levels in 339,256 UK Biobank participants. Mendelian Randomisation (MR) analyses were performed to replicate significant findings using various GWAS consortia data. Sensitivity analyses were conducted to examine possible pleiotropic effects on metabolic traits of the genetic variants used as instruments for serum urate.\n\nResultsPheWAS analysis, examining the association with 1,431 disease outcomes, identified a multitude of disease outcomes including gout, hypertension, hypercholesterolemia, and heart diseases (e.g., coronary atherosclerosis, myocardial infarction, and ischaemic heart disease) that were associated (p<3.35e-04) with genetically determined serum urate levels. TreeWAS analysis, examining 10,750 ICD-10 diagnostic terms, identified more sub-phenotypes of cardiovascular and cerebrovascular diseases (e.g., angina pectoris, heart failure, cerebral infarction). MR analysis successfully replicated the association with gout, hypertension, heart diseases and blood lipid levels, but indicated the existence of genetic pleiotropy. Sensitivity analyses support an inference that pleiotropic effects of genetic variants on urate and metabolic traits contribute to the observational associations with cardiovascular diseases.\n\nConclusionsHigh serum urate levels are associated with different types of cardiac events. The finding of genetic pleiotropy indicates the existence of common upstream pathological elements influencing both urate and metabolic traits, and this may suggest new opportunities and challenges for developing drugs targeting a more distal mediator that would be beneficial for both the treatment of gout and the prevention of cardiovascular comorbidities.

epidemiology↗

bioRxiv2019

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

BackgroundThe 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 the times at which events occur. 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).\n\nResultsUsing simulated data, 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 electronic health records of 49 792 genotyped individuals. In terms of effect sizes, the hazard ratios estimated by Cox regression were nearly identical to the odds ratios estimated by logistic regression. 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.\n\nConclusionsAs 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.

bioinformatics↗

bioRxiv2019

A rare variant on a common risk haplotype of HFE causes increased risk of hereditary hemochromatosis

Hereditary hemochromatosis (HH) is an autosomal recessive disorder of excess iron absorption. The most common form, HH1, is caused by loss of function variants in HFE. HFE encodes a cell surface protein that binds to the Transferrin Receptor (TfR1), reducing TfR1s affinity for the transferrin/iron complex and thereby limiting cellular iron uptake. Two common missense alleles for HH1 have been identified, HFE C282Y and HFE H63D; H63D is considered to be a less penetrant allele. When we deployed Phenotype Risk Scores (PheRS), a method that aggregates multiple symptoms together in Electronic Health Records (EHRs), we identified HFE E168Q as a novel variant associated with HH. E168Q is on the same haplotype as H63D, and in a crystal structure HFE E168 lies at the interface of the HFE-TfR1 interaction and makes multiple salt bridge connections with TfR1. In in vitro cell surface abundance experiments, the HFE E168Q+H63D double mutation surprisingly increased cell surface abundance of HFE by 10-fold compared to wildtype. In coimmunoprecipitation experiments, however, HFE C282Y, E168Q, and E168Q+H63D completely abolished the interaction between HFE and TfR1, while H63D alone only partially reduced binding. These findings provide mechanistic insight to validate the PheRS result that HFE E168Q is an HH1-associated allele and lead to the reclassification of E168Q from a variant of uncertain significance to a pathogenic variant, according to ACMG guidelines. HFE E168Q results in loss of HFE function by disrupting the HFE-TfR1 interaction. In addition, some disease manifestations attributed to H63D may reflect the functional effects of E168Q.

genetics↗

bioRxiv2018

Genetic variants related to antihypertensive targets inform drug efficacy and side effects

BackgroundDrug effects can be investigated through natural variation in the genes for their protein targets. We aimed to use this approach to explore the potential side effects and repurposing potential of antihypertensive drugs, which are amongst the most commonly used medications worldwide.\n\nMethodsWe identified genetic instruments for antihypertensive drug classes as variants in the gene for the corresponding target that associated with systolic blood pressure at genome-wide significance. To validate the instruments, we compared Mendelian randomisation (MR) estimates for drug effects on coronary heart disease (CHD) and stroke risk to randomised controlled trial (RCT) results. Phenome-wide association study (PheWAS) in the UK Biobank was performed to identify potential side effects and repurposing opportunities, with findings investigated in the Vanderbilt University Biobank (BioVU) and in observational analysis of the UK Biobank.\n\nFindingsWe identified suitable genetic instruments for beta-blockers (BBs) and calcium channel blockers (CCBs). MR estimates for their effect on CHD and stroke risk respectively were comparable to results from RCTs against placebo. PheWAS in the UK Biobank identified an association of the CCB genetic risk score (scaled to drug effect) with increased risk of diverticulosis (odds ratio [OR] 1.23, 95%CI 1.10-1.38), with a consistent estimate found in BioVU (OR 1.16, 95%CI 0.94-1.44). Association with diverticulosis was further supported in observational analysis of CCB use in the UK Biobank (OR 1.08, 95%CI 1.02-1.15).\n\nInterpretationWe identified valid genetic instruments for BBs and CCBs. Using genetic and observational approaches, we highlighted a previously unreported potential detrimental effect of CCBs on risk of diverticulosis. This work serves as a proof of concept that investigation of genetic variants can offer a complementary approach to exploring the efficacy and side effects of anti-hypertensive medications.\n\nFundingWellcome Trust.

genetics↗

bioRxiv2018

Developing and Evaluating Mappings of ICD-10 and ICD-10-CM codes to Phecodes

BackgroundThe PheCode system was built upon the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) for phenome-wide association studies (PheWAS) in the electronic health record (EHR).\n\nObjectiveHere, we present our work on the development and evaluation of maps from ICD-10 and ICD-10-CM codes to PheCodes.\n\nMethodsWe mapped ICD-10 and ICD-10-CM codes to PheCodes using a number of methods and resources, such as concept relationships and explicit mappings from the Unified Medical Language System (UMLS), Observational Health Data Sciences and Informatics (OHDSI), Systematized Nomenclature of Medicine - Clinical Terms (SNOMED CT), and National Library of Medicine (NLM). We assessed the coverage of the maps in two databases: Vanderbilt University Medical Center (VUMC) using ICD-10-CM and the UK Biobank (UKBB) using ICD-10. We assessed the fidelity of the ICD-10-CM map in comparison to the gold-standard ICD-9-CM[->]PheCode map by investigating phenotype reproducibility and conducting a PheWAS.\n\nResultsWe mapped >75% of ICD-10-CM and ICD-10 codes to PheCodes. Of the unique codes observed in the VUMC (ICD-10-CM) and UKBB (ICD-10) cohorts, >90% were mapped to PheCodes. We observed 70-75% reproducibility for chronic diseases and <10% for an acute disease. A PheWAS with a lipoprotein(a) (LPA) genetic variant, rs10455872, using the ICD-9-CM and ICD-10-CM maps replicated two genotype-phenotype associations with similar effect sizes: coronary atherosclerosis (ICD-9-CM: P < .001, OR = 1.60 vs. ICD-10-CM: P < .001, OR = 1.60) and with chronic ischemic heart disease (ICD-9-CM: P < .001, OR = 1.5 vs. ICD-10-CM: P < .001, OR = 1.47).\n\nConclusionsThis study introduces the initial \"beta\" versions of ICD-10 and ICD-10-CM to PheCode maps that will enable researchers to leverage accumulated ICD-10 and ICD-10-CM data for high-throughput PheWAS in the EHR.

bioinformatics↗

bioRxiv2018

Pulling the covers in electronic health records for an association study with self-reported sleep behaviors

The electronic health record (EHR) contains rich histories of clinical care, but has not traditionally been mined for information related to sleep habits. Here we performed a retrospective EHR study and derived a cohort of 3,652 individuals with self-reported sleep behaviors, documented from visits to the sleep clinic. These individuals were obese (mean body mass index 33.6 kg/m2) and had a high prevalence of sleep apnea (60.5%), however we found sleep behaviors largely concordant with prior prospective cohort studies. In our cohort, average wake time was one hour later and average sleep duration was 40 minutes longer on weekends than on weekdays (p<1{middle dot}10-12). Sleep duration also varied considerably as a function of age, and tended to be longer in females and in whites. Additionally, through phenome-wide association analyses, we found an association of long weekend sleep with depression, and an unexpectedly large number of associations of long weekday sleep with mental health and neurological disorders (q<0.05). We then sought to replicate previously published genetic associations with morning/evening preference on a subset of our cohort with extant genotyping data (n=555). While those findings did not replicate in our cohort, a polymorphism (rs3754214) in high linkage disequilibrium with a previously published polymorphism near TARS2 was associated with long sleep duration (p<0.01). Collectively, our results highlight the potential of the EHR for uncovering the correlates of human sleep in real-world populations.

bioinformatics↗

bioRxiv2018

Using Topic Modeling via Non-negative Matrix Factorization to Identify Relationships between Genetic Variants and Disease Phenotypes: A Case Study of Lipoprotein(a) (LPA)

Genome-wide and phenome-wide association studies are commonly used to identify important relationships between genetic variants and phenotypes. Most of these studies have treated diseases as independent variables and suffered from heavy multiple adjustment burdens due to the large number of genetic variants and disease phenotypes. In this study, we propose using topic modeling via non-negative matrix factorization (NMF) for identifying associations between disease phenotypes and genetic variants. Topic modeling is an unsupervised machine learning approach that can be used to learn the semantic patterns from electronic health record data. We chose rs10455872 in LPA as the predictor since it has been shown to be associated with increased risk of hyperlipidemia and cardiovascular diseases (CVD). Using data of 12,759 individuals from the biobank at Vanderbilt University Medical Center, we trained a topic model using NMF from 1,853 distinct phecodes extracted from the cohorts electronic health records and generated six topics. We quantified their associations with rs10455872 in LPA. Topics indicating CVD had positive correlations with rs10455872 (P < 0.001), replicating a previous finding. We also identified a negative correlation between LPA and a topic representing lung cancer (P < 0.001). Our results demonstrate the applicability of topic modeling in exploring the relationship between the genome and clinical diseases.\n\nAuthor summaryIdentifying the clinical associations of genetic variants remains crucial in understanding how the human genome modulates disease risk. Traditional phenome-wide association studies consider each disease phenotype as an independent variable, however, diseases often present as complex clusters of comorbid conditions. In this study, we propose using topic modeling to model electronic health record data as a mixture of topics (e.g., disease clusters or relevant comorbidities) and testing associations between topics and genetic variants. Our results demonstrated the feasibility of using topic modeling to replicate and discover novel associations between the human genome and clinical diseases.

genetics↗

bioRxiv2017

An atlas of genetic variation for linking pathogen-induced cellular traits to human disease

Genome-wide association studies (GWAS) have identified thousands of genetic variants associated with disease. To facilitate moving from associations to disease mechanisms, we leveraged the role of pathogens in shaping human evolution with the Hi-HOST Phenome Project (H2P2): a catalog of cellular GWAS comprised of 79 phenotypes in response to 8 pathogens in 528 lymphoblastoid cell lines. Seventeen loci surpass genome-wide significance (p<5x10-8) for phenotypes ranging from pathogen replication to cytokine production. Combining H2P2 with clinical association data from the eMERGE Network and experimental validation revealed evidence for mechanisms of action and connections with diseases. We identified a SNP near CXCL10 as a cis-cytokine-QTL and a new risk factor for inflammatory bowel disease. A SNP in ZBTB20 demonstrated pleiotropy, partially mediated through NF{kappa}B signaling, and was associated with viral hepatitis. Data are available in an H2P2 web portal to facilitate further interpreting human genome variation through the lens of cell biology.

genetics↗