Biology subjects

Bastarache, L.

Publications and source records attributed to Bastarache, L..

10 recordsLinked to original sources

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↗

bioRxiv2022

The phers R package: using phenotype risk scores based on electronic health records to study Mendelian disease and rare genetic variants

Electronic health record (EHR) data linked to DNA biobanks are a valuable resource for understanding the phenotypic effects of human genetic variation. We previously developed the phenotype risk score (PheRS) as an approach to quantify the extent to which a patients clinical features resemble a given Mendelian disease. Using PheRS, we have uncovered novel associations between Mendelian diseaselike phenotypes and rare genetic variants, and identified patients who may have undiagnosed Mendelian disease. Although the PheRS approach is conceptually simple, it involves multiple mapping steps and was previously only available as custom scripts, limiting the approachs usability. Thus, we developed the phers R package, a complete and user-friendly set of functions and maps for performing a PheRS-based analysis on linked clinical and genetic data. The package includes up-to-date maps between EHR-based phenotypes (i.e., ICD codes and phecodes), human phenotype ontology (HPO) terms, and Mendelian diseases. Starting with occurrences of ICD codes, the package enables the user to calculate phenotype risk scores, validate the scores using case-control analyses, and perform genetic association analyses. By increasing PheRSs transparency and usability, the phers R package will help improve our understanding of the relationships between rare genetic variants and clinically meaningful human phenotypes. AvailabilityThe phers R package is free and open-source, and available on CRAN and at https://phers.hugheylab.org. Contactjakejhughey@gmail.com Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2019

PhenomeXcan: Mapping the genome to the phenome through the transcriptome

Large-scale genomic and transcriptomic initiatives offer unprecedented ability to study the biology of complex traits and identify target genes for precision prevention or therapy. Translation to clinical contexts, however, has been slow and challenging due to lack of biological context for identified variant-level associations. Moreover, many translational researchers lack the computational or analytic infrastructures required to fully use these resources. We integrate genome-wide association study (GWAS) summary statistics from multiple publicly available sources and data from Genotype-Tissue Expression (GTEx) v8 using PrediXcan and provide a user-friendly platform for translational researchers based on state-of-the-art algorithms. We develop a novel Bayesian colocalization method, fastENLOC, to prioritize the most likely causal gene-trait associations. Our resource, PhenomeXcan, synthesizes 8.87 million variants from GWAS on 4,091 traits with transcriptome regulation data from 49 tissues in GTEx v8 into an innovative, gene-based resource including 22,255 genes. Across the entire genome/phenome space, we find 65,603 significant associations (Bonferroni-corrected p-value of 5.5 x 10-10), where 19,579 (29.8 percent) were colocalized (locus regional colocalization probability > 0.1). We successfully replicate associations from PheWAS Catalog (AUC=0.61) and OMIM (AUC=0.64). We provide examples of (a) finding novel and underreported genome-to-phenome associations, (b) exploring complex gene-trait clusters within PhenomeXcan, (c) studying phenome-to-phenome relationships between common and rare diseases via further integration of PhenomeXcan with ClinVar, and (d) evaluating potential therapeutic targets. PhenomeXcan (phenomexcan.org) broadens access to complex genomic and transcriptomic data and empowers translational researchers. One-Sentence SummaryPhenomeXcan is a gene-based resource of gene-trait associations with biological context that supports translational research.

genetics↗

bioRxiv2019

Widespread dose-dependent effects of RNA expression and splicing on complex diseases and traits

The resources generated by the GTEx consortium offer unprecedented opportunities to advance our understanding of the biology of human diseases. Here, we present an in-depth examination of the phenotypic consequences of transcriptome regulation and a blueprint for the functional interpretation of genome-wide association study-discovered loci. Across a broad set of complex traits and diseases, we demonstrate widespread dose-dependent effects of RNA expression and splicing. We develop a data-driven framework to benchmark methods that prioritize causal genes and find no single approach outperforms the combination of multiple approaches. Using colocalization and association approaches that take into account the observed allelic heterogeneity of gene expression, we propose potential target genes for 47% (2,519 out of 5,385) of the GWAS loci examined. Our results demonstrate the translational relevance of the GTEx resources and highlight the need to increase their resolution and breadth to further our understanding of the genotype-phenotype link.

genetics↗

bioRxiv2019

WikiMedMap: Expanding the Phenotyping Mapping Toolbox Using Wikipedia

Researchers utilizing phenotypic data from diverse sources require matching of phenotypes to standard clinical vocabularies. Mapping phenotypes to vocabulary can be difficult, as existing tools are often incomplete, can be difficult to access, and can be cumbersome to use, especially for non-experts. We created WikiMedMap as a simple tool that leverages Wikipedia and maps phenotype strings to standard clinical vocabularies. We assessed WikiMedMap by mapping phenotype strings from questionnaires in the UK Biobank and from Mendelian diseases in Online Mendelian Inheritance in Man (OMIM) database to eight vocabularies: International Classification of Diseases, Ninth Revision (ICD-9), ICD-10, ICD-O, Medical Subject Headings (MeSH), OMIM, Disease Database, and MedlinePlus. WikiMedMap outperformed conventional mapping tools in finding potential matches for phenotype strings. We envision WikiMedMap as a technique that complements existing and established tools to map strings to clinical vocabularies that usually do not coexist in one source.

bioinformatics↗

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

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

PROTEIN-CODING VARIANTS IMPLICATE NOVEL GENES RELATED TO LIPID HOMEOSTASIS CONTRIBUTING TO BODY FAT DISTRIBUTION

Body fat distribution is a heritable risk factor for a range of adverse health consequences, including hyperlipidemia and type 2 diabetes. To identify protein-coding variants associated with body fat distribution, assessed by waist-to-hip ratio adjusted for body mass index, we analyzed 228,985 predicted coding and splice site variants available on exome arrays in up to 344,369 individuals from five major ancestries for discovery and 132,177 independent European-ancestry individuals for validation. We identified 15 common (minor allele frequency, MAF[≥]5%) and 9 low frequency or rare (MAF<5%) coding variants that have not been reported previously. Pathway/gene set enrichment analyses of all associated variants highlight lipid particle, adiponectin level, abnormal white adipose tissue physiology, and bone development and morphology as processes affecting fat distribution and body shape. Furthermore, the cross-trait associations and the analyses of variant and gene function highlight a strong connection to lipids, cardiovascular traits, and type 2 diabetes. In functional follow-up analyses, specifically in Drosophila RNAi-knockdown crosses, we observed a significant increase in the total body triglyceride levels for two genes (DNAH10 and PLXND1). By examining variants often poorly tagged or entirely missed by genome-wide association studies, we implicate novel genes in fat distribution, stressing the importance of interrogating low-frequency and protein-coding variants.

genetics↗

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↗