Faculty Bibliography
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61
Quantifying the phenome-wide disease burden of obesity using electronic health records and genomics
OBJECTIVE:High BMI is associated with many comorbidities and mortality. This study aimed to elucidate the overall clinical risk of obesity using a genome- and phenome-wide approach. METHODS:This study performed a phenome-wide association study of BMI using a clinical cohort of 736,726 adults. This was followed by genetic association studies using two separate cohorts: one consisting of 65,174 adults in the Electronic Medical Records and Genomics (eMERGE) Network and another with 405,432 participants in the UK Biobank. RESULTS:Class 3 obesity was associated with 433 phenotypes, representing 59.3% of all billing codes in individuals with severe obesity. A genome-wide polygenic risk score for BMI, accounting for 7.5% of variance in BMI, was associated with 296 clinical diseases, including strong associations with type 2 diabetes, sleep apnea, hypertension, and chronic liver disease. In all three cohorts, 199 phenotypes were associated with class 3 obesity and polygenic risk for obesity, including novel associations such as increased risk of renal failure, venous insufficiency, and gastroesophageal reflux. CONCLUSIONS:This combined genomic and phenomic systematic approach demonstrated that obesity has a strong genetic predisposition and is associated with a considerable burden of disease across all disease classes.
PMCID:9691570
PMID: 36372681
ISSN: 1930-739x
CID: 5710602
International Classification of Diseases codes do not capture all cases of hidradenitis suppurativa in the electronic health record: a retrospective cohort [Letter]
PMCID:10266923
PMID: 35656711
ISSN: 1365-2133
CID: 5710592
Whole exome sequencing in Alopecia Areata identifies rare variants in KRT82
Alopecia areata is a complex genetic disease that results in hair loss due to the autoimmune-mediated attack of the hair follicle. We previously defined a role for both rare and common variants in our earlier GWAS and linkage studies. Here, we identify rare variants contributing to Alopecia Areata using a whole exome sequencing and gene-level burden analyses approach on 849 Alopecia Areata patients compared to 15,640 controls. KRT82 is identified as an Alopecia Areata risk gene with rare damaging variants in 51 heterozygous Alopecia Areata individuals (6.01%), achieving genome-wide significance (p = 2.18E-07). KRT82 encodes a hair-specific type II keratin that is exclusively expressed in the hair shaft cuticle during anagen phase, and its expression is decreased in Alopecia Areata patient skin and hair follicles. Finally, we find that cases with an identified damaging KRT82 variant and reduced KRT82 expression have elevated perifollicular CD8 infiltrates. In this work, we utilize whole exome sequencing to successfully identify a significant Alopecia Areata disease-relevant gene, KRT82, and reveal a proposed mechanism for rare variant predisposition leading to disrupted hair shaft integrity.
PMCID:8831607
PMID: 35145093
ISSN: 2041-1723
CID: 5710582
Clinical translation of hidradenitis suppurativa genetic studies requires global collaboration
PMCID:8738108
PMID: 34498254
ISSN: 1365-2133
CID: 5710792
Monogenic mutations implicate STAT1 in hidradenitis suppurativa pathogenesis [Meeting Abstract]
ISI:000829693000100
ISSN: 0022-202x
CID: 5714252
Distribution of hidradenitis suppurativa monogenic etiologies in a racially diverse specialty clinic cohort [Meeting Abstract]
ISI:000829693001029
ISSN: 0022-202x
CID: 5713732
Distribution of monogenic etiologies in a racially diverse hidradenitis suppurativa (HS) cohort [Meeting Abstract]
ISI:000849696800028
ISSN: 0906-6705
CID: 5714242
Neptune: an environment for the delivery of genomic medicine
PURPOSE:Genomic medicine holds great promise for improving health care, but integrating searchable and actionable genetic data into electronic health records (EHRs) remains a challenge. Here we describe Neptune, a system for managing the interaction between a clinical laboratory and an EHR system during the clinical reporting process. METHODS:We developed Neptune and applied it to two clinical sequencing projects that required report customization, variant reanalysis, and EHR integration. RESULTS:Neptune has been applied for the generation and delivery of over 15,000 clinical genomic reports. This work spans two clinical tests based on targeted gene panels that contain 68 and 153 genes respectively. These projects demanded customizable clinical reports that contained a variety of genetic data types including single-nucleotide variants (SNVs), copy-number variants (CNVs), pharmacogenomics, and polygenic risk scores. Two variant reanalysis activities were also supported, highlighting this important workflow. CONCLUSION:Methods are needed for delivering structured genetic data to EHRs. This need extends beyond developing data formats to providing infrastructure that manages the reporting process itself. Neptune was successfully applied on two high-throughput clinical sequencing projects to build and deliver clinical reports to EHR systems. The software is open source and available at https://gitlab.com/bcm-hgsc/neptune .
PMCID:8487966
PMID: 34257418
ISSN: 1530-0366
CID: 5479332
A semi-supervised model to predict regulatory effects of genetic variants at single nucleotide resolution using massively parallel reporter assays
MOTIVATION:Predicting regulatory effects of genetic variants is a challenging but important problem in functional genomics. Given the relatively low sensitivity of functional assays, and the pervasiveness of class imbalance in functional genomic data, popular statistical prediction models can sharply underestimate the probability of a regulatory effect. We describe here the presence-only model (PO-EN), a type of semi-supervised model, to predict regulatory effects of genetic variants at sequence-level resolution in a context of interest by integrating a large number of epigenetic features and massively parallel reporter assays (MPRAs). RESULTS:Using experimental data from a variety of MPRAs we show that the presence-only model produces better calibrated predicted probabilities and has increased accuracy relative to state-of-the-art prediction models. Furthermore, we show that the predictions based on pre-trained PO-EN models are useful for prioritizing functional variants among candidate eQTLs and significant SNPs at GWAS loci. In particular, for the costimulatory locus, associated with multiple autoimmune diseases, we show evidence of a regulatory variant residing in an enhancer 24.4 kb downstream of CTLA4, with evidence from capture Hi-C of interaction with CTLA4. Furthermore, the risk allele of the regulatory variant is on the same risk increasing haplotype as a functional coding variant in exon 1 of CTLA4, suggesting that the regulatory variant acts jointly with the coding variant leading to increased risk to disease. AVAILABILITY:The presence-only model is implemented in the R package 'PO.EN', freely available on CRAN. A vignette describing a detailed demonstration of using the proposed PO-EN model can be found on github at https://github.com/Iuliana-Ionita-Laza/PO.EN/. SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.
PMCID:8337004
PMID: 33515242
ISSN: 1367-4811
CID: 5710552
Medical Records-Based Genetic Studies of the Complement System
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:). 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.
PMID: 33941608
ISSN: 1533-3450
CID: 5710572
