DNA Biobank Research Articles

A polygenic score for height identifies an unmeasured genetic predisposition among pediatric patients with idiopathic short stature.

Background A subset of children with short stature do not have an identified clinical explanation and are assigned a diagnosis of idiopathic short stature (ISS). We hypothesized that a polygenic score for height (PGS height ) could identify children with ISS who have an unrecognized heritable predisposition to shorter height. Methods We examined 534 pediatric participants in an EHR-linked DNA biobank (BioVU) who had undergone an evaluation for short stature by an endocrinologist. We used a previously validated PGS height and standardized it to a standard deviation (SDS) of 1. PGS height differences between short stature subtypes was estimated using Tukey's HSD. The PGS height and mid-parental height (MPH) were then used to predict adult heights for each participant and these predictions were compared using Cohen's d stratifying by short stature subtype. The ability of the PGS height to discriminate between ISS and short stature due to underlying disease was evaluated using logistic regression models with area under the ROC curve (AUC) analyses and testing the incremental benefit (ΔAUC) of adding the PGS height to prediction models. Results Among the 534 participants, 22.1% had ISS (median [IQR] PGS height SDS = -1.31 [-2.15 to -0.47]), 6.6% had familial (genetic) short stature (FSS) (-1.62 [-2.13 to -0.54]), and 45.1% had short stature due to underlying pathology (-0.74 [-1.23 to -0.19]). Children with ISS had similar PGS height values as those with FSS (ΔPGS height [95% CI] = 0.19 [-0.31 to 0.70], p = 0.75), but predicted heights generated by the PGS height were lower than the MPH estimate for children with ISS ( d = -0.64; p = 4.0×10 - 18 ) but not FSS ( d = 0.05; p = 0.46), suggesting that MPH underestimates height in the ISS group. Children with ISS had lower PGS height values than children with pathology (ΔPGS height = -0.60 SDS [-0.89 to -0.31], p < 0.001), suggesting children with ISS have a larger predisposition to shorter height. In addition, the PGS height improved model discrimination between ISS and pathologic short stature (ΔAUC, + 0.07 [95% CI, 0.01 to 0.11]). Conclusions Some children with ISS have a clinically unrecognized polygenic predisposition to shorter height that is comparable to children with FSS and larger than those with underlying pathology. A PGS height could help clinicians identify children who have a benign predisposition to shorter height.

Electronic health record biobank cohort recapitulates an association between the MUC5B promoter polymorphism and ARDS in critically ill adults.

Large population-based DNA biobanks linked to electronic health records (EHRs) may provide advantages over traditional study designs for identifying genetic drivers of ARDS. Can ARDS be identified in an EHR biobank, and can this approach validate a previously reported genetic risk factor for ARDS? We analyzed two genotyped cohorts from one academic medical center: a prospective biomarker study of critically ill adults (VALID cohort), and hospitalized participants in a de-identified EHR biobank (BioVU). ARDS status was assessed by clinician-investigator review in VALID and an EHR-derived algorithm in BioVU (EHR-ARDS). We tested the association between the MUC5B promoter polymorphism (rs35705950) with development of ARDS/EHR-ARDS in each cohort. In VALID, 2,795 patients were included, age was 55 [43, 66] (median [IQR]) years, and 718 (25.7%) developed ARDS. In BioVU, 9,025 hospitalized participants were included, age was 60 [48, 70] , and 1,056 (11.7%) developed EHR-ARDS. We observed a significant interaction between age and rs35705950 on ARDS risk in VALID: in older patients rs35705950 was associated with increased ARDS risk (OR: 1.44; 95%CI 1.08-1.92; p=0.012) whereas among younger patients this effect was attenuated (OR: 0.84; 95%CI: 0.62-1.14; p=0.26). In BioVU, rs35705950 was associated with increased risk for EHR-ARDS among all participants (OR: 1.20; 95%CI: 1.00-1.43, p=0.043) and this relationship did not vary by age. The polymorphism was also associated with more severe oxygenation impairment among BioVU participants who required mechanical ventilation. The MUC5B promoter polymorphism was associated with ARDS in two cohorts of at-risk hospitalized adults. Although age-related effect modification was observed only in the prospective biomarker cohort, the EHR cohort identified a consistent association between MUC5B and ARDS risk regardless of age and a novel association with oxygenation impairment. Our study highlights the potential for EHR biobanks to enable precision-medicine ARDS studies.

A meta-analysis GWAS of taxane-induced peripheral neuropathy in patients with breast cancer.

12089 Background: Taxane-induced peripheral neuropathy (TIPN) is a common dose-limiting toxicity in the medical management of breast cancer patients. Despite the clinical efficacy of taxane treatment, there is significant heterogeneity in the development of neuropathy and its severity, and no clear genetic etiologies have yet been identified. We sought to discover genetic risk variants in two large electronic health record linked DNA biobanks. Methods: A meta-analysis genome wide association study (GWAS) was performed with participants in the All of Us Research Program and BioVU biobanks. All participants of the study population were of female sex at birth and had a breast cancer diagnosis. Cases were defined as taxane-exposed participants who received their first neuropathy diagnosis code within one year after first taxane exposure. Controls were defined as taxane-exposed participants without a neuropathy diagnosis code and without gabapentin initiation after taxane exposure. GWAS was performed with PLINK version 2.0 using age at taxane initiation, number of taxane cycles received, and the first 10 principal components as covariates. Expression quantitative trait loci (eQTL) analysis was performed with the GTEx 8.0 and EyeGEx databases. Results: The All of Us cohort had 196 cases and 254 controls. The BioVU cohort had 244 cases and 528 controls. In our meta-analysis GWAS, rs3065465 (chr11:69138693; closest gene by distance is LOC338694) was associated with TIPN (OR=2.14, P= 8.27E-09) and was similar across different genetic ancestries (Table). The eQTL analysis of rs3065465 shows significant association with mRNA expression of TPCN2(P= 6.44E-06), a cation channel involved in neuronal differentiation and intracellular calcium homeostasis. Conclusions: rs3065465 represents a potential novel genetic marker for TIPN risk in breast cancer patients.[Table: see text]

Genotype-based "virtual" metabolomics in a clinical biobank identifies novel metabolite-disease associations.

Introduction: Circulating metabolites act as biomarkers of dysregulated metabolism and may inform disease pathophysiology. A portion of the inter-individual variability in circulating metabolites is influenced by common genetic variation. We evaluated whether a genetics-based "virtual" metabolomics approach can identify novel metabolite-disease associations. Methods: We examined the association between polygenic scores for 724 metabolites with 1,247 clinical phenotypes in the BioVU DNA biobank, comprising 57,735 European ancestry and 15,754 African ancestry participants. We applied Mendelian randomization (MR) to probe significant relationships and validated significant MR associations using independent GWAS of candidate phenotypes. Results and Discussion: We found significant associations between 336 metabolites and 168 phenotypes in European ancestry and 107 metabolites and 56 phenotypes in African ancestry. Of these metabolite-disease pairs, MR analyses confirmed associations between 73 metabolites and 53 phenotypes in European ancestry. Of 22 metabolitephenotype pairs evaluated for replication in independent GWAS, 16 were significant (false discovery rate p < 0.05). These included associations between bilirubin and X-21796 with cholelithiasis, phosphatidylcholine (16:0/22:5n3,18:1/20:4) and arachidonate with inflammatory bowel disease and Crohn's disease, and campesterol with coronary artery disease and myocardial infarction. These associations may represent biomarkers or potentially targetable mediators of disease risk.

The broad impact of cell death genes on the human disease phenome

Cell death mediated by genetically defined signaling pathways influences the health and dynamics of all tissues, however the tissue specificity of cell death pathways and the relationships between these pathways and human disease are not well understood. We analyzed the expression profiles of an array of 44 cell death genes involved in apoptosis, necroptosis, and pyroptosis cell death pathways across 49 human tissues from GTEx, to elucidate the landscape of cell death gene expression across human tissues, and the relationship between tissue-specific genetically determined expression and the human phenome. We uncovered unique cell death gene expression profiles across tissue types, suggesting there are physiologically distinct cell death programs in different tissues. Using summary statistics-based transcriptome wide association studies (TWAS) on human traits in the UK Biobank (n ~ 500,000), we evaluated 513 traits encompassing ICD-10 defined diagnoses and laboratory-derived traits. Our analysis revealed hundreds of significant (FDR < 0.05) associations between genetically regulated cell death gene expression and an array of human phenotypes encompassing both clinical diagnoses and hematologic parameters, which were independently validated in another large-scale DNA biobank (BioVU) at Vanderbilt University Medical Center (n = 94,474) with matching phenotypes. Cell death genes were highly enriched for significant associations with blood traits versus non-cell-death genes, with apoptosis-associated genes enriched for leukocyte and platelet traits. Our findings are also concordant with independently published studies (e.g. associations between BCL2L11/BIM expression and platelet & lymphocyte counts). Overall, these results suggest that cell death genes play distinct roles in their contribution to human phenotypes, and that cell death genes influence a diverse array of human traits.

47 Cross-ancestry GWAS meta-analysis of keloids discovers novel susceptibility loci in diverse populations

OBJECTIVES/GOALS: We aimed to conduct an updated genome-wide meta-analysis of keloids in expanded populations, including those most afflicted by keloids. Our overall objective was to improve understanding of keloid development though the identification and further characterization of keloid-associated genes with genetically predicted gene expression (GPGE). METHODS/STUDY POPULATION: We used publicly available summary statistics from several large-scale DNA biobanks, including the UK Biobank, FinnGen, and Biobank Japan. We also leveraged data from the Million Veterans Program and performed genome-wide association studies of keloids in BioVU and eMERGE. For each of these datasets, cases were determined from ICD-9/ICD-10 codes and phecodes. With these data we conducted fixed effects meta-analysis, both across ancestries and stratified by broad ancestry groups. This approach allowed us to consider cumulative evidence for genetic risk factors for keloids and explore potential ancestry-specific components of risk. We used FUMA for functional annotation of results and LDSC to estimate ancestry-specific heritability. We performed GPGE analysis using S-PrediXcan with GTEx v8 tissues. RESULTS/ANTICIPATED RESULTS: We detected 30 (23 novel) genomic risk loci in the cross-ancestry analysis. Major risk loci were broadly consistent between ancestries, with variable effects. Keloid heritability estimates from LDSC were 6%, 21%, and 34% for European, East Asian, and African ancestry, respectively. The top hit (P = 1.7e-77) in the cross-ancestry analysis was at a replicated variant (rs10863683) located downstream of LINC01705. GPGE analysis identified an association between decreased risk of keloids and increased expression of LINC01705 in fibroblasts (P = 3.6e10-20), which are important in wound healing. The top hit in the African-ancestry analysis (P = 5.5e-31) was a novel variant (rs34647667) in a conserved region downstream of ITGA11. ITGA11 encodes a collagen receptor and was previously associated with uterine fibroids. DISCUSSION/SIGNIFICANCE: This work significantly increases the yield of discoveries from keloid genetic association studies, describing both common and ancestry-specific effects. Stark differences in heritability support a potential adaptive origin for keloid disparities. Further work will continue to examine keloids in the broader context of other fibrotic diseases.

Population genetic testing and SERPINA1 sequencing identifies unidentified alpha-1 antitrypsin deficiency alleles and gene-environment interaction with hepatitis C infection.

Alpha-1 antitrypsin deficiency (AATD), a relatively common autosomal recessive genetic disorder, is underdiagnosed in symptomatic individuals. We sought to compare the risk of liver transplantation associated with hepatitis C infection with AATD heterozygotes and homozygotes and determine if SERPINA1 sequencing would identify undiagnosed AATD. We performed a retrospective cohort study in a deidentified Electronic Health Record (EHR)-linked DNA biobank with 72,027 individuals genotyped for the M, Z, and S alleles in SERPINA1. We investigated liver transplantation frequency by genotype group and compared with hepatitis C infection. We performed SERPINA1 sequencing in carriers of pathogenic AATD alleles who underwent liver transplantation. Liver transplantation was associated with the Z allele (ZZ: odds ratio [OR] = 1.31, p<2e-16; MZ: OR = 1.02, p = 1.2e-13) and with hepatitis C (OR = 1.20, p<2e-16). For liver transplantation, there was a significant interaction between genotype and hepatitis C (ZZ: interaction OR = 1.23, p = 4.7e-4; MZ: interaction OR = 1.11, p = 6.9e-13). Sequencing uncovered a second, rare, pathogenic SERPINA1 variant in six of 133 individuals with liver transplants and without hepatitis C. Liver transplantation was more common in individuals with AATD risk alleles (including heterozygotes), and AATD and hepatitis C demonstrated evidence of a gene-environment interaction in relation to liver transplantation. The current AATD screening strategy may miss diagnoses whereas SERPINA1 sequencing may increase diagnostic yield for AATD, stratify risk for liver disease, and inform clinical management for individuals with AATD risk alleles and liver disease risk factors.

A test of automated use of electronic health records to aid in diagnosis of genetic disease

PurposeAutomated use of electronic health records may aid in decreasing the diagnostic delay for rare diseases. The phenotype risk score (PheRS) is a weighted aggregate of syndromically related phenotypes that measures the similarity between an individual’s conditions and features of a disease. For some diseases, there are individuals without a diagnosis of that disease who have scores similar to diagnosed patients. These individuals may have that disease but not yet be diagnosed. MethodsWe calculated the PheRS for cystic fibrosis (CF) for 965,626 subjects in the Vanderbilt University Medical Center electronic health record. ResultsOf the 400 subjects with the highest PheRS for CF, 248 (62%) had been diagnosed with CF. Twenty-six of the remaining participants, those who were alive and had DNA available in the linked DNA biobank, underwent clinical review and sequencing analysis of CFTR and SERPINA1. This uncovered a potential diagnosis for 2 subjects, 1 with CF and 1 with alpha-1-antitrypsin deficiency. An additional 7 subjects had pathogenic or likely pathogenic variants, 2 in CFTR and 5 in SERPINA1. ConclusionThese findings may be clinically actionable for the providers caring for these patients. Importantly, this study highlights feasibility and challenges for future implications of this approach.

Genetics of Pulmonary Pressure and Right Ventricle Stress Identify Diabetes as a Causal Risk Factor.

Background Epidemiologic studies have identified risk factors associated with pulmonary hypertension and right heart failure, but causative drivers of pulmonary hypertension and right heart adaptation are not well known. We sought to leverage unbiased genetic approaches to determine clinical conditions that share genetic architecture with pulmonary pressure and right ventricular dysfunction. Methods and Results We leveraged Vanderbilt University's deidentified electronic health records and DNA biobank to identify 14 861 subjects of European ancestry who underwent at least 1 echocardiogram with available estimates of pulmonary pressure and right ventricular function. Analyses of the study were performed between 2020 and 2022. The final analytical sample included 14 861 participants (mean [SD] age, 63 [15] years and mean [SD] body mass index, 29 [7] kg/m2). An unbiased phenome-wide association study identified diabetes as the most statistically significant clinical International Classifications of Diseases, Ninth Revision (ICD-9) code associated with polygenic risk for increased pulmonary pressure. We validated this finding further by finding significant associations between genetic risk for diabetes and a related condition, obesity, with pulmonary pressure estimate. We then used 2-sample univariable Mendelian randomization and multivariable Mendelian randomization to show that diabetes, but not obesity, was independently associated with genetic risk for increased pulmonary pressure and decreased right ventricle load stress. Conclusions Our findings show that genetic risk for diabetes is the only significant independent causative driver of genetic risk for increased pulmonary pressure and decreased right ventricle load stress. These findings suggest that therapies targeting genetic risk for diabetes may also potentially be beneficial in treating pulmonary hypertension and right heart dysfunction.

Phenotype Risk Score but Not Genetic Risk Score Aids in Identifying Individuals With Systemic Lupus Erythematosus in the Electronic Health Record.

Systemic lupus erythematosus (SLE) poses diagnostic challenges. We undertook this study to evaluate the utility of a phenotype risk score (PheRS) and a genetic risk score (GRS) to identify SLE individuals in a real-world setting. Using a de-identified electronic health record (EHR) database with an associated DNA biobank, we identified 789 SLE cases and 2,261 controls with available MEGAEX genotyping. A PheRS for SLE was developed using billing codes that captured American College of Rheumatology SLE criteria. We developed a GRS with 58 SLE risk single-nucleotide polymorphisms (SNPs). SLE cases had a significantly higher PheRS (mean ± SD 7.7 ± 8.0 versus 0.8 ± 2.0 in controls; P < 0.001) and GRS (mean ± SD 12.2 ± 2.3 versus 11.0 ± 2.0 in controls; P < 0.001). Black individuals with SLE had a higher PheRS compared to White individuals (mean ± SD 10.0 ± 10.1 versus 7.1 ± 7.2, respectively; P = 0.002) but a lower GRS (mean ± SD 9.0 ± 1.4 versus 12.3 ± 1.7, respectively; P < 0.001). Models predicting SLE that used only the PheRS had an area under the curve (AUC) of 0.87. Adding the GRS to the PheRS resulted in a minimal difference with an AUC of 0.89. On chart review, controls with the highest PheRS and GRS had undiagnosed SLE. We developed a SLE PheRS to identify established and undiagnosed SLE individuals. A SLE GRS using known risk SNPs did not add value beyond the PheRS and was of limited utility in Black individuals with SLE. More work is needed to understand the genetic risks of SLE in diverse populations.

Genome-Wide Association Study of CKD Progression.

Rapid progression of CKD is associated with poor clinical outcomes. Most previous studies looking for genetic factors associated with low eGFR have used cross-sectional data. The authors conducted a meta-analysis of genome-wide association studies of eGFR decline among 116,870 participants with CKD, focusing on longitudinal data. They identified three loci (two of them novel) associated with longitudinal eGFR decline. In addition to the known UMOD/PDILT locus, variants within BICC1 were associated with significant differences in longitudinal eGFR slope. Variants within HEATR4 also were associated with differences in eGFR decline, but only among Black/African American individuals without diabetes. These findings help characterize molecular mechanisms of eGFR decline in CKD and may inform new therapeutic approaches for progressive kidney disease. Rapid progression of CKD is associated with poor clinical outcomes. Despite extensive study of the genetics of cross-sectional eGFR, only a few loci associated with eGFR decline over time have been identified. We performed a meta-analysis of genome-wide association studies of eGFR decline among 116,870 participants with CKD-defined by two outpatient eGFR measurements of <60 ml/min per 1.73 m 2 , obtained 90-365 days apart-from the Million Veteran Program and Vanderbilt University Medical Center's DNA biobank. The primary outcome was the annualized relative slope in outpatient eGFR. Analyses were stratified by ethnicity and diabetes status and meta-analyzed thereafter. In cross-ancestry meta-analysis, the strongest association was rs77924615, near UMOD / PDILT ; each copy of the G allele was associated with a 0.30%/yr faster eGFR decline ( P = 4.9×10 -27 ). We also observed an association within BICC1 (rs11592748), where every additional minor allele was associated with a 0.13%/yr slower eGFR decline ( P = 5.6×10 -9 ). Among participants without diabetes, the strongest association was the UMOD/PDILT variant rs36060036, associated with a 0.27%/yr faster eGFR decline per copy of the C allele ( P = 1.9×10 -17 ). Among Black participants, a significantly faster eGFR decline was associated with variant rs16996674 near APOL1 (R 2 =0.29 with the G1 high-risk genotype); among Black participants with diabetes, lead variant rs11624911 near HEATR4 also was associated with a significantly faster eGFR decline. We also nominally replicated loci with known associations with eGFR decline, near PRKAG2, FGF5, and C15ORF54. Three loci were significantly associated with longitudinal eGFR change at genome-wide significance. These findings help characterize molecular mechanisms of eGFR decline and may contribute to the development of new therapeutic approaches for progressive CKD.

305 Leveraging multi-timepoint blood samples to characterize cancer-associated mutations in the blood over time

OBJECTIVES/GOALS: Clonal hematopoiesis of indeterminate potential (CHIP) is a common age-related condition that confers an increased risk of blood cancer, cardiovascular disease, and overall mortality. Larger proportions of blood cells with the CHIP mutation (clones) lead to worse outcomes. The goal of this study was to characterize CHIP clonal behavior over time. METHODS/STUDY POPULATION: While DNA biobanks have the ability to identify large cohorts of individuals with CHIP, they typically only contain blood from a single timepoint, limiting the ability to infer how CHIP clones change over time. In this preliminary study, we utilized multi-timepoint blood samples from 101 individuals with CHIP in Vanderbilt’s biobank (BioVU) to characterize clonal behavior over time. Using a CHIP gene-specific sequencing pipeline, we were able to characterize each individual’s CHIP mutation(s) and how the fraction of cells with the CHIP mutation expanded/reduced over time. By Spring 2023, we will also include ~300 additional individuals with CHIP in this study. RESULTS/ANTICIPATED RESULTS: CHIP mutations occurred 48% of the time in DNMT3A and 23% of the time in TET2, consistent with previous studies. 21% of individuals had more than one CHIP mutation. The mean difference in time between the two timepoints was 5.2 years (SD=2.9). Surprisingly, we observed both clonal expansion and clonal reduction across timepoints with 30% of DNMT3A, 0.6% of TET2, and 46% of JAK2 clones shrinking over time. The fastest average expansion was seen in TET2 clones (2% growth/year) and the slowest in DNMT3A clones (0.4% growth/year), but there was a significant amount of variation between individuals. In DNMT3A clones, there were no differences observed between loss of function mutations, missense mutations or DNMT3A R882 hotspot mutations. Clonal competition was observed in individuals with multiple driver mutations. DISCUSSION/SIGNIFICANCE: We used multi-timepoint blood samples to quantify the change in CHIP cell fraction over time on a per individual basis and observed novel clonal behavior and competition. Understanding the factors that influence the rate of CHIP progression can lead to personalized disease risk assessment for individuals with CHIP.

KiT-GENIE, the French genetic biobank of kidney transplantation.

KiT-GENIE is a monocentric DNA biobank set up to consolidate the very rich and homogeneous DIVAT French cohort of kidney donors and recipients (D/R) in order to explore the molecular factors involved in kidney transplantation outcomes. We collected DNA samples for kidney transplantations performed in Nantes, and we leveraged GWAS genotyping data for securing high-quality genetic data with deep SNP and HLA annotations through imputations and for inferring D/R genetic ancestry. Overall, the biobank included 4217 individuals (n = 1945 D + 2,272 R, including 1969 D/R pairs), 7.4 M SNPs and over 200 clinical variables. KiT-GENIE represents an accurate snapshot of kidney transplantation clinical practice in Nantes between 2002 and 2018, with an enrichment in living kidney donors (17%) and recipients with focal segmental glomerulosclerosis (4%). Recipients were predominantly male (63%), of European ancestry (93%), with a mean age of 51yo and 86% experienced their first graft over the study period. D/R pairs were 93% from European ancestry, and 95% pairs exhibited at least one HLA allelic mismatch. The mean follow-up time was 6.7 years with a hindsight up to 25 years. Recipients experienced biopsy-proven rejection and graft loss for 16.6% and 21.3%, respectively. KiT-GENIE constitutes one of the largest kidney transplantation genetic cohorts worldwide to date. It includes homogeneous high-quality clinical and genetic data for donors and recipients, hence offering a unique opportunity to investigate immunogenetic and genetic factors, as well as donor-recipient interactions and mismatches involved in rejection, graft survival, primary disease recurrence and other comorbidities.

Evaluating resources composing the PheMAP knowledge base to enhance high-throughput phenotyping.

A previous study, PheMAP, combined independent, online resources to enable high-throughput phenotyping (HTP) using electronic health records (EHRs). However, online resources offer distinct quality descriptions of diseases which may affect phenotyping performance. We aimed to evaluate the phenotyping performance of single resource-based PheMAPs and investigate an optimized strategy for HTP. We compared how each resource produced top-ranked concept unique identifiers (CUIs) by term frequency-inverse document frequency with Jaccard matrices comparing single resources and the original PheMAP. We correlated top-ranked concepts from each resource to features used in established Phenotype KnowledgeBase (PheKB) algorithms for hypothyroidism, type II diabetes mellitus (T2DM), and dementias. Using resources separately, we calculated multiple phenotype risk scores for individuals from Vanderbilt University Medical Center's BioVU DNA Biobank and compared phenotyping performance against rule-based eMERGE algorithms. Lastly, we implemented an ensemble strategy which classified patient case/control status based upon PheMAP resource agreement. Jaccard similarity matrices indicate that the similarity of CUIs comprising single resource-based PheMAPs varies. Single resource-based PheMAPs generated from MedlinePlus and MedicineNet outperformed others but only encompass 81.6% of overall disease phenotypes. We propose the PheMAP-Ensemble which provides higher average accuracy and precision than the combined average accuracy and precision of single resource-based PheMAPs. While offering complete phenotype coverage, PheMAP-Ensemble significantly increases phenotyping recall compared to the original iteration. Resources comprising the PheMAP produce different phenotyping performance when implemented individually. The ensemble method significantly improves the quality of PheMAP by fully utilizing dissimilar resources to capture accurate phenotyping data from EHRs.

Abstract 13318: Shared Genetic Architecture of RV-PA Coupling and Measures of Adiposity

Introduction: The right ventricle’s ability to adapt to load stress, termed RV-PA coupling, is a major determinant of survival in pulmonary hypertension. However, the etiologic drivers of impaired RV-PA coupling are incompletely understood. One approach to identifying etiologic factors is through the study of the genetic architecture underlying impaired RV-PA coupling. We previously showed shared genetic risk between adiposity and RV size. Thus, we interrogated a polygenic predictor of RV-PA coupling to determine whether it shares genetic risk with adiposity. Methods: In 3493 adult subjects of European ancestry in Vanderbilt Univ. Medical Center’s de-identified DNA biobank (BioVU) with an available echocardiogram, RV-PA coupling was calculated as the ratio of tricuspid annular plane systolic excursion (TAPSE) to estimated RV systolic pressure (RVSP). The association between three measures of obesity and RV-PA coupling was examined using 2-sample inverse variance weighted (IVW) meta-analysis, and MR-Egger to assess for pleiotropy. Genetic instruments for obesity were constructed using prior summary statistics for body mass index (BMI), waist circumference, and whole body fat mass. Results: Median age of cohort was 63, 54% female. Median and IQR of variables were as follows: TAPSE 2.0 [1.6, 2.4], RVSP 31 [25, 40], TAPSE/RVSP 0.6 [0.4, 0.8]. By 2-sample MR, we found a significant negative association between genetically determined waist circumference and RV-PA coupling (β=-0.34, p=0.004) (Figure). We found similar negative associations between RV-PA coupling and BMI (β=-0.41, p=0.01) and whole body fat mass (β=-0.55 p=0.005). Units for estimates were log change in outcome (TAPSE/RVSP) per change in exposure. Conclusions: Our findings suggest shared genetic architecture between measures of adiposity and RV-PA coupling, supporting the notion that pathophysiologic mechanisms underlying adiposity may be play a causal role in impaired RV-PA coupling.

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 patient's clinical features resemble a given Mendelian disease. Using PheRS, we have uncovered novel associations between Mendelian disease-like 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 approach's 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 terms and Mendelian diseases. Starting with occurrences of ICD codes, the package enables the user to calculate PheRSs, validate the scores using case-control analyses, and perform genetic association analyses. By increasing PheRS's transparency and usability, the phers R package will help improve our understanding of the relationships between rare genetic variants and clinically meaningful human phenotypes. The phers R package is free and open-source and available on CRAN and at https://phers.hugheylab.org. Supplementary data are available at Bioinformatics online.

GENOMIC POLITICS AND EQUALITY

GENOMIC POLITICS AND EQUALITY

M043 Serum, plasma and DNA biobanking project from Korean National Health and Nutrition Survey

M043 Serum, plasma and DNA biobanking project from Korean National Health and Nutrition Survey

Novel Analysis Methods to Mine Immune-Mediated Phenotypes and Find Genetic Variation Within the Electronic Health Record (Roadmap for Phenotype to Genotype: Immunogenomics)

The field of immunogenomics has the opportunity for accelerated genetic discovery aided by the maturation of electronic health records (EHRs) linked to DNA biobanks. Novel analysis methods in deep phenotyping of EHR data allow the full realization of the paired and increasingly dense genetic/phenotypic information available. This enables researchers to uncover genetic risk factors for the prevention and optimal treatment of immune-mediated diseases and immune-mediated adverse drug reactions. This article reviews the background of EHRs linked to DNA biobanks, potential applications to immunogenomic discovery, and current and emerging techniques in EHR-based deep phenotyping.

EP325: Medically actionable DNA variation from the GENCOV COVID-19 Genome Sequencing Study

DNA biobanks developed for COVID-19 research have the potential to discover medically actionable findings that may be relevant not only for identifying individuals at high risk for COVID-19 related disease, but that also predict genetic conditions for which prevention, treatment or management strategies may be available.