Comparison of phenomic profiles in the All of Us Research Program against the US general population and the UK Biobank.

IMPORTANCE: Knowledge gained from cohort studies has dramatically advanced both public and precision health. The All of Us Research Program seeks to enroll 1 million diverse participants who share multiple sources of data, providing unique opportunities for research. It is important to understand the phenomic profiles of its participants to conduct research in this cohort. OBJECTIVES: More than 280 000 participants have shared their electronic health records (EHRs) in the All of Us Research Program. We aim to understand the phenomic profiles of this cohort through comparisons with those in the US general population and a well-established nation-wide cohort, UK Biobank, and to test whether association results of selected commonly studied diseases in the All of Us cohort were comparable to those in UK Biobank. MATERIALS AND METHODS: We included participants with EHRs in All of Us and participants with health records from UK Biobank. The estimates of prevalence of diseases in the US general population were obtained from the Global Burden of Diseases (GBD) study. We conducted phenome-wide association studies (PheWAS) of 9 commonly studied diseases in both cohorts. RESULTS: This study included 287 012 participants from the All of Us EHR cohort and 502 477 participants from the UK Biobank. A total of 314 diseases curated by the GBD were evaluated in All of Us, 80.9% (N = 254) of which were more common in All of Us than in the US general population [prevalence ratio (PR) >1.1, P < 2 × 10-5]. Among 2515 diseases and phenotypes evaluated in both All of Us and UK Biobank, 85.6% (N = 2152) were more common in All of Us (PR >1.1, P < 2 × 10-5). The Pearson correlation coefficients of effect sizes from PheWAS between All of Us and UK Biobank were 0.61, 0.50, 0.60, 0.57, 0.40, 0.53, 0.46, 0.47, and 0.24 for ischemic heart diseases, lung cancer, chronic obstructive pulmonary disease, dementia, colorectal cancer, lower back pain, multiple sclerosis, lupus, and cystic fibrosis, respectively. DISCUSSION: Despite the differences in prevalence of diseases in All of Us compared to the US general population or the UK Biobank, our study supports that All of Us can facilitate rapid investigation of a broad range of diseases. CONCLUSION: Most diseases were more common in All of Us than in the general US population or the UK Biobank. Results of disease-disease association tests from All of Us are comparable to those estimated in another well-studied national cohort.

Uterine fibroid polygenic risk score (PRS) associates and predicts risk for uterine fibroid.

Uterine fibroids (UF) are common pelvic tumors in women, heritable, and genome-wide association studies (GWAS) have identified ~ 30 loci associated with increased risk in UF. Using summary statistics from a previously published UF GWAS performed in a non-Hispanic European Ancestry (NHW) female subset from the Electronic Medical Records and Genomics (eMERGE) Network, we constructed a polygenic risk score (PRS) for UF. UF-PRS was developed using PRSice and optimized in the separate clinical population of BioVU. PRS was validated using parallel methods of 10-fold cross-validation logistic regression and phenome-wide association study (PheWAS) in a seperate subset of eMERGE NHW females (validation set), excluding samples used in GWAS. PRSice determined pt < 0.001 and after linkage disequilibrium pruning (r2 < 0.2), 4458 variants were in the PRS which was significant (pseudo-R2 = 0.0018, p = 0.041). 10-fold cross-validation logistic regression modeling of validation set revealed the model had an area under the curve (AUC) value of 0.60 (95% confidence interval [CI] 0.58-0.62) when plotted in a receiver operator curve (ROC). PheWAS identified six phecodes associated with the PRS with the most significant phenotypes being 218 'benign neoplasm of uterus' and 218.1 'uterine leiomyoma' (p = 1.94 × 10-23, OR 1.31 [95% CI 1.26-1.37] and p = 3.50 × 10-23, OR 1.32 [95% CI 1.26-1.37]). We have developed and validated the first PRS for UF. We find our PRS has predictive ability for UF and captures genetic architecture of increased risk for UF that can be used in further studies.

Lossless integration of multiple electronic health records for identifying pleiotropy using summary statistics.

Increasingly, clinical phenotypes with matched genetic data from bio-bank linked electronic health records (EHRs) have been used for pleiotropy analyses. Thus far, pleiotropy analysis using individual-level EHR data has been limited to data from one site. However, it is desirable to integrate EHR data from multiple sites to improve the detection power and generalizability of the results. Due to privacy concerns, individual-level patients' data are not easily shared across institutions. As a result, we introduce Sum-Share, a method designed to efficiently integrate EHR and genetic data from multiple sites to perform pleiotropy analysis. Sum-Share requires only summary-level data and one round of communication from each site, yet it produces identical test statistics compared with that of pooled individual-level data. Consequently, Sum-Share can achieve lossless integration of multiple datasets. Using real EHR data from eMERGE, Sum-Share is able to identify 1734 potential pleiotropic SNPs for five cardiovascular diseases.

Heritability and genome-wide association study of benign prostatic hyperplasia (BPH) in the eMERGE network.

Benign prostatic hyperplasia (BPH) results in a significant public health burden due to the morbidity caused by the disease and many of the available remedies. As much as 70% of men over 70 will develop BPH. Few studies have been conducted to discover the genetic determinants of BPH risk. Understanding the biological basis for this condition may provide necessary insight for development of novel pharmaceutical therapies or risk prediction. We have evaluated SNP-based heritability of BPH in two cohorts and conducted a genome-wide association study (GWAS) of BPH risk using 2,656 cases and 7,763 controls identified from the Electronic Medical Records and Genomics (eMERGE) network. SNP-based heritability estimates suggest that roughly 60% of the phenotypic variation in BPH is accounted for by genetic factors. We used logistic regression to model BPH risk as a function of principal components of ancestry, age, and imputed genotype data, with meta-analysis performed using METAL. The top result was on chromosome 22 in SYN3 at rs2710383 (p-value = 4.6 × 10-7; Odds Ratio = 0.69, 95% confidence interval = 0.55-0.83). Other suggestive signals were near genes GLGC, UNCA13, SORCS1 and between BTBD3 and SPTLC3. We also evaluated genetically-predicted gene expression in prostate tissue. The most significant result was with increasing predicted expression of ETV4 (chr17; p-value = 0.0015). Overexpression of this gene has been associated with poor prognosis in prostate cancer. In conclusion, although there were no genome-wide significant variants identified for BPH susceptibility, we present evidence supporting the heritability of this phenotype, have identified suggestive signals, and evaluated the association between BPH and genetically-predicted gene expression in prostate.

Probing the Virtual Proteome to Identify Novel Disease Biomarkers.

BACKGROUND: Proteomic approaches allow measurement of thousands of proteins in a single specimen, which can accelerate biomarker discovery. However, applying these technologies to massive biobanks is not currently feasible because of the practical barriers and costs of implementing such assays at scale. To overcome these challenges, we used a "virtual proteomic" approach, linking genetically predicted protein levels to clinical diagnoses in >40 000 individuals. METHODS: We used genome-wide association data from the Framingham Heart Study (n=759) to construct genetic predictors for 1129 plasma protein levels. We validated the genetic predictors for 268 proteins and used them to compute predicted protein levels in 41 288 genotyped individuals in the Electronic Medical Records and Genomics (eMERGE) cohort. We tested associations for each predicted protein with 1128 clinical phenotypes. Lead associations were validated with directly measured protein levels and either low-density lipoprotein cholesterol or subclinical atherosclerosis in the MDCS (Malmö Diet and Cancer Study; n=651). RESULTS: In the virtual proteomic analysis in eMERGE, 55 proteins were associated with 89 distinct diagnoses at a false discovery rate q<0.1. Among these, 13 associations involved lipid (n=7) or atherosclerosis (n=6) phenotypes. We tested each association for validation in MDCS using directly measured protein levels. At Bonferroni-adjusted significance thresholds, levels of apolipoprotein E isoforms were associated with hyperlipidemia, and circulating C-type lectin domain family 1 member B and platelet-derived growth factor receptor-ß predicted subclinical atherosclerosis. Odds ratios for carotid atherosclerosis were 1.31 (95% CI, 1.08-1.58; P=0.006) per 1-SD increment in C-type lectin domain family 1 member B and 0.79 (0.66-0.94; P=0.008) per 1-SD increment in platelet-derived growth factor receptor-ß. CONCLUSIONS: We demonstrate a biomarker discovery paradigm to identify candidate biomarkers of cardiovascular and other diseases.

A study paradigm integrating prospective epidemiologic cohorts and electronic health records to identify disease biomarkers.

Defining the full spectrum of human disease associated with a biomarker is necessary to advance the biomarker into clinical practice. We hypothesize that associating biomarker measurements with electronic health record (EHR) populations based on shared genetic architectures would establish the clinical epidemiology of the biomarker. We use Bayesian sparse linear mixed modeling to calculate SNP weightings for 53 biomarkers from the Atherosclerosis Risk in Communities study. We use the SNP weightings to computed predicted biomarker values in an EHR population and test associations with 1139 diagnoses. Here we report 116 associations meeting a Bonferroni level of significance. A false discovery rate (FDR)-based significance threshold reveals more known and undescribed associations across a broad range of biomarkers, including biometric measures, plasma proteins and metabolites, functional assays, and behaviors. We confirm an inverse association between LDL-cholesterol level and septicemia risk in an independent epidemiological cohort. This approach efficiently discovers biomarker-disease associations.

PR interval genome-wide association meta-analysis identifies 50 loci associated with atrial and atrioventricular electrical activity.

Electrocardiographic PR interval measures atrio-ventricular depolarization and conduction, and abnormal PR interval is a risk factor for atrial fibrillation and heart block. Our genome-wide association study of over 92,000 European-descent individuals identifies 44 PR interval loci (34 novel). Examination of these loci reveals known and previously not-yet-reported biological processes involved in cardiac atrial electrical activity. Genes in these loci are over-represented in cardiac disease processes including heart block and atrial fibrillation. Variants in over half of the 44 loci were associated with atrial or blood transcript expression levels, or were in high linkage disequilibrium with missense variants. Six additional loci were identified either by meta-analysis of ~105,000 African and European-descent individuals and/or by pleiotropic analyses combining PR interval with heart rate, QRS interval, and atrial fibrillation. These findings implicate developmental pathways, and identify transcription factors, ion-channel genes, and cell-junction/cell-signaling proteins in atrio-ventricular conduction, identifying potential targets for drug development.

Rare Variants in the Gene ALPL That Cause Hypophosphatasia Are Strongly Associated With Ovarian and Uterine Disorders.

Context: Mutations in alkaline phosphatase (AlkP), liver/bone/kidney (ALPL), which encodes tissue-nonspecific isozyme AlkP, cause hypophosphatasia (HPP). HPP is suspected by a low-serum AlkP. We hypothesized that some patients with bone or dental disease have undiagnosed HPP, caused by ALPL variants. Objective: Our objective was to discover the prevalence of these gene variants in the Vanderbilt University DNA Biobank (BioVU) and to assess phenotypic associations. Design: We identified subjects in BioVU, a repository of DNA, that had at least one of three known, rare HPP disease-causing variants in ALPL: rs199669988, rs121918007, and/or rs121918002. To evaluate for phenotypic associations, we conducted a sequential phenome-wide association study of ALPL variants and then performed a de-identified manual record review to refine the phenotype. Results: Out of 25,822 genotyped individuals, we identified 52 women and 53 men with HPP disease-causing variants in ALPL, 7/1000. None had a clinical diagnosis of HPP. For patients with ALPL variants, the average serum AlkP levels were in the lower range of normal or lower. Forty percent of men and 62% of women had documented bone and/or dental disease, compatible with the diagnosis of HPP. Forty percent of the female patients had ovarian pathology or other gynecological abnormalities compared with 15% seen in controls. Conclusions: Variants in the ALPL gene cause bone and dental disease in patients with and without the standard biomarker, low plasma AlkP. ALPL gene variants are more prevalent than currently reported and underdiagnosed. Gynecologic disease appears to be associated with HPP-causing variants in ALPL.

Clinical Features Associated With Nascent Left Ventricular Diastolic Dysfunction in a Population Aged 40 to 55 Years.

Diastolic dysfunction (DD), an abnormality in cardiac left ventricular (LV) chamber compliance, is associated with increased morbidity and mortality. Although DD has been extensively studied in older populations, co-morbidity patterns are less well characterized in middle-aged subjects. We screened 156,434 subjects with transthoracic echocardiogram reports available through Vanderbilt's electronic heath record and identified 6,612 subjects 40 to 55 years old with an LV ejection fraction ≥50% and diastolic function staging. We tested 452 incident and prevalent clinical diagnoses for associations with early-stage DD (n = 1,676) versus normal function. There were 44 co-morbid diagnoses associated with grade 1 DD including hypertension (odds ratio [OR] = 2.02, 95% confidence interval [CI] 1.78 to 2.28, p <5.3 × 10-29), type 2 diabetes (OR 1.96, 95% CI 1.68 to 2.29, p = 2.1 × 10-17), tachycardia (OR 1.38, 95% CI 0.53 to 2.19, p = 2.9 × 10-6), obesity (OR 1.76, 95% CI 1.51 to 2.06, p = 1.7 × 10-12), and clinical end points, including end-stage renal disease (OR 3.29, 95% CI 2.19 to 4.96, p = 1.2 × 10-8) and stroke (OR 1.5, 95% CI 1.12 to 2.02, p = 6.9 × 10-3). Among the 60 incident diagnoses associated with DD, heart failure with preserved ejection fraction (OR 4.63, 95% CI 3.39 to 6.32, p = 6.3 × 10-22) had the most significant association. Among subjects with normal diastolic function and blood pressure at baseline, a blood pressure measurement in the hypertensive range at the time of the second echocardiogram was associated with progression to stage 1 DD (p = 0.04). In conclusion, DD was common among subjects 40 to 55 years old and was associated with a heavy burden of co-morbid disease.

Investigating the Genetic Architecture of the PR Interval Using Clinical Phenotypes.

BACKGROUND: One potential use for the PR interval is as a biomarker of disease risk. We hypothesized that quantifying the shared genetic architectures of the PR interval and a set of clinical phenotypes would identify genetic mechanisms contributing to PR variability and identify diseases associated with a genetic predictor of PR variability. METHODS AND RESULTS: We used ECG measurements from the ARIC study (Atherosclerosis Risk in Communities; n=6731 subjects) and 63 genetically modulated diseases from the eMERGE network (Electronic Medical Records and Genomics; n=12 978). We measured pairwise genetic correlations (rG) between PR phenotypes (PR interval, PR segment, P-wave duration) and each of the 63 phenotypes. The PR segment was genetically correlated with atrial fibrillation (rG=-0.88; P=0.0009). An analysis of metabolic phenotypes in ARIC also showed that the P wave was genetically correlated with waist circumference (rG=0.47; P=0.02). A genetically predicted PR interval phenotype based on 645 714 single-nucleotide polymorphisms was associated with atrial fibrillation (odds ratio=0.89 per SD change; 95% confidence interval, 0.83-0.95; P=0.0006). The differing pattern of associations among the PR phenotypes is consistent with analyses that show that the genetic correlation between the P wave and PR segment was not significantly different from 0 (rG=-0.03 [0.16]). CONCLUSIONS: The genetic architecture of the PR interval comprises modulators of atrial fibrillation risk and obesity.

Systematic Evaluation of Pleiotropy Identifies 6 Further Loci Associated With Coronary Artery Disease.

BACKGROUND: Genome-wide association studies have so far identified 56 loci associated with risk of coronary artery disease (CAD). Many CAD loci show pleiotropy; that is, they are also associated with other diseases or traits. OBJECTIVES: This study sought to systematically test if genetic variants identified for non-CAD diseases/traits also associate with CAD and to undertake a comprehensive analysis of the extent of pleiotropy of all CAD loci. METHODS: In discovery analyses involving 42,335 CAD cases and 78,240 control subjects we tested the association of 29,383 common (minor allele frequency >5%) single nucleotide polymorphisms available on the exome array, which included a substantial proportion of known or suspected single nucleotide polymorphisms associated with common diseases or traits as of 2011. Suggestive association signals were replicated in an additional 30,533 cases and 42,530 control subjects. To evaluate pleiotropy, we tested CAD loci for association with cardiovascular risk factors (lipid traits, blood pressure phenotypes, body mass index, diabetes, and smoking behavior), as well as with other diseases/traits through interrogation of currently available genome-wide association study catalogs. RESULTS: We identified 6 new loci associated with CAD at genome-wide significance: on 2q37 (KCNJ13-GIGYF2), 6p21 (C2), 11p15 (MRVI1-CTR9), 12q13 (LRP1), 12q24 (SCARB1), and 16q13 (CETP). Risk allele frequencies ranged from 0.15 to 0.86, and odds ratio per copy of the risk allele ranged from 1.04 to 1.09. Of 62 new and known CAD loci, 24 (38.7%) showed statistical association with a traditional cardiovascular risk factor, with some showing multiple associations, and 29 (47%) showed associations at p < 1 × 10-4 with a range of other diseases/traits. CONCLUSIONS: We identified 6 loci associated with CAD at genome-wide significance. Several CAD loci show substantial pleiotropy, which may help us understand the mechanisms by which these loci affect CAD risk.

PRECISION MEDICINE: DATA AND DISCOVERY FOR IMPROVED HEALTH AND THERAPY.

The major goal of precision medicine is to improve human health. A feature that unites much research in the field is the use of large datasets such as genomic data and electronic health records. Research in this field includes examination of variation in the core bases of DNA and their methylation status, through variations in metabolic and signaling molecules, all the way up to broader systems level changes in physiology and disease presentation. Intermediate goals include understanding the individual drivers of disease that differentiate the cause of disease in each individual. To match this development of approaches to physical and activitybased measurements, computational approaches to using these new streams of data to better understand improve human health are being rapidly developed by the thriving biomedical informatics research community. This session of the 2017 Pacific Symposium of Biocomputing presents some of the latest advances in the capture, analysis and use of diverse biomedical data in precision medicine.

Defining a Contemporary Ischemic Heart Disease Genetic Risk Profile Using Historical Data.

BACKGROUND: Continued reductions in morbidity and mortality attributable to ischemic heart disease (IHD) require an understanding of the changing epidemiology of this disease. We hypothesized that we could use genetic correlations, which quantify the shared genetic architectures of phenotype pairs and extant risk factors from a historical prospective study to define the risk profile of a contemporary IHD phenotype. METHODS AND RESULTS: We used 37 phenotypes measured in the ARIC study (Atherosclerosis Risk in Communities; n=7716, European ancestry subjects) and clinical diagnoses from an electronic health record (EHR) data set (n=19 093). All subjects had genome-wide single-nucleotide polymorphism genotyping. We measured pairwise genetic correlations (rG) between the ARIC and EHR phenotypes using linear mixed models. The genetic correlation estimates between the ARIC risk factors and the EHR IHD were modestly linearly correlated with hazards ratio estimates for incident IHD in ARIC (Pearson correlation [r]=0.62), indicating that the 2 IHD phenotypes had differing risk profiles. For comparison, this correlation was 0.80 when comparing EHR and ARIC type 2 diabetes mellitus phenotypes. The EHR IHD phenotype was most strongly correlated with ARIC metabolic phenotypes, including total:high-density lipoprotein cholesterol ratio (rG=-0.44, P=0.005), high-density lipoprotein (rG=-0.48, P=0.005), systolic blood pressure (rG=0.44, P=0.02), and triglycerides (rG=0.38, P=0.02). EHR phenotypes related to type 2 diabetes mellitus, atherosclerotic, and hypertensive diseases were also genetically correlated with these ARIC risk factors. CONCLUSIONS: The EHR IHD risk profile differed from ARIC and indicates that treatment and prevention efforts in this population should target hypertensive and metabolic disease.

Coding Variation in ANGPTL4, LPL, and SVEP1 and the Risk of Coronary Disease.

BACKGROUND: The discovery of low-frequency coding variants affecting the risk of coronary artery disease has facilitated the identification of therapeutic targets. METHODS: Through DNA genotyping, we tested 54,003 coding-sequence variants covering 13,715 human genes in up to 72,868 patients with coronary artery disease and 120,770 controls who did not have coronary artery disease. Through DNA sequencing, we studied the effects of loss-of-function mutations in selected genes. RESULTS: We confirmed previously observed significant associations between coronary artery disease and low-frequency missense variants in the genes LPA and PCSK9. We also found significant associations between coronary artery disease and low-frequency missense variants in the genes SVEP1 (p.D2702G; minor-allele frequency, 3.60%; odds ratio for disease, 1.14; P=4.2×10(-10)) and ANGPTL4 (p.E40K; minor-allele frequency, 2.01%; odds ratio, 0.86; P=4.0×10(-8)), which encodes angiopoietin-like 4. Through sequencing of ANGPTL4, we identified 9 carriers of loss-of-function mutations among 6924 patients with myocardial infarction, as compared with 19 carriers among 6834 controls (odds ratio, 0.47; P=0.04); carriers of ANGPTL4 loss-of-function alleles had triglyceride levels that were 35% lower than the levels among persons who did not carry a loss-of-function allele (P=0.003). ANGPTL4 inhibits lipoprotein lipase; we therefore searched for mutations in LPL and identified a loss-of-function variant that was associated with an increased risk of coronary artery disease (p.D36N; minor-allele frequency, 1.9%; odds ratio, 1.13; P=2.0×10(-4)) and a gain-of-function variant that was associated with protection from coronary artery disease (p.S447*; minor-allele frequency, 9.9%; odds ratio, 0.94; P=2.5×10(-7)). CONCLUSIONS: We found that carriers of loss-of-function mutations in ANGPTL4 had triglyceride levels that were lower than those among noncarriers; these mutations were also associated with protection from coronary artery disease. (Funded by the National Institutes of Health and others.).

Evaluation of the F2R IVS-14A/T PAR1 polymorphism with subsequent cardiovascular events and bleeding in patients who have undergone percutaneous coronary intervention.

Abnormal platelet reactivity is associated with recurrent ischemia and bleeding following percutaneous coronary intervention (PCI). Protease-activated receptor-1 (PAR1), encoded by F2R, is a high affinity thrombin receptor on platelets and the target of the antiplatelet drug vorapaxar. The intronic single nucleotide polymorphism F2R IVS-14 A/T affects PAR1 receptor density and function. We hypothesized that carriers of the T allele, who have been shown to have decreased platelet reactivity, would be at lower risk for thrombotic events, but higher risk for bleeding following PCI. Using BioVU, the Vanderbilt DNA repository linked to the electronic medical record, we studied 660 patients who underwent PCI for unstable or stable coronary artery disease. Primary outcome measures were major adverse cardiovascular events (MACE, composite of revascularization, MI, stroke, death) and bleeding (assessed by Bleeding Academic Research Consortium scale) over 24 months. The minor allele (T) frequency was 14.8 %. There were no genotypic differences in the frequency of MACE (33.7, 28.8, and 31.6 % for A/A, A/T, and T/T respectively, P = 0.50) or bleeding (15.7, 14.7, and 18.8 % for A/A, A/T, and T/T respectively, P = 0.90). In a Cox regression model, fully adjusted for age, race, sex, BMI, and smoking status, carrying a T allele was not associated with MACE (HR 1.19, 95 % CI 0.89-1.59, P = 0.23) or bleeding (HR 0.73, 95 % CI 0.37-1.4, P = 0.34). In conclusion, in our population, F2R IVS-14 PAR1 variability does not affect risk of MACE or bleeding following PCI.

Clinical and Genetic Factors Associated with Cutaneous Squamous Cell Carcinoma in Kidney and Heart Transplant Recipients.

BACKGROUND: Cutaneous squamous cell carcinoma (cSCC) occurs with higher frequency and recurrence rates, increased morbidity and mortality, and more aggressive metastasis in kidney and heart transplant recipients compared to the general population but all transplant recipients do not develop cSCC. In addition, the phenotypic expression of cSCC among transplant recipients can vary between mild disease to extensive recurrent metastatic disease. These clinically observed differences in occurrence and severity of cSCC among transplant recipients suggest the possibility that an underlying genetic component might modify risk. METHODS: We identified 88 white post-transplant cSCC cases (71 kidney and 17 heart) and 300 white post-transplant controls (265 kidney and 35 heart) using a DNA biobank linked with de-identified electronic medical records. Logistic regression was used to determine adjusted odds ratios (OR) for clinical characteristics and single nucleotide polymorphisms (SNP) associated with cSCC in both a candidate SNP and genome wide analysis. RESULTS: Age (OR 1.08 [1.05-1.11], p<0.001) and azathioprine exposure (OR 8.64 [3.92-19.03], p<0.001) were significantly associated while gender, smoking tobacco use, dialysis duration and immunosuppression duration were not. Ten candidate SNPs previously associated with non-melanoma skin cancer in the general population were significantly associated with cSCC in transplant recipients. Genome wide association analysis implicated SNPs in genes previously associated with malignancy, CSMD1 (OR 3.14 [1.90-5.20]) and CACNA1D (OR 2.67 [1.73-4.10]). CONCLUSIONS: This study shows an association of increasing age and azathioprine exposure with cSCC and confirms a genetic contribution for cSCC development in kidney and heart transplant recipients.

Genome-Wide Association Study of Serum Creatinine Levels during Vancomycin Therapy.

Vancomycin, a commonly used antibiotic, can be nephrotoxic. Known risk factors such as age, creatinine clearance, vancomycin dose / dosing interval, and concurrent nephrotoxic medications fail to accurately predict nephrotoxicity. To identify potential genomic risk factors, we performed a genome-wide association study (GWAS) of serum creatinine levels while on vancomycin in 489 European American individuals and validated findings in three independent cohorts totaling 439 European American individuals. In primary analyses, the chromosome 6q22.31 locus was associated with increased serum creatinine levels while on vancomycin therapy (most significant variant rs2789047, risk allele A, ß = -0.06, p = 1.1 x 10(-7)). SNPs in this region had consistent directions of effect in the validation cohorts, with a meta-p of 1.1 x 10(-7). Variation in this region on chromosome 6, which includes the genes TBC1D32/C6orf170 and GJA1 (encoding connexin43), may modulate risk of vancomycin-induced kidney injury.

CTNNA3 and SEMA3D: Promising loci for asthma exacerbation identified through multiple genome-wide association studies.

BACKGROUND: Asthma exacerbations are a major cause of morbidity and medical cost. OBJECTIVE: The objective of this study was to identify genetic predictors of exacerbations in asthmatic subjects. METHODS: We performed a genome-wide association study meta-analysis of acute asthma exacerbation in 2 pediatric clinical trials: the Childhood Asthma Management Program (n = 581) and the Childhood Asthma Research and Education (n = 205) network. Acute asthma exacerbations were defined as treatment with a 5-day course of oral steroids. We obtained a replication cohort from Biobank of Vanderbilt University Medical Center (BioVU; n = 786), the Vanderbilt University electronic medical record-linked DNA biobank. We used CD4(+) lymphocyte genome-wide mRNA expression profiling to identify associations of top single nucleotide polymorphisms with mRNA abundance of nearby genes. RESULTS: A locus in catenin (cadherin-associated protein), alpha 3 (CTNNA3), reached genome-wide significance (rs7915695, P = 2.19 × 10(-8); mean exacerbations, 6.05 for minor alleles vs 3.71 for homozygous major alleles). Among the 4 top single nucleotide polymorphisms replicated in BioVU, rs993312 in Sema domain, immunoglobulin domain (Ig), short basic domain, secreted, (semaphorin) 3D (SEMA3D) was significant (P = .0083) and displayed stronger association among African Americans (P = .0004 in BioVU [mean exacerbations, 3.91 vs 1.53]; P = .0089 in the Childhood Asthma Management Program [mean exacerbations, 6.0 vs 3.25]). CTNNA3 variants did not replicate in BioVU. A regulatory variant in the CTNNA3 locus was associated with CTNNA3 mRNA expression in CD4(+) cells from asthmatic patients (P = .00079). CTNNA3 appears to be active in the immune response, and SEMA3D has a plausible role in airway remodeling. We also provide a replication of a previous association of purinergic receptor P2X, ligand-gated ion channel, 7 (P2RX7), with asthma exacerbation. CONCLUSIONS: We identified 2 loci associated with exacerbations through a genome-wide association study. CTNNA3 met genome-wide significance thresholds, and SEMA3D replicated in a clinical biobank database.

Integrating EMR-linked and in vivo functional genetic data to identify new genotype-phenotype associations.

The coupling of electronic medical records (EMR) with genetic data has created the potential for implementing reverse genetic approaches in humans, whereby the function of a gene is inferred from the shared pattern of morbidity among homozygotes of a genetic variant. We explored the feasibility of this approach to identify phenotypes associated with low frequency variants using Vanderbilt's EMR-based BioVU resource. We analyzed 1,658 low frequency non-synonymous SNPs (nsSNPs) with a minor allele frequency (MAF)<10% collected on 8,546 subjects. For each nsSNP, we identified diagnoses shared by at least 2 minor allele homozygotes and with an association p<0.05. The diagnoses were reviewed by a clinician to ascertain whether they may share a common mechanistic basis. While a number of biologically compelling clinical patterns of association were observed, the frequency of these associations was identical to that observed using genotype-permuted data sets, indicating that the associations were likely due to chance. To refine our analysis associations, we then restricted the analysis to 711 nsSNPs in genes with phenotypes in the On-line Mendelian Inheritance in Man (OMIM) or knock-out mouse phenotype databases. An initial comparison of the EMR diagnoses to the known in vivo functions of the gene identified 25 candidate nsSNPs, 19 of which had significant genotype-phenotype associations when tested using matched controls. Twleve of the 19 nsSNPs associations were confirmed by a detailed record review. Four of 12 nsSNP-phenotype associations were successfully replicated in an independent data set: thrombosis (F5,rs6031), seizures/convulsions (GPR98,rs13157270), macular degeneration (CNGB3,rs3735972), and GI bleeding (HGFAC,rs16844401). These analyses demonstrate the feasibility and challenges of using reverse genetics approaches to identify novel gene-phenotype associations in human subjects using low frequency variants. As increasing amounts of rare variant data are generated from modern genotyping and sequence platforms, model organism data may be an important tool to enable discovery.

Mechanistic phenotypes: an aggregative phenotyping strategy to identify disease mechanisms using GWAS data.

A single mutation can alter cellular and global homeostatic mechanisms and give rise to multiple clinical diseases. We hypothesized that these disease mechanisms could be identified using low minor allele frequency (MAF<0.1) non-synonymous SNPs (nsSNPs) associated with "mechanistic phenotypes", comprised of collections of related diagnoses. We studied two mechanistic phenotypes: (1) thrombosis, evaluated in a population of 1,655 African Americans; and (2) four groupings of cancer diagnoses, evaluated in 3,009 white European Americans. We tested associations between nsSNPs represented on GWAS platforms and mechanistic phenotypes ascertained from electronic medical records (EMRs), and sought enrichment in functional ontologies across the top-ranked associations. We used a two-step analytic approach whereby nsSNPs were first sorted by the strength of their association with a phenotype. We tested associations using two reverse genetic models and standard additive and recessive models. In the second step, we employed a hypothesis-free ontological enrichment analysis using the sorted nsSNPs to identify functional mechanisms underlying the diagnoses comprising the mechanistic phenotypes. The thrombosis phenotype was solely associated with ontologies related to blood coagulation (Fisher's p = 0.0001, FDR p = 0.03), driven by the F5, P2RY12 and F2RL2 genes. For the cancer phenotypes, the reverse genetics models were enriched in DNA repair functions (p = 2×10-5, FDR p = 0.03) (POLG/FANCI, SLX4/FANCP, XRCC1, BRCA1, FANCA, CHD1L) while the additive model showed enrichment related to chromatid segregation (p = 4×10-6, FDR p = 0.005) (KIF25, PINX1). We were able to replicate nsSNP associations for POLG/FANCI, BRCA1, FANCA and CHD1L in independent data sets. Mechanism-oriented phenotyping using collections of EMR-derived diagnoses can elucidate fundamental disease mechanisms.