Multi-ancestry genome- and phenome-wide association studies of diverticular disease in electronic health records with natural language processing enriched phenotyping algorithm.

OBJECTIVE: Diverticular disease (DD) is one of the most prevalent conditions encountered by gastroenterologists, affecting ~50% of Americans before the age of 60. Our aim was to identify genetic risk variants and clinical phenotypes associated with DD, leveraging multiple electronic health record (EHR) data sources of 91,166 multi-ancestry participants with a Natural Language Processing (NLP) technique. MATERIALS AND METHODS: We developed a NLP-enriched phenotyping algorithm that incorporated colonoscopy or abdominal imaging reports to identify patients with diverticulosis and diverticulitis from multicenter EHRs. We performed genome-wide association studies (GWAS) of DD in European, African and multi-ancestry participants, followed by phenome-wide association studies (PheWAS) of the risk variants to identify their potential comorbid/pleiotropic effects in clinical phenotypes. RESULTS: Our developed algorithm showed a significant improvement in patient classification performance for DD analysis (algorithm PPVs ≥ 0.94), with up to a 3.5 fold increase in terms of the number of identified patients than the traditional method. Ancestry-stratified analyses of diverticulosis and diverticulitis of the identified subjects replicated the well-established associations between ARHGAP15 loci with DD, showing overall intensified GWAS signals in diverticulitis patients compared to diverticulosis patients. Our PheWAS analyses identified significant associations between the DD GWAS variants and circulatory system, genitourinary, and neoplastic EHR phenotypes. DISCUSSION: As the first multi-ancestry GWAS-PheWAS study, we showcased that heterogenous EHR data can be mapped through an integrative analytical pipeline and reveal significant genotype-phenotype associations with clinical interpretation. CONCLUSION: A systematic framework to process unstructured EHR data with NLP could advance a deep and scalable phenotyping for better patient identification and facilitate etiological investigation of a disease with multilayered data.

Pleiotropy of systemic lupus erythematosus risk alleles and cardiometabolic disorders: A phenome-wide association study and inverse-variance weighted meta-analysis.

OBJECTIVES: To test the hypothesis that genetic predisposition to systemic lupus erythematosus (SLE) increases the risk of cardiometabolic disorders. METHODS: Using 41 single nucleotide polymorphisms (SNPs) associated with SLE, we calculated a weighted genetic risk score (wGRS) for SLE. In a large biobank we tested the association between this wGRS and 9 cardiometabolic phenotypes previously associated with SLE: atrial fibrillation, ischemic stroke, coronary artery disease, type 1 and type 2 diabetes, obesity, chronic kidney disease, hypertension, and hypercholesterolemia. Additionally, we performed a phenome-wide association analysis (pheWAS) to discover novel clinical associations with a genetic predisposition to SLE. Findings were replicated in the Electronic Medical Records and Genomics (eMERGE) Network. To further define the association between SLE-related risk alleles and the selected cardiometabolic phenotypes, we performed an inverse variance weighted regression (IVWR) meta-analysis. RESULTS: The wGRS for SLE was calculated in 74,759 individuals of European ancestry. Among the pre-selected phenotypes, the wGRS was significantly associated with type 1 diabetes (OR [95%CI] =1.11 [1.06, 1.17], P-value = 1.05x10-5). In the PheWAS, the wGRS was associated with several autoimmune phenotypes, kidney disorders, and skin neoplasm; but only the associations with autoimmune phenotypes were replicated. In the IVWR meta-analysis, SLE-related risk alleles were nominally associated with type 1 diabetes (P = 0.048) but the associations were heterogeneous and did not meet the adjusted significance threshold. CONCLUSION: A weighted GRS for SLE was associated with an increased risk of several autoimmune-related phenotypes including type I diabetes but not with cardiometabolic disorders.

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.

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.

Identification of Four Novel Loci in Asthma in European American and African American Populations.

RATIONALE: Despite significant advances in knowledge of the genetic architecture of asthma, specific contributors to the variability in the burden between populations remain uncovered. OBJECTIVES: To identify additional genetic susceptibility factors of asthma in European American and African American populations. METHODS: A phenotyping algorithm mining electronic medical records was developed and validated to recruit cases with asthma and control subjects from the Electronic Medical Records and Genomics network. Genome-wide association analyses were performed in pediatric and adult asthma cases and control subjects with European American and African American ancestry followed by metaanalysis. Nominally significant results were reanalyzed conditioning on allergy status. MEASUREMENTS AND MAIN RESULTS: The validation of the algorithm yielded an average of 95.8% positive predictive values for both cases and control subjects. The algorithm accrued 21,644 subjects (65.83% European American and 34.17% African American). We identified four novel population-specific associations with asthma after metaanalyses: loci 6p21.31, 9p21.2, and 10q21.3 in the European American population, and the PTGES gene in African Americans. TEK at 9p21.2, which encodes TIE2, has been shown to be involved in remodeling the airway wall in asthma, and the association remained significant after conditioning by allergy. PTGES, which encodes the prostaglandin E synthase, has also been linked to asthma, where deficient prostaglandin E2 synthesis has been associated with airway remodeling. CONCLUSIONS: This study adds to understanding of the genetic architecture of asthma in European Americans and African Americans and reinforces the need to study populations of diverse ethnic backgrounds to identify shared and unique genetic predictors of asthma.

Performance of an electronic health record-based phenotype algorithm to identify community associated methicillin-resistant Staphylococcus aureus cases and controls for genetic association studies.

BACKGROUND: Community associated methicillin-resistant Staphylococcus aureus (CA-MRSA) is one of the most common causes of skin and soft tissue infections in the United States, and a variety of genetic host factors are suspected to be risk factors for recurrent infection. Based on the CDC definition, we have developed and validated an electronic health record (EHR) based CA-MRSA phenotype algorithm utilizing both structured and unstructured data. METHODS: The algorithm was validated at three eMERGE consortium sites, and positive predictive value, negative predictive value and sensitivity, were calculated. The algorithm was then run and data collected across seven total sites. The resulting data was used in GWAS analysis. RESULTS: Across seven sites, the CA-MRSA phenotype algorithm identified a total of 349 cases and 7761 controls among the genotyped European and African American biobank populations. PPV ranged from 68 to 100% for cases and 96 to 100% for controls; sensitivity ranged from 94 to 100% for cases and 75 to 100% for controls. Frequency of cases in the populations varied widely by site. There were no plausible GWAS-significant (p < 5 E -8) findings. CONCLUSIONS: Differences in EHR data representation and screening patterns across sites may have affected identification of cases and controls and accounted for varying frequencies across sites. Future work identifying these patterns is necessary.

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.

Epistatic Gene-Based Interaction Analyses for Glaucoma in eMERGE and NEIGHBOR Consortium.

Primary open angle glaucoma (POAG) is a complex disease and is one of the major leading causes of blindness worldwide. Genome-wide association studies have successfully identified several common variants associated with glaucoma; however, most of these variants only explain a small proportion of the genetic risk. Apart from the standard approach to identify main effects of variants across the genome, it is believed that gene-gene interactions can help elucidate part of the missing heritability by allowing for the test of interactions between genetic variants to mimic the complex nature of biology. To explain the etiology of glaucoma, we first performed a genome-wide association study (GWAS) on glaucoma case-control samples obtained from electronic medical records (EMR) to establish the utility of EMR data in detecting non-spurious and relevant associations; this analysis was aimed at confirming already known associations with glaucoma and validating the EMR derived glaucoma phenotype. Our findings from GWAS suggest consistent evidence of several known associations in POAG. We then performed an interaction analysis for variants found to be marginally associated with glaucoma (SNPs with main effect p-value <0.01) and observed interesting findings in the electronic MEdical Records and GEnomics Network (eMERGE) network dataset. Genes from the top epistatic interactions from eMERGE data (Likelihood Ratio Test i.e. LRT p-value <1e-05) were then tested for replication in the NEIGHBOR consortium dataset. To replicate our findings, we performed a gene-based SNP-SNP interaction analysis in NEIGHBOR and observed significant gene-gene interactions (p-value <0.001) among the top 17 gene-gene models identified in the discovery phase. Variants from gene-gene interaction analysis that we found to be associated with POAG explain 3.5% of additional genetic variance in eMERGE dataset above what is explained by the SNPs in genes that are replicated from previous GWAS studies (which was only 2.1% variance explained in eMERGE dataset); in the NEIGHBOR dataset, adding replicated SNPs from gene-gene interaction analysis explain 3.4% of total variance whereas GWAS SNPs alone explain only 2.8% of variance. Exploring gene-gene interactions may provide additional insights into many complex traits when explored in properly designed and powered association studies.

Identification of genetic interaction networks via an evolutionary algorithm evolved Bayesian network.

BACKGROUND: The future of medicine is moving towards the phase of precision medicine, with the goal to prevent and treat diseases by taking inter-individual variability into account. A large part of the variability lies in our genetic makeup. With the fast paced improvement of high-throughput methods for genome sequencing, a tremendous amount of genetics data have already been generated. The next hurdle for precision medicine is to have sufficient computational tools for analyzing large sets of data. Genome-Wide Association Studies (GWAS) have been the primary method to assess the relationship between single nucleotide polymorphisms (SNPs) and disease traits. While GWAS is sufficient in finding individual SNPs with strong main effects, it does not capture potential interactions among multiple SNPs. In many traits, a large proportion of variation remain unexplained by using main effects alone, leaving the door open for exploring the role of genetic interactions. However, identifying genetic interactions in large-scale genomics data poses a challenge even for modern computing. RESULTS: For this study, we present a new algorithm, Grammatical Evolution Bayesian Network (GEBN) that utilizes Bayesian Networks to identify interactions in the data, and at the same time, uses an evolutionary algorithm to reduce the computational cost associated with network optimization. GEBN excelled in simulation studies where the data contained main effects and interaction effects. We also applied GEBN to a Type 2 diabetes (T2D) dataset obtained from the Marshfield Personalized Medicine Research Project (PMRP). We were able to identify genetic interactions for T2D cases and controls and use information from those interactions to classify T2D samples. We obtained an average testing area under the curve (AUC) of 86.8 %. We also identified several interacting genes such as INADL and LPP that are known to be associated with T2D. CONCLUSIONS: Developing the computational tools to explore genetic associations beyond main effects remains a critically important challenge in human genetics. Methods, such as GEBN, demonstrate the utility of considering genetic interactions, as they likely explain some of the missing heritability.

CYP2C19 and CES1 polymorphisms and efficacy of clopidogrel and aspirin dual antiplatelet therapy in patients with symptomatic intracranial atherosclerotic disease.

OBJECT Symptomatic intracranial atherosclerotic disease (ICAD) has a high risk of recurrent stroke. Genetic polymorphisms in CYP2C19 and CES1 are associated with adverse outcomes in cardiovascular patients, but have not been studied in ICAD. The authors studied CYP2C19 and CES1 single-nucleotide polymorphisms (SNPs) in symptomatic ICAD patients. METHODS Genotype testing for CYP2C19*2, (*)3, (*)8, (*)17 and CES1 G143E was performed on 188 adult symptomatic ICAD patients from 3 medical centers who were medically managed with clopidogrel and aspirin. Testing was performed prospectively at 1 center, and retrospectively from a DNA sample biorepository at 2 centers. Multiple logistic regression and Cox regression analysis were performed to assess the association of these SNPs with the primary endpoint, which was a composite of transient ischemic attack (TIA), stroke, myocardial infarction, or death within 12 months. RESULTS The primary endpoint occurred in 14.9% of the 188 cases. In multiple logistic regression analysis, the presence of the CYP2C19 loss of function (LOF) alleles *2, *3, and *8 in the medically managed patients was associated with lower odds of primary endpoint compared with wild-type homozygotes (odds ratio [OR] 0.13, 95% CI 0.03-0.62, p = 0.0101). Cox regression analysis demonstrated the CYP2C19 LOF carriers had a lower risk for the primary endpoint, with hazard ratio (HR) of 0.27 (95% CI 0.08-0.95), p = 0.041. A sensitivity analysis of a secondary composite endpoint of TIA, stroke, or death demonstrated a significant trend in multiple logistic regression analysis of CYP2C19 variants, with lower odds of secondary endpoint in patients carrying at least 1 LOF allele (*2, *3, *8) than in wild-type homozygotes (OR 0.27, 95% CI 0.06-1.16, p = 0.078). Cox regression analysis demonstrated that the carriers of CYP2C19 LOF alleles had a lower risk forthe secondary composite endpoint (HR 0.22, 95% CI 0.05-1.04, p = 0.056). CONCLUSIONS This is the first study examining genetic variants and their effects in symptomatic ICAD. Variant alleles of CYP2C19 (*2, *3, *8) were associated with lower odds of the primary and secondary composite endpoints. However, the direction of the association was opposite of what is expected based on this SNP. This may reflect an incomplete understanding of this genetic variation and its effect in symptomatic ICAD and warrants further investigations.

A Robust e-Epidemiology Tool in Phenotyping Heart Failure with Differentiation for Preserved and Reduced Ejection Fraction: the Electronic Medical Records and Genomics (eMERGE) Network.

Identifying populations of heart failure (HF) patients is paramount to research efforts aimed at developing strategies to effectively reduce the burden of this disease. The use of electronic medical record (EMR) data for this purpose is challenging given the syndromic nature of HF and the need to distinguish HF with preserved or reduced ejection fraction. Using a gold standard cohort of manually abstracted cases, an EMR-driven phenotype algorithm based on structured and unstructured data was developed to identify all the cases. The resulting algorithm was executed in two cohorts from the Electronic Medical Records and Genomics (eMERGE) Network with a positive predictive value of >95 %. The algorithm was expanded to include three hierarchical definitions of HF (i.e., definite, probable, possible) based on the degree of confidence of the classification to capture HF cases in a whole population whereby increasing the algorithm utility for use in e-Epidemiologic research.

ePhenotyping for Abdominal Aortic Aneurysm in the Electronic Medical Records and Genomics (eMERGE) Network: Algorithm Development and Konstanz Information Miner Workflow.

BACKGROUND AND OBJECTIVE: We designed an algorithm to identify abdominal aortic aneurysm cases and controls from electronic health records to be shared and executed within the "electronic Medical Records and Genomics" (eMERGE) Network. MATERIALS AND METHODS: Structured Query Language, was used to script the algorithm utilizing "Current Procedural Terminology" and "International Classification of Diseases" codes, with demographic and encounter data to classify individuals as case, control, or excluded. The algorithm was validated using blinded manual chart review at three eMERGE Network sites and one non-eMERGE Network site. Validation comprised evaluation of an equal number of predicted cases and controls selected at random from the algorithm predictions. After validation at the three eMERGE Network sites, the remaining eMERGE Network sites performed verification only. Finally, the algorithm was implemented as a workflow in the Konstanz Information Miner, which represented the logic graphically while retaining intermediate data for inspection at each node. The algorithm was configured to be independent of specific access to data and was exportable (without data) to other sites. RESULTS: The algorithm demonstrated positive predictive values (PPV) of 92.8% (CI: 86.8-96.7) and 100% (CI: 97.0-100) for cases and controls, respectively. It performed well also outside the eMERGE Network. Implementation of the transportable executable algorithm as a Konstanz Information Miner workflow required much less effort than implementation from pseudo code, and ensured that the logic was as intended. DISCUSSION AND CONCLUSION: This ePhenotyping algorithm identifies abdominal aortic aneurysm cases and controls from the electronic health record with high case and control PPV necessary for research purposes, can be disseminated easily, and applied to high-throughput genetic and other studies.

CMTR1 is associated with increased asthma exacerbations in patients taking inhaled corticosteroids.

Inhaled corticosteroids (ICS) are the most effective controller medications for asthma, and variability in ICS response is associated with genetic variation. Despite ICS treatment, some patients with poor asthma control experience severe asthma exacerbations, defined as a hospitalization or emergency room visit. We hypothesized that some individuals may be at increased risk of asthma exacerbations, despite ICS use, due to genetic factors. A GWAS of 237,726 common, independent markers was conducted in 806 Caucasian asthmatic patients from two population-based biobanks: BioVU, at Vanderbilt University Medical Center (VUMC) in Tennessee (369 patients), and Personalized Medicine Research Project (PMRP) at the Marshfield Clinic in Wisconsin (437 patients). Using a case-control study design, the association of each SNP locus with the outcome of asthma exacerbations (defined as asthma-related emergency department visits or hospitalizations concurrent with oral corticosteroid use), was evaluated for each population by logistic regression analysis, adjusting for age, gender and the first four principal components. A meta-analysis of the results was conducted. Validation of expression of selected candidate genes was determined by evaluating an independent microarray expression data set. Our study identified six novel SNPs associated with differential risk of asthma exacerbations (P < 10(-05)). The top GWAS result, rs2395672 in CMTR1, was associated with an increased risk of exacerbations in both populations (OR = 1.07, 95% CI 1.03-1.11; joint P = 2.3 × 10(-06)). Two SNPs (rs2395672 and rs279728) were associated with increased risk of exacerbations, while the remaining four SNPs (rs4271056, rs6467778, rs2691529, and rs9303988) were associated with decreased risk. Three SNPs (rs2395672, rs6467778, and rs2691529) were present in three genes: CMTR1, TRIM24 and MAGI2. The CMTR1 mRNA transcript was significantly differentially expressed in nasal lavage samples from asthmatics during acute exacerbations, suggesting potential involvement of this gene in the development of this phenotype. We show that genetic variability may contribute to asthma exacerbations in patients taking ICS. Furthermore, our studies implicate CMTR1 as a novel candidate gene with potential roles in the pathogenesis of asthma exacerbations.

Electronic medical records and genomics (eMERGE) network exploration in cataract: several new potential susceptibility loci.

PURPOSE: Cataract is the leading cause of blindness in the world, and in the United States accounts for approximately 60% of Medicare costs related to vision. The purpose of this study was to identify genetic markers for age-related cataract through a genome-wide association study (GWAS). METHODS: In the electronic medical records and genomics (eMERGE) network, we ran an electronic phenotyping algorithm on individuals in each of five sites with electronic medical records linked to DNA biobanks. We performed a GWAS using 530,101 SNPs from the Illumina 660W-Quad in a total of 7,397 individuals (5,503 cases and 1,894 controls). We also performed an age-at-diagnosis case-only analysis. RESULTS: We identified several statistically significant associations with age-related cataract (45 SNPs) as well as age at diagnosis (44 SNPs). The 45 SNPs associated with cataract at p<1×10(-5) are in several interesting genes, including ALDOB, MAP3K1, and MEF2C. All have potential biologic relationships with cataracts. CONCLUSIONS: This is the first genome-wide association study of age-related cataract, and several regions of interest have been identified. The eMERGE network has pioneered the exploration of genomic associations in biobanks linked to electronic health records, and this study is another example of the utility of such resources. Explorations of age-related cataract including validation and replication of the association results identified herein are needed in future studies.

Asymmetry in noise-induced hearing loss: evaluation of two competing theories.

Competing theories exist about why asymmetry is observed in noise-induced hearing loss (NIHL). We evaluated these theories using a cohort of young workers studied over 16 years. The study aim was to describe and evaluate patterns of hearing loss and asymmetry by gender, agricultural exposure and gunfire exposure. This was a secondary analysis of data collected from young adults during follow-up of a randomized controlled trial. This follow-up study evaluated long-term effects of a hearing conservation intervention for rural students. The sample consisted of 392 of 690 participants from the original trial. In total, 355 young adults (aged 29-33 years) completed baseline and follow-up noise exposure surveys and clinical audiometric examinations. Data are displayed graphically as thresholds by frequency and ear and degree of asymmetry between ears (left minus right). In the primary group comparisons, low and high frequency averages and mean high frequency asymmetry were analyzed using mixed linear models. At frequencies >2000 Hz, men showed more hearing loss, with greater asymmetry and a different asymmetry pattern, than women. For men with documented hearing loss, there was a trend toward increasing asymmetry with increasing levels of hearing loss. Asymmetry at high frequencies varied substantially by level of shooting exposure. While "head shadowing" is accepted as the primary explanation for asymmetric hearing loss in the audiologic and related public health literature, our findings are more consistent with physiological differences as the primary cause of asymmetric hearing loss, with greater susceptibility to NIHL in the left ear of men.

Development and validation of an electronic phenotyping algorithm for chronic kidney disease.

Twenty-six million Americans are estimated to have chronic kidney disease (CKD) with increased risk for cardiovascular disease and end stage renal disease. CKD is frequently undiagnosed and patients are unaware, hampering intervention. A tool for accurate and timely identification of CKD from electronic medical records (EMR) could improve healthcare quality and identify patients for research. As members of eMERGE (electronic medical records and genomics) Network, we developed an automated phenotyping algorithm that can be deployed to identify rapidly diabetic and/or hypertensive CKD cases and controls in health systems with EMRs It uses diagnostic codes, laboratory results, medication and blood pressure records, and textual information culled from notes. Validation statistics demonstrated positive predictive values of 96% and negative predictive values of 93.3. Similar results were obtained on implementation by two independent eMERGE member institutions. The algorithm dramatically outperformed identification by ICD-9-CM codes with 63% positive and 54% negative predictive values, respectively.

Determinants of early-stage hearing loss among a cohort of young workers with 16-year follow-up.

OBJECTIVES: The authors had a unique opportunity to study the early impacts of occupational and recreational exposures on the development of noise-induced hearing loss (NIHL) in a cohort of 392 young workers. The objectives of this study were to estimate strength of associations between occupational and recreational exposures and occurrence of early-stage NIHL and to determine the extent to which relationships between specific noise exposures and early-stage NIHL were mitigated through the use of hearing protection. METHODS: Participants were young adults who agreed to participate in a follow-up of a randomised controlled trial. While the follow-up study was designed to observe long-term effects (up to 16 years) of a hearing conservation intervention for high school students, it also provided opportunity to study the potential aetiology of NIHL in this worker cohort. Study data were collected via exposure history questionnaires and clinical audiometric examinations. RESULTS: Over the 16-year study period, the authors documented changes to hearing acuity that exceeded 15 dB at high frequencies in 42.8% of men and 27.7% of women. Analyses of risk factors for NIHL were limited to men, who comprised 68% of the cohort, and showed that risks increased in association with higher levels of the most common recreational and occupational noise sources, as well as chemical exposures with ototoxic potential. Use of hearing protection and other safety measures, although not universal and sometimes modest, appeared to offer some protection. CONCLUSIONS: Early-stage NIHL can be detected in young workers by measuring high-frequency changes in hearing acuity. Hearing conservation programmes should focus on a broader range of exposures, whether in occupational or non-occupational settings. Priority exposures include gunshots, chainsaws, power tools, smoking and potentially some chemical exposures.