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BackgroundMultiple sclerosis (MS) is a complex autoimmune disease in which both the roles of genetic susceptibility and environmental/microbial factors have been investigated. More than 200 genetic susceptibility variants have been identified along with the dysbiosis of gut microbiota, both independently have been shown to be associated with MS. We hypothesize that MS patients harboring genetic susceptibility variants along with gut microbiome dysbiosis are at a greater risk of exhibiting the disease. We investigated the polygenic risk score for MS in conjunction with gut microbiota in the same cohort of 117 relapsing remitting MS (RRMS) and 26 healthy controls. DNA samples were genotyped using Illumina’s Infinium Immuno array-24 v2 chip followed by calculating polygenic risk score and the microbiota was determined by sequencing the V4 hypervariable region of the 16S rRNA gene.ResultsWe identified two clusters of MS patients, Cluster A and B both having a higher polygenic risk score than the control group. The Cluster B with the higher polygenic risk score had a distinct gut microbiota, different than the Cluster A. MS group whose microbiome was similar to that of the control group despite a higher genetic risk score than the control group. This could be due to i) the non-active state of the disease in that group of MS patients at the time of fecal sample collection and/or ii) the restoration of the gut microbiome post disease modifying therapy to treat the MS.ConclusionOur study showed that there seems to be association between polygenic risk score and gut microbiome dysbiosis in triggering the disease in a small cohort of MS patients. The MS Cluster A who have a higher polygenic risk score but microbiome profile similar to that of healthy controls could be due to the remitting phase of the disease or due to the effect of disease modifying therapies.

Genomic medicine holds great promise for improving health care, but integrating searchable and actionable genetic data into electronic health records (EHRs) remains a challenge. Here we describe Neptune, a system for managing the interaction between a clinical laboratory and an EHR system during the clinical reporting process. We developed Neptune and applied it to two clinical sequencing projects that required report customization, variant reanalysis, and EHR integration. Neptune has been applied for the generation and delivery of over 15,000 clinical genomic reports. This work spans two clinical tests based on targeted gene panels that contain 68 and 153 genes respectively. These projects demanded customizable clinical reports that contained a variety of genetic data types including single-nucleotide variants (SNVs), copy-number variants (CNVs), pharmacogenomics, and polygenic risk scores. Two variant reanalysis activities were also supported, highlighting this important workflow. Methods are needed for delivering structured genetic data to EHRs. This need extends beyond developing data formats to providing infrastructure that manages the reporting process itself. Neptune was successfully applied on two high-throughput clinical sequencing projects to build and deliver clinical reports to EHR systems. The software is open source and available at https://gitlab.com/bcm-hgsc/neptune .

The advancement of precision medicine requires new methods to coordinate and deliver genetic data from heterogeneous sources to physicians and patients. The eMERGE III Network enrolled >25,000 participants from biobank and prospective cohorts of predominantly healthy individuals for clinical genetic testing to determine clinically actionable findings. The network developed protocols linking together the 11 participant collection sites and 2 clinical genetic testing laboratories. DNA capture panels targeting 109 genes were used for testing of DNA and sample collection, data generation, interpretation, reporting, delivery, and storage were each harmonized. A compliant and secure network enabled ongoing review and reconciliation of clinical interpretations, while maintaining communication and data sharing between clinicians and investigators. A total of 202 individuals had positive diagnostic findings relevant to the indication for testing and 1,294 had additional/secondary findings of medical significance deemed to be returnable, establishing data return rates for other testing endeavors. This study accomplished integration of structured genomic results into multiple electronic health record (EHR) systems, setting the stage for clinical decision support to enable genomic medicine. Further, the established processes enable different sequencing sites to harmonize technical and interpretive aspects of sequencing tests, a critical achievement toward global standardization of genomic testing. The eMERGE protocols and tools are available for widespread dissemination.

Standard analyses applied to genome-wide association data are well designed to detect additive effects of moderate strength. However, the power for standard genome-wide association study (GWAS) analyses to identify effects from recessive diplotypes is not typically high. We proposed and conducted a gene-based compound heterozygosity test to reveal additional genes underlying complex diseases. With this approach applied to iron overload, a strong association signal was identified between the fibroblast growth factor–encoding gene, FGF6, and hemochromatosis in the central Wisconsin population. Functional validation showed that fibroblast growth factor 6 protein (FGF-6) regulates iron homeostasis and induces transcriptional regulation of hepcidin. Moreover, specific identified FGF6 variants differentially impact iron metabolism. In addition, FGF6 downregulation correlated with iron-metabolism dysfunction in systemic sclerosis and cancer cells. Using the recessive diplotype approach revealed a novel susceptibility hemochromatosis gene and has extended our understanding of the mechanisms involved in iron metabolism.

Importance Advanced age-related macular degeneration (AMD) is a leading cause of blindness in Western countries. Causal, modifiable risk factors need to be identified to develop preventive measures for advanced AMD. Objective To assess whether smoking, alcohol consumption, blood pressure, body mass index, and glycemic traits are associated with increased risk of advanced AMD. Design, Setting, Participants This study used 2-sample mendelian randomization. Genetic instruments composed of variants associated with risk factors at genome-wide significance (P

Genome-wide, imputed, sequence, and structural data are now available for exceedingly large sample sizes. The needs for data management, handling population structure and related samples, and performing associations have largely been met. However, the infrastructure to support analyses involving complexity beyond genome-wide association studies is not standardized or centralized. We provide the PLatform for the Analysis, Translation, and Organization of large-scale data (PLATO), a software tool equipped to handle multi-omic data for hundreds of thousands of samples to explore complexity using genetic interactions, environment-wide association studies and gene–environment interactions, phenome-wide association studies, as well as copy number and rare variant analyses. Using the data from the Marshfield Personalized Medicine Research Project, a site in the electronic Medical Records and Genomics Network, we apply each feature of PLATO to type 2 diabetes and demonstrate how PLATO can be used to uncover the complex etiology of common traits., Centralized infrastructure to support analyses involving complexity beyond genome-wide association studies is broadly needed. Here, Ritchie and colleagues develop PLATO, a software tool to process and integrate various methods for this task.

Individuals participating in biobanks and other large research projects are increasingly asked to provide broad consent for open-ended research use and widespread sharing of their biosamples and data. We assessed willingness to participate in a biobank using different consent and data sharing models, hypothesizing that willingness would be higher under more restrictive scenarios. Perceived benefits, concerns, and information needs were also assessed. In this experimental survey, individuals from 11 US healthcare systems in the Electronic Medical Records and Genomics (eMERGE) Network were randomly allocated to one of three hypothetical scenarios: tiered consent and controlled data sharing; broad consent and controlled data sharing; or broad consent and open data sharing. Of 82,328 eligible individuals, exactly 13,000 (15.8%) completed the survey. Overall, 66% (95% CI: 63%-69%) of population-weighted respondents stated they would be willing to participate in a biobank; willingness and attitudes did not differ between respondents in the three scenarios. Willingness to participate was associated with self-identified white race, higher educational attainment, lower religiosity, perceiving more research benefits, fewer concerns, and fewer information needs. Most (86%, CI: 84%-87%) participants would want to know what would happen if a researcher misused their health information; fewer (51%, CI: 47%-55%) would worry about their privacy. The concern that the use of broad consent and open data sharing could adversely affect participant recruitment is not supported by these findings. Addressing potential participants' concerns and information needs and building trust and relationships with communities may increase acceptance of broad consent and wide data sharing in biobank research.

Translational multidisciplinary research is important for the Center for Devices and Radiological Health's efforts for utilizing real‐world data (RWD) to enhance predictive evaluation of medical device performance in patient subpopulations. As part of our efforts for developing new RWD‐based evidentiary approaches, including in silico discovery of device‐related risk predictors and biomarkers, this study aims to characterize the sex/race‐related trends in hip replacement outcomes and identify corresponding candidate single nucleotide polymorphisms (SNPs). Adverse outcomes were assessed by deriving RWD from a retrospective analysis of hip replacement hospital discharge data from the National Inpatient Sample (NIS). Candidate SNPs were explored using pre‐existing data from the Personalized Medicine Research Project (PMRP). High‐Performance Integrated Virtual Environment was used for analyzing and visualizing putative associations between SNPs and adverse outcomes. Ingenuity Pathway Analysis (IPA) was used for exploring plausibility of the sex‐related candidate SNPs and characterizing gene networks associated with the variants of interest. The NIS‐based epidemiologic evidence showed that periprosthetic osteolysis (PO) was most prevalent among white men. The PMRP‐based genetic evidence associated the PO‐related male predominance with rs7121 (odds ratio = 4.89; 95% confidence interval = 1.41−17.05) and other candidate SNPs. SNP‐based IPA analysis of the expected gene expression alterations and corresponding signaling pathways suggested possible role of sex‐related metabolic factors in development of PO, which was substantiated by ad hoc epidemiologic analysis identifying the sex‐related differences in metabolic comorbidities in men vs. women with hip replacement‐related PO. Thus, our in silico study illustrates RWD‐based evidentiary approaches that may facilitate cost/time‐efficient discovery of biomarkers for informing use of medical products.

BackgroundWe characterised the phenotypic consequence of genetic variation at the PCSK9 locus and compared findings with recent trials of pharmacological inhibitors of PCSK9.MethodsPublished and individual participant level data (300,000+ participants) were combined to construct a weighted PCSK9 gene-centric score (GS). Fourteen randomized placebo controlled PCSK9 inhibitor trials were included, providing data on 79,578 participants. Results were scaled to a one mmol/L lower LDL-C concentrationResultsThe PCSK9 GS (comprising 4 SNPs) associations with plasma lipid and apolipoprotein levels were consistent in direction with treatment effects. The GS odds ratio (OR) for myocardial infarction (MI) was 0.53 (95%CI 0.42; 0.68), compared to a PCSK9 inhibitor effect of 0.90 (95%CI 0.86; 0.93). For ischemic stroke ORs were 0.84 (95%CI 0.57; 1.22) for the GS, compared to 0.85 (95%CI 0.78; 0.93) in the drug trials. ORs with type 2 diabetes mellitus (T2DM) were 1.29 (95% CI 1.11; 1.50) for the GS, as compared to 1.00 (95%CI 0.96; 1.04) for incident T2DM in PCSK9 inhibitor trials. No genetic associations were observed for cancer, heart failure, atrial fibrillation, chronic obstructive pulmonary disease, or Alzheimer’s disease – outcomes for which large-scale trial data were unavailable.ConclusionsGenetic variation at the PCSK9 locus recapitulates the effects of therapeutic inhibition of PCSK9 on major blood lipid fractions and MI. Apparent discordance between genetic associations and trial outcome for T2DM might be explained lack by a of statistical precision, or differences in the nature and duration of genetic versus pharmacological perturbation of PCSK9.FundingThis research was funded by the British Heart Foundation (SP/13/6/30554, RG/10/12/28456, FS/18/23/33512), UCL Hospitals NIHR Biomedical Research Centre, by the Rosetrees and Stoneygate Trusts.Condensed abstractEvidence on the long-term efficacy and safety of therapeutic inhibition of PCSK9 is lacking. To explore potential long-term effects of PCSK9 inhibition, we characterised the phenotypic consequence of LDL-cholesterol lowering variants at the PCSK9 locus. A PCSK9 gene score comprising 4 SNPs recapitulated the effects of therapeutic inhibition of PCSK9 on major blood lipid fractions and risk of myocardial infarction, and was associated with an increased risk of type 2 diabetes. No associations with safety outcomes such as cancer, COPD, Alzheimer’s disease or atrial fibrillation were identified. Our findings suggest PCSK9 inhibition may be safe and effective during prolonged use.

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 qP =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-β. Conclusions: We demonstrate a biomarker discovery paradigm to identify candidate biomarkers of cardiovascular and other diseases.

Genetic variation can affect drug response in multiple ways, although it remains unclear how rare genetic variants affect drug response. The electronic Medical Records and Genomics (eMERGE) Network, collaborating with the Pharmacogenomics Research Network, began eMERGE‐PGx, a targeted sequencing study to assess genetic variation in 82 pharmacogenes critical for implementation of “precision medicine.” The February 2015 eMERGE‐PGx data release includes sequence‐derived data from ∼5,000 clinical subjects. We present the variant frequency spectrum categorized by variant type, ancestry, and predicted function. We found 95.12% of genes have variants with a scaled Combined Annotation‐Dependent Depletion score above 20, and 96.19% of all samples had one or more Clinical Pharmacogenetics Implementation Consortium Level A actionable variants. These data highlight the distribution and scope of genetic variation in relevant pharmacogenes, identifying challenges associated with implementing clinical sequencing for drug treatment at a broader level, underscoring the importance for multifaceted research in the execution of precision medicine.

Rationale and Objectives The discovery of germline genetic variants associated with breast cancer has engendered interest in risk stratification for improved, targeted detection and diagnosis. However, there has yet to be a comparison of the predictive ability of these genetic variants with mammography abnormality descriptors. Materials and Methods Our institutional review board-approved, Health Insurance Portability and Accountability Act-compliant study utilized a personalized medicine registry in which participants consented to provide a DNA sample and to participate in longitudinal follow-up. In our retrospective, age-matched, case-controlled study of 373 cases and 395 controls who underwent breast biopsy, we collected risk factors selected a priori based on the literature, including demographic variables based on the Gail model, common germline genetic variants, and diagnostic mammography findings according to Breast Imaging Reporting and Data System (BI-RADS). We developed predictive models using logistic regression to determine the predictive ability of (1) demographic variables, (2) 10 selected genetic variants, or (3) mammography BI-RADS features. We evaluated each model in turn by calculating a risk score for each patient using 10-fold cross-validation, used this risk estimate to construct Receiver Operator Characteristic Curve (ROC) curves, and compared the area under the ROC curve (AUC) of each using the DeLong method. Results The performance of the regression model using demographic risk factors was not statistically different from the model using genetic variants (P = 0.9). The model using mammography features (AUC = 0.689) was superior to both the demographic model (AUC = .598; P Conclusions BI-RADS features exceeded the ability of demographic and 10 selected germline genetic variants to predict breast cancer in women recommended for biopsy.

Standard analyses applied to genome-wide association data are well designed to detect additive effects of moderate strength. However, the power for standard genome-wide association study (GWAS) analyses to identify effects from recessive diplotypes is not typically high. We proposed and conducted a gene-based compound heterozygosity test to reveal additional genes underlying complex diseases. With this approach applied to iron overload, a strong association signal was identified between the fibroblast growth factor–encoding gene, FGF6, and hemochromatosis in the central Wisconsin population. Functional validation showed that fibroblast growth factor 6 protein (FGF-6) regulates iron homeostasis and induces transcriptional regulation of hepcidin. Moreover, specific identified FGF6 variants differentially impact iron metabolism. In addition, FGF6 downregulation correlated with iron-metabolism dysfunction in systemic sclerosis and cancer cells. Using the recessive diplotype approach revealed a novel susceptibility hemochromatosis gene and has extended our understanding of the mechanisms involved in iron metabolism.

Background Electronic health records (EHRs) are increasingly used for clinical and translational research through the creation of phenotype algorithms. Currently, phenotype algorithms are most commonly represented as noncomputable descriptive documents and knowledge artifacts that detail the protocols for querying diagnoses, symptoms, procedures, medications, and/or text-driven medical concepts, and are primarily meant for human comprehension. We present desiderata for developing a computable phenotype representation model (PheRM).Methods A team of clinicians and informaticians reviewed common features for multisite phenotype algorithms published in PheKB.org and existing phenotype representation platforms. We also evaluated well-known diagnostic criteria and clinical decision-making guidelines to encompass a broader category of algorithms.Results We propose 10 desired characteristics for a flexible, computable PheRM: (1) structure clinical data into queryable forms; (2) recommend use of a common data model, but also support customization for the variability and availability of EHR data among sites; (3) support both human-readable and computable representations of phenotype algorithms; (4) implement set operations and relational algebra for modeling phenotype algorithms; (5) represent phenotype criteria with structured rules; (6) support defining temporal relations between events; (7) use standardized terminologies and ontologies, and facilitate reuse of value sets; (8) define representations for text searching and natural language processing; (9) provide interfaces for external software algorithms; and (10) maintain backward compatibility.Conclusion A computable PheRM is needed for true phenotype portability and reliability across different EHR products and healthcare systems. These desiderata are a guide to inform the establishment and evolution of EHR phenotype algorithm authoring platforms and languages.

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

Bioinformatics approaches to examine gene-gene models provide a means to discover interactions between multiple genes that underlie complex disease. Extensive computational demands and adjusting for multiple testing make uncovering genetic interactions a challenge. Here, we address these issues using our knowledge-driven filtering method, Biofilter, to identify putative single nucleotide polymorphism (SNP) interaction models for cataract susceptibility, thereby reducing the number of models for analysis. Models were evaluated in 3,377 European Americans (1,185 controls, 2,192 cases) from the Marshfield Clinic, a study site of the Electronic Medical Records and Genomics (eMERGE) Network, using logistic regression. All statistically significant models from the Marshfield Clinic were then evaluated in an independent dataset of 4,311 individuals (742 controls, 3,569 cases), using independent samples from additional study sites in the eMERGE Network: Mayo Clinic, Group Health/University of Washington, Vanderbilt University Medical Center, and Geisinger Health System. Eighty-three SNP-SNP models replicated in the independent dataset at likelihood ratio test P < 0.05. Among the most significant replicating models was rs12597188 (intron of CDH1)–rs11564445 (intron of CTNNB1). These genes are known to be involved in processes that include: cell-to-cell adhesion signaling, cell-cell junction organization, and cell-cell communication. Further Biofilter analysis of all replicating models revealed a number of common functions among the genes harboring the 83 replicating SNP-SNP models, which included signal transduction and PI3K-Akt signaling pathway. These findings demonstrate the utility of Biofilter as a biology-driven method, applicable for any genome-wide association study dataset.

Ten organizations within the Electronic Medical Records and Genomics Network developed programs to implement pharmacogenomic sequencing and clinical decision support into clinical settings. Recognizing the importance of informed prescribers, a variety of strategies were used to incorporate provider education to support implementation. Education experiences with pharmacogenomics are described within the context of each organization's prior involvement, including the scope and scale of implementation specific to their Electronic Medical Records and Genomics projects. We describe common and distinct education strategies, provide exemplars and share challenges. Lessons learned inform future perspectives. Future pharmacogenomics clinical implementation initiatives need to include funding toward implementing provider education and evaluating outcomes.

Motivation Pedigree analysis is a longstanding and powerful approach to gain insight into the underlying genetic factors in human health, but identifying, recruiting and genotyping families can be difficult, time consuming and costly. Development of high throughput methods to identify families and foster downstream analyses are necessary. Results This paper describes simple methods that allowed us to identify 173 368 family pedigrees with high probability using basic demographic data available in most electronic health records (EHRs). We further developed and validate a novel statistical method that uses EHR data to identify families more likely to have a major genetic component to their diseases risk. Lastly, we showed that incorporating EHR-linked family data into genetic association testing may provide added power for genetic mapping without additional recruitment or genotyping. The totality of these results suggests that EHR-linked families can enable classical genetic analyses in a high-throughput manner. Availability and implementation Pseudocode is provided as supplementary information Supplementary information Supplementary data are available at Bioinformatics online.

Several reports have shown that statin treatment benefits patients with asthma; however, inconsistent effects have been observed. The mir-152 family (148a, 148b and 152) has been implicated in asthma. These microRNAs suppress HLA-G expression, and rs1063320, a common SNP in the HLA-G 3'UTR that is associated with asthma risk, modulates miRNA binding. We report that statins upregulate mir-148b and 152, and affect HLA-G expression in an rs1063320-dependent fashion. In addition, we found that individuals who carried the G minor allele of rs1063320 had reduced asthma-related exacerbations (emergency department visits, hospitalizations or oral steroid use) compared with non-carriers (P=0.03) in statin users ascertained in the Personalized Medicine Research Project at the Marshfield Clinic (n=421). These findings support the hypothesis that rs1063320 modifies the effect of statin benefit in asthma, and thus may contribute to variation in statin efficacy for the management of this disease.

Age related macular degeneration (AMD) is a chronic, progressive disease resulting in the loss of central vision. AMD is characterized by the formation of extracellular deposits (drusen) between the retinal pigment epithelium (RPE) and Bruch’s membrane. The presence of drusen is considered the earliest clinical sign of pathological changes associated with AMD.1 As the disease progresses, the development of geographic atrophy (GA) of the RPE and choriocapillaris, as well as choroidal neovascularization can occur. AMD represents the leading cause of vision loss in the developed world,2 and owing to the increasing life expectancy, morbidity related to AMD is projected to increase in prevalence in the coming decades.3 Of those diagnosed with AMD, 85 – 90% of patients present with nonexudative, or, “dry” AMD.4 Current clinical management of these patients involves supplementation with antioxidant vitamins and zinc,5 as well as monitoring for metamorphopsia, which would suggest choroidal neovascularization and progression to “wet” AMD. However, many patients remain asymptomatic for quite some time, representing a window of opportunity to intervene before devastating vision loss occurs. Currently lacking, however, are objective structural biomarkers that can identify the earliest anatomical changes associated with AMD. Recent advances in clinical imaging, such as spectral domain optical coherence tomography (SD-OCT), have greatly aided in the diagnosis and assessment of the progression of AMD, and have provided critical information for clinical decision-making. With its excellent axial resolution, SD-OCT has been used to characterize and classify different types of drusen.6 However, the lateral resolution of commercial SD-OCT is insufficient to visualize structures at the cellular level, impeding the ability to characterize and recognize changes associated with the disease in its earliest stages. Adaptive optics (AO) imaging tools represent a potential solution to the inadequate resolution of traditional imaging modalities. While histologic studies have shown photoreceptor loss in both exudative and nonexudative AMD,7 the use of AO imaging tools represents an opportunity for in vivo analysis of photoreceptor involvement in AMD. Recent studies using AO retinal imaging in the assessment of photoreceptor density in patients with AMD have shown differing results. Obata and Yanagi (2014) did not observe a difference in photoreceptor density between subjects with AREDS grade 1 fundi and those with an AREDS grade of 2 or 3.8 However, a decrease in density corresponding to age and axial length was noted.8 Boretsky et al. (2012) showed that patients exhibiting extensive drusen or GA display diminished cone density when compared to patients with small or intermediate drusen.9 However, Zayit-Soudry et al. (2013) showed that patients with GA displayed continuous cone mosaics that were often of normal spacing, and that spacing was stable over time despite signs of clinical progression.10 Godara et al. (2010) showed that photoreceptor density and mosaic were well-preserved overlying a basal laminar druse in an asymptomatic patient.11 In addition, Querques et al. (2012) found an intact photoreceptor mosaic in a patient with large drusen, further supporting that photoreceptor loss may not occur until later in the disease process.12 Mrejen et al. (2013) examined subjects with either conventional or pseudodrusen and found a significant reduction in photoreceptor density overlying both conventional drusen (nearly 22%) and pseudodrusen (over 90%).13 They also reported altered photoreceptor reflectivity overlying pseudodrusen. In most cases this was manifested as a ring of hyporeflectivity surrounding the deposit, however, photoreceptor structure was not discernible over many of the subretinal drusenoid deposits.13 We sought to identify the earliest anatomical changes associated with AMD by screening previously undiagnosed subjects (over the age of 50) selected primarily on the basis of having a high or low genetic relative risk (RR) for developing AMD. Examining these subjects provides an opportunity to further our understanding of the early stage of the disease process as well as to correlate changes seen with standard clinical imaging to those seen with AO imaging tools. This multimodal imaging approach may provide a more precise method for monitoring disease progression and identifying those at risk for development of the disease, ultimately allowing for intervention before vision loss.

Importance Large-scale DNA sequencing identifies incidental rare variants in established Mendelian disease genes, but the frequency of related clinical phenotypes in unselected patient populations is not well established. Phenotype data from electronic medical records (EMRs) may provide a resource to assess the clinical relevance of rare variants. Objective To determine the clinical phenotypes from EMRs for individuals with variants designated as pathogenic by expert review in arrhythmia susceptibility genes. Design, Setting, and Participants This prospective cohort study included 2022 individuals recruited for nonantiarrhythmic drug exposure phenotypes from October 5, 2012, to September 30, 2013, for the Electronic Medical Records and Genomics Network Pharmacogenomics project from 7 US academic medical centers. Variants in SCN5A and KCNH2 , disease genes for long QT and Brugada syndromes, were assessed for potential pathogenicity by 3 laboratories with ion channel expertise and by comparison with the ClinVar database. Relevant phenotypes were determined from EMRs, with data available from 2002 (or earlier for some sites) through September 10, 2014. Exposures One or more variants designated as pathogenic in SCN5A or KCNH2 . Main Outcomes and Measures Arrhythmia or electrocardiographic (ECG) phenotypes defined by International Classification of Diseases, Ninth Revision ( ICD-9 ) codes, ECG data, and manual EMR review. Results Among 2022 study participants (median age, 61 years [interquartile range, 56-65 years]; 1118 [55%] female; 1491 [74%] white), a total of 122 rare (minor allele frequency ICD-9 code for arrhythmia was found in 11 of 63 (17%) variant carriers vs 264 of 1959 (13%) of those without variants (difference, +4%; 95% CI, −5% to +13%; P = .35). In the 1270 (63%) with ECGs, corrected QT intervals were not different in variant carriers vs those without (median, 429 vs 439 milliseconds; difference, −10 milliseconds; 95% CI, −16 to +3 milliseconds; P = .17). After manual review, 22 of 63 participants (35%) with designated variants had any ECG or arrhythmia phenotype, and only 2 had corrected QT interval longer than 500 milliseconds. Conclusions and Relevance Among laboratories experienced in genetic testing for cardiac arrhythmia disorders, there was low concordance in designating SCN5A and KCNH2 variants as pathogenic. In an unselected population, the putatively pathogenic genetic variants were not associated with an abnormal phenotype. These findings raise questions about the implications of notifying patients of incidental genetic findings.

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.

Objective To determine whether the apolipoprotein E4 (Apo E4) allele may be a genetic risk factor for fibromyalgia syndrome (FMS). Design A retrospective assessment of associations between Apo E4 genotype and selected environmental exposures among a cohort diagnosed with FMS compared with control subjects. Setting Marshfield Clinic Research Foundation's Personalized Medicine Research Project (PMRP) biobank. Participants One hundred fifty-one case subjects with fibromyalgia and 300 age- and gender-matched control subjects. Methods Fibromyalgia case subjects were identified according to a strict phenotypic definition from among the nearly 20,000 subjects enrolled in the PMRP. Age- and gender-matched control subjects also were identified from the PMRP in a 2:1 control/case ratio. Apo E4 genotype was determined by single nucleotide polymorphism analysis for both case subjects with fibromyalgia and control subjects. Case subjects with fibromyalgia and control subjects were asked to assess their level of function and stress by completing the Short Form-36 and the Perceived Stress Scale. Main Outcome Measures Statistical associations between the Apo E4 genotype and phenotypic criteria (diagnosis of FMS) as well as historical environmental exposures as documented in the electronic medical record were assessed. Results Approximately one quarter of both case subjects with fibromyalgia and control subjects were found to carry at least one Apo E4 allele. The odds ratio (OR) for case subjects with fibromyalgia who had ever been in a motor vehicle accident and subsequently had been diagnosed with FMS was increased among those with at least one copy of the Apo E4 allele (OR 7.04) compared with those without an Apo E4 allele (OR 1.90). The presence of an Apo E4 allele did not influence the degree of pain or level of function among those with FMS. Conclusions These data suggest that specific interactions between genetically susceptible individuals (eg, those with at least one copy of the Apo E4 allele) and the environment (eg, involvement in a motor vehicle accident) may contribute to the risk of being diagnosed with FMS, although Apo E4 allele status does not appear to modulate perceived FMS severity.

The National Lung Screening Trial (NLST) is a randomized multicenter study comparing low-dose helical computed tomography (CT) with chest radiography in the screening of older current and former heavy smokers for early detection of lung cancer, which is the leading cause of cancer-related death in the United States. Five-year survival rates approach 70% with surgical resection of stage IA disease; however, more than 75% of individuals have incurable locally advanced or metastatic disease, the latter having a 5-year survival of less than 5%. It is plausible that treatment should be more effective and the likelihood of death decreased if asymptomatic lung cancer is detected through screening early enough in its preclinical phase. For these reasons, there is intense interest and intuitive appeal in lung cancer screening with low-dose CT. The use of survival as the determinant of screening effectiveness is, however, confounded by the well-described biases of lead time, length, and overdiagnosis. Despite previous attempts, no test has been shown to reduce lung cancer mortality, an endpoint that circumvents screening biases and provides a definitive measure of benefit when assessed in a randomized controlled trial that enables comparison of mortality rates between screened individuals and a control group that does not undergo the screening intervention of interest. The NLST is such a trial. The rationale for and design of the NLST are presented.

A cohort of postmenopausal osteoporotic females and controls with normal bone mineral density, the interleukin 6 (IL6) −634G > C (rs1800796) C allele of the promoter region showed association with osteoporosis. The lipoprotein receptor-related protein 5 (LRP5) gene showed association between C135242T C/T alleles and osteoporosis only in smokers, suggesting a role for environmental interaction. A nested case–control study within a population-based cohort was undertaken to assess the relative impact of cigarette smoking, statin use, genetic polymorphisms, and one-way interaction of these factors on development of osteoporosis in postmenopausal women. Genotyping of 14 single-nucleotide polymorphisms (SNPs) corresponding to vitamin D receptor gene, estrogen receptor 1, collagen type 1 alpha 1, IL6, transcription growth factor beta, apolipoprotein E, and LRP5 genes was performed in cases (n = 309) with osteoporosis and controls (n = 293) with normal bone mineral density drawn from a homogeneous Caucasian population. SNPs were chosen based on known functional consequences or prior evidence for association and genotyped using matrix-assisted laser desorption ionization time-of-flight technology. Cases differed from controls relative to body mass index, age, and smoking but not statin use. After adjusting for age, the IL6 −634G > C (rs1800796) allele showed association with osteoporosis (odds ratio (OR) for CC + CG = 2.51, p = 0.0047)), independent of statin use or smoking status. On stratification for smoking, association with LRP5 C135242T (rs545382) and osteoporosis emerged (OR 2.8 in smokers for CT alleles, p = 0.03)), suggestive of potential environmental interaction. Evidence suggested a role for genetic variation in IL6 and LRP5 in conferring risk for osteoporosis in Caucasian women, with the latter manifest only in smokers.

Statin use can be accompanied by a variety of musculoskeletal complaints. The authors describe the clinical characteristics of case patients who experienced adverse statin-induced musculoskeletal symptoms within a large population-based cohort in Central Wisconsin. Case status was determined based on elevated serum creatine kinase (CK) levels and the presence of at least 1 physician note reflecting an increased index of suspicion for statin intolerance. From the medical records of nearly 2 million unique patients, the authors identified more than 20,000 potential study patients ( approximately 1%) having CK data and at least 1 exposure to a statin drug. Manual screening was completed on 2227 patients with CK levels in the upper 10th percentile. Of those screened, 267 met inclusion criteria (12.0% eligibility) and 218 agreed to participate in a retrospective study characterizing the risk determinants of statin-induced muscle toxicity. Three categoric pain variables were graded retrospectively (distribution, location, and severity of pain). The presenting complaints of the case patients were extremely heterogeneous. The number of patients with a compelling pain syndrome (diffuse, proximal muscle pain of high intensity) increased at higher serum CK levels; the number of patients with indeterminate pain variables decreased at higher serum CK levels. The lines reflecting these relationships cross at a CK level of approximately 1175 U/L, approximately half the threshold level needed to make a clinical diagnosis of "myopathy" (ie, CK >10-fold the upper limit of normal).

Diabetes mellitus is a rapidly increasing and costly public health problem. Large studies are needed to understand the complex gene-environment interactions that lead to diabetes and its complications. The Marshfield Clinic Personalized Medicine Research Project (PMRP) represents one of the largest population-based DNA biobanks in the United States. As part of an effort to begin phenotyping common diseases within the PMRP, we now report on the construction of a diabetes case-finding algorithm using electronic medical record data from adult subjects agedor =50 years living in one of the target PMRP ZIP codes. Based upon diabetic diagnostic codes alone, we observed a false positive case rate ranging from 3.0% (in subjects with the highest glycosylated hemoglobin values) to 44.4% (in subjects with the lowest glycosylated hemoglobin values). We therefore developed an improved case finding algorithm that utilizes diabetic diagnostic codes in combination with clinical laboratory data and medication history. This algorithm yielded an estimated prevalence of 24.2% for diabetes mellitus in adult subjects agedor =50 years.

Investigating the association between biobank derived genomic data and the information of linked electronic health records (EHRs) is an emerging area of research for dissecting the architecture of complex human traits, where cases and controls for study are defined through the use of electronic phenotyping algorithms deployed in large EHR systems. For our study, cataract cases and controls were identified within the Marshfield Personalized Medicine Research Project (PMRP) biobank and linked EHR, which is a member of the NHGRI-funded electronic Medical Records and Genomics (eMERGE) Network. Our goal was to explore potential gene-gene and gene-environment interactions within these data for 527,953 and 527,936 single nucleotide polymorphisms (SNPs) for gene-gene and gene-environment analyses, respectively, with minor allele frequency1%, in order to explore higher level associations with cataract risk beyond investigations of single SNP-phenotype associations. To build our SNP-SNP interaction models we utilized a prior-knowledge driven filtering method called Biofilter to minimize the multiple testing burden of exploring the vast array of interaction models possible from our extensive number of SNPs. Using Biofilter, we developed 57,376 prior-knowledge directed SNP-SNP models to test for association with cataract status. We selected models that required 6 sources of external domain knowledge. We identified 13 statistically significant SNP-SNP models with an interaction with p-value1 × 10(-4), as well as an overall model with p-value0.01 associated with cataract status. We also conducted gene-environment interaction analyses for all GWAS SNPs and a set of environmental factors from the PhenX Toolkit: smoking, UV exposure, and alcohol use;these environmental factors have been previously associated with the formation of cataracts. We found a total of 782 gene-environment models that exhibit an interaction with a p-value1 × 10(-4) associatedwith cataract status. Our results show these approaches enable advanced searches for epistasis and gene-environment interactions beyond GWAS, and that the EHR based approach provides an additional source of data for seeking these advanced explanatory models of the etiology of complex disease/outcome such as cataracts.

Introduction Liver enzyme levels and total serum bilirubin are under genetic control and in recent years genome-wide population-based association studies have identified different susceptibility loci for these traits. We conducted a genome-wide association study in European ancestry participants from the Electronic Medical Records and Genomics (eMERGE) Network dataset of patient medical records with available genotyping data in order to identify genetic contributors to variability in serum bilirubin levels and other liver function tests and to compare the effects between adult and pediatric populations. Methods The process of whole genome imputation of eMERGE samples with standard quality control measures have been described previously. After removing missing data and outliers based on principal components (PC) analyses, 3294 samples from European ancestry were used for the GWAS study. The association between each single nucleotide polymorphism (SNP) and total serum bilirubin and other liver function tests was tested using linear regression, adjusting for age, gender, site, platform and ancestry principal components (PC). Results Consistent with previous results, a strong association signal has been detected for UGT1A gene cluster (best SNP rs887829, beta = 0.15, p = 1.30x10-118) for total serum bilirubin level. Indeed, in this region more than 176 SNPs (or indels) had p

Population-based genetic analyses, such as the Genome-Wide Association Study (GWAS), have proven powerful for describing the genetic complexities of common disease in epidemiologic research. However, the significant challenges faced by population-based study designs have resulted in revitalization of family-based approaches, including twin studies. Twin studies are unique in their ability to ascertain both heritable and environmental contributions to human disease. Several regional and national twin registries have been constructed using a variety of methods to identify potential twins. A significant challenge in constructing these large twin registries includes the substantial resources required to recruit participants, collect phenotypic data, and update the registries as time progresses. Here we describe the use of the Marshfield Clinic electronic medical record (EMR) to identify a cohort of 19,226 patients enriched for twins or multiples. This cohort defines the Marshfield Clinic Twin/Multiple Birth Cohort (MCTC). An EMR system provides both a mechanism to identify potential twins and a source of detailed phenotypic data in near real time without the need for patient contact outside standard medical care. To demonstrate that the MCTC can be used for genetic-based epidemiologic research, concordance rates for muscular dystrophy (MD) and fragile-X syndrome-two highly heritable diseases-were assessed. Observations indicate that both MD and fragile-X syndrome are highly correlated among affected twins in the MCTC (P ≅ 3.7 × 10(-6) and 1.1 × 10(-4) , respectively). These findings suggest that EMR systems may not only be an effective resource for predicting families of twins, but can also be rapidly applied to epidemiologic research.

As more research studies incorporate next-generation sequencing (including whole-genome or whole-exome sequencing), investigators and institutional review boards face difficult questions regarding which genomic results to return to research participants and how. An American College of Medical Genetics and Genomics 2013 policy paper suggesting that pathogenic mutations in 56 specified genes should be returned in the clinical setting has raised the question of whether comparable recommendations should be considered in research settings. The Clinical Sequencing Exploratory Research (CSER) Consortium and the Electronic Medical Records and Genomics (eMERGE) Network are multisite research programs that aim to develop practical strategies for addressing questions concerning the return of results in genomic research. CSER and eMERGE committees have identified areas of consensus regarding the return of genomic results to research participants. In most circumstances, if results meet an actionability threshold for return and the research participant has consented to return, genomic results, along with referral for appropriate clinical follow-up, should be offered to participants. However, participants have a right to decline the receipt of genomic results, even when doing so might be viewed as a threat to the participants’ health. Research investigators should be prepared to return research results and incidental findings discovered in the course of their research and meeting an actionability threshold, but they have no ethical obligation to actively search for such results. These positions are consistent with the recognition that clinical research is distinct from medical care in both its aims and its guiding moral principles.

Investigating the association between biobank derived genomic data and the information of linked electronic health records (EHRs) is an emerging area of research for dissecting the architecture of complex human traits, where cases and controls for study are defined through the use of electronic phenotyping algorithms deployed in large EHR systems. For our study, cataract cases and controls were identified within the Marshfield Personalized Medicine Research Project (PMRP) biobank and linked EHR, which is a member of the NHGRI-funded electronic Medical Records and Genomics (eMERGE) Network. Our goal was to explore potential gene-gene and gene-environment interactions within these data for 527,953 and 527,936 single nucleotide polymorphisms (SNPs) for gene-gene and gene-environment analyses, respectively, with minor allele frequency > 1%, in order to explore higher level associations with cataract risk beyond investigations of single SNP-phenotype associations. To build our SNP-SNP interaction models we utilized a prior-knowledge driven filtering method called Biofilter to minimize the multiple testing burden of exploring the vast array of interaction models possible from our extensive number of SNPs. Using Biofilter, we developed 57,376 prior-knowledge directed SNP-SNP models to test for association with cataract status. We selected models that required 6 sources of external domain knowledge. We identified 13 statistically significant SNP-SNP models with an interaction with p-value < 1 × 10(-4), as well as an overall model with p-value < 0.01 associated with cataract status. We also conducted gene-environment interaction analyses for all GWAS SNPs and a set of environmental factors from the PhenX Toolkit: smoking, UV exposure, and alcohol use;these environmental factors have been previously associated with the formation of cataracts. We found a total of 782 gene-environment models that exhibit an interaction with a p-value < 1 × 10(-4) associatedwith cataract status. Our results show these approaches enable advanced searches for epistasis and gene-environment interactions beyond GWAS, and that the EHR based approach provides an additional source of data for seeking these advanced explanatory models of the etiology of complex disease/outcome such as cataracts.

To establish a well-defined cohort for genetic epidemiology studies of endometriosis and conduct a pilot study to confirm validity using existing data associated with endometriosis.Between January and May 2010, a nested cohort within a population-based biobank was established in Marshfield, Wisconsin, USA. The inclusion criteria were women who had laparoscopy or hysterectomy. Fifty-one pleiotropic genetic polymorphisms and other established risk factors, such as smoking status and body mass index, were compared between endometriosis cases and controls.From the existing biobank, 796 cases and 501 controls were identified, and 259 women with endometriosis were enrolled specifically for the nested cohort within this biobank. A single nucleotide polymorphism in the MMP1 gene significantly differed between cases and controls only when stratified by smoking status. Minor allele frequency was higher in control women who smoked than in women with endometriosis who smoked (55.5% versus 45.5%, χ(2)=8.2, P=0.017); the inverse relationship was found in non-smoker control women.Women with endometriosis were successfully recruited to participate in a general biobank, and a novel gene-environment interaction was identified. The findings suggest that important potential genetic associations may be missed if gene-environment interactions with known epidemiologic risk factors are not considered.

PURPOSE To estimate glaucoma and ocular hypertension prevalence and to describe temporal trends in prescribing patterns and intraocular pressure (IOP) response to topical medications used in glaucoma and ocular hypertension. MATERIALS AND METHODS The medical records of adult subjects enrolled in the population-based Marshfield Clinic Personalized Medicine Research Project were searched to identify participants who had been diagnosed with ocular hypertension or glaucoma and prescribed agent(s) to lower IOP. All IOPs before and after prescription of the IOP agents were recorded. RESULTS As of December 31, 2005, 18,773 adults were enrolled in the Personalized Medicine Research Project, 57.1% were female, and their mean age was 50.3 years (range, 18 to 101 y). The overall rate of definite glaucoma in subjects aged 50 years and above was 2.1% (95% confidence interval=1.2, 2.4) and the rate of treated ocular hypertension was 1.4% (95% confidence interval=1.2, 1.7). Topical beta-blockers were the agents prescribed for the majority of subjects until the year 2000, when prostaglandins, first used in 1995, became the primary agent prescribed. In 2005, 75% of subjects used prostaglandin analogs and 46% used topical beta-blockers. The largest relative reduction in IOP in the first 3 months after prescription was observed for prostaglandin analogs (21.4% mean relative reduction), followed by beta-blockers (20.9% mean relative reduction). There has been a significant decrease over time in mean IOP before initiating medical therapy (linear regression beta coefficient=-0.30, P

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, β = -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.

Background/Aims KRAS testing is a pharmacogenomic test to help decide how to treat patients with metastatic colorectal cancer (mCRC). The CERGEN (Comparative Effectiveness Research in Genomics & Personalized Medicine for Colorectal Cancer) project, a collaboration among eight institutions of the NCI-funded Cancer Research Network (CRN) and four academic partners, utilized KRAS test results to evaluate overall survival in mCRC patients. As a part of this project, we needed to retrospectively test all patients that had not had the testing done as part of their clinical care. This project is a large, multi-site CRN project utilizing archived tumor tissue.

Hereditary hemochromatosis (HH) is a common autosomal-recessive disorder associated with pathogenic HFE variants, most commonly those resulting in p.Cys282Tyr and p.His63Asp. Recommendations on returning incidental findings of HFE variants in individuals undergoing genome-scale sequencing should be informed by penetrance estimates of HH in unselected samples. We used the eMERGE Network, a multicenter cohort with genotype data linked to electronic medical records, to estimate the diagnostic rate and clinical penetrance of HH in 98 individuals homozygous for the variant coding for HFE p.Cys282Tyr and 397 compound heterozygotes with variants resulting in p.[His63Asp];[Cys282Tyr]. The diagnostic rate of HH in males was 24.4% for p.Cys282Tyr homozygotes and 3.5% for compound heterozygotes (p < 0.001); in females, it was 14.0% for p.Cys282Tyr homozygotes and 2.3% for compound heterozygotes (p < 0.001). Only males showed differences across genotypes in transferrin saturation levels (100% of homozygotes versus 37.5% of compound heterozygotes with transferrin saturation > 50%; p = 0.003), serum ferritin levels (77.8% versus 33.3% with serum ferritin > 300 ng/ml; p = 0.006), and diabetes (44.7% versus 28.0%; p = 0.03). No differences were found in the prevalence of heart disease, arthritis, or liver disease, except for the rate of liver biopsy (10.9% versus 1.8% [p = 0.013] in males; 9.1% versus 2% [p = 0.035] in females). Given the higher rate of HH diagnosis than in prior studies, the high penetrance of iron overload, and the frequency of at-risk genotypes, in addition to other suggested actionable adult-onset genetic conditions, opportunistic screening should be considered for p.[Cys282Tyr];[Cys282Tyr] individuals with existing genomic data.

Background The purpose of this paper is to describe the data collection efforts and validation of PhenX measures in the Personalized Medicine Research Project (PMRP) cohort. Methods Thirty-six measures were chosen from the PhenX Toolkit within the following domains: demographics; anthropometrics; alcohol, tobacco and other substances; cardiovascular; environmental exposures; cancer; psychiatric; neurology; and physical activity and physical fitness. Eligibility criteria for the current study included: living PMRP subjects with known addresses who consented to future contact and were not currently living in a nursing home, available GWAS data from eMERGE I for subjects where age-related cataract, HDL, dementia and resistant hypertension were the primary phenotypes, thus biasing the sample to the older PMRP participants. The questionnaires were mailed twice. Data from the PhenX measures were compared with information from PMRP questionnaires and data from Marshfield Clinic electronic medical records. Results Completed PhenX questionnaires were returned by 2271 subjects for a final response rate of 70%. The mean age reported on the PhenX questionnaire (73.1 years) was greater than the PMRP questionnaire (64.8 years) because the data were collected at different time points. The mean self-reported weight, and subsequently calculated BMI, were less on the PhenX survey than the measured values at the time of enrollment into PMRP (PhenX means 173.5 pounds and BMI 28.2 kg/m2 versus PMRP 182.9 pounds and BMI 29.6 kg/m2). There was 95.3% agreement between the two questionnaires about having ever smoked at least 100 cigarettes. 139 (6.2%) of subjects indicated on the PhenX questionnaire that they had been told they had a stroke. Of them, only 15 (10.8%) had no electronic indication of a prior stroke or TIA. All of the age-and gender-specific 95% confidence limits around point estimates for major depressive episodes overlap and show that 31% of women aged 50–64 reported symptoms associated with a major depressive episode. Conclusions The approach employed resulted in a high response rate and valuable data for future gene/environment analyses. These results and high response rate highlight the utility of the PhenX Toolkit to collect valid phenotypic data that can be shared across groups to facilitate gene/environment studies.