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Abstract Background To describe the dietary intake of participants in the Personalized Medicine Research Project (PMRP), and to quantify differences in nutrient intake by smoking status and APOE4-a genetic marker that has been shown to modify the association between risk factors and outcomes. Methods The PMRP is a population-based DNA, plasma and serum biobank of more than 20,000 adults aged 18 years and older in central Wisconsin. A questionnaire at enrollment captures demographic information as well as self-reported smoking and alcohol intake. The protocol was amended to include the collection of dietary intake and physical activity via self-reported questionnaires: the National Cancer Institute 124-item Diet History Questionnaire and the Baecke Physical Activity Questionnaire. These questionnaires were mailed out to previously enrolled participants. APOE was genotyped in all subjects. Results The response rate to the mailed questionnaires was 68.2% for subjects who could still be contacted (alive with known address). Participants ranged in age from 18 to 98 years (mean 54.7) and 61% were female. Dietary intake is variable when comparing gender, age, smoking, and APOE4. Over 50% of females are dietary supplement users; females have higher supplement intake than males, but both have increasing supplement use as age increases. Food energy, total fat, cholesterol, protein, and alcohol intake decreases as both males and females age. Female smokers had higher macronutrient intake, whereas male nonsmokers had higher macronutrient intake. Nonsmokers in both genders use more supplements. In females, nonsmokers and smokers with APOE4 had higher supplement use. In males, nonsmokers with APOE4 had higher supplement use between ages 18-39 only, and lower supplement use at ages above 39. Male smokers with APOE4 had lower supplement use. Conclusion Dietary intake in PMRP subjects is relatively consistent with data from the National Health and Nutrition Examination Survey (NHANES). Findings suggest a possible correlation between the use of supplements and APOE4. The PMRP dietary data can benefit studies of gene-environment interactions and the development of common diseases.

Background: Renal glucosuria is a rare inheritable trait caused by loss-of-function variants in the gene that encodes SGLT2 (i.e., SLC5A2). The genetics of renal glucosuria is poorly understood and even less is known on how loss-of-function variants in SLC5A2 may affect response to SGLT2 inhibitors, a new class of medication gaining popularity to treat diabetes by artificially inducing glucosuria., Methods: We used two biobanks that link genomic with electronic health record data to study the genetics of renal glucosuria. This included 245,394 participants enrolled in the All of Us (AoU) Research Program and 11,011 enrolled in Marshfield Clinic's Personalized Research Project (PMRP). Association studies in AoU and PMRP identified 10 variants that reached an experiment-wise Bonferroni threshold in either cohort, nine were novel. PMRP was further used as a recruitment source for a prospective SGLT2 pharmacogenetic trial. During a glucose tolerance test, the trial measured urine glucose concentrations in 15 SLC5A2 variant-positive individuals and 15 matched wild types with and without an SGLT2 inhibitor., Results: This trial demonstrated that carriers of SLC5A2 risk variants may be more sensitive to SGLT2 inhibitors compared to wild types (P=0.075). Based on population data, 2% of an ethnically diverse population carry rare variants in SLC5A2 and are at risk for renal glucosuria., Conclusions: As a result, 2% of individuals being treated with SGLT2 inhibitors may respond differently to this new class of medication compared to the general population suggesting a larger investigation into SLC5A2 variants and SGLT2 inhibitors is needed., (Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American Society of Nephrology.)

Globally, half a billion people are employed in animal agriculture and are directly exposed to the associated microorganisms. However, the extent to which such exposures affect resident human microbiomes is unclear. Here we conducted a longitudinal profiling of the nasal and faecal microbiomes of 66 dairy farmers and 166 dairy cows over a year-long period. We compare farmer microbiomes to those of 60 age-, sex- and ZIP code-matched people with no occupational exposures to farm animals (non-farmers). We show that farming is associated with microbiomes containing livestock-associated microbes; this is most apparent in the nasal bacterial community, with farmers harbouring a richer and more diverse nasal community than non-farmers. Similarly, in the gut microbial communities, we identify more shared microbial lineages between cows and farmers from the same farms. Additionally, we find that shared microbes are associated with antibiotic resistance genes. Overall, our study demonstrates the interconnectedness of human and animal microbiomes., (© 2024. The Author(s), under exclusive licence to Springer Nature Limited.)

Objective: To determine if host genetics may be a risk factor for severe blastomycosis. Design: A cohort of patients who had contracted blastomycosis underwent targeted SNP (single nucleotide polymorphism) genotyping. The genetics of these patients were compared to a set of age and gender-matched controls and between patients with severe versus mild to moderate blastomycosis. Setting: The Marshfield Clinic Health System in central and northern Wisconsin Participants: Patients with a diagnosis of blastomycosis prior to 2017 were contacted for enrollment in this study. A phone hotline was also set up to allow interested participants from outside the Marshfield Clinic Health System to request enrollment. Methods: SNP frequency was assessed for significant differences between the patient cohort and controls and between patients with severe versus mild to moderate blastomycosis. We also tested the effect of Blastomyces species identified in clinical isolates on disease symptoms and severity. Results: No significant differences were found in SNP frequency between cases and controls or between those with severe or mild to moderate blastomycosis. We did detect significant differences in symptom frequency and disease severity by Blastomyces species. Conclusions: Our study did not identify any genetic risk factors for blastomycosis. Instead, the species of Blastomyces causing the infection had a significant effect on disease severity., (Copyright © 2024 Marshfield Clinic Health System.)

Background: Although inhaled corticosteroids (ICS) are the first-line therapy for patients with persistent asthma, many patients continue to have exacerbations. We developed machine learning models to predict the ICS response in patients with asthma., Methods: The subjects included asthma patients of European ancestry ( n = 1371; 448 children; 916 adults). A genome-wide association study was performed to identify the SNPs associated with ICS response. Using the SNPs identified, two machine learning models were developed to predict ICS response: (1) least absolute shrinkage and selection operator (LASSO) regression and (2) random forest., Results: The LASSO regression model achieved an AUC of 0.71 (95% CI 0.67-0.76; sensitivity: 0.57; specificity: 0.75) in an independent test cohort, and the random forest model achieved an AUC of 0.74 (95% CI 0.70-0.78; sensitivity: 0.70; specificity: 0.68). The genes contributing to the prediction of ICS response included those associated with ICS responses in asthma ( TPSAB1, FBXL16 ), asthma symptoms and severity ( ABCA7, CNN2, PTRN3, and BSG/CD147 ), airway remodeling ( ELANE, FSTL3 ), mucin production ( GAL3ST ), leukotriene synthesis ( GPX4 ), allergic asthma ( ZFPM1, SBNO2 ), and others., Conclusions: An accurate risk prediction of ICS response can be obtained using machine learning methods, with the potential to inform personalized treatment decisions. Further studies are needed to examine if the integration of richer phenotype data could improve risk prediction.

It is well known that common variants in specific genes influence drug metabolism and response, but it is currently unknown what fraction of patients are given prescriptions over a lifetime that could be contraindicated by their pharmacogenomic profiles. To determine the clinical utility of pharmacogenomics over a lifetime in a general patient population, we sequenced the genomes of 300 deceased Marshfield Clinic patients linked to lifelong medical records. Genetic variants in 33 pharmacogenes were evaluated for their lifetime impact on drug prescribing using extensive electronic health records. Results show that 93% of the 300 deceased patients carried clinically relevant variants. Nearly 80% were prescribed approximately three medications on average that may have been impacted by these variants. Longitudinal data suggested that the optimal age for pharmacogenomic testing was prior to age 50, but the optimal age is greatly influenced by the stability of the population in the healthcare system. This study emphasizes the broad clinical impact of pharmacogenomic testing over a lifetime and demonstrates the potential application of genomic medicine in a general patient population for the advancement of precision medicine., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Ye, Mayer, Leary, Kitchner, Dart, Brilliant and Hebbring.)

Multiple 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 genetic 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 genetic risk score and the microbiota was determined by sequencing the V4 hypervariable region of the 16S rRNA gene. We identified two clusters of MS patients, Cluster A and B, both having a higher genetic risk score than the control group. However, the MS cases in cluster B not only had a higher genetic risk score but also showed a distinct gut microbiome than that of cluster A. Interestingly, cluster A which included both healthy control and MS cases had similar gut microbiome composition. 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. Our study showed that there seems to be an association between genetic 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 genetic 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., (© 2023. Springer Nature Limited.)

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., (© 2019 The Authors. Clinical and Translational Science published by Wiley Periodicals Inc. on behalf of the American Society of Clinical Pharmacology & Therapeutics. This article has been contributed to by US Government employees and their work is in the public domain in the USA.)

Background: We characterised the phenotypic consequence of genetic variation at the PCSK9 locus and compared findings with recent trials of pharmacological inhibitors of PCSK9., Methods: Published and individual participant level data (300,000+ participants) were combined to construct a weighted PCSK9 gene-centric score (GS). Seventeen 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 concentration., Results: The 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., Conclusions: Genetic variation at the PCSK9 locus recapitulates the effects of therapeutic inhibition of PCSK9 on major blood lipid fractions and MI. While indicating an increased risk of T2DM, no other possible safety concerns were shown; although precision was moderate.

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., (© 2019 by The American Society of Hematology.)

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., Contact: HEBBRING.SCOTT@marshfieldresearch.org., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com)

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.

Background: Statin treatment and variants in the gene encoding HMG-CoA reductase are associated with reductions in both the concentration of LDL cholesterol and the risk of coronary heart disease, but also with modest hyperglycaemia, increased bodyweight, and modestly increased risk of type 2 diabetes, which in no way offsets their substantial benefits. We sought to investigate the associations of LDL cholesterol-lowering PCSK9 variants with type 2 diabetes and related biomarkers to gauge the likely effects of PCSK9 inhibitors on diabetes risk., Methods: In this mendelian randomisation study, we used data from cohort studies, randomised controlled trials, case control studies, and genetic consortia to estimate associations of PCSK9 genetic variants with LDL cholesterol, fasting blood glucose, HbA 1c , fasting insulin, bodyweight, waist-to-hip ratio, BMI, and risk of type 2 diabetes, using a standardised analysis plan, meta-analyses, and weighted gene-centric scores., Findings: Data were available for more than 550 000 individuals and 51 623 cases of type 2 diabetes. Combined analyses of four independent PCSK9 variants (rs11583680, rs11591147, rs2479409, and rs11206510) scaled to 1 mmol/L lower LDL cholesterol showed associations with increased fasting glucose (0·09 mmol/L, 95% CI 0·02 to 0·15), bodyweight (1·03 kg, 0·24 to 1·82), waist-to-hip ratio (0·006, 0·003 to 0·010), and an odds ratio for type diabetes of 1·29 (1·11 to 1·50). Based on the collected data, we did not identify associations with HbA 1c (0·03%, -0·01 to 0·08), fasting insulin (0·00%, -0·06 to 0·07), and BMI (0·11 kg/m 2 , -0·09 to 0·30)., Interpretation: PCSK9 variants associated with lower LDL cholesterol were also associated with circulating higher fasting glucose concentration, bodyweight, and waist-to-hip ratio, and an increased risk of type 2 diabetes. In trials of PCSK9 inhibitor drugs, investigators should carefully assess these safety outcomes and quantify the risks and benefits of PCSK9 inhibitor treatment, as was previously done for statins., Funding: British Heart Foundation, and University College London Hospitals NHS Foundation Trust (UCLH) National Institute for Health Research (NIHR) Biomedical Research Centre., (Copyright © 2017 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY license. Published by Elsevier Ltd.. All rights reserved.)

Background: As biobanks play an increasing role in the genomic research that will lead to precision medicine, input from diverse and large populations of patients in a variety of health care settings will be important in order to successfully carry out such studies. One important topic is participants' views towards consent and data sharing, especially since the 2011 Advanced Notice of Proposed Rulemaking (ANPRM), and subsequently the 2015 Notice of Proposed Rulemaking (NPRM) were issued by the Department of Health and Human Services (HHS) and Office of Science and Technology Policy (OSTP). These notices required that participants consent to research uses of their de-identified tissue samples and most clinical data, and allowing such consent be obtained in a one-time, open-ended or "broad" fashion. Conducting a survey across multiple sites provides clear advantages to either a single site survey or using a large online database, and is a potentially powerful way of understanding the views of diverse populations on this topic., Methods: A workgroup of the Electronic Medical Records and Genomics (eMERGE) Network, a national consortium of 9 sites (13 separate institutions, 11 clinical centers) supported by the National Human Genome Research Institute (NHGRI) that combines DNA biorepositories with electronic medical record (EMR) systems for large-scale genetic research, conducted a survey to understand patients' views on consent, sample and data sharing for future research, biobank governance, data protection, and return of research results., Results: Working across 9 sites to design and conduct a national survey presented challenges in organization, meeting human subjects guidelines at each institution, and survey development and implementation. The challenges were met through a committee structure to address each aspect of the project with representatives from all sites. Each committee's output was integrated into the overall survey plan. A number of site-specific issues were successfully managed allowing the survey to be developed and implemented uniformly across 11 clinical centers., Conclusions: Conducting a survey across a number of institutions with different cultures and practices is a methodological and logistical challenge. With a clear infrastructure, collaborative attitudes, excellent lines of communication, and the right expertise, this can be accomplished successfully.

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 < 0.001) and the genetic model (AUC = .601; P < 0.001)., Conclusions: BI-RADS features exceeded the ability of demographic and 10 selected germline genetic variants to predict breast cancer in women recommended for biopsy., (Copyright © 2016 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

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., (© 2015 The Authors. *Genetic Epidemiology published by Wiley Periodicals, Inc.)

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.

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., (© 2014 WILEY PERIODICALS, INC.)

Purpose: Utilize high-resolution imaging to examine retinal anatomy in patients with known genetic relative risk (RR) for developing age-related macular degeneration (AMD)., Methods: Forty asymptomatic subjects were recruited (9 men, 31 women; age range, 51 to 69 years; mean age, 61.4 years). Comprehensive eye examination, fundus photography, and high-resolution retinal imaging using spectral domain optical coherence tomography and adaptive optics were performed on each patient. Genetic RR scores were developed using an age-independent algorithm. Adaptive optics scanning light ophthalmoscope images were acquired in the macula extending to 10 degrees temporal and superior from fixation and were used to calculate cone density in up to 35 locations for each subject., Results: Relative risk was not significantly predictive of fundus grade (p = 0.98). Only patients with a high RR displayed drusen on Cirrus or Bioptigen OCT. Compared to an eye with a grade of 0, an eye with a fundus grade equal to or greater than 1 had a 12% decrease in density (p < 0.0001) and a 5% increase in spacing (p = 0.0014). No association between genetic RR and either cone density (p = 0.435) or spacing (p = 0.538) was found. Three distinct adaptive optics scanning light ophthalmoscope phenotypical variations of photoreceptor appearance were noted in patients with grade 1 to 3 fundi. These included variable reflectivity of photoreceptors, decreased waveguiding, and altered photoreceptor mosaic overlying drusen., Conclusions: Our data demonstrate the potential of multimodal assessment in the understanding of early anatomical changes associated with AMD. Adaptive optics scanning light ophthalmoscope imaging reveals a decrease in photoreceptor density and increased spacing in patients with grade 1 to 3 fundi, as well as a spectrum of photoreceptor changes, ranging from variability in reflectivity to decreased density. Future longitudinal studies are needed in genetically characterized subjects to assess the significance of these findings with respect to the development and progression of AMD.

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.

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, 2580 cataract cases and 1367 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 529,431 single nucleotide polymorphisms (SNPs) 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 the 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 5 statistically significant models with an interaction term with p-value < 0.05, as well as an overall model with p-value < 0.05 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 288 models that exhibit an interaction term with a p-value ≤ 1×10(-4) associated with 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.

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., (Copyright © 2011 American Academy of Physical Medicine and Rehabilitation. Published by Elsevier Inc. All rights reserved.)

Background: To describe the dietary intake of participants in the Personalized Medicine Research Project (PMRP), and to quantify differences in nutrient intake by smoking status and APOE4-a genetic marker that has been shown to modify the association between risk factors and outcomes., Methods: The PMRP is a population-based DNA, plasma and serum biobank of more than 20,000 adults aged 18 years and older in central Wisconsin. A questionnaire at enrollment captures demographic information as well as self-reported smoking and alcohol intake. The protocol was amended to include the collection of dietary intake and physical activity via self-reported questionnaires: the National Cancer Institute 124-item Diet History Questionnaire and the Baecke Physical Activity Questionnaire. These questionnaires were mailed out to previously enrolled participants. APOE was genotyped in all subjects., Results: The response rate to the mailed questionnaires was 68.2% for subjects who could still be contacted (alive with known address). Participants ranged in age from 18 to 98 years (mean 54.7) and 61% were female. Dietary intake is variable when comparing gender, age, smoking, and APOE4. Over 50% of females are dietary supplement users; females have higher supplement intake than males, but both have increasing supplement use as age increases. Food energy, total fat, cholesterol, protein, and alcohol intake decreases as both males and females age. Female smokers had higher macronutrient intake, whereas male nonsmokers had higher macronutrient intake. Nonsmokers in both genders use more supplements. In females, nonsmokers and smokers with APOE4 had higher supplement use. In males, nonsmokers with APOE4 had higher supplement use between ages 18-39 only, and lower supplement use at ages above 39. Male smokers with APOE4 had lower supplement use., Conclusion: Dietary intake in PMRP subjects is relatively consistent with data from the National Health and Nutrition Examination Survey (NHANES). Findings suggest a possible correlation between the use of supplements and APOE4. The PMRP dietary data can benefit studies of gene-environment interactions and the development of common diseases.

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)., ((c) 2009 Wiley Periodicals, Inc.)