Ethical and practical challenges of sharing data from genome-wide association studies: the eMERGE Consortium experience.

In 2007, the National Human Genome Research Institute (NHGRI) established the Electronic MEdical Records and GEnomics (eMERGE) Consortium (www.gwas.net) to develop, disseminate, and apply approaches to research that combine DNA biorepositories with electronic medical record (EMR) systems for large-scale, high-throughput genetic research. One of the major ethical and administrative challenges for the eMERGE Consortium has been complying with existing data-sharing policies. This paper discusses the challenges of sharing genomic data linked to health information in the electronic medical record (EMR) and explores the issues as they relate to sharing both within a large consortium and in compliance with the National Institutes of Health (NIH) data-sharing policy. We use the eMERGE Consortium experience to explore data-sharing challenges from the perspective of multiple stakeholders (i.e., research participants, investigators, and research institutions), provide recommendations for researchers and institutions, and call for clearer guidance from the NIH regarding ethical implementation of its data-sharing policy.

A rural community's involvement in the design and usability testing of a computer-based informed consent process for the Personalized Medicine Research Project.

Many informed consent studies demonstrate that research subjects poorly retain and understand information in written consent documents. Previous research in multimedia consent is mixed in terms of success for improving participants' understanding, satisfaction, and retention. This failure may be due to a lack of a community-centered design approach to building the interventions. The goal of this study was to gather information from the community to determine the best way to undertake the consent process. Community perceptions regarding different computer-based consenting approaches were evaluated, and a computer-based consent was developed and tested. A second goal was to evaluate whether participants make truly informed decisions to participate in research. Simulations of an informed consent process were videotaped to document the process. Focus groups were conducted to determine community attitudes towards a computer-based informed consent process. Hybrid focus groups were conducted to determine the most acceptable hardware device. Usability testing was conducted on a computer-based consent prototype using a touch-screen kiosk. Based on feedback, a computer-based consent was developed. Representative study participants were able to easily complete the consent, and all were able to correctly answer the comprehension check questions. Community involvement in developing a computer-based consent proved valuable for a population-based genetic study. These findings may translate to other types of informed consents, including those for trials involving treatment of genetic disorders. A computer-based consent may serve to better communicate consistent, clear, accurate, and complete information regarding the risks and benefits of study participation. Additional analysis is necessary to measure the level of comprehension of the check-question answers by larger numbers of participants. The next step will involve contacting participants to measure whether understanding of what they consented to is retained over time.

Return of individual research results from genome-wide association studies: experience of the Electronic Medical Records and Genomics (eMERGE) Network.

PURPOSE: Return of individual genetic results to research participants, including participants in archives and biorepositories, is receiving increased attention. However, few groups have deliberated on specific results or weighed deliberations against relevant local contextual factors. METHODS: The Electronic Medical Records and Genomics (eMERGE) Network, which includes five biorepositories conducting genome-wide association studies, convened a return of results oversight committee to identify potentially returnable results. Network-wide deliberations were then brought to local constituencies for final decision making. RESULTS: Defining results that should be considered for return required input from clinicians with relevant expertise and much deliberation. The return of results oversight committee identified two sex chromosomal anomalies, Klinefelter syndrome and Turner syndrome, as well as homozygosity for factor V Leiden, as findings that could warrant reporting. Views about returning findings of HFE gene mutations associated with hemochromatosis were mixed due to low penetrance. Review of electronic medical records suggested that most participants with detected abnormalities were unaware of these findings. Local considerations relevant to return varied and, to date, four sites have elected not to return findings (return was not possible at one site). CONCLUSION: The eMERGE experience reveals the complexity of return of results decision making and provides a potential deliberative model for adoption in other collaborative contexts.

Cataract research using electronic health records.

BACKGROUND: The eMERGE (electronic MEdical Records and Genomics) network, funded by the National Human Genome Research Institute, is a national consortium formed to develop, disseminate, and apply approaches to research that combine DNA biorepositories with electronic health record (EHR) systems for large-scale, high-throughput genetic research. Marshfield Clinic is one of five sites in the eMERGE network and primarily studied: 1) age-related cataract and 2) HDL-cholesterol levels. The purpose of this paper is to describe the approach to electronic evaluation of the epidemiology of cataract using the EHR for a large biobank and to assess previously identified epidemiologic risk factors in cases identified by electronic algorithms. METHODS: Electronic algorithms were used to select individuals with cataracts in the Personalized Medicine Research Project database. These were analyzed for cataract prevalence, age at cataract, and previously identified risk factors. RESULTS: Cataract diagnoses and surgeries, though not type of cataract, were successfully identified using electronic algorithms. Age specific prevalence of both cataract (22% compared to 17.2%) and cataract surgery (11% compared to 5.1%) were higher when compared to the Eye Diseases Prevalence Research Group. The risk factors of age, gender, diabetes, and steroid use were confirmed. CONCLUSIONS: Using electronic health records can be a viable and efficient tool to identify cataracts for research. However, using retrospective data from this source can be confounded by historical limits on data availability, differences in the utilization of healthcare, and changes in exposures over time.

Next-generation analysis of cataracts: determining knowledge driven gene-gene interactions using biofilter, and gene-environment interactions using the Phenx Toolkit*.

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.

Attachment, parental bonding and borderline personality disorder features in young adults.

The relations between parental bonding and attachment constructs and borderline personality disorder features were examined in a sample of 393 18-year-old participants. Hierarchical regression analyses revealed that parental bonding and attachment scores (especially insecure attachment, anxious or ambivalent attachment, and a perception of a relative lack of caring from one's mother) were uniquely associated with borderline features beyond what could be accounted for by gender, childhood adversity experiences, Axis I disorder, and nonborderline Axis II symptoms. Although relatively modest, these relations suggest that bonding and attachment constructs might be considered in comprehensive etiological models of borderline personality disorder.

Alcohol, tobacco, and drug use disorders and personality disorder symptoms.

In a nonclinical sample of 395 young adults, the authors evaluated the relations between major personality traits, Diagnostic and Statistical Manual of Mental Disorders (4th ed.; DSM-IV; American Psychiatric Association, 1994) personality disorder symptoms, and DSM-IV alcohol use disorders (AUDs). Consistent with previous findings, traits related to disinhibition and negative affectivity were consistently associated with AUDs, as were Cluster B personality disorder symptoms (especially antisocial and borderline disorder symptoms). Multivariate analyses revealed that Cluster B symptoms were significantly associated with AUDs above and beyond what was accounted for by personality traits. Further, the authors found differential patterns of relations between other substance use disorders (SUDs; i.e., tobacco dependence and drug use diagnoses) and personality disorder symptoms. Overall, these results suggest that personality disorder symptoms predict unique variance in SUDs that reflect maladaptive aspects of personality traits.

Validation of PhenX measures in the personalized medicine research project for use in gene/environment studies.

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.

Next-generation analysis of cataracts: determining knowledge driven gene-gene interactions using Biofilter, and gene-environment interactions using the PhenX Toolkit.

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.

Importance of multi-modal approaches to effectively identify cataract cases from electronic health records.

OBJECTIVE: There is increasing interest in using electronic health records (EHRs) to identify subjects for genomic association studies, due in part to the availability of large amounts of clinical data and the expected cost efficiencies of subject identification. We describe the construction and validation of an EHR-based algorithm to identify subjects with age-related cataracts. MATERIALS AND METHODS: We used a multi-modal strategy consisting of structured database querying, natural language processing on free-text documents, and optical character recognition on scanned clinical images to identify cataract subjects and related cataract attributes. Extensive validation on 3657 subjects compared the multi-modal results to manual chart review. The algorithm was also implemented at participating electronic MEdical Records and GEnomics (eMERGE) institutions. RESULTS: An EHR-based cataract phenotyping algorithm was successfully developed and validated, resulting in positive predictive values (PPVs) >95%. The multi-modal approach increased the identification of cataract subject attributes by a factor of three compared to single-mode approaches while maintaining high PPV. Components of the cataract algorithm were successfully deployed at three other institutions with similar accuracy. DISCUSSION: A multi-modal strategy incorporating optical character recognition and natural language processing may increase the number of cases identified while maintaining similar PPVs. Such algorithms, however, require that the needed information be embedded within clinical documents. CONCLUSION: We have demonstrated that algorithms to identify and characterize cataracts can be developed utilizing data collected via the EHR. These algorithms provide a high level of accuracy even when implemented across multiple EHRs and institutional boundaries.

Community engagement in biobanking: Experiences from the eMERGE Network.

Advances in genomic technologies and the promise of "personalised medicine" have spurred the interest of researchers, healthcare systems, and the general public. However, the success of population-based genetic studies depends on the willingness of large numbers of individuals and diverse communities to grant researchers access to detailed medical and genetic information. Certain features of this kind of research - such as the establishment of biobanks and prospective data collection from participants' electronic medical records - make the potential risks and benefits to participants difficult to specify in advance. Therefore, community input into biobank processes is essential. In this report, we describe community engagement efforts undertaken by six United States biobanks, various outcomes from these engagements, and lessons learned. Our aim is to provide useful insights and potential strategies for the various disciplines that work with communities involved in biobank-based genomic research.