Holm, Ingrid A.

Background: Susceptibility to cardiometabolic diseases (CDs) is shaped by genetic, socio-environmental, and lifestyle factors. We evaluated the associations of disease-specific polysocial risk scores (PSSs)— derived from social determinants of health (SDOH) and lifestyle factors—and genetic risk factors with six CDs, across self-identified race/ethnicity (SIRE) groups in the eMERGE IV cohort. Methods: The six CDs included: atrial fibrillation, coronary heart disease, chronic kidney disease, hyperlipidemia, obesity, and type 2 diabetes. Disease-specific PSSs were developed using machine-learning models based on 65 SDOH and lifestyle factors. Participants were grouped based on their SIRE as Asian, Black, Hispanic/Latino, and White. We evaluated PSS distributions across SIRE groups. Genetic risk was assessed through disease-specific polygenic risk scores (PRSs), monogenic etiologies, and family history (FamHx). High PSS and high PRS were defined as the top 5th percentile. Associations were tested using multivariable logistic regression; model performance was assessed using the C-statistic. Results: Among 20178 participants (50±15 years, 68% female, 40% non-White), the most frequently associated PSS components were physical activity, sleep, self-perceived health, and income. PSS distributions varied across SIRE groups, with Whites having the lowest and Black and Hispanic/Latinos the highest scores for most CDs (Figure 1). All PSSs were significantly associated with their respective CDs, independent of confounders (OR per 1 SD increase: 1.19–2.68; Figure 2-A). No significant interactions were observed between PSSs and PRSs or monogenic etiologies. However, significant interactions were noted between PSSs and FamHx for CKD and obesity. PSS and PRS effects on the odds of CDs were independent (Figure 2-B) and additive (Figure 2-C). Incorporating PSSs into models that included clinical and genetic risk factors improved predictive performance of risk prediction models (Figure 3). Conclusion: PSSs, reflecting socio-environmental and lifestyle disadvantage, varied significantly across SIRE groups and were associated with risk of six CDs, independent of and additive to PRS. Given the substantial variation in socio-environmental and lifestyle risks across SIRE groups, integrating these factors into risk prediction models for CDs may help address health disparities and enhance preventive efforts, especially in marginalized populations.

Sonic hedgehog signaling regulates processes of embryonic development across multiple tissues, yet factors regulating context-specific Shh signaling remain poorly understood. Exome sequencing of families with polymicrogyria (disordered cortical folding) revealed multiple individuals with biallelic deleterious variants in TMEM161B , which encodes a multi-pass transmembrane protein of unknown function. Tmem161b null mice demonstrated holoprosencephaly, craniofacial midline defects, eye defects, and spinal cord patterning changes consistent with impaired Shh signaling, but were without limb defects, suggesting a CNS-specific role of Tmem161b. Tmem161b depletion impaired the response to Smoothened activation in vitro and disrupted cortical histogenesis in vivo in both mouse and ferret models, including leading to abnormal gyration in the ferret model. Tmem161b localizes non-exclusively to the primary cilium, and scanning electron microscopy revealed shortened, dysmorphic, and ballooned ventricular zone cilia in the Tmem161b null mouse, suggesting that the Shh-related phenotypes may reflect ciliary dysfunction. Our data identify TMEM161B as a regulator of cerebral cortical gyration, as involved in primary ciliary structure, as a regulator of Shh signaling, and further implicate Shh signaling in human gyral development.

Purpose: The electronic Medical Records and Genomics (eMERGE) Phase III study was undertaken to assess clinical utility of returning medically actionable genomic screening results. We assessed pediatric clinical outcomes following return of pathogenic/likely pathogenic (P/LP) variants in autosomal dominant conditions with available effective interventions. Methods: The two eMERGE III pediatric sites collected outcome data and assessed changes in medical management at 6 and 12 months. Results: We returned P/LP results to 29 participants with outcome data. For 23 of the 29 participants, the P/LP results were previously unknown. Five of the 23 participants were already followed for conditions related to the P/LP variant. Of those receiving novel results and not being followed for the condition related to the P/LP result (n = 18), 14 (77.8%) had a change in healthcare after return of results (RoR). Following RoR, cascade testing of family members occurred for 10 of 23 (43.5%). Conclusions: The most common outcomes post-RoR included imaging/laboratory testing and health behavior recommendations. A change in healthcare was documented in 77.8% of those receiving results by 6 months. Our findings demonstrate how return of genomic screening results impacts healthcare in pediatric populations.

Abstract The public health impact of genomic screening can be enhanced by cascade testing. However, cascade testing depends on communication of results to family members. While the barriers and facilitators of family communication have been researched following clinical genetic testing, the factors impacting the dissemination of genomic screening results are unknown. Using the pragmatic Electronic Medical Records and Genomics Network‐3 (eMERGE‐3) study, we explored the reported sharing practices of participants who underwent genomic screening across the United States. Six eMERGE‐3 sites returned genomic screening results for mostly dominant medically actionable disorders and surveyed adult participants regarding communication of results with first‐degree relatives. Across the sites, 279 participants completed a 1‐month and/or 6‐month post‐results survey. By 6 months, only 34% of the 156 respondents shared their results with all first‐degree relatives and 4% did not share with any. Over a third (39%) first‐degree relatives were not notified of the results. Half (53%) of participants who received their results from a genetics provider shared them with all first‐degree relatives compared with 11% of participants who received their results from a non‐genetics provider. The most frequent reasons for sharing were a feeling of obligation (72%) and that the information could help family members make medical decisions (72%). The most common reasons indicated for not sharing were that the family members were too young (38%), or they were not in contact (25%) or not close to them (25%). These data indicate that the professional returning the results may impact sharing patterns, suggesting that there is a need to continue to educate healthcare providers regarding approaches to facilitate sharing of genetic results within families. Finally, these data suggest that interventions to increase sharing may be universally effective regardless of the origin of the genetic result.

There is a need for multimodal strategies to keep research participants informed about study results. Our aim was to characterize preferences of genomic research participants from two institutions along four dimensions of general research result updates: content, timing, mechanism, and frequency. Methods: We conducted a web-based cross-sectional survey that was administered from 25 June 2018 to 5 December 2018. Results: 397 participants completed the survey, most of whom (96%) expressed a desire to receive research updates. Preferences with high endorsement included: update content (brief descriptions of major findings, descriptions of purpose and goals, and educational material); update timing (when the research is completed, when findings are reviewed, when findings are published, and when the study status changes); update mechanism (email with updates, and email newsletter); and update frequency (every three months). Hierarchical cluster analyses based on the four update preferences identified four profiles of participants with similar preference patterns. Very few participants in the largest profile were comfortable with budgeting less money for research activities so that researchers have money to set up services to send research result updates to study participants. Conclusion: Future studies may benefit from exploring preferences for research result updates, as we have in our study. In addition, this work provides evidence of a need for funders to incentivize researchers to communicate results to participants.

AbstractPopulation‐based genomic screening has the potential to improve health outcomes by identifying genetic causes of disease before they occur. While much attention has been paid to supporting the needs of the small percentage of patients who will receive a life‐altering positive genomic screening result that requires medical attention, little attention has been given to the communication of negative screening results. As there are currently no best practices for returning negative genomic screening results, we drew on experiences across the electronic medical records and genomics (eMERGE) III Network to highlight the diversity of reporting methods employed, challenges encountered in reporting negative test results, and “lessons learned” across institutions. A 60‐item survey that consisted of both multiple choice and open‐ended questions was created to gather data across institutions. Even though institutions independently developed procedures for reporting negative results, and had very different study populations, we identified several similarities of approach, including but not limited to: returning results by mail, placing results in the electronic health record via an automated process, reporting results to participants' primary care provider, and providing genetic counseling to interested patients at no cost. Differences in procedures for reporting negative results included: differences in terminology used to describe negative results, definitions of negative results, guidance regarding the meaning of negative results for participants and their family members, and recommendations for clinical follow up. Our findings highlight emerging practices for reporting negative genomic screening results and highlight the need to create patient education and clinical support tools for reporting negative screening results.

A challenge in returning genomic test results to research participants is how best to communicate complex and clinically nuanced findings to participants in a manner that is scalable to the large numbers of participants enrolled. The purpose of this study was to examine the features of genetic results letters produced at each Electronic Medical Records and Genomics (eMERGE3) Network site to assess their readability and content. Letters were collected from each site, and a qualitative analysis of letter content and a quantitative analysis of readability statistics were performed. Because letters were produced independently at each eMERGE site, significant heterogeneity in readability and content was found. The content of letters varied widely from a baseline of notifying participants that results existed to more detailed information about positive or negative results, as well as materials for sharing with family members. Most letters were significantly above the Centers for Disease Control-suggested reading level for health communication. While continued effort should be applied to make letters easier to understand, the ongoing challenge of explaining complex genomic information, the implications of negative test results, and the uncertainty that comes with some types of test and result makes simplifying letter text challenging.

A goal of the 3rd phase of the Electronic Medical Records and Genomics (eMERGE3) Network was to examine the return of results (RoR) of actionable variants in more than 100 genes to consenting participants and their healthcare providers. Each of the 10 eMERGE sites developed plans for three essential elements of the RoR process: Disclosure to the participant, notification of the health care provider, and integration of results into the electronic health record (EHR). Procedures and protocols around these three elements were adapted as appropriate to individual site requirements and limitations. Detailed information about the RoR procedures at each site was obtained through structured telephone interviews and follow-up surveys with the clinical investigator leading or participating in the RoR process at each eMERGE3 institution. Because RoR processes at each of the 10 sites allowed for taking into account differences in population, disease focus and institutional requirements, significant heterogeneity of process was identified, including variability in the order in which patients and clinicians were notified and results were placed in the EHR. This heterogeneity in the process flow for eMERGE3 RoR reflects the “real world” of genomic medicine in which RoR procedures must be shaped by the needs of the patients and institutional environments.

Abstract Objective We describe a stratified sampling design that combines electronic health records (EHRs) and United States Census (USC) data to construct the sampling frame and an algorithm to enrich the sample with individuals belonging to rarer strata. Materials and Methods This design was developed for a multi-site survey that sought to examine patient concerns about and barriers to participating in research studies, especially among under-studied populations (eg, minorities, low educational attainment). We defined sampling strata by cross-tabulating several socio-demographic variables obtained from EHR and augmented with census-block-level USC data. We oversampled rarer and historically underrepresented subpopulations. Results The sampling strategy, which included USC-supplemented EHR data, led to a far more diverse sample than would have been expected under random sampling (eg, 3-, 8-, 7-, and 12-fold increase in African Americans, Asians, Hispanics and those with less than a high school degree, respectively). We observed that our EHR data tended to misclassify minority races more often than majority races, and that non-majority races, Latino ethnicity, younger adult age, lower education, and urban/suburban living were each associated with lower response rates to the mailed surveys. Discussion We observed substantial enrichment from rarer subpopulations. The magnitude of the enrichment depends on the accuracy of the variables that define the sampling strata and the overall response rate. Conclusion EHR and USC data may be used to define sampling strata that in turn may be used to enrich the final study sample. This design may be of particular interest for studies of rarer and understudied populations.