Master of Science, The Ohio State University, 2022, Genetic Counseling

Although many clinical genetic testing laboratories collect race, ethnicity, and ancestry (REA) information, there are documented issues inherent to the process. Obtaining a better understanding of clinical genetic testing laboratory practices surrounding REA data provides an opportunity to better understand how they contribute to and mitigate social inequities in genetic medicine. To investigate current REA data practices, this study aimed to characterize clinical genetic testing laboratory REA data collection, use, and storage practices as reported by laboratory employees. Participants (n=57) completed a survey addressing current collection, use, and storage practices, as well as opinions regarding REA data. Most laboratories reportedly collect (95%, n=41), use (82%, n=33), and store (71%, n=34) REA data. REA data collection and use varies in relation to test type, clinical specialty, admixed ancestry, and collection source. All (100%, 10/10) employees who perform variant interpretation (VI) report inclusion of population-based criteria in their VI protocol, but only half use REA data in VI very frequently (50%, 4/8), while half use it very infrequently (50%, 4/8). Participants had a greater endorsing than refuting opinion about the need for improved REA data practices (67%, 24/36) and transparency (38%, 13/34). Nearly half of participants reported REA data practices contribute to systemic racism (41%, 13/42) and healthcare inequities (47%, 14/30). Most participants reported it is the responsibility of laboratories to assess their REA data practices (70%, 21/30) and expressed at least some willingness to contribute to developing REA data practice guidelines (45%, 13/29). Quantifiably characterizing laboratory practices via employee reports builds opportunities for research further identifying factors exacerbating and mitigating any contributions REA data practices make to systemic issues, and may aid in the development of REA data practice guidelines.

Committee: Laiken Peterson (Advisor); Jordan Brown (Committee Member); Matthew Avenarius Dr. (Committee Member) Subjects: Biology; Biomedical Research; Demographics; Genetics; Health; Health Care; Health Care Management; Health Sciences; Medical Ethics; Medicine; Molecular Biology

MS, University of Cincinnati, 2023, Medicine: Genetic Counseling

Health care provider (HCP) continuing education with regard to genomic testing remains an area of concern. Numerous studies have shown the benefits of continuing education for HCPs, including through online modules, in improving confidence for utilizing clinical genomic testing. It remains unclear if these approaches have changed how HCPs perceive their own knowledge. We utilized a modified version of a validated genetic counseling self-efficacy scale to assess what changes in self-efficacy can be observed when comparing HCP response before and after completion of a series of case-based, online, genomic education modules (GEMs). Focusing in on 12 genetic counseling competencies, participants were surveyed before and after completion of online education modules on genomic testing. We also considered whether completion of the GEMs could affect other variables such as changes in practice behavior including referrals to clinical genetics providers and genomic tests ordered. We hypothesized that HCP self-efficacy in the 12 target areas would improve after completion of the modules. Sixteen HCPs, across various specialties, completed the modules and both pre and post-test surveys. Using a paired t-test, we found significant improvements in mean self-efficacy in 8 of the 12 categories for which participants were surveyed (p ? 0.004) after their completion of the online modules. We saw a trend towards increase in both absolute genomic test ordering and referrals to HCPs in genetics though below the established significance threshold. The results of this study suggest improvement in HCP self-efficacy after completion of educational modules and provide an approach to measure HCP self-efficacy for other genomic educational modules. Additional studies involving a larger sample size are needed.

Committee: Laura Ramsey Ph.D. (Committee Chair); Cynthia Prows R.N. M.S.N. (Committee Member); Carrie Atzinger M.S. C.G.C. (Committee Member) Subjects: Health Sciences

Doctor of Philosophy, The Ohio State University, 2023, Horticulture and Crop Science

Breeders have increasingly adopted genomic selection (GS) and sparse testing as methods to improve genetic gains and reduce field testing costs in early-stage trials. However, the impact of the number of sparse testing environments (TE) on breeding success and cost-effectiveness remains uncertain. In this study, we utilized both empirical and simulated yield data from three environments and three years of field testing to evaluate the influence of TE on prediction accuracy, genetic gain (∆G), and the probability of identifying a new cultivar (Pc). Additionally, we investigated the effects of incorporating late-stage data into early-stage predictions, modeling marker-by-environment interaction (MEI), and increasing the number of sparse-tested full-sibs on prediction accuracy. Our findings con-sistently demonstrated that increasing the number of TE enhanced prediction accuracy, with the greatest improvement observed when transitioning from zero TE to one TE, where zero TE involved training with late-stage lines only. GS with two TE was nearly as accurate as three TE. Furthermore, ∆G and Pc were highest at one TE when testing equal number of plots. Interestingly, when the total cost of evaluation was even, the ∆G and Pc were similar for one to three TE. Modeling MEI increased prediction accuracy in trials with high MEI variance relative to genetic variance. The most significant improvement in accuracy occurred when 3-6 full-sibs per family were sparse-tested. This research contributes to the improvement of GS in sparse testing designs and provides valuable insights to enhance breeding success while optimizing the use of resources in early-stage trials.

Committee: Clay Sneller (Advisor); William Notz (Committee Member); Leah McHale (Committee Member); David Francis (Committee Member) Subjects: Agriculture; Genetics; Plant Sciences; Statistics

Master of Science, The Ohio State University, 2022, Genetic Counseling

Background: Race, ethnicity, and ancestry (REA) are distinct terms that are often used interchangeably to refer to ascribed social identities. Within the medical setting, REA is commonly collected as demographic information with race and ethnicity being frequently used as surrogates for ancestral background. Currently, patient- or provider-reported REA is being used in biomedical and healthcare research instead of genetic ancestry, which is scientifically interpreted. The utilization of patient- or provider-reported REA in the clinical interpretation of potentially disease-associated variants may result in inaccurate risk assessment. Genetic counselors (GCs) often collect patient-reported REA as part of the pedigree construction process. Methods for obtaining patient-reported REA are currently not well characterized. This study aims to do the following: determine the proportion of genetic counselors who currently collect patient-reported REA during routine genetic counseling encounters, characterize how genetic counselors ask their patients about REA, and describe the characteristics of genetic counselors that do collect REA information as well as those that do not. An additional exploratory aim of investigating whether or not genetic counselors can determine race, ethnicity, and ancestry emerged during survey construction. Methods: 239 board-certified genetic counselors were recruited by electronic means to complete a 20-question online survey assessing GCs' perception of race, ethnicity, and ancestry, the current practices of GCs, and the demographics of GCs. Data regarding GCs' REA perception, current practices, and demographics were analyzed using descriptive statistics and chi-squared tests. Statistical analysis was not significant. Results: More participants ask patients for ancestry data (93%) in comparison to ethnicity (65%) or race data (40%). 75% of participants collect REA data from patients directly. Phrases and/or terms associated with “ethnicity”, “cou (open full item for complete abstract)

Committee: Jordan Brown (Advisor); Leigha Senter-Jamieson (Committee Member); Damara Hamlin (Committee Member); Vivian Pan (Committee Member); Barbara Harrison (Committee Member) Subjects: Genetics; Health Care; Health Sciences

MS, University of Cincinnati, 2018, Medicine: Genetic Counseling

Next generation sequencing integration into clinical and research settings has sparked debate about the return of genomic sequencing results, particularly for minors. The American College of Medical Genetics (ACMG) states that when undergoing clinical whole exome or genome sequencing, medically actionable results should be returned for 59 genes, regardless of the age of the individual. These recommendations conflict with long standing recommendations to defer predictive testing for adult onset conditions until the age of the majority. The ACMG recommendations do support parents' choices to opt in or out of secondary analysis for their child's 59 genes; yet there is no consideration of soliciting adolescents' preferences and choices in these decisions. While adult and parental preferences have been studied, there is limited data about the involvement of adolescents in the decision-making process and their choices in the return of genomic sequencing results, particularly for adult onset disorders and carrier-status. This study aimed to provide empirical evidence for the reasons adolescents choose to learn, or not learn, sequencing results for carrier-status, conditions that are or are not preventable, treatable, or are adult-onset. We also aimed to capture the type of involvement adolescents wanted when making these testing decisions. Methods: After making decisions about learning genomic research results, we interviewed adolescents and one of their parents to explore the reasoning behind their choices. Interviews were audio-recorded and transcribed. Transcripts were analyzed using a constant comparative method, and deductive and inductive codes were used for thematic analysis. Results: Among 64 adolescents sampled, aged 13-17, thirty-three adolescents chose to learn all results, and thirty-one adolescents chose to exclude certain results. Reasons expressed among adolescents who chose to learn all results included an importance of having knowledge, being able t (open full item for complete abstract)

Committee: Cynthia Prows R.N. M.S.N. (Committee Chair); Michelle Mcgowan (Committee Member); Melanie Myers Ph.D. (Committee Member) Subjects: Genetics