Skip to main content

Implementation and evaluation of personal genetic testing as part of genomics analysis courses in German universities



Due to the increasing application of genome analysis and interpretation in medical disciplines, professionals require adequate education. Here, we present the implementation of personal genotyping as an educational tool in two genomics courses targeting Digital Health students at the Hasso Plattner Institute (HPI) and medical students at the Technical University of Munich (TUM).


We compared and evaluated the courses and the students’ perceptions on the course setup using questionnaires.


During the course, students changed their attitudes towards genotyping (HPI: 79% [15 of 19], TUM: 47% [25 of 53]). Predominantly, students became more critical of personal genotyping (HPI: 73% [11 of 15], TUM: 72% [18 of 25]) and most students stated that genetic analyses should not be allowed without genetic counseling (HPI: 79% [15 of 19], TUM: 70% [37 of 53]). Students found the personal genotyping component useful (HPI: 89% [17 of 19], TUM: 92% [49 of 53]) and recommended its inclusion in future courses (HPI: 95% [18 of 19], TUM: 98% [52 of 53]).


Students perceived the personal genotyping component as valuable in the described genomics courses. The implementation described here can serve as an example for future courses in Europe.

Peer Review reports


Decreasing genome sequencing costs, new technologies, and an improved knowledge of the human genome brought genomic research to a level of application in life sciences and medicine that exceeds expectations from three decades ago when the Human Genome Project was launched [1]. Translation of genomic research towards an application in clinical care is continuously progressing, with the aim of improving diagnosis, disease prevention, risk prediction, as well as drug efficacy and safety assessments [2,3,4,5,6]. An understanding of data science has become a requirement for genomic researchers [1]. In addition, the interest of European citizens in direct-to-consumer (DTC) genetic testing in a non-clinical setting is high, even though genetic education and understanding is low in the general population [7].

To train a new generation of experts at the interface between computer science and medicine, the Digital Health (DH) Master’s program was initiated in 2018 at the Digital Engineering Faculty of the University of Potsdam, the Hasso Plattner Institute (HPI) [8]. In 2020, we introduced the Analyze Your Personal Genome (AYPG) course that incorporates voluntary personal genetic testing (PGT). Its aim is to enhance the understanding and to develop a sensitivity towards genetic testing by affecting the students on a personal level. The underlying rationale is based on self-determination theory: the students’ interest in the course content and their motivation for engaging in the course shifts from extrinsic motivation driven by grading to the intrinsic motivation of wanting to learn about their personal genomic background [9]. In addition to increased motivation, we aimed for enhanced compassion with patients and individuals undergoing PGT. Because DH students have heterogeneous backgrounds, a particular challenge was to design a course that provides learning opportunities to everyone, while ensuring that students take informed decisions whether to undergo PGT, regardless of their previous knowledge.

In response to the rising application of clinical and DTC genetic testing, the necessity of a profound genetic education in medical training was already recognized more than a decade ago [10]. At Tufts University and Stanford School of Medicine, discussions about introducing PGT into genomics courses to improve learning outcomes and motivation began in 2008 and 2009, respectively, and, after careful consideration, resulted in similar course concepts including voluntary genetic testing [11, 12]. At the Icahn School of Medicine at Mount Sinai, New York, personal genome sequencing was first applied in medical training in 2012 [13]. Strategies and results of these three schools were summarized and discussed by Garber et al. [14]. Briefly, several schools aimed to improve the genomics education for healthcare providers by adding an innovative and engaging component to their curricula, such as personal genotyping or working with anonymized or donated genomic data. Enhanced educational outcomes of students participating in PGT were indicated in studies accompanying the courses at Stanford School of Medicine and Icahn School of Medicine at Mount Sinai [15, 16].

Next to medical schools, PGT is increasingly applied in the genomics training of prospective pharmacists, with a special focus on pharmacogenomics. At the Eshelman School of Pharmacy at the University of North Carolina and at the University of Pittsburgh, voluntary genotyping is offered to advanced pharmacy and students in the Doctor of Pharmacy (PharmD) program to increase the understanding and acceptance of pharmacogenomics in the clinical context [17,18,19]. The classes included up to 145 students and genotyping was conducted by a DTC company. At the University of Florida, a smaller number of third-year PharmD students could undergo PGT of selected pharmacogenomic markers as part of the elective pharmacogenomics class [20]. The only published genomics course including PGT for undergraduate science students rather than medical or pharmaceutical students took place at the Brigham Young University [21]. Here, PGT was offered as part of the Advanced Molecular Biology and Genomics course. The students received their personal genotyping data not during, but after completion of the course, assuming that the students’ motivation would be increased by undergoing genetic testing. At the "Bring Your Genes to Cal” course at UC Berkeley, undergraduate students were offered to receive genetic information regarding three non-disease-causing variants. In 2010, upon discussions with the California Department of Public Health, the university decided not to return individualized results anymore [22].

Many of the offered courses in the US rely on genotyping performed by private DTC companies [12, 15, 17,18,19, 21]. In Europe, there is no uniform legislative framework covering DTC genetic testing across the 27 individual member states [23]. In Germany, genetic testing is regulated by the Genetic Diagnostics Act (Gendiagnostikgesetz, GenDG) [24], which requires testing to be carried out by medical doctors, following written informed consent; in case of preemptive testing, genetic counseling by certified medical doctors is required. Therefore, the GenDG does not allow for delivery of clinically relevant results within a DTC genetic testing framework [25]. However, it is allowed to obtain raw genetic data. Additionally, genetic testing for research purposes is exempt.

The present paper describes the implementation and evaluation of the AYPG course at HPI in Potsdam, Germany. Moreover, the AYPG course is compared to the Genomic Medicine (GM) elective course at the School of Medicine of the Technical University of Munich (TUM), which, to our knowledge, was the first course at a German university to include a PGT component in 2017. The AYPG and GM courses were evaluated using questionnaires to assess the students’ attitudes concerning PGT and its usefulness as part of an educational program.

Materials and methods

This section describes the implementation of the AYPG course and the course questionnaires.

Course implementation

The elective AYPG course was offered to DH Master’s students at HPI. After a course briefing at the beginning of the summer term 2020, covering relevant information about the course and establishing the voluntary nature of the PGT, 16 students in their second year of Master’s studies participated in the course. The course consisted of four three-hour remote lectures throughout the semester and a five-day in-person block course in August 2020 (Fig. 1). A second iteration of the course was conducted in summer term 2021.

Fig. 1
figure 1

Timeline of the analyze your personal genome course. Magenta items indicate questionnaire periods, blue items teaching activities, and yellow items events related to data

We structured the learning outcomes for the AYPG course according to Kraiger et al. [30] into cognitive, skill-based, and affective learning (Table 1). While cognitive learning outcomes mainly refer to the accumulation and organization of knowledge, skill-based learning outcomes are related to the acquisition of skills and automaticity, and affective learning outcomes are related to attitude and motivation. The course started with an emphasis on cognitive learning outcomes, including the basics of human genetics, pharmacogenomics, and clinical genomics, preceding the practical modules. In the second part, the focus was set on skill-based learning outcomes in combination with knowledge transfer.

Table 1 AYPG course schedule containing learning objectives: the course was structured into 12 sessions of three hours

After the introductory lecture, which included information about the risks and challenges of genetic testing as well as ethical aspects, students could choose to participate in the free-of-charge PGT. Additionally, a course information sheet and consent form were provided to ensure informed consent (Additional file 1). Students had to pass a quiz to make sure they had read and understood the course information sheet. In case HPI students had questions regarding their genetic test results, they were pointed towards existing external genetic counselling offers accessible to them. A study coordinator and honest broker who was not associated with the course coordinated the genotyping process, including communication with students, obtaining consent, collection of samples for PGT, and handing out the pseudonymized data to the students. The honest broker had no access to PGT information and researchers conducting quality control of the genetic data worked with pseudonymized data. The course instructors did not know which students participated in the PGT and which did not. Personal data was processed in compliance with the EU General Data Protection Regulation and the Brandenburg Data Protection Act. The described genomics course with PGT opportunity was approved by the ethics board of the University of Potsdam with application number 17/2021.

Genotyping was conducted using the Illumina Infinium Global Screening Array (GSA v3-MD). Throughout the course, only selected single-nucleotide variants (SNVs) were accessible to the students: specific pharmacogenomic markers, SNVs determining the carrier status of rare diseases with recessive inheritance patterns, and backbone SNVs not suitable for determining the individual risk of rare monogenic diseases and limited to assessing risk of common multifactorial traits or diseases (Additional file 2). The full PGT information was encrypted and password-protected. Only after successful course completion, students could request the password to their own complete encrypted data from the honest broker. To obtain additional large genotyping data sets, open-access data from the openSNP database was downloaded using the opensnp-cohort-maker pipeline [26, 27]. The final openSNP data set included 509,712 variants in 3,581 individuals. Furthermore, students who chose not to participate in the PGT component of the course or who chose not to analyze their personal genetic data during the course were assigned anonymous openSNP data for analysis.

Course questionnaire

To evaluate the course and to assess the impact of the course on students’ attitudes towards genetic testing and analyses, we established questionnaires that were completed at four different time points: a pre-course questionnaire (Q1) after the introductory course, an intermediate questionnaire (Q2) before working with one’s own data, an end-of-course questionnaire (Q3) after working with one’s own data, and a retrospective questionnaire (Q4) about three months after completion of the course (Fig. 1, Additional file 3).

The overall questionnaire design was inspired by the questionnaires used to evaluate a course including genome sequencing at the Icahn School of Medicine at Mount Sinai [16, 28]. The first iteration of the Q1, Q2, and Q3 questionnaires contained questions about the motivation to participate in the course, the usage of personal genome data, usefulness of array-based genotyping data in clinical practice, usefulness of genome sequencing data in clinical practice compared to array-based genotyping data, and opinions on genetic analysis legislation, specifically the German GenDG. A Human-centered design thinking expert from the HPI was consulted to help with the design and implementation of the questionnaires as well as usability testing.

After the first three questionnaires were conducted at HPI, the survey was re-designed and shortened considerably, in coordination with co-authors from TUM. This re-design was conducted to make the questionnaire more accessible and increase participation rates at HPI and TUM, especially when the survey was only administered remotely. Instead of Likert scale questions with several sub-items and open-ended questions, much simpler questions with fewer answer options were introduced, focusing on the motivation to participate in the course and participation and attitudes regarding genotyping and data analysis in the course context. All questionnaires following the first three HPI questionnaires were adapted to this revised version.

To foster truthful answers, the questionnaire invitations and introductions clearly stated that participation is completely voluntary and anonymous. At HPI, pseudonymization was implemented with the help of the honest broker, enabling the anonymous connection of single students’ responses across questionnaires. Students agreed to the complete anonymization of their data after the last questionnaire by deleting the mapping of names to pseudonyms.

The questionnaires were created with SoSci Survey [29] and were conducted with students of the AYPG course in the summer term 2020, the TUM GM course in the winter term 2020/21, and the AYPG course in the summer term 2021. Additionally, a slightly adapted version of Q4 was conducted at TUM for all past GM classes (Q4’). HPI questionnaires were conducted in English, which is the teaching language of the DH curriculum, TUM questionnaires in German.

Statistical analyses

Questionnaire answers were exported from SoSci Survey in CSV format and analyzed using R 4.2.1. Responses were compared in two dimensions:

First, HPI and TUM responses were contrasted using Fisher’s exact test (stats::fisher.test; stats v4.2.1). For effect sizes, Cramer’s V statistic was computed (lsr::cramersV; lsr v0.5). Effect sizes were interpreted according to the standard interpretation [29].

Second, longitudinal differences in questionnaire responses were assessed over the course (each iteration of the course was analyzed separately). For pseudonymized questionnaires, as conducted at HPI, paired categorical Likert-type responses over the course were compared with Wilcoxon’s Signed Rank test (stats::wilcox.test with option paired = TRUE). The r value was used as effect size (rstatix::wilcox_effsize; rstatix v0.7.2). 5-point Likert scale responses from long-version questionnaires were simplified by grouping them together regarding whether they indicated agreement (value of 1) or disagreement (value of − 1) while keeping neutral answers as a third category (value of 0). Paired binary responses were assessed using McNemar's test (stats::mcnemar.test), using odds ratio as effect size (DescTools:: OddsRatio; DescTools v0.99.47). Completely anonymous questionnaires, as conducted at TUM, could not be matched longitudinally to individuals; therefore, responses over the course were compared with Fisher’s exact test.

The code used for statistical analyses is available on GitHub (


This section compares the AYPG and GM courses and presents the main questionnaire results. In total, 13 questionnaires were conducted (Additional file 4): Q1 to Q4 for HPI 2020 (16 course participants), HPI 2021 (16 course participants [15 during the block week]), and TUM 2020/21 (10 course participants [8 in the block week]); as well as TUM Q4’ (102 course participants before 2020/214). Most questionnaire results in the following sections focus on Q4 and Q4’ questionnaires (HPI 2020: N = 10; TUM 2020/21: N = 6; HPI 2021: N = 9; TUM before 2020/21: N = 47).

Course content and interests differed between student populations

The AYPG course at HPI, in particular the implementation of the PGT component, but also parts of its content and structure, was inspired by the GM course, which was first implemented at TUM in 2017. Nevertheless, the curriculum of each course was optimized for the different student populations and learning objectives (Table 2).

Table 2 Comparison of existing genomics courses in Germany

The courses covered introductions to genetics and molecular biology, genetic epidemiology, and statistics, pharmacogenomics, medical genomics, and ethical aspects of genetic testing. The weighting of the individual topics was adjusted according to the students’ prior knowledge. Both courses covered the topics of variant annotation, interpretation, and risk estimation for individuals and costumers of DTC testing. The students were assigned small projects and presentation tasks to each student, accompanied by discussion rounds on specific genetic variants, their annotation, and associated risk.

A strong focus of AYPG was computational data analysis using a publicly available data set, covering standard computational tools and analyses in genetics (e.g., genome-wide association analysis and polygenic risk scores). Using the openSNP dataset, students learned how to read and filter genotyping data, perform quality control and basic genome-wide association studies, and calculate polygenic risk score using the PLINK tool [31]. Further discussion on the risks and benefits regarding genetic testing and a direct question and answer session with human geneticists were designed to stimulate a more sophisticated attitude and reflection on the topic.

In the GM course, on the other hand, the two major learning objectives were to gain knowledge on interpreting and analyzing genetic variants based on public databases and to understand options for DTC genetic testing and the associated benefits and risks. Accordingly, more emphasis was placed on interpreting DTC test results. Future physicians might be confronted with patients who have purchased DTC products. Thus, a critical understanding of the companies’ products and their benefits and risks is beneficial to medicine students. The analysis of genetic variants of individual patients was discussed in more depth during the TUM course, which is relevant for the clinical setting.

Regarding the students’ motivation to participate, only few differences between the two courses became apparent (Table 3): compared to TUM students, HPI students were significantly more interested in pharmacogenomics (p = 0.002) and showed a tendency for an increased interest in receiving or analyzing their own genomic data (p = 0.07). Overall, the highest motivation to participate in the courses was the general interest in genomics or genomic analyses (92% at HPI, 91% at TUM).

Table 3 Comparison of motivation to participate in the course between HPI and TUM

Students find the genotyping component useful

Retrospectively, most students felt that genotyping in the course was useful for the learning experience (HPI: 89% [17 of 19], TUM: 92% [49 of 53]); 9 students who indicated that genotyping was a useful learning experience did not participate in the PGT component (Table 4). Almost all students would recommend offering PGT again in future courses (HPI: 95% [18 of 19], TUM: 98% [52 of 53]). 64% (34 of 53) of TUM participants expressed the opinion that a similar course should be an obligatory component of the medicine curriculum at universities.

Table 4 Combined results from retrospective questionnaires

The majority of questionnaire respondents participated in PGT (HPI: 89% [17 of 19], TUM: 83% [44 of 53]). Many students also collected the passwords to access their full genetic data (HPI: 76% [13 of 17], TUM: 66% [35 of 53]) or still planned to collect it at the time of the questionnaire (HPI: 3 of 4). Overall, less than half of the questionnaire participants who collected their password had conducted their own analyses with their full data at the time of Q4 (HPI: 54% [7 of 13], TUM: 40% (14 of 35)). Of all students who did or still planned to collect their passwords, most still intended conduct further analyses (HPI: 88% [14 of 16], TUM: 63% [22 of 35]).

Of all HPI and TUM Q4 2020/21 survey participants who collected their passwords (N = 19), 6 analyzed pharmacogenomic markers (HPI: 2 of 11, TUM: 4 of 6), 5 wellness traits (HPI: 4 of 11, TUM: 1 of 6), 4 genetic ancestry (HPI: 2 of 11, TUM: 2 of 6), 3 carrier statuses (HPI: 1 of 11, TUM: 2 of 6), and 2 their risk for polygenic diseases (HPI: 0 of 11, TUM: 2 of 6). Notably, only 40% (10 of 25) of students felt adequately trained to interpret genomic data outside of the course (HPI: 42% [8 of 19], TUM: 33% [2 of 6]).

Most students stated retrospectively that they would not have participated in the PGT component if it had been conducted by a non-European private company such as 23andMe: 63% (12 of 19) of HPI students and 79% (42 of 53) of TUM students. In responses to open-ended questions at the AYPG course in 2020 regarding whether the students would participate in PGT (if it was allowed in Germany), 9 stated that they would not have been able to afford WGS-based PGT. Furthermore, 10 students voiced concerns regarding data security and handling and would make the decision contingent on which institution provided the WGS and how this institution handled the data. Finally, the majority of HPI and TUM participants felt that personal genotyping should not be allowed without prior genetic counseling (HPI: 79% [15 of 19], TUM: 70% [37 of 53]). Interestingly, the two groups of students had different opinions about whether they would recommend PGT to relatives and friends who have not taken the course: 30% (16 of 53) of TUM students would recommend it, compared to 68% (13 of 19) of HPI students (p = 0.01).

Students changed their attitude towards genotyping throughout the course

The course changed the attitudes of many—especially HPI—students towards personal genotype analyses (HPI: 79% [15 of 19], TUM: 47% [25 of 53]; p = 0.02), with students typically becoming “more critical” (HPI: 73% [11 of 15], TUM: 72% [18 of 25]).

This can partially be confirmed by following the responses across the questionnaires (Table 4 and Additional file 5). During the course conducted in 2020, HPI students showed a trend of becoming more skeptical regarding the interpretability and usefulness of genetic data. Specifically, students mostly agreed that PGT results are not predictive (average values reported for each time point after grouping the values to agreement = 1, neutral response = 0, and disagreement = − 1; time point Q1: 0 (neutral), Q2: 0.57 (agree), Q3: 0.75 (agree); p for Q1 vs. Q3 = 0.0477; question not asked in Q4). They eventually rather disagreed that PGT is useful to inform family members about health risks (Q1–Q3) and that they would recommend PGT to relatives and friends (Q4) (time point Q1: 0.67 (agree), Q2: 0.83 (agree), Q3: − 0.5 (disagree), Q4: 0.33 (rather agree); p for Q2 vs. Q3 = 0.053). The students also eventually rather disagreed that PGT is an opportunity to obtain information that can help them improve their health and wellbeing (time point Q1: 0.62 (agree), Q2: 0.75 (agree), Q3: − 0.12 (rather disagree); p for Q2 vs. Q3 = 0.053; question not asked in Q4). In addition, HPI students disagreed during the entire course that genetic testing should be allowed without counselling (time point Q1: − 0.5 (disagree), Q2: − 0.5 (disagree), Q3: − 1 (disagree), Q4: − 0.67 (disagree); p for Q2 vs. Q3 = 0.149) and were more likely to believe that they could learn unwanted information from PGT (time point Q1: − 0.62 (disagree), Q2: − 0.25 (rather disagree), Q3: 0 (neutral); p for Q1 vs. Q3 = 0.088; question not asked in Q4). Consistent with the fact that fewer TUM students reported an attitude change, their responses did not change as much over the course as they did for HPI students, although HPI responses changed less in 2021 (mean absolute change of average responses HPI 2020: 0.71; TUM 2020/21: 0.32; HPI 2021: 0.48). In both groups, most students had not yet conducted the genetic analyses they had initially planned at the time of Q3 or Q4.

In open-ended questions at the AYPG course 2020, students provided information regarding the analyses they planned to carry out and their reasons. In Q1, students stated overall curiosity (n = 6) and improvement of analysis skills relevant for their future careers (n = 3) as reasons for further analyses. In Q2, the students stated that they were interested in finding out more about their healthcare risks (n = 4) and to improve their skillset (n = 3). In Q3 (after the course), some students expected to require further support for being able to properly understand their data (n = 2) or were concerned about learning unwanted information (n = 3). Others specifically did not want to further investigate risk for serious healthcare conditions but instead planned to analyze their genetic ancestry (n = 2) or acquire pharmacogenomic information (n = 2).


In the USA, PGT was already implemented as an educational method for university students in several, mostly medical and pharmaceutical schools. By contrast, at European universities, the genomics courses for DH students at the HPI Potsdam and for medical students at the TU Munich are, to the best of our knowledge, the only courses that implemented genetic testing with genome-wide coverage at the time when we collected our questionnaire data. The Medical University of Innsbruck has established a PGT course in Genomic Medicine, modelled on the GM course at TUM as part of their Master’s program in Genetic and Genomic Counselling in 2022.

While the course content differed between the courses at HPI and TUM, the implementation of PGT within the context of the courses was conducted in a similar manner. The use of PGT as part of the offered courses lies outside of the legal restriction of the GenDG, as it is only intended for research and educational purposes. At both Universities, ethical boards explicitly approved the courses and genetic testing for students, performed by a research institution without monetary gain. Students preferred this test setup to the possible alternative of conducting DTC testing via non-European private companies. The implementation described here could serve as a blueprint for universities in Europe when establishing a similar concept for genomics courses at their site.

The concept of personal genotyping for educational purposes can be beneficial for genomic courses of different study programs, as described for the AYPG course at HPI and the GM course at TUM. The idea of adding this educational component to a genomics course was motivated by the assumption that the students’ engagement and learning would be increased during the course and that personal reflection would make them more sensitive to genetic testing in general. Overall, the course questionnaires confirmed that students perceived the PGT component as useful and motivating. The vast majority of students would recommend keeping the PGT component as part of the course and advised to add similar courses to the curriculum of other universities. Similarly, medical, PharmD and undergraduate students from several US universities described PGT in the context of their genomic education as an important part of their curriculum [15, 20, 21] and as useful for their future practice [19, 32].

Our data did not allow for a comparison regarding whether knowledge gain differed between genotyped and non-genotyped students because of the high participation rates in the PGT and small total sample sizes. Nevertheless, both higher test scores and self-reported knowledge, as well as a higher engagement in the course by students participating in PGT were previously reported [15, 17, 21]. However, the benefits must outweigh any risks associated with the genetic testing, and risks must be minimized to justify the implementation of such educational techniques.

In the university context, these risks include anonymity, confidentiality, and coercion [11, 14, 33]. The course itself is an elective part of the study program, and PGT was not a requirement for course participation. An honest broker system made sure that the genetic data and corresponding personal information were strictly separated. The costs of genetic testing were covered by the Life & Brain Research Center (HPI) and the Institute of Human Genetics (TUM) so that students could undergo PGT regardless of their financial situation. Additionally, genetic testing was not conducted through a private company to mitigate the risk of conflicts of interests and of privacy issues including data leakage.

Furthermore, a major risk of genetic testing in general is that unwanted information may be learned. For the two courses in Potsdam and Munich, just a fraction of low-risk personal genetic information was returned to the students during the course. Only after completion of the course, students could request their complete data, minimizing the risk of making an uninformed decision to explore personal genetic information. At different stages of the courses, risks and benefits of genetic testing were discussed, so that continuous learning of the students regarding this topic was assured.

Most students who participated in the PGT component of the course requested or planned to request the password to decrypt their genetic data, but primarily non-disease related analyses had been conducted at the time of Q4 and Q4’. Many students did not conduct subsequent analyses with their data at all. The questionnaire responses also indicate a change in attitude towards genetic testing among HPI and TUM students: Most students answered that they had become more critical (as opposed developing a more positive attitude). However, it is not explicitly clear how the students interpreted the predefined answers “more critical” and “more positive”, available for the question on how students changed their attitude. These ambiguous answering options constitute a limitation of our study; in future questionnaires, either the wording should be more precise or further follow-up questions should be included. However, based on the overall trend of the students’ responses over the course and given our personal interactions with the students during the courses, e.g., in discussions rounds, we feel confident that the response “more critical” can typically be interpreted as “more reflective/aware of ethical implications”. We presume that this trend reflects the intense discussions of ethical aspects conducted throughout the courses (Table 1, Additional file 6). As shown with the example of the AYPG and GM courses, the content and structure of genomic courses should be tailored to the specific student population. One important aspect is that the desired learning outcomes differed between medical and DH students. While the study program in Medicine requires a stronger focus on patient-doctor interactions and patient-oriented interpretation of results, DH students were more intensely trained in computational analyses of larger genomic datasets.

Questionnaire results showed that the different focus areas were consistent with the students’ motivation to participate in the course. However, the interest of students may have been modified by the preceding contents and lectures provided at HPI and TUM. For instance, the high interest in pharmacogenomics within the HPI student cohort can be explained by a preceding lecture covering basic concepts of pharmacogenomics that all students from the course were required to attend. This heterogeneity in the student populations constitutes a limitation for our comparisons of the survey data across the two institutions.

There are further limitations related to the course questionnaires: One major limitation is the small sample size, which is why the results were not further stratified by educational background (and for Q4’ by course years). This may have introduced biases. Moreover, the voluntary survey participants could represent a subset of more positive and motivated or more critical students. While the survey results can certainly provide insights and qualitative trends, no clear conclusions can be drawn from the quantitative analyses. Furthermore, the re-design of the survey after the first HPI iterations limits the comparability of the responses between all iterations of the questionnaires. Nonetheless, we consider our approach a valuable evaluation of the course concept.

Finally, understanding genomic information is important not only in the context of the DH and Medicine study programs, but also for the general population, especially regarding personalized health services that provide genetic information to their customers. The majority of students from HPI and TUM supported the idea that genetic counseling should be mandatory when personal genotyping is conducted, as is required by law in Germany. This response is to some degree surprising, as the students received their own raw genetic data without further counseling and at least the HPI students would recommend PGT to family and friends who did not even take the course.

These results are in line with the observations made by Weitzel et al.: Even though most students participating in PGT of selected pharmacogenomic SNPs agreed that genotyping was useful for the course and improved their understanding of the content, the number of students considering recommending DTC to patients decreased over the course. As a possible reason, the authors suggested the potential increase in understanding the complexity and therefore the demand for professional support in interpreting results [20].


The application of voluntary PGT in two genomic courses at HPI and TUM was perceived as useful by most students across study programs and demonstrates the feasibility of the implementation of an innovative course design at German universities. Further investigation including higher sample sizes are required for more robust quantitative analyses.

Availability of data and materials

The datasets generated and analyzed during the current study are not publicly available due to data privacy restrictions but are available from T.S. ( on reasonable request. The questionnaire responses are available in aggregated form, with pseudonyms and open text responses removed (Additional file 7).


  1. Green ED, Gunter C, Biesecker LG, et al. Strategic vision for improving human health at The Forefront of Genomics. Nature. 2020;586:683–92.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Blagec K, Koopmann R, Crommentuijn-Van Rhenen M, et al. Implementing pharmacogenomics decision support across seven European countries: the Ubiquitous Pharmacogenomics (U-PGx) project. J Am Med Inform Assoc. 2018;25(7):893–8.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Widén E, Junna N, Ruotsalainen S, et al. How communicating polygenic and clinical risk for atherosclerotic cardiovascular disease impacts health behavior: an observational follow-up study. Circ Genomic Precis Med. 2022;15(2):e003459.

    Article  Google Scholar 

  4. Manolio TA, Rowley R, Williams MS, et al. Opportunities, resources, and techniques for implementing genomics in clinical care. Lancet. 2019;394(10197):511–20.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Wray NR, Lin T, Austin J, et al. From basic science to clinical application of polygenic risk scores: a primer. JAMA Psychiat. 2021;78(1):101–9.

    Article  Google Scholar 

  6. Lewis CM, Vassos E. Polygenic risk scores: from research tools to clinical instruments. Genome Med. 2020;12(1):1–11.

    Article  Google Scholar 

  7. Hoxhaj I, Stojanovic J, Boccia S. European citizens’ perspectives on direct-to-consumer genetic testing: an updated systematic review. Eur J Public Health. 2014;2020:1–7.

    Article  Google Scholar 

  8. HPI Digital Health Master’s Program. Accessed May 20, 2022.

  9. Deci EL, Ryan RM. The “what” and “why” of goal pursuits: human needs and the self-determination of behavior. Psychol Inq. 2000;11(4):227–68.

    Article  Google Scholar 

  10. Haspel RL, Arnaout R, Briere L, et al. A call to action training pathology residents in genomics and personalized medicine. Am J Clin Pathol. 2010;133(6):832–4.

    Article  PubMed  Google Scholar 

  11. Salari K, Pizzo PA, Prober CG. Commentary: to genotype or not to genotype? Addressing the debate through the development of a genomics and personalized medicine curriculum. Acad Med. 2011;86(8):925–7.

    Article  PubMed  Google Scholar 

  12. Walt DR, Kuhlik A, Epstein SK, et al. Lessons learned from the introduction of personalized genotyping into a medical school curriculum. Genet Med. 2011;13(1):63–6.

    Article  PubMed  Google Scholar 

  13. Sanderson SC, Linderman MD, Kasarskis A, et al. Informed decision-making among students analyzing their personal genomes on a whole genome sequencing course: a longitudinal cohort study. Genome Med. 2013.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Garber KB, Hyland KM, Dasgupta S. Participatory genomic testing as an educational experience. Trends Genet. 2016;32(6):317–20.

    Article  CAS  PubMed  Google Scholar 

  15. Salari K, Karczewski KJ, Hudgins L, Ormond KE. Evidence that personal genome testing enhances student learning in a course on genomics and personalized medicine. PLoS ONE. 2013;8(7):1–8.

    Article  CAS  Google Scholar 

  16. Linderman MD, Sanderson SC, Bashir A, et al. Impacts of incorporating personal genome sequencing into graduate genomics education: a longitudinal study over three course years. BMC Med Genom. 2018;11(1):1–12.

    Article  Google Scholar 

  17. Adams SM, Anderson KB, Coons JC, et al. Advancing pharmacogenomics education in the core pharmd curriculum through student personal genomic testing. Am J Pharm Educ. 2016.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Frick A, Benton CS, Scolaro KL, et al. Transitioning pharmacogenomics into the clinical setting: training future pharmacists. Front Pharmacol. 2016;7:241.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Frick A, Benton C, Suzuki O, et al. Implementing clinical pharmacogenomics in the classroom: student pharmacist impressions of an educational intervention including personal genotyping. Pharmacy. 2018;6(4):115.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Weitzel KW, McDonough CW, Elsey AR, Burkley B, Cavallari LH, Johnson JA. Effects of using personal genotype data on student learning and attitudes in a pharmacogenomics course. Am J Pharm Educ. 2016.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Weber KS, Jensen JL, Johnson SM. Anticipation of personal genomics data enhances interest and learning environment in genomics and molecular biology undergraduate courses. PLoS ONE. 2015.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Sanders R. UC Berkeley alters DNA testing program. Berkeley News. Accessed January 30, 2023.

  23. Hoxhaj I, Stojanovic J, Sassano M, Acampora A, Boccia S. A review of the legislation of direct-to-consumer genetic testing in EU member states. Eur J Med Genet. 2020;63(4):103841.

    Article  PubMed  Google Scholar 

  24. Human Genetic Examination Act (Genetic Diagnosis Act - GenDG) from 24.04.2009. Bundesrat Printed Matter 374/09

  25. Statement of the German Society of Human Genetics (GfH) on “Direct-to-Consumer” (DTC) Genetic Testing; 2011.

  26. Greshake B, Bayer PE, Rausch H, Reda J. openSNP: a crowdsourced web resource for personal genomics. PLoS ONE. 2014;9(3):e89204.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. OpenSNP cohort maker. Accessed May 20, 2022.

  28. Sanderson SC, Linderman MD, Zinberg R, et al. How do students react to analyzing their own genomes in a whole-genome sequencing course? Outcomes of a longitudinal cohort study. Genet Med. 2015;17(11):866–74.

    Article  PubMed  Google Scholar 

  29. Leiner D. SoSci Survey (Version 3.1. 06). Published online 2019.

  30. Kraiger K, Ford JK, Salas E. Application of cognitive, skill-based, and affective theories of learning outcomes to new methods of training evaluation. J Appl Psychol. 1993;78(2):311–28.

    Article  Google Scholar 

  31. Chang CC, Chow CC, Tellier LC, Vattikuti S, Purcell SM, Lee JJ. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience. 2015;4(1):7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Krynetskiy E, Calligaro IL. Introducing pharmacy students to pharmacogenomic analysis. Am J Pharm Educ. 2009.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Korf BR. Pushing the envelope in genomics education. Genet Med. 2015;17(11):857–8.

    Article  CAS  PubMed  Google Scholar 

Download references


We thank the students of the AYPG and Medical Genomics courses at HPI and TUM for the participation in our surveys and their support of our work. We thank Dr. Per Hoffmann and Stefan Herms (Life & Brain Research Center) for their support with genotyping HPI students as a voluntary part of the course. Further, we thank our honest broker Dr. Jasmin Cirillo (HPI) for all her work, and all additional external lecturers of the HPI course (in alphabetical order): Dr. Aniwaa Owusu Obeng (Icahn School of Medicine at Mount Sinai), Prof. Dr. Johannes Zschocke (Medical University Innsbruck), Dr. Noura S. Abul-Husn (Icahn School of Medicine at Mount Sinai), Prof. Dr. Thomas Meitinger (TUM). We also thank Dr. Jonathan Edelman (HPI) for his help in developing the initial course questionnaires.


Open Access funding enabled and organized by Projekt DEAL and the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – Projektnummer 491466077. The research leading to these results has received funding from the Horizon 2020 Programme of the European Commission under Grant Agreement No. 826117.

Author information

Authors and Affiliations



Conceptualization: TS, SI, CS; Data Curation and Formal Analysis: TS; Methodology: TS, SI, CS, HOH; Resources: BS, TFMA; Visualization: TS, SI; Writing—original draft: SI, TS, Writing—review and editing: CS, HOH, TFMA, BS, EB. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Claudia Schurmann.

Ethics declarations

Ethics approval and consent to participate

The described genomics course with PGT opportunity was approved by the ethics board of the University of Potsdam with the application number 17/2021. All methods were performed in accordance with the relevant guidelines and regulations. Written informed consent was obtained from all study participants.

Consent for publication

Not applicable

Competing interests

Drs. Andlauer and Schurmann are salaried employees of Boehringer Ingelheim Pharma and Bayer AG, respectively. All other authors declare that they have no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1

. AYPG course information sheet with consent form and appendix containing list of traits that can be assessed from partial data.

Additional file 2.

List of variants that were returned to students during the AYPG course.

Additional file 3.

Single PDF questionnaires.

Additional file 4.

All conducted questionnaires with survey periods and number of participants.

Additional file 5.

Comparison of questionnaire results between different questionnaires in one course year (p-values < 0.1).

Additional file 6.

Schedule of the Genomic Medicine course at TUM (winter term 2020/21).

Additional file 7.

Aggregated questionnaire response data.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Slosarek, T., Ibing, S., Schormair, B. et al. Implementation and evaluation of personal genetic testing as part of genomics analysis courses in German universities. BMC Med Genomics 16, 73 (2023).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: