Factors that influence a patient’s decision to engage in genetic research

IntroductionThe most challenging step in clinical research studies is patient recruitment. Many research studies do not reach their targets because of participant rejection. The purpose of this study was to assess patient as well as the community knowledge, motivation, and barriers to participate in genetic research.MethodsA cross-section study was conducted between September 2018 and February 2020 using face-to-face interviews with candidate patients from outpatient clinics at King Fahad Medical City (KFMC), Riyadh, Saudi Arabia. Additionally, an online survey was conducted to assess the community’s knowledge, motivation and barriers to participate in genetic research studies.ResultsIn total, 470 patients were interviewed for this study, with 341 being successfully recruited for the face to face interview, and the other patients being refused owing to time constraints. The majority percentage of the respondents were females. The respondents’ mean age was 30, and 52.6% reported having a college degree. The survey results from 388 participants illustrated that around 90% of the participants, participated voluntarily due to a good understanding of genetics studies. The majority held positive attitudes toward being part of genetic research, which exceeded the reported motivation score of >75%. The survey indicated that >90% of individuals were willing to participate to acquire therapeutic benefits or to receive continued aftercare. However, 54.6% of survey participants were worried about the side effects and the risks involved in genetic testing. A higher proportion (71.4%) of respondents reported that lack of knowledge about genetic research was one of the barriers to rejecting participation.ConclusionRespondents reported relatively high motivation and knowledge for participation in genetic research. However, study participants reported “do not know enough about genetic research” and “lack of time during clinic visit” as a barrier for participation in genetic research

Starvar: symptom-based tool for automatic ranking of variants using evidence from literature and genomes

Abstract Background Identifying variants associated with diseases is a challenging task in medical genetics research. Current studies that prioritize variants within individual genomes generally rely on known variants, evidence from literature and genomes, and patient symptoms and clinical signs. The functionalities of the existing tools, which rank variants based on given patient symptoms and clinical signs, are restricted to the coverage of ontologies such as the Human Phenotype Ontology (HPO). However, most clinicians do not limit themselves to HPO while describing patient symptoms/signs and their associated variants/genes. There is thus a need for an automated tool that can prioritize variants based on freely expressed patient symptoms and clinical signs. Results STARVar is a Symptom-based Tool for Automatic Ranking of Variants using evidence from literature and genomes. STARVar uses patient symptoms and clinical signs, either linked to HPO or expressed in free text format. It returns a ranked list of variants based on a combined score from two classifiers utilizing evidence from genomics and literature. STARVar improves over related tools on a set of synthetic patients. In addition, we demonstrated its distinct contribution to the domain on another synthetic dataset covering publicly available clinical genotype–phenotype associations by using symptoms and clinical signs expressed in free text format. Conclusions STARVar stands as a unique and efficient tool that has the advantage of ranking variants with flexibly expressed patient symptoms in free-form text. Therefore, STARVar can be easily integrated into bioinformatics workflows designed to analyze disease-associated genomes. Availability STARVar is freely available from https://github.com/bio-ontology-research-group/STARVar