SwissGenVar: A Platform for Clinical-Grade Interpretation of Genetic Variants to Foster Personalized Healthcare in Switzerland

Large-scale next-generation sequencing (NGS) germline testing is technically feasible today, but variant interpretation represents a major bottleneck in analysis workflows. This includes extensive variant prioritization, annotation, and time-consuming evidence curation. The scale of the interpretation problem is massive, and variants of uncertain significance (VUSs) are a challenge to personalized medicine. This challenge is further compounded by the complexity and heterogeneity of the standards used to describe genetic variants and the associated phenotypes when searching for relevant information to support clinical decision making. To address this, all five Swiss academic institutions for Medical Genetics joined forces with the Swiss Institute of Bioinformatics (SIB) to create SwissGenVar as a user-friendly nationwide repository and sharing platform for genetic variant data generated during routine diagnostic procedures and research sequencing projects. Its aim is to provide a protected environment for expert evidence sharing about individual variants to harmonize and upscale their significance interpretation at the clinical grade according to international standards. To corroborate the clinical assessment, the variant-related data will be combined with consented high-quality clinical information. Broader visibility will be achieved by interfacing with international databases, thus supporting global initiatives in personalized healthcare

SwissGenVar: A platform for clinical grade interpretation of genetic variants to foster personalized health care in Switzerland

Large-scale next-generation sequencing (NGS) germline testing is technically feasible today, but variant interpretation represents a major bottleneck in analysis workflows including the extensive variant prioritization, annotation, and time-consuming evidence curation. The scale of the interpretation problem is massive, and variants of uncertain significance (VUS) are a challenge to personalized medicine. This challenge is further compounded by the complexity and heterogeneity of standards used to describe genetic variants and associated phenotypes when searching for relevant information to inform clinical decision-making. For this purpose, all five Swiss academic Medical Genetics Institutions joined forces with the Swiss Institute of Bioinformatics (SIB) to create SwissGenVar as a user-friendly nationwide repository and sharing platform for genetic variant data generated during routine diagnostic procedures and research sequencing projects. Its objective is to provide a protected environment for expert evidence sharing about individual variants to harmonize and up-scale their significance interpretation at clinical grade following international standards. To corroborate the clinical assessment, the variant-related data are combined with consented high-quality clinical information. Broader visibility will be gained by interfacing with international databases, thus supporting global initiatives in personalized health care

Mapping genetic variations to three- dimensional protein structures to enhance variant interpretation: a proposed framework

The translation of personal genomics to precision medicine depends on the accurate interpretation of the multitude of genetic variants observed for each individual. However, even when genetic variants are predicted to modify a protein, their functional implications may be unclear. Many diseases are caused by genetic variants affecting important protein features, such as enzyme active sites or interaction interfaces. The scientific community has catalogued millions of genetic variants in genomic databases and thousands of protein structures in the Protein Data Bank. Mapping mutations onto three-dimensional (3D) structures enables atomic-level analyses of protein positions that may be important for the stability or formation of interactions; these may explain the effect of mutations and in some cases even open a path for targeted drug development. To accelerate progress in the integration of these data types, we held a two-day Gene Variation to 3D (GVto3D) workshop to report on the latest advances and to discuss unmet needs. The overarching goal of the workshop was to address the question: what can be done together as a community to advance the integration of genetic variants and 3D protein structures that could not be done by a single investigator or laboratory? Here we describe the workshop outcomes, review the state of the field, and propose the development of a framework with which to promote progress in this arena. The framework will include a set of standard formats, common ontologies, a common application programming interface to enable interoperation of the resources, and a Tool Registry to make it easy to find and apply the tools to specific analysis problems. Interoperability will enable integration of diverse data sources and tools and collaborative development of variant effect prediction methods

General resources in Genetics and/or Oncology

Review on General resources in Genetics and/or Oncolog

Precision health approaches: ethical considerations for health data processing

This thesis provides insights and recommendations on some of the most crucial elements necessary for an effective, legally and ethically sound implementation of precision health approaches in the Swiss context (and beyond), specifically for precision medicine and precision public health. In this regard, this thesis recognizes the centrality of data in these two abovementioned domains, and the ethical and scientific imperative of ensuring the widespread and responsible sharing of high quality health data between the numerous stakeholders involved in healthcare, public health and associated research domains. It also recognizes the need to protect not only the interests of data subjects but also those of data processors. Indeed, it is only through a comprehensive assessment of the needs and expectations of each and every one regarding data sharing activities that sustainable solutions to known ethical and scientific conundrums can be devised and implemented. In addition, the included chapters in this thesis emphasize recommending solutions that could be convincingly applied to real world problems, with the ultimate objective of having a concrete impact on clinical and public health practice and policies, including research activities. Indeed, the strengths of this thesis reside in a careful and in-depth interdisciplinary assessment of the different issues at stake in precision health approaches, with the elaboration of the least disruptive solutions (as far as possible) and recommendations for an easy evaluation and subsequent adoption by relevant stakeholders active in these two domains. This thesis has three main objectives, namely (i) to investigate and identify factors influencing the processing of health data in the Swiss context and suggest some potential solutions and recommendations. A better understanding of these factors is paramount for an effective implementation of precision health approaches given their strong dependence on high quality and easily accessible health datasets; (ii) to identify and explore the ethical, legal and social issues (ELSI) of innovative participatory disease surveillance systems – also falling under precision health approaches – and how research ethics are coping within this field. In addition, this thesis aims to strengthen the ethical approaches currently used to cater for these ELSIs by providing a robust ethical framework; and lastly, (iii) to investigate how precision health approaches might not be able to achieve their social justice and health equity goals, if the impact of structural racism on these initiatives is not given due consideration. After a careful assessment, this thesis provides recommendations and potential actions that could help these precision health approaches adhere to their social justice and health equity goals. This thesis has investigated these three main objectives using both empirical and theoretical research methods. The empirical branch consists of systematic and scoping reviews, both adhering to the PRISMA guidelines, and two interview-based studies carried out with Swiss expert stakeholders. The theoretical branch consists of three chapters, each addressing important aspects concerning precision health approaches

Pharmacogenomic testing in paediatrics: Clinical implementation strategies

Pharmacogenomics (PGx) relates to the study of genetic factors determining variability in drug response. Implementing PGx testing in paediatric patients can enhance drug safety, helping to improve drug efficacy or reduce the risk of toxicity. Despite its clinical relevance, the implementation of PGx testing in paediatric practice to date has been variable and limited. As with most paediatric pharmacological studies, there are well-recognised barriers to obtaining high-quality PGx evidence, particularly when patient numbers may be small, and off-label or unlicensed prescribing remains widespread. Furthermore, trials enrolling small numbers of children can rarely, in isolation, provide sufficient PGx evidence to change clinical practice, so extrapolation from larger PGx studies in adult patients, where scientifically sound, is essential. This review paper discusses the relevance of PGx to paediatrics and considers implementation strategies from a child health perspective. Examples are provided from Canada, the Netherlands and the UK, with consideration of the different healthcare systems and their distinct approaches to implementation, followed by future recommendations based on these cumulative experiences. Improving the evidence base demonstrating the clinical utility and cost-effectiveness of paediatric PGx testing will be critical to drive implementation forwards. International, interdisciplinary collaborations will enhance paediatric data collation, interpretation and evidence curation, while also supporting dedicated paediatric PGx educational initiatives. PGx consortia and paediatric clinical research networks will continue to play a central role in the streamlined development of effective PGx implementation strategies to help optimise paediatric pharmacotherapy

Exploring Strategies to Integrate Disparate Bioinformatics Datasets

Distinct bioinformatics datasets make it challenging for bioinformatics specialists to locate the required datasets and unify their format for result extraction. The purpose of this single case study was to explore strategies to integrate distinct bioinformatics datasets. The technology acceptance model was used as the conceptual framework to understand the perceived usefulness and ease of use of integrating bioinformatics datasets. The population of this study included bioinformatics specialists of a research institution in Lebanon that has strategies to integrate distinct bioinformatics datasets. The data collection process included interviews with 6 bioinformatics specialists and reviewing 27 organizational documents relating to integrating bioinformatics datasets. Thematic analysis was used to identify codes and themes related to integrating distinct bioinformatics datasets. Key themes resulting from data analysis included a focus on integrating bioinformatics datasets, adding metadata with the submitted bioinformatics datasets, centralized bioinformatics database, resources, and bioinformatics tools. I showed throughout analyzing the findings of this study that specialists who promote standardizing techniques, adding metadata, and centralization may increase efficiency in integrating distinct bioinformatics datasets. Bioinformaticians, bioinformatics providers, the health care field, and society might benefit from this research. Improvement in bioinformatics affects poistevely the health-care field which has a positive social change. The results of this study might also lead to positive social change in research institutions, such as reduced workload, less frustration, reduction in costs, and increased efficiency while integrating distinct bioinformatics datasets