A BLOCKCHAIN INFRASTRUCTURE TO SUPPORT SMART CONTRACTS-BASED CONSENT MODELS FOR CLINICAL GENOMICS

Abstract

The advent of fast, effective next-generation sequencing (NGS) technologies has led to the identification of 4,187 genes responsible for about 50% of approximately 6,000–8,000 rare diseases, as their causes can now be accurately determined by variants in patients’ genomes. These variants are classified into one of five categories, ranging from pathogenic (disease- causing) to benign (with no impact on health). In the clinical genomics context, sharing rare genetic disease information between genetic databases and laboratories is essential to determine the pathogenic significance of variants to enable the diagnosis of rare genetic diseases. However, significant concerns regarding data governance and security have reduced this sharing in practice; hence, genetic data are often kept in centralised restricted-access repositories. This makes them difficult to locate or unavailable outside of the laboratories that own them. In this context, a key problem facing researchers today is how to promote responsible clinical genomic data sharing on a large scale. As emerging technologies, blockchain and smart contracts might have the potential to provide a secure method for sharing genomic data between the involved parties and thus help overcome the concerns that hinder the large-scale sharing of genomic data. This thesis aims to contribute to the growing knowledge of the potential role of blockchain technology in supporting the sharing of clinical genomic data. To this end, we propose an architecture design that leverages blockchain and smart contract. First, we provide an in-depth analysis of a deliberative focus group study with National Health Service (NHS) Genomic Medicine Service patients regarding public opinion on sharing genomic data to identify the design requirements and their implications for blockchain-based applications in healthcare. Second, we designed a decentralised architecture that supports clinical genomic data sharing on a large scale to meet each of the identified requirements. Third, we demonstrate a proof-of-concept implementation called ConsentChain by walking through a case study of clinical data sharing. Fourth, we applied the principles learned from designing and developing ConsentChain to identify software design patterns that could be used to guide the design and implementation processes of blockchain-based healthcare applications. Last, we applied the software design patterns to develop a proof-of-concept implementation for another case study of clinical data sharing called PGxChain.