Privacy and ethical challenges in next-generation sequencing

ABSTRACT

Introduction: Next-generation sequencing (NGS) is expected to revolutionize health care. NGS allows for sequencing of the whole genome more cheaply and quickly than previous techniques. NGS offers opportunities to advance medical diagnostics and treatments, but also raises complicated ethical questions that need to be addressed.

Areas considered: This article draws from the literature on research and clinical ethics, as well as next-generation sequencing, in order to provide an overview of the ethical challenges involved in next-generation sequencing. This article includes a discussion of the ethics of NGS in research and clinical contexts.

Expert opinion: The use of NGS in clinical and research contexts has features that pose challenges for traditional ethical frameworks for protecting research participants and patients. NGS generates massive amounts of data and results that vary in terms of known clinical relevance. It is important to determine appropriate processes for protecting, managing and communicating the data. The use of machine learning for sequencing and interpretation of genomic data also raises concerns in terms of the potential for bias and potential implications for fiduciary obligations. NGS poses particular challenges in three main ethical areas: privacy, informed consent, and return of results.

KEYWORDS:

• Next generation sequencing
• ethics
• privacy

Article highlights

• The use of NGS in research and clinical contexts offers tremendous opportunities to advance understandings of health and disease, as well as improve treatments. At the same time, certain features of the research and clinical landscape for NGS present challenges for ethical frameworks for research and clinical care. Efforts to increase research collaboration and data sharing, massive data sets and the use of Big Data approaches for genomic research present challenges for privacy, in particular.

• NGS data often moves back and forth between research and clinical contexts. There are differences in the ethical obligations and standards applicable in research and clinical contexts. A large portion of NGS data is generated in research laboratories, which have different standards than those applied to clinical laboratories. The boundary between research and clinical contexts for NGS has therefore blurred. This blurred boundary also has implications for informed consent and the return of results that need to be considered.

• The availability of personal information in online data sets, data sharing and advances in data analysis have made it easier to re-identify individuals from their genomic data. NGS is also potentially useful for non-medical applications, such as forensic uses, which presents additional privacy concerns regarding NGS-generated data. Institutions that utilize NGS will need to ensure that there are appropriate security and storage standards for the massive data sets generated by NGS. There will also need to be attentive to protecting people from potential misuse or harmful repercussions from their genomic data.

• Machine learning techniques are being applied to NGS. Machine learning applications raise concerns regarding the potential for bias. It will be necessary to educate relevant stakeholders regarding issues such as potential bias in the data and algorithms, the appropriate uses machine learning systems and their limitations. Machine learning tools also have implications for fiduciary obligations in health care that will need to be studied and addressed.

Declaration of interest

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Reviewer disclosures

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Additional information

Funding

This paper was not funded.