Anti-Selection is Only the Beginning.

This commentary proposes the need for greater normative debate about when, if ever, it is appropriate for insurers to access genetic information of insureds to combat anti-selection.

Functional genomics data: privacy risk assessment and technological mitigation.

The generation of functional genomics data by next-generation sequencing has increased greatly in the past decade. Broad sharing of these data is essential for research advancement but poses notable privacy challenges, some of which are analogous to those that occur when sharing genetic variant data. However, there are also unique privacy challenges that arise from cryptic information leakage during the processing and summarization of functional genomics data from raw reads to derived quantities, such as gene expression values. Here, we review these challenges and present potential solutions for mitigating privacy risks while allowing broad data dissemination and analysis.

Familial DNA analysis and criminal investigation: Usage, downsides and privacy concerns.

Since the discovery of Deoxyribonucleic acid (DNA) capability in forensic investigation, it has been an important part of the criminal justice system. In most criminal cases DNA profile originating from evidence sample collected from the crime scene is compared with the DNA profile from the reference sample. However, when a reference sample is not available for comparison, familial DNA analysis can provide important investigation leads in a criminal investigation process by identifying an individual. Moreover, this analysis is also proving effective in the identification of ethnicity and ancestry of an individual. A number of different methodologies and software are being used for familial DNA analysis. This review describes the importance of familial DNA analysis, methodologies used for familial DNA searching and identification, and its advantages in forensic. Moreover, ethical, legal and social issues associated with familial DNA analysis have also been discussed along with future directions for the proper implementation of this technology.

Can Genetic Nondiscrimination Laws Save Lives?

Numerous state laws and the federal Genetic Information Nondiscrimination Act (GINA) have been enacted to prevent or redress genetic discrimination in employment and health insurance, but laws protecting against genetic discrimination in life insurance have been less common and weak. Consequently, some individuals with a genetic risk of a serious illness have declined presymptomatic genetic testing, thereby decreasing their prevention and treatment options and increasing their mortality risk. In 2020, Florida became the first state to prohibit life insurance companies from using the results of presymptomatic genetic tests in underwriting. Although the law was "only" intended to prevent genetic discrimination, a possible or even likely consequence of the law will be to encourage timely genetic testing by at-rick individuals and thereby save lives.

Demographic and prosocial intrapersonal characteristics of biobank participants and refusers: the findings of a survey in the Netherlands.

Research in genetics relies heavily on voluntary contributions of personal data. We aimed to acquire insights into the differences between participants and refusers of participation in a Dutch population-based biobank. Accordingly, we assessed the demographic and prosocial intrapersonal characteristics of respondents who participated (n = 2615) or refused to participate (n = 404) in the Lifelines biobank and databank. Our results indicated that health-related values critically influence participation decisions. The participation threshold for Lifelines was determined by an absence of health-related values and of trust in government. Therefore, considering these factors in communication and recruitment strategies could enhance participation in biomedical research. No indications were found of a stronger general prosociality of participants or their trust in researchers beyond the context of biobanking. This emphasizes the contextual understanding of the decision of participation in biobanking. Our findings may contribute to improving recruitment strategies by incorporating relevant values and/or highlighting prosocial benefits. Moreover, they foreground the need to address trust issues in collaborations between data repositories and commercial companies. Future research should explore how prosocial intrapersonal characteristics drive participation and withdrawal decisions and relate to contextual attributes.

Review of policies of companies and databases regarding access to customers' genealogy data for law enforcement purposes.

The rapidly evolving popularity of direct-to-consumer genetic genealogy companies has made it possible to retrieve genomic information for unintended reasons by third parties, including the emerging use for law enforcement purposes. The question remains whether users of direct-to-consumer genetic genealogy companies and genealogical databases are aware that their genetic and/or genealogical data could be used as means to solving forensic cases. Our review of 22 companies' and databases' policies showed that only four companies have provided additional information on how law enforcement agencies should request permission to use their services for law enforcement purposes. Moreover, two databases have adopted a different approach by providing a special service for law enforcement. Although all companies and databases included in the study provide at least some provisions about police access, there is an ongoing debate over the ethics of these practices, and how to balance users' privacy with law enforcement requests.

Protecting Genomic Data Privacy with Probabilistic Modeling.

The proliferation of sequencing technologies in biomedical research has raised many new privacy concerns. These include concerns over the publication of aggregate data at a genomic scale (e.g. minor allele frequencies, regression coefficients). Methods such as differential privacy can overcome these concerns by providing strong privacy guarantees, but come at the cost of greatly perturbing the results of the analysis of interest. Here we investigate an alternative approach for achieving privacy-preserving aggregate genomic data sharing without the high cost to accuracy of differentially private methods. In particular, we demonstrate how other ideas from the statistical disclosure control literature (in particular, the idea of disclosure risk) can be applied to aggregate data to help ensure privacy. This is achieved by combining minimal amounts of perturbation with Bayesian statistics and Markov Chain Monte Carlo techniques. We test our technique on a GWAS dataset to demonstrate its utility in practice.

Precision Medicine in Internal Medicine.

Medicine has long sought to match diagnostic and treatment approaches to the particular needs and risks of individual patients. The decreasing cost and increasing ease of genetic sequencing have propelled the rise of precision medicine. Precision medicine aims to use genetic and other information to provide care tailored to the individual patient, with the goal of improving clinical outcomes and minimizing unnecessary diagnostic and therapeutic interventions. Although developments in genetic sequencing have the potential to transform clinical care, there are important limitations, including uncertainty in the clinical interpretation of many genetic variants and concerns about privacy, discrimination, and cost. To help clinicians understand the basics of genetic sequencing and how to apply it in clinical practice, Annals of Internal Medicine is launching a new "Precision Medicine" series. This introduction provides a general overview of clinical sequencing, with a focus on germline variation. Subsequent articles will use a case-based format to provide concise summaries of specific clinical precision medicine scenarios that are relevant to the practice of internal medicine. These cases will highlight specific clinical indications; interpretation of genetic test results; and ethical, legal, cost, and privacy issues related to genetic testing. The goal is to provide practical information on the appropriate application and interpretation of genomics in routine clinical practice.

Considerations for Genomic Data Privacy and Security when Working in the Cloud.

Next-generation sequencing produces large amounts of data. The complexity and data management issues associated with next-generation sequencing have led many laboratories to turn to cloud services, especially when internal information technology infrastructure is inadequate to support data requirements. In addition, public cloud repositories of variants are being increasingly utilized, and their data sets are being populated through crowdsourcing submissions of human genetic variation identified within laboratories. The purpose of this review is to describe the challenges of managing genomic data in the cloud and to discuss potential strategies to surmount these challenges in a compliant manner. The definitions and advantages of cloud systems are outlined. Special regulatory considerations for laboratories are included, and strategies for compliance in the US regulatory environment for genetic information in clinical patient care as well as in research and public databases are also discussed.

Genomic Contextualism: Shifting the Rhetoric of Genetic Exceptionalism.

As genomic science has evolved, so have policy and practice debates about how to describe and evaluate the ways in which genomic information is treated for individuals, institutions, and society. The term genetic exceptionalism, describing the concept that genetic information is special or unique, and specifically different from other kinds of medical information, has been utilized widely, but often counterproductively in these debates. We offer genomic contextualism as a new term to frame the characteristics of genomic science in the debates. Using stasis theory to draw out the important connection between definitional issues and resulting policies, we argue that the framework of genomic contextualism is better suited to evaluating genomics and its policy-relevant features to arrive at more productive discussion and resolve policy debates.