Obtaining informed consent (IC) for genetic/genomic research is complicated because most of the ethical issues associated with genetic research are considerations to be disclosed by researchers and weighed by prospective participants during the consent process. These include complicated questions associated with return of results, management of incidental findings (results beyond the aims of the study that are of potential significance to individual participants), and responsibility for reinterpretation of findings. Moreover, some research designs and contexts—such as recruitment based on genotype, or research with direct-to-consumer testing company data or among indigenous peoples—present distinct IC challenges. Sharing samples and data to address a wide range of research questions, combining population-based cohorts to create ancestrally diverse study populations, and NIH data sharing requirements have prompted suggestions that traditional, study-specific IC be replaced by broad consent and dynamic consent. This shift, as well as practicalities of conducting research during the COVID19 pandemic, accelerated the use—and calls for evaluation—of interactive digital interfaces for IC. In addition, to protect participants’ interests, some have urged shifting from reliance on IC alone toward focus on data and sample stewardship practices that involve robust governance of research involving biobanks and biological samples. 

Traditional elements of disclosure for informed consent are particularly complicated in the context of genetic research. Communicating the right to withdraw research participation requires nuanced explanation of what is meant by ‘participation’ and the multiple ways in which a participants’ data and biological samples may be used. Research participation is voluntary, and participants may withdraw from the study. However, they cannot withdraw their information from findings generated by analyses already completed, and may not be able to prevent future, secondary use of their deidentified data and samples. 

Risks to be disclosed during IC may include the possibility of psychosocial harm to the participant, to family members who may share inherited genetic variations, and to community or population members to whom research findings may be applied (accurately or not), resulting in group harm. Currently, explanation of the risks of discrimination may refer to the protections afforded by the Genetic Information Nondiscrimination Act (GINA)—namely, that GINA affords protection in employment and health insurance, but not for life, disability, and long-term care insurance (or, in education and myriad other contexts). The possibility of this legislation being repealed, or of scientific advances thoroughly undermining the concept of pooling health risks, on which insurance is based, may increase the risks associated with learning one’s genetic risk of disease or other traits. 

IC must also include an explanation of how participants’ information will be kept secure and their privacy protected, while acknowledging that a person’s genotype is uniquely identifying. Relatively new statistical and cryptographic approaches to privacy protection and data security methods may be challenging to explain. In the United States, there is little access to compensation for those harmed by research participation or privacy breach. IC disclosure should reflect this fact or explain study-specific avenues for seeking compensation. If biological samples or data are to be deidentified, IC must include an explanation of whether reidentification can occur, under what conditions, and for whose potential benefit (the participant or the research enterprise and other people in the future). 

Disclosure of potential benefits of genetic research is less straightforward than in other research domains. In studies where no individualized results are offered, there is no prospect of direct benefit to research participants. Even when individual results or incidental findings will be offered, reaping benefit from this genetic information often depends on the ability of recipients (or their family members) to act on the information. The ability to act, in turn, depends on insurance and economic status, geographic location, circumstances and life events competing for attention, and other factors affecting health behavior change. Because people’s values and circumstances differ, learning genetic information could constitute a risk for some participants, but be a potential benefit of participation for others. 

In the translational genomic research context, IC must seek to dispel participants’ therapeutic misconception, the tendency to attribute therapeutic intent to research procedures. This is made more challenging because, since at least the inception of the Human Genome Project, genetic research has been accompanied by hype suggesting that just around the corner lie cures for both rare and common diseases. A more recent example may be President Obama’s 2015 articulation of the promise of precision medicine as providing “the right treatments [or preventive measures], at the right time, every time to the right person,” coupled with his statement that “for a small but growing number of patients, that future is already here.”  

Genetic research is also conducted in the shadow of 20th century eugenics, within societies with increasingly visible and widening wealth and education gaps, and alongside persistent racism, which warrants mistrust of both science and genetic explanations of disease and difference. These and other factors have affected enrollment in genomic studies resulting in the vast majority of participants (78%) being of European ancestry. As a result, some genetic research findings (e.g., polygenic risk scores) are valid and useful only for European-ancestry populations. Thus, research participation with potential benefit for members of one population may currently offer none, or far less, for others. How to acknowledge this during IC while seeking to enroll diverse study populations is challenging. In addition, new study designs, employment of computationally efficient methods to correct for population structure and increase generalizability of findings, and the coupling of genetic studies with data mining and artificial intelligence/machine learning, may alter the risk-benefit profile of research participation and make explaining both research processes and findings more challenging. 

Empirical research on IC for genetic research can suggest ways to improve disclosure and increase understanding. As the articles below demonstrate, by assessing participants’ and other stakeholders’ views regarding the components of IC and the interests IC seeks to protect, empirical research can inform—though not substitute for—normative analysis of IC and the risks and potential benefits of genetic research.