Time for a change: considering the rights of study participants to ownership of their personal research-grade genomic data

Health Avatar: An Informatics Platform for Personal and Private Big Data

This article provides a comprehensive analysis of the various dimensions in South African law applicable to personal genomic sequence data. This analysis includes property rights, personality rights, and intellectual property rights. Importantly, the under-investigated question of whether personal genomic sequence data are capable of being owned is investigated and answered affirmatively. In addition to being susceptible of ownership, personal genomic sequence data are also the object of data subjects’ personality rights, and can also be the object of intellectual property rights: whether on their own qua trade secret or as part of a patented invention or copyrighted dataset. It is shown that personality rights constrain ownership rights, while the exploitation of intellectual property rights is constrained by both personality rights and ownership rights. All of these rights applicable to personal genomic sequence data should be acknowledged and harmonized for such data to be used effectively.

Causative mutations for human genetic disorders have mainly been identified in exonic regions that code for amino acid sequences. Recently, however, it has been reported that mutations in deep intronic regions can also cause certain human genetic disorders by creating novel splice sites, leading to pseudo-exon activation. To investigate how frequently pseudo-exon activation events occur in normal individuals, we conducted in silico identification of such events using personal genome data and corresponding high-quality transcriptome data. With rather stringent conditions, on average, 2.6 pseudo-exon activation events per individual were identified. More pseudo-exon activation events were found in 5′ donor splice sites than in 3′ acceptor splice sites. Although pseudo-exon activation events have sporadically been reported as causative mutations in genetic disorders, it is revealed in this study that such events can be observed in normal individuals at a certain frequency. We estimate that human genomes typically contain on average at least 10 pseudo-exon activation events. The actual number should be higher than this, because we used stringent criteria to identify pseudo-exon activation events. This suggests that it is worth considering the possibility of pseudo-exon activation when searching for causative mutations of genetic disorders if candidate mutations are not identified in coding regions or RNA splice sites.

The increase in the availability of personal genomic data to lay consumers using online services poses a challenge to HCI researchers: such data are complex and sensitive, involve multiple dimensions of uncertainty, and can have substantial implications for individuals' well-being. Personal genomic data are also unique because unlike other personal data, which constantly change, genomic data are largely stable during a person's lifetime; it is their interpretation and implications that change over time as new medical research exposes relationships between genes and health. In this paper, we present a novel tool for self exploration of personal genomic data. To evaluate the usability and utility of the tool, we conducted the first study of a genome interpretation tool to date, in which users used their own personal genomic data. We conclude by offering design implications for the development of interactive personal genomic reports.

BackgroundIn recent years, people who sought direct-to-consumer genetic testing services have been increasingly confronted with an unprecedented amount of personal genomic information, which influences their decisions, emotional state, and well-being. However, these users of direct-to-consumer genetic services, who vary in their education and interests, frequently have little relevant experience or tools for understanding, reasoning about, and interacting with their personal genomic data. Online interactive techniques can play a central role in making personal genomic data useful for these users.ObjectiveWe sought to (1) identify the needs of diverse users as they make sense of their personal genomic data, (2) consequently develop effective interactive visualizations of genomic trait data to address these users’ needs, and (3) evaluate the effectiveness of the developed visualizations in facilitating comprehension.MethodsThe first two user studies, conducted with 63 volunteers in the Personal Genome Project and with 36 personal genomic users who participated in a design workshop, respectively, employed surveys and interviews to identify the needs and expectations of diverse users. Building on the two initial studies, the third study was conducted with 730 Amazon Mechanical Turk users and employed a controlled experimental design to examine the effectiveness of different design interventions on user comprehension.ResultsThe first two studies identified searching, comparing, sharing, and organizing data as fundamental to users’ understanding of personal genomic data. The third study demonstrated that interactive and visual design interventions could improve the understandability of personal genomic reports for consumers. In particular, results showed that a new interactive bubble chart visualization designed for the study resulted in the highest comprehension scores, as well as the highest perceived comprehension scores. These scores were significantly higher than scores received using the industry standard tabular reports currently used for communicating personal genomic information.ConclusionsDrawing on multiple research methods and populations, the findings of the studies reported in this paper offer deep understanding of users’ needs and practices, and demonstrate that interactive online design interventions can improve the understandability of personal genomic reports for consumers. We discuss implications for designers and researchers.

In order to study the relationship between genes and diseases, the increasing availability and sharing of phenotypic and genotypic data have been promoted as an imperative within the scientific community. In parallel with data sharing practices by clinicians and researchers, recent initiatives have been observed in which individuals are sharing personal genomic data. The involvement of individuals in such initiatives is facilitated by the increased accessibility of personal genomic data, offered by private test providers along with availability of online networks. Personal webpages and on-line data sharing platforms such as Consent to Research (Portable Legal Consent), Free the Data, and Genomes Unzipped are being utilized to host and share genotypes, electronic health records and family history uploaded by individuals. Although personal genomic data sharing initiatives vary in nature, the emphasis on the individuals’ control on their data in order to benefit research and ultimately health care has seen as a key theme across these initiatives. In line with the growing practice of personal genomic data sharing, this paper aims to shed light on the potential challenges surrounding these initiatives. As in the course of these initiatives individuals are solicited to individually balance the risks and benefits of sharing their genomic data, their awareness of the implications of personal genomic data sharing for themselves and their family members is a necessity. Furthermore, given the sensitivity of genomic data and the controversies around their complete de-identifiability, potential privacy risks and harms originating from unintended uses of data have to be taken into consideration.Electronic supplementary materialThe online version of this article (doi:10.1186/s40504-014-0022-7) contains supplementary material, which is available to authorized users.

People choose how and if to generate and disclose not just personal genomic data, but also multiple other types of personal health and non-health related data. To contextualize choices about genetic testing and genetic data disclosure, we explored perspectives of genomic data privacy and disclosure compared to other types of data. We conducted a qualitative focus group study with adult members of an integrated U.S. health system, using administrative data to stratify our sample by age and by race/ethnicity. Discussion topics included qualities, rights, benefits and harms of disclosure of genomic, health, family history and non-health related data. We conducted thematic template analysis using verbatim transcripts. The sample (n = 24) was 67% female, mean age 54.1 years (range 23-88), and 37% people of color; 71% reported college degree. Participants considered genetic data, but not other data types, as a permanent, core part of the individual self and as protected health information under current laws. Participants did not feel that individuals had a right to family medical history disclosure from their relatives. Participants assumed high levels of privacy protections of genetic and other health-related data, but no perceived privacy or protection around other personal data. Participants weighed benefits and risks of generation and disclosure of all data types; harms were more far-reaching for non-health data, possibly related to the perceived lack of protections around these data. People make privacy-related choices about genetic testing in the context of related considerations for multiple types of data and rely on perceived privacy protections under current U.S. health privacy laws. Genetic research and screening programs should consider providing clear guidance on privacy protections afforded to genetic information in U.S. clinical settings. Future research should examine connections between privacy-related views on genetic and multiple other types of personal data.

Data-sharing among genomic researchers is promoted for its potential to accelerate our understanding of the molecular basis of cancer. However, with genomic data sharing the risks of re-identifying study participants, revealing personal genomic information and data misuse might increase. This study aims at exploring perceptions of patients and physicians in Oncology regarding their assessment of the informational risks resulting from participating in whole genomic research studies in order to improve the informed consent process. For this purpose, we conducted a qualitative focus group study at the National Center for Tumor Diseases (NCT). Patients and oncologists assessed the informational risks either as minimal or as greater than minimal, depending on the context factors of occupational status, age, and patients’ prognosis. Interestingly, even patients who assumed a greater risk did not refrain from participating in genomic research, provided that certain informational and institutional safeguards are implemented. Moreover, they expected comprehensive disclosure of the risks resulting from genomic data sharing. These results suggest (1) comprehensive disclosure of the risks of genomic research to potential study participants in genomic research to facilitate risk assessment and sound decision making, (2) establishing independent governance entities in order to minimize the informational risks of genomic research, and (3) implementing data sharing policies which offer guidance for physicians and researchers involved in genomic research.

Genomic research is increasingly intersecting with questions of personal and group identity. This narrative review synthesizes open-access, peer-reviewed literature to examine how genomic information influences individual identity (health self-concept, ancestry, disability) and group identity (racial, ethnic, national, social classifications). Key findings indicate that personal genomic data (e.g. direct-to-consumer ancestry tests) can reshape individuals’ self-perception of heritage and health, though reactions vary widely. Group identities, especially concepts of race and ethnicity, are both challenged and reinforced by genomic research. While genetic evidence undermines simplistic racial categories, the use of such categories in research can inadvertently reify them. Disability studies document tensions between genomic interventions and identity in the disabled community, suggesting concerns that reducing genetic conditions may devalue those identities. Major debates emerge around genomic essentialism versus social constructs of identity, data privacy and the ethics of defining group membership via DNA. Across domains, identity outcomes hinge not on DNA per se but on how genomic data are interpreted within socio‑historical frames. Thus, further interdisciplinary research is needed to address these dynamics and inform policies that respect both the scientific and social dimensions of identity.

BackgroundThe rapid development of genetic and genomic technologies, such as next-generation sequencing and genome editing, has made disease treatment much more precise and effective. The technologies’ value can only be realized by the aggregation and analysis of people’s genomic and health data. However, the collection and sharing of genomic data has many obstacles, including low data quality, information islands, tampering distortions, missing records, leaking of private data, and gray data transactions.ObjectiveThis study aimed to prove that emerging blockchain technology provides a solution for the protection and management of sensitive personal genomic data because of its decentralization, traceability, encryption algorithms, and antitampering features.MethodsThis paper describes the case of a blockchain-based genomic big data platform, LifeCODE.ai, to illustrate the means by which blockchain enables the storage and management of genomic data from the perspectives of data ownership, data sharing, and data security.ResultsBlockchain opens up new avenues for dealing with data ownership, data sharing, and data security issues in genomic big data platforms and realizes the psychological empowerment of individuals in the platform.ConclusionsThe blockchain platform provides new possibilities for the management and security of genetic data and can help realize the psychological empowerment of individuals in the process, and consequently, the effects of data self-governance, incentive-sharing, and security improvement can be achieved. However, there are still some problems in the blockchain that have not been solved, and which require continuous in-depth research and innovation in the future.

Personal genomic data constitute one important part of personal health data. However, due to the large amount of personal genomic data obtained by the next-generation sequencing technology, special tools are needed to analyze these data. In this article, we will explore a tool analyzing cloud-based large-scale genome sequencing data. Analyzing and identifying genomic variations from amplicon-based next-generation sequencing data are necessary for the clinical diagnosis and treatment of cancer patients. When processing the amplicon-based next-generation sequencing data, one essential step is removing primer sequences from the reads to avoid detecting false-positive mutations introduced by nonspecific primer binding and primer extension reactions. At present, the removing primer tools usually discard primer sequences from the FASTQ file instead of BAM file, but this method could cause some downstream analysis problems. Only one tool (BAMClipper) removes primer sequences from BAM files, but it only modified the CIGAR value of the BAM file, and false-positive mutations falling in the primer region could still be detected based on its processed BAM file. So, we developed one cutting primer tool (rmvPFBAM) removing primer sequences from the BAM file, and the mutations detected based on the processed BAM file by rmvPFBAM are highly credible. Besides that, rmvPFBAM runs faster than other tools, such as cutPrimers and BAMClipper.

In recent years, personal genomic data can be quickly generated in an affordable price. Abundant research results on genetic diseases have also been published in the past two decade. Therefore, it is desired to utilize updated genetic disease research results into personal genomic data analysis and apply them into genomics-based personalized healthcare. However, this is a challenging task for current healthcare professionals because the desired clinically relevant information is hidden in highly complex genomics data sets and in various types of databases, which were typically created for genomics researchers in the past. In this project, an integrated patient genomic information analysis and management system is created for healthcare professionals, especially physicians, so that they can conveniently access the desired patient genetic information and current research results related to the genetic makeup, and utilize the information in personalized healthcare practice. The accuracy of the data integrated in the system and analysis results from the system were evaluated and a usability study was conducted to determine the usability of the system by physicians. These evaluations indicated that the results obtained in this system were the same as the ones obtained from a manual but more tedious approach, and physicians could easily finish all the designed tasks and obtain desired data using the system.

Pharmacogenetic screening for drug therapy: From single gene markers to decision making in the next generation sequencing era

In this paper, we describe the potential correlation between translation-writing error types and students' language gene single nucleic polymorphism (SNP) sites. One SNP site, SNP4, or Rs1852469, seems more closely associated with four types of translation-writing error types. The SNP information was decoded by DNA sequencing of the language gene FOXP2 extracted from participants' vein blood samples. Considering the sampling size is very small (21 students), the conclusion of this study is very preliminary, but the combination of classroom performance and personal gene information may lead to novel education technology derived from personal genome data in the future.

The high availability of genome sequencing data and advancement in data mining stimulate the progress of biomedical breakthroughs and the hope of personalized medicine. Meanwhile, the popularity of personalized and commercialized genome testing brings about increasing privacy concerns. Moreover, personal genomic data is a kind of Big Data, sensitive and highly correlated among relative by blood. Therefore, it is challenging to quantitatively evaluate the risks of kin genomic data privacy. To address this problem, we construct a genome data model to capture the characteristics of kin genomic data. Besides, we develop an inference attack method by operating belief propagation on a factor graph.