ArticleAmerican journal of human genetics2019
Third-Party Genetic Interpretation Tools: A Mixed-Methods Study of Consumer Motivation and Behavior.
Article in American journal of human genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Sharing behavior and health care utilization following direct-to-consumer genetic testing: a systematic review.Open research Europe · 2025Pooled it
- Direct-to-Consumer Genetic Testing.NEJM evidence · 2025Review
- Changes in acceptability, consideration, intention, and uptake of direct-to-consumer genetic tests in the Netherlands from 2017 to 2022.Journal of genetic counseling · 2025Article
- Valuable Data "Gain" and "Loss": The Quantitative Impact of Information Choice on Consumers' Decision to Buy Selenium-Rich Agricultural Products.Foods (Basel, Switzerland) · 2024Article
- Article
- Searching across-cohort relatives in 54,092 GWAS samples via encrypted genotype regression.PLoS genetics · 2024Article
- An Environmental Scan of Consumer-Initiated Germline Genetic Testing for Health Risks.Mayo Clinic proceedings · 2023Review
- Ethical considerations when co-analyzing ancient DNA and data from private genetic databases.American journal of human genetics · 2023Review
- A game theoretic approach to balance privacy risks and familial benefits.Scientific reports · 2023Article
- A qualitative study exploring the consumer experience of receiving self-initiated polygenic risk scores from a third-party website.European journal of human genetics : EJHG · 2023Article
- Polygenic Scores in the Direct-to-Consumer Setting: Challenges and Opportunities for a New Era in Consumer Genetic Testing.Journal of personalized medicine · 2023Review
- New insights from the last decade of research in psychiatric genetics: discoveries, challenges and clinical implications.World psychiatry : official journal of the World Psychiatric Association (WPA) · 2023Article
- Genomic health data generation in the UK: a 360 view.European journal of human genetics : EJHG · 2022Article
- Returning raw genomic data: rights of research participants and obligations of health care professionals.The Medical journal of Australia · 2022Article
- PAGEANT: personal access to genome and analysis of natural traits.Nucleic acids research · 2022Article
- Family secrets: Experiences and outcomes of participating in direct-to-consumer genetic relative-finder services.American journal of human genetics · 2022Article
- Environmental scan of family chart linking for genetic cascade screening in a U.S. integrated health system.Frontiers in genetics · 2022Article
- Use of SNP chips to detect rare pathogenic variants: retrospective, population based diagnostic evaluation.BMJ (Clinical research ed.) · 2021Article
- Complicated legacies: The human genome at 20.Science (New York, N.Y.) · 2021Article
- False Alarms in Consumer Genomics Add to Public Fear and Potential Health Care Burden.Journal of personalized medicine · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
In an effort to meet ethical obligations and/or participant expectations, researchers may consider offering "raw" or uninterpreted genetic data for result return. It is therefore important to understand the motivations, behaviors, and perspectives of individuals who might choose to access raw data before such return becomes routine. In the direct-to-consumer (DTC) context, where raw data are often made available to customers, the use of third-party interpretation tools has raised concerns about genotype accuracy, data privacy, reliability of interpretation, and consumption of limited health care resources. However, relatively little is known about why individuals access raw data or what they do with the information received from third-party interpretation. Accordingly, we conducted a survey on raw data access and third-party tool usage among 1,137 DTC customers recruited through social media. Most survey respondents (89%) reported downloading their raw data. Among downloaders, 94% used at least one tool, most commonly Promethease (63%) or GEDmatch (84%). More than half (56%) used both health-related and non-health-related tools and differed significantly from those who used only one tool type in terms of demographics, participation in research, DTC tests ordered, and testing motivations. Exploratory interviews were conducted with 10 respondents and illustrated how social networking, initial lack of interesting findings, and general curiosity contributed to use of multiple tool types. These results suggest that even when initially motivated by ancestry and genealogy, consumers frequently also pursue health information in a largely unregulated and expanding suite of third-party tools, raising both challenges and opportunities for the professional genetics community.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.