ArticleGenetics in medicine : official journal of the American College of Medical Genetics2014
Variations in predicted risks in personal genome testing for common complex diseases.
Article in Genetics in medicine : official journal of the American College of Medical Genetics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 2 of them syntheses that pooled it.
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Who cites it
34 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Multigene panels in prostate cancer risk assessment: a systematic review.Genetics in medicine : official journal of the American College of Medical Genetics · 2016Pooled it
- Internet-Based Direct-to-Consumer Genetic Testing: A Systematic Review.Journal of medical Internet research · 2015Pooled it
- Considerations for developing regulations for direct-to-consumer genetic testing: a scoping review using the 3-I framework.Journal of community genetics · 2022Article
- Public reactions to direct-to-consumer genetic health tests: A comparison across the US, UK, Japan and Australia.European journal of human genetics : EJHG · 2020Article
- Psoriasis and Genetics.Acta dermato-venereologica · 2020Review
- Development of a general logistic model for disease risk prediction using multiple SNPs.FEBS open bio · 2019Article
- Patients' willingness to share digital health and non-health data for research: a cross-sectional study.BMC medical informatics and decision making · 2019Article
- Proprietary Algorithms for Polygenic Risk: Protecting Scientific Innovation or Hiding the Lack of It?Genes · 2019Article
- Cost Analyses of Genomic Sequencing: Lessons Learned from the MedSeq Project.Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research · 2018Article
- Medicine, market and communication: ethical considerations in regard to persuasive communication in direct-to-consumer genetic testing services.BMC medical ethics · 2018Article
- Article
- Complement System and Age-Related Macular Degeneration: Implications of Gene-Environment Interaction for Preventive and Personalized Medicine.BioMed research international · 2018Review
- Systems-Mapping of Herbal Effects on Complex Diseases Using the Network-Perturbation Signatures.Frontiers in pharmacology · 2018Article
- Utilization of Genetic Counseling after Direct-to-Consumer Genetic Testing: Findings from the Impact of Personal Genomics (PGen) Study.Journal of genetic counseling · 2017Article
- Genetic risk models: Influence of model size on risk estimates and precision.Genetic epidemiology · 2017Article
- Estimation of the risk of a qualitative phenotype: dependence on population risk.Journal of human genetics · 2017Article
- Reclassification of genetic-based risk predictions as GWAS data accumulate.Genome medicine · 2016Article
- Constructing Hypothetical Risk Data from the Area under the ROC Curve: Modelling Distributions of Polygenic Risk.PloS one · 2016Article
- Crowdsourced direct-to-consumer genomic analysis of a family quartet.BMC genomics · 2015Article
- The impact of direct-to-consumer personal genomic testing on perceived risk of breast, prostate, colorectal, and lung cancer: findings from the PGen study.BMC medical genomics · 2015Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
purposeThe promise of personalized genomics for common complex diseases depends, in part, on the ability to predict genetic risks on the basis of single nucleotide polymorphisms. We examined and compared the methods of three companies (23andMe, deCODEme, and Navigenics) that have offered direct-to-consumer personal genome testing.
methodsWe simulated genotype data for 100,000 individuals on the basis of published genotype frequencies and predicted disease risks using the methods of the companies. Predictive ability for six diseases was assessed by the AUC.
resultsAUC values differed among the diseases and among the companies. The highest values of the AUC were observed for age-related macular degeneration, celiac disease, and Crohn disease. The largest difference among the companies was found for celiac disease: the AUC was 0.73 for 23andMe and 0.82 for deCODEme. Predicted risks differed substantially among the companies as a result of differences in the sets of single nucleotide polymorphisms selected and the average population risks selected by the companies, and in the formulas used for the calculation of risks.
conclusionFuture efforts to design predictive models for the genomics of common complex diseases may benefit from understanding the strengths and limitations of the predictive algorithms designed by these early companies.
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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.