ArticlePLoS genetics2009
Needles in the haystack: identifying individuals present in pooled genomic data.
Article in PLoS genetics, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
What it found
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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
25 citing papers in PubMed.
- Anonymization: The imperfect science of using data while preserving privacy.Science advances · 2024Review
- Assessing Privacy Vulnerabilities in Genetic Data Sets: Scoping Review.JMIR bioinformatics and biotechnology · 2024Article
- Sociotechnical safeguards for genomic data privacy.Nature reviews. Genetics · 2022Review
- Article
- Proof-of-concept study: Homomorphically encrypted data can support real-time learning in personalized cancer medicine.BMC medical informatics and decision making · 2019Article
- A Secure Alignment Algorithm for Mapping Short Reads to Human Genome.Journal of computational biology : a journal of computational molecular cell biology · 2018Article
- Big Data in Public Health: Terminology, Machine Learning, and Privacy.Annual review of public health · 2018Review
- On the privacy risks of sharing clinical proteomics data.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2016Article
- Privacy in the Genomic Era.ACM computing surveys · 2015Article
- Genetic data sharing and privacy.Neuroinformatics · 2015Article
- One Size Doesn't Fit All: Measuring Individual Privacy in Aggregate Genomic Data.Proceedings. IEEE Symposium on Security and Privacy. Workshops · 2015Article
- Scalable privacy-preserving data sharing methodology for genome-wide association studies.Journal of biomedical informatics · 2014Article
- Routes for breaching and protecting genetic privacy.Nature reviews. Genetics · 2014Review
- Systems analysis of high-throughput data.Advances in experimental medicine and biology · 2014Review
- Attention-deficit/hyperactivity disorder genomics: update for clinicians.Current psychiatry reports · 2012Review
- Participant identification in genetic association studies: improved methods and practical implications.International journal of epidemiology · 2011Article
- Assessing and managing risk when sharing aggregate genetic variant data.Nature reviews. Genetics · 2011Review
- Temporal trends in results availability from genome-wide association studies.PLoS genetics · 2011Article
- Biobanking residual tissues.Human genetics · 2011Review
- Pathways of distinction analysis: a new technique for multi-SNP analysis of GWAS data.PLoS genetics · 2011Article
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5 authors.
Funding
Abstract
Recent publications have described and applied a novel metric that quantifies the genetic distance of an individual with respect to two population samples, and have suggested that the metric makes it possible to infer the presence of an individual of known genotype in a sample for which only the marginal allele frequencies are known. However, the assumptions, limitations, and utility of this metric remained incompletely characterized. Here we present empirical tests of the method using publicly accessible genotypes, as well as analytical investigations of the method's strengths and limitations. The results reveal that the null distribution is sensitive to the underlying assumptions, making it difficult to accurately calibrate thresholds for classifying an individual as a member of the population samples. As a result, the false-positive rates obtained in practice are considerably higher than previously believed. However, despite the metric's inadequacies for identifying the presence of an individual in a sample, our results suggest potential avenues for future research on tuning this method to problems of ancestry inference or disease prediction. By revealing both the strengths and limitations of the proposed method, we hope to elucidate situations in which this distance metric may be used in an appropriate manner. We also discuss the implications of our findings in forensics applications and in the protection of GWAS participant privacy.
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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.