ArticleNature genetics2012
Bayesian method to predict individual SNP genotypes from gene expression data.
Article in Nature genetics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 72 papers, 3 of them syntheses that pooled it.
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Who cites it
72 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Flimma: a federated and privacy-aware tool for differential gene expression analysis.Genome biology · 2021Pooled it
- A meta-analysis of gene expression quantitative trait loci in brain.Translational psychiatry · 2014Pooled it
- Bayesian test for colocalisation between pairs of genetic association studies using summary statistics.PLoS genetics · 2014Pooled it
- NoisyFlow: differentially private optimal transport using neural networks for secure biomedical data sharing across multiple institutions.Bioinformatics (Oxford, England) · 2026Article
- Innovative Single-Cell Sequencing Techniques for B cell Analysis and Their Implications for Rational HIV-1 Vaccine Design.Current HIV research · 2026Review
- Peripheral blood microarray-based transcriptomic and epigenetic analyses identify immune, inflammation, and metabolic dysregulation in Alzheimer's disease.NPJ dementia · 2026Article
- Secure and scalable gene expression quantification with pQuant.Nature communications · 2025Article
- Secure and federated quantitative trait loci mapping with privateQTL.Cell genomics · 2025Article
- Article
- Privacy of single-cell gene expression data.Patterns (New York, N.Y.) · 2024Article
- Privacy-Enhancing Technologies in Biomedical Data Science.Annual review of biomedical data science · 2024Review
- Large-scale genotype prediction from RNA sequence data necessitates a new ethical and policy framework.Nature genetics · 2024Article
- Assessing Privacy Vulnerabilities in Genetic Data Sets: Scoping Review.JMIR bioinformatics and biotechnology · 2024Article
- Deep Learning for Epidemiologists: An Introduction to Neural Networks.American journal of epidemiology · 2023Review
- Assessing transcriptomic reidentification risks using discriminative sequence models.Genome research · 2023Article
- Large scale proteomic studies create novel privacy considerations.Scientific reports · 2023Article
- Predictive network analysis identifies JMJD6 and other potential key drivers in Alzheimer's disease.Communications biology · 2023Article
- Sequre: a high-performance framework for secure multiparty computation enables biomedical data sharing.Genome biology · 2023Article
- Functional genomics data: privacy risk assessment and technological mitigation.Nature reviews. Genetics · 2022Review
- Ethical implications of epigenetics in the era of personalized medicine.Clinical epigenetics · 2022Review
12 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
RNA profiling can be used to capture the expression patterns of many genes that are associated with expression quantitative trait loci (eQTLs). Employing published putative cis eQTLs, we developed a Bayesian approach to predict SNP genotypes that is based only on RNA expression data. We show that predicted genotypes can accurately and uniquely identify individuals in large populations. When inferring genotypes from an expression data set using eQTLs of the same tissue type (but from an independent cohort), we were able to resolve 99% of the identities of individuals in the cohort at P(adjusted) ≤ 1 × 10(-5). When eQTLs derived from one tissue were used to predict genotypes using expression data from a different tissue, the identities of 90% of the study subjects could be resolved at P(adjusted) ≤ 1 × 10(-5). We discuss the implications of deriving genotypic information from RNA data deposited in the public domain.
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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.