Identification of individuals by trait prediction using whole-genome sequencing data [Genetics]
![Identification of individuals by trait prediction using whole-genome sequencing data [Genetics]](/image/eyJ1cmkiOiJodHRwOi8vc3RhY2thZGVtaWMuaGVyb2t1YXBwLmNvbS9pbWFnZT9pbWFnZV9pZD0xOTEzMyIsImZvcm1hdCI6IndlYnAiLCJxdWFsaXR5IjoxMDAsIm5vQ2FjaGUiOnRydWV9.webp)
Prediction of human physical traits and demographic information from genomic data challenges privacy and data deidentification in personalized medicine. To explore the current capabilities of phenotype-based genomic identification, we applied whole-genome sequencing, detailed phenotyping, and statistical modeling to predict biometric traits in a cohort of 1,061 participants of diverse ancestry. Individually, for a large fraction of the traits, their predictive accuracy beyond ancestry and demographic information is limited. However, we have developed a maximum entropy algorithm that integrates multiple predictions to determine which genomic samples and phenotype measurements originate from the same person. Using this algorithm, we have reidentified an average of >8 of 10 held-out individuals in an ethnically mixed cohort and an average of 5 of either 10 African Americans or 10 Europeans. This work challenges current conceptions of personal privacy and may have far-reaching ethical and legal implications.
Publisher URL: http://feedproxy.google.com/~r/Pnas-RssFeedOfEarlyEditionArticles/~3/k1fhEnymhXI/1711125114.short
DOI: 10.1073/pnas.1711125114
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