Researchers from Mount Sinai School of Medicine have developed a method to derive enough DNA information from non-DNA sourcessuch as RNAto clearly identify individuals whose biological data are stored in massive research repositories. The approach may raise questions regarding the ability to protect individual identity when high-dimensional data are collected for research purposes. A paper introducing the technique appears in the April 8 online edition of Nature Genetics.
DNA contains the genetic instructions used in the development and functioning of every living cell. RNA acts as a messenger that relays genetic information in the cell so that the great majority of processes needed for tissue to function properly can be carried out.
To date, access to data bases with DNA information has been restricted and protected as it has long been considered the sole genetic fingerprint for every individual. However, vast amounts of RNA data have been made publicly available via a number of databases in the United States and Europe. These databases contain thousands of genomic studies from around the world.
In this study, lead authors Eric E. Schadt, PhD, and Ke Hao, PhD, developed a technique whereby a person's DNA could be inferred from RNA data using gene-expression levels monitored in any of a number of tissues. In contrast, most studies involving DNA and RNA begin with DNA sequences and then seek to associate expression patterns with changes in DNA between individuals in a population. This is the first time going from RNA levels to DNA sequence has been described.
"By observing RNA levels in a given tissue, we can infer a genotypic barcode that uniquely tags an individual in ways that enables matching the individual to an independently derived DNA sample," said Dr. Schadt, Director of the Institute for Genomics and Multiscale Biology, the Jean C. and James W. Crystal Professor of Genomics, and Chair of the Department of Ge
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