Preprint Article Version 1 This version is not peer-reviewed

Genotype Fingerprints Enable Fast and Private Comparison of Genetic Testing Results for Research and Direct-to-Consumer Applications

Version 1 : Received: 31 August 2018 / Approved: 31 August 2018 / Online: 31 August 2018 (04:59:28 CEST)

A peer-reviewed article of this Preprint also exists.

Robinson, M.; Glusman, G. Genotype Fingerprints Enable Fast and Private Comparison of Genetic Testing Results for Research and Direct-to-Consumer Applications. Genes 2018, 9, 481. Robinson, M.; Glusman, G. Genotype Fingerprints Enable Fast and Private Comparison of Genetic Testing Results for Research and Direct-to-Consumer Applications. Genes 2018, 9, 481.

Journal reference: Genes 2018, 9, 481
DOI: 10.3390/genes9100481

Abstract

Genetic testing has expanded out of the research laboratory into medical practice and the direct-to-consumer market, and rapid analysis of the resulting genotype data can now have significant impact. We present a method for summarizing personal genotypes as ‘genotype fingerprints’ that meet these needs. Genotype fingerprints can be derived from any single nucleotide polymorphism (SNP)-based assay, and remain comparable as chip designs evolve to higher marker densities. We demonstrate that they support distinguishing types of relationships among closely related individuals and closely related individuals from individuals from the same background population, as well as high-throughput identification of identical genotypes, individuals in known background populations, and de novo separation of subpopulations within a large cohort through extremely rapid comparisons. While fingerprints do not preserve anonymity, they provide a useful degree of privacy by summarizing a genotype in a way that prevents reconstruction of individual marker states. Genotype fingerprints are therefore well-suited as a format for public aggregation of genetic information to support ancestry and relatedness determination without revealing personal health risk status.

Subject Areas

computational genomics; genome comparison; algorithms; genetic testing; privacy; direct-to-consumer; study design; population genetics

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our diversity statement.

Leave a public comment
Send a private comment to the author(s)
Views 0
Downloads 0
Comments 0
Metrics 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.