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Bradbury C, Köttgen A, Staubach F. Off-target phenotypes in forensic DNA phenotyping and biogeographic ancestry inference: A resource. Forensic Sci Int Genet 2018; 38:93-104. [PMID: 30391626 DOI: 10.1016/j.fsigen.2018.10.010] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2018] [Revised: 08/27/2018] [Accepted: 10/13/2018] [Indexed: 01/04/2023]
Abstract
With recent advances in DNA sequencing technologies it has become feasible and cost effective to genotype larger marker sets for forensic purposes. Two technologies that make use of the larger marker sets have come into focus in forensic research and applications; inference of biogeographic ancestry (BGA) and forensic DNA phenotyping (FDP). These methods hold the promise to reveal information about a yet unknown perpetrator from a DNA sample. In contrast, DNA-profiling, that is a standard practice in case work, relies on matching DNA-profiles between crime scene material and suspects on a database of DNA-profiles. Markers for DNA-profiling were developed under the premise to reveal as little additional information about the human source of the profile as possible, the rationale being that personal privacy rights have to be balanced against the public interest in solving a crime. The same argument holds for markers used in BGA and FDP; these markers might also reveal information on off-target phenotypes (OTPs), that go beyond BGA and the phenotypes targeted in FDP. In particular, health related OTPs might shift the balance between privacy protection and public interest. However, to our knowledge, there is currently no convenient resource available to incorporate knowledge on OTPs in BGA and FDP assay design and application. In order to provide such a resource, we performed a systematic search for OTPs associated with a comprehensive set of markers (1766 SNPs) used or suggested to be used for BGA inference and FDP. In this set, we identified a relatively small number of 27 SNPs (1.53%) that convey information on diverse health related OTPs such as cancer risk, induced asthma, or risk of alcoholism. Some of these SNPs are commonly used for FDP and BGA across different marker sets. We conclude that the effects of SNP markers used in FDP and BGA on OTPs are currently limited, with few exceptions that should be considered in a balanced decision on assay design and application.
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Affiliation(s)
- Cedric Bradbury
- University College Freiburg, Albert-Ludwigs-University Freiburg, Freiburg, Germany
| | - Anna Köttgen
- Institute of Genetic Epidemiology, Dept. of Biometry, Epidemiology and Medical Bioinformatics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Fabian Staubach
- Institute of Biology I, Dept. of Evolutionary Biology and Ecology, Albert-Ludwigs-University Freiburg, Freiburg, Germany.
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Chaitanya L, Breslin K, Zuñiga S, Wirken L, Pośpiech E, Kukla-Bartoszek M, Sijen T, Knijff PD, Liu F, Branicki W, Kayser M, Walsh S. The HIrisPlex-S system for eye, hair and skin colour prediction from DNA: Introduction and forensic developmental validation. Forensic Sci Int Genet 2018; 35:123-135. [DOI: 10.1016/j.fsigen.2018.04.004] [Citation(s) in RCA: 138] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2017] [Revised: 03/05/2018] [Accepted: 04/06/2018] [Indexed: 11/29/2022]
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Lippert C, Sabatini R, Maher MC, Kang EY, Lee S, Arikan O, Harley A, Bernal A, Garst P, Lavrenko V, Yocum K, Wong T, Zhu M, Yang WY, Chang C, Lu T, Lee CWH, Hicks B, Ramakrishnan S, Tang H, Xie C, Piper J, Brewerton S, Turpaz Y, Telenti A, Roby RK, Och FJ, Venter JC. Identification of individuals by trait prediction using whole-genome sequencing data. Proc Natl Acad Sci U S A 2017; 114:10166-10171. [PMID: 28874526 PMCID: PMC5617305 DOI: 10.1073/pnas.1711125114] [Citation(s) in RCA: 96] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
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.
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Affiliation(s)
| | | | | | | | | | - Okan Arikan
- Human Longevity, Inc., Mountain View, CA 94303
| | | | - Axel Bernal
- Human Longevity, Inc., Mountain View, CA 94303
| | - Peter Garst
- Human Longevity, Inc., Mountain View, CA 94303
| | | | - Ken Yocum
- Human Longevity, Inc., Mountain View, CA 94303
| | | | - Mingfu Zhu
- Human Longevity, Inc., Mountain View, CA 94303
| | | | - Chris Chang
- Human Longevity, Inc., Mountain View, CA 94303
| | - Tim Lu
- Human Longevity, Inc., San Diego, CA 92121
| | | | - Barry Hicks
- Human Longevity, Inc., Mountain View, CA 94303
| | | | - Haibao Tang
- Human Longevity, Inc., Mountain View, CA 94303
| | - Chao Xie
- Human Longevity Singapore, Pte. Ltd., Singapore 138542
| | - Jason Piper
- Human Longevity Singapore, Pte. Ltd., Singapore 138542
| | | | - Yaron Turpaz
- Human Longevity, Inc., San Diego, CA 92121
- Human Longevity Singapore, Pte. Ltd., Singapore 138542
| | | | - Rhonda K Roby
- Human Longevity, Inc., San Diego, CA 92121
- J. Craig Venter Institute, La Jolla, CA 92037
| | - Franz J Och
- Human Longevity, Inc., Mountain View, CA 94303
| | - J Craig Venter
- Human Longevity, Inc., San Diego, CA 92121;
- J. Craig Venter Institute, La Jolla, CA 92037
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Walsh S, Chaitanya L, Breslin K, Muralidharan C, Bronikowska A, Pospiech E, Koller J, Kovatsi L, Wollstein A, Branicki W, Liu F, Kayser M. Global skin colour prediction from DNA. Hum Genet 2017; 136:847-863. [PMID: 28500464 PMCID: PMC5487854 DOI: 10.1007/s00439-017-1808-5] [Citation(s) in RCA: 73] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2017] [Accepted: 05/03/2017] [Indexed: 12/14/2022]
Abstract
Human skin colour is highly heritable and externally visible with relevance in medical, forensic, and anthropological genetics. Although eye and hair colour can already be predicted with high accuracies from small sets of carefully selected DNA markers, knowledge about the genetic predictability of skin colour is limited. Here, we investigate the skin colour predictive value of 77 single-nucleotide polymorphisms (SNPs) from 37 genetic loci previously associated with human pigmentation using 2025 individuals from 31 global populations. We identified a minimal set of 36 highly informative skin colour predictive SNPs and developed a statistical prediction model capable of skin colour prediction on a global scale. Average cross-validated prediction accuracies expressed as area under the receiver-operating characteristic curve (AUC) ± standard deviation were 0.97 ± 0.02 for Light, 0.83 ± 0.11 for Dark, and 0.96 ± 0.03 for Dark-Black. When using a 5-category, this resulted in 0.74 ± 0.05 for Very Pale, 0.72 ± 0.03 for Pale, 0.73 ± 0.03 for Intermediate, 0.87±0.1 for Dark, and 0.97 ± 0.03 for Dark-Black. A comparative analysis in 194 independent samples from 17 populations demonstrated that our model outperformed a previously proposed 10-SNP-classifier approach with AUCs rising from 0.79 to 0.82 for White, comparable at the intermediate level of 0.63 and 0.62, respectively, and a large increase from 0.64 to 0.92 for Black. Overall, this study demonstrates that the chosen DNA markers and prediction model, particularly the 5-category level; allow skin colour predictions within and between continental regions for the first time, which will serve as a valuable resource for future applications in forensic and anthropologic genetics.
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Affiliation(s)
- Susan Walsh
- Department of Biology, Indiana University Purdue University Indianapolis (IUPUI), Indianapolis, IN, USA.
| | - Lakshmi Chaitanya
- Department of Genetic Identification, Erasmus MC University Medical Centre Rotterdam, Rotterdam, The Netherlands
| | - Krystal Breslin
- Department of Biology, Indiana University Purdue University Indianapolis (IUPUI), Indianapolis, IN, USA
| | - Charanya Muralidharan
- Department of Biology, Indiana University Purdue University Indianapolis (IUPUI), Indianapolis, IN, USA
| | - Agnieszka Bronikowska
- Department of Dermatology, Collegium Medicum of the Jagiellonian University, Kraków, Poland
| | - Ewelina Pospiech
- Faculty of Biology and Earth Sciences, Institute of Zoology, Jagiellonian University, Kraków, Poland
- Malopolska Centre of Biotechnology, Jagiellonian University, Kraków, Poland
| | - Julia Koller
- Department of Genetic Identification, Erasmus MC University Medical Centre Rotterdam, Rotterdam, The Netherlands
| | - Leda Kovatsi
- Laboratory of Forensic Medicine and Toxicology, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Andreas Wollstein
- Section of Evolutionary Biology, Department of Biology II, University of Munich LMU, Planegg-Martinsried, Germany
| | - Wojciech Branicki
- Malopolska Centre of Biotechnology, Jagiellonian University, Kraków, Poland
- Central Forensic Laboratory of the Police, Warsaw, Poland
| | - Fan Liu
- Department of Genetic Identification, Erasmus MC University Medical Centre Rotterdam, Rotterdam, The Netherlands
- Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Manfred Kayser
- Department of Genetic Identification, Erasmus MC University Medical Centre Rotterdam, Rotterdam, The Netherlands.
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