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Phenotype prediction accuracy: A Swedish perspective
National Forensic Centre, Sweden.
Linköping University, Department of Clinical and Experimental Medicine, Division of Hematopoiesis and Developmental Biology. Linköping University, Faculty of Medicine and Health Sciences. National Board of Forensic Medicine, Sweden.
National Forensic Centre, Sweden; Lund Univ, Sweden.
Linköping University, Department of Clinical and Experimental Medicine, Division of Hematopoiesis and Developmental Biology. Linköping University, Faculty of Medicine and Health Sciences. National Board of Forensic Medicine, Sweden.
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2019 (English)In: Forensic Science International: Genetics Supplement Series, ISSN 1875-1768, E-ISSN 1875-175X, Vol. 7, no 1, p. 384-386Article in journal (Refereed) Published
Abstract [en]

Methods for SNP-based phenotype prediction have recently been developed, but prediction accuracy data for several populations and regions are missing. We analysed the accuracy of hair and eye colour predictions for 111 individuals residing in Sweden, using the ForenSeq system and the MiSeq FGx instrument (Verogen). Observed colours were compared to predicted colours, using the colour with the highest probability value for each prediction. Overall, 80% of eye colour predictions were correct, but the system failed to predict intermediate/green eye colour in our cohort. For hair colour, 58% of predictions were correct, and the majority of incorrect predictions were related to brown hair. To assess if prediction accuracy could be improved by the exclusion of predictions with low probabilities, we applied a threshold of amp;gt;= 0.7. The threshold improved eye colour prediction, from 80% to 85% correct predictions, whereas hair colour prediction accuracy was virtually unaffected (58% versus 57% correct predictions). In summary, the phenotype prediction accuracy was acceptable in our cohort and the use of a threshold was only useful for eye colour predictions.

Place, publisher, year, edition, pages
Elsevier, 2019. Vol. 7, no 1, p. 384-386
Keywords [en]
EVC; Massively parallel sequencing; Phenotype; SNP
National Category
Analytical Chemistry
Identifiers
URN: urn:nbn:se:liu:diva-163761DOI: 10.1016/j.fsigss.2019.10.022ISI: 000508217000150Scopus ID: 2-s2.0-85073055664OAI: oai:DiVA.org:liu-163761DiVA, id: diva2:1415777
Available from: 2020-03-19 Created: 2020-03-19 Last updated: 2020-03-26Bibliographically approved

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Tillmar, Andreas

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Division of Hematopoiesis and Developmental BiologyFaculty of Medicine and Health Sciences
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