Emergent biomarker derived from next-generation sequencing to identify pain patients requiring uncommonly high opioid doses

  • Next-generation sequencing (NGS) provides unrestricted access to the genome, but it produces ‘big data’ exceeding in amount and complexity the classical analytical approaches. We introduce a bioinformatics-based classifying biomarker that uses emergent properties in genetics to separate pain patients requiring extremely high opioid doses from controls. Following precisely calculated selection of the 34 most informative markers in the OPRM1, OPRK1, OPRD1 and SIGMAR1 genes, pattern of genotypes belonging to either patient group could be derived using a k-nearest neighbor (kNN) classifier that provided a diagnostic accuracy of 80.6±4%. This outperformed alternative classifiers such as reportedly functional opioid receptor gene variants or complex biomarkers obtained via multiple regression or decision tree analysis. The accumulation of several genetic variants with only minor functional influences may result in a qualitative consequence affecting complex phenotypes, pointing at emergent properties in genetics.

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Author:Dario Kringel, Alfred Ultsch, Michael Zimmermann, Peter Jansen, Wilfried Ilias, Rainer Freynhagen, Norbert Griessinger, Andreas Kopf, Christoph Stein, Alexandra Doehring, Eduard Resch, Jörn LötschORCiDGND
URN:urn:nbn:de:hebis:30:3-465524
DOI:https://doi.org/10.1038/tpj.2016.28
ISSN:1470-269X
ISSN:1473-1150
Pubmed Id:https://pubmed.ncbi.nlm.nih.gov/27139154
Parent Title (English):The pharmacogenomics journal
Publisher:Nature Publishing Group
Place of publication:Basingstoke
Document Type:Article
Language:English
Year of Completion:2016
Date of first Publication:2016/05/03
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Release Date:2018/05/29
Tag:Clinical genetics; Genetics research; Predictive medicine
Volume:[16]
Issue:5
Page Number:8
First Page:1
Last Page:8
Note:
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/4.0/
HeBIS-PPN:435646370
Institutes:Medizin / Medizin
Dewey Decimal Classification:6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit
Sammlungen:Universitätspublikationen
Licence (German):License LogoCreative Commons - Namensnennung-Nicht kommerziell - Keine Bearbeitung 4.0