Computational functional genomics-based AmpliSeq™ panel for next-generation sequencing of key genes of pain

  • The genetic background of pain is becoming increasingly well understood, which opens up possibilities for predicting the individual risk of persistent pain and the use of tailored therapies adapted to the variant pattern of the patient’s pain-relevant genes. The individual variant pattern of pain-relevant genes is accessible via next-generation sequencing, although the analysis of all “pain genes” would be expensive. Here, we report on the development of a cost-effective next generation sequencing-based pain-genotyping assay comprising the development of a customized AmpliSeq™ panel and bioinformatics approaches that condensate the genetic information of pain by identifying the most representative genes. The panel includes 29 key genes that have been shown to cover 70% of the biological functions exerted by a list of 540 so-called “pain genes” derived from transgenic mice experiments. These were supplemented by 43 additional genes that had been independently proposed as relevant for persistent pain. The functional genomics covered by the resulting 72 genes is particularly represented by mitogen-activated protein kinase of extracellular signal-regulated kinase and cytokine production and secretion. The present genotyping assay was established in 61 subjects of Caucasian ethnicity and investigates the functional role of the selected genes in the context of the known genetic architecture of pain without seeking functional associations for pain. The assay identified a total of 691 genetic variants, of which many have reports for a clinical relevance for pain or in another context. The assay is applicable for small to large-scale experimental setups at contemporary genotyping costs.
Author:Dario KringelORCiDGND, Sebastian MalkuschORCiDGND, Eija KalsoORCiDGND, Jörn LötschORCiDGND
Parent Title (English):International journal of molecular sciences
Publisher:Molecular Diversity Preservation International
Place of publication:Basel
Document Type:Article
Date of Publication (online):2021/01/18
Date of first Publication:2021/01/16
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Release Date:2021/01/18
Tag:computational functional genomics; data science; human genomics; knowledge discovery; next generation sequencing; pain genetics; pharmacogenomics
Issue:art. 878
Page Number:33
First Page:1
Last Page:33
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (
Institutes:Medizin / Medizin
Dewey Decimal Classification:6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit
Licence (German):License LogoCreative Commons - Namensnennung 4.0