Machine-learned association of next-generation sequencing-derived variants in thermosensitive ion channels genes with human thermal pain sensitivity phenotypes

  • Genetic association studies have shown their usefulness in assessing the role of ion channels in human thermal pain perception. We used machine learning to construct a complex phenotype from pain thresholds to thermal stimuli and associate it with the genetic information derived from the next-generation sequencing (NGS) of 15 ion channel genes which are involved in thermal perception, including ASIC1, ASIC2, ASIC3, ASIC4, TRPA1, TRPC1, TRPM2, TRPM3, TRPM4, TRPM5, TRPM8, TRPV1, TRPV2, TRPV3, and TRPV4. Phenotypic information was complete in 82 subjects and NGS genotypes were available in 67 subjects. A network of artificial neurons, implemented as emergent self-organizing maps, discovered two clusters characterized by high or low pain thresholds for heat and cold pain. A total of 1071 variants were discovered in the 15 ion channel genes. After feature selection, 80 genetic variants were retained for an association analysis based on machine learning. The measured performance of machine learning-mediated phenotype assignment based on this genetic information resulted in an area under the receiver operating characteristic curve of 77.2%, justifying a phenotype classification based on the genetic information. A further item categorization finally resulted in 38 genetic variants that contributed most to the phenotype assignment. Most of them (10) belonged to the TRPV3 gene, followed by TRPM3 (6). Therefore, the analysis successfully identified the particular importance of TRPV3 and TRPM3 for an average pain phenotype defined by the sensitivity to moderate thermal stimuli.

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Author:Jörn LötschORCiDGND, Dario KringelORCiDGND, Gerd GeisslingerORCiDGND, Bruno Georg OertelORCiDGND, Eduard Resch, Sebastian MalkuschORCiDGND
Parent Title (English):International journal of molecular sciences
Place of publication:Basel
Document Type:Article
Date of Publication (online):2020/06/19
Date of first Publication:2020/06/19
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Release Date:2020/08/13
Tag:data science; experimental pain models; human genomics; machine learning; next-generation sequencing; pain
Issue:12, art. 4367
Page Number:23
First Page:1
Last Page:23
© 2020 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 (
Dewey Decimal Classification:6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit
Licence (German):License LogoCreative Commons - Namensnennung 4.0