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Computational Methods for the Pharmacogenetic Interpretation of Next Generation Sequencing Data

Up to half of all patients do not respond to pharmacological treatment as intended. A substantial fraction of these inter-individual differences is due to heritable factors and a growing number of associations between genetic variations and drug response phenotypes have been identified. Importantly,...

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Autores principales: Zhou, Yitian, Fujikura, Kohei, Mkrtchian, Souren, Lauschke, Volker M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6288784/
https://www.ncbi.nlm.nih.gov/pubmed/30564131
http://dx.doi.org/10.3389/fphar.2018.01437
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author Zhou, Yitian
Fujikura, Kohei
Mkrtchian, Souren
Lauschke, Volker M.
author_facet Zhou, Yitian
Fujikura, Kohei
Mkrtchian, Souren
Lauschke, Volker M.
author_sort Zhou, Yitian
collection PubMed
description Up to half of all patients do not respond to pharmacological treatment as intended. A substantial fraction of these inter-individual differences is due to heritable factors and a growing number of associations between genetic variations and drug response phenotypes have been identified. Importantly, the rapid progress in Next Generation Sequencing technologies in recent years unveiled the true complexity of the genetic landscape in pharmacogenes with tens of thousands of rare genetic variants. As each individual was found to harbor numerous such rare variants they are anticipated to be important contributors to the genetically encoded inter-individual variability in drug effects. The fundamental challenge however is their functional interpretation due to the sheer scale of the problem that renders systematic experimental characterization of these variants currently unfeasible. Here, we review concepts and important progress in the development of computational prediction methods that allow to evaluate the effect of amino acid sequence alterations in drug metabolizing enzymes and transporters. In addition, we discuss recent advances in the interpretation of functional effects of non-coding variants, such as variations in splice sites, regulatory regions and miRNA binding sites. We anticipate that these methodologies will provide a useful toolkit to facilitate the integration of the vast extent of rare genetic variability into drug response predictions in a precision medicine framework.
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spelling pubmed-62887842018-12-18 Computational Methods for the Pharmacogenetic Interpretation of Next Generation Sequencing Data Zhou, Yitian Fujikura, Kohei Mkrtchian, Souren Lauschke, Volker M. Front Pharmacol Pharmacology Up to half of all patients do not respond to pharmacological treatment as intended. A substantial fraction of these inter-individual differences is due to heritable factors and a growing number of associations between genetic variations and drug response phenotypes have been identified. Importantly, the rapid progress in Next Generation Sequencing technologies in recent years unveiled the true complexity of the genetic landscape in pharmacogenes with tens of thousands of rare genetic variants. As each individual was found to harbor numerous such rare variants they are anticipated to be important contributors to the genetically encoded inter-individual variability in drug effects. The fundamental challenge however is their functional interpretation due to the sheer scale of the problem that renders systematic experimental characterization of these variants currently unfeasible. Here, we review concepts and important progress in the development of computational prediction methods that allow to evaluate the effect of amino acid sequence alterations in drug metabolizing enzymes and transporters. In addition, we discuss recent advances in the interpretation of functional effects of non-coding variants, such as variations in splice sites, regulatory regions and miRNA binding sites. We anticipate that these methodologies will provide a useful toolkit to facilitate the integration of the vast extent of rare genetic variability into drug response predictions in a precision medicine framework. Frontiers Media S.A. 2018-12-04 /pmc/articles/PMC6288784/ /pubmed/30564131 http://dx.doi.org/10.3389/fphar.2018.01437 Text en Copyright © 2018 Zhou, Fujikura, Mkrtchian and Lauschke. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Pharmacology
Zhou, Yitian
Fujikura, Kohei
Mkrtchian, Souren
Lauschke, Volker M.
Computational Methods for the Pharmacogenetic Interpretation of Next Generation Sequencing Data
title Computational Methods for the Pharmacogenetic Interpretation of Next Generation Sequencing Data
title_full Computational Methods for the Pharmacogenetic Interpretation of Next Generation Sequencing Data
title_fullStr Computational Methods for the Pharmacogenetic Interpretation of Next Generation Sequencing Data
title_full_unstemmed Computational Methods for the Pharmacogenetic Interpretation of Next Generation Sequencing Data
title_short Computational Methods for the Pharmacogenetic Interpretation of Next Generation Sequencing Data
title_sort computational methods for the pharmacogenetic interpretation of next generation sequencing data
topic Pharmacology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6288784/
https://www.ncbi.nlm.nih.gov/pubmed/30564131
http://dx.doi.org/10.3389/fphar.2018.01437
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