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Prediction of lithium response using genomic data
Predicting lithium response prior to treatment could both expedite therapy and avoid exposure to side effects. Since lithium responsiveness may be heritable, its predictability based on genomic data is of interest. We thus evaluate the degree to which lithium response can be predicted with a machine...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7806976/ https://www.ncbi.nlm.nih.gov/pubmed/33441847 http://dx.doi.org/10.1038/s41598-020-80814-z |
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author | Stone, William Nunes, Abraham Akiyama, Kazufumi Akula, Nirmala Ardau, Raffaella Aubry, Jean-Michel Backlund, Lena Bauer, Michael Bellivier, Frank Cervantes, Pablo Chen, Hsi-Chung Chillotti, Caterina Cruceanu, Cristiana Dayer, Alexandre Degenhardt, Franziska Del Zompo, Maria Forstner, Andreas J. Frye, Mark Fullerton, Janice M. Grigoroiu-Serbanescu, Maria Grof, Paul Hashimoto, Ryota Hou, Liping Jiménez, Esther Kato, Tadafumi Kelsoe, John Kittel-Schneider, Sarah Kuo, Po-Hsiu Kusumi, Ichiro Lavebratt, Catharina Manchia, Mirko Martinsson, Lina Mattheisen, Manuel McMahon, Francis J. Millischer, Vincent Mitchell, Philip B. Nöthen, Markus M. O’Donovan, Claire Ozaki, Norio Pisanu, Claudia Reif, Andreas Rietschel, Marcella Rouleau, Guy Rybakowski, Janusz Schalling, Martin Schofield, Peter R. Schulze, Thomas G. Severino, Giovanni Squassina, Alessio Veeh, Julia Vieta, Eduard Trappenberg, Thomas Alda, Martin |
author_facet | Stone, William Nunes, Abraham Akiyama, Kazufumi Akula, Nirmala Ardau, Raffaella Aubry, Jean-Michel Backlund, Lena Bauer, Michael Bellivier, Frank Cervantes, Pablo Chen, Hsi-Chung Chillotti, Caterina Cruceanu, Cristiana Dayer, Alexandre Degenhardt, Franziska Del Zompo, Maria Forstner, Andreas J. Frye, Mark Fullerton, Janice M. Grigoroiu-Serbanescu, Maria Grof, Paul Hashimoto, Ryota Hou, Liping Jiménez, Esther Kato, Tadafumi Kelsoe, John Kittel-Schneider, Sarah Kuo, Po-Hsiu Kusumi, Ichiro Lavebratt, Catharina Manchia, Mirko Martinsson, Lina Mattheisen, Manuel McMahon, Francis J. Millischer, Vincent Mitchell, Philip B. Nöthen, Markus M. O’Donovan, Claire Ozaki, Norio Pisanu, Claudia Reif, Andreas Rietschel, Marcella Rouleau, Guy Rybakowski, Janusz Schalling, Martin Schofield, Peter R. Schulze, Thomas G. Severino, Giovanni Squassina, Alessio Veeh, Julia Vieta, Eduard Trappenberg, Thomas Alda, Martin |
author_sort | Stone, William |
collection | PubMed |
description | Predicting lithium response prior to treatment could both expedite therapy and avoid exposure to side effects. Since lithium responsiveness may be heritable, its predictability based on genomic data is of interest. We thus evaluate the degree to which lithium response can be predicted with a machine learning (ML) approach using genomic data. Using the largest existing genomic dataset in the lithium response literature (n = 2210 across 14 international sites; 29% responders), we evaluated the degree to which lithium response could be predicted based on 47,465 genotyped single nucleotide polymorphisms using a supervised ML approach. Under appropriate cross-validation procedures, lithium response could be predicted to above-chance levels in two constituent sites (Halifax, Cohen’s kappa 0.15, 95% confidence interval, CI [0.07, 0.24]; and Würzburg, kappa 0.2 [0.1, 0.3]). Variants with shared importance in these models showed over-representation of postsynaptic membrane related genes. Lithium response was not predictable in the pooled dataset (kappa 0.02 [− 0.01, 0.04]), although non-trivial performance was achieved within a restricted dataset including only those patients followed prospectively (kappa 0.09 [0.04, 0.14]). Genomic classification of lithium response remains a promising but difficult task. Classification performance could potentially be improved by further harmonization of data collection procedures. |
format | Online Article Text |
id | pubmed-7806976 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-78069762021-01-14 Prediction of lithium response using genomic data Stone, William Nunes, Abraham Akiyama, Kazufumi Akula, Nirmala Ardau, Raffaella Aubry, Jean-Michel Backlund, Lena Bauer, Michael Bellivier, Frank Cervantes, Pablo Chen, Hsi-Chung Chillotti, Caterina Cruceanu, Cristiana Dayer, Alexandre Degenhardt, Franziska Del Zompo, Maria Forstner, Andreas J. Frye, Mark Fullerton, Janice M. Grigoroiu-Serbanescu, Maria Grof, Paul Hashimoto, Ryota Hou, Liping Jiménez, Esther Kato, Tadafumi Kelsoe, John Kittel-Schneider, Sarah Kuo, Po-Hsiu Kusumi, Ichiro Lavebratt, Catharina Manchia, Mirko Martinsson, Lina Mattheisen, Manuel McMahon, Francis J. Millischer, Vincent Mitchell, Philip B. Nöthen, Markus M. O’Donovan, Claire Ozaki, Norio Pisanu, Claudia Reif, Andreas Rietschel, Marcella Rouleau, Guy Rybakowski, Janusz Schalling, Martin Schofield, Peter R. Schulze, Thomas G. Severino, Giovanni Squassina, Alessio Veeh, Julia Vieta, Eduard Trappenberg, Thomas Alda, Martin Sci Rep Article Predicting lithium response prior to treatment could both expedite therapy and avoid exposure to side effects. Since lithium responsiveness may be heritable, its predictability based on genomic data is of interest. We thus evaluate the degree to which lithium response can be predicted with a machine learning (ML) approach using genomic data. Using the largest existing genomic dataset in the lithium response literature (n = 2210 across 14 international sites; 29% responders), we evaluated the degree to which lithium response could be predicted based on 47,465 genotyped single nucleotide polymorphisms using a supervised ML approach. Under appropriate cross-validation procedures, lithium response could be predicted to above-chance levels in two constituent sites (Halifax, Cohen’s kappa 0.15, 95% confidence interval, CI [0.07, 0.24]; and Würzburg, kappa 0.2 [0.1, 0.3]). Variants with shared importance in these models showed over-representation of postsynaptic membrane related genes. Lithium response was not predictable in the pooled dataset (kappa 0.02 [− 0.01, 0.04]), although non-trivial performance was achieved within a restricted dataset including only those patients followed prospectively (kappa 0.09 [0.04, 0.14]). Genomic classification of lithium response remains a promising but difficult task. Classification performance could potentially be improved by further harmonization of data collection procedures. Nature Publishing Group UK 2021-01-13 /pmc/articles/PMC7806976/ /pubmed/33441847 http://dx.doi.org/10.1038/s41598-020-80814-z Text en © The Author(s) 2021 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Stone, William Nunes, Abraham Akiyama, Kazufumi Akula, Nirmala Ardau, Raffaella Aubry, Jean-Michel Backlund, Lena Bauer, Michael Bellivier, Frank Cervantes, Pablo Chen, Hsi-Chung Chillotti, Caterina Cruceanu, Cristiana Dayer, Alexandre Degenhardt, Franziska Del Zompo, Maria Forstner, Andreas J. Frye, Mark Fullerton, Janice M. Grigoroiu-Serbanescu, Maria Grof, Paul Hashimoto, Ryota Hou, Liping Jiménez, Esther Kato, Tadafumi Kelsoe, John Kittel-Schneider, Sarah Kuo, Po-Hsiu Kusumi, Ichiro Lavebratt, Catharina Manchia, Mirko Martinsson, Lina Mattheisen, Manuel McMahon, Francis J. Millischer, Vincent Mitchell, Philip B. Nöthen, Markus M. O’Donovan, Claire Ozaki, Norio Pisanu, Claudia Reif, Andreas Rietschel, Marcella Rouleau, Guy Rybakowski, Janusz Schalling, Martin Schofield, Peter R. Schulze, Thomas G. Severino, Giovanni Squassina, Alessio Veeh, Julia Vieta, Eduard Trappenberg, Thomas Alda, Martin Prediction of lithium response using genomic data |
title | Prediction of lithium response using genomic data |
title_full | Prediction of lithium response using genomic data |
title_fullStr | Prediction of lithium response using genomic data |
title_full_unstemmed | Prediction of lithium response using genomic data |
title_short | Prediction of lithium response using genomic data |
title_sort | prediction of lithium response using genomic data |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7806976/ https://www.ncbi.nlm.nih.gov/pubmed/33441847 http://dx.doi.org/10.1038/s41598-020-80814-z |
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