Prediction of lithium response using genomic data

Abstract 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...

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Autores principales:	William Stone, Abraham Nunes, Kazufumi Akiyama, Nirmala Akula, Raffaella Ardau, Jean-Michel Aubry, Lena Backlund, Michael Bauer, Frank Bellivier, Pablo Cervantes, Hsi-Chung Chen, Caterina Chillotti, Cristiana Cruceanu, Alexandre Dayer, Franziska Degenhardt, Maria Del Zompo, Andreas J. Forstner, Mark Frye, Janice M. Fullerton, Maria Grigoroiu-Serbanescu, Paul Grof, Ryota Hashimoto, Liping Hou, Esther Jiménez, Tadafumi Kato, John Kelsoe, Sarah Kittel-Schneider, Po-Hsiu Kuo, Ichiro Kusumi, Catharina Lavebratt, Mirko Manchia, Lina Martinsson, Manuel Mattheisen, Francis J. McMahon, Vincent Millischer, Philip B. Mitchell, Markus M. Nöthen, Claire O’Donovan, Norio Ozaki, Claudia Pisanu, Andreas Reif, Marcella Rietschel, Guy Rouleau, Janusz Rybakowski, Martin Schalling, Peter R. Schofield, Thomas G. Schulze, Giovanni Severino, Alessio Squassina, Julia Veeh, Eduard Vieta, Thomas Trappenberg, Martin Alda
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/3122c8062c9b4f39a9a4aa0e6220ae8c
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spelling	oai:doaj.org-article:3122c8062c9b4f39a9a4aa0e6220ae8c2021-12-02T14:01:20ZPrediction of lithium response using genomic data10.1038/s41598-020-80814-z2045-2322https://doaj.org/article/3122c8062c9b4f39a9a4aa0e6220ae8c2021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-80814-zhttps://doaj.org/toc/2045-2322Abstract 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.William StoneAbraham NunesKazufumi AkiyamaNirmala AkulaRaffaella ArdauJean-Michel AubryLena BacklundMichael BauerFrank BellivierPablo CervantesHsi-Chung ChenCaterina ChillottiCristiana CruceanuAlexandre DayerFranziska DegenhardtMaria Del ZompoAndreas J. ForstnerMark FryeJanice M. FullertonMaria Grigoroiu-SerbanescuPaul GrofRyota HashimotoLiping HouEsther JiménezTadafumi KatoJohn KelsoeSarah Kittel-SchneiderPo-Hsiu KuoIchiro KusumiCatharina LavebrattMirko ManchiaLina MartinssonManuel MattheisenFrancis J. McMahonVincent MillischerPhilip B. MitchellMarkus M. NöthenClaire O’DonovanNorio OzakiClaudia PisanuAndreas ReifMarcella RietschelGuy RouleauJanusz RybakowskiMartin SchallingPeter R. SchofieldThomas G. SchulzeGiovanni SeverinoAlessio SquassinaJulia VeehEduard VietaThomas TrappenbergMartin AldaNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q William Stone Abraham Nunes Kazufumi Akiyama Nirmala Akula Raffaella Ardau Jean-Michel Aubry Lena Backlund Michael Bauer Frank Bellivier Pablo Cervantes Hsi-Chung Chen Caterina Chillotti Cristiana Cruceanu Alexandre Dayer Franziska Degenhardt Maria Del Zompo Andreas J. Forstner Mark Frye Janice M. Fullerton Maria Grigoroiu-Serbanescu Paul Grof Ryota Hashimoto Liping Hou Esther Jiménez Tadafumi Kato John Kelsoe Sarah Kittel-Schneider Po-Hsiu Kuo Ichiro Kusumi Catharina Lavebratt Mirko Manchia Lina Martinsson Manuel Mattheisen Francis J. McMahon Vincent Millischer Philip B. Mitchell Markus M. Nöthen Claire O’Donovan Norio Ozaki Claudia Pisanu Andreas Reif Marcella Rietschel Guy Rouleau Janusz Rybakowski Martin Schalling Peter R. Schofield Thomas G. Schulze Giovanni Severino Alessio Squassina Julia Veeh Eduard Vieta Thomas Trappenberg Martin Alda Prediction of lithium response using genomic data
description	Abstract 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	article
author	William Stone Abraham Nunes Kazufumi Akiyama Nirmala Akula Raffaella Ardau Jean-Michel Aubry Lena Backlund Michael Bauer Frank Bellivier Pablo Cervantes Hsi-Chung Chen Caterina Chillotti Cristiana Cruceanu Alexandre Dayer Franziska Degenhardt Maria Del Zompo Andreas J. Forstner Mark Frye Janice M. Fullerton Maria Grigoroiu-Serbanescu Paul Grof Ryota Hashimoto Liping Hou Esther Jiménez Tadafumi Kato John Kelsoe Sarah Kittel-Schneider Po-Hsiu Kuo Ichiro Kusumi Catharina Lavebratt Mirko Manchia Lina Martinsson Manuel Mattheisen Francis J. McMahon Vincent Millischer Philip B. Mitchell Markus M. Nöthen Claire O’Donovan Norio Ozaki Claudia Pisanu Andreas Reif Marcella Rietschel Guy Rouleau Janusz Rybakowski Martin Schalling Peter R. Schofield Thomas G. Schulze Giovanni Severino Alessio Squassina Julia Veeh Eduard Vieta Thomas Trappenberg Martin Alda
author_facet	William Stone Abraham Nunes Kazufumi Akiyama Nirmala Akula Raffaella Ardau Jean-Michel Aubry Lena Backlund Michael Bauer Frank Bellivier Pablo Cervantes Hsi-Chung Chen Caterina Chillotti Cristiana Cruceanu Alexandre Dayer Franziska Degenhardt Maria Del Zompo Andreas J. Forstner Mark Frye Janice M. Fullerton Maria Grigoroiu-Serbanescu Paul Grof Ryota Hashimoto Liping Hou Esther Jiménez Tadafumi Kato John Kelsoe Sarah Kittel-Schneider Po-Hsiu Kuo Ichiro Kusumi Catharina Lavebratt Mirko Manchia Lina Martinsson Manuel Mattheisen Francis J. McMahon Vincent Millischer Philip B. Mitchell Markus M. Nöthen Claire O’Donovan Norio Ozaki Claudia Pisanu Andreas Reif Marcella Rietschel Guy Rouleau Janusz Rybakowski Martin Schalling Peter R. Schofield Thomas G. Schulze Giovanni Severino Alessio Squassina Julia Veeh Eduard Vieta Thomas Trappenberg Martin Alda
author_sort	William Stone
title	Prediction of lithium response using genomic data
title_short	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_sort	prediction of lithium response using genomic data
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/3122c8062c9b4f39a9a4aa0e6220ae8c
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Prediction of lithium response using genomic data

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