Inclusion of genetic variants in an ensemble of gradient boosting decision trees does not improve the prediction of citalopram treatment response

Abstract Identifying in advance who is unlikely to respond to a specific antidepressant treatment is crucial to precision medicine efforts. The current work leverages genome-wide genetic variation and machine learning to predict response to the antidepressant citalopram using data from the Sequenced...

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Autores principales:	Jason Shumake, Travis T. Mallard, John E. McGeary, Christopher G. Beevers
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/6e984ede38424cfeb69f6eeec4c072b3
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spelling	oai:doaj.org-article:6e984ede38424cfeb69f6eeec4c072b32021-12-02T13:30:22ZInclusion of genetic variants in an ensemble of gradient boosting decision trees does not improve the prediction of citalopram treatment response10.1038/s41598-021-83338-22045-2322https://doaj.org/article/6e984ede38424cfeb69f6eeec4c072b32021-02-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-83338-2https://doaj.org/toc/2045-2322Abstract Identifying in advance who is unlikely to respond to a specific antidepressant treatment is crucial to precision medicine efforts. The current work leverages genome-wide genetic variation and machine learning to predict response to the antidepressant citalopram using data from the Sequenced Treatment Alternatives to Relieve Depression (STARD) trial (n = 1257 with both valid genomic and outcome data). A confirmatory approach selected 11 SNPs previously reported to predict response to escitalopram in a sample different from the current study. A novel exploratory approach selected SNPs from across the genome using nested cross-validation with elastic net logistic regression with a predominantly lasso penalty (alpha = 0.99). SNPs from each approach were combined with baseline clinical predictors and treatment response outcomes were predicted using a stacked ensemble of gradient boosting decision trees. Using pre-treatment clinical and symptom predictors only, out-of-fold prediction of a novel treatment response definition based on STARD treatment guidelines was acceptable, AUC = .659, 95% CI [0.629, 0.689]. The inclusion of SNPs using confirmatory or exploratory selection methods did not improve the out-of-fold prediction of treatment response (AUCs were .662, 95% CI [0.632, 0.692] and .655, 95% CI [0.625, 0.685], respectively). A similar pattern of results were observed for the secondary outcomes of the presence or absence of distressing side effects regardless of treatment response and achieving remission or satisfactory partial response, assuming medication tolerance. In the current study, incorporating SNP variation into prognostic models did not enhance the prediction of citalopram response in the STAR*D sample.Jason ShumakeTravis T. MallardJohn E. McGearyChristopher G. BeeversNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jason Shumake Travis T. Mallard John E. McGeary Christopher G. Beevers Inclusion of genetic variants in an ensemble of gradient boosting decision trees does not improve the prediction of citalopram treatment response
description	Abstract Identifying in advance who is unlikely to respond to a specific antidepressant treatment is crucial to precision medicine efforts. The current work leverages genome-wide genetic variation and machine learning to predict response to the antidepressant citalopram using data from the Sequenced Treatment Alternatives to Relieve Depression (STARD) trial (n = 1257 with both valid genomic and outcome data). A confirmatory approach selected 11 SNPs previously reported to predict response to escitalopram in a sample different from the current study. A novel exploratory approach selected SNPs from across the genome using nested cross-validation with elastic net logistic regression with a predominantly lasso penalty (alpha = 0.99). SNPs from each approach were combined with baseline clinical predictors and treatment response outcomes were predicted using a stacked ensemble of gradient boosting decision trees. Using pre-treatment clinical and symptom predictors only, out-of-fold prediction of a novel treatment response definition based on STARD treatment guidelines was acceptable, AUC = .659, 95% CI [0.629, 0.689]. The inclusion of SNPs using confirmatory or exploratory selection methods did not improve the out-of-fold prediction of treatment response (AUCs were .662, 95% CI [0.632, 0.692] and .655, 95% CI [0.625, 0.685], respectively). A similar pattern of results were observed for the secondary outcomes of the presence or absence of distressing side effects regardless of treatment response and achieving remission or satisfactory partial response, assuming medication tolerance. In the current study, incorporating SNP variation into prognostic models did not enhance the prediction of citalopram response in the STAR*D sample.
format	article
author	Jason Shumake Travis T. Mallard John E. McGeary Christopher G. Beevers
author_facet	Jason Shumake Travis T. Mallard John E. McGeary Christopher G. Beevers
author_sort	Jason Shumake
title	Inclusion of genetic variants in an ensemble of gradient boosting decision trees does not improve the prediction of citalopram treatment response
title_short	Inclusion of genetic variants in an ensemble of gradient boosting decision trees does not improve the prediction of citalopram treatment response
title_full	Inclusion of genetic variants in an ensemble of gradient boosting decision trees does not improve the prediction of citalopram treatment response
title_fullStr	Inclusion of genetic variants in an ensemble of gradient boosting decision trees does not improve the prediction of citalopram treatment response
title_full_unstemmed	Inclusion of genetic variants in an ensemble of gradient boosting decision trees does not improve the prediction of citalopram treatment response
title_sort	inclusion of genetic variants in an ensemble of gradient boosting decision trees does not improve the prediction of citalopram treatment response
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/6e984ede38424cfeb69f6eeec4c072b3
work_keys_str_mv	AT jasonshumake inclusionofgeneticvariantsinanensembleofgradientboostingdecisiontreesdoesnotimprovethepredictionofcitalopramtreatmentresponse AT travistmallard inclusionofgeneticvariantsinanensembleofgradientboostingdecisiontreesdoesnotimprovethepredictionofcitalopramtreatmentresponse AT johnemcgeary inclusionofgeneticvariantsinanensembleofgradientboostingdecisiontreesdoesnotimprovethepredictionofcitalopramtreatmentresponse AT christophergbeevers inclusionofgeneticvariantsinanensembleofgradientboostingdecisiontreesdoesnotimprovethepredictionofcitalopramtreatmentresponse
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Inclusion of genetic variants in an ensemble of gradient boosting decision trees does not improve the prediction of citalopram treatment response

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