A genetic risk score using human chromosomal-scale length variation can predict schizophrenia

Abstract Studies indicate that schizophrenia has a genetic component, however it cannot be isolated to a single gene. We aimed to determine how well one could predict that a person will develop schizophrenia based on their germ line genetics. We compared 1129 people from the UK Biobank dataset who h...

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Autores principales:	Christopher Toh, James P. Brody
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/1c343b803ff6463eb810f03984a954f2
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spelling	oai:doaj.org-article:1c343b803ff6463eb810f03984a954f22021-12-02T18:48:09ZA genetic risk score using human chromosomal-scale length variation can predict schizophrenia10.1038/s41598-021-97983-02045-2322https://doaj.org/article/1c343b803ff6463eb810f03984a954f22021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-97983-0https://doaj.org/toc/2045-2322Abstract Studies indicate that schizophrenia has a genetic component, however it cannot be isolated to a single gene. We aimed to determine how well one could predict that a person will develop schizophrenia based on their germ line genetics. We compared 1129 people from the UK Biobank dataset who had a diagnosis of schizophrenia to an equal number of age matched people drawn from the general UK Biobank population. For each person, we constructed a profile consisting of numbers. Each number characterized the length of segments of chromosomes. We tested several machine learning algorithms to determine which was most effective in predicting schizophrenia and if any improvement in prediction occurs by breaking the chromosomes into smaller chunks. We found that the stacked ensemble, performed best with an area under the receiver operating characteristic curve (AUC) of 0.545 (95% CI 0.539–0.550). We noted an increase in the AUC by breaking the chromosomes into smaller chunks for analysis. Using SHAP values, we identified the X chromosome as the most important contributor to the predictive model. We conclude that germ line chromosomal scale length variation data could provide an effective genetic risk score for schizophrenia which performs better than chance.Christopher TohJames P. BrodyNature 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 Christopher Toh James P. Brody A genetic risk score using human chromosomal-scale length variation can predict schizophrenia
description	Abstract Studies indicate that schizophrenia has a genetic component, however it cannot be isolated to a single gene. We aimed to determine how well one could predict that a person will develop schizophrenia based on their germ line genetics. We compared 1129 people from the UK Biobank dataset who had a diagnosis of schizophrenia to an equal number of age matched people drawn from the general UK Biobank population. For each person, we constructed a profile consisting of numbers. Each number characterized the length of segments of chromosomes. We tested several machine learning algorithms to determine which was most effective in predicting schizophrenia and if any improvement in prediction occurs by breaking the chromosomes into smaller chunks. We found that the stacked ensemble, performed best with an area under the receiver operating characteristic curve (AUC) of 0.545 (95% CI 0.539–0.550). We noted an increase in the AUC by breaking the chromosomes into smaller chunks for analysis. Using SHAP values, we identified the X chromosome as the most important contributor to the predictive model. We conclude that germ line chromosomal scale length variation data could provide an effective genetic risk score for schizophrenia which performs better than chance.
format	article
author	Christopher Toh James P. Brody
author_facet	Christopher Toh James P. Brody
author_sort	Christopher Toh
title	A genetic risk score using human chromosomal-scale length variation can predict schizophrenia
title_short	A genetic risk score using human chromosomal-scale length variation can predict schizophrenia
title_full	A genetic risk score using human chromosomal-scale length variation can predict schizophrenia
title_fullStr	A genetic risk score using human chromosomal-scale length variation can predict schizophrenia
title_full_unstemmed	A genetic risk score using human chromosomal-scale length variation can predict schizophrenia
title_sort	genetic risk score using human chromosomal-scale length variation can predict schizophrenia
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/1c343b803ff6463eb810f03984a954f2
work_keys_str_mv	AT christophertoh ageneticriskscoreusinghumanchromosomalscalelengthvariationcanpredictschizophrenia AT jamespbrody ageneticriskscoreusinghumanchromosomalscalelengthvariationcanpredictschizophrenia AT christophertoh geneticriskscoreusinghumanchromosomalscalelengthvariationcanpredictschizophrenia AT jamespbrody geneticriskscoreusinghumanchromosomalscalelengthvariationcanpredictschizophrenia
_version_	1718377607673675776

A genetic risk score using human chromosomal-scale length variation can predict schizophrenia

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