Automated screening for Fragile X premutation carriers based on linguistic and cognitive computational phenotypes

Abstract Millions of people globally are at high risk for neurodegenerative disorders, infertility or having children with a disability as a result of the Fragile X (FX) premutation, a genetic abnormality in FMR1 that is underdiagnosed. Despite the high prevalence of the FX premutation and its effec...

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Autores principales:	Arezoo Movaghar, Marsha Mailick, Audra Sterling, Jan Greenberg, Krishanu Saha
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/a12a8eef4c964f3096346a8e67d3e32c
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spelling	oai:doaj.org-article:a12a8eef4c964f3096346a8e67d3e32c2021-12-02T15:06:00ZAutomated screening for Fragile X premutation carriers based on linguistic and cognitive computational phenotypes10.1038/s41598-017-02682-42045-2322https://doaj.org/article/a12a8eef4c964f3096346a8e67d3e32c2017-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-02682-4https://doaj.org/toc/2045-2322Abstract Millions of people globally are at high risk for neurodegenerative disorders, infertility or having children with a disability as a result of the Fragile X (FX) premutation, a genetic abnormality in FMR1 that is underdiagnosed. Despite the high prevalence of the FX premutation and its effect on public health and family planning, most FX premutation carriers are unaware of their condition. Since genetic testing for the premutation is resource intensive, it is not practical to screen individuals for FX premutation status using genetic testing. In a novel approach to phenotyping, we have utilized audio recordings and cognitive profiling assessed via self-administered questionnaires on 200 females. Machine-learning methods were developed to discriminate FX premutation carriers from mothers of children with autism spectrum disorders, the comparison group. By using a random forest classifier, FX premutation carriers could be identified in an automated fashion with high precision and recall (0.81 F1 score). Linguistic and cognitive phenotypes that were highly associated with FX premutation carriers were high language dysfluency, poor ability to organize material, and low self-monitoring. Our framework sets the foundation for computational phenotyping strategies to pre-screen large populations for this genetic variant with nominal costs.Arezoo MovagharMarsha MailickAudra SterlingJan GreenbergKrishanu SahaNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-11 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Arezoo Movaghar Marsha Mailick Audra Sterling Jan Greenberg Krishanu Saha Automated screening for Fragile X premutation carriers based on linguistic and cognitive computational phenotypes
description	Abstract Millions of people globally are at high risk for neurodegenerative disorders, infertility or having children with a disability as a result of the Fragile X (FX) premutation, a genetic abnormality in FMR1 that is underdiagnosed. Despite the high prevalence of the FX premutation and its effect on public health and family planning, most FX premutation carriers are unaware of their condition. Since genetic testing for the premutation is resource intensive, it is not practical to screen individuals for FX premutation status using genetic testing. In a novel approach to phenotyping, we have utilized audio recordings and cognitive profiling assessed via self-administered questionnaires on 200 females. Machine-learning methods were developed to discriminate FX premutation carriers from mothers of children with autism spectrum disorders, the comparison group. By using a random forest classifier, FX premutation carriers could be identified in an automated fashion with high precision and recall (0.81 F1 score). Linguistic and cognitive phenotypes that were highly associated with FX premutation carriers were high language dysfluency, poor ability to organize material, and low self-monitoring. Our framework sets the foundation for computational phenotyping strategies to pre-screen large populations for this genetic variant with nominal costs.
format	article
author	Arezoo Movaghar Marsha Mailick Audra Sterling Jan Greenberg Krishanu Saha
author_facet	Arezoo Movaghar Marsha Mailick Audra Sterling Jan Greenberg Krishanu Saha
author_sort	Arezoo Movaghar
title	Automated screening for Fragile X premutation carriers based on linguistic and cognitive computational phenotypes
title_short	Automated screening for Fragile X premutation carriers based on linguistic and cognitive computational phenotypes
title_full	Automated screening for Fragile X premutation carriers based on linguistic and cognitive computational phenotypes
title_fullStr	Automated screening for Fragile X premutation carriers based on linguistic and cognitive computational phenotypes
title_full_unstemmed	Automated screening for Fragile X premutation carriers based on linguistic and cognitive computational phenotypes
title_sort	automated screening for fragile x premutation carriers based on linguistic and cognitive computational phenotypes
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/a12a8eef4c964f3096346a8e67d3e32c
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Automated screening for Fragile X premutation carriers based on linguistic and cognitive computational phenotypes

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