MADloy: robust detection of mosaic loss of chromosome Y from genotype-array-intensity data

Background: Accurate protocols and methods to robustly detect the mosaic loss of chromosome Y (mLOY) are needed given its reported role in cancer, several age-related disorders and overall male mortality. Intensity SNP-array data have been used to infer mLOY status and to determine its prominent rol...

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Autores: González, Juan Ramón, López Sánchez, Marcos, 1986-, Cáceres, Alejandro, Puig, Pedro, Esko, Tõnu, Pérez Jurado, Luis Alberto
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2020
País:España
Institución:Universitat Pompeu Fabra
Repositorio:Repositorio Digital de la UPF
OAI Identifier:oai:repositori.upf.edu:10230/45954
Acceso en línea:http://hdl.handle.net/10230/45954
http://dx.doi.org/10.1186/s12859-020-03768-z
Access Level:acceso abierto
Palabra clave:Bioconductor
Loss of chromosome Y
SNP array
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spelling MADloy: robust detection of mosaic loss of chromosome Y from genotype-array-intensity dataGonzález, Juan RamónLópez Sánchez, Marcos, 1986-Cáceres, AlejandroPuig, PedroEsko, TõnuPérez Jurado, Luis AlbertoBioconductorLoss of chromosome YSNP arrayBackground: Accurate protocols and methods to robustly detect the mosaic loss of chromosome Y (mLOY) are needed given its reported role in cancer, several age-related disorders and overall male mortality. Intensity SNP-array data have been used to infer mLOY status and to determine its prominent role in male disease. However, discrepancies of reported findings can be due to the uncertainty and variability of the methods used for mLOY detection and to the differences in the tissue-matrix used. Results: We created a publicly available software tool called MADloy (Mosaic Alteration Detection for LOY) that incorporates existing methods and includes a new robust approach, allowing efficient calling in large studies and comparisons between methods. MADloy optimizes mLOY calling by correctly modeling the underlying reference population with no-mLOY status and incorporating B-deviation information. We observed improvements in the calling accuracy to previous methods, using experimentally validated samples, and an increment in the statistical power to detect associations with disease and mortality, using simulation studies and real dataset analyses. To understand discrepancies in mLOY detection across different tissues, we applied MADloy to detect the increment of mLOY cellularity in blood on 18 individuals after 3 years and to confirm that its detection in saliva was sub-optimal (41%). We additionally applied MADloy to detect the down-regulation genes in the chromosome Y in kidney and bladder tumors with mLOY, and to perform pathway analyses for the detection of mLOY in blood. Conclusions: MADloy is a new software tool implemented in R for the easy and robust calling of mLOY status across different tissues aimed to facilitate its study in large epidemiological studies.BioMed Central202020202020info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/10230/45954http://dx.doi.org/10.1186/s12859-020-03768-zreponame:Repositorio Digital de la UPFinstname:Universitat Pompeu FabraInglésBMC Bioinformatics. 2020; 21(1):533© The Author(s) 2020. Open Access This 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/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data mahttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:repositori.upf.edu:10230/459542026-06-12T07:21:37Z
dc.title.none.fl_str_mv MADloy: robust detection of mosaic loss of chromosome Y from genotype-array-intensity data
title MADloy: robust detection of mosaic loss of chromosome Y from genotype-array-intensity data
spellingShingle MADloy: robust detection of mosaic loss of chromosome Y from genotype-array-intensity data
González, Juan Ramón
Bioconductor
Loss of chromosome Y
SNP array
title_short MADloy: robust detection of mosaic loss of chromosome Y from genotype-array-intensity data
title_full MADloy: robust detection of mosaic loss of chromosome Y from genotype-array-intensity data
title_fullStr MADloy: robust detection of mosaic loss of chromosome Y from genotype-array-intensity data
title_full_unstemmed MADloy: robust detection of mosaic loss of chromosome Y from genotype-array-intensity data
title_sort MADloy: robust detection of mosaic loss of chromosome Y from genotype-array-intensity data
dc.creator.none.fl_str_mv González, Juan Ramón
López Sánchez, Marcos, 1986-
Cáceres, Alejandro
Puig, Pedro
Esko, Tõnu
Pérez Jurado, Luis Alberto
author González, Juan Ramón
author_facet González, Juan Ramón
López Sánchez, Marcos, 1986-
Cáceres, Alejandro
Puig, Pedro
Esko, Tõnu
Pérez Jurado, Luis Alberto
author_role author
author2 López Sánchez, Marcos, 1986-
Cáceres, Alejandro
Puig, Pedro
Esko, Tõnu
Pérez Jurado, Luis Alberto
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Bioconductor
Loss of chromosome Y
SNP array
topic Bioconductor
Loss of chromosome Y
SNP array
description Background: Accurate protocols and methods to robustly detect the mosaic loss of chromosome Y (mLOY) are needed given its reported role in cancer, several age-related disorders and overall male mortality. Intensity SNP-array data have been used to infer mLOY status and to determine its prominent role in male disease. However, discrepancies of reported findings can be due to the uncertainty and variability of the methods used for mLOY detection and to the differences in the tissue-matrix used. Results: We created a publicly available software tool called MADloy (Mosaic Alteration Detection for LOY) that incorporates existing methods and includes a new robust approach, allowing efficient calling in large studies and comparisons between methods. MADloy optimizes mLOY calling by correctly modeling the underlying reference population with no-mLOY status and incorporating B-deviation information. We observed improvements in the calling accuracy to previous methods, using experimentally validated samples, and an increment in the statistical power to detect associations with disease and mortality, using simulation studies and real dataset analyses. To understand discrepancies in mLOY detection across different tissues, we applied MADloy to detect the increment of mLOY cellularity in blood on 18 individuals after 3 years and to confirm that its detection in saliva was sub-optimal (41%). We additionally applied MADloy to detect the down-regulation genes in the chromosome Y in kidney and bladder tumors with mLOY, and to perform pathway analyses for the detection of mLOY in blood. Conclusions: MADloy is a new software tool implemented in R for the easy and robust calling of mLOY status across different tissues aimed to facilitate its study in large epidemiological studies.
publishDate 2020
dc.date.none.fl_str_mv 2020
2020
2020
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10230/45954
http://dx.doi.org/10.1186/s12859-020-03768-z
url http://hdl.handle.net/10230/45954
http://dx.doi.org/10.1186/s12859-020-03768-z
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv BMC Bioinformatics. 2020; 21(1):533
dc.rights.none.fl_str_mv http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv BioMed Central
publisher.none.fl_str_mv BioMed Central
dc.source.none.fl_str_mv reponame:Repositorio Digital de la UPF
instname:Universitat Pompeu Fabra
instname_str Universitat Pompeu Fabra
reponame_str Repositorio Digital de la UPF
collection Repositorio Digital de la UPF
repository.name.fl_str_mv
repository.mail.fl_str_mv
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