sPLINK: a hybrid federated tool as a robust alternative to meta-analysis in genome-wide association studies

Meta-analysis has been established as an effective approach to combining summary statistics of several genome-wide association studies (GWAS). However, the accuracy of meta-analysis can be attenuated in the presence of cross-study heterogeneity. We present sPLINK, a hybrid federated and user-friendl...

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Autores principales: Nasirigerdeh, Reza, Torkzadehmahani, Reihaneh, Matschinske, Julian, Frisch, Tobias, List, Markus, Späth, Julian, Weiss, Stefan, Völker, Uwe, Pitkänen, Esa, Heider, Dominik, Wenke, Nina Kerstin, Kaissis, Georgios, Rueckert, Daniel, Kacprowski, Tim, Baumbach, Jan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Method
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8785575/
https://www.ncbi.nlm.nih.gov/pubmed/35073941
http://dx.doi.org/10.1186/s13059-021-02562-1
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author Nasirigerdeh, Reza
Torkzadehmahani, Reihaneh
Matschinske, Julian
Frisch, Tobias
List, Markus
Späth, Julian
Weiss, Stefan
Völker, Uwe
Pitkänen, Esa
Heider, Dominik
Wenke, Nina Kerstin
Kaissis, Georgios
Rueckert, Daniel
Kacprowski, Tim
Baumbach, Jan
author_facet Nasirigerdeh, Reza
Torkzadehmahani, Reihaneh
Matschinske, Julian
Frisch, Tobias
List, Markus
Späth, Julian
Weiss, Stefan
Völker, Uwe
Pitkänen, Esa
Heider, Dominik
Wenke, Nina Kerstin
Kaissis, Georgios
Rueckert, Daniel
Kacprowski, Tim
Baumbach, Jan
author_sort Nasirigerdeh, Reza
collection PubMed
description Meta-analysis has been established as an effective approach to combining summary statistics of several genome-wide association studies (GWAS). However, the accuracy of meta-analysis can be attenuated in the presence of cross-study heterogeneity. We present sPLINK, a hybrid federated and user-friendly tool, which performs privacy-aware GWAS on distributed datasets while preserving the accuracy of the results. sPLINK is robust against heterogeneous distributions of data across cohorts while meta-analysis considerably loses accuracy in such scenarios. sPLINK achieves practical runtime and acceptable network usage for chi-square and linear/logistic regression tests. sPLINK is available at https://exbio.wzw.tum.de/splink. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at (10.1186/s13059-021-02562-1).
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spelling pubmed-87855752022-01-24 sPLINK: a hybrid federated tool as a robust alternative to meta-analysis in genome-wide association studies Nasirigerdeh, Reza Torkzadehmahani, Reihaneh Matschinske, Julian Frisch, Tobias List, Markus Späth, Julian Weiss, Stefan Völker, Uwe Pitkänen, Esa Heider, Dominik Wenke, Nina Kerstin Kaissis, Georgios Rueckert, Daniel Kacprowski, Tim Baumbach, Jan Genome Biol Method Meta-analysis has been established as an effective approach to combining summary statistics of several genome-wide association studies (GWAS). However, the accuracy of meta-analysis can be attenuated in the presence of cross-study heterogeneity. We present sPLINK, a hybrid federated and user-friendly tool, which performs privacy-aware GWAS on distributed datasets while preserving the accuracy of the results. sPLINK is robust against heterogeneous distributions of data across cohorts while meta-analysis considerably loses accuracy in such scenarios. sPLINK achieves practical runtime and acceptable network usage for chi-square and linear/logistic regression tests. sPLINK is available at https://exbio.wzw.tum.de/splink. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at (10.1186/s13059-021-02562-1). BioMed Central 2022-01-24 /pmc/articles/PMC8785575/ /pubmed/35073941 http://dx.doi.org/10.1186/s13059-021-02562-1 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/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/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Method
Nasirigerdeh, Reza
Torkzadehmahani, Reihaneh
Matschinske, Julian
Frisch, Tobias
List, Markus
Späth, Julian
Weiss, Stefan
Völker, Uwe
Pitkänen, Esa
Heider, Dominik
Wenke, Nina Kerstin
Kaissis, Georgios
Rueckert, Daniel
Kacprowski, Tim
Baumbach, Jan
sPLINK: a hybrid federated tool as a robust alternative to meta-analysis in genome-wide association studies
title sPLINK: a hybrid federated tool as a robust alternative to meta-analysis in genome-wide association studies
title_full sPLINK: a hybrid federated tool as a robust alternative to meta-analysis in genome-wide association studies
title_fullStr sPLINK: a hybrid federated tool as a robust alternative to meta-analysis in genome-wide association studies
title_full_unstemmed sPLINK: a hybrid federated tool as a robust alternative to meta-analysis in genome-wide association studies
title_short sPLINK: a hybrid federated tool as a robust alternative to meta-analysis in genome-wide association studies
title_sort splink: a hybrid federated tool as a robust alternative to meta-analysis in genome-wide association studies
topic Method
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8785575/
https://www.ncbi.nlm.nih.gov/pubmed/35073941
http://dx.doi.org/10.1186/s13059-021-02562-1
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