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

doi:10.1186/s13059-021-02562-1

2022

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Title:: sPLINK: a hybrid federated tool as a robust alternative to meta-analysis in genome-wide association studies.
Document type:: Journal Article; Research Support, Non-U.S. Gov't
Author(s):: 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
Abstract:: 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 . «
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-a... »
Journal title abbreviation:: Genome Biol
Year:: 2022
Journal volume:: 23
Journal issue:: 1
Fulltext / DOI:: doi:10.1186/s13059-021-02562-1
Pubmed ID:: http://view.ncbi.nlm.nih.gov/pubmed/35073941
Print-ISSN:: 1474-7596
TUM Institution:: Institut für Diagnostische und Interventionelle Radiologie (Prof. Makowski); Institut für KI und Informatik in der Medizin (Prof. Rückert)
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