Speaker
Mohammad Bakhtiari
(Data scientist)
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://featurecloud.ai/app/splink .
Primary authors
Co-authors
Julian Matschinske
Mr
Reza Nasirigerdeh
( AI in Medicine and Healthcare, Technical University of Munich, Munich, Germany)
Reihaneh Torkzadehmahani
(AI in Medicine and Healthcare, Technical University of Munich, Munich, Germany)