  Genome-wide association studies (GWAS) have achieved great success in the
genetic study of Alzheimer's disease (AD). Collaborative imaging genetics
studies across different research institutions show the effectiveness of
detecting genetic risk factors. However, the high dimensionality of GWAS data
poses significant challenges in detecting risk SNPs for AD. Selecting relevant
features is crucial in predicting the response variable. In this study, we
propose a novel Distributed Feature Selection Framework (DFSF) to conduct the
large-scale imaging genetics studies across multiple institutions. To speed up
the learning process, we propose a family of distributed group Lasso screening
rules to identify irrelevant features and remove them from the optimization.
Then we select the relevant group features by performing the group Lasso
feature selection process in a sequence of parameters. Finally, we employ the
stability selection to rank the top risk SNPs that might help detect the early
stage of AD. To the best of our knowledge, this is the first distributed
feature selection model integrated with group Lasso feature selection as well
as detecting the risk genetic factors across multiple research institutions
system. Empirical studies are conducted on 809 subjects with 5.9 million SNPs
which are distributed across several individual institutions, demonstrating the
efficiency and effectiveness of the proposed method.
