Leveraging the use of meta‐analysis summary statistics to improve gene expression prediction models

Genetically regulated gene expression as assessed via Transcriptome‐wide association studies (TWAS) can be used to elucidate the mechanistic basis Alzheimer’s Disease. A limitation of current TWAS approaches is the requirement for individual‐level datasets with both genotypes and gene expression measures to produce effective prediction models. We sought to improve the predictive performance of existing gene‐based models by leveraging easily accessible summary statistics that are powered by large sample sizes.