Background: Recent genetic studies showed that polygenic risk score (PRS) could be used to identify individuals at high risk of Alzheimer's disease (AD). Despite this success, prediction and early intervention of AD still remain challenging. In this study, we suggest a novel PRS approach that is weighted by predicted tissue-specific gene expression levels.

Method: We used whole-genome sequencing and phenotypic data of 446 European participants (207 late-onset AD cases and 239 cognitively normal controls) from the Alzheimer’s disease Neuroimaging Initiative (ADNI). We performed transcriptome-wide association studies (TWAS) in 13 brain regions by using MetaXcan (weights from GTEx V8) and GWAS summary statistics (IGAP stage 1 excluding ADNI participants) and integrated these 13 TWAS results using MultiXcan. To generate transcriptome-based weighting (TW)-PRS, expression weights in each gene were mapped to SNPs in the gene, and these were applied as additional weights to the SNPs’ beta coefficients in the GWAS summary statistics.

Result: An AD prediction using conventional PRS yielded a pseudo-R2 of 0.0462 (P=1E-04). Compared with the conventional PRS, the TW-PRS improved the performance and statistical power with a pseudo-R2 of 0.0574 (P=1.74E-05). A fully adjusted model achieved an AUC of 0.794 in which TW-PRS were significant even when APOE ε4 status and demographic information were adjusted (P=2.33E-05).

Conclusion: Our finding suggests that tissue-specific transcriptomic factors may be independent and complementary to conventional PRS and provide additional information for tissue-specific regulatory effects in AD.