Novel polygenic risk score approach with transcriptome-based weighting for genetic risk prediction of late-onset Alzheimer's disease.

Abstract

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, early detection and 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. 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) (Table 1). 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. Then, PRS was derived based on the weighted GWAS summary statistics using PRSice-2. Finally, we evaluated prediction performance of a set of different models that incorporated demographic information and TW-PRS. A total of 17,588 gene expression weights were obtained from 13 brain tissues. These gene expression weights covered about 220,000 SNPs. Among them, expression weights were applied to about 140,000 SNPs overlapped with GWAS summary statistics. An AD prediction using conventional PRS yielded a pseudo-R2 of 0.0462 (P=1.30E-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). As shown in Table 2, 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). This study proposes a novel TW-PRS approach that combines predicted tissue-specific transcriptomic weights and PRS. Expression weights of brain regions critical to AD progression enhanced the performance of conventional PRS. 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.