Abstract
Transcriptome-wide Association Studies (TWAS) extend genome-wide association studies (GWAS) by integrating genetically-regulated gene expression models. We performed the most powerful AD-TWAS to date, using summary statistics from
-eQTL meta-analyses and the largest clinically-adjudicated Alzheimer's Disease (AD) GWAS.
We implemented the OTTERS TWAS pipeline, leveraging
-eQTL data from cortical brain tissue (MetaBrain; N=2,683) and blood (eQTLGen; N=31,684) to predict gene expression, then applied these models to AD-GWAS data (Cases=21,982; Controls=44,944).
We identified and validated five novel gene associations in cortical brain tissue (
,
,
,
,
) and six genes proximal to known AD-related GWAS loci (Blood:
; Brain:
,
,
,
,
). Further, using causal eQTL fine-mapping, we generated sparse models that retained the strength of the AD-TWAS association for
,
,
,
, and
.
Our comprehensive AD-TWAS discovered new gene associations and provided insights into the functional relevance of previously associated variants.