Abstract
BackgroundGenome-wide association studies (GWAS) have identified numerous genetic variants associated with Alzheimer's disease (AD), but their functional implications remain unclear. Transcriptome-wide association studies (TWAS) offer enhanced statistical power by analyzing genetic associations at the gene level rather than at the variant level, enabling assessment of how genetically-regulated gene expression influences AD risk. However, previous AD-TWAS have been limited by small expression quantitative trait loci (eQTL) reference datasets or reliance on AD-by-proxy phenotypes.ObjectiveTo perform the most powerful AD-TWAS to date using summary statistics from the largest available brain and blood
-eQTL meta-analyses applied to the largest clinically-adjudicated AD GWAS.MethodsWe implemented the OTTERS TWAS pipeline to predict gene expression using the largest available
-eQTL data from cortical brain tissue (MetaBrain; N = 2683) and blood (eQTLGen; N = 31,684), and then applied these models to AD-GWAS data (Cases = 21,982; Controls = 44,944).ResultsWe identified and validated five novel gene associations in cortical brain tissue (
,
,
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) 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
.ConclusionsOur comprehensive AD-TWAS discovered new gene associations and provided insights into the functional relevance of previously associated variants, which enables us to further understand the genetic architecture underlying AD risk.