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Drug Development.

Noah J Lorincz-Comi1, Feixiong Cheng2, Wenqiang Song3

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This study introduces a new gene-based test to find Alzheimer's disease (AD) drug targets using genetic and multi-omic data. It identified 151 potential targets, including NTRK1, which showed promise in reducing tau pathology.

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Area of Science:

  • Genetics
  • Neuroscience
  • Pharmacology

Background:

  • Genome-wide association studies (GWAS) have limitations in identifying Alzheimer's disease (AD) drug targets.
  • Gene-based association tests offer greater statistical power than single nucleotide polymorphism (SNP)-based methods.
  • Existing gene-based approaches do not fully utilize functional data from relevant tissues.

Purpose of the Study:

  • To develop and apply a novel gene-based association test (GenT) for efficient genome-wide screening of AD drug targets.
  • To integrate multi-omic data with large GWAS data for enhanced target identification.
  • To experimentally validate a candidate AD drug target.

Main Methods:

  • Developed GenT, a gene-based association test integrating multi-omic data (eQTLs, pQTLs) with AD GWAS data.
  • Applied GenT to analyze up to 18,273 genes using data from ROSMAP, MetaBrain, and GTEx cohorts.
  • Experimentally validated candidate targets in vitro using AD patient-derived iPSC neurons.

Main Results:

  • Identified 151 druggable, potentially causal AD genes, including RIPK2, NTRK1, and RIOK1, not found by SNP-based GWAS.
  • Discovered 103 eQTL-associated genes and 74 pQTL-associated genes, with 26 shared.
  • Demonstrated that an NTRK1 inhibitor significantly reduced tau hyper-phosphorylation in AD iPSC neurons.

Conclusions:

  • Gene-based association testing integrating multi-omic data is a powerful strategy for AD drug target discovery.
  • This approach offers advantages over traditional SNP-based inference, which has lower statistical power.
  • Findings support the use of human genetic and genomic data for identifying and validating AD therapeutics.