Back to search

Article

Machine Learning-Based Prediction of Cell-type Resolved Brain eQTLs Enhances Discovery of Variants Explaining Alzheimer’s Disease Heritability

2025-12-04

Abstract excerpt

The majority of causal genome-wide association studies (GWAS) variants for Alzheimer’s disease (AD) are believed to reside in noncoding regions of the genome, where they likely affect gene regulation, particularly in microglia. Although expression Quantitative Trait Loci (eQTL) studies offer valuable insights into gene regulation, they tend to identify variants in the promoter regions of genes under weaker selecti...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work
5d648864-f19b-5737-a466-6fedf1eb1afc
DOI
10.64898/2025.12.03.25341562
Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest
Interactive article relationship graphSelect a related publication card to move it into the centre and load its closest explainable connections. Solid lines are source-backed structured connections. Dashed lines are semantic discovery signals and are not scientific evidence.
Machine Learning-Based Prediction of Cell-type Resolved Brain eQTLs Enhances Discovery of Variants Explaining Alzheimer’s Disease HeritabilityDOI 10.64898/2025.12.03.25341562
Select a neighboring publication to make it the new centre.