Estimating shared polygenicity identifies novel druggable genes for Alzheimer's disease

The set of AD exclusive genes which are viable druggable candidates is likely very small. This highlights the utility of evaluating the set of repurposable candidates, which we show may be significantly larger. Together, our work highlights the power of our method to identify putatively safer and efficacious drug targets for AD and other AD-related dementia if broadly applied.

Authors

Institutions

Publication Details

Journal
Alzheimer s & Dementia
Published
2025-12-01
DOI
https://doi.org/10.1002/alz70855_102953
Primary Topic
Genetic Associations and Epidemiology
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Estimating shared polygenicity identifies novel druggable genes for Alzheimer's disease

Noah J Lorincz‐Comi, Feixiong Cheng
Alzheimer s & Dementia
Genetic Associations and Epidemiology
article

Estimating shared polygenicity identifies novel druggable genes for Alzheimer's disease

Noah J Lorincz‐Comi, Feixiong Cheng
article en

Abstract

The set of AD exclusive genes which are viable druggable candidates is likely very small. This highlights the utility of evaluating the set of repurposable candidates, which we show may be significantly larger. Together, our work highlights the power of our method to identify putatively safer and efficacious drug targets for AD and other AD-related dementia if broadly applied.

Alzheimer s & DementiaVol. 21(S1)
Cleveland Clinic (US)
Openalex Percentile: Top 23%
Genetic Associations and Epidemiology
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.