Bayesian estimation of shared polygenicity identifies drug targets and repurposable medicines for human complex diseases DOI Creative Commons
Noah Lorincz‐Comi, Feixiong Cheng

medRxiv (Cold Spring Harbor Laboratory), Journal Year: 2025, Volume and Issue: unknown

Published: March 17, 2025

Abstract Background Complex diseases may share portions of their polygenic architectures which can be leveraged to identify drug targets with low off-target potential or repurposable candidates. However, the literature lacks methods make these inferences at scale using publicly available data. Methods We introduce a Bayesian model estimate structure trait only gene-based association test statistics from GWAS summary data and returns gene-level posterior risk probabilities (PRPs). PRPs were used infer shared polygenicity between 496 pairs we measures that prioritize effects repurposing potential. Results Across 32 traits, estimated 69.5 97.5% disease-associated genes are multiple number druggable associated single disease ranged 1 (multiple sclerosis) 59 (schizophrenia). Estimating genetic architecture ALS all other traits identified KIT gene as potentially harmful target because its deleterious triglycerides, but also TBK1 SCN11B putatively safer non-association any 31 traits. additionally found 21 candidate repourposable for Alzheimer’s (AD) (e.g., PLEKHA1, PPIB ) 5 GAK, DGKQ ). Conclusions The sets have limited generally smaller compared pleiotropic targets, both represent promising directions future experimental studies.

Language: Английский

Bayesian estimation of shared polygenicity identifies drug targets and repurposable medicines for human complex diseases DOI Creative Commons
Noah Lorincz‐Comi, Feixiong Cheng

medRxiv (Cold Spring Harbor Laboratory), Journal Year: 2025, Volume and Issue: unknown

Published: March 17, 2025

Abstract Background Complex diseases may share portions of their polygenic architectures which can be leveraged to identify drug targets with low off-target potential or repurposable candidates. However, the literature lacks methods make these inferences at scale using publicly available data. Methods We introduce a Bayesian model estimate structure trait only gene-based association test statistics from GWAS summary data and returns gene-level posterior risk probabilities (PRPs). PRPs were used infer shared polygenicity between 496 pairs we measures that prioritize effects repurposing potential. Results Across 32 traits, estimated 69.5 97.5% disease-associated genes are multiple number druggable associated single disease ranged 1 (multiple sclerosis) 59 (schizophrenia). Estimating genetic architecture ALS all other traits identified KIT gene as potentially harmful target because its deleterious triglycerides, but also TBK1 SCN11B putatively safer non-association any 31 traits. additionally found 21 candidate repourposable for Alzheimer’s (AD) (e.g., PLEKHA1, PPIB ) 5 GAK, DGKQ ). Conclusions The sets have limited generally smaller compared pleiotropic targets, both represent promising directions future experimental studies.

Language: Английский

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