Leveraging Random Effects in Cistrome-Wide Association Studies for Decoding the Genetic Determinants of Prostate Cancer

Cistrome-wide association studies (CWAS) are pivotal for identifying genetic determinants of diseases by correlating genetically regulated cistrome states with phenotypes. Traditional CWAS typically develops a model based on cistrome and genotype data to associate predicted cistrome states with phen...

Full description

Saved in:
Bibliographic Details
Published in:Advanced science 2024-09, Vol.11 (36), p.e2400815-n/a
Main Authors: Shao, Mengting, Tian, Min, Chen, Kaiyang, Jiang, Hangjin, Zhang, Shuting, Li, Zhenghui, Shen, Yan, Chen, Feng, Shen, Baixin, Cao, Chen, Gu, Ning
Format: Article
Language:English
Subjects:
Causality
cistrome‐wide association studies
Datasets
Disease
DNA methylation
Gene expression
Genetic Predisposition to Disease - genetics
Genome-Wide Association Study - methods
Genomics
Genotype
Genotype & phenotype
Humans
kernel method
Male
Methods
nonlinear effect
Performance evaluation
Phenotype
Polymorphism, Single Nucleotide - genetics
Prostate cancer
Prostatic Neoplasms - genetics
Simulation
Statistical power
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Cistrome-wide association studies (CWAS) are pivotal for identifying genetic determinants of diseases by correlating genetically regulated cistrome states with phenotypes. Traditional CWAS typically develops a model based on cistrome and genotype data to associate predicted cistrome states with phenotypes. The random effect cistrome-wide association study (RECWAS), reevaluates the necessity of cistrome state prediction in CWAS. RECWAS utilizes either a linear model or marginal effect for initial feature selection, followed by kernel-based feature aggregation for association testing is introduced. Through simulations and analysis of prostate cancer data, a thorough evaluation of CWAS and RECWAS is conducted. The results suggest that RECWAS offers improved power compared to traditional CWAS, identifying additional genomic regions associated with prostate cancer. CWAS identified 102 significant regions, while RECWAS found 50 additional significant regions compared to CWAS, many of which are validated. Validation encompassed a range of biological evidence, including risk signals from the GWAS catalog, susceptibility genes from the DisGeNET database, and enhancer-domain scores. RECWAS consistently demonstrated improved performance over traditional CWAS in identifying genomic regions associated with prostate cancer. These findings demonstrate the benefits of incorporating kernel methods into CWAS and provide new insights for genetic discovery in complex diseases.
ISSN:2198-3844
2198-3844
DOI:10.1002/advs.202400815