Cargando…
A Bayesian framework that integrates multi-omics data and gene networks predicts risk genes from schizophrenia GWAS data
Genome-wide association studies (GWAS) have identified >100 schizophrenia (SCZ)-associated loci, but using these findings to illuminate disease biology remains a challenge. Here, we present integrative RIsk Gene Selector (iRIGS), a Bayesian framework that integrates multi-omics data and gene netw...
Autores principales: | Wang, Quan, Chen, Rui, Cheng, Feixiong, Wei, Qiang, Ji, Ying, Yang, Hai, Zhong, Xue, Tao, Ran, Wen, Zhexing, Sutcliffe, James S., Liu, Chunyu, Cook, Edwin H., Cox, Nancy J., Li, Bingshan |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6646046/ https://www.ncbi.nlm.nih.gov/pubmed/30988527 http://dx.doi.org/10.1038/s41593-019-0382-7 |
Ejemplares similares
-
Integration of multidimensional splicing data and GWAS summary statistics for risk gene discovery
por: Ji, Ying, et al.
Publicado: (2022) -
A Bayesian framework to integrate multi-level genome-scale data for Autism risk gene prioritization
por: Ji, Ying, et al.
Publicado: (2022) -
Prioritization of schizophrenia risk genes from GWAS results by integrating multi-omics data
por: He, Dan, et al.
Publicado: (2021) -
IntOMICS: A Bayesian Framework for Reconstructing Regulatory Networks Using Multi-Omics Data
por: Pačínková, Anna, et al.
Publicado: (2023) -
Pathway- and network-based analysis of GWAS data revealed susceptibility gene sets to schizophrenia
por: Jia, Peilin, et al.
Publicado: (2010)