eMAGMA: An eQTL-informed method to identify risk genes using genome-wide association study summary statistics
Zachary F. Gerring, Angela Mina-Vargas, Eric R. Gamazon, Eske M. Derks
eMAGMA converts SNP-level GWAS summary statistics into gene-level associations using tissue-specific eQTL data, outperforming existing methods in identifying causal genes for complex diseases.
Bioinformatics · Feb 19, 2021Peer-reviewedGenetics