Exploring potential causal genes for uterine leiomyomas: A summary data-based Mendelian randomization and FUMA analysis

Abstract

AbstractObjectiveTo explore potential causal genetic variants and genes underlying the pathogenesis of uterine leiomyomas (ULs).MethodsWe conducted the summary data-based Mendelian randomization (SMR) analysis and performed functional mapping and annotation using FUMA to examine genetic variants and genes that are potentially involved in the pathogenies of ULs.Both analyses used summarized data of a recent genome-wide association study (GWAS) on ULs, which has a total sample size of 244,324 (20,406 cases and 223,918 controls). For the SMR analysis, we performed separate analysis using CAGE and GTEx eQTL data.ResultsUsing the CAGE eQTL data, our SMR analysis identified 13 probes tagging 10 unique genes that were pleiotropically/potentially causally associated with ULs, with the top three probes being ILMN_1675156 (tagging CDC42, PSMR=8.03×10−9), ILMN_1705330 (tagging CDC42, PSMR=1.02×10−7) and ILMN_2343048 (tagging ABCB9, PSMR=9.37×10−7). Using GTEx eQTL data, our SMR analysis did not identify any significant genes after correction for multiple testing. FUMA analysis identified 106 independent SNPs, 24 genomic loci and 137 genes that are potentially involved in the pathogenesis of ULs, seven of which were also identified by the SMR analysis.ConclusionsWe identified many genetic variants, genes, and genomic loci that are potentially involved in the pathogenesis of ULs. More studies are needed to explore the exact underlying mechanisms in the etiology of ULs.