A microRNA Transcriptome-wide Association Study of Prostate Cancer Risk

Large genome-wide association studies have identified hundreds of single-nucleotide polymorphisms associated with increased risk of prostate cancer (PrCa), and many of these risk loci is presumed to confer regulatory effects on gene expression. While eQTL studies of long RNAs has yielded many potential risk genes, the relationship between PrCa risk genetics and microRNA expression dysregulation is understudied. We performed an microRNA transcriptome-wide association study of PrCa risk using small RNA sequencing and genome-wide genotyping data from N = 441 normal prostate epithelium tissue samples along with N = 411 prostate adenocarcinoma tumor samples from the Cancer Genome Atlas (TCGA). Genetically regulated expression prediction models were trained for all expressed microRNAs using the FUSION TWAS software. TWAS for PrCa risk was performed with both sets of models using single-SNP summary statistics from the recent PRACTICAL consortium PrCa case-control OncoArray GWAS meta-analysis. A total of 613 and 571 distinct expressed microRNAs were identified in the normal and tumor tissue datasets, respectively (overlap: 480). Among these, 79 (13%) normal tissue microRNAs demonstrated significant cis-heritability (median cis-h2 = 0.15, range: 0.03–0.79) for model training. Similar results were obtained from TCGA tumor samples, with 48 (9%) microRNA expression models successfully trained (median cis-h2 = 0.14, range: 0.06–0.60). Using normal tissue models, we identified two significant TWAS microRNA associations with PrCa risk: over-expression of mir-941 family microRNAs (P_TWAS = 2.9E-04) and reduced expression of miR-3617-5p (P_TWAS = 1.0E-03). The TCGA tumor TWAS also identified a significant association with miR-941 overexpression (P_TWAS = 9.7E-04). Subsequent finemapping of the TWAS results using a multi-tissue database indicated limited evidence of causal status for each microRNA with PrCa risk (posterior inclusion probabilities <0.05). Future work will examine downstream regulatory effects of microRNA dysregulation as well as microRNA-mediated risk mechanisms via competing endogenous RNA relationships.