Multiple Functional Variants at 13q14 Risk Locus for Osteoporosis Regulate RANKL Expression Through Long-Range Super-Enhancer.

RANKL is a key regulator involved in bone metabolism, and a drug target for osteoporosis. The clinical diagnosis and assessment of osteoporosis are mainly based on bone mineral density (BMD). Previous powerful genomewide association studies (GWASs) have identified multiple intergenic single-nucleotide polymorphisms (SNPs) located over 100 kb upstream of RANKL and 65 kb downstream of AKAP11 at 13q14.11 for osteoporosis. Whether these SNPs exert their roles on osteoporosis through RANKL is unknown. In this study, we conducted integrative analyses combining expression quantitative trait locus (eQTL), genomic chromatin interaction (high-throughput chromosome conformation capture [Hi-C]), epigenetic annotation, and a series of functional assays. The eQTL analysis identified six potential functional SNPs (rs9533090, rs9594738, r8001611, rs9533094, rs9533095, and rs9594759) exclusively correlated with RANKL gene expression (p < 0.001) at 13q14.11. Co-localization analyses suggested that eQTL signal for RANKL and BMD-GWAS signal shared the same causal variants. Hi-C analysis and functional annotation further validated that the first five osteoporosis SNPs are located in a super-enhancer region to regulate the expression of RANKL via long-range chromosomal interaction. Particularly, dual-luciferase assay showed that the region harboring rs9533090 in the super-enhancer has the strongest enhancer activity, and rs9533090 is an allele-specific regulatory SNP. Furthermore, deletion of the region harboring rs9533090 using CRISPR/Cas9 genome editing significantly reduced RANKL expression in both mRNA level and protein level. Finally, we found that the rs9533090-C robustly recruits transcription factor NFIC, which efficiently elevates the enhancer activity and increases the RANKL expression. In summary, we provided a feasible method to identify regulatory noncoding SNPs to distally regulate their target gene underlying the pathogenesis of osteoporosis by using bioinformatics data analyses and experimental validation. Our findings would be a potential and promising therapeutic target for precision medicine in osteoporosis.