An Osteoporosis Risk SNP at 1p36.12 Acts as an Allele-Specific Enhancer to Modulate LINC00339 Expression via Long-Range Loop Formation

Abstract

Genome-wide association studies (GWASs) have reproducibly associated variants within intergenic regions of 1p36.12 locus with osteoporosis, but the functional roles underlying these noncoding variants are unknown. Through an integrative functional genomic and epigenomic analyses, we prioritized rs6426749 as a potential causal SNP for osteoporosis at 1p36.12. Dual-luciferase assay and CRISPR/Cas9 experiments demonstrate that rs6426749 acts as a distal allele-specific enhancer regulating expression of a lncRNA (LINC00339) (∼360 kb) via long-range chromatin loop formation and that this loop is mediated by CTCF occupied near rs6426749 and LINC00339 promoter region. Specifically, rs6426749-G allele can bind transcription factor TFAP2A, which efficiently elevates the enhancer activity and increases LINC00339 expression. Downregulation of LINC00339 significantly increases the expression of CDC42 in osteoblast cells, which is a pivotal regulator involved in bone metabolism. Our study provides mechanistic insight into how a noncoding SNP affects osteoporosis by long-range interaction, a finding that could indicate promising therapeutic targets for osteoporosis.

A road map for understanding molecular and genetic determinants of osteoporosis

“…Data in cultured primary osteoclasts from 158 individuals were used to identify colocalizing eQTL for genes associated with BMD 123 . Moreover, eQTL analysis on other bone-related cells and tissues, such as circulating monocytes 120 , lymphocytes 124 or whole blood 124,125 , have also been widely applied to decipher the regulatory roles of GWAS variants in osteoporosis. For example, lymphocytes and peripheral blood eQTL were used to identify LINC00339 as a potentially causal gene for BMD GWAS 124 .…”

Multi-omics Data Integration for Identifying Osteoporosis Biomarkers and Their Biological Interaction and Causal Mechanisms

“…Notably, Mendelian randomization (MR) analysis has been widely used to assess potential causal relationships of genetic/environmental risk factors and diseases (Davey Smith and Hemani, 2014). Recently, MR analysis has been adopted to inspect the causality of biomarkers in disease etiology, utilizing multiple independent SNPs identified by QTL analysis (QTL SNPs) as instrumental variables (Taylor et al, 2019;Yao et al, 2018;Chen et al, 2018). As an example, by applying eQTLs as genetic instruments, Chen et al recently revealed a causal relationship between LINC00339 gene expression and BMD variation (Chen et al, 2018).…”

A functional variant near XCL1 gene improves breast cancer survival via promoting cancer immunity

“…The MR approach provides supportive evidence for a causal relationship between exposure, mediator and disease outcome . In this regard, we considered gene expression to be more directly regulated by genetic variants than environmental factors and biological traits and used it as the proxy for exposure, whereas environmental factors and biological traits have been more commonly considered the proxy for exposure in published MR studies . Notably, the eQTLs used as IVs in the first step of MR ( XCL1 eQTLs rs1024176 and rs982143) and in the second step of MR ( cis ‐eQTLs for BTLA , C1orf54 , CADM1 and CLEC9A ) were retrieved from the METABRIC dataset, which is completely independent of our TWBC cohort.…”