eQTL Colocalization Analyses Identify NTN4 as a Candidate Breast Cancer Risk Gene

Abstract

Breast cancer genome-wide association studies (GWAS) have identified 150 genomic risk regions containing more than 13,000 credible causal variants (CCVs). The CCVs are predominantly noncoding and enriched in regulatory elements. However, the genes underlying breast cancer risk associations are largely unknown. Here, we used genetic colocalization analysis to identify loci at which gene expression could potentially explain breast cancer risk phenotypes. Using data from the Breast Cancer Association Consortium (BCAC) and quantitative trait loci (QTL) from the Genotype-Tissue Expression (GTEx) project and The Cancer Genome Project (TCGA), we identify shared genetic relationships and reveal novel associations between cancer phenotypes and effector genes.Seventeen genes, including NTN4, were identified as potential mediators of breast cancer risk. For NTN4, we showed the rs61938093 CCV at this region was located within an enhancer element that physically interacts with the NTN4 promoter, and the risk allele reduced NTN4 promoter activity. Furthermore, knockdown of NTN4 in breast cells increased cell proliferation in vitro and tumor growth in vivo. These data provide evidence linking risk-associated variation to genes that may contribute to breast cancer predisposition.The influence of common genetic variation on gene expression underlies a considerable proportion of the heritability associated with complex traits. Mapping of expression QTL (eQTL), where genetic variants are tested for association with gene expression levels, is widely used to identify genes that are regulated by trait-associated variants. Several studies have shown that eQTLs are enriched in cell types relevant to the trait of interest1; 2. For example, T cell-specific eQTLs are over-represented for autoimmune risk alleles and monocyte-specific eQTLs for Alzheimer's [MIM: 104300] and Parkinson's [MIM: 168600] disease alleles2. For breast cancer [MIM: 114480], several studies have used eQTL data from tumor and normal tissues datasets to identify candidate target genes3-6. Recent studies have also showed that breast cancer risk variants could regulate genes in cells of the tumor microenvironment, such as immune cells and fibroblasts7; 8. Because eQTLs are widespread, overlap between GWAS and eQTL signals is likely to occur by chance when using nominal significance

Integrated study of miR‐215 promoting breast cancer cell apoptosis by targeting RAD54B

“…Its high morbidity and high mortality have attracted the attention of clinicians and scientific researchers. It is a heterogeneous disease, and current researches have found that its occurrence and development were related to numerous genetic changes 2‐4 . A large number of biologically active molecules participate in the regulation of signalling pathways in BC cells.…”

CRISPR screens identify gene targets and drug repositioning opportunities at breast cancer risk loci

“…Although a major goal of these studies is to identify new strategies for cancer prevention or treatment, a major obstacle in translating these findings to meaningful biological insights is that most risk variants are non-coding and the gene targets of the associations are not clear. Following fine mapping to identify the CCVs for BC, prioritizing loci with relatively few CCVs, chromatin conformation capture (3C) and luciferase assays, have been performed at 16 BC risk loci implicating regulation of TERT 40 , CCND1 50 , FGFR2 51 , IGFBP5 52 , MAP3K1 53 , ESR1, RMND1 and CCDC170 41 , KLF4 54 , NRBF2 55 , ABHD8 56 , FGF10 and MRPS30 57 , KLHDC7A, PIDD1, CITED4, PRKRIP1 and RASA4 4 , DUSP4 28 , NTN4 58 , TBX3 34 and novel lncRNAs, CUPID1 and CUPID2 59 .…”

CRISPR screens identify gene targets and drug repositioning opportunities at breast cancer risk loci

“…However, a major obstacle in translating these findings to meaningful biological insights is that the majority of risk variants are non-coding and the gene targets of the associations are not clear. Following fine mapping to identify the CCVs for BC, prioritizing loci with relatively few CCVs, chromatin conformation capture (3C) and luciferase assays, have been performed at 16 BC risk loci implicating regulation of TERT (Bojesen et al, 2013), CCND1 (French et al, 2013), FGFR2 (Meyer et al, 2013), IGFBP5 (Baxter et al, 2021), MAP3K1 (Glubb et al, 2015), ESR1, RMND1 and CCDC170 (Dunning et al, 2016), KLF4 (Orr et al, 2015), NRBF2 (Darabi et al, 2015), ABHD8 (Lawrenson et al, 2016), FGF10 and MRPS30 (Ghoussaini et al, 2016), KLHDC7A, PIDD1, CITED4, PRKRIP1 and RASA4 (Michailidou et al, 2017), DUSP4 (Glubb et al, 2020), NTN4 (Beesley et al, 2020b), TBX3 (Beesley et al, 2020a) and novel lncRNAs, CUPID1 and CUPID2 (Betts et al, 2017).…”