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EQTL Colocalization Analyses Identify NTN4 as a Candidate Breast Cancer Risk Gene.

ABSTRACT: Breast cancer genome-wide association studies (GWASs) 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.

SUBMITTER: Beesley J

PROVIDER: S-EPMC7536644 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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

Beesley Jonathan J Sivakumaran Haran H Moradi Marjaneh Mahdi M Shi Wei W Hillman Kristine M KM Kaufmann Susanne S Hussein Nehal N Kar Siddhartha S Lima Luize G LG Ham Sunyoung S Möller Andreas A Chenevix-Trench Georgia G Edwards Stacey L SL French Juliet D JD

American journal of human genetics 20200831 4

Breast cancer genome-wide association studies (GWASs) 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 Associat ...[more]

PMID: 32871102

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Project description:We previously identified a locus at 21q22.3, tagged by the single nucleotide polymorphism (SNP) rs35418111, being associated with breast cancer risk at a genome-wide significance level; however, the underlying causal functional variants and gene(s) responsible for this association are unknown. We performed functional genomic analyses to identify potential functional variants and target genes that may mediate this association. Functional annotation for SNPs in high linkage disequilibrium (LD, r2 > 0.8) with rs35418111 in Asians showed evidence of promoter and/or enhancer activities, including rs35418111, rs2078203, rs8134832, rs57385578, and rs8126917. These five variants were assessed for interactions with nuclear proteins by electrophoretic mobility shift assays. Our results showed that the risk alleles for rs2078203 and rs35418111 altered DNA-protein interaction patterns. Cis-expression quantitative loci (cis-eQTL) analysis, using data from the Genotype-Tissue Expression database (GTEx) European-ancestry female normal breast tissue, indicated that the risk allele of rs35418111 was associated with a decreased expression of the YBEY gene, a relatively uncharacterized endoribonuclease in humans. We investigated the biological effects of YBEY on breast cancer cell lines by transient knock-down of YBEY expression in MCF-7, T47D, and MDA-MB-231 cell lines. Knockdown of YBEY mRNA in breast cancer cell lines consistently decreased cell proliferation, colony formation, and migration/invasion, regardless of estrogen receptor status. We performed RNA sequencing in MDA-MB-231 cells transfected with siRNA targeting YBEY and subsequent gene set enrichment analysis to identify gene networks associated with YBEY knockdown. These data indicated YBEY was involved in networks associated with inflammation and metabolism. Finally, we showed trends in YBEY expression patterns in breast tissues from The Cancer Genome Atlas (TCGA); early-stage breast cancers had elevated YBEY expression compared with normal tissue, but significantly decreased expression in late-stage disease. Our study provides evidence of a significant role for the human YBEY gene in breast cancer pathogenesis and the association between the rs35418111/21q22.3 locus and breast cancer risk, which may be mediated through functional SNPs, rs35418111 and rs2078203, that regulate expression of YBEY.

Project description:Breast cancer includes several subtypes with distinct characteristic biological, pathologic, and clinical features. Elucidating subtype-specific genetic etiology could provide insights into the heterogeneity of breast cancer to facilitate the development of improved prevention and treatment approaches. In this study, we conducted pairwise case-case comparisons among five breast cancer subtypes by applying a case-case genome-wide association study (CC-GWAS) approach to summary statistics data of the Breast Cancer Association Consortium. The approach identified 13 statistically significant loci and eight suggestive loci, the majority of which were identified from comparisons between triple-negative breast cancer (TNBC) and luminal A breast cancer. Associations of lead variants in 12 loci remained statistically significant after accounting for previously reported breast cancer susceptibility variants, among which, two were genome-wide significant. Fine mapping implicated putative functional/causal variants and risk genes at several loci, e.g., 3q26.31/TNFSF10, 8q22.3/NACAP1/GRHL2, and 8q23.3/LINC00536/TRPS1, for TNBC as compared with luminal cancer. Functional investigation further identified rs16867605 at 8q22.3 as a SNP that modulates the enhancer activity of GRHL2. Subtype-informative polygenic risk scores (PRS) were derived, and patients with a high subtype-informative PRS had an up to two-fold increased risk of being diagnosed with TNBC instead of luminal cancers. The CC-GWAS PRS remained statistically significant after adjusting for TNBC PRS derived from traditional case-control GWAS in The Cancer Genome Atlas and the African Ancestry Breast Cancer Genetic Consortium. The CC-GWAS PRS was also associated with overall survival and disease-specific survival among patients with breast cancer. Overall, these findings have advanced our understanding of the genetic etiology of breast cancer subtypes, particularly for TNBC. Significance: The discovery of subtype-informative genetic risk variants for breast cancer advances our understanding of the etiologic heterogeneity of breast cancer, which could accelerate the identification of targets and personalized strategies for prevention and treatment.

Project description:BackgroundA detailed understanding of genetic variants that affect beef merit helps maximize the efficiency of breeding for improved production merit in beef cattle. To prioritize the putative variants and genes, we ran a comprehensive genome-wide association studies (GWAS) analysis for 21 agronomic traits using imputed whole-genome variants in Simmental beef cattle. Then, we applied expression quantitative trait loci (eQTL) mapping between the genotype variants and transcriptome of three tissues (longissimus dorsi muscle, backfat, and liver) in 120 cattle.ResultsWe identified 1,580 association signals for 21 beef agronomic traits using GWAS. We then illuminated 854,498 cis-eQTLs for 6,017 genes and 46,970 trans-eQTLs for 1,903 genes in three tissues and built a synergistic network by integrating transcriptomics with agronomic traits. These cis-eQTLs were preferentially close to the transcription start site and enriched in functional regulatory regions. We observed an average of 43.5% improvement in cis-eQTL discovery using multi-tissue eQTL mapping. Fine-mapping analysis revealed that 111, 192, and 194 variants were most likely to be causative to regulate gene expression in backfat, liver, and muscle, respectively. The transcriptome-wide association studies identified 722 genes significantly associated with 11 agronomic traits. Via the colocalization and Mendelian randomization analyses, we found that eQTLs of several genes were associated with the GWAS signals of agronomic traits in three tissues, which included genes, such as NADSYN1, NDUFS3, LTF and KIFC2 in liver, GRAMD1C, TMTC2 and ZNF613 in backfat, as well as TIGAR, NDUFS3 and L3HYPDH in muscle that could serve as the candidate genes for economic traits.ConclusionsThe extensive atlas of GWAS, eQTL, fine-mapping, and transcriptome-wide association studies aid in the suggestion of potentially functional variants and genes in cattle agronomic traits and will be an invaluable source for genomics and breeding in beef cattle.

Dataset Information

EQTL Colocalization Analyses Identify NTN4 as a Candidate Breast Cancer Risk Gene.

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

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