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. 2021 Mar 30;11(1):7192.
doi: 10.1038/s41598-021-86690-5.

Variable expression quantitative trait loci analysis of breast cancer risk variants

Affiliations

Variable expression quantitative trait loci analysis of breast cancer risk variants

George A R Wiggins et al. Sci Rep. .

Abstract

Genome wide association studies (GWAS) have identified more than 180 variants associated with breast cancer risk, however the underlying functional mechanisms and biological pathways which confer disease susceptibility remain largely unknown. As gene expression traits are under genetic regulation we hypothesise that differences in gene expression variability may identify causal breast cancer susceptibility genes. We performed variable expression quantitative trait loci (veQTL) analysis using tissue-specific expression data from the Genotype-Tissue Expression (GTEx) Common Fund Project. veQTL analysis identified 70 associations (p < 5 × 10-8) consisting of 60 genes and 27 breast cancer risk variants, including 55 veQTL that were observed in breast tissue only. Pathway analysis of genes associated with breast-specific veQTL revealed an enrichment of four genes (CYP11B1, CYP17A1 HSD3B2 and STAR) involved in the C21-steroidal biosynthesis pathway that converts cholesterol to breast-related hormones (e.g. oestrogen). Each of these four genes were significantly more variable in individuals homozygous for rs11075995 (A/A) breast cancer risk allele located in the FTO gene, which encodes an RNA demethylase. The A/A allele was also found associated with reduced expression of FTO, suggesting an epi-transcriptomic mechanism may underlie the dysregulation of genes involved in hormonal biosynthesis leading to an increased risk of breast cancer. These findings provide evidence that genetic variants govern high levels of expression variance in breast tissue, thus building a more comprehensive insight into the underlying biology of breast cancer risk loci.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Schematic of study rationale to identify veQTL and eQTL in four different tissue types. Firstly, gene expression data from GTEx was filtered to remove lowly expressed genes and samples with tissue-specific expression data was matched with genotype calls. Tissue-specific QTL analyses was performed on the breast cancer risk variants identified by previous studies.
Figure 2
Figure 2
Characteristics of gene expression variability in veQTLs. Three class of veQTLs were observed with respect to the minor allele. Boxplots represent genes at veQTL for each of the proposed classes. Each point represent the expression of a single sample. The table (bottom) presents the number of significant veQTL associations categorised by class. Significant breast veQTLs were represented in all three classes with the majority (39/70) class I.
Figure 3
Figure 3
Tissue-specific performance of veQTL and eQTL analysis. (a) Tissue specific q–q plots and genomic inflation factors (λ) for the associations of breast cancer risk variants and gene expression variability, with observed p-values plotted as a function of expected p-values under the null hypothesis of no association; red lines indicate the a null distribution of p values. (b) Tissue-specific p-value distribution for BC variants eQTLs (red) and veQTLs (blue). (c) Tissue-specific correlations of –log10(p) for eQTL (x-axis) and veQTL (y-axis).
Figure 4
Figure 4
Pathway enrichment of candidate breast cancer risk genes identified through veQTL analysis. Fifty-five gene SNP pairs were observed only in breast tissues, 47 of these were veQTL but not eQTL associations. The candidate genes identified by these 47 associations were enriched for pathways involved in C21-steroid hormone metabolic process. The significance of pathway enrichment, − log10(p), are shown graphically alongside a heatmap for genes involved (in blue) in the respective pathways. Pathway analysis was performed in R using GO terms and using the DOSE and ClusterProfiler packages.
Figure 5
Figure 5
Co-localisation of ER negative breast cancer GWAS and trans-veQTL signals. (a) Regional association plots for ER negative breast cancer risk for rs11075995 from Michailidou et al.. (b) Regional association plots for trans-veQTL at rs11075995. Points indicate individual SNPs at their chromosomal location and significance (− log10(p value)) for either GWAS (a) or trans-veQTL (b). The blue line represents the recombination rate and the colour of the points indicate the strength of the LD with rs10075995 measured as r2 in the EUR population from 1000 genomes (hg19). All plots were generated using LocusZoom.
Figure 6
Figure 6
Schematic of part of C21-steroird biosynthesis pathway. Genes shown in red were associated with a significant increase in variability in individuals homozygous for the rs1105995 risk allele (A) in breast tissue (i.e. 4 of the 70 breast-derived genes from Fig. 4).
Figure 7
Figure 7
cis-effects of rs11075995 minor allele and FTO expression. (a) Ideogram and chromosomal location of the rs11075995 variant within in the second intron of the FTO gene. (b) Tissue-specific expression of FTO stratified by genotypes at the rs11075995 location. T/T homozygous major allele (Green), A/T heterozygous (Orange), A/A homozygous minor allele (Blue).

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