Fine-mapping of 150 breast cancer risk regions identifies 191 likely target genes

Abstract

Genome-wide association studies have identified breast cancer risk variants in over 150 genomic regions, but the mechanisms underlying risk remain largely unknown. These regions were explored by combining association analysis with in silico genomic feature annotations. We defined 205 independent risk-associated signals with the set of credible causal variants in each one. In parallel, we used a Bayesian approach (PAINTOR) that combines genetic association, linkage disequilibrium and enriched genomic features to determine variants with high posterior probabilities of being causal. Potentially causal variants were significantly over-represented in active gene regulatory regions and transcription factor binding sites. We applied our INQUSIT pipeline for prioritizing genes as targets of those potentially causal variants, using gene expression (expression quantitative trait loci), chromatin interaction and functional annotations. Known cancer drivers, transcription factors and genes in the developmental, apoptosis, immune system and DNA integrity checkpoint gene ontology pathways were over-represented among the highest-confidence target genes.

Figure 1. Flowchart summarizing the study design.

Logistic regression summary statistics were used to select the final set of variants to run stepwise multinomial regression. These results were meta-analysed with CIMBA to provide the final set of strong independent signals and their CCVs. Through a case-only analysis we identified significant differences in effect sizes between ER-positive and ER-negative breast cancer and used this to classify the phenotype for each independent signal. With these strong CCVs, we ran the bio-features enrichment analysis, which identified the features to be included in the PAINTOR models, together with the OncoArray logistic regression summary statistics, and the OncoArray LD. Both multinomial regression CCVs and PAINTOR high Posterior Probability variants were analyzed with INQUISIT to determine high confidence target genes. Finally, we used the set of high confidence target genes to identify enriched pathways. ^a conditional on the index variants from BCAC strong signals.

Figure 2. Determining independent risk signals and credible candidate variants (CCVs).

(a) Number of independent signals per region identified through multinomial stepwise logistic regression. (b) Signal classification according to their confidence into strong and moderate confidence signals. (c) Number of CCVs per signal at strong confidence signals identified through multinomial stepwise logistic regression. (d) Number of CCVs per signal at moderate confidence signals identified through multinomial stepwise regression. (e) Subtype classification of strong signals into ER-positive, ER-negative and signals equally associated with both phenotypes (ER-neutral) from BCAC analysis. (f) Subtype classification from the meta-analysis of BCAC and CIMBA. Between brackets, number of CCVs from the meta-analysis of BCAC and CIMBA. (g) Number of variants at different posterior probability thresholds. 15 variants reach a PP ≥ 80% by at least one of the three models (ER-all, ER-positive, ER-negative).

Figure 3. Overlap of CCVs with gene regulatory regions gene bodies and transcription factor binding sites.

(a) Breast cancer CCVs overlap with chromatin states and broad breast cells epigenetic marks. (b) Breast cancer CCVs overlap with breast cells epigenetic marks. (c) Autoimmune CCVs overlap with breast cells epigenetic marks. (d) Breast cancer CCVs overlap with autoimmune-related epigenetic marks. (e) Autoimmune CCVs overlap with autoimmune-related epigenetic marks. (f) Significant ER-positive CCVs overlap with transcription factors binding sites. TFBSs found significant for ER-positive CCVs are highlighted in red (x axis labels). (g) Significant ER-negative CCVs overlap with transcription factors binding sites. (h) Significant ER-neutral CCVs overlap with transcription factors binding sites. Strong column: analysis with all CCVs at strong signals. ER-positive, ER-negative, ER-neutral: analysis of CCVs at strong signals stratified by phenotype. Logistic regression robust variance estimation for clustered observations, Wald test Χ² p-values estimated using 67,136 ER-positive and 17,506 ER-negative cases, together with 88,937 controls. Non-significant p-values are noted as dark grey. Significance defined as FDR 5%, which corresponds to the following P-value thresholds^: Strong signals P-value = 1.66x10^-2, ER-positive P-value = 2.42x10^-2; ER-negative P-value 3.02x10^-3; ER-neutral P-value = 1.76x10^-3.

Figure 4. Predicted target genes are enriched in known breast cancer driver genes and transcription factors.

79 target genes that fulfil at least one of the following criteria: are targeted by more than one independent signal, are known driver genes, transcription factor genes, or their binding sites (ChIP-Seq BS) or consensus motif (TF Motif) are significantly overlapped by CCVs. *Genes with published functional follow up.

Figure 5. Predicted target genes by phenotype and significantly enriched pathways.

(a) Venn diagram showing the associated phenotype (ER-positive, ER-negative, ER-neutral) for the Level 1 target genes, predicted by the CCVs and HPPVs. * ER-positive or ER-negative target genes also targeted by ER-neutral signals. (b) Heatmap showing clustering of pathway themes over-represented by INQUISIT Level 1 target genes. Color represents the relative number of genes per phenotype within enriched pathways, grouped by common themes. ER-positive, ER-negative, ER-neutral, and all phenotypes together (strong).