Supplementary Information containing supplementary methods, figures and tables with their legends. This file includes: Supplementary Materials and Methods; Supplementary Table 1 - Breast cancer cell line DNase-seq data used for SNP prioritization; Supplementary Table 2 - Breast cancer cell line ChIP-seq data used in this study; Supplementary Table 3 - Transcription factor motif databases used in this study; Supplementary Table 4 - A comparison of eQTL genes for all three 5p12 breast cancer GWAS SNPs; Supplementary Table 5 - eQTL results of rs4415084 for genes within TAD; Supplementary Table 6 - Significant isoform-QTL results of rs4415084; Supplementary Table 7 - Differential chromatin interaction analysis between MCF-7 and T-47D based on Hi-C data; Supplementary Figure 1 - A schematic representation of the phasing certainty measure in local chromosome allele-specific expression (LCASE) analysis; Supplementary Figure 2 - A schematic representation of TF prioritization based on TF-gene expression correlation analyses; Supplementary Figure 3 - Transcription elongation downstream of MRPS30; Supplementary Figure 4 - eQTL results for rs4415084 based on normal breast tissue data from GTEx; Supplementary Figure 5 - LCASE SNPs in MRPS30 processed transcripts; Supplementary Figure 6 - LCASE SNPs in RP11-53O19.3; Supplementary Figure 7 - LCASE SNPs in RP11-53O19.1; Supplementary Figure 8 - Other candidate causative SNPs and TFs.
ARTICLE ABSTRACT
Previous genome-wide association studies (GWAS) have identified several common genetic variants that may significantly modulate cancer susceptibility. However, the precise molecular mechanisms behind these associations remain largely unknown; it is often not clear whether discovered variants are themselves functional or merely genetically linked to other functional variants. Here, we provide an integrated method for identifying functional regulatory variants associated with cancer and their target genes by combining analyses of expression quantitative trait loci, a modified version of allele-specific expression that systematically utilizes haplotype information, transcription factor (TF)–binding preference, and epigenetic information. Application of our method to a breast cancer susceptibility region in 5p12 demonstrates that the risk allele rs4415084-T correlates with higher expression levels of the protein-coding gene mitochondrial ribosomal protein S30 (MRPS30) and lncRNA RP11-53O19.1. We propose an intergenic SNP rs4321755, in linkage disequilibrium (LD) with the GWAS SNP rs4415084 (r2 = 0.988), to be the predicted functional SNP. The risk allele rs4321755-T, in phase with the GWAS rs4415084-T, created a GATA3-binding motif within an enhancer, resulting in differential GATA3 binding and chromatin accessibility, thereby promoting transcription of MRPS30 and RP11-53O19.1. MRPS30 encodes a member of the mitochondrial ribosomal proteins, implicating the role of risk SNP in modulating mitochondrial activities in breast cancer. Our computational framework provides an effective means to integrate GWAS results with high-throughput genomic and epigenomic data and can be extended to facilitate rapid functional characterization of other genetic variants modulating cancer susceptibility.Significance: Unification of GWAS results with information from high-throughput genomic and epigenomic profiles provides a direct link between common genetic variants and measurable molecular perturbations. Cancer Res; 78(7); 1579–91. ©2018 AACR.
