Cis-eQTL analysis and functional validation of candidate susceptibility genes for high-grade serous ovarian cancer

Abstract

Genome-wide association studies have reported eleven regions conferring risk of high-grade serous epithelial ovarian cancer (HGSOC). Expression quantitative trait locus (eQTL) analyses can identify candidate susceptibility genes at risk loci. Here we evaluate cis-eQTL associations at 47 regions associated with HGSOC risk (P≤10−5). For three cis-eQTL associations (P<1.4×10−3, FDR<0.05) at 1p36 (CDC42), 1p34 (CDCA8) and 2q31 (HOXD9), we evaluate the functional role of each candidate by perturbing expression of each gene in HGSOC precursor cells. Overexpression of HOXD9 increases anchorage-independent growth, shortens population-doubling time and reduces contact inhibition. Chromosome conformation capture identifies an interaction between rs2857532 and the HOXD9 promoter, suggesting this SNP is a leading causal variant. Transcriptomic profiling after HOXD9 overexpression reveals enrichment of HGSOC risk variants within HOXD9 target genes (P=6×10−10 for risk variants (P<10−4) within 10kb of a HOXD9 target gene in ovarian cells), suggesting a broader role for this network in genetic susceptibility to HGSOC.

The OncoArray Consortium: A Network for Understanding the Genetic Architecture of Common Cancers

“…Many groups have applied this concept to identify transcript expression correlated with trait-associated SNPs(78–80). For example, GAME-ON investigators have successfully used eQTL analysis to identify susceptibility genes at several breast, prostate and ovarian cancer loci, and confirmed the significance of these genes through their functional analysis in disease models(42, 81, 82). …”

“…Generally, selection of SNPs were based on 1) candidate SNPs from loci enriched showing some evidence of association (e.g. p<10 −5 ) from previous GWAS of common cancers (breast, ovarian, prostate, colon and lung) (30–37); 2) fine mapping of risk loci based on 1000 Genomes Project data and resequencing studies(38); 3) candidate rare variants from whole genome and whole exome studies, and exome arrays(39); 4) findings from previously published studies of other cancers provided by the NHGRI SNP catalogue (40) and other online resources; and 5) other “wild-card” variants, for example variants of potential functional significance(18, 41, 42). The majority of SNP selection was based on regions previously identified from GWAS in European populations, but disease sites also allocated tagging SNPs to capture variability for Asian and African descent populations.…”

Genetically regulated hepatic transcripts and pathways orchestrate haematological, biochemical and body composition traits

“…To determine the genetic impact on expression, we focused on cis-eQTLs by considering SNPs located in genomic windows near the transcripts. These regions include promoters, enhancers and UTRs encompassing transcription factor-binding sites and regulatory elements1516.…”

Supervised dimension reduction for large-scale “omics” data with censored survival outcomes under possible non-proportional hazards

“…Furthermore, the proposed methods are broadly applicable to a variety of high-throughput "omics" data such as feature expression data arising from next-generation sequencing, allelespecific expression, methylation, microarrays and SNP arrays as well as large-scale data from proteomics, metabolomics and DNA copy number studies, many of which have been utilized in this study. There has been a recent surge in integrative "omic" analyses that simultaneously involve different data types as well as other quantitative outcome variables using publicly available data from repositories such as TCGA and GEO (Ramakodi et al, 2016;Li et al, 2013;Lawrenson et al, 2015). Within this context, the proposed unifying framework offers a robust platform for analysis and interpretation.…”