2015
DOI: 10.1038/ncomms9234
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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 e… Show more

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Cited by 59 publications
(53 citation statements)
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“…Many groups have applied this concept to identify transcript expression correlated with trait-associated SNPs(7880). 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). …”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…Many groups have applied this concept to identify transcript expression correlated with trait-associated SNPs(7880). 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). …”
Section: Discussionmentioning
confidence: 99%
“…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) (3037); 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.…”
Section: Methodsmentioning
confidence: 99%
“…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.…”
Section: Discussionmentioning
confidence: 99%
“…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.…”
Section: Conclusion and Discussionmentioning
confidence: 99%