Rare germline genetic variation in PAX8 transcription factor binding sites and susceptibility to epithelial ovarian cancer
Ezquina, S. A. M.; Jones, M.; Dicks, E.; de Vries, A.; Peng, P.-C.; Corona, R. I.; Lawrenson, K.; Tyrer, J. P.; Hazelett, D.; Brenton, J. D.; Antoniou, A. C.; Gayther, S. A.; Pharoah, P. D. P.
Show abstract
Common genetic variation throughout the genome together with rare coding variants identified to date explain about a half of the inherited genetic component of epithelial ovarian cancer risk. It is likely that rare variation in the non-coding genome will explain some of the unexplained heritability, but identifying such variants is challenging. The primary problem is lack of statistical power to identifying individual risk variants by association as power is a function of sample size, effect size and allele frequency. Power can be increased by using burden tests which test for association of carriers of any variant in a specified genomic region. This has the effect of increasing the putative effect allele frequency. PAX8 is a transcription factor that plays a critical role in tumour progression, migration and invasion. Furthermore, regulatory elements proximal to target genes of PAX8 are enriched for common ovarian cancer risk variants. We hypothesised that rare variation in PAX8 binding sites are also associated with ovarian cancer risk, but unlikely to be associated with risk of breast, colorectal or endometrial cancer. We have used publicly-available, whole-genome sequencing data from the UK 100,000 Genomes Project to evaluate the burden of rare variation in PAX8 binding sites across the genome. Data were available for 522 ovarian cancers, 2560 breast cancers, 2465 colorectal cancers and 729 endometrial cancers and 2253 non-cancer controls. Active binding sites were defined using data from multiple PAX8 and H3K27 ChIPseq experiments. We found no association between the burden of rare variation in PAX8 binding sites (defined in several ways) and risk of ovarian, breast or endometrial cancer. An apparent association with colorectal cancer was likely to be a technical artefact as a similar association was also detected for rare variation in random regions of the genome. Despite the null result this study provides a proof-of -principle for using burden testing to identify rare, non-coding germline genetic variation associated with disease. Larger sample sizes available from large-scale sequencing projects together with improved understanding of the function of the non-coding genome will increase the potential of similar studies in the future.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Estimating cancer risk in carriers of Lynch syndrome variants in UK Biobank 95%
- A Comprehensive Epithelial Tubo-Ovarian Cancer Risk Prediction Model Incorporating Genetic and Epidemiological Risk Factors 94%
- Heritable genetic variants in key cancer genes link cancer risk with anthropometric traits 93%
Similar papers in this journal
- Performance of polygenic risk scores for cancer prediction in a racially diverse academic biobank 94%
- Classification of Variants of Reduced Penetrance in High Penetrance Cancer Susceptibility Genes: Framework for Genetics Clinicians and Clinical Scientists by CanVIG-UK (Cancer Variant Interpretation Group-UK) 93%
- Genome Alert!: a standardized procedure for genomic variant reinterpretation and automated genotype-phenotype reassessment in clinical routine 93%
Similar papers in this journal
Similar papers in this journal
- Cross-cancer genome-wide association study of endometrial cancer and epithelial ovarian cancer identifies genetic risk regions associated with risk of both cancers 93%
- Genetic analysis of functional rare germline variants across 9 cancer types from the DiscovEHR study 93%
- Hereditary haemochromatosis beyond liver cancer: increased risk of prostate cancer during an 11-year follow-up 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.