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The Ovarian Cancer Association Consortium OncoArray genome-wide association study

A custom Illumina genotyping array, the OncoArray. The array includes a genome-wide backbone, comprising 230,000 SNPs tagging most common genetic variants, together with dense mapping of known susceptibility regions, rare variants from sequencing experiments, pharmacogenetic markers, and cancer-related traits. A total of 494,763 SNPs passed quality control steps with a sample success rate of 97% of the samples. Participating sites performed ancestry analysis using a common set of markers and a scoring algorithm based on principal components analysis as described in PMID:27697780.The array was used to genotype ovarian cancer cases and controls from multiple studies participating in the Ovarian Cancer Association Consortium, of which 54,747 passes QC.An initial principal components analysis was carried out using a method described in PMID:27697780 to assign subject to intercontinental ancestry groups: “European”, “African”, “Asian” and "Other". Within ancestry principal components analysis was carried using a set of ~33k unlinked markers. Subsequent analyses were adjusted for the ancestry specific PCs: 9 Eur, 10 Asian, 1 African and 6 Mixed. These PCs are included in the imputed dataset phenotypes file

Click on a Dataset ID in the table below to learn more, and to find out who to contact about access to these data

Dataset ID Description Technology Samples
EGAD00010001192 Illumina OncoArray 56479
Publications Citations
The OncoArray Consortium: A Network for Understanding the Genetic Architecture of Common Cancers.
Cancer Epidemiol Biomarkers Prev 26: 2017 126-135
Molecular signatures of X chromosome inactivation and associations with clinical outcomes in epithelial ovarian cancer.
Hum Mol Genet 28: 2019 1331-1342
Functional analysis of the 1p34.3 risk locus implicates GNL2 in high-grade serous ovarian cancer.
Am J Hum Genet 109: 2022 116-135
Polygenic risk modeling for prediction of epithelial ovarian cancer risk.
Eur J Hum Genet 30: 2022 349-362