Assessing the genetic architecture of epithelial ovarian cancer histological subtypes
until further notice
SourceHuman Genetics, 135, 7, (2016), pp. 741-756
Article / Letter to editor
Display more detailsDisplay less details
SubjectRadboudumc 15: Urological cancers RIHS: Radboud Institute for Health Sciences; Radboudumc 17: Women's cancers RIMLS: Radboud Institute for Molecular Life Sciences
Epithelial ovarian cancer (EOC) is one of the deadliest common cancers. The five most common types of disease are high-grade and low-grade serous, endometrioid, mucinous and clear cell carcinoma. Each of these subtypes present distinct molecular pathogeneses and sensitivities to treatments. Recent studies show that certain genetic variants confer susceptibility to all subtypes while other variants are subtype-specific. Here, we perform an extensive analysis of the genetic architecture of EOC subtypes. To this end, we used data of 10,014 invasive EOC patients and 21,233 controls from the Ovarian Cancer Association Consortium genotyped in the iCOGS array (211,155 SNPs). We estimate the array heritability (attributable to variants tagged on arrays) of each subtype and their genetic correlations. We also look for genetic overlaps with factors such as obesity, smoking behaviors, diabetes, age at menarche and height. We estimated the array heritabilities of high-grade serous disease ([Formula: see text] = 8.8 +/- 1.1 %), endometrioid ([Formula: see text] = 3.2 +/- 1.6 %), clear cell ([Formula: see text] = 6.7 +/- 3.3 %) and all EOC ([Formula: see text] = 5.6 +/- 0.6 %). Known associated loci contributed approximately 40 % of the total array heritability for each subtype. The contribution of each chromosome to the total heritability was not proportional to chromosome size. Through bivariate and cross-trait LD score regression, we found evidence of shared genetic backgrounds between the three high-grade subtypes: serous, endometrioid and undifferentiated. Finally, we found significant genetic correlations of all EOC with diabetes and obesity using a polygenic prediction approach.
Upload full text
Use your RU credentials (u/z-number and password) to log in with SURFconext to upload a file for processing by the repository team.