Assessing the genetic architecture of epithelial ovarian cancer histological subtypes
Van Nieuwenhuysen, Els
Doherty, Jennifer Anne
Rossing, Mary Anne
Gilks, C. Blake
Australian Ovarian Cancer Study
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CitationCuellar-Partida, Gabriel, Yi Lu, Suzanne C. Dixon, Peter A. Fasching, Alexander Hein, Stefanie Burghaus, et al. 2016. “Assessing the Genetic Architecture of Epithelial Ovarian Cancer Histological Subtypes.” Human Genetics 135 (7): 741–56. https://doi.org/10.1007/s00439-016-1663-9.
AbstractEpithelial 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 ( = 8.8 +/- 1.1 %), endometrioid ( = 3.2 +/- 1.6 %), clear cell ( = 6.7 +/- 3.3 %) and all EOC ( = 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.
Citable link to this pagehttp://nrs.harvard.edu/urn-3:HUL.InstRepos:41292470
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