Assessing the genetic architecture of epithelial ovarian cancer histological subtypes
Author(s) -
Gabriel Cuéllar-Partida,
Yi Lu,
Suzanne C. DixonSuen,
Peter A. Fasching,
Alexander Hein,
Stefanie Burghaus,
Matthias W. Beckmann,
Diether Lambrechts,
Els Van Nieuwenhuysen,
Ignace Vergote,
Adriaan Vanderstichele,
Jennifer A. Doherty,
Mary Anne Rossing,
Jenny ChangClaude,
Anja Rudolph,
Shan WangGohrke,
Marc T. Goodman,
Natalia Bogdanova,
Thilo Dörk,
Matthias Dürst,
Peter Hillemanns,
Ingo B. Runnebaum,
Natalia Antonenkova,
Ralf Bützow,
Arto Leminen,
Heli Nevanlinna,
Liisa M. Pelttari,
Robert P. Edwards,
Joseph L. Kelley,
Francesmary Modugno,
Kirsten B. Moysich,
Roberta B. Ness,
Rikki A. Cannioto,
Estrid Høgdall,
Claus Høgdall,
Allan Jensen,
Graham G. Giles,
Fiona Bruinsma,
Susanne K. Kjær,
Michelle A.T. Hildebrandt,
Dong Liang,
Karen H. Lu,
Xifeng Wu,
Maria Bisogna,
Fanny Dao,
Douglas A. Levine,
Daniel W. Cramer,
Kathryn L. Terry,
Shelley S. Tworoger,
Meir J. Stampfer,
Stacey A. Missmer,
Line Bjørge,
Helga B. Salvesen,
Reidun Kristin Kopperud,
Katharina Bischof,
Katja K.H. Aben,
Lambertus A. Kiemeney,
Leon F.A.G. Massuger,
Angela BrooksWilson,
Sara H. Olson,
Valerie McGuire,
Joseph H. Rothstein,
Weiva Sieh,
Alice S. Whittemore,
Linda S. Cook,
Nhu D. Le,
C. Blake Gilks,
Jacek Gronwald,
Anna Jakubowska,
Jan Lubiński,
Tomasz Kluz,
Honglin Song,
Jonathan P. Tyrer,
Nicolas Wentzensen,
Louise A. Brinton,
Britton Trabert,
Jolanta Lissowska,
Esther M. John,
Steven A. Narod,
Catherine Phelan,
Hoda AntonCulver,
Argyrios Ziogas,
Diana Eccles,
Ian Campbell,
Simon A. Gayther,
Aleksandra GentryMaharaj,
Usha Me,
Susan J. Ramus,
Anna H. Wu,
Agnieszka DansonkaMieszkowska,
Jolanta Kupryjańczyk,
Agnieszka Timorek,
Lukasz M. Szafron,
Julie M. Cunningham,
Brooke L. Fridley,
Stacey J. Winham,
Elisa V. Bandera,
Elizabeth M. Poole,
Terry K. Morgan,
Ellen L. Goode,
Joellen M. Schildkraut,
Celeste Leigh Pearce,
Andrew Berchuck,
Paul D.P. Pharoah,
Penelope M. Webb,
Georgia ChenevixTrench,
Harvey A. Risch,
Stuart MacGregor
Publication year - 2016
Publication title -
human genetics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.351
H-Index - 137
eISSN - 1432-1203
pISSN - 0340-6717
DOI - 10.1007/s00439-016-1663-9
Subject(s) - biology , serous fluid , heritability , genetic architecture , ovarian cancer , single nucleotide polymorphism , genetics , cancer , genetic association , clear cell , disease , oncology , quantitative trait locus , genotype , medicine , gene , carcinoma , biochemistry
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.
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