Assessing the genetic architecture of epithelial ovarian cancer histological subtypes

TY - JOUR

T1 - Assessing the genetic architecture of epithelial ovarian cancer histological subtypes

AU - Cuellar-Partida, Gabriel

AU - Lu, Yi

AU - Dixon, Suzanne C

AU - Fasching, Peter A

AU - Hein, Alexander

AU - Burghaus, Stefanie

AU - Beckmann, Matthias W

AU - Lambrechts, Diether

AU - Van Nieuwenhuysen, Els

AU - Vergote, Ignace

AU - Vanderstichele, Adriaan

AU - Doherty, Jennifer Anne

AU - Rossing, Mary Anne

AU - Chang-Claude, Jenny

AU - Rudolph, Anja

AU - Wang-Gohrke, Shan

AU - Goodman, Marc T

AU - Bogdanova, Natalia

AU - Dörk, Thilo

AU - Dürst, Matthias

AU - Hillemanns, Peter

AU - Runnebaum, Ingo B

AU - Antonenkova, Natalia

AU - Butzow, Ralf

AU - Leminen, Arto

AU - Nevanlinna, Heli

AU - Pelttari, Liisa M

AU - Edwards, Robert P

AU - Kelley, Joseph L

AU - Modugno, Francesmary

AU - Moysich, Kirsten B

AU - Ness, Roberta B

AU - Cannioto, Rikki

AU - Høgdall, Estrid

AU - Høgdall, Claus

AU - Jensen, Allan

AU - Giles, Graham G

AU - Bruinsma, Fiona

AU - Kjaer, Susanne K

AU - Hildebrandt, Michelle A T

AU - Liang, Dong

AU - Lu, Karen H

AU - Wu, Xifeng

AU - Bisogna, Maria

AU - Dao, Fanny

AU - Levine, Douglas A

AU - Cramer, Daniel W

AU - Terry, Kathryn L

AU - Tworoger, Shelley S

AU - Stampfer, Meir

AU - Missmer, Stacey

AU - Bjorge, Line

AU - Salvesen, Helga B

AU - Kopperud, Reidun K

AU - Bischof, Katharina

AU - Aben, Katja K H

AU - Kiemeney, Lambertus A

AU - Massuger, Leon F A G

AU - Brooks-Wilson, Angela

AU - Olson, Sara H

AU - McGuire, Valerie

AU - Rothstein, Joseph H

AU - Sieh, Weiva

AU - Whittemore, Alice S

AU - Cook, Linda S

AU - Le, Nhu D

AU - Blake Gilks, C

AU - Gronwald, Jacek

AU - Jakubowska, Anna

AU - Lubiński, Jan

AU - Kluz, Tomasz

AU - Song, Honglin

AU - Tyrer, Jonathan P

AU - Wentzensen, Nicolas

AU - Brinton, Louise

AU - Trabert, Britton

AU - Lissowska, Jolanta

AU - McLaughlin, John R

AU - Narod, Steven A

AU - Phelan, Catherine

AU - Anton-Culver, Hoda

AU - Ziogas, Argyrios

AU - Eccles, Diana

AU - Campbell, Ian

AU - Gayther, Simon A

AU - Gentry-Maharaj, Aleksandra

AU - Menon, Usha

AU - Ramus, Susan J

AU - Wu, Anna H

AU - Dansonka-Mieszkowska, Agnieszka

AU - Kupryjanczyk, Jolanta

AU - Timorek, Agnieszka

AU - Szafron, Lukasz

AU - Cunningham, Julie M

AU - Fridley, Brooke L

AU - Winham, Stacey J

AU - Bandera, Elisa V

AU - Poole, Elizabeth M

AU - Morgan, Terry K

AU - Goode, Ellen L

AU - Schildkraut, Joellen M

AU - Pearce, Celeste L

AU - Berchuck, Andrew

AU - Pharoah, Paul D P

AU - Webb, Penelope M

AU - Chenevix-Trench, Georgia

AU - Risch, Harvey A

AU - MacGregor, Stuart

AU - Australian Ovarian Cancer Study

PY - 2016/7

Y1 - 2016/7

N2 - 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.

AB - 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.

U2 - 10.1007/s00439-016-1663-9

DO - 10.1007/s00439-016-1663-9

M3 - SCORING: Journal article

C2 - 27075448

VL - 135

SP - 741

EP - 756

JO - HUM GENET

JF - HUM GENET

SN - 0340-6717

IS - 7

ER -