Abstract
We performed genome-wide association studies of five gynecologic diseases using data of 46,837 subjects (5236 uterine fibroid, 645 endometriosis, 647 ovarian cancer (OC), 909 uterine endometrial cancer (UEC), and 538 uterine cervical cancer (UCC) cases allowing overlaps, and 39,556 shared female controls) from Biobank Japan Project. We used the population-specific imputation reference panel (n = 3541), yielding 7,645,193 imputed variants. Analyses performed under logistic model, linear mixed model, and model incorporating correlations identified nine significant associations with three gynecologic diseases including four novel findings (rs79219469:C > T, LINC02183, P = 3.3 × 10−8 and rs567534295:C > T, BRCA1, P = 3.1 × 10−8 with OC, rs150806792:C > T, INS-IGF2, P = 4.9 × 10−8 and rs140991990:A > G, SOX9, P = 3.3 × 10−8 with UCC). Random-effect meta-analysis of the five GWASs correcting for the overlapping subjects suggested one novel shared risk locus (rs937380553:A > G, LOC730100, P = 2.0 × 10−8). Reverse regression analysis identified three additional novel associations (rs73494486:C > T, GABBR2, P = 4.8 × 10−8, rs145152209:A > G, SH3GL3/BNC1, P = 3.3 × 10−8, and rs147427629:G > A, LOC107985484, P = 3.8 × 10−8). Estimated heritability ranged from 0.026 for OC to 0.220 for endometriosis. Genetic correlations were relatively strong between OC and UEC, endometriosis and OC, and uterine fibroid and OC (rg > 0.79) compared with relatively weak correlations between UCC and the other four (rg = −0.08 ~ 0.25). We successfully identified genetic associations with gynecologic diseases in the Japanese population. Shared genetic effects among multiple related diseases may help understanding the pathophysiology.
Highlights
Supplementary information The online version of this article contains supplementary material, which is available to authorized users.Uterine fibroma (UF), endometriosis, ovarian cancer (OC), uterine endometrial cancer (UEC), and uterine cervical cancer (UCC) are all common proliferative diseases arising10 Kyoto-McGill International Collaborative School in Genomic Medicine, Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan11 Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita 565-0871, Japan from gynecologic organs
Since MTAG utilizes bivariate linkage disequilibrium (LD) score regression, where linear regression with liability threshold model is assumed and regression z-scores are assumed to follow standard normal distribution, which is different from the linear mixed model and non-normal distribution of regression z-scores assumed in BOLT-LMM, we utilized the results of mach2dat for applying MTAG
Numbers of the subjects eligible for each genome-wide association study (GWAS) were as follows; 5236 for UF, 645 for endometriosis, 647 for OC, 909 for UEC, and 538 for UCC cases where those who have multiple diseases were allowed to enroll in each corresponding GWAS, and 39,556 shared female controls
Summary
The common risk genes wellknown from pedigree studies, such as BRCA1 and BRCA2 [11], have not been reported as either ovarian or breast cancer-susceptibility genes in the context of genome-wide association study (GWAS) [7, 12] This is largely because risk variants found in pedigree studies are usually rare among general population, which is unlikely detected in GWAS. MTAG utilizes summary level data and comparison between analyses based on row genotype data and summary level data might not be straightforward, the MTAG results were comparable to those from analyses under the common logistic model (mach2dat) and linear mixed model (BOLT-LMM) in a disease-specific manner. We considered that SCOPA results could be comparable to those of joint analysis of all cases versus shared controls and random effect GWAS meta-analysis of different diseases in a multiple-disease-combined manner
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