Expressive

rs11246001 - RIC8A

Magnitude 2.8 · 3 studies on file

Reported associations

  • GWAS of five gynecologic diseases and cross-trait analysis in Japanese - Unknown journal (n.d.) · Unknown authors · PubMed 31488892

    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

  • Variants associating with uterine leiomyoma highlight genetic background shared by various cancers and hormone-related traits - Unknown journal (n.d.) · Unknown authors · PubMed 30194396

    ABSTRACT: Uterine leiomyomas are common benign tumors of the myometrium. We performed a meta-analysis of two genome-wide association studies of leiomyoma in European women (16,595 cases and 523,330 controls), uncovering 21 variants at 16 loci that associate with the disease. Five variants were previously reported to confer risk of various malignant or benign tumors (rs78378222 in TP53, rs10069690 in TERT, rs1800057 and rs1801516 in ATM, and rs7907606 at OBFC1) and four signals are located at established risk loci for hormone-related traits (endometriosis and breast cancer) at 1q36.12 (CDC42/WNT4), 2p25.1 (GREB1), 20p12.3 (MCM8), and 6q26.2 (SYNE1/ESR1). Polygenic score for leiomyoma, computed using UKB data, is significantly correlated with risk of cancer in the Icelandic population. Funct

  • Multitrait analyses identify genetic variants associated with aortic valve function and aortic stenosis risk - Unknown journal (n.d.) · Unknown authors · PubMed 41419685

    ABSTRACT: The genetic influences on normal aortic valve function and their impact on aortic stenosis risk are of substantial interest. We used deep learning to measure peak velocity, mean gradient and aortic valve area from magnetic resonance imaging and conducted genome-wide association studies (GWAS) in 59,571 participants in the UK Biobank. Incorporating the aortic valve measurement GWAS with aortic stenosis GWAS using multitrait analysis of GWAS (MTAG), we identified 166 distinct loci (134 with aortic valve traits, 134 with aortic stenosis and 166 unique loci across all GWAS), including PCSK9 and LDLR. The MTAG aortic stenosis PGS was associated with aortic stenosis in All of Us (hazard ratio (HR) = 3.32 for top 5% versus all others, P = 8.8 × 10−22) and Mass General Bri

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