Family history of uterine fibroids significantly modifies associations of GWAS loci with the disease risk: molecular-genetics and bioinformatic analysis

Abstract Uterine fibroids (UF) are among the most common benign tumors in women, yet their genetic underpinnings remain largely unexplored. Given the importance of genetic predisposition and the significance of GWAS-identified loci in UF, we aimed to investigate how family history of UF influences the association between these loci and the disease risk. DNA samples from 241 women with a family history of UF and 947 women without were genotyped for seven GWAS SNPs using probe-based PCR. In patients without a family history of UF, rs547025 SIRT3 was associated with a reduced risk (effect allele C, OR = 0.59, 95 % CI = 0.41–0.83, p = 0.002). Conversely, rs7986407 FOXO1 was linked to an increased risk of UF in those with a family history (effect allele G, OR = 1.94, 95 % CI = 1.03–3.68, p = 0.03). The most significant gene-environment interactions in patients with a family history were observed with pelvic inflammatory disease and GWAS SNPs, while in those without a family history, interactions additionally involved medical abortions. Our bioinformatic analysis uncovered the SNPs’ molecular mechanisms, contributing to UF development.

Authors

Institutions

Publication Details

Journal
Berichte aus der medizinischen Informatik und Bioinformatik/Journal of integrative bioinformatics
Published
2026-10-14
DOI
https://doi.org/10.1515/jib-2024-0045
Primary Topic
Uterine Myomas and Treatments
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Family history of uterine fibroids significantly modifies associations of GWAS loci with the disease risk: molecular-genetics and bioinformatic analysis

Ksenia Kobzeva, Olga Y. Bushueva, Valerii Kuzminov, Liubov Ponomareva
Berichte aus der medizinischen Informatik und Bioinformatik/Journal of integrative bioinformatics
Uterine Myomas and Treatments
article

Family history of uterine fibroids significantly modifies associations of GWAS loci with the disease risk: molecular-genetics and bioinformatic analysis

Ksenia Kobzeva, Olga Y. Bushueva, Valerii Kuzminov, Liubov Ponomareva
article en

Abstract

Abstract Uterine fibroids (UF) are among the most common benign tumors in women, yet their genetic underpinnings remain largely unexplored. Given the importance of genetic predisposition and the significance of GWAS-identified loci in UF, we aimed to investigate how family history of UF influences the association between these loci and the disease risk. DNA samples from 241 women with a family history of UF and 947 women without were genotyped for seven GWAS SNPs using probe-based PCR. In patients without a family history of UF, rs547025 SIRT3 was associated with a reduced risk (effect allele C, OR = 0.59, 95 % CI = 0.41–0.83, p = 0.002). Conversely, rs7986407 FOXO1 was linked to an increased risk of UF in those with a family history (effect allele G, OR = 1.94, 95 % CI = 1.03–3.68, p = 0.03). The most significant gene-environment interactions in patients with a family history were observed with pelvic inflammatory disease and GWAS SNPs, while in those without a family history, interactions additionally involved medical abortions. Our bioinformatic analysis uncovered the SNPs’ molecular mechanisms, contributing to UF development.

Berichte aus der medizinischen Informatik und Bioinformatik/Journal of integrative bioinformatics
Kursk State Medical University (RU)
Good health and well-being
Openalex Percentile: Top 9%
Uterine Myomas and Treatments
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.