ArticlePloS one2019
Integrating genome-wide association and eQTLs studies identifies the genes associated with age at menarche and age at natural menopause.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed, 10 citations in OpenAlex.
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- Associations between abdominal adipose tissue, reproductive span, and brain characteristics in post-menopausal women.NeuroImage. Clinical · 2022Article
- Genetic Regulation of Physiological Reproductive Lifespan and Female Fertility.International journal of molecular sciences · 2021Review
- Pubertal Growth, IGF-1, and Windows of Susceptibility: Puberty and Future Breast Cancer Risk.The Journal of adolescent health : official publication of the Society for Adolescent Medicine · 2021Article
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveAn early onset of menarche and, later, menopause are well-established risk factors for the development of breast cancer and endometrial cancer. Although the largest GWASs have identified 389 independent signals for age at menarche (AAM) and 44 regions for age at menopause (ANM), GWAS can only identify the associations between variants and traits. The aim of this study was to identify genes whose expression levels were associated with AAM or ANM due to pleiotropy or causality by integrating GWAS data with genome-wide expression quantitative trait loci (eQTLs) data. We also aimed to identify the pleiotropic genes that influenced both phenotypes.
methodWe employed GWAS data of AAM and ANM and genome-wide eQTL data from whole blood. The summary data-based Mendelian randomization method was used to prioritize the associated genes for further study. The colocalization analysis was used to identify the pleiotropic genes associated with both phenotypes.
resultsWe identified 31 genes whose expression was associated with AAM and 24 genes whose expression was associated with ANM due to pleiotropy or causality. Two pleiotropic genes were identified to be associated with both phenotypes.
conclusionThe results point out the most possible genes which were responsible for the association. Our study prioritizes the associated genes for further functional mechanistic study of AAM and ANM and illustrates the benefit of integrating different omics data into the study of complex traits.
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