Jiajun Shi1, Lang Wu1, Bingshan Li2,3, Yingchang Lu1, Xingyi Guo1, Qiuyin Cai1, Jirong Long1, Wanqing Wen1, Wei Zheng1, Xiao-Ou Shu1. 1. 1 Department of Medicine, Vanderbilt Epidemiology Center and Division of Epidemiology, Vanderbilt University School of Medicine, Nashville, TN, USA. 2. 2 Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, USA. 3. 3 Vanderbilt Genetics Institute, Nashville, TN, USA.
Abstract
OBJECTIVE: To identify novel susceptibility genes for age at natural menopause (ANM). METHODS: Using transcription data generated in tissues from normal hypothalami (n = 73) and ovaries (n = 68) and high-density genotyping data provided by the Genotype-Tissue Expression (GTEx) database, we built 16 164 genetic models to predict gene expression across the transcriptome in these tissues. We used these models and summary statistics data from genome-wide association studies (GWAS) of ANM generated in 69 360 women of European ancestry to identify genes with their predicted expression related to ANM. RESULTS: We found the predicted expression of 34 genes to be significantly associated with ANM at a Bonferroni-corrected threshold of P < 3.09 ×10-6. These include 4 genes located more than 1 Mb away from any previously GWAS-identified ANM-associated variants, 24 genes that reside in known GWAS-identified loci but have not been previously implicated, and 6 genes previously implicated as ANM-associated genes. CONCLUSION: Results from this transcriptome-wide association study, which integrated Expression quantitative trait loci (eQTL) data with summary statistics of GWAS of ANM, improves our understanding of the genetics and biology of female reproductive aging.
OBJECTIVE: To identify novel susceptibility genes for age at natural menopause (ANM). METHODS: Using transcription data generated in tissues from normal hypothalami (n = 73) and ovaries (n = 68) and high-density genotyping data provided by the Genotype-Tissue Expression (GTEx) database, we built 16 164 genetic models to predict gene expression across the transcriptome in these tissues. We used these models and summary statistics data from genome-wide association studies (GWAS) of ANM generated in 69 360 women of European ancestry to identify genes with their predicted expression related to ANM. RESULTS: We found the predicted expression of 34 genes to be significantly associated with ANM at a Bonferroni-corrected threshold of P < 3.09 ×10-6. These include 4 genes located more than 1 Mb away from any previously GWAS-identified ANM-associated variants, 24 genes that reside in known GWAS-identified loci but have not been previously implicated, and 6 genes previously implicated as ANM-associated genes. CONCLUSION: Results from this transcriptome-wide association study, which integrated Expression quantitative trait loci (eQTL) data with summary statistics of GWAS of ANM, improves our understanding of the genetics and biology of female reproductive aging.
Entities:
Keywords:
age at natural menopause; expression quantitative trait loci; genome-wide association studies; transcriptome-wide association studies
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