Dataset Information

Integration of genome-wide association study and expression quantitative trait locus mapping for identification of endometriosis-associated genes.

ABSTRACT: To determine whether genetic predisposition to endometriosis varies depending on ethnicity and in association with expression quantitative trait loci (eQTL) in a Taiwanese population. We conducted a genome-wide association study (GWAS) and replicated it in 259 individuals with laparoscopy-confirmed stage III or IV endometriosis (cases) and 171 women without endometriosis (controls). Their genomic DNA was extracted from blood and evaluated by the GWAS of Taiwan Biobank Array. Novel genetic variants that predispose individuals to endometriosis were identified using GWAS and replication, including rs10739199 (P?=?6.75?×?10^-5) and rs2025392 (P?=?8.01?×?10^-5) at chromosome 9, rs1998998 (P?=?6.5?×?10^-6) at chromosome 14, and rs6576560 (P?=?9.7?×?10^-6) at chromosome 15. After imputation, strong signals were exhibited by rs10822312 (P?=?1.80?×?10^-7) at chromosome 10, rs58991632 (P?=?1.92?×?10^-6) and rs2273422 (P?=?2.42?×?10^-6) at chromosome 20, and rs12566078 (P?=?2.5?×?10^-6) at chromosome 1. We used the Genotype-Tissue Expression (GTEx) database to observe eQTL. Among these SNPs, the cis-eQTL rs13126673 of inturned planar cell polarity protein (INTU) showed significant association with INTU expression (P?=?5.1?×?10^-33). Moreover, the eQTL analysis was performed on endometriotic tissues from women with endometriosis. The expression of INTU in 78 endometriotic tissue of women with endometriosis is associated with rs13126673 genotype (P?=?0.034). To our knowledge, this is the first GWAS to link endometriosis and eQTL in a Taiwanese population.

SUBMITTER: Chou YC

PROVIDER: S-EPMC7803948 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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Integration of genome-wide association study and expression quantitative trait locus mapping for identification of endometriosis-associated genes.

Chou Ya-Ching YC Chen Ming-Jer MJ Chen Pi-Hua PH Chang Ching-Wen CW Yu Mu-Hsien MH Chen Yi-Jen YJ Tsai Eing-Mei EM Tsai Shih-Feng SF Kuo Wun-Syuan WS Tzeng Chii-Ruey CR

Scientific reports 20210112 1

To determine whether genetic predisposition to endometriosis varies depending on ethnicity and in association with expression quantitative trait loci (eQTL) in a Taiwanese population. We conducted a genome-wide association study (GWAS) and replicated it in 259 individuals with laparoscopy-confirmed stage III or IV endometriosis (cases) and 171 women without endometriosis (controls). Their genomic DNA was extracted from blood and evaluated by the GWAS of Taiwan Biobank Array. Novel genetic varian ...[more]

PMID: 33436679

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Project description:The term epistasis refers to interactions between multiple genetic loci. Genetic epistasis is important in regulating biological function and is considered to explain part of the 'missing heritability,' which involves marginal genetic effects that cannot be accounted for in genome-wide association studies. Thus, the study of epistasis is of great interest to geneticists. However, estimating epistatic effects for quantitative traits is challenging due to the large number of interaction effects that must be estimated, thus significantly increasing computing demands. Here, we present a new web server-based tool, the Pipeline for estimating EPIStatic genetic effects (PEPIS), for analyzing polygenic epistatic effects. The PEPIS software package is based on a new linear mixed model that has been used to predict the performance of hybrid rice. The PEPIS includes two main sub-pipelines: the first for kinship matrix calculation, and the second for polygenic component analyses and genome scanning for main and epistatic effects. To accommodate the demand for high-performance computation, the PEPIS utilizes C/C++ for mathematical matrix computing. In addition, the modules for kinship matrix calculations and main and epistatic-effect genome scanning employ parallel computing technology that effectively utilizes multiple computer nodes across our networked cluster, thus significantly improving the computational speed. For example, when analyzing the same immortalized F2 rice population genotypic data examined in a previous study, the PEPIS returned identical results at each analysis step with the original prototype R code, but the computational time was reduced from more than one month to about five minutes. These advances will help overcome the bottleneck frequently encountered in genome wide epistatic genetic effect analysis and enable accommodation of the high computational demand. The PEPIS is publically available at http://bioinfo.noble.org/PolyGenic_QTL/.

Dataset Information

Integration of genome-wide association study and expression quantitative trait locus mapping for identification of endometriosis-associated genes.

Publications

Integration of genome-wide association study and expression quantitative trait locus mapping for identification of endometriosis-associated genes.

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