We aimed at identifying transcripts whose expression is regulated by a SNP-SNP interaction. Out of 47,294 expression phenotypes we used 3107 transcripts that survived an extensive quality control and 86,613 linkage disequilibrium-pruned SNP markers that have been genotyped in 210 individuals. For each transcript we defined cis-SNPs, tested them for epistasis with all trans-SNPs, and corrected all observed cis-trans-regulated expression effects for multiple testing. We determined that the expression of about 15% of all included transcripts is regulated by a significant two-locus interaction, which is more than expected (P = 2.86 × 10(-144)). Our findings suggest further that cis-markers with so called 'marginal effects' are more likely to be involved in two-locus gene regulation than expected (P = 8.27 × 10(-05)), although the majority of interacting cis-markers showed no one-locus regulation. Furthermore, we found evidence that gene-mediated trans-effects are not a major source of epistasis, as no enrichment of genes has been found in close vicinity of trans-SNPs. In addition, our data support the notion that neither chromosomal regions nor cellular processes are enriched in epistatic interactions. Finally, some of the cis-trans regulated genes have been found in genome-wide association studies, which might be interesting for follow-up studies of the corresponding disorders. In summary, our results provide novel insights into the complex genome-transcriptome regulation.
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http://dx.doi.org/10.1038/ejhg.2011.156 | DOI Listing |
Background: Analyzing disease-linked genetic variants via expression quantitative trait loci (eQTLs) is important for identifying potential disease-causing genes. Previous research prioritized genes by integrating Genome-Wide Association Study (GWAS) results with tissue- level eQTLs. Recent studies have explored brain cell type-specific eQTLs, but they lack a systematic analysis across various Alzheimer's disease (AD) GWAS datasets, nor did they compare effects between tissue and cell type levels or across different cell type-specific eQTL datasets.
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January 2025
Medical Oncology Department of Gastrointestinal Cancer, Cancer Hospital of Dalian University of Technology, Liaoning Cancer Hospital & Institute, No.44 Xiaoheyan Road, Dadong District, Shenyang 110042, Liaoning Province, China.
Gastric cancer (GC) remains a significant global health challenge. This study aimed to comprehensively analyze GC epidemiology and risk factors to inform prevention and intervention strategies. We analyzed the Global Burden of Disease Study 2021 data, conducted 16 different machine learning (ML) models of NHANES data, performed Mendelian randomization (MR) studies on disease phenotypes, dietary preferences, microbiome, blood-based markers, and integrated differential gene expression and expression quantitative trait loci (eQTL) data from multiple cohorts to identify factors associated with GC risk.
View Article and Find Full Text PDFExp Gerontol
January 2025
Department of Otorhinolaryngology, Qilu Hospital of Shandong University, Jinan, Shandong, China; NHC Key Laboratory of Otorhinolaryngology, Shandong University, Jinan, Shandong, China. Electronic address:
Background: Age-related hearing loss (ARHL) is a common sensory disorder with significant public health implications. However, few effective treatment options are available. Mendelian randomization (MR) has been used to repurpose existing drugs and identify new therapeutic targets.
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December 2024
Cancer Research Institute, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Peking University Cancer Hospital Yunnan, Kunming, 650106, China.
Background: Hyperemesis gravidarum (HG), excessive vomiting in pregnancy, occurs in 0.3-10.8% of pregnancies and is associated with maternal and fetal morbidity.
View Article and Find Full Text PDFComput Struct Biotechnol J
December 2024
Aix-Marseille Univ, INSERM U1090, TAGC, Marseille 13288, France.
Integrating expression quantitative trait loci (eQTL) data with genome-wide association studies (GWAS) enables the discovery of pleiotropic gene regulatory variants that influence a wide range of traits and disease susceptibilities. However, a comprehensive understanding of the distribution of pleiotropic QTLs across the genome and their phenotypic associations remain limited. In this study, we systematically annotated genetic variants associated with both trait variation and gene expression changes, focusing specifically on the unique characteristics of pleiotropic eQTLs.
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