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Systematic Exploration in Tissue-Pathway Associations of Complex Traits Using Comprehensive eQTLs Catalog
Boqi Wang1, James Yang2, Steven Qiu3
1Emory University, Atlanta, GA, United States.
Frontiers in Big Data
|November 22, 2021
Summary
This study used loci2path software to analyze expression quantitative trait loci (eQTLs) and identify tissue-specific pathways involved in complex traits like Alzheimer's disease and Parkinson's disease.
Area of Science:
- Genomics
- Systems Biology
- Complex Trait Genetics
Background:
- Expression quantitative trait loci (eQTLs) link genetic variations to gene expression, crucial for understanding complex traits.
- Most genes function in pathways, necessitating pathway-level analysis for comprehensive trait and disease mechanism insights.
- Previous research focused on individual eQTL-trait associations, often overlooking the systemic roles of gene pathways.
Purpose of the Study:
- To expand and apply the loci2path software for large-scale eQTL enrichment analysis at the pathway level.
- To identify tissue-specific enriched pathways within genomic regions associated with complex traits.
- To generate novel hypotheses and reveal underlying mechanisms of disease pathogenesis for ten complex traits.
Main Methods:
- Utilized 13,791,909 eQTLs from Genotype-Tissue Expression (GTEx) V8 data across 49 tissues.
- Integrated 2,893 pathway sets from MSigDB and query regions from the Phenotype-Genotype Integrator (PheGenI) catalog.
- Performed eQTLs' target genes (eGenes) enrichment analysis using the loci2path software.
Main Results:
- Identified significant biological pathways associated with ten complex traits, including Alzheimer's disease (AD), Parkinson's disease (PD), and schizophrenia.
- Highlighted specific pathways for AD, such as BioCarta D4-GDI and WikiPathways sulfation biotransformation.
- Provided evidence for novel hypotheses in AD pathogenesis, including caspase-3 involvement, and revealed consistent mechanisms for other studied traits.
Conclusions:
- The loci2path eQTL enrichment test is a valuable tool for uncovering novel biological mechanisms of complex traits.
- The study identified potential pathogenesis mechanisms for multiple complex diseases, requiring further experimental validation.
- Findings offer new avenues for research into the genetic underpinnings and systemic regulation of complex traits and diseases.
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