gsQTL: Associating genetic risk variants with gene sets by exploiting their shared variability
Gerard A Bouland1,2, Niccolò Tesi1,3, Ahmed Mahfouz1,2
1Delft Bioinformatics Lab, Delft University of Technology, Delft, The Netherlands.
Biorxiv : the Preprint Server for Biology
|September 30, 2024
Summary
We introduce gene set QTL (gsQTL), a novel method to link genetic risk variants to gene sets. gsQTL improves upon traditional expression quantitative trait loci (eQTL) analysis by examining collective gene set variation, offering enhanced identification of functional gene sets.
Area of Science:
- Genetics
- Systems Biology
- Bioinformatics
Background:
- Genome-wide association studies (GWASs) identify genetic risk loci, but linking them to specific gene functions remains challenging.
- Current methods like expression quantitative trait loci (eQTL) analysis face limitations due to small effect sizes and multiple testing burdens.
- Gene set analyses are used to infer functionality but can be hampered by these limitations.
Purpose of the Study:
- To develop a more effective method for identifying functional relationships between genetic risk variants and gene sets.
- To introduce and validate the gene set QTL (gsQTL) approach as an alternative to conventional eQTL and gene set analysis.
- To demonstrate the broader applicability of analyzing shared variability within gene sets.
Main Methods:
- Proposed the gene set QTL (gsQTL) method, which analyzes the collective variation of entire gene sets instead of individual genes.
- Compared gsQTL performance against conventional methods in identifying links between genetic risk variants and gene sets.
- Evaluated the robustness of gsQTL against inflation or deflation of significant enrichments.
Main Results:
- gsQTL demonstrates superior adeptness at identifying links between genetic risk variants and specific gene sets compared to traditional approaches.
- The gsQTL method shows reduced susceptibility to inflation or deflation of significant enrichments.
- Demonstrated the broader applicability of gsQTL in scenarios involving coordinated gene regulation, such as transcription factor activity or differential expression.
Conclusions:
- gsQTL offers a more powerful and robust approach for functional interpretation of genetic risk loci.
- Analyzing collective gene set variation provides deeper insights into the functional consequences of genetic variants.
- The gsQTL framework has broad implications for understanding complex genetic diseases and biological pathways.
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