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Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
Published on: January 16, 2019
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AeQTL: eQTL analysis using region-based aggregation of rare genomic variants
Guanlan Dong1, Michael C Wendl, Bin Zhang
1Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA.
Summary
AeQTL software enhances the detection of rare genetic variants linked to gene expression changes. This tool improves power for identifying expression quantitative trait loci (eQTLs) and aids in cancer gene discovery.
Area of Science:
- Genomics
- Bioinformatics
- Cancer Research
Background:
- Genomic and transcriptomic data enable expression quantitative trait loci (eQTL) discovery.
- Existing eQTL tools have limitations in detecting rare variants and file format compatibility.
Purpose of the Study:
- To develop a novel software tool, AeQTL, for enhanced eQTL analysis.
- To improve the detection power for rare variant eQTLs.
- To apply AeQTL in identifying genetic variants associated with gene expression in tumors.
Main Methods:
- AeQTL aggregates variants by user-specified regions for analysis.
- The tool accommodates standard genomic file formats.
- Comparison of AeQTL with single-variant tests for power assessment.
Main Results:
- AeQTL demonstrated comparable or superior power in identifying rare variant eQTLs.
- Discovered associations between aggregated rare germline truncations and BRCA1/SLC25A39 expression in breast tumors.
- Identified differential associations of aggregated somatic mutations with cancer driver gene expression and developed a multi-omic classifier for oncogenes/tumor-suppressors.
Conclusions:
- AeQTL is an effective tool for discovering rare coding and noncoding variants associated with gene expression.
- AeQTL facilitates novel multi-omic classifications of cancer genes.
- The software is user-friendly and broadly applicable in genetic research.
