Reconstructing SNV-SNV interaction networks via computational detection of variant co-expression in RNA-seq data

The detection of co-expressed variant alleles is suggested as a strategy to improve genetic association studies and address the missing heritability problem but is currently limited by the availability of methods and demanding computational requirements. In response, this work presents two RNA-seq based eQTL methods – ReQTL and SNV2 – to address these shortcomings. These applications employ a computational approach to rapidly assess genome-wide relationships among genetic variants and gene expression (ReQTL), as well as pairwise correlations between single nucleotide variants (SNV2) by assessing expressed variant allele frequencies (VAFs) for correlation across multiple individuals. These approaches are exemplified on sets of RNA-sequencing data from the Genotype-Tissue Expression (GTEx) project in which both known and novel molecular relationships are identified. The results suggest that ReQTL and SNV2 reveal both known and novel molecular associations. In our data SNVs involved in significant ReQTL and SNV2 correlations appear to be enriched in RNA-editing sites. ReQTL and SNV2 analyses are shown to be computationally feasible and can be applied to study: (1) RNA-mediated molecular interactions, (2) RNA-editing implicated networks, and (3) imprinting and co-transcribed genes/isoforms. Given the quickly growing accessibility of RNA-sequencing data matched by improving algorithms for RNA-seq variant calls, ReQTL and SNV2 holds a strong potential to facilitate the discovery of novel molecular interactions in a time and cost-effective manner.

Author

• Bousounis, Pavlos

Language

• English

Keyword

• SNV
• quantitative traits
• eQTL
• allelic co-expression
• RNA-seq

Date created

• 2019

Type of Work

• Master's Thesis

License

• All rights reserved

Rights statement

• In Copyright

Degree

• M.S.

Advisor

• Horvath, Anelia

Committee Member(s)

• Villagra, Alejandro

Persistent URL

•  https://scholarspace.library.gwu.edu/etd/vt150k16q