Differential allelic representation (DAR) identifies candidate eQTLs and improves transcriptome analysis

Area of Science:

• Genomics and Transcriptomics
• Population Genetics
• Evolutionary Biology

Background:

• Transcriptome analysis comparing mutant and wild-type genotypes reveals mutation impacts and biological responses.
• Genes near mutations are often over-represented in differentially expressed (DE) gene lists, potentially due to expression quantitative trait loci (eQTLs) rather than functional responses.
• Distinguishing true DE genes from eQTL effects is challenging, complicating mutation impact assessment.

Purpose of the Study:

• To develop a method to differentiate functional DE genes from eQTL-driven expression changes.
• To quantify localized differential allelic representation (DAR) in RNA-sequencing data.
• To improve functional enrichment analyses and investigate chromosome evolution.

Main Methods:

• Defined and quantified localized differential allelic representation (DAR) in RNA-sequencing data.
• Applied the DAR metric to predict regions susceptible to eQTL-driven differential expression.
• Compared zebrafish and medaka genomes to identify chromosomal aggregation of DE genes.

Main Results:

• Chromosomally co-located differentially expressed genes (CC-DEGs) are observed in both recessive and dominant mutations.
• The DAR metric effectively predicts eQTL-prone regions and improves gene-based analyses.
• Identified CC-DEGs likely functionally related to mutant phenotypes and observed potential chromosomal aggregation during zebrafish evolution.

Conclusions:

• The DAR method accurately distinguishes functional DE genes from eQTL effects using only RNA-seq data.
• This approach enhances the interpretation of mutation impacts and aids in identifying functionally relevant genes.
• Findings support theories on linkage disequilibrium influencing chromosome evolution and provide insights into teleost genome evolution.