PUBLICATION
Differential allelic representation (DAR) identifies candidate eQTLs and improves transcriptome analysis
- Authors
- Baer, L., Barthelson, K., Postlethwait, J.H., Adelson, D.L., Pederson, S.M., Lardelli, M.
- ID
- ZDB-PUB-240213-4
- Date
- 2024
- Source
- PLoS Computational Biology 20: e1011868e1011868 (Journal)
- Registered Authors
- Lardelli, Michael, Postlethwait, John H.
- Keywords
- none
- Datasets
- GEO:GSE164466, GEO:GSE217196
- MeSH Terms
-
- Animals
- Gene Expression Profiling
- Genotype
- Quantitative Trait Loci*/genetics
- RNA
- Transcriptome/genetics
- Zebrafish*/genetics
- PubMed
- 38346074 Full text @ PLoS Comput. Biol.
Citation
Baer, L., Barthelson, K., Postlethwait, J.H., Adelson, D.L., Pederson, S.M., Lardelli, M. (2024) Differential allelic representation (DAR) identifies candidate eQTLs and improves transcriptome analysis. PLoS Computational Biology. 20:e1011868e1011868.
Abstract
In comparisons between mutant and wild-type genotypes, transcriptome analysis can reveal the direct impacts of a mutation, together with the homeostatic responses of the biological system. Recent studies have highlighted that, when the effects of homozygosity for recessive mutations are studied in non-isogenic backgrounds, genes located proximal to the mutation on the same chromosome often appear over-represented among those genes identified as differentially expressed (DE). One hypothesis suggests that DE genes chromosomally linked to a mutation may not reflect functional responses to the mutation but, instead, result from an unequal distribution of expression quantitative trait loci (eQTLs) between sample groups of mutant or wild-type genotypes. This is problematic because eQTL expression differences are difficult to distinguish from genes that are DE due to functional responses to a mutation. Here we show that chromosomally co-located differentially expressed genes (CC-DEGs) are also observed in analyses of dominant mutations in heterozygotes. We define a method and a metric to quantify, in RNA-sequencing data, localised differential allelic representation (DAR) between those sample groups subjected to differential expression analysis. We show how the DAR metric can predict regions prone to eQTL-driven differential expression, and how it can improve functional enrichment analyses through gene exclusion or weighting-based approaches. Advantageously, this improved ability to identify probable eQTLs also reveals examples of CC-DEGs that are likely to be functionally related to a mutant phenotype. This supports a long-standing prediction that selection for advantageous linkage disequilibrium influences chromosome evolution. By comparing the genomes of zebrafish (Danio rerio) and medaka (Oryzias latipes), a teleost with a conserved ancestral karyotype, we find possible examples of chromosomal aggregation of CC-DEGs during evolution of the zebrafish lineage. Our method for DAR analysis requires only RNA-sequencing data, facilitating its application across new and existing datasets.
Genes / Markers
Expression
Phenotype
Mutations / Transgenics
Human Disease / Model
Sequence Targeting Reagents
Fish
Orthology
Engineered Foreign Genes
Mapping