Al-Tobasei, R. | bioRxiv Science

bioRxiv2025

Splicing Retention and Enhancer Divergence Govern the Evolutionary Fate of Ohnologues Following Whole-Genome Duplication in Rainbow Trout

Whole-genome duplication (WGD) produces gene duplicates--known as ohnologues--that expand evolutionary potential while posing challenges for regulatory coordination and functional balance. While divergence in gene expression has been widely studied post-WGD, the long-term evolutionary dynamics of alternative splicing (AS) and its regulatory basis remain unresolved. Here, we investigate the evolution of AS in rainbow trout (Oncorhynchus mykiss), a species that underwent a salmonid-specific WGD [~]100 million years ago. Using a high-quality genome assembly, transcriptome data across six tissues, and ChIP-seq profiling of histone modifications, we classify ohnologue pairs based on their expression divergence, splicing complexity, and epigenetic signatures. We find that most ohnologues are retained through conservation, with a gradual reduction in AS diversity over time. Contrary to earlier models, a substantial fraction of ohnologues follows an independent splicing model, maintaining splicing complexity similar to their unduplicated ancestors. Strikingly, we show that enhancer-associated histone marks, particularly H3K27ac, diverge significantly between neofunctionalized and independently splicing gene pairs, implicating enhancer rewiring as a key driver of regulatory and functional divergence. These findings reveal that AS evolution after WGD is shaped by both selective pressures and epigenetic modulation, challenging assumptions of rapid splicing loss and highlighting the independent model as a dominant long-term fate. Our results provide a unified framework for understanding how splicing and regulatory landscapes evolve following genome duplication, with broad implications for vertebrate genome evolution and functional innovation.

genomics↗

bioRxiv2018

Genome-wide association analysis with a 50K transcribed gene SNP-chip identifies QTL affecting muscle yield in rainbow trout

Detection of coding/functional SNPs that change the biological function of a gene may lead to identification of putative causative alleles within QTL regions and discovery of genetic markers with large effects on phenotypes. Two bioinformatics pipelines, GATK and SAMtools, were used to identify ~21K transcribed SNPs with allelic imbalances associated with important aquaculture production traits including body weight, muscle yield, muscle fat content, shear force, and whiteness in addition to resistance/susceptibility to bacterial cold-water disease (BCWD). SNPs were identified from pooled RNA-Seq data collected from ~620 fish, representing 98 families from growth- and 54 families from BCWD-selected lines with divergent phenotypes. In addition, ~29K transcribed SNPs without allelic-imbalances were strategically added to build a 50K Affymetrix SNP-chip. SNPs selected included two SNPs per gene from 14K genes and ~5K non-synonymous SNPs. The SNP-chip was used to genotype 1728 fish. The average SNP calling-rate for samples passing quality control (QC; 1,641 fish) was [≥] 98.5%. Genome-wide association (GWA) study on 878 fish (representing 197 families from 2 consecutive generations) with muscle yield phenotypes and genotyped for 35K polymorphic markers (passing QC) identified several QTL regions explaining together up to 28.40% of the additive genetic variance for muscle yield in this rainbow trout population. The most significant QTLs were on chromosomes 14 and 16 with 12.71% and 10.49% of the genetic variance, respectively. Many of the annotated genes in the QTL regions were previously reported as important regulators of muscle development and cell signaling. No major QTLs were identified in a previous GWA study using a 57K genomic SNP chip on the same fish population. These results indicate improved detection power of the transcribed gene SNP-chip in the target trait and population, allowing identification of large-effect QTLs for important traits in rainbow trout.

genomics↗