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Ibeagha-Awemu, E. M.

Publications and source records attributed to Ibeagha-Awemu, E. M..

2 recordsLinked to original sources

The enhanced multi-tissue atlas of regulatory effects in cattle

Cattle are integral to global food security, yet the molecular architecture of their complex traits remains poorly understood. Here, we present the Cattle Genotype-Tissue Expression (CattleGTEx) Phase 1 resource (https://cattlegtex.farmgtex.org/), a substantial expansion of the pilot study. By leveraging 12,422 RNA-seq profiles across 43 tissues and 82 breeds, we characterized 433,972 primary and 161,428 non-primary regulatory effects spanning seven molecular phenotypes. This high-resolution atlas resolves 75% of GWAS signals for 44 complex traits, significantly addressing the "missing regulation" in livestock. We propose a genetic regulatory model demonstrating how variants across multiple biological layers interact with specific biological contexts to shape phenotypic variation. Furthermore, CattleGTEx elucidates mechanisms underlying adaptive evolution between Bos taurus and Bos indicus, as well as artificial selection in dairy and beef breeds. Finally, by mapping evolutionary constraints on these regulatory effects, we demonstrate the translational value of this resource for prioritizing causal variants in human complex diseases. Together, Phase 1 of CattleGTEx provides a transformative framework for functional genomics, precision breeding, and comparative genetics.

genetics↗

The first sheep graph pan-genome reveals the spectrum of structural variations and their effects on different tail phenotypes

Structural variations (SVs) are a major contributor to genetic diversity and phenotypic variations, but their prevalence and functions in domestic animals are largely unexplored. Here, we assembled 26 haplotype-resolved genome assemblies from 13 genetically diverse sheep using PacBio HiFi sequencing. We constructed a graph-based ovine pan-genome and discovered 142,422 biallelic insertions and deletions, 7,028 divergent alleles and 13,419 multiallelic variations. We then used a graph-based approach to genotype the biallelic SVs in 684 individuals from 45 domestic breeds and two wild species. Integration with RNA-seq data allows to identify candidate expression-associated SVs. We demonstrate a direct link of SVs and phenotypes by localizing the putative causative insertion in HOXB13 gene responsible for the long-tail trait and identifying multiple large SVs associated with the fat-tail. Beyond generating a benchmark resource for ovine structural variants, our study highlights that animal genetic research will greatly benefit from using a pan-genome graph rather than a single reference genome.

genomics↗