bioRxivGeneticsPreprint
100 years domestication of penaeid shrimp and meta-analysis of breeding traits
Shengjie ReniD, José Manuel YañeziD, Ricardo Pérez-EnríqueziD, Morten RyeiD, Ross D. HoustoniD, David A. HurwoodiD, José Reyes González-GalavíziD, Marcela SalazariD, Dean R. JerryiD
Abstract
Penaeid shrimp farming plays a pivotal role in ensuring future food security and promoting economic sustainability. Compared to the extensive long history of domestication observed in terrestrial agriculture species, the domestication and selective breeding of penaeids are relatively recent endeavors. Selective breeding aimed at improving production traits holds significant promise for enhancing efficiency and reducing the environmental impact of shrimp farming, thereby contributing to its long-term sustainability. Assessing genotype-by-environment (G-by-E) interactions is essential in breeding programs to ensure that improved penaeid shrimp strains perform consistently across different production environments, with genomic selection proving more effective than sib-testing alone in mitigating environmental sensitivity. Genome editing tools like CRISPR/Cas9 offer significant potential to accelerate genetic gains in penaeid shrimp by enabling rapid introduction of desired genetic changes, with recent advancements showing promising results in achieving high transfection efficiency in shrimp embryos. Additionally, artificial intelligence and machine learning are being leveraged to streamline phenotyping and enhance decision-making in shrimp breeding and farming, improving efficiency and accuracy in managing traits and predicting disease outbreaks. Herein, we provide an overview and update on the domestication of penaeid shrimp, including the current status of domestication for principal farmed species, key milestones in domestication history, targeted breeding traits in selective breeding programs, the advantages of integrating genomeic selection for enhancing production traits, and future directions for selective breeding of penaeid shrimp.
- research focus: benchmarking
- research focus: database / resource
- research focus: software / tool
- approach: statistical model
- approach: machine learning (classical)
- Citations
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