TY - ABST
T1 - Selective breeding in commercial insects with a house fly model system
AU - Hansen, Laura Skrubbeltrang
AU - Laursen, Stine Frey
AU - Bahrndorff, Simon
AU - Kargo, Morten
AU - Sørensen, Jesper Givskov
AU - Sahana, Goutam
AU - Nielsen, Hanne Marie
AU - Kristensen, Torsten Nygaard
PY - 2024
Y1 - 2024
N2 - The rapid expansion of the human population combined with the concerning environmental consequences of resource-intensive agriculture has sparked the interest for alternative protein production from insects. Selective breeding has the potential to optimise insect production through genetic improvement of key traits in commercial insect species, but despite many ongoing efforts, several challenges and questions remain to be solved. We utilise a house fly laboratory model system to reveal and address challenges encountered when implementing selective breeding in commercial insect populations. The research is organised into distinct parts, each addressing a specific aspect of insect breeding such as high-throughput phenotyping, genetic parameter estimation, possibilities and limitations of different breeding plan designs, inbreeding risk and management and, ultimately, the design and implementation of a breeding plan in a house fly population. We present methods that increases throughput of the phenotyping process allowing for rapid acquisition of accurate phenotypic records in house fly larvae, enabling fast selection decisions. We establish a population with known genetic relationship, to unveil important sources of phenotypic variance in larval and adult traits in the house fly, both of biological and environmental origin, and utilise these results to design an optimal insect breeding plan. We demonstrate how selective breeding can be implemented in species with high fecundity and rapid development, such as dipterans, without keeping track of and isolating single individuals. These advances bring us closer to established insect breeding practises which could be an important stride towards advancing sustainable agriculture and ensuring global food security in the future.
AB - The rapid expansion of the human population combined with the concerning environmental consequences of resource-intensive agriculture has sparked the interest for alternative protein production from insects. Selective breeding has the potential to optimise insect production through genetic improvement of key traits in commercial insect species, but despite many ongoing efforts, several challenges and questions remain to be solved. We utilise a house fly laboratory model system to reveal and address challenges encountered when implementing selective breeding in commercial insect populations. The research is organised into distinct parts, each addressing a specific aspect of insect breeding such as high-throughput phenotyping, genetic parameter estimation, possibilities and limitations of different breeding plan designs, inbreeding risk and management and, ultimately, the design and implementation of a breeding plan in a house fly population. We present methods that increases throughput of the phenotyping process allowing for rapid acquisition of accurate phenotypic records in house fly larvae, enabling fast selection decisions. We establish a population with known genetic relationship, to unveil important sources of phenotypic variance in larval and adult traits in the house fly, both of biological and environmental origin, and utilise these results to design an optimal insect breeding plan. We demonstrate how selective breeding can be implemented in species with high fecundity and rapid development, such as dipterans, without keeping track of and isolating single individuals. These advances bring us closer to established insect breeding practises which could be an important stride towards advancing sustainable agriculture and ensuring global food security in the future.
M3 - Conference abstract for conference
T2 - Insects for the Green Economy: Sustainable Food<br/>Systems and Livelihoods in Africa
Y2 - 28 February 2024 through 29 February 2024
ER -