Aarhus University Seal / Aarhus Universitets segl


  • Title: Sustainable and efficient insect production for livestock feed through selective breeding (FLYgene)
  • Funded by: Danida
  • AU Project manager: Seniorforsker Goutam Sahana, Center for Quantitative Genetics and Genomics
  • Business partners
    • Aarhus University, Center for Quantitative Genetics and Genomics (AU-QGG)
    • Aarhus University, Electronics and Computer Engineering (AU-ECE)
    • Copenhagen University, Department of Nutrition, Exercise and Sports
    • Makarere University, Electronics and Computer Engineering (MU-ECE)
    • Makarere University, Department of Food Technology & Nutrition
    • Makarere University, Department of Agricultural Production (MU-DAP)
    • Jomo Kenyatta University of Agriculture and Technology (JKUAT), Department of Biochemistry
    • University of Nairobi, Department of Animal Production
    • University of Nairobi, Department of Food Science, Nutrition and Technology
    • International Center for Insect Physiology and Ecology (ICIPE)
    • InsectiPro ltd. Private sector partner.
    • Marula ProTeen ltd. Private sector partner.
  • Project period: 5 years, from 2022 - 2026
  • Grant: DKK 11,999,759

Project description:

This project aims to generate new knowledge of the Black Soldier Fly (BSF) genetics, genomics, and phenomics to inform the design of sustainable breeding programs in Kenya and Uganda. With the assembly of a multidisciplinary team of entomologists, geneticists, bioinformaticians, electronic and computer engineers, and nutritionists, the project will explore innovative BSF phenotyping and family identification systems and quantify the genetic parameters of BSF traits, followed by the design of breeding schemes. High-throughput phenotyping techniques, including those employing cameras, exist for the precise breeding of plants and livestock, and their use for the phenotyping of invertebrates and plant–insect interactions is being investigated. We have previously described methods for the monitoring of Varroa destructor infestation in honeybees using computer vision systems. More recently, we demonstrated the promise of image-based methods for thermal tolerance and larval body size measurement in houseflies and BSFs in a laboratory setting. We aim to further these advances by pursuing a novel investigation on the applicability of image-based methods for BSF phenotyping and family identification under routine rearing conditions in Kenya and Uganda. The use of genetic polymorphism data has brought about a new era in population and conservation genetics, enabling the accurate inference of genetic diversity within and between populations. With advances in genomic technology, it is now feasible to collect whole genome–level polymorphism data at moderate expense and effort. In this project, we will use whole-genome sequencing data to study the genetic diversity of the BSF in Kenya and Uganda, thereby informing the design of breeding schemes that maintain this diversity.