Sustainable and efficient insect production for livestock feed through selective breeding (FLYgene)

Project: Research

Project Details


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.
Short titleFLYgene
Effective start/end date01/01/202231/12/2026


Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint.