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Development of microsatellite markers with the SSR-seq method on Glossina palpalis gambiensis, and G. p. palpalis, and analysis of corresponding field samples from three sleeping sickness foci: Boffa, Dubreka (Guinea), and Bonon (Cote d'Ivoire).

DE MEEUS, T.; Ravel, S.; Kagbadouno, M.; BERTE, D.; SEGARD, A.; LEPAIS, O.

2026-09-24 genomics
10.64898/2025.12.19.695359 bioRxiv
Show abstract

Tsetse flies are strictly found is sub-Saharan Africa where they are responsible for the transmission and maintenance of African trypanosomiases in humans (HAT) and animals (AAT). Vector control has been recognized as an essential tool to fight against these diseases. Nevertheless, it requires the best possible knowledge of the biology of the targeted population. Population genetics tools can prove very useful to obtain such information but require the use of polymorphic and reliable genetic markers. In this paper we present the development of microsatellite markers using a new high-throughput sequencing based technology (SSRseq). We applied it on two species of tsetse flies from three HAT foci and obtained more accurate results as compared to microsatellite loci developed with classic methods. We could indeed use 9 to 14 SSRseq loci without the several problems generally met with classic microsatellites, as all were located in autosomes, without short allele dominance or stuttering and very few null alleles. SSRseq loci appeared much more polymorphic in tsetse flies as compared to other species (fungi, trees, bees, or fishes), which suggested much higher effective population sizes, much higher mutation rates of the genome or both, suggesting high capacities for evolutionary adaptation. With the 9-14 loci kept, we confirmed the propensity of these flies to move almost freely in the whole zones investigated, and also highlighted the possible evolutionary response of one of the unkept loci regarding vector control devices used in these HAT foci, which will require further studies. We suggest for further population structure studies to use only loci with less than 1% missing data, which proved being a good and fast selection strategy to get the most reliable results.

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