Abstract
Bioassays and molecular diagnostics are routinely used for the monitoring of malaria vector populations to support insecticide resistance management (IRM), guiding operational decisions on which insecticides ought to be used for effective vector control. Previously developed TaqMan assays were optimised to distinguish the wild-type L1014 from the knockdown resistance (kdr) point mutations 1014F and 1014S (triplex reaction), and the N1575 wild-type from the point mutation 1575Y (duplex reaction). Subsequently, artificial pools of Anopheles gambiae (An. gambiae) specimens with known genotypes of L1014F, L1014S, and N1575Y were created, nucleic acids were extracted, and kdr mutations were detected. These data were then used to define a linear regression model that predicts the allelic frequency within a pool of mosquitoes as a function of the measured ΔCt values (Ct mutant - Ct wild type probe). Polynomial regression models showed r2 values of >0.99 (p < 0.05). The method was validated with populations of variable allelic frequencies, and found to be precise (1.66⁻2.99%), accurate (3.3⁻5.9%), and able to detect a single heterozygous mosquito mixed with 9 wild type individuals in a pool of 10. Its pilot application in field-caught samples showed minimal differences from individual genotyping (0.36⁻4.0%). It allowed the first detection of the super-kdr mutation N1575Y in An. gambiae from Mali. Using pools instead of individuals allows for more efficient resistance allele screening, facilitating IRM.