A Multi-Approach for In Silico Detection of Chromosome Inversions in Mosquito Vectors

In Brazil, Nyssorhynchus darlingi stands out as the primary malaria vector. Chromosome inversions have long been recognized as critical evolutionary mechanisms in diverse organisms. In this study, we used biallelic SNPs to show that it is possible to detect chromosome inversions reliably with low coverage sequence data. We estimated chromosome inversions in an Amazon Basin sample of Ny. darlingi and compared them with Anopheles gambiae and Anopheles albimanus genomes in synteny analysis. The An. gambiae dataset benchmarked the inversion detection pipeline with known inversions. Genotyping by sequencing was performed using the LCSeqTools workflow for the lcWGS dataset with an average sequencing depth of 2x. A synteny analysis was performed for Ny. darlingi inversions regions with An. gambiae and An. albimanus genomes. The sliding window analysis of PCA components revealed 10 high-confidence candidate regions for chromosome inversions in Ny. darlingi genome and two known inversions for An. gambiae with possible identification of breakpoints and adjacent regions at lower resolution. We demonstrate that lcWGS is a cost-effective and accurate method for detecting chromosome inversions. We reliably detected chromosome inversions in Ny. darlingi from the Brazilian Amazon that does not share similar inversion arrangements in An. gambiae or An. albimanus genomes.