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

In Brazil, <i>Nyssorhynchus darlingi</i> 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 <i>Ny. darlingi</i> and compared them with <i>Anopheles gambiae</i> and <i>Anopheles albimanus</i> genomes in synteny analysis. The <i>An. gambiae</i> 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 <i>Ny. darlingi</i> inversions regions with <i>An. gambiae</i> and <i>An. albimanus</i> genomes. The sliding window analysis of PCA components revealed 10 high-confidence candidate regions for chromosome inversions in <i>Ny. darlingi</i> genome and two known inversions for <i>An. gambiae</i> 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 <i>Ny. darlingi</i> from the Brazilian Amazon that does not share similar inversion arrangements in <i>An. gambiae</i> or <i>An. albimanus</i> genomes.