Calling structural variants with confidence from short-read data in wild bird populations.

Comprehensive characterisation of structural variation in natural populations has only become feasible in the last decade. To investigate the population genomic nature of structural variation (SV), reproducible and high-confidence SV callsets are first required. We created a population-scale reference of the genome-wide landscape of structural variation across 33 Nordic house sparrows (Passer domesticus) individuals. To produce a consensus callset across all samples using short-read data, we compare heuristic-based quality-filtering and visual curation (Samplot/PlotCritic and Samplot-ML) approaches. We demonstrate that curation of SVs is important for reducing putative false positives and that the time invested in this step outweighs the potential costs of analysing short-read discovered SV datasets that include many potential false positives. We find that even a lenient manual curation strategy (e.g. applied by a single curator) can reduce the proportion of putative false positives by up to 80%, thus enriching the proportion of high-confidence variants. Crucially, in applying a lenient manual curation strategy with a single curator, nearly all (>99%) variants rejected as putative false positives were also classified as such by a more stringent curation strategy using three additional curators. Furthermore, variants rejected by manual curation failed to reflect expected population structure from SNPs, whereas variants passing curation did. Combining heuristic-based quality-filtering with rapid manual curation of structural variants in short-read data can therefore become a time- and cost-effective first step for functional and population genomic studies requiring high-confidence SV callsets.