High performance imputation of structural and single nucleotide variants in Atlantic salmon using low-coverage whole genome sequencing

Whole genome sequencing (WGS), despite its advantages, is yet to replace alternative methods for genotyping single nucleotide variants (SNVs). Structural variants (SVs) have larger effects on traits than SNVs, but are more challenging to accurately genotype. Using low-coverage WGS with genotype imputation offers a cost-effective strategy to achieve genome-wide variant coverage, but is yet to be tested for SVs. Here, we investigate combined SNV and SV imputation with low-coverage WGS data in Atlantic salmon (Salmo salar). As the reference panel, we used genotypes for high-confidence SVs and SNVs for n=445 wild individuals sampled from diverse populations. We also generated 15x WGS data (n=20 samples) for a commercial population out-with the reference panel, and called SVs and SNVs with gold-standard approaches. An imputation method (GLIMPSE) was tested at WGS depths of 1x, 2x, 3x and 4x for samples within and out-with the reference panel. SNVs were imputed with high accuracy and recall across all WGS depths, including for samples out-with the reference panel. For SVs, we compared imputation based purely on linkage disequilibrium (LD) with SNVs, to that supplemented with SV genotype likelihoods (GLs) from low-coverage WGS. Including SV GLs increased imputation accuracy, but as a trade-off with recall, requiring 3-4x coverage for best performance. Combining strategies allowed us to capture 84% of the reference panel deletions with 87% accuracy at 1x WGS. This study highlights the promise of reference panel imputation using low-coverage WGS, including novel opportunities to enhance the resolution of genome-wide association studies by capturing SVs. ### Competing Interest Statement The authors have declared no competing interest.