Abstract
BACKGROUND: Whole genome sequencing (WGS), despite its advantages, is yet to replace methods for genotyping single nucleotide variants (SNVs) such as SNP arrays and targeted genotyping assays. 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. METHODS: 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 = 365 wild individuals sampled from diverse populations. We also generated 15 × WGS data (n = 20 samples) for a commercial population external to the reference panel, and called SVs and SNVs with gold-standard approaches. An imputation method selected for its established performance using low-coverage sequencing data (GLIMPSE) was tested at WGS depths of 1 × , 2 × , 3 × , and 4 × for samples within and external to the reference panel. RESULTS: 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 3-4 × depth for best performance. Combining strategies allowed us to capture 84% of the reference panel deletions with 87% accuracy at 1 × depth. We also show that SV length affects imputation performance, with provision of SV GLs greatly enhancing accuracy for the longest SVs in the dataset. CONCLUSIONS: 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.