Genotype imputation, integration for genome-wide association studies and genomic prediction of blackleg resistance in Canola

Background:Integrating germplasm populations genotyped by different genotyping platforms via genotype imputation is a way to utilize accumulated genetic resources. In this study, we used 278 canola samples genotyped via whole-genome sequencing (WGS) at 10X coverage to evaluate the imputation accuracy of three imputation approaches. The optimal imputation methods were used to impute and integrate two Canola genotype datasets: a diverse canola collection genotyped by genotyping-by-sequencing via transcriptome (GBS-t) and a double haploid (DH) line collection genotyped with low-coverage WGS (skim-WGS). The genomic prediction accuracy (GP) and detection power of marker‒trait association (GWAS) of the combined population for blackleg resistance were evaluated.Results:The empirical imputation accuracy (r2) measured as the squared correlation between observed and imputed genotypes was moderate for Minimac3 when imputing from the GBS-t density to the WGS. The accuracy dramatically improved from 0.64 to 0.82 by removing SNPs with poor Minimac3-reported R2 (R2 Conclusion:It is feasible to impute and integrate germplasms from different sequencing platforms for downstream analyses. However, genetic heterogeneity across populations could add complexity to the analyses. Increasing the sample size by combining datasets showed slightly greater prediction accuracy and greater detection power in GWASs in the present study.