Using genotype imputation to integrate Canola populations for genome-wide association and genomic prediction of blackleg resistance

作者：Huanhuan Zhao, Iona M. MacLeod, Gabriel Keeble‐Gagnère, Denise M. Barbulescu, Josquin Tibbits, Sukhjiwan Kaur, Matthew Hayden · 发表于：BMC Genomics · 年份：2025 · DOI：10.1186/s12864-025-11250-4 · 被引用次数：4 · 研究领域：Genetic Associations and Epidemiology、Genetic Mapping and Diversity in Plants and Animals、Genetic diversity and population structure

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 10× 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 predictive ability (GP) and detection power of marker‒trait association (GWAS) of the combined population for blackleg resistance were evaluated. RESULTS: for GLIMPSE was higher than that for Beagle when imputing from different low-coverage to full-coverage WGS. We imputed and integrated the diverse canola collection and the DH lines, and the combined population showed similar or slightly greater predictive ability (PA) for blackleg resistance traits than did each of the single populations with ~ 921 K SNPs. Higher marker-trait association (MTA) detection powers were indicated with the combined population; however, similar numbers of MTAs were discovered when each single population was combined in a meta-GWAS. CONCLUSION: It is feasible to impute and integrate germplasms from different sequencing platforms for downstream analyses. However, genetic heterogeneity across...