Single-cross prediction with imputed multiomic data: a case study in rapeseed

Advancements in sequencing technologies enabled the assembly and characterization of plant genomes with high resolution. In breeding programs, these data are combined with phenotypic information in genomic prediction to select genotypes based on their genetic profiles. Although single nucleotide polymorphism (SNP) arrays are commonly used for genotyping, they capture only a fraction of the genomewide diversity. To address this, one approach involves genotyping the entire population with arrays, sequence a subset using whole-genome sequencing (WGS), or assessing gene expression profiles, followed by imputing the data across the entire population. This study evaluates the effect of imputed WGS markers (SNPs and structural variants) and expression data on genomic prediction in a rapeseed hybrid breeding population. A combination of SNP arrays, WGS, and RNA sequencing was employed, followed by imputation of marker and expression data. Genomic prediction was utilized to estimate general and specific combining ability effects in untested hybrids. However, while adding imputed whole-genome and expression data increased marker density and linkage disequilibrium, it did not enhance prediction accuracy compared to SNP array data. This is attributed to redundancy in relationship, imputation errors, or environmental influences on gene expressions. This suggests that SNP arrays continue to be reliable for genomic prediction in rapeseed hybrid breeding.