Low-call-rate SNPs and presence–absence variation identified in the rice pan-genome can improve genomic prediction of rice gene bank accessions

Substantial improvements in genomic prediction accuracy for rice gene bank accessions were achieved by incorporating SNPs of low call rate identified in a recently published rice pan-genome. Introduction of useful genetic variation to breeding populations is a key factor in achieving genetic gain in crop breeding. However, identifying donors from genetic diversity stored in gene banks requires extensive phenotyping, which is not feasible for many traits of interest. Genomic prediction (GP) of phenotypic values has been proposed to overcome this phenotyping bottleneck. A key challenge for GP is the identification of appropriate markers representative of genetic variation causal for phenotypes. Here we report on utilizing single nucleotide polymorphisms (SNPs) from the core and dispensable genomes of a rice pan-genome resource comprising 16 reference sequences. Using a published pan-genome graph, we identified SNPs within structural variations of the dispensable genome. In this SNP set, SNPs of low call rate (CR) were common. Presence-absence variation (PAV) of these SNPs was associated with subpopulation structure, indicating that SNP absence reflects on underlying sequence PAV rather than being solely due to technical errors in SNP detection. To incorporate these SNPs in GP models, we employed modified encoding, retaining information of PAV and nucleotide variation by one-hot encoding (OHE). Adding these to SNP matrices increased prediction accuracies of GP for some traits and subpopulations. Improvements could largely be attributed to the inclusion of PAV. Our results show that the traditional approach of applying strict CR filters to SNPs located in the dispensable genome disregards potentially valuable genetic information not in linkage with SNPs of high CR. The proposed strategy provides a straightforward way to enhance GP performance in rice gene bank accessions.