Leveraging ancestral recombination graphs for quantitative genetic analysis of rice yield in indica and japonica subspecies

Rice (Oryza sativa L.) has two main subspecies, indica and japonica, which coexist in many regions but are often treated separately during breeding. Combining both subspecies in quantitative genetic analyses could enhance genetic improvement, however, this requires appropriately modelling their genetic history. The ancestral recombination graph (ARG) is an effective population genetics tool that comprehensively and succinctly represents a species genetic history. This study evaluated the use of an ARG, encoded as a tree sequence, to improve quantitative genetic analyses of indica and japonica rice. Using data from Uruguays National Rice Breeding Program, we inferred ancestral alleles, constructed and dated an ARG, and examined its application in genomic prediction and genome-wide association studies. We compared the predictive ability of a branch-based relationship matrix (BRM) built from an ARG against conventional relationship matrices from pedigree and single nucleotide polymorphism (SNP) site data. We then estimated BRMs SNP site effects to identify potential sites of interest and better understand how these map onto the tree sequence branches. The results showed that the ARG captured key biological signals, encoded genomic data more efficiently than conventional formats, and resulted in the highest predictive ability when combining both subspecies. Although the ARG-based approach did not substantially outperform conventional approaches for between-species prediction, this approach holds promise for plant breeding with larger datasets and could enhance genome-wide association studies by elucidating haplotype ancestry and the evolution of their value. Overall, our results demonstrated the potential of ARGs for the quantitative genetic analysis of diverse populations.