Rice Genomics and Genetics 2025, Vol.16, No.3, 159-179 http://cropscipublisher.com/index.php/rgg 175 certain alleles (and gene presences) that make it less suitable for agriculture, such as shattering or dormancy alleles, but it also contains many beneficial alleles absent in cultivars, like stress tolerance genes. Using CRISPR/Cas9 gene editing, it is now feasible to introduce domestication-related mutations into wild rice in a designed way. The pan-genome guides this by listing all key differences: e.g., loss-of-function of sh4 and prog1 for non-shattering and erect growth, respectively; deletion in Rc for white pericarp; perhaps a semi-dwarf allele in Sd1; and so forth. Recent work on de novo domestication of wild allotetraploid rice successfully edited a suite of genes to create a phenotype approaching cultivated rice in a single generation. The pan-genome ensures that while we are modifying those known domestication genes, we retain the wild rice’s novel genetic content (such as additional disease resistance genes or high nutrient content genes) that we ultimately want in the new crop. Pan-genomic data offers new direction for improving traits in existing rice varieties. Instead of relying only on a single reference genome, pan-genomes show a broader picture, revealing genes that may have been missed before. For example, if a wild rice species has a unique gene that helps it resist a certain disease-and this gene isn’t found in any cultivated types-we could introduce it into elite lines using gene editing or transformation. In other cases, genome-wide association studies (GWAS) based on pan-genomes might find that a key promoter sequence is missing in high-yielding lines. That missing piece can then be added through precise editing to boost performance. One recent example used a graph-based pan-genome to identify two new QTLs for grain size. Researchers confirmed these by knocking out the genes with CRISPR/Cas9, showing their clear effect on grain shape. This approach-from pan-genome data to editing and trait validation-shows how structural variants can be efficiently turned into breeding targets. It speeds up the entire process of bringing useful traits from wild relatives into cultivated rice. 8.4 Integration into breeding pipelines and seed industry As rice pan-genome data become more accessible, they are being integrated into breeding pipelines and even seed industry practices. Modern rice breeding increasingly uses decision support tools that incorporate genomic information at various stages-from parental selection to line advancement. Pan-genome databases and browsers (e.g., the RFGB-Rice Functional & Genomic Breeding platform-which includes pan-genomic info) allow breeders to check if a parent line possesses certain presence/absence alleles or structural variants of interest. For example, if a breeder wants to improve a popular rice cultivar by adding a gene that is only present in aus rice, pan-genome resources will identify which aus accessions carry that gene and what markers tag it. This guides the choice of donor parent. After making the cross, breeders can use markers (based on that gene’s presence) to track the introgression in progeny, an approach that has been made more efficient with the advent of pan-genome-informed arrays (like the 80K SNP-array which captures pan-genome variation) (Daware et al., 2022). This ensures the desired genomic segment is retained while background genome is recovered. Pan-genome research plays a key role in keeping genetic diversity broad within breeding programs. It helps breeders see how much variation in a species is actually captured by current elite lines. If essential parts of the pan-genome-such as genes from wild relatives-are missing, targeted efforts like introgression or pre-breeding can bring in useful traits, especially for stress tolerance or new disease threats. Seed companies also value pan-genomic markers for identifying and protecting their varieties. A complete set of presence/absence markers allows for high-precision genetic fingerprinting, even among closely related lines. This strengthens seed purity checks and safeguards intellectual property by linking each variety to a distinct genomic signature. On the industry scale, pan-genome data enable the creation of customized breeding panels-subsets of diverse lines that maximize pan-genome coverage. For instance, the discovery that only a few accessions harbor a given rare allele could prompt including those accessions in a breeding consortium to ensure that allele isn’t lost. In summary, integration of pan-genomes into breeding is making selection more precise and comprehensive: breeders can now select not just for known genes and SNPs, but for the presence of entire genomic regions that were previously outside their awareness. As a result, the seed industry is moving toward more data-driven breeding decisions, leveraging the full genetic potential outlined by the rice pan-genome.
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