Rice Genomics and Genetics 2025, Vol.16, No.3, 159-179 http://cropscipublisher.com/index.php/rgg 174 8 Applications of Rice Pan-genome Research 8.1 Germplasm utilization and genome-wide association studies (GWAS) Rice pan-genome resources greatly enhance the utilization of germplasm collections and improve the power of genome-wide association studies. Traditional GWAS in rice often struggled with “missing heritability,” partly because they ignored structural variants and presence/absence variation. By providing a more complete set of markers, including those from non-reference sequence, pan-genome-based GWAS can capture previously untagged genetic effects. As described in Case 4, using a pan-genome genotyping array led to the discovery of multiple novel QTLs for grain traits that single-reference SNP chips failed to detect. Similarly, researchers performing GWAS with a graph-based pan-genome approach were able to pinpoint trait-associated structural variants (like an insertion affecting plant height) that were hidden to linear reference analysis. Thus, integrating pan-genomic variants into GWAS increases QTL detection power and can explain additional phenotypic variance (closing some of the “missing heritability” gap) (Yang et al., 2025). Beyond GWAS, pan-genomes improve germplasm characterization. For instance, breeders and gene bank managers can use pan-genome data to more thoroughly genotype diverse landraces and wild accessions. Each accession can be characterized not just by SNPs relative to Nipponbare, but by its unique gene content. This helps identify accessions carrying novel genes of interest-for example, a particular landrace might be the only one (among those sequenced) harboring a wild-derived disease resistance gene. Such information directs breeders to germplasm that should be tapped for specific traits. In practice, national and international gene banks are beginning to integrate pan-genomic markers into their characterization protocols. The International Rice Research Institute (IRRI), for example, now has access to variations identified by 3K RGP and subsequent pan-genome studies to guide germplasm mining. Pan-genome data makes it possible to bring together results from different studies, even when they used different reference genomes or analysis methods. For example, a recent meta-GWAS on rice used a graph-based pan-genome to combine six separate datasets. This led to the identification of 156 QTLs related to traits like yield-116 of which weren’t found in individual studies. Many of these were linked to structural variations or presence/absence markers only visible through the pan-genome. 8.2 Marker development and genomic selection The rise of rice pan-genome studies has really changed how we develop molecular markers for breeding. Instead of relying only on SNPs, breeders now have access to a broader range of genetic variations-like structural variants and new genes tied to important traits (Daware et al., 2022). These can be turned into practical tools, such as PCR markers. Take disease resistance, for instance: if researchers find that deleting a specific gene makes a plant resistant to rice blast, it’s straightforward to design a simple InDel marker to test whether breeding lines carry that deletion. This is better than using a nearby SNP because it targets the actual cause of resistance. Genomic selection isn’t just riding on the success of marker-assisted breeding-it’s growing with it. With the rise of pan-genome data, we now catch details that used to slip through the cracks, like gene deletions or duplications. These subtle variations matter, especially when trying to improve complex traits such as yield or stress resistance. By factoring in this broader range of genetic markers, our prediction models become more accurate and useful in real breeding scenarios. One tool I find particularly promising is the Practical Haplotype Graph (PHG). Instead of sequencing the whole genome every time-which costs a lot-we can now sample a small portion, and PHG helps fill in the blanks using a reference built from pan-genome data. It’s efficient, cost-saving, and still leverages full genetic diversity. 8.3 Implications for denovo domestication and gene editing The rice pan-genome provides a blueprint for de novo domestication-the idea of taking wild species or unadapted germplasm and rapidly domesticating them (or improving them) using modern tools. By comparing genomes of domesticated rice with wild relatives, pan-genome studies pinpoint which genes and structural variants were critical in domestication (Shang et al., 2022). For example, pan-genome analysis confirms that wild rice contains
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