Rice Pangenome Array (RPGA): an efficient genotyping solution for pangenome-based accelerated crop improvement in rice.

The advent of the pangenome era has unraveled previously unknown genetic variation existing within diverse crop plants, including rice. This untapped genetic variation is believed to account for a major portion of phenotypic variation existing in crop plants. However, the use of conventional single reference-guided genotyping often fails to capture large portion of this genetic variation leading to a reference bias. This makes it difficult to identify and utilize novel population/cultivar-specific genes for crop improvement. Thus, we developed a rice pangenome genotyping array (RPGA) harboring probes assaying 80K single nucleotide polymorphisms (SNPs) and presence-absence variants (PAVs) spanning the entire 3K rice pangenome. This array provides a simple, user-friendly and cost-effective (60 to 80 USD per sample) solution for rapid pangenome-based genotyping in rice. The GWAS conducted using RPGA-SNP genotyping data of a rice diversity panel detected a total of 42 loci, including previously known as well as novel genomic loci regulating grain size/weight traits in rice. Eight of these identified trait-associated loci (dispensable loci) could not be detected with conventional single reference genome-based GWAS. A WD repeat-containing PROTEIN 12 gene underlying one of such dispensable locus on chromosome 7 (qLWR7) along with other non-dispensable loci were subsequently detected using high-resolution QTL mapping confirming authenticity of RPGA-led GWAS. This demonstrates the potential of RPGA-based genotyping to overcome reference bias. The application of RPGA-based genotyping for population structure analysis, hybridity testing, ultra-high-density genetic map construction and chromosome-level genome assembly, and marker-assisted selection was also demonstrated. A web application (http://www.rpgaweb.com) was further developed to provide easy to use platform for the imputation of RPGA-based genotyping data using 3K Rice Reference Panel and subsequent GWAS.