ABSTRACT
The advent of the pan-genome 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 and might be responsible for missing heritability. 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. To overcome this challenge, we developed a rice pan-genome genotyping array (RPGA) includes 80K genome-wide SNPs which provides simple, user-friendly and cost-effective solution for rapid pan-genome-based genotyping in rice. The GWAS conducted using RPGA-SNP genotyping data of a rice diversity panel detected total of 42 loci, including previously known as well as novel genomic loci regulating grain size/weight traits in rice. Eight of the identified trait-associated loci (dispensable loci) could not be detected with conventional single reference genome-based GWAS and found to be missing from the commonly used Nipponbare reference genome. WD repeat-containing PROTEIN 12 gene underlying one of such dispensable locus on chromosome 7 (qLWR7) along with few other non-dispensable loci was 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. Besides GWAS, the application of RPGA-based genotyping for natural allelic diversity and population structure analysis, seed purity and hybridity testing, ultra-high-density genetic map construction and chromosome level genome assembly, and marker-assisted foreground/background selection was successfully demonstrated. Based on these salient outcomes, a web application (http://www.rpgaweb.com) was also developed to provide easy to use platform for imputation of RPGA-based genotyping data using 3K Rice Reference Panel and subsequent GWAS in order to drive genetic improvement of rice.
Competing Interest Statement
The authors have declared no competing interest.