Rice Pangenome Genotyping Array: an efficient genotyping solution for pangenome-based accelerated genetic improvement...

Area of Science:

• Plant genetics and genomics
• Crop improvement and breeding
• Bioinformatics and computational biology

Background:

• The pangenome era reveals extensive genetic variation in crops like rice, crucial for phenotypic diversity.
• Conventional single reference-guided genotyping introduces reference bias, hindering the capture of valuable genetic variation.
• This limitation impedes the identification and utilization of novel population/cultivar-specific genes for crop enhancement.

Purpose of the Study:

• To develop a cost-effective and user-friendly tool for rapid pangenome-based genotyping in rice.
• To overcome the limitations of reference bias in identifying genetic variations for crop improvement.
• To facilitate advanced genomic analyses including GWAS, population structure, and marker-assisted selection.

Main Methods:

• Development of the Rice Pangenome Genotyping Array (RPGA) with 80K single-nucleotide polymorphisms (SNPs) and presence-absence variants.
• Application of RPGA for genotyping a rice diversity panel and conducting genome-wide association studies (GWAS).
• Utilizing high-resolution quantitative trait loci (QTL) mapping to validate RPGA-led GWAS findings.
• Development of a web application for data imputation and subsequent GWAS analysis.

Main Results:

• RPGA enabled rapid, cost-effective pangenome-based genotyping in rice.
• GWAS using RPGA data identified 42 loci regulating grain size/weight, including 8 novel dispensable loci missed by conventional methods.
• A WD repeat-containing PROTEIN 12 gene was identified at a dispensable locus, confirming RPGA's efficacy.
• Demonstrated utility of RPGA for population structure analysis, hybridity testing, genetic mapping, genome assembly, and marker-assisted selection.

Conclusions:

• The RPGA effectively overcomes reference bias, enabling comprehensive capture of rice genetic variation.
• RPGA facilitates the discovery of novel trait-associated loci, crucial for accelerating rice breeding and crop improvement.
• The developed web application provides a user-friendly platform for pangenome data analysis and GWAS.