Prioritization of candidate genes for major QTLs governing yield traits employing integrated multi-omics approach in rice (Oryza sativa L.)

Abstract

Abstract Rapidly identifying candidate genes underlying major QTLs is crucial for improving rice (Oryza sativa L.). In this study, we developed a workflow to rapidly prioritize candidate genes underpinning 99 major QTLs governing yield component traits. This workflow integrates multiomics databases, including sequence variation, gene expression, gene ontology, co-expression analysis, and protein–protein interaction. We predicted 206 candidate genes for 99 reported QTLs governing ten economically important yield-contributing traits using this approach. Among these, transcription factors belonging to families of MADS-box, WRKY, helix–loop–helix, TCP, MYB, GRAS, auxin response factor, and nuclear transcription factor Y subunit were promising. Validation of key prioritized candidate genes in contrasting rice genotypes for sequence variation and differential expression identified Leucine-Rich Repeat family protein (LOC_Os03g28270) and cytochrome P450 (LOC_Os02g57290) as candidate genes for the major QTLs GL1 and pl2.1, which govern grain length and panicle length, respectively. In conclusion, this study demonstrates that our workflow can significantly narrow down a large number of annotated genes in a QTL to a very small number of the most probable candidates, achieving approximately a 21-fold reduction. These candidate genes have potential implications for enhancing rice yield.