# Genome-Wide Association Study of Yield-Related Traits and Photoperiod Response in Rice

## TL;DR

This study uses a new method to identify genes affecting rice yield and how photoperiod influences these traits, helping improve rice breeding.

## Contribution

The study introduces Fast3VmrMLM, a novel GWAS method effective in detecting small-effect loci for rice yield traits.

## Key findings

- 141 significant QTNs were detected for 10 yield-related traits in rice.
- 182 photoperiod-responsive candidate genes were identified through expression and GO analysis.
- Six pleiotropic genes, including LOC_Os02g02210, were confirmed to influence grain width and yield traits.

## Abstract

Yield-related traits of rice (Oryza sativa L.) are pivotal to safeguarding global food security. As a powerful and efficient strategy, genome-wide association study (GWAS) has identified numerous genes for yield-related traits in rice over recent decades, providing critical resources for germplasm improvement. Most yield-related traits are complex quantitative traits controlled by multiple genes with diverse effect sizes, and traditional GWAS approaches have limited power to detect small-effect loci. In this study, we employed Fast3VmrMLM, a compressed mixed linear model integrating genome-wide scanning and machine learning, to perform GWAS for 10 key yield-related traits using a panel of 529 rice accessions and 4,945,006 single-nucleotide polymorphisms (SNPs). The traits included heading date, plant height, panicle number, effective panicle number, yield per plant, spikelet length, grain length, grain width, grain weight, and grain thickness. We detected 141 significant quantitative trait nucleotides (QTNs) associated with target traits and identified 92 previously validated genes located near these QTNs. As a key environmental regulator, photoperiod directly controls flowering and indirectly modulates yield-related traits, and we further identified 182 photoperiod-responsive candidate genes via differential expression and Gene Ontology (GO) enrichment analysis. Through tissue-specific expression analysis, homology analysis with Arabidopsis genes, and haplotype-phenotype differential analysis, six pleiotropic candidate genes were confirmed; notably, LOC_Os02g02210 appears to contribute substantially to grain width and yield-related traits. In conclusion, Fast3VmrMLM proved effective for dissecting the genetic basis of yield-related traits, especially in detecting small-effect loci. These results not only establish a potential genetic link between photoperiod regulation and rice yield formation but also provide high-confidence candidate genes and loci that will accelerate functional genomic studies and precision molecular breeding for high-yield rice.