Multi-model genome-wide association studies for appearance quality in rice

Area of Science:

• Plant genetics
• Agricultural science
• Molecular biology

Background:

• Improving rice grain quality is crucial for market acceptance and consumer satisfaction.
• Identifying genetic factors controlling grain appearance traits is key for developing advanced breeding strategies.
• Genome-wide association studies (GWAS) are powerful tools for dissecting complex genetic architectures of quantitative traits.

Purpose of the Study:

• To identify genetic markers and candidate genes associated with key rice grain quality traits.
• To leverage single-locus (SL-GWAS) and multi-locus (ML-GWAS) approaches for comprehensive trait dissection.
• To discover novel quantitative trait nucleotides (QTNs) for enhancing rice breeding programs.

Main Methods:

• Conducted SL-GWAS (CMLM, MLM) and ML-GWAS (FASTmrEMMA, mrMLM, FASTmrMLM) on 198 rice accessions using 553,831 SNP markers.
• Utilized mixed linear models to identify SNP markers associated with grain quality traits.
• Detected quantitative trait nucleotides (QTNs) linked to grain aroma (AR), head rice recovery (HRR), and percentage of grains with chalkiness (PGC).

Main Results:

• Identified 594 SNP markers associated with grain quality traits using mixed linear models.
• Detected 70 QTNs strongly associated with AR, HRR, and PGC via ML-GWAS.
• Discovered a total of 39 reliable QTNs, including 20 novel ones, across single- and multi-locus GWAS.
• Pinpointed four functional candidate genes (LOC_Os01g66110, LOC_Os01g66140, LOC_Os07g44910, LOC_Os02g14120) influencing AR, HRR, and PGC.

Conclusions:

• The identified QTNs and candidate genes provide valuable genetic resources for marker-assisted selection in rice breeding.
• These findings can accelerate the development of rice varieties with superior grain appearance and market value.
• The study highlights the effectiveness of combining SL-GWAS and ML-GWAS for robust genetic discovery in rice quality traits.