Genome-Wide Association Studies Using 3VmrMLM Model Provide New Insights into Branched-Chain Amino Acid Contents in Rice Grains

Abstract

Rice (Oryza sativa L.) is a globally important food source providing carbohydrates, amino acids, and dietary fiber for humans and livestock. The branched-chain amino acid (BCAA) level is a complex trait related to the nutrient quality of rice. However, the genetic mechanism underlying the BCAA (valine, leucine, and isoleucine) accumulation in rice grains remains largely unclear. In this study, the grain BCAA contents and 239,055 SNPs of a diverse panel containing 422 rice accessions were adopted to perform a genome-wide association study (GWAS) using a recently proposed 3VmrMLM model. A total of 357 BCAA-content-associated main-effect quantitative trait nucleotides (QTNs) were identified from 15 datasets (12 BCAA content datasets and 3 BLUP datasets of BCAA). Furthermore, the allelic variation of two novel candidate genes, LOC_Os01g52530 and LOC_Os06g15420, responsible for the isoleucine (Ile) content alteration were identified. To reveal the genetic basis of the potential interactions between the gene and environmental factor, 53 QTN-by-environment interactions (QEIs) were detected using the 3VmrMLM model. The LOC_Os03g24460, LOC_Os01g55590, and LOC_Os12g31820 were considered as the candidate genes potentially contributing to the valine (Val), leucine (Leu), and isoleucine (Ile) accumulations, respectively. Additionally, 10 QTN-by-QTN interactions (QQIs) were detected using the 3VmrMLM model, which were putative gene-by-gene interactions related to the Leu and Ile contents. Taken together, these findings suggest that the implementation of the 3VmrMLM model in a GWAS may provide new insights into the deeper understanding of BCAA accumulation in rice grains. The identified QTNs/QEIs/QQIs serve as potential targets for the genetic improvement of rice with high BCAA levels.

New Insights into the Genetic Basis of Lysine Accumulation in Rice Revealed by Multi-Model GWAS

“…For instance, QTN-sf0825353310 was co-detected by GLM, MLM, CMLM, EMMAX, FASTm-rMLM, ISIS EM-BLASSO, mrMLM, and pLARmEB in Grain_BLUP dataset (Supplementary Table S2, Supplementary Figure S2). However, the most common QTNs (38) in leaf lysine datasets were identified by the GLM and mrMLM-ML models (Table 2), such as QTN-sf0702729577 detected by GLM, MLM, CMLM, EMMAX, FASTmrEMMA, FASTmrMLM, ISIS EM-BLASSO, mrMLM, and pLARmEB in the Leaf_env3_r2 dataset (Supplementary Table S2, Supplementary Figure S3). The position and number of all detected putative and common QTNs associated with the lysine content in rice grain/leaf are shown on a CIRCOS map (Supplementary Figure S4).…”

“…Thus, a range of multi-locus models have been proposed to detect the QTNs/QTLs with large and small effects in an accurate and robust manner [35][36][37][38]. According to the results obtained in our previous studies, the combined use of multiple statistical models for GWAS facilitates a better understanding of the genetic mechanism of the complex and multi-omics trait, particularly for the free amino acid content in rice [36][37][38][39].…”

“…For example, MLM and its derived single-locus models have been proven to control spurious associations and show high performance in the detection of quantitative trait nucleotide/locus (QTN/QTL) with a large effect [30,34,35]. Thus, a range of multi-locus models have been proposed to detect the QTNs/QTLs with large and small effects in an accurate and robust manner [35][36][37][38]. According to the results obtained in our previous studies, the combined use of multiple statistical models for GWAS facilitates a better understanding of the genetic mechanism of the complex and multi-omics trait, particularly for the free amino acid content in rice [36][37][38][39].…”

“…To unravel the genetic basis underlying the primary and secondary metabolic interface, various QTLs/QTNs and candidate genes have been exploited using this approach [37,38,[40][41][42][43][44][45][46][47]. Previous studies generally focused on the structural genes in the biosynthesis and metabolism pathways.…”

Genome-wide association analyses for revealing QTN, QTN-by-environment and QTN-by-QTN interactions in soybean phenology

“…QTN-by-QTN interaction (QQI) detection is an important and approach for the full genetic view of the phenological variation in soybean accessions (Sui et al, 2023). For a long time, there are many limitations in identifying QQI in natural populations, and the emergence of the 3VmrMLM model provides an e cient tool for epistasis detection in the whole genome (Li et al, 2022).…”