Rice (Oryza sativa L.) is one of the important staple foods for human consumption and livestock use. As a complex quality trait, free amino acid (FAA) content in rice is of nutritional importance. To dissect the genetic mechanism of FAA level, five amino acids’ (Val, Leu, Ile, Arg, and Trp) content and 4,325,832 high-quality SNPs of 448 rice accessions were used to conduct genome-wide association studies (GWAS) with nine different methods. Of these methods, one single-locus method (GEMMA), seven multi-locus methods (mrMLM, pLARmEB, FASTmrEMMA, pKWmEB, FASTmrMLM, ISIS EM-BLASSO, and FarmCPU), and the recent released 3VmrMLM were adopted for methodological comparison of quantitative trait nucleotide (QTN) detection and identification of stable quantitative trait nucleotide loci (QTLs). As a result, 987 QTNs were identified by eight multi-locus GWAS methods; FASTmrEMMA detected the most QTNs (245), followed by 3VmrMLM (160), and GEMMA detected the least QTNs (0). Among 88 stable QTLs identified by the above methods, 3VmrMLM has some advantages, such as the most common QTNs, the highest LOD score, and the highest proportion of all detected stable QTLs. Around these stable QTLs, candidate genes were found in the GO classification to be involved in the primary metabolic process, biosynthetic process, and catalytic activity, and shown in KEGG analysis to have participated in metabolic pathways, biosynthesis of amino acids, and tryptophan metabolism. Natural variations of candidate genes resulting in the content alteration of five FAAs were identified in this association panel. In addition, 95 QTN-by-environment interactions (QEIs) of five FAA levels were detected by 3VmrMLM only. GO classification showed that the candidate genes got involved in the primary metabolic process, transport, and catalytic activity. Candidate genes of QEIs played important roles in valine, leucine, and isoleucine degradation (QEI_09_03978551 and candidate gene LOC_Os09g07830 in the Leu dataset), tryptophan metabolism (QEI_01_00617184 and candidate gene LOC_Os01g02020 in the Trp dataset), and glutathione metabolism (QEI_12_09153839 and candidate gene LOC_Os12g16200 in the Arg dataset) pathways through KEGG analysis. As an alternative of the multi-locus GWAS method, these findings suggested that the application of 3VmrMLM may provide new insights into better understanding FAA accumulation and facilitate the molecular breeding of rice with high FAA level.

Introduction: Flax (Linum usitatissimum L.) is an economically important crop due to its oil and fiber. However, it is prone to various diseases, including pasmo caused by the fungus Septoria linicola.Methods: In this study, we conducted field evaluations of 445 flax accessions over a five-year period (2012–2016) to assess their resistance to pasmo A total of 246,035 single nucleotide polymorphisms (SNPs) were used for genetic analysis. Four statistical models, including the single-locus model GEMMA and the multi-locus models FarmCPU, mrMLM, and 3VmrMLM, were assessed to identify quantitative trait nucleotides (QTNs) associated with pasmo resistance.Results: We identified 372 significant QTNs or 132 tag QTNs associated with pasmo resistance from five pasmo resistance datasets (PAS2012–PAS2016 and the 5-year average, namely PASmean) and three genotypic datasets (the all SNPs/ALL, the gene-based SNPs/GB and the RGA-based SNPs/RGAB). The tag QTNs had R² values of 0.66–16.98% from the ALL SNP dataset, 0.68–20.54%from the GB SNP dataset, and 0.52–22.42% from the RGAB SNP dataset. Of these tag QTNs, 93 were novel. Additionally, 37 resistance gene analogs (RGAs)co-localizing with 39 tag QTNs were considered as potential candidates for controlling pasmo resistance in flax and 50 QTN-by-environment interactions(QEIs) were identified to account for genes by environmental interactions. Nine RGAs were predicted as candidate genes for ten QEIs.Discussion: Our results suggest that pasmo resistance in flax is polygenic and potentially influenced by environmental factors. The identified QTNs provide potential targets for improving pasmo resistance in flax breeding programs. This study sheds light on the genetic basis of pasmo resistance and highlights the importance of considering both genetic and environmental factors in breeding programs for flax.

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.

Simultaneous achievement of high yield and excellent quality in rice is essential for food security and human health. The coordinated application of nitrogen (N) and sulfur (S) can effectively increase the grain yield (GY) and grain protein concentration (GPC) of rice. A two-season field experiment was conducted to investigate the synergistic effects of combined N and S application on the GY and GPC of rice. This study employed four N rates (0, 120, 180, and 240 kg ha⁻¹, designated as N0, N1, N2, and N3, respectively) and four S rates (0, 30, 45, and 60 kg ha⁻¹, designated as S0, S1, S2, and S3, respectively) using two rice cultivars: Jiujiuxiang (JJX) and Jiuxiangyou (JXY). The experimental results demonstrate that N and S exert significant effects on the GY and GPC of rice, with notable interactive effects between these two nutrient elements. The synergistic fertilization of N and S enhanced the GY by improving rice plant photosynthesis and dry matter accumulation while increasing GPC through elevated cysteine concentration in grains. Compared to the unfertilized treatment, the GY of the JJX cultivar showed increases of 68.3–143.2% (Season I) and 59.4–133.4% (Season II) under combined N and S applications, while the GY of the JXY cultivar increased by 53.2–144.1% (Season I) and 66.0–192.9% (Season II). Similarly, the GPC of the JJX cultivar showed increases of 7.5–43.4% (Season I) and 5.7–43.9% (Season II) under combined N and S applications, while the GPC of the JXY cultivar increased by 13.1–66.7% (Season I) and 13.3–61.0% (Season II). Overall, whether on the JJX or JXY cultivars, the application of 180 kg ha⁻¹ of N combined with 45 kg ha⁻¹ of S (i.e., the N2S2 treatment) synergistically enhances GY and GPC in rice. The synergistic fertilization of N and S synergistically enhances both rice yield and nutritional quality by regulating plant growth dynamics, which meet the requirements for healthy and sustainable development in rice production systems.

Rice ( Oryza sativa ) is an important staple food, feeding more than half of the global population. A feasible improvement of rice yield is necessary to meet the ever–growing food demands. Genomic selection (GS), as an advanced breeding technique, enables the prediction of phenotypes solely based on genotypic data using a constructed genomic prediction model. A benchmarking study is required to evaluate the prediction performance of various available GS models for accelerating the breeding process. To address this concern, a diverse population containing 688 rice accessions with 66,456 haplotype‐based single nucleotide polymorphisms (SNPs) (HA‐SNPs) were used to construct 17 GS models for grain length, grain width, and 1000‐grain weight separately. The effect of population size on the GS prediction accuracy was slightly greater than that of marker size. Among these models, ridge regression best linear unbiased prediction (RRBLUP) offered several advantages in terms of robustness, simplicity, and computational efficiency. Regarding the predictive ability, machine learning and deep learning models are also viable alternatives. Additionally, the prediction accuracy of genome‐wide association study–detected SNP based GS was significantly higher than that of HA‐SNP based GS. Furthermore, a subset including 63 selected accessions was used to construct the RRBLUP model, and followed by the evaluation of elite germplasm for practical breeding. Taken together, these findings have the potential to reduce the rice yield breeding cycle and enhance the genetic gain.