Rice yield is a complex trait affected by many related traits.  Traditional single-trait genome-wide association studies (GWAS) have limitations when studying complex traits, as they cannot account for the genetic relationships among multiple traits.  Multi-trait GWAS can consider the relationships among multiple traits and identify pleiotropic loci, so it is more suitable for complex traits such as rice yield than single-trait GWAS.  In this study, we conducted a multi-trait GWAS on 11 two-trait combinations of yield and yield-related traits with 575 hybrid rice varieties across two environments.  All these yield-related traits showed significant genetic correlations with yield (YD), including filled grains per panicle (FGPP), 1,000-grain weight (KGW), tillers per plant (TP), primary branch number (PB), secondary branch number (SB), and main panicle length (MPL).  In total, we identified 44 pleiotropic quantitative trait loci (pQTLs), including 29 new pQTLs not found in a single-trait GWAS.  We then screened 23 pQTLs showing common effects in two traits as key pQTLs.  These key pQTLs were subsequently analyzed by haplotype analysis, which identified 13 pleiotropic candidate genes.  Finally, we identified two optimal yield-enhancing allele combinations by pyramiding the superior haplotypes: GS3-GL3.1-OsCIPK17 for the YD-KGW combination and GNP12 for the YD-FGPP and YD-SB combinations.  This study provides pleiotropic candidate genes and allele combinations that exhibit superior differences in both yield and yield-related traits, offering valuable information for future high-yielding rice breeding.

Thirty-six walnut cultivars were analyzed for apparent, nutritional, processing and protein properties. Systematic cluster analysis (SCA) was applied to classify 36 walnut cultivars, while multivariate linear regression (MLR) analysis was used to develop a model for evaluating walnut protein solubility. The walnut cultivars were classified into three distinct clusters. Wen 185 and Xinguang had protein purity of 64.42, 70.57, and solubility of 27.04, 30.04%, respectively. Wen 185 and Xinguang were identified as the more suitable cultivars for extracting and processing soluble proteins. The MLR model revealed critical factors influencing protein solubility, such as arginine (Arg), glutamic acid (Glu), threonine (Thr), lysine (Lys), histidine (His), and crude fat. Glu (r=-0.64) and Arg (r=-0.57) showed a significant negative correlation with solubility. With a R² of 0.832 between predicted and experimental values, the model was validated. This study has improved the efficiency of walnut protein during the processing and pointed out the direction for the processing and utilization of different cultivars of walnuts.