Common carp (Cyprinus carpio) is one of the most significant fish species worldwide, with its natural distribution spanning Europe and Asia. To conduct a genome-wide association study (GWAS) and compare the prediction accuracy of genomic selection (GS) models for the growth traits of common carp in spring and autumn at 2 years of age, a total of 325 carp individuals were re-sequenced and phenotypic measurements were taken. Three GWAS methods (FarmCPU, GEMMA, and GLM) were applied and their performance was evaluated in conjunction with various GS models, using significance levels based on p-values. GWAS analyses were performed on eight traits (including the body length, body weight, fat content of fillet, and condition factor) for both spring and autumn seasons. Eleven different GS models (such as Bayes A, Bayes B, and SVR-linear) were combined to evaluate their performance in genomic selection. The results demonstrate that the FarmCPU method consistently exhibits superior stability and predictive accuracy across most traits, particularly under higher SNP densities (e.g., 5K), where prediction accuracies frequently exceed 0.8. Notably, when integrated with Bayesian approaches, FarmCPU achieves a substantial performance boost, with the prediction accuracy reaching as high as 0.95 for the autumn body weight, highlighting its potential for high-resolution genomic prediction. In contrast, GEMMA and GLM exhibited a more variable performance at lower SNP densities. Overall, the integration of FarmCPU with genomic selection (GS) models offers one of the most reliable and efficient frameworks for trait prediction, particularly for complex traits with substantial genetic variation. This approach proves especially powerful when coupled with Bayesian methodologies, further enhancing its applicability in advanced breeding programs.

Soybean (Glycine max (L.) Merr.) is one of the most important global economic crops, extensively utilized in the production of food, animal feed, and industrial raw materials. As the demand for soybeans continues to rise, improving both the yield and quality of soybeans has become a central focus of agricultural research. To accelerate the genetic improvement of soybean, genome selection (GS) and genome-wide association studies (GWAS) have emerged as effective tools and have been widely applied in various crops. In this study, we conducted GWAS and GS model evaluations across five soybean phenotypes (Glycitin content, Oil, Pod, Total isoflavone content, and Total tocopherol content) to explore the effectiveness of different GWAS methods and GS models in soybean genetic improvement. We applied several GWAS methods, including fastGWA, BOLT-LMM, FarmCPU, GLM, and MLM, and compared the predictive performance of various GS models, such as BayesA, BayesB, BayesC, BL, BRR, SVR_poly, SVR_linear, Ridge, PLS_Regression, and Linear_Regression. Our results indicate that markers selected through GWAS, when used in GS, achieved a prediction accuracy of 0.94 at a 5 K density. Furthermore, Bayesian models proved to be more stable than machine learning models. Overall, this study offers new insights into soybean genome selection and provides a scientific foundation for future soybean breeding strategies.

Agronomic traits and quality traits of alfalfa are of great importance to the feed industry. Genomic selection (GS) based on genotyping-by-sequencing (GBS) data, if it achieves moderate to high accuracy, has the potential to significantly shorten breeding cycles for complex traits and accelerate genetic progress. This study aims to investigate the effect of different reference genomes on the prediction accuracy of genomic selection. A total of 11 Bayesian and machine learning models and nine different reference genomes were used to conduct genomic selection on five traits in 385 alfalfa accessions. The accuracy of GS was evaluated using five-fold cross-validation, based on the correlation between genomic estimated breeding values (GEBVs) and estimated breeding values (EBVs). For the five traits, it was found that traits with high heritability exhibited significantly higher prediction accuracy. The prediction accuracy fluctuated minimally across different reference genomes, with the diploid genome showing relatively higher accuracy. For two high-heritability traits, fall dormancy and plant height, predictions were made after SNP density reduction, and it was observed that density had little effect on prediction accuracy. However, for the fall dormancy trait in the diploid genome, more than half of the models showed regular fluctuations, with prediction accuracy increasing as SNP density increased. In conclusion, this study provides a theoretical basis for precision breeding of alfalfa and other polyploid crops by combining different reference genomes and models, and offers important guidance for optimizing future genomic selection strategies.

Soybean (Glycine max (L.) Merr.), as a crucial source of oil and protein globally, is widely cultivated in many countries. Low-temperature stress has become one of the major environmental factors affecting soybean production, especially in colder regions, making the improvement of cold tolerance traits in soybean a key breeding objective. This study integrates Genome-Wide Association Studies (GWAS) and Marker-Assisted Selection (MAS) to enhance the predictive performance of soybean cold tolerance traits. First, three GWAS methods—Fast3VmrMLM, fastGWA, and FarmCPU—were used to analyze soybean cold tolerance traits, and significant SNP markers were identified. Principal Component Analysis (PCA) was employed to reveal genetic differences among various soybean germplasm. Then, based on the identified SNP markers, multiple Genomic Selection (GS) models, such as GBLUP, BayesA, BayesB, BayesC, BL, and BRR, were used for prediction to evaluate the contribution of genetic effects to phenotypic variation. The results showed that the markers selected through GWAS significantly improved the prediction accuracy of genomic selection, especially with the Fast3VmrMLM and FarmCPU methods in larger datasets. Finally, Gene Ontology (GO) analysis was performed to further identify candidate genes associated with cold tolerance traits and their biological functions, providing theoretical support for molecular breeding of cold-tolerant soybean varieties.

Recent advances in plant genomics have characterized transposable elements (TEs) as key contributors to genome structure and gene regulation. This study focuses on the remarkably high abundance of short interspersed nuclear elements (SINEs) in the genus Trifolium. Using the allotetraploid horticultural plant white clover (Trifolium repens L.) as the study organism, we systematically investigate lineage-specific SINE amplification, genomic distribution, insertional preferences, and their regulatory effects on gene expression. Our analyses reveal that SINEs are significantly more abundant in Trifolium than in other angiosperms. Comparative genomic analyses further indicate that SINE accumulation is closely associated with polyploidization and domestication. Gene Ontology (GO) enrichment analyses demonstrate that SINEs are preferentially enriched in stress responsive genes. Expression analyses further showed that, within duplicated gene pairs, genes with SINE insertions in their upstream promoter regions exhibit significantly higher transcript levels compared with genes without such insertions. Under drought, cold, and cadmium stress, these SINE-associated genes exhibit upregulation, and our data analysis shows a strong correlation between the presence of SINE insertions and stress-induced upregulation of gene expression. This study demonstrates that SINE insertions in upstream promoter regions modulate transcriptional regulatory networks involved in stress responses, contributing to broad ecological adaptation in white clover.

Flowering time is an important trait affecting regional adaptation and yield stability in rapeseed (Brassica napus). To dissect the genetic basis of flowering time and vernalization-type differentiation in B. napus, this study integrated genome-wide SNPs, flowering-time phenotypes, vernalization types, and geographic origin information to establish a candidate QTL discovery framework combining genome-wide association study (GWAS) and machine learning. Based on 8,387,529 high-quality SNPs, GWAS was performed using the FarmCPU model under two covariate settings: a PC-corrected model and a PC + vernalization type-corrected model. These two models identified 32 and 27 suggestive SNPs, respectively. In parallel, ExtraTreesRegressor was used to estimate the feature contribution of SNPs to flowering-time prediction, leading to the identification of 18,854 ML_delta10SD high-contribution SNPs, which were further merged into 997 ML-derived candidate QTL intervals, hereafter referred to as ML-QTLs. By integrating GWAS-derived QTLs and ML-QTLs at the interval level, 34 GWAS- and ML-supported candidate regions were prioritized. Candidate gene annotation revealed that these regions contained genes related to flowering regulation, including FLC, FRIGIDA-like, VIN3-like, SOC1, ELF3, CONSTANS-like, APETALA1-like, and genes involved in the gibberellin pathway. Top-SNP genotypic effect analysis and multi-SNP genotype combination analysis further showed that genetic variation within representative candidate regions was significantly associated with flowering-time differences and vernalization-type differentiation. Predictive modeling indicated that candidate markers selected by GWAS and machine learning showed predictive potential within the present population. Overall, this study prioritized a set of GWAS- and ML-supported candidate regions and putative candidate genes associated with flowering time in B. napus from the complementary perspectives of statistical association and machine learning-based predictive contribution, providing candidate resources for future marker development, functional validation, and adaptive improvement in rapeseed.