The interaction between genotype and environment (GEI) significantly influences plant performance, crucial for breeding programs and ultimately boosting crop productivity. Alongside GEI, breeders encounter another hurdle in their quest for yield improvement, notably adverse and negative correlations among pivotal traits. This study delved into the stability of white sugar yield (WSY), root yield (RY), sugar content (SC), extraction coefficient of sugar (ECS), and the interplay among essential traits including RY, SC, alpha amino nitrogen (N), sodium (Na+), and potassium (K+) across 15 sugar beet hybrids and three control varieties. The investigation spanned two locations over two consecutive years (2022-2023), employing a randomized complete block design with four replications to comprehensively analyze these factors. The analysis of variance highlighted the significant effects of environment, genotype, and GEI at the 1% probability level. Notably, the AMMI analysis of GEI revealed the significance of the first component for WSY, RY, and SC, with the first two components proving significant for ECS. Within the linear mixed model (LMM), WSY, RY, SC, and ECS demonstrated significant effects from both genotype and GEI. In the WAASB biplot, genotypes 10, 8, 17, 6, 13, 14, 15, 7, 12, and 16 exhibited stability in WSY, while genotypes 9, 10, 6, 14, 7, 8, 13, 12, 18, and 15 displayed stability in RY. Additionally, genotypes 10, 15, 12, 13, 16, 17, 6, and 14 were stable for SC, and genotypes 8, 10, 7, 6, 13, 12, 16, 17, 15, 14, and 18 showcased stability in ECS, boasting above-average yield values. In the genotype by yield × trait (GYT) biplot, genotypes 15, 18, and 16 emerged as top performers when combining RY with SC, Na+, N, and K+, suggesting their potential for inclusion in breeding programs.

Heat stress poses a significant environmental challenge that profoundly impacts wheat productivity. It disrupts vital physiological processes such as photosynthesis, by impeding the functionality of the photosynthetic apparatus and compromising plasma membrane stability, thereby detrimentally affecting grain development in wheat. The scarcity of identified marker trait associations pertinent to thermotolerance presents a formidable obstacle in the development of marker-assisted selection strategies against heat stress. To address this, wheat accessions were systematically exposed to both normal and heat stress conditions and phenotypic data were collected on physiological traits including proline content, canopy temperature depression, cell membrane injury, photosynthetic rate, transpiration rate (at vegetative and reproductive stage and \'stay-green\'. Principal component analysis elucidated the most significant contributors being proline content, transpiration rate, and canopy temperature depression, which exhibited a synergistic relationship with grain yield. Remarkably, cluster analysis delineated the wheat accessions into four discrete groups based on physiological attributes. Moreover, to explore the relationship between physiological traits and DNA markers, 158 wheat accessions were genotyped with 186 SSRs. Allelic frequency and polymorphic information content value were found to be highest on genome A (4.94 and 0.688), chromosome 1A (5.00 and 0.712), and marker Xgwm44 (13.0 and 0.916). Population structure, principal coordinate analysis and cluster analysis also partitioned the wheat accessions into four subpopulations based on genotypic data, highlighting their genetic homogeneity. Population diversity and presence of linkage disequilibrium established the suitability of population for association mapping. Additionally, linkage disequilibrium decay was most pronounced within a 15-20 cM region on chromosome 1A. Association mapping revealed highly significant marker trait associations at Bonferroni correction P < 0.00027. Markers Xwmc418 (located on chromosome 3D) and Xgwm233 (chromosome 7A) demonstrated associations with transpiration rate, while marker Xgwm494 (chromosome 3A) exhibited an association with photosynthetic rates at both vegetative and reproductive stages under heat stress conditions. Additionally, markers Xwmc201 (chromosome 6A) and Xcfa2129 (chromosome 1A) displayed robust associations with canopy temperature depression, while markers Xbarc163 (chromosome 4B) and Xbarc49 (chromosome 5A) were strongly associated with cell membrane injury at both stages. Notably, marker Xbarc49 (chromosome 5A) exhibited a significant association with the \'stay-green\' trait under heat stress conditions. These results offers the potential utility in marker-assisted selection, gene pyramiding and genomic selection models to predict performance of wheat accession under heat stress conditions.

We present a comparison of machine learning methods for the prediction of four quantitative traits in Arabidopsis thaliana. High prediction accuracies were achieved on individuals grown under standardized laboratory conditions from the 1001 Arabidopsis Genomes Project. An existing body of evidence suggests that linear models may be impeded by their inability to make use of non-additive effects to explain phenotypic variation at the population level. The results presented here use a nested cross-validation approach to confirm that some machine learning methods have the ability to statistically outperform linear prediction models, with the optimal model dependent on availability of training data and genetic architecture of the trait in question. Linear models were competitive in their performance as per previous work, though the neural network class of predictors was observed to be the most accurate and robust for traits with high heritability. The extent to which non-linear models exploit interaction effects will require further investigation of the causal pathways that lay behind their predictions. Future work utilizing more traits and larger sample sizes, combined with an improved understanding of their respective genetic architectures, may lead to improvements in prediction accuracy.

This study aimed to reveal the diversity and variation in panicle traits of the Chinese prickly ash and clarify their influence on the its systematic classification to provide a theoretical basis and technical support for the efficient utilization of Chinese prickly ash germplasm resources and breeding. Sixteen panicle traits were identified from 35 Chinese prickly ash germplasm resources from 2021 to 2022. The diversity of these panicle traits and their role in the plant\'s systematic classification were studied using variance, correlation, cluster, and principal component analyses. Cluster analysis showed that the 35 Chinese prickly ash germplasm resources could be divided into two groups with Euclidean distances of 25. Further analysis showed that yield traits such as panicle length, panicle width, primary branching, grain number per panicle, and grain weight per panicle were significantly positively correlated with grain chlorophyll content, while grain anthocyanin content was negatively correlated with both panicle (panicle length, panicle width, panicle length to width ratio, primary branching, grain number per panicle, and grain weight per panicle) and grain characteristics (single grain weight, thousand-grain weight, grain length, grain width and fruit shape index). In conclusion, Chinese prickly ash germplasms have diverse panicle traits. Z. armatum has dark green grains, long and wide panicles, a long conical shape, many primary branches, high grain weight, and high grain number per panicle. In contrast, Z. bungeanum has bright red seeds, a panicle width larger than its length, short and conical panicles, a small number of primary branches, and low grain weight per panicle and number of grains per panicle. Overall, Z. armatum had a significant yield advantage over Z. bungeanum.

Betong chicken (KU line) is a slow-growing Thai native chicken used for meat production. The objectives of this study were to identify polymorphisms of the calpain1 (CAPN1) and calpain3 (CAPN3) genes and to investigate their effects on growth, carcass, and meat quality traits in Betong chickens (KU line). A sample of 252 Betong chickens (KU line) was screened for CAPN1 and CAPN3 polymorphisms. The polymorphisms of CAPN1 were detected using gel electrophoresis and DNA sequencing, whereas the polymorphisms of CAPN3 were identified using restriction fragment length polymorphism. Polymorphisms were detected in both CAPN1 (AA, AB, and BB genotypes) and CAPN3 (CC, CT, and TT genotypes). The frequency of the B allele was higher than for the A allele (0.78 and 0.22, respectively) in CAPN1, while the C allelic frequency was higher than for the T allele (0.54 and 0.46, respectively) in CAPN3. The CAPN1 genotype and the combination of the CAPN1 and CAPN3 genotypes could be used as genetic markers for meat lightness. The CAPN3 could be useful for increasing body weight, live weight, and breast meat weight in Betong chickens (KU line).

The genetic improvement of cattle is able to provide the highest economic efficiency. Animals with best external features often have the highest indicators of productivity and nonspecific resistance. In order to identify single nucleotide polymorphism (SNP) markers and genes, potentially responsible for conformation traits of Ayrshire cattle, we conducted a genome-wide association study (GWAS). Blood samples were collected from 495 first-calf Ayrshire heifers. For all animals, a linear assessment was carried out, which consisted of 17 conformation traits and 13 exterior flaws. Our study revealed a total of 43 SNPs on chromosomes BTA1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 14, 16, 18, 19, 22, 25, 27, and 29 associated with conformation traits, four of which have met the significance threshold. Among these four significant SNPs, genes associated with exterior flaws such as tilted udder floor and weak loin and SNPs associated with weak back were identified. The markers and genes identified in the corresponding genomic regions are promising candidates for further investigation of the biological processes involved in cow exterior formation and function. The results obtained, including the MYO1B gene associated with weak loin, PEPD and ZFC3H1 genes related to tilted udder floor, and EDNRB gene associated with weak back can be effectively used as molecular markers in genomic selection programs.

BACKGROUND: Regions of genome-wide marker data may have differing influences on the evaluated traits. This can be reflected in the genomic models by assigning different weights to the markers, which can enhance the accuracy of genomic prediction. However, the standard multi-trait single-step genomic evaluation model can be computationally infeasible when the traits are allowed to have different marker weights.
RESULTS: In this study, we developed and implemented a multi-trait single-step single nucleotide polymorphism best linear unbiased prediction (SNPBLUP) model for large genomic data evaluations that allows for the use of precomputed trait-specific marker weights. The modifications to the standard single-step SNPBLUP model were minor and did not significantly increase the preprocessing workload. The model was tested using simulated data and marker weights precomputed using BayesA. Based on the results, memory requirements and computing time per iteration slightly increased compared to the standard single-step model without weights. Moreover, convergence of the model was slower when using marker weights, which resulted in longer total computing time. The use of marker weights, however, improved prediction accuracy.
CONCLUSIONS: We investigated a single-step SNPBLUP model that can be used to accommodate trait-specific marker weights. The marker-weighted single-step model improved prediction accuracy. The approach can be used for large genomic data evaluations using precomputed marker weights.

Field pea (Pisum sativum L.) needs improvement to increase productivity due to its high price and demand. However, the incidence of powdery mildew (PM) disease limits its production. This study aimed to analyze the diversity of qualitative and quantitative traits against powdery mildew resistance by utilizing cluster and principal component analysis to explore PM resistance high-yield potential field peas. Shannon-Weaver\'s diversity index (H\') displayed high intra-genotype diversity for quantitative and qualitative aspects. Heterogeneity was identified for resistance against powdery mildew infections. Eighty-five genotypes were divided into five groups using Mohalanobis generalized distance (D2) statistics. The highest inter-cluster D2 value was observed between clusters 2 and 3 (11.89) while the lowest value was found between clusters 3 and 4 (2.06). Most of the genotypes had noticeable differences, so these could be employed in a crossing scheme. Twelve genotypes were extremely resistant, 29 genotypes were resistant, 25 genotypes were moderately resistant, 18 genotypes were fairly susceptible, and 1 genotype was susceptible to powdery mildew disease. Among 29 resistant genotypes, BFP77, BFP74, BFP63, BFP62, BFP43, and BFP80 were high yielders and, could be used directly and/or transferred through hybridization to high-yielding disease-susceptible genotypes. Among the 25 moderately resistant genotypes, BFP78, BFP45, BFP79, and BFP48 were found to be high yielders. In principal component analysis (PCA), the first four PCs with Eigen values > 1 accounted for 88.4% variability for quantitative traits. Clustering sorted genotypes into five groups, where groups 1 to 5 assembled 37, 28, 1, 8, and 11 genotypes, respectively. Genotypes of cluster 4 were identified as high yielders with its attributes. Pearson correlation significantly and positively correlated across all traits except for PM. This variation suggested that there is a mechanism to select promising genotypes for field pea breeding. Considering all features, BFP78, BFP77, BFP74, BFP63, BFP62, BFP45, BFP79, and BFP80 could be preferred as high yielders and PM resistance owing to longer pod lengths, seeds per pod and pods per plant.

We present a novel approach to genome-wide association studies (GWAS) by leveraging unstructured, spoken phenotypic descriptions to identify genomic regions associated with maize traits. Utilizing the Wisconsin Diversity panel, we collected spoken descriptions of Zea mays ssp. mays traits, converting these qualitative observations into quantitative data amenable to GWAS analysis. First, we determined that visually striking phenotypes could be detected from unstructured spoken phenotypic descriptions. Next, we developed two methods to process the same descriptions to derive the trait plant height, a well-characterized phenotypic feature in maize: (1) a semantic similarity metric that assigns a score based on the resemblance of each observation to the concept of \'tallness\' and (2) a manual scoring system that categorizes and assigns values to phrases related to plant height. Our analysis successfully corroborated known genomic associations and uncovered novel candidate genes potentially linked to plant height. Some of these genes are associated with gene ontology terms that suggest a plausible involvement in determining plant stature. This proof-of-concept demonstrates the viability of spoken phenotypic descriptions in GWAS and introduces a scalable framework for incorporating unstructured language data into genetic association studies. This methodology has the potential not only to enrich the phenotypic data used in GWAS and to enhance the discovery of genetic elements linked to complex traits but also to expand the repertoire of phenotype data collection methods available for use in the field environment.

BACKGROUND: The selection of individuals based on their predicted breeding values and mating of related individuals can increase the proportion of identical-by-descent alleles. In this context, the objectives of this study were to estimate inbreeding coefficients based on alternative metrics and data sources such as pedigree (FPED), hybrid genomic relationship matrix H (FH), and ROH of different length (FROH); and calculate Pearson correlations between the different metrics in a closed Nellore cattle population selected for body weight adjusted to 378 days of age (W378). In addition to total FROH (all classes) coefficients were also estimated based on the size class of the ROH segments: FROH1 (1-2 Mb), FROH2 (2-4 Mb), FROH3 (4-8 Mb), FROH4 (8-16 Mb), and FROH5 (> 16 Mb), and for each chromosome (FROH_CHR). Furthermore, we assessed the effect of each inbreeding metric on birth weight (BW), body weights adjusted to 210 (W210) and W378, scrotal circumference (SC), and residual feed intake (RFI). We also evaluated the chromosome-specific effects of inbreeding on growth traits.
RESULTS: The correlation between FPED and FROH was 0.60 while between FH and FROH and FH and FPED were 0.69 and 0.61, respectively. The annual rate of inbreeding was 0.16% for FPED, 0.02% for FH, and 0.16% for FROH. A 1% increase in FROH5 resulted in a reduction of up to -1.327 ± 0.495 kg in W210 and W378. Four inbreeding coefficients (FPED, FH, FROH2, and FROH5) had a significant effect on W378, with reductions of up to -3.810 ± 1.753 kg per 1% increase in FROH2. There was an unfavorable effect of FPED on RFI (0.01 ± 0.0002 kg dry matter/day) and of FROH on SC (-0.056 ± 0.022 cm). The FROH_CHR coefficients calculated for BTA3, BTA5, and BTA8 significantly affected the growth traits.
CONCLUSIONS: Inbreeding depression was observed for all traits evaluated. However, these effects were greater for the criterion used for selection of the animals (i.e., W378). The increase in the genomic inbreeding was associated with a higher inbreeding depression on the traits evaluated when compared to pedigree-based inbreeding. Genomic information should be used as a tool during mating to optimize control of inbreeding and, consequently, minimize inbreeding depression in Nellore cattle.