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Background Polygenic risk score (PRS) analysis is used to predict disease risk. Although PRS has been shown to have great potential in improving clinical care, PRS accuracy assessment has been mainly focused on European ancestry. This study aimed to develop an accurate genetic risk score for knee osteoarthritis (OA) using a multi-population PRS and leveraging a multi-trait PRS in the Japanese population. Methods We calculated PRS using PRS-CS-auto, derived from genome-wide association study (GWAS) summary statistics for knee OA in the Japanese population (same ancestry) and multi-population. We further identified risk factor traits for which PRS could predict knee OA and subsequently developed an integrated PRS based on multi-trait analysis of GWAS (MTAG), including genetically correlated risk traits. PRS performance was evaluated in participants of the Nagahama cohort study who underwent radiographic evaluation of the knees ( n  = 3,279). PRSs were incorporated into knee OA integrated risk models along with clinical risk factors. Results A total of 2,852 genotyped individuals were included in the PRS analysis. The PRS based on Japanese knee OA GWAS was not associated with knee OA ( p  = 0.228). In contrast, PRS based on multi-population knee OA GWAS showed a significant association with knee OA ( p  = 6.7 × 10 −5 , odds ratio (OR) per standard deviation = 1.19), whereas PRS based on MTAG of multi-population knee OA, along with risk factor traits such as body mass index GWAS, displayed an even stronger association with knee OA ( p  = 5.4 × 10 −7 , OR = 1.24). Incorporating this PRS into traditional risk factors improved the predictive ability of knee OA (area under the curve, 74.4% to 74.7%; p  = 0.029). Conclusions This study showed that multi-trait PRS based on MTAG, combined with traditional risk factors, and using large sample size multi-population GWAS, significantly improved predictive accuracy for knee OA in the Japanese population, even when the sample size of GWAS of the same ancestry was small. To the best of our knowledge, this is the first study to show a statistically significant association between the PRS and knee OA in a non-European population. Trial registration No. C278.

Genomic and phenomic selection have transformed modern breeding by enabling data-driven prediction of complex traits. Deep learning (DL) can further enhance predictive ability by capturing nonlinear patterns that classical and Bayesian approaches often fail to represent. However, despite its potential, the adoption of DL in breeding programs remains limited due to its computational demands and the lack of accessible tools for users without extensive programming experience. This study introduces the MTMEGPS (Multi-Trait and Multi-Environment Genomic and Phenomic Selection), an R package that provides a streamlined end-to-end workflow for Uni- and Multi-Trait (UT and MT, respectively) and Uni- and Multi-Environment (UE and ME, respectively) genomic and phenomic prediction. The package supports data preparation, hyperparameter optimization, model training, and DL-based evaluation. To assess its performance, MTMEGPS was applied to the two default datasets included in the package: Maize (genomic data) and Eucalyptus (near-infrared spectroscopy, NIR, data), as well as to an independent publicly available multi-environment validation dataset. Across most scenarios, MTMEGPS showed superior predictive ability compared with all benchmark models, particularly under UT for the internal datasets and MT for the independent multi-environment dataset. Mean squared error (MSE) values were similar across models, all falling within a moderate range. Overall, these results demonstrate the efficiency and practical utility of MTMEGPS for genomic and phenomic selection, even in scenarios where prediction errors remain moderate.

ABSTRACT The objective of this work was to assess the breeding influences in different agronomic and physiological traits in Brazilian soybean cultivars, released between 1965 and 2011, to identify traits associated with modern cultivars. A total of 29 cultivars were evaluated in two locations in the 2016/17 crop season. Genotype selection based on agronomic and physiological traits was determined using GYT (Grain Yield*Trait) methodology, which uses the Superiority Index to rank genotypes by mean of all traits. Grain Yield is combined with other target traits and shows the strengths and weaknesses of each genotype. Soybean breeding improved desirable traits during the 46 years of evaluation. Superiority index can be a powerful tool for breeders to obtain high genetic gains in the future. The cultivars DMario 58i, TMG 7161RR and TMG 7262 RR stand out as the best cultivars but present different sets of desirable traits. The traits grain yield, harvest index, number of pods per plant, reproductive-vegetative ratio, photosynthetic rate and transpiration rate are core traits which can be evaluated in soybean breeding programs.

Genotype selection based on multiple traits in multi-years is frequently influenced by unpredictable rainfed conditions. The main objective of the study was to apply the new methodology of genotype by yield*trait (GYT) biplot for genotype selection and trait profiles in durum wheat genotypes based on multi-traits and multi-year data under rainfed conditions of Iran. A superiority index was applied based on GYT table for ranking of genotypes by the mean of all traits. The GYT biplot ranked the genotypes based on their levels in combining yield with other key traits. Grain yield was combined with target traits and showed the strengths and weaknesses of each genotype. Based on GYT-biplots the relationships among the studied traits were not repeatable across years, but they facilitated visual genotype comparisons and selection. The breeding lines G13, G10 and G15 ranked as the best in combination of the morph-physiological traits i.e., SPAD-reading, early heading, flag-leaf length and number of grain per spike with grain yield under rainfed conditions. The results indicate that there is a potential for simultaneous improvement of some characteristics of durum wheat under rainfed conditions. The GYT biplot was a useful tool for exploring the combination of yield with traits and trait profiles of the durum genotypes to obtain high genetic gains in the durum breeding programs.

Arabica coffee (Coffea arabica L.) is one of the top commercial commodities worldwide. The effectiveness of the selection of the coffee‐soaking treatment is significant in determining the best treatment for all traits tested. This study aimed to (1) evaluate the variation in soaking treatments on the tested traits, (2) identify the strengths and weaknesses of each treatment and determine the optimal treatment using the multitrait genotype‐ideotype distance index (MGIDI). The experiment was conducted in the Weeds Laboratory, Faculty of Agriculture, Universitas Padjadjaran. The treatment uses four times of soaking (F) (0,12,24,36) hours combined with two kinds of harvesting techniques (selective K1 and strip picking K2). The results showed significant differences across all tested traits at the p < 0.01 level, except for length after drying (LAD) (p < 0.05). Treatments selective picking + 12 h of soaking time and selective picking + 24 h of soaking time contributed to the related properties, namely weight before drying, weight after drying, length before drying (LBD), width before drying (WBD), thickness before drying (TBD), LAD, width after drying (WAD), thickness after drying (TAD), and water content before drying (WCBD). On the other hand, strip picking + 36 h of soaking time, strip picking + 24 h of soaking time, and strip picking + 12 h of soaking time have strengths related to the last bean weight (LBW) and water content after drying (WCAD). The MGIDI identified selective picking + 36 h of soaking time (F3K1V1) as the best treatment across the 11 quality traits tested, while control strip picking + no soaking time (KRV1) was rated the least effective. This study highlights the utility of factor analysis within the MGIDI as an efficient tool for assessing the strengths and weaknesses of coffee‐soaking treatments across multiple traits.

Several studies have been done all around Europe to find the possible reasons for changing the current common pet diet to plant-based, and the results indicated that animal welfare, ethical, and moral concerns are the three most important concerns that make pet owners willingly select the plant-based diet for their pets (4,7–9). [...]efficient and effective plant-based meat production is now a high trend in pet industries in order to keep pace with high plant-based meat demands in the near future. [...]there are some nutrients from TVP sources that may not be well absorbed in the body (12). [...]additional nutrients and/ or various plant protein sources should be embedded/adjusted into TVP to be considered as nutritionally valuable/complete as meat for plant-based food diets (Figure 1). [...]by 2050, worldwide meat consumption is expected to treble from 2008 despite the negative impacts that can be made on environmental sustainability, human health, and ethical considerations, improving public health and minimizing animal suffering (17). [...]the COVID-19 pandemic has not had a large detrimental influence on the market for plant-based pet food, while the demand for plant-based pet food online shopping increased significantly (Figure 1) (22).

Background Glaucoma causes permanent blindness. Current treatments have limited effectiveness, necessitating novel therapeutic strategies. We aimed to identify potential drug targets for glaucoma by integrating multi-trait and multi-omic analyses. Methods We sourced druggable gene expression and protein abundance summary-level data from quantitative trait loci studies, and genetic associations with glaucoma from a large-scale multi-trait analysis. We employed proteome and transcriptome Mendelian randomization (MR) and colocalisation to identify potential therapeutic targets, glaucoma endophenotype MR to explore the potential mechanisms of identified associations, and phenome-wide MR to investigate possible adverse effects of candidate targets. Results We identified CPXM1 and FLT4 as tier 1; INSR as tier 2; and CPZ and PXDN as tier 3 druggable genes. Genetically predicted higher levels of CPXM1 [odds ratio (OR): 0.86, 95% confidence interval (CI): 0.81–0.91, P FDR  < 0.001], FLT4 (OR: 0.74, 95% CI: 0.64 − 0.87, P FDR  = 0.033), INSR (OR: 0.58, 95% CI: 0.43 − 0.78, P FDR  = 0.042), and CPZ (OR: 0.55, 95% CI: 0.40 − 0.74, P FDR  = 0.033) were associated with decreased glaucoma risk while those of PXDN (OR: 1.33, 95% CI: 1.15 − 1.54, P FDR  = 0.033) with increased risk. The associations for CPXM1 (OR: 0.53, 95% CI: 0.39 − 0.73, P  < 0.001) and FLT4 (OR: 0.86, 95% CI: 0.78 − 0.95, P  = 0.005) were confirmed transcriptome-wide and colocalisation was confirmed for CPXM1 [posterior probability H4 (PPH 4 ) = 0.940], FLT4 (PPH 4  = 0.701), and INSR (PPH 4  = 0.706). The protective effects of CPXM1 and CPZ may be attributed to intraocular pressure-lowering activities. The risk associated with PXDN is due to its involvement in glaucomatous neuropathy. No significant adverse effects were identified. Conclusions This study provides novel insights into glaucoma pathophysiology and promotes pharmaceutical target innovation.

The performance of genomic prediction (GP) on genetically correlated traits can be improved through an interdependence multi-trait model under a multi-environment context. In this study, a panel of 237 soft facultative wheat (Triticum aestivum L.) lines was evaluated to compare single- and multi-trait models for predicting grain yield (GY), harvest index (HI), spike fertility (SF), and thousand grain weight (TGW). The panel was phenotyped in two locations and two years in Florida under drought and moderately drought stress conditions, while the genotyping was performed using 27,957 genotyping-by-sequencing (GBS) single nucleotide polymorphism (SNP) makers. Five predictive models including Multi-environment Genomic Best Linear Unbiased Predictor (MGBLUP), Bayesian Multi-trait Multi-environment (BMTME), Bayesian Multi-output Regressor Stacking (BMORS), Single-trait Multi-environment Deep Learning (SMDL), and Multi-trait Multi-environment Deep Learning (MMDL) were compared. Across environments, the multi-trait statistical model (BMTME) was superior to the multi-trait DL model for prediction accuracy in most scenarios, but the DL models were comparable to the statistical models for response to selection. The multi-trait model also showed 5 to 22% more genetic gain compared to the single-trait model across environment reflected by the response to selection. Overall, these results suggest that multi-trait genomic prediction can be an efficient strategy for economically important yield component related traits in soft wheat.

Multi-trait and multi-environment data are common in animal and plant breeding programs. However, what is lacking are more powerful statistical models that can exploit the correlation between traits to improve prediction accuracy in the context of genomic selection (GS). Multi-trait models are more complex than univariate models and usually require more computational resources, but they are preferred because they can exploit the correlation between traits, which many times helps improve prediction accuracy. For this reason, in this paper we explore the power of multi-trait deep learning (MTDL) models in terms of prediction accuracy. The prediction performance of MTDL models was compared to the performance of the Bayesian multi-trait and multi-environment (BMTME) model proposed by Montesinos-López et al. (2016), which is a multi-trait version of the genomic best linear unbiased prediction (GBLUP) univariate model. Both models were evaluated with predictors with and without the genotype×environment interaction term. The prediction performance of both models was evaluated in terms of Pearson’s correlation using cross-validation. We found that the best predictions in two of the three data sets were found under the BMTME model, but in general the predictions of both models, BTMTE and MTDL, were similar. Among models without the genotype×environment interaction, the MTDL model was the best, while among models with genotype×environment interaction, the BMTME model was superior. These results indicate that the MTDL model is very competitive for performing predictions in the context of GS, with the important practical advantage that it requires less computational resources than the BMTME model.

Plant breeders regularly evaluate multiple traits across multiple environments, which opens an avenue for using multiple traits in genomic prediction models. We assessed the potential of multi-trait (MT) genomic prediction model through evaluating several strategies of incorporating multiple traits (eight agronomic and malting quality traits) into the prediction models with two cross-validation schemes (CV1, predicting new lines with genotypic information only and CV2, predicting partially phenotyped lines using both genotypic and phenotypic information from correlated traits) in barley. The predictive ability was similar for single (ST-CV1) and multi-trait (MT-CV1) models to predict new lines. However, the predictive ability for agronomic traits was considerably increased when partially phenotyped lines (MT-CV2) were used. The predictive ability for grain yield using the MT-CV2 model with other agronomic traits resulted in 57% and 61% higher predictive ability than ST-CV1 and MT-CV1 models, respectively. Therefore, complex traits such as grain yield are better predicted when correlated traits are used. Similarly, a considerable increase in the predictive ability of malting quality traits was observed when correlated traits were used. The predictive ability for grain protein content using the MT-CV2 model with both agronomic and malting traits resulted in a 76% higher predictive ability than ST-CV1 and MT-CV1 models. Additionally, the higher predictive ability for new environments was obtained for all traits using the MT-CV2 model compared to the MT-CV1 model. This study showed the potential of improving the genomic prediction of complex traits by incorporating the information from multiple traits (cost-friendly and easy to measure traits) collected throughout breeding programs which could assist in speeding up breeding cycles.