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[This corrects the article DOI: 10.3389/fpls.2026.1754820.].

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.

Water salinity and scarcity constitute major limitations to crop production in arid and semi-arid regions. Introduction of nutritious and stress-tolerant underutilized crops is a promising approach for dietary enrichment, cropping system diversification, remediation of marginal and degraded lands, and building climate resilience. The primary objectives of this study were to investigate the effect of water salinity and managed water-deficit stress on grain and fodder yield, identify multi-trait ideotypes, and validate the stability and genetic gain in finger millet ideotypes over a 2-year period. A total of 80 finger millet accessions were evaluated under fresh water (0 dS/m) and two saline irrigation water (6 and 10 dS/m) in Dubai during the 2020/2021 cropping season. Validation of a selected elite subset was conducted under a combination of optimum, salinity, and drought-stress regimes (0 dS/m, 6 dS/m, 10 dS/m, and 50% irrigation) during the 2021/2022 cropping season. Initial analysis showed a grain yield (GYLD) reduction of 87% under 10 dS/m saline irrigation water compared with the control, and the genotype-by-treatment (G × T) interaction revealed highly significant effects for GYLD. Using multi-trait genotype–ideotype distance index (MGIDI), 20 elite accessions were identified, demonstrating a remarkable increase in mean GYLD under high saline irrigation water, corresponding to a genetic gain of 167% over the reference population mean. Validation trials confirmed the success of the selection by showing a non-significant G × T for GYLD and dry fodder yield (DFYLD) across the four validation treatments, alongside a significant increase in heritability ( H 2 ) for GYLD from 0.60 to 0.78. Comparative analysis revealed that managed water-deficit stress was the most limiting factor for GYLD in the elite subset, causing an average loss of 42.7% compared to 20.4% under high saline water irrigation. However, DFYLD displayed exceptional stability across both saline water and water-deficit stress types. The comparative analysis presented in Venn diagrams ultimately identified a core group of stable, broadly adapted accessions, including IE 4028 and IE 4570, which are recommended as high- impact parental lines for combined stress tolerance. These findings establish a reliable selection framework for enhancing the climate-resilience of underutilized crops in marginal environments.

With the rapid development of the sharing economy and the progress of social ecological civilization, social media influencers (SMIs) have garnered significant from academia and practitioners for their pivotal role in fostering pro-environmental behavioral intentions within the tourism consumption context. Drawing on the two-step flow theory, social influence theory, and social learning theory, this study establishes an integrated analytical framework to elucidate how SMIs facilitate the balance between tourism development and ecosystem preservation by activating pro-environmental behavioral behavior. This study conceptualizes the SMIs’ multi-traits as a higher-order construct (a third-order reflective structure), which integrates content-determined and personality-determined attributes, viewing SMIs’ effectiveness as a coherent system of influence rather than a series of fragmented traits. Based on survey data collected from 598 Chinese social media users, the study utilized Covariance-Based Structural Equation Modeling (CB-SEM) to test the proposed model. The results demonstrate that SMIs’ multi-traits exert significant positive effects on parasocial relationships and wishful identification, which in turn enhance individuals’ willingness to mimic. This willingness to mimic serves as a core behavioral conversion mechanism, bridging digital influence on three pro-environmental behavioral intentions: general, specific and online advocacy intentions. Furthermore, robustness analyses reveal marked heterogeneity across education- and income-based groups, indicating that the efficacy of SMI traits and the psychological-to-behavioral conversion efficiency are contingent upon the recipients’ socioeconomic resources and cognitive capital. Overall, this study characterizes social media influencer marketing as a scalable, socially driven phenomenon that can effectively activate and promote pro-environmental behavioral intentions, providing valuable insights for environmental education and sustainable tourism development in the digital age.

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.

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.

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.