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PubMed2025

Yield and yield component trait analysis with DArT genotyping for GWAS in soybean grown in drought conditions of Kazakhstan.

Development of drought tolerant cultivars of soybean is the single best way to address the challenge of global climate change and very limited water resources for crop irrigation in Central Asia including Kazakhstan. A set of 188 soybean cultivars with diverse origins was assessed for genome-wide association study (GWAS) for yield and eight yield-related traits in both irrigated (well-watered, WW) and non-irrigated (drought) conditions during 2 years in field trials in South-Eastern Kazakhstan. The 295K Diversity array technology (DArT) analysis was applied, and 16K filtered DArT markers were used for genotyping of 183 soybean accessions. In the results, 41 quantitative trait nucleotides (QTN) were identified as significantly associated with nine studied traits. To verify these results, bulk segregant analysis (BSA) was carried out in six breeding lines originating from two crosses between high-yielding under drought cvs, Sponsor and Zen, with drought sensitive cv Lastochka. The evaluation of combined results revealed 10 most significant QTN and eight most promising putative candidate genes, which were selected and tested for their gene expression using RT-qPCR under drought compared with WW controls. Among them, glucose-6-phosphate isomerase (G6PI), pentatricopeptide repeats (PPR) protein, and ABC transporter, associated with seed yield, seed weight per plant, and plant height, were highly upregulated in drought tolerant genotypes. In contrast, two other genes, Rab-GDP dissociation inhibitor (Rab-GDI) and Transducin with WD40 repeats, associated with seed yield, showed repression in the same genotypes. These verified genes involved in the control of yield and yield-related traits can be used for marker-assisted selection to develop novel genotypes and new soybean cultivars tolerant to strong drought in Kazakhstan and in other countries with similar conditions.

Diversity array technology (DArT)↗

PubMed2026

Combining ability and gene action for grain yield and biofortification traits in pearl millet [Pennisetum glaucum (L.) R. Br.]: implications for breeding high-yielding biofortified hybrids in arid regions.

Hybrid RIB-9184 × RIB-15131 combines high yield (18.84 g plant⁻¹) with iron (46.16 mg kg⁻¹), zinc (38.86 mg kg⁻¹), and protein (11.91%); Fe-Zn correlation (rg = 0.82) permits simultaneous biofortification. Pearl millet [Pennisetum glaucum (L.) R. Br., syn. Cenchrus americanus (L.) Morrone] is a climate-resilient cereal with inherently high micronutrient levels, making it a priority crop for biofortification. Understanding gene action for yield and nutritional traits is essential for designing effective breeding strategies. Ten diverse inbred lines were crossed in a half-diallel design (Griffing's Method 2, Model 1), and the 55 entries (45 F1 hybrids + 10 parents) were evaluated across two sowing-date environments in a randomised complete block design with three replications at Jaipur, Rajasthan, India. Biofortification traits (Fe, Zn, protein) showed predominantly additive gene action (Baker's ratio 0.71-0.91) with high heritability (0.90-0.94). G×E interaction was significant for Fe and Zn but genotypic variance was substantially larger, maintaining high heritability; protein showed no G×E interaction. Grain yield was governed largely by non-additive effects (Baker's ratio 0.54) with significant G×E interaction, favouring hybrid breeding. Among parents, RIB-9205 had the highest GCA for Fe (6.65, P < 0.001), RIB-9184 for Zn (3.85, P < 0.001) and protein (0.78, P < 0.001), and RIB-9185 was a balanced combiner for yield (1.39, P < 0.001) and micronutrients. The hybrid RIB-9184 × RIB-15131 ranked first across all five weighting schemes of the multi-trait performance index (1.31), combining grain yield of 18.84 g plant⁻1 with Fe of 46.16 mg kg⁻1, Zn of 38.86 mg kg⁻1, and protein of 11.91%. The strong Fe-Zn correlation (rg = 0.82, P < 0.01) permits simultaneous micronutrient improvement. An integrated approach combining hybrid development for yield with population improvement for micronutrient density is recommended for biofortified pearl millet cultivars in arid regions.

Pennisetum↗

PubMed2026

Finlay-Wilkinson random regression for yield and yield stability prediction in cereals.

Year-to-year climate variability poses a challenge for agriculture by increasing crop yield variability; therefore, there is a need to identify genotypes that can withstand these fluctuations. With the right selection criteria, genotypes with yield stability across variable environmental conditions can be selected. Methods such as Finlay-Wilkinson random regression (FWRR) may allow us to use sparse datasets-common in plant breeding pipelines-and incorporate genomic data to leverage phenotypic information from related genotypes to predict yield stability. Our objective was to examine how the number of environments and the variance among those environments affect stability predictions. We also integrate FWRR as a genomic prediction tool for characterizing yield stability, comparing it to the traditional genomic prediction models as a reference. We used three datasets: one highly unbalanced dataset for oats (Avena sativa L.) and two completely balanced datasets with different numbers of environments for barley (Hordeum vulgare L.) and wheat (Triticum aestivum L.). We fit standard Finlay-Wilkinson (FW) and FWRR models to estimate grain yield and stability under various scenarios. We found that the estimated stability values obtained were similar using balanced datasets for FW or FWRR. FWRR also achieved moderate predictive ability for stability using unbalanced datasets under 10-fold cross-validation (CV1) with new genotypes. In terms of environmental representation, selecting the right set of environments for inclusion in the model was more important than adding more environments. Our results suggest the possibility of using FWRR to select stable genotypes earlier in line development, as well as to design resource-efficient stability-testing schemes.

Hordeum↗

PubMed2026

Quantitative trait loci associated with improved fruit yield under heat-stress conditions in fresh-market tomato.

Rising temperatures and more frequent heat stress events pose a major challenge to global tomato production, particularly in tropical and subtropical regions such as the southern United States. High temperatures during flowering and fruit set lead to poor fruit set and reduced yield. Although several commercial cultivars and breeding lines are described as heat-tolerant, the genetic basis of yield performance under heat stress conditions in fresh-market tomato remains poorly understood. This study aimed to identify genomic regions associated with fruit yield under natural heat stress. A biparental recombinant inbred line (RIL) population developed by the UF/IFAS tomato breeding program was evaluated under natural field heat stress in the fall seasons of 2016, 2017, and 2018, with fruit yield recorded as the primary trait. Genotyping of RILs was performed with the AgriPlex commercial tomato panel. Multi-environment QTL analysis was conducted to identify loci associated with fruit yield under heat stress. A major locus on chromosome 12 was selected for validation. Backcross populations segregating for this region were evaluated in a randomized block design during the fall of 2020 at the Gulf Coast Research and Education Center (GCREC), Balm, Florida. Multi-environment QTL analysis identified several loci on chromosome 4, 5, 6, and 12 associated with fruit yield under natural heat stress conditions. Among these, a locus on chromosome 12 showed consistent effects across multiple harvests and environments and explained a relatively larger proportion of phenotypic variance. Validation using backcross populations confirmed that genotype carrying the chromosome 12 QTL produced significantly higher yield under natural heat stress than susceptible genotypes. Overall, this study identified an agronomically important region on chromosome 12 that can be targeted to improve tomato yield under heat stress. The results also highlight multiple genomic regions contributing to higher yield under heat stress. These findings provide a foundation for developing breeding strategies for developing heat-tolerant fresh-market tomato cultivars.

QTL analysis↗

PubMed2026

XsiAMT1.1a was identified as a novel ammonium uptake functional gene and its overexpression combined with GA4 application significantly increased yield in Arabidopsis thaliana.

Nitrogen (N) is a key limiting factor for plant yield. Ammonium is one of the main N forms absorbed by plants. Overexpression of ammonium uptake functional genes, such as ammonium transporter (AMT), can increase yield. However, the AMTs reported to enhance yield significantly is still limited. No researches have focused on the effect of overexpressing AMT combined with hormone application on yield improvement. In this study, we first investigated the role of XsiAMT1.1a, a potential ammonium uptake functional gene in an ammonium preference plant Xanthium sibiricum, in ammonium uptake by the analysis of bioinformatics, gene expression and subcellular localization, and the determination of ammonium uptake rate in endogenous silencing and heterologous overexpression plants. Subsequently, the effect of XsiAMT1.1a overexpression combined with hormone application on yield increase was further investigated in model plant Arabidopsis thaliana. Our results showed that XsiAMT1.1a shared the same conserved domains with AtAMT1 subfamily members and localized on the plasma membrane. XsiAMT1.1a was induced by N deficiency and highly expressed during the reproductive period. XsiAMT1.1a endogenous silencing and heterologous overexpression significantly decreased and increased ammonium uptake rates in X. sibiricum and A. thaliana, respectively. Overexpression of XsiAMT1.1a significantly improved total N accumulation, biomass and yield in A. thaliana, while XsiAMT1.1a overexpression combined with GA4 application had a stronger promoting effect on the above indicators. Our research identified a novel ammonium uptake functional gene, XsiAMT1.1a, and provided a new yield-increasing strategy which was verified in A. thaliana.

Arabidopsis↗

PubMed2026

Molecular diagnostic yield and barriers in inherited retinal diseases: a retrospective cohort study.

OBJECTIVE: To evaluate the diagnostic yield of panel-based genetic testing for inherited retinal diseases (IRDs) and identify barriers to molecular resolution. DESIGN: Retrospective cohort. PARTICIPANTS: A total of 404 patients with clinically confirmed IRDs who were evaluated at the Adult Inherited Retinal Dystrophy Service, Ontario, Canada (October 2021-September 2024). METHODS: Patients underwent targeted massive parallel sequencing panel testing. Diagnostic yield was calculated, and unresolved cases were reviewed. Associations between yield, phenotype, ethnicity, and sex were assessed using χ² analysis. RESULTS: Of 685 referrals, 570 had confirmed IRDs. After we excluded 140 pending results and 26 patients who declined testing, 404 patients were analyzed. At referral, 94 patients (23.2%) had a previous molecular diagnosis, and 138 (34.0%) were diagnosed through clinic-initiated testing, giving an overall yield of 57.4%. Yield varied significantly by phenotype (χ², P = 1.4 × 10⁻⁶), from 94.4% in vitelliform macular dystrophies to 25.0% in vitreoretinopathies, with no sex association (P = 1.0). Disease-causing variants were identified in 83 IRD-associated genes, most frequently ABCA4, USH2A, and BEST1. Of 172 unresolved cases, 62 (36.0%) had negative panels, and 110 (63.9%) were inconclusive, including 30 with unphased pathogenic variants in recessive genes and 10 with high-suspicion variants of uncertain significance. Key barriers included limited family availability for phasing, restricted access to functional assays, and lack of public coverage for whole-exome or whole-genome sequencing. CONCLUSIONS: Massive parallel sequencing-based panel testing achieved a 57% diagnostic yield in this IRD population. Success was strongly phenotype-dependent with substantial heterogeneity. Whole-exome sequencing, whole-genome sequencing, family segregation, and functional genomics could improve diagnostic outcomes and management.

Humans↗

PubMed2026

Genetic legacy effects in a mungbean-wheat rotation reveal potential to breed for system-level yield gains.

Legume crops provide protein-rich food, serve as critical disease breaks in cereal rotations, and contribute to soil fertility through symbiotic nitrogen fixation. However, crop improvement programs typically focus on within-crop performance rather than system-level benefits. We hypothesize that legacy effects (the influence of one crop's genotype on subsequent crop performance) are under genetic control and could be targeted in breeding programs. To test this, we evaluated how 309 genetically diverse mungbean genotypes influenced subsequent wheat performance. The mungbean panel was grown, followed by a single wheat cultivar sown in the same plots. Remarkably, wheat yield varied by nearly 1 t ha-1 (2.52-3.49 t ha-1), depending solely on the preceding mungbean genotype. Legacy effects showed moderate heritability (H2: 0.43-0.65), suggesting untapped genetic potential for breeding. However, these estimates were derived from a single site and season and require validation across environments. Analyses of mungbean traits, soil properties, and volatile organic compounds identified root architecture, symbiotic nitrogen fixation, and the soil microbiome as potential contributors to legacy effects, although these mechanisms remain to be tested directly. Haplotype mapping identified genomic regions in mungbean associated with wheat yield and, to a lesser extent, grain protein, revealing trade-offs between within-crop performance and legacy effects. Genetic simulations based on empirically derived marker effects compared genomic selection strategies targeting mungbean yield, wheat yield, or both simultaneously. A selection strategy placing equal weight on mungbean yield and subsequent wheat yield (50:50 weighting) achieved simultaneous gains in both crops (19.5% and 7.6%), highlighting the potential to breed for system-level productivity with reduced input requirements.

crop rotations↗

PubMed2026

Grain protein and yield stability study in rainfed durum wheat RILs.

Developing durum wheat cultivars with stable grain yield across diverse environments remains a key breeding objective. This study evaluated 118 recombinant inbred lines (RILs) derived from a cross between the drought-adapted cultivar 'Zardak' (Triticum durum) and the landrace 'Iran-249' (T. turanicum) with desirable seed characteristics, across four heterogeneous rainfed environments in Italy and Iran. The assessment focused on grain yield (GY) and grain protein content (GPC) stability. Combined analysis of variance revealed significant (p < 0.01) effects for genotype, environment, and their interaction for both traits. Line ZD-050 showed the highest GY (3.91 t ha⁻¹), while ZD-032 had the highest GPC (14.27%). Stability analysis using parametric and non-parametric methods, along with AMMI and GGE biplot modeling, identified ZD-050 as among the most promising genotypes according to yield-integrating and dynamic-stability approaches. This line showed high grain yield in methods such as the Superiority Index and Kang's rank-sum, although stability rankings differed across the used methods. This line maintained superior yield, demonstrated broad adaptability across environments, and had moderate protein levels, identifying it as an optimal candidate for breeding programs targeting yield stability and wide adaptation under rainfed conditions.

Triticum↗

PubMed2025

Genomics control of biostimulant-induced stress tolerance and crop yield enhancement.

Biostimulants are changing modern agriculture, as they have the potential to secure healthy and sustainable food production while preserving the environment. They have two main biological effects: growth promotion and stress protection. Both effects can lead to enhancement of the yield and improvement of the marketable grade of the produce in crops, without compromising crop quality. Their use increased exponentially in the past decade, as they are highly efficient, ecologically friendly (non-toxic, biodegradable), and applicable to all major crops. While exponential data on the physiological mechanisms of stress protection is accumulating in recent years, the information as to how biostimulants act at the molecular level is still rather limited. Here we review the growing evidence of the biostimulants role in stress protection and yield enhancement of crops, as well as the recent transcriptomic and metabolomic data, which indicate biostimulants' molecular mode of action. In particular, we outline the role of genes encoding signaling components, plant hormones (abscisic acid, brassinosteroids, and ethylene), genes encoding transcription factors from ERF, WRKY, NAC, and MYB families, and genes related to growth, photosynthesis, and stress response. Finally, we describe strategies to study the genetic and genomics control of biostimulants mode of action, with foci on stress tolerance and yield enhancement. In Arabidopsis, established systems for biostimulants-induced protection against drought and oxidative stress will allow both forward and reverse genetics approaches to identify key genes from the biostimulants network. Mutations in such genes compromise the stress-protective effect of biostimulants. In major crops such as pepper and tomato, large Genome Wide Association Studies (GWAS) panels can be utilized to study crops responses to biostimulants in terms of drought tolerance, fruit qualities, and yield in order to pinpoint genes controlling biostimulants-induced stress protection and yield enhancement. The combination of these approaches allows identification and verification of important genes involved in the pathways of biostimulant-induced stress protection and yield enhancement, as well as deciphering parts of the intricate biostimulant-signaling network.

Crops, Agricultural↗

PubMed2026

From stress signaling to yield stability: physiological and molecular mechanisms of wheat resilience to heat and drought stress.

Wheat resilience depends on coordinated signaling, reproductive protection, and source-sink regulation, providing a framework to breed robust trait combinations that stabilize yield under combined heat and drought. Climate change is increasing the frequency and severity of heat and drought events, posing a major threat to wheat productivity, yield stability, and food security. Because these stresses often coincide in the field, their combined effects can impair growth, reproductive development, grain filling, and final yield more severely than either stress alone. Wheat resilience under such conditions depends on coordinated physiological adjustment and molecular regulation that sustain cellular homeostasis, protect reproductive tissues, and preserve yield-related traits. This review synthesizes current knowledge on the physiological and molecular bases of wheat resilience to heat and drought, with emphasis on their combined effects. We discuss major physiological responses, including photosynthetic adjustment, stomatal regulation, canopy cooling, osmotic balance, antioxidant defense, membrane stability, and source-sink coordination. We also examine key regulatory pathways involved in stress perception and adaptation, including calcium and reactive oxygen species signaling, mitogen-activated protein kinase cascades, phytohormonal crosstalk, transcriptional regulation, heat shock proteins, late embryogenesis abundant proteins, and osmoprotective and redox-associated pathways. In addition, we highlight the growing contribution of transcriptomics, proteomics, metabolomics, and phenomics to the identification of candidate genes, biomarkers, and adaptive traits. Finally, we consider how mechanistic insights can be translated into wheat improvement through molecular markers, genomic selection, gene editing, and climate-realistic phenotyping. An integrated understanding of stress signaling and adaptive trait deployment will be essential for developing wheat cultivars with improved resilience and yield stability under future climates.

Triticum↗

PubMed2025

A meta-analysis of diagnostic yield and clinical utility of genome and exome sequencing in pediatric rare and undiagnosed genetic diseases.

PURPOSE: To systematically evaluate the diagnostic yield and clinical utility of genome sequencing (GS) and exome sequencing (ES; genome-wide sequencing [GWS]) in pediatric patients with rare and undiagnosed genetic diseases. METHODS: We conducted a meta-analysis of studies published between 2011 and 2023. To address study heterogeneity, comparative analyses included within-cohort studies using random-effects models. RESULTS: We identified 108 studies including 24,631 probands with diverse clinical indications. The pooled diagnostic yield among within-cohort studies (N = 13) for GWS was 34.2% (95% CI: 27.6-41.5; I2: 86%) vs 18.1% (95% CI: 13.1-24.6; I2: 89%) for non-GWS, with 2.4-times odds of diagnosis (95% CI: 1.40-4.04; P < .05). The pooled diagnostic yield among within-cohort studies (N = 3) for GS was 30.6% (95% CI: 18.6-45.9; I2: 79%) vs 23.2% (95% CI: 18.5-28.7; I2: 58%) for ES, with 1.7-times the odds of diagnosis (95% CI: 0.94-2.92; P = .13). In first-line testing, the diagnostic yield tended to be higher for GS than for ES across clinical subgroups. The pooled clinical utility among patients with a positive diagnosis was 58.7% (95% CI: 47.3-69.2; I2: 81%) for GS and 54.5% (95% CI: 40.7-67.6; I2: 87%) for ES. CONCLUSION: GS appears to have a higher diagnostic yield than ES, with similar clinical utility per positive diagnosis.

Child↗

PubMed2026

Investigating the Role of MicroRNA396 (miR396) Gene in Regulating Wheat Yield and Grain Nitrogen Concentration.

Nitrogen (N) is essential for crop growth, yet excessive fertilization causes environmental issues, highlighting the need to sustain yield and grain N concentration under reduced N input. miR396s are known to regulate plant development and stress responses. Here, we examined whether and how miR396 affects wheat yield and N status under high and low N conditions. TaMIM396 (transforming with the target mimicry construct of miR396) overexpression significantly increased plant height, spike length, grain yield, and grain N concentration under both N treatments. Physiological data showed TaMIM396 enhanced dry matter (DM) and N accumulation at anthesis and maturity, as well as improved post-anthesis remobilization of DM and N to grains. RNA-seq analysis revealed that, under low N, TaMIM396 specifically upregulated key photosynthetic antenna genes, including Lhca3 and Lhcb1/2/3/5, which are critical for light harvesting, suggesting improved photosynthetic efficiency that promotes DM accumulation under N limitation. Collectively, our results demonstrate that TaMIM396 acts as a broad-spectrum N-efficiency gene, coordinating carbon and N remobilization while boosting photosynthetic capacity, thereby supporting stable yield and grain N concentration across N supply levels. Therefore, TaMIM396 is a promising candidate for breeding N-efficient wheat cultivars compatible with sustainable high-yield agriculture.

TaMIM396↗

PubMed2026

Transcriptional and phytohormonal regulation of positional ear development reveals yield strategies in maize.

Maize (Zea mays L.) is a vital global crop, contributing ∼37% of annual grain production. Enhancing yield per unit area is crucial for food security, yet research has primarily focused on single-ear traits, overlooking the regulation of double ears-a key determinant of prolificacy. While secondary ears drive yield variability under prolificacy-favoring conditions, the mechanisms governing ear formation across shoot positions remain poorly understood. Here, we performed high-resolution transcriptomic analysis of 66 samples from three ear types (primary, secondary and third) in maize inbred B73. We uncovered distinct hormonal developmental dynamics: strigolactone (SL) signaling genes, particularly SBP transcription factors, dominated in primary (I) ears, whereas ethylene-related genes (e.g., ZmEREB131, ZmACCO35) were enriched in third (III) ears. Functional validation confirmed that knockout of ZmEREB131 and ZmACCO35 accelerated development and elongated ears compared to wild-type, implicating ethylene (ETH) signaling in ear maturation arrest. Notably, SL inhibitor application synchronized primary and secondary ear development, boosting total yield by >20% without compromising primary ear performance. Our study elucidates the transcriptional networks underlying differential ear development and provides actionable strategies for yield improvement through targeted hormonal modulation. These findings advance the understanding of maize inflorescence biology and offer molecular tools for breeding high-yielding varieties.

RNA-seq↗

PubMed2026

Prenatal SNP-array chromosomal microarray analysis in 3,549 pregnancies: indication-specific yields and clinical implications.

BACKGROUND: SNP-based chromosomal microarray analysis (CMA) is widely used in invasive prenatal diagnosis, yet real-world performance across contemporary referral pathways, especially in the NIPT era, remains incompletely characterized. METHODS: We retrospectively analyzed 3,549 prenatal invasive samples tested by SNP array, and evaluated diagnostic yield overall and by referral indication and ultrasound phenotype. RESULTS: In total, we identified 398 pathogenic or likely pathogenic (P/LP) variants across 386 fetuses, resulting in an overall diagnostic yield of 10.9% (386/3,549). These findings comprised 223 aneuploidies and 175 pathogenic CNVs. In contrast, variants of uncertain significance (VOUS) were detected in 12.0% (426/3,549) of cases. Diagnostic yields were heavily stratified by indication: yields peaked in NIPT high-risk referrals (38.9%) and were intermediate in ultrasound-based cases (~ 11%), but dropped significantly in the advanced maternal age (AMA; 4.2%) and serum screening (~ 5-6%) groups. Conversely, VOUS rates remained remarkably stable across all referral categories. Sub-analysis of ultrasound abnormalities revealed that multisystem anomalies conferred the highest risk (27.3%), driven predominantly by aneuploidies; among soft markers, increased nuchal translucency (NT) emerged as the strongest predictor of chromosomal pathology. CONCLUSIONS: In our cohort, SNP-array identified clinically actionable findings in 10.9% of cases. NIPT enriched diagnostic yields, particularly for aneuploidies, and NT thickness was strongly associated with pathogenic findings. These results support an indication-based approach to genomic testing, with NIPT as a triage tool for aneuploidy and CMA for high-risk populations, while improving VOUS counseling.

Humans↗

PubMed2026

Integrative haplotype and SNP-based GWAS supports the identification of stable genomic loci controlling yield-related traits in soybean.

Soybean yield is vulnerable to environmental variation, therefore, it is important to detect and implement stable genomic regions associated with yield-related traits in soybean breeding programs. In this study, SNP and haplotype-based GWAS were conducted to reveal important candidate genomic regions and putative candidate genes associated with soybean yield-related traits. This study demonstrates that the integration of haplotype and SNP-based GWAS could improve the detection of genomic regions associated with complex traits, enhance statistical power, and facilitate the identification of biologically relevant candidate genes. Ten stable haplotype blocks and six stable SNPs were detected based on the integration of haplotype and SNP-based GWAS, respectively. Furthermore, multiple candidate genes associated with the yield-related traits were identified. For instance, six genes were identified as transporters, including Glyma.15G092800, encoding serine-type endopeptidase activity, Glyma.15G203300 encoding a major facilitator superfamily (MFS) sugar transporter, Glyma.04G163000, transmembrane transporter, and Glyma.04G164100, leucine-rich repeat receptor-like protein kinase (LRR-RLK), as the most promising candidate genes. Additionally, three genes involved in signaling and pathways of various phytohormones can be promising candidates for increasing seed yield through improving plant architecture in soybean plants. The identified superior haplotypes with favourable alleles will be useful for marker-assisted selection in future breeding programs in soybean.

DArT markers↗

PubMed2025

Natural variation in the cytokinin oxidase gene ZmCKX6 influences leaf morphology and yield-related traits in maize.

Leaf width (LW) is a critical determinant of maize architecture and yield. To uncover its genetic basis, we performed a genome-wide association study (GWAS) on 348 maize inbred lines and identified ZmCKX6, encoding cytokinin oxidase/dehydrogenase, as a key gene associated with LW. Natural variation in the ZmCKX6 promoter significantly influenced its expression levels, leading to differences in LW across various haplotypes. Functional validation using CRISPR/Cas9 revealed that ZmCKX6 knockout results in pleiotropic effects, including narrower leaves, reduced plant height, and decreased grain yield components. These phenotypes were accompanied by elevated levels of active cytokinins but reduced levels of auxin, gibberellins, and salicylic acid. Transcriptome analysis revealed a significant downregulation of photosynthesis-related genes, corresponding to reduced photosynthetic rates in knockout lines. Evolutionary analysis demonstrated that the allele associated with narrower leaves were preferentially selected during maize domestication and breeding. This study highlights the role of ZmCKX6 in modulating cytokinin homeostasis and its subsequent impact on multiple agronomic traits in maize, providing insights into the complex genetic control of plant architecture and yield. The identified natural variations could be valuable for marker-assisted selection aimed at optimizing plant architecture and improving yield.

Zea mays↗

PubMed2025

Diagnostic Yield After Postnatal Reanalysis of Prenatal Exome Sequencing Results.

OBJECTIVE: Analysis of exome sequencing (ES) relies on correlation with phenotypic features, but fetal phenotyping is often incomplete. The additional yield of postnatal follow-up in cases with negative or inconclusive prenatal ES has not been demonstrated. Our objective was to assess the incremental diagnostic yield of ES reanalysis after initially negative prenatal ES for congenital anomalies incorporating features identified postnatally. METHODS: This was a secondary analysis of two prospective cohort studies of ES for fetal anomalies. We included cases in which initial ES utilizing the prenatal phenotype was not diagnostic. The primary outcome was incremental diagnostic yield of ES when incorporating postnatal findings. RESULTS: Eighty-seven cases with negative or inconclusive prenatal ES and postnatal follow-up available were included. Of those, 56 (64%) had new findings postnatally. There was an incremental yield of 2% in the entire cohort, and 7% in those with new postnatal findings. In two additional cases, postnatal evaluation suggested a specific genetic diagnosis that was not detectable with ES. CONCLUSION: Among pregnancies with fetal anomalies and no clear diagnosis identified by prenatal ES, postnatal follow-up is recommended. Reanalysis of ES results can result in a genetic diagnosis in 7% of cases with new findings.

Humans↗

PubMed2026

Sparse phenotyping for wheat grain yield enabled by multiomics prediction.

Grain yield is a central target in wheat breeding, yet accurately predicting it remains challenging because it depends on many genes and responds strongly to environmental variation. Genomic selection (GS) has improved breeding efficiency by enabling genome-based prediction of genetic merit, but predictability (PA) for grain yield is often limited under stress environments. At the same time, advances in high-throughput phenotyping (HTP) using unmanned aerial vehicles (UAVs) provide phenomic data that capture environment-responsive plant performance and may complement genomic information. In this study, we evaluated genomic and phenomic models for predicting grain yield in elite bread wheat lines across irrigated, drought, and heat-stress environments. Using a sparse phenotyping framework, we compared parametric and non-parametric models. PA was evaluated within environments and under cross-environment sparse phenotyping scenarios. Genomic models provided a stable baseline and enabled effective information sharing across environments when phenotypic data were incomplete. Phenomics-only models captured environment-specific plant responses but were more sensitive to environmental context. Multiomics models that integrated genomic and phenomic information consistently achieved the highest PA, with the largest gains observed under stress conditions. Overall, our results demonstrate that integrating genomics and UAV-based phenomics within sparse phenotyping designs offers a practical and scalable approach to improve grain yield prediction in wheat.

Triticum↗