Aug 2026· Genetics and Molecular Research· 0 citations· 22 references
Abstract
Crop productivity is increasingly threatened by climate change and multiple abiotic stresses that significantly reduce agricultural sustainability and food security worldwide. Molecular breeding and genomics-assisted crop improvement strategies have emerged as effective approaches for developing high-yielding and stress-resilient crop varieties. The present study evaluated quantitative agronomic traits and their significance in molecular breeding applications using a rice genotype–phenotype dataset containing quantitative trait information and SNP-based genomic data. Quantitative trait analysis, correlation analysis, Principal Component Analysis, and machine learning-based predictive modeling were performed to assess phenotypic variability and yield-associated trait relationships. The results demonstrated substantial phenotypic diversity among rice accessions, particularly for grain morphology, plant architecture, and reproductive traits. Correlation analysis revealed significant positive associations among several agronomic traits, while PCA identified plant architecture and grain morphology as major contributors to phenotypic variation. A Random Forest regression model was further developed to predict grain weight using agronomic traits, where grain width and grain length emerged as the most influential predictors of yield-associated performance. The findings highlight the importance of integrating quantitative trait analysis, predictive modeling, and molecular breeding approaches for improving crop yield and adaptive performance. The study also demonstrates the potential application of computational and genomics-assisted breeding frameworks in developing climate-resilient rice cultivars. Overall, the integration of machine learning and molecular breeding strategies may contribute substantially to sustainable crop improvement and future agricultural productivity.
Rice (
Oryza sativa
L.) is a critical staple crop, necessitating continuous genetic improvement to enhance yield and quality traits to meet global food demands. This study aims to identify high‐yielding genotypes with desirable quality traits and elucidate the relationships among traits to inform breeding strategies. Twenty‐seven rice genotypes were evaluated for genetic variability, heritability, genetic advance, and trait associations. Nine agronomic traits were measured: grain yield, days to maturity, plant height, productive tiller number, 1000‐grain weight, grain length, grain width, grain shape, and awn length during the 2021–2023 cropping seasons. Analysis of variance revealed highly significant differences (
p
< 0.01) among genotypes for all traits, demonstrating pronounced phenotypic variations that suggest a diverse genetic background among the studied genotypes. Grain yield ranged from 4.38 t ha
−
1
(Gerdeh) to 7.73 t ha
−
1
(Shiroudi), with a mean of 6.052 t ha
−
1
. Broad‐sense heritability (
H
2
) was high (0.926–1.0) for all traits. Traits with both high heritability and high genetic advance as percentage of mean (GA% > 20%)—namely, grain shape, productive tiller number, grain yield, grain width, and grain length—are likely to respond effectively to direct phenotypic selection. Correlation analysis showed significant positive associations between grain yield and days to maturity (
r
= 0.532,
p
< 0.01), 1000‐grain weight (
r
= 0.266,
p
< 0.05), grain length (
r
= 0.269,
p
< 0.05), and grain shape (
r
= 0.269,
p
< 0.05), but a negative correlation with awn length (
r
= −0.406,
p
< 0.01). Path analysis identified days to maturity (direct effect = 0.341,
p
< 0.001) and grain shape (0.241) as key yield contributors. Hierarchical clustering grouped genotypes into four clusters, with Cluster 4 (including Shiroudi, Keshvari, and Neda) containing high‐yielding genotypes. These results provide clear guidance for designing efficient breeding programs through targeted selection of superior genotypes and trait‐based strategies. Shiroudi and Cluster 4 genotypes are promising candidates for breeding high‐yielding, quality rice varieties.
Background. Winter bread wheat (Triticum aestivum L.) is one of the world’s most important cereal crops, and its productivity is critical for global food security. Under ongoing climate change, the impact of biotic and abiotic stresses, including drought, high temperature, salinity, and frost, is increasing, leading to significant yield losses and reduced grain quality. This creates a strong need to integrate conventional breeding with modern molecular genetic and bioinformatics approaches to develop stress-resilient cultivars.
This review summarizes current breeding and molecular genetic strategies aimed at improving the resistance of winter bread wheat (T. aestivum L.) to biotic and abiotic stresses under changing climatic conditions. We analyze the applications of genetic markers, quantitative trait loci (QTL) mapping, genome-wide association studies (GWAS), genomic selection, and genome-editing technologies. Particular attention is given to integrating high-throughput phenotyping, molecular research, and digital tools into modern breeding pipelines, as well as to emerging sources of resistance and the development of an adaptive wheat gene pool.
Overall, this review highlights the importance of combining conventional breeding approaches with advanced molecular and genomic tools to enhance breeding efficiency. Such integration provides a coherent framework for developing next-generation wheat cultivars capable of maintaining stable yield performance under climate-related stress conditions and thereby contributing to global food security. The review also outlines key future research directions for improving breeding efficiency under increasing environmental variability.
Yegor Kucherenko, A. Yarosh, Z. Usova et al.· Studia Biologica· 0 citations
Ethiopian durum wheat (
Triticum turgidum
ssp.
durum
) is a globally significant crop recognized for its rich genetic diversity, stress adaptation, and high grain quality. This review consolidates over 20 years of research on the phenotypic and genotypic characterization of Ethiopian durum wheat landraces, focusing on trait discovery, genome‐wide association studies (GWAS), quantitative trait loci (QTLs) mapping, and implications for breeding and conservation. Studies are categorized into phenotypic diversity, genetic variation, GWAS and QTL‐based trait identification, grain quality, and participatory breeding. Ethiopian landraces display extensive variation in morphological, agronomic, and molecular traits shaped by ecological gradients and farmer selection. While modern cultivars are selected for high yield potential under favorable conditions, specific landraces demonstrate superior yield stability under environmental stress, targeted resilience to abiotic and biotic factors (such as terminal drought, soil acidity, and disease pressure), and desirable nutritional and industrial grain qualities (including high protein content and novel glutenin alleles). Advances in genomics have pinpointed key loci associated with drought and heat tolerance, disease resistance, root architecture, and grain pigmentation. Furthermore, participatory breeding strategies that incorporate traditional farmer preferences alongside genomic tools highlight valuable multi‐trait combinations often missed by standard metric screening. Utilizing these landrace resources via targeted breeding and pre‐breeding strategies can effectively bridge the gap between stress resilience and high yield potential in changing climates.
Agegnehu Mekonnen Tessema, F. Abebe, Y. G. Kidane et al.· Agrosystems, Geosciences &am...· 0 citations
By combining genomic data with precision breeding techniques, researchers are developing crops that are better adapted to a growing population and a changing climate, positioning the integration of molecular breeding and bioinformatics as a central pillar of future global food security.
Muhammad Shahid Iqbal, Z. Sarfraz, Muhammad Mujahid et al.· Frontiers in Plant Science· 0 citations
Barley is an important cereal crop with extensive genetic variation, which is essential for sustainable crop improvement and food security. This investigation evaluated the genetic diversity among twenty barley genotypes through morphological traits using a genotype × trait biplot approach. A field trial was conducted in the Moghan district and fertile tillers (FT), tillers per plant (TP), seeds per plant (SP), single-plant yield (YSP), seed yield performance (SYP), thousand-seed weight (TSW), plant height (PH), straw yield (SY), spike length (SL), and biological yield (BY) were measured. The biplot explained 66% of the variation (43% and 23% by the first and second components, respectively), effectively capturing genotype-trait interaction. Vector analysis revealed positive associations among yield-related traits, with SP correlated YSP, SYP associated with TSW, and SY, PH, SL, and BY forming a correlated group. Tiller-related traits were independent of biomass-related traits, indicating that selection for higher tillers may improve specific yield components without affecting plant size or straw yield. The polygon-view biplot identified vertex genotypes exhibiting trait-specific superiority; G10 excelled in SP, YSP, SYP, and TSW; G12 was superior in FT and TP; and G13 performed best for SY, PH, SL, and BY. Genotypes such as G5, G7, and G16 were located in less favorable sectors, indicating suboptimal performance. The ideal trait view highlighted SYP as the most informativeness trait, followed by YSP, TSW, and SP, suggesting that these traits are most valuable for for multi-trait selection. The biplot approach visualized phenotypic diversity and trait relationships and identified promising barley genotypes for selection.
N. Sabaghnia, H. Ghorbanian, A. Ebadi-Segherloo et al.· Current Applied Science and...· 0 citations
A comprehensive analysis of the methodological, theoretical, and practical frameworks underlying molecular-marker applications in modern plant breeding is provided, offering a thorough perspective on how molecular biotechnology can be optimized to ensure future global food security.
Eva Jansen· International Journal of Com...· 0 citations
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