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Root system architecture (RSA), also known as root morphology, is critical in plant acquisition of soil resources, plant growth, and yield formation. Many QTLs associated with RSA or root traits in maize have been identified using several bi-parental populations, particularly in response to various environmental factors. In the present study, a meta-analysis of QTLs associated with root traits was performed in maize using 917 QTLs retrieved from 43 mapping studies published from 1998 to 2020. A total of 631 QTLs were projected onto a consensus map involving 19,714 markers, which led to the prediction of 68 meta-QTLs (MQTLs). Among these 68 MQTLs, 36 MQTLs were validated with the marker-trait associations available from previous genome-wide association studies for root traits. The use of comparative genomics approaches revealed several gene models conserved among the maize, sorghum, and rice genomes. Among the conserved genomic regions, the ortho-MQTL analysis uncovered 20 maize MQTLs syntenic to 27 rice MQTLs for root traits. Functional analysis of some high-confidence MQTL regions revealed 442 gene models, which were then subjected to in silico expression analysis, yielding 235 gene models with significant expression in various tissues. Furthermore, 16 known genes viz., DXS2, PHT, RTP1, TUA4, YUC3, YUC6, RTCS1, NSA1, EIN2, NHX1, CPPS4, BIGE1, RCP1, SKUS13, YUC5, and AW330564 associated with various root traits were present within or near the MQTL regions. These results could aid in QTL cloning and pyramiding in developing new maize varieties with specific root architecture for proper plant growth and development under optimum and abiotic stress conditions. [ABSTRACT FROM AUTHOR]

A meta-QTL analysis was conducted in Indian mustard to identify robust and stable meta-QTLs (MQTLs) by utilizing 1504 available QTLs, which included 891 QTLs for yield-related traits and 613 QTLs for quality traits. For yield-related traits, a total of 57 MQTLs (YRTs_MQTLs) were uncovered from the clustering of 560 projected QTLs, which had a 4.18-fold smaller confidence interval (CI) than that of the initial QTLs, whereas, for quality traits, as many as 51 MQTLs (Quality_MQTLs) were derived from 324 projected QTLs, which had a 2.65-fold smaller CI than that of the initial QTLs. Sixteen YRTs_MQTLs were observed to share chromosomal positions with 16 Quality_MQTLs. Moreover, four most promising YRTs_MQTLs and eight Quality-MQTLs were also selected and recommended for use in breeding programs. Four of these selected MQTLs were also validated with significant SNPs that were identified in previously published genome-wide association studies. Further, in silico functional analysis of some promising MQTLs allowed the detection of as many as 1435 genes, which also involved 15 high-confidence candidate genes (CGs) for yield-related traits and 46 high-confidence CGs for quality traits. After validation, the identified CGs can also be exploited to model the plant architecture and to improve quality traits through marker-assisted breeding, genetic engineering, and genome editing approaches. [ABSTRACT FROM AUTHOR]

Wheat is an essential constituent of cereal-based diets, and one of the most significant sources of calories. However, modern wheat varieties are low in proteins and minerals. Biofortification is a method for increasing the availability of essential elements in the edible portions of crops through agronomic or genetic and genomic interventions. Wheat biofortification, as a research topic, has become increasingly prevalent. Recent accomplishments in genomic biofortification could potentially be helpful for the development of biofortified wheat grains, as a sustainable solution to the issue of "hidden hunger". Genomic interventions mainly include quantitative trait loci (QTL) mapping, marker-assisted selection (MAS), and genomic selection (GS). Developments in the identification of QTL and in the understanding of the physiological and molecular bases of the QTLs controlling the biofortification traits in wheat have revealed new horizons for the improvement of modern wheat varieties. Markers linked with the QTLs of desirable traits can be identified through QTL mapping, which can be employed for MAS. Besides MAS, a powerful tool, GS, also has great potential for crop improvement. We have compiled information from QTL mapping studies on wheat, carried out for the identification of the QTLs associated with biofortification traits, and have discussed the present status of MAS and different prospects of GS for wheat biofortification. Accelerated mapping studies, as well as MAS and GS schemes, are expected to improve wheat breeding efficiency further. [ABSTRACT FROM AUTHOR]

Vegetables require an optimum supply of mineral elements like silicon (Si). Si is second to oxygen in its abundance in the earth crust, and its role is quite significant in tackling biotic and abiotic stresses of vegetables. Si application also improves several agronomic and quality traits of vegetables. Hence, Si application is recommended as a strategy for the improvement of vegetable crops production. Although the research about the role of Si in vegetable dicots still lags far behind than cereals. Recently, omics-based approaches were used to provide a deeper understanding of the role of Si in vegetable protection. Here, we have compiled the studies focusing on the role of Si for vegetables, thus, enabling all of the important information regarding the effect Si application to vegetables at one place. [ABSTRACT FROM AUTHOR]