In wheat, a meta-analysis was performed using previously identified QTLs associated with drought stress (DS), heat stress (HS), salinity stress (SS), water-logging stress (WS), pre-harvest sprouting (PHS), and aluminium stress (AS) which predicted a total of 134 meta-QTLs (MQTLs) that involved at least 28 consistent and stable MQTLs conferring tolerance to five or all six abiotic stresses under study. Seventy-six MQTLs out of the 132 physically anchored MQTLs were also verified with genome-wide association studies. Around 43% of MQTLs had genetic and physical confidence intervals of less than 1 cM and 5 Mb, respectively. Consequently, 539 genes were identified in some selected MQTLs providing tolerance to 5 or all 6 abiotic stresses. Comparative analysis of genes underlying MQTLs with four RNA-seq based transcriptomic datasets unravelled a total of 189 differentially expressed genes which also included at least 11 most promising candidate genes common among different datasets. The promoter analysis showed that the promoters of these genes include many stress responsiveness cis-regulatory elements, such as ARE, MBS, TC-rich repeats, As-1 element, STRE, LTR, WRE3, and WUN-motif among others. Further, some MQTLs also overlapped with as many as 34 known abiotic stress tolerance genes. In addition, numerous ortho-MQTLs among the wheat, maize, and rice genomes were discovered. These findings could help with fine mapping and gene cloning, as well as marker-assisted breeding for multiple abiotic stress tolerances in wheat.

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.

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.

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.