Defining genomic landscape for identification of potential candidate resistance genes associated with major rice diseases through MetaQTL analysis.

Rice production is severely affected by various diseases such as bacterial leaf blight (BLB), brown spot (BS), false smut (FS), foot rot (FR), rice blast (RB), and sheath blight (SB). In recent years, several quantitative trait loci (QTLs) studies involving different populations have been carried out, resulting in the identification of hundreds of resistance QTLs for each disease. These QTLs can be integrated and analyzed using meta-QTL (MQTL) analysis for better understanding of the genetic architecture underlying multiple disease resistance (MDR). This study involved an MQTL analysis on 661 QTLs (378, 161, 21, 41, 44, and 16 QTLs for SB, RB, BLB, BS, FS, and FR, respectively) retrieved from 50 individual studies published from 1995 to 2021. Of these, 503 QTLs were projected finally onto the consensus map saturated with 6,275 markers, resulting in 73 MQTLs, including 27 MDR-MQTLs conferring resistance to three or more diseases. Forty-seven MQTLs were validated using marker-trait associations identified in published genome-wide association studies. A total of 3,310 genes, including both R and defense genes, were also identified within some selected high-confidence MQTL regions that were investigated further for the syntenic relationship with barley, wheat, and maize genomes. Thirty-nine high-confidence candidate genes were selected based on their expression patterns and recommended for future studies involving functional validation, genetic engineering, and gene editing. Nineteen MQTLs were co-localized with 39 known R genes for BLB and RB diseases. These results could pave the way to utilize candidate genes in a marker-assisted breeding program for MDR in rice.

Silage maize as a potent candidate for sustainable animal husbandry development-perspectives and strategies for genetic enhancement.

Maize is recognized as the queen of cereals, with an ability to adapt to diverse agroecologies (from 58oN to 55oS latitude) and the highest genetic yield potential among cereals. Under contemporary conditions of global climate change, C4 maize crops offer resilience and sustainability to ensure food, nutritional security, and farmer livelihood. In the northwestern plains of India, maize is an important alternative to paddy for crop diversification in the wake of depleting water resources, reduced farm diversity, nutrient mining, and environmental pollution due to paddy straw burning. Owing to its quick growth, high biomass, good palatability, and absence of anti-nutritional components, maize is also one of the most nutritious non-legume green fodders. It is a high-energy, low-protein forage commonly used for dairy animals like cows and buffalos, often in combination with a complementary high-protein forage such as alfalfa. Maize is also preferred for silage over other fodders due to its softness, high starch content, and sufficient soluble sugars required for proper ensiling. With a rapid population increase in developing countries like China and India, there is an upsurge in meat consumption and, hence, the requirement for animal feed, which entails high usage of maize. The global maize silage market is projected to grow at a compound annual growth rate of 7.84% from 2021 to 2030. Factors such as increasing demand for sustainable and environment-friendly food sources coupled with rising health awareness are fueling this growth. With the dairy sector growing at about 4%-5% and the increasing shortage faced for fodder, demand for silage maize is expected to increase worldwide. The progress in improved mechanization for the provision of silage maize, reduced labor demand, lack of moisture-related marketing issues as associated with grain maize, early vacancy of farms for next crops, and easy and economical form of feed to sustain household dairy sector make maize silage a profitable venture. However, sustaining the profitability of this enterprise requires the development of hybrids specific for silage production. Little attention has yet been paid to breeding for a plant ideotype for silage with specific consideration of traits such as dry matter yield, nutrient yield, energy in organic matter, genetic architecture of cell wall components determining their digestibility, stalk standability, maturity span, and losses during ensiling. This review explores the available information on the underlying genetic mechanisms and gene/gene families impacting silage yield and quality. The trade-offs between yield and nutritive value in relation to crop duration are also discussed. Based on available genetic information on inheritance and molecular aspects, breeding strategies are proposed to develop maize ideotypes for silage for the development of sustainable animal husbandry.

Genome-Wide Meta-Analysis of QTLs Associated with Root Traits and Implications for Maize Breeding.

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.