Integrated meta-analysis and transcriptomics pinpoint genomic loci and novel candidate genes associated with submergence tolerance in rice.

BACKGROUND: Due to rising costs, water shortages, and labour shortages, farmers across the globe now prefer a direct seeding approach. However, submergence stress remains a major bottleneck limiting the success of this approach in rice cultivation. The merger of accumulated rice genetic resources provides an opportunity to detect key genomic loci and candidate genes that influence the flooding tolerance of rice. RESULTS: In the present study, a whole-genome meta-analysis was conducted on 120 quantitative trait loci (QTL) obtained from 16 independent QTL studies reported from 2004 to 2023. These QTL were confined to 18 meta-QTL (MQTL), and ten MQTL were successfully validated by independent genome-wide association studies from diverse natural populations. The mean confidence interval (CI) of the identified MQTL was 3.44 times narrower than the mean CI of the initial QTL. Moreover, four core MQTL loci with genetic distance less than 2 cM were obtained. By combining differentially expressed genes (DEG) from two transcriptome datasets with 858 candidate genes identified in the core MQTL regions, we found 38 common differentially expressed candidate genes (DECGs). In silico expression analysis of these DECGs led to the identification of 21 genes with high expression in embryo and coleoptile under submerged conditions. These DECGs encode proteins with known functions involved in submergence tolerance including WRKY, F-box, zinc fingers, glycosyltransferase, protein kinase, cytochrome P450, PP2C, hypoxia-responsive family, and DUF domain. By haplotype analysis, the 21 DECGs demonstrated distinct genetic differentiation and substantial genetic distance mainly between indica and japonica subspecies. Further, the MQTL7.1 was successfully validated using flanked marker S2329 on a set of genotypes with phenotypic variation. CONCLUSION: This study provides a new perspective on understanding the genetic basis of submergence tolerance in rice. The identified MQTL and novel candidate genes lay the foundation for marker-assisted breeding/engineering of flooding-tolerant cultivars conducive to direct seeding.

Fashion meets science: how advanced breeding approaches could revolutionize the textile industry.

Natural fibers have garnered considerable attention owing to their desirable textile properties and advantageous effects on human health. Nevertheless, natural fibers lag behind synthetic fibers in terms of both quality and yield, as these attributes are largely genetically determined. In this article, a comprehensive overview of the natural and synthetic fiber production landscape over the last 10 years is presented, with a particular focus on the role of scientific breeding techniques in improving fiber quality traits in key crops like cotton, hemp, ramie, and flax. Additionally, the article delves into cutting-edge genomics-assisted breeding techniques, including QTL mapping, genome-wide association studies, transgenesis, and genome editing, and their potential role in enhancing fiber quality traits in these crops. A user-friendly compendium of 11226 available QTLs and significant marker-trait associations derived from 136 studies, associated with diverse fiber quality traits in these crops is furnished. Furthermore, the potential applications of transcriptomics in these pivotal crops, elucidating the distinct genes implicated in augmenting fiber quality attributes are investigated. Additionally, information on 11257 candidate/characterized or cloned genes sourced from various studies, emphasizing their key role in the development of high-quality fiber crops is collated. Additionally, the review sheds light on the current progress of marker-assisted selection for fiber quality traits in each crop, providing detailed insights into improved cultivars released for different fiber crops. In conclusion, it is asserted that the application of modern breeding tools holds tremendous potential in catalyzing a transformative shift in the textile industry.

Natural fibers possess desirable properties, but they often lag behind synthetic fibers in terms of both quality and quantity. Genomic-assisted breeding has the potential to improve fiber quality traits in cotton, hemp, ramie, and flax. Utilizing available QTLs, marker-trait associations, and candidate genes can contribute to the development of superior fiber crops, underscoring the significance of advanced breeding tools.

Multi-GWAS reveals significant genomic regions for Mungbean yellow mosaic India virus resistance in urdbean (Vigna mungo (L.) across multiple environments.

KEY MESSAGE: Unraveling genetic markers for MYMIV resistance in urdbean, with 8 high-confidence marker-trait associations identified across diverse environments, provides crucial insights for combating MYMIV disease, informing future breeding strategies. Globally, yellow mosaic disease (YMD) causes significant yield losses, reaching up to 100% in favorable environments within major urdbean cultivating regions. The introgression of genomic regions conferring resistance into urdbean cultivars is crucial for combating YMD, including resistance against mungbean yellow mosaic India virus (MYMIV). To uncover the genetic basis of MYMIV resistance, we conducted a genome-wide association study (GWAS) using three multi-locus models in 100 diverse urdbean genotypes cultivated across six individual and two combined environments. Leveraging 4538 high-quality single nucleotide polymorphism (SNP) markers, we identified 28 unique significant marker-trait associations (MTAs) for MYMIV resistance, with 8 MTAs considered of high confidence due to detection across multiple GWAS models and/or environments. Notably, 4 out of 28 MTAs were found in proximity to previously reported genomic regions associated with MYMIV resistance in urdbean and mungbean, strengthening our findings and indicating consistent genomic regions for MYMIV resistance. Among the eight highly significant MTAs, one localized on chromosome 6 adjacent to previously identified quantitative trait loci for MYMIV resistance, while the remaining seven were novel. These MTAs contain several genes implicated in disease resistance, including four common ones consistently found across all eight MTAs: receptor-like serine-threonine kinases, E3 ubiquitin-protein ligase, pentatricopeptide repeat, and ankyrin repeats. Previous studies have linked these genes to defense against viral infections across different crops, suggesting their potential for further basic research involving cloning and utilization in breeding programs. This study represents the first GWAS investigation aimed at identifying resistance against MYMIV in urdbean germplasm.

Comprehensive meta-QTL analysis for dissecting the genetic architecture of stripe rust resistance in bread wheat.

BACKGROUND: Yellow or stripe rust, caused by the fungus Puccinia striiformis f. sp. tritici (Pst) is an important disease of wheat that threatens wheat production. Since developing resistant cultivars offers a viable solution for disease management, it is essential to understand the genetic basis of stripe rust resistance. In recent years, meta-QTL analysis of identified QTLs has gained popularity as a way to dissect the genetic architecture underpinning quantitative traits, including disease resistance. RESULTS: Systematic meta-QTL analysis involving 505 QTLs from 101 linkage-based interval mapping studies was conducted for stripe rust resistance in wheat. For this purpose, publicly available high-quality genetic maps were used to create a consensus linkage map involving 138,574 markers. This map was used to project the QTLs and conduct meta-QTL analysis. A total of 67 important meta-QTLs (MQTLs) were identified which were refined to 29 high-confidence MQTLs. The confidence interval (CI) of MQTLs ranged from 0 to 11.68 cM with a mean of 1.97 cM. The mean physical CI of MQTLs was 24.01 Mb, ranging from 0.0749 to 216.23 Mb per MQTL. As many as 44 MQTLs colocalized with marker-trait associations or SNP peaks associated with stripe rust resistance in wheat. Some MQTLs also included the following major genes- Yr5, Yr7, Yr16, Yr26, Yr30, Yr43, Yr44, Yr64, YrCH52, and YrH52. Candidate gene mining in high-confidence MQTLs identified 1,562 gene models. Examining these gene models for differential expressions yielded 123 differentially expressed genes, including the 59 most promising CGs. We also studied how these genes were expressed in wheat tissues at different phases of development. CONCLUSION: The most promising MQTLs identified in this study may facilitate marker-assisted breeding for stripe rust resistance in wheat. Information on markers flanking the MQTLs can be utilized in genomic selection models to increase the prediction accuracy for stripe rust resistance. The candidate genes identified can also be utilized for enhancing the wheat resistance against stripe rust after in vivo confirmation/validation using one or more of the following methods: gene cloning, reverse genetic methods, and omics approaches.

Multi-omics assisted breeding for biotic stress resistance in soybean.

Biotic stress is a critical factor limiting soybean growth and development. Soybean responses to biotic stresses such as insects, nematodes, fungal, bacterial, and viral pathogens are governed by complex regulatory and defense mechanisms. Next-generation sequencing has availed research techniques and strategies in genomics and post-genomics. This review summarizes the available information on marker resources, quantitative trait loci, and marker-trait associations involved in regulating biotic stress responses in soybean. We discuss the differential expression of related genes and proteins reported in different transcriptomics and proteomics studies and the role of signaling pathways and metabolites reported in metabolomic studies. Recent advances in omics technologies offer opportunities to reshape and improve biotic stress resistance in soybean by altering gene regulation and/or other regulatory networks. We suggest using 'integrated omics' to precisely understand how soybean responds to different biotic stresses. We also discuss the potential challenges of integrating multi-omics for the functional analysis of genes and their regulatory networks and the development of biotic stress-resistant cultivars. This review will help direct soybean breeding programs to develop resistance against different biotic stresses.

Genome-wide association study unravels genomic regions associated with chlorophyll fluorescence parameters in wheat (Triticum aestivum L.) under different sowing conditions.

KEY MESSAGE: Genome-wide association study identified 205 significant marker-trait associations for chlorophyll fluorescence parameters in wheat. Candidate gene mining, in silico expression, and promoter analyses revealed the potential candidate genes associated with the studied parameters. The present study investigated the effect of varied sowing conditions (viz., early, timely, and late) on different chlorophyll fluorescence parameters in diverse wheat germplasm set comprising of 198 lines over two cropping seasons (2020-2021 and 2021-2022). Further, a genome-wide association study was conducted to identify potential genomic regions associated with these parameters. The results revealed significant impacts of sowing conditions on all fluorescence parameters, with the maximum and minimum effects on FI (26.64%) and FV/FM (2.12%), respectively. Among the 205 marker-trait associations (MTAs) identified, 11 high-confidence MTAs were chosen, exhibiting substantial effects on multiple fluorescence parameters, and each explaining more than 10% of the phenotypic variation. Through gene mining of genomic regions encompassing high-confidence MTAs, we identified a total of 626 unique gene models. In silico expression analysis revealed 42 genes with an expression value exceeding 2 TPM. Among them, 10 genes were identified as potential candidate genes with functional relevance to enhanced photosynthetic efficiency. These genes mainly encoded for the following important proteins/products-ankyrin repeat protein, 2Fe-2S ferredoxin-type iron-sulfur-binding domain, NADH-ubiquinone reductase complex-1 MLRQ subunit, oxidoreductase FAD/NAD(P)-binding, photosystem-I PsaF, and protein kinases. Promoter analysis revealed the presence of light-responsive (viz., GT1-motif, TCCC-motif, I-box, GT1-motif, TCT-motif, and SP-1) and stress-responsive (viz., ABRE, AuxRR-core, GARE-motif, and ARE) cis-regulatory elements, which may be involved in the regulation of identified putative candidate genes. Findings from this study could directly help wheat breeders in selecting lines with favorable alleles for chlorophyll fluorescence, while the identified markers will facilitate marker-assisted selection of potential genomic regions for improved photosynthesis.

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.

Meta-QTLs, ortho-meta QTLs and related candidate genes for yield and its component traits under water stress in wheat (Triticum aestivum L.).

Meta-QTLs (MQTLs), ortho-MQTLs, and related candidate genes (CGs) for yield and its seven component traits evaluated under water deficit conditions were identified in wheat. For this purpose, a high density consensus map and 318 known QTLs were used for identification of 56 MQTLs. Confidence intervals (CIs) of the MQTLs were narrower (0.7-21 cM; mean = 5.95 cM) than the CIs of the known QTLs (0.4-66.6 cM; mean = 12.72 cM). Forty-seven MQTLs were co-located with marker trait associations reported in previous genome-wide association studies. Nine selected MQTLs were declared as 'breeders MQTLs' for use in marker-assisted breeding (MAB). Utilizing known MQTLs and synteny/collinearity among wheat, rice and maize, 12 ortho-MQTLs were also identified. A total of 1497 CGs underlying MQTLs were also identified, which were subjected to in-silico expression analysis, leading to identification of 64 differentially expressed CGs (DECGs) under normal and water deficit conditions. These DECGs encoded a variety of proteins, including the following: zinc finger, cytochrome P450, AP2/ERF domain-containing proteins, plant peroxidase, glycosyl transferase, glycoside hydrolase. The expression of 12 CGs at seedling stage (3 h stress) was validated using qRT-PCR in two wheat genotypes, namely Excalibur (drought tolerant) and PBW343 (drought sensitive). Nine of the 12 CGs were up-regulated and three down-regulated in Excalibur. The results of the present study should prove useful for MAB, for fine mapping of promising MQTLs and for cloning of genes across the three cereals studied. Supplementary Information: The online version contains supplementary material available at 10.1007/s12298-023-01301-z.

Unravelling consensus genomic regions associated with quality traits in wheat using meta-analysis of quantitative trait loci.

MAIN CONCLUSION: Meta-analysis in wheat for three major quality traits identified 110 meta-QTL (MQTL) with reduced confidence interval (CI). Five GWAS validated MQTL (viz., 1A.1, 1B.2, 3B.4, 5B.2, and 6B.2), each involving more than 20 initial QTL and reduced CI (95%) (< 2 cM), were selected for quality breeding programmes. Functional characterization including candidate gene mining and expression analysis discovered 44 high confidence candidate genes associated with quality traits. A meta-analysis of quantitative trait loci (QTL) associated with dough rheology properties, nutritional traits, and processing quality traits was conducted in wheat. For this purpose, as many as 2458 QTL were collected from 50 interval mapping studies published during 2013-2020. Of the total QTL, 1126 QTL were projected onto the consensus map saturated with 249,603 markers which led to the identification of 110 meta-QTL (MQTL). These MQTL exhibited an 18.84-fold reduction in the average CI compared to the average CI of the initial QTL (ranging from 14.87 to 95.55 cM with an average of 40.35 cM). Of the 110, 108 MQTL were physically anchored to the wheat reference genome, including 51 MQTL verified with marker-trait associations (MTAs) reported from earlier genome-wide association studies. Candidate gene (CG) mining allowed the identification of 2533 unique gene models from the MQTL regions. In-silico expression analysis discovered 439 differentially expressed gene models with > 2 transcripts per million expressions in grains and related tissues, which also included 44 high-confidence CGs involved in the various cellular and biochemical processes related to quality traits. Nine functionally characterized wheat genes associated with grain protein content, high-molecular-weight glutenin, and starch synthase enzymes were also found to be co-localized with some of the MQTL. Synteny analysis between wheat and rice MQTL regions identified 23 wheat MQTL syntenic to 16 rice MQTL associated with quality traits. Furthermore, 64 wheat orthologues of 30 known rice genes were detected in 44 MQTL regions. Markers flanking the MQTL identified in the present study can be used for marker-assisted breeding and as fixed effects in the genomic selection models for improving the prediction accuracy during quality breeding. Wheat orthologues of rice genes and other CGs available from MQTLs can be promising targets for further functional validation and to better understand the molecular mechanism underlying the quality traits in wheat.

Meta-QTLs, ortho-meta-QTLs and candidate genes for grain yield and associated traits in wheat (Triticum aestivum L.).

KEY MESSAGE: In wheat, 2852 major QTLs of 8998 QTLs available for yield and related traits were used for meta-analysis; 141 meta-QTLs were identified, which included 13 breeder's MQTLs and 24 ortho-MQTLs; 1202 candidate genes and 50 homologues of genes for yield from other cereals were also identified. Meta-QTL analysis was conducted using 2852 of the 8998 known QTLs, retrieved from 230 reports published during 1999-2020 (including 19 studies on tetraploid wheat) for grain yield (GY) and the following ten component traits: (i) grain weight (GWei), (ii) grain morphology-related traits (GMRTs), (iii) grain number (GN), (iv) spikes-related traits (SRTs), (v) plant height (PH), (vi) tiller number (TN), (vii) harvest index (HI), (viii) biomass yield (BY), (ix) days to heading/flowering and maturity (DTH/F/M), and (x) grain filling duration (GFD). The study resulted in the identification of 141 meta-QTLs (MQTLs), with an average confidence interval (CI) of 1.4 cM as against a CI of > 12.1 cM (8.8 fold reduction) in the QTLs that were used. The corresponding physical length of CI ranged from 0.01 Mb to 661.9 Mb (mean, 31.5 Mb). Seventy-seven (77) of these 141 MQTLs overlapped marker-trait associations (MTAs) reported in genome-wide association studies. Also, 63 MQTLs (each based on at least 10 QTLs) were considered stable and robust, with 13 MQTLs described as breeder's MQTLs (selected based on small CI, large LOD, and high level of phenotypic variation explained). Thirty-five yield-related genes from rice, barley, and maize were also utilized to identify 50 wheat homologues in MQTLs. Further, the use of synteny and collinearity allowed the identification of 24 ortho-MQTLs which were common among the wheat, barley, rice, and maize. The results of the present study should prove useful for wheat breeding and future basic research in cereals including wheat, barley, rice, and maize.

Meta-QTLs for multiple disease resistance involving three rusts in common wheat (Triticum aestivum L.).

KEY MESSAGE: In wheat, multiple disease resistance meta-QTLs (MDR-MQTLs) and underlying candidate genes for the three rusts were identified which may prove useful for development of resistant cultivars. Rust diseases in wheat are a major threat to global food security. Therefore, development of multiple disease-resistant cultivars (resistant to all three rusts) is a major goal in all wheat breeding programs worldwide. In the present study, meta-QTLs and candidate genes for multiple disease resistance (MDR) involving all three rusts were identified using 152 individual QTL mapping studies for resistance to leaf rust (LR), stem rust (SR), and yellow rust (YR). From these 152 studies, a total of 1,146 QTLs for resistance to three rusts were retrieved, which included 368 QTLs for LR, 291 QTLs for SR, and 487 QTLs for YR. Of these 1,146 QTLs, only 718 QTLs could be projected onto the consensus map saturated with 2, 34,619 markers. Meta-analysis of the projected QTLs resulted in the identification of 86 MQTLs, which included 71 MDR-MQTLs. Ten of these MDR-MQTLs were referred to as the 'Breeders' MQTLs'. Seventy-eight of the 86 MQTLs could also be anchored to the physical map of the wheat genome, and 54 MQTLs were validated by marker-trait associations identified during earlier genome-wide association studies. Twenty MQTLs (including 17 MDR-MQTLs) identified in the present study were co-localized with 44 known R genes. In silico expression analysis allowed identification of several differentially expressed candidate genes (DECGs) encoding proteins carrying different domains including the following: NBS-LRR, WRKY domains, F-box domains, sugar transporters, transferases, etc. The introgression of these MDR loci into high-yielding cultivars should prove useful for developing high yielding cultivars with resistance to all the three rusts.

Meta-analysis reveals consensus genomic regions associated with multiple disease resistance in wheat (Triticum aestivum L.).

In wheat, meta-QTLs (MQTLs) and candidate genes (CGs) were identified for multiple disease resistance (MDR). For this purpose, information was collected from 58 studies for mapping QTLs for resistance to one or more of the five diseases. As many as 493 QTLs were available from these studies, which were distributed in five diseases as follows: septoria tritici blotch (STB) 126 QTLs; septoria nodorum blotch (SNB), 103 QTLs; fusarium head blight (FHB), 184 QTLs; karnal bunt (KB), 66 QTLs; and loose smut (LS), 14 QTLs. Of these 493 QTLs, only 291 QTLs could be projected onto a consensus genetic map, giving 63 MQTLs. The CI of the MQTLs ranged from 0.04 to 15.31 cM with an average of 3.09 cM per MQTL. This is a ~ 4.39 fold reduction from the CI of QTLs, which ranged from 0 to 197.6 cM, with a mean of 13.57 cM. Of 63 MQTLs, 60 were anchored to the reference physical map of wheat (the physical interval of these MQTLs ranged from 0.30 to 726.01 Mb with an average of 74.09 Mb). Thirty-eight (38) of these MQTLs were verified using marker-trait associations (MTAs) derived from genome-wide association studies. As many as 874 CGs were also identified which were further investigated for differential expression using data from five transcriptome studies, resulting in 194 differentially expressed candidate genes (DECGs). Among the DECGs, 85 genes had functions previously reported to be associated with disease resistance. These results should prove useful for fine mapping and cloning of MDR genes and marker-assisted breeding. Supplementary Information: The online version contains supplementary material available at 10.1007/s11032-022-01282-z.

WheatQTLdb V2.0: a supplement to the database for wheat QTL.

We recently developed a database for hexaploid wheat QTL (WheatQTLdb; www.wheatqtldb.net), which included 11,552 QTL affecting various traits of economic importance. However, that database did not include valuable QTL from other wheat species and/or progenitors of hexaploid wheat. Therefore, an updated and improved version of wheat QTL database (WheatQTLdb V2.0) was developed, which now includes information on hexaploid wheat (Triticum aestivum) and the following seven other related species: T. durum, T. turgidum, T. dicoccoides, T. dicoccum, T. monococcum, T. boeoticum, and Aegilops tauschii. WheatQTLdb V2.0 includes a much-improved list of QTL, including 27,518 main effect QTL, 202 epistatic QTL, and 1321 metaQTL. This newly released WheatQTLdb V2.0 also has additional valuable options to search and choose the QTL, category-wise, and trait-wise data for their use in research or breeding programs.

Meta-QTLs, ortho-MQTLs, and candidate genes for thermotolerance in wheat (Triticum aestivum L.).

Meta-QTL analysis for thermotolerance in wheat was conducted to identify robust meta-QTLs (MQTLs). In this study, 441 QTLs related to 31 heat-responsive traits were projected on the consensus map with 50,310 markers. This exercise resulted in the identification of 85 MQTLs with confidence interval (CI) ranging from 0.11 to 34.9 cM with an average of 5.6 cM. This amounted to a 2.96-fold reduction relative to the mean CI (16.5 cM) of the QTLs used. Seventy-seven (77) of these MQTLs were also compared and verified with the results of recent genome-wide association studies (GWAS). The 85 MQTLs included seven MQTLs that are particularly useful for breeding purposes (we called them breeders' MQTLs). Seven ortho-MQTLs between wheat and rice genomes were also identified using synteny and collinearity. The MQTLs were used for the identification of 1,704 candidate genes (CGs). In silico expression analysis of these CGs permitted identification of 182 differentially expressed genes (DEGs), which included 36 high confidence CGs with known functions previously reported to be important for thermotolerance. These high confidence CGs encoded proteins belonging to the following families: protein kinase, WD40 repeat, glycosyltransferase, ribosomal protein, SNARE associated Golgi protein, GDSL lipase/esterase, SANT/Myb domain, K homology domain, etc. Thus, the present study resulted in the identification of MQTLs (including breeders' MQTLs), ortho-MQTLs, and underlying CGs, which could prove useful not only for molecular breeding for the development of thermotolerant wheat cultivars but also for future studies focused on understanding the molecular basis of thermotolerance. Supplementary Information: The online version contains supplementary material available at 10.1007/s11032-021-01264-7.

Mathematical model, forecast and analysis on the spread of COVID-19.

Pandemic COVID-19 which has infected more than 35,027,546 people and death more than 1,034,837 people in 235 countries as on October 05, 2020 has created a chaos across the globe. In this paper, we develop a compartmental epidemic model to understand the spreading behaviour of the disease in human population with a special case of Bhilwara, a desert town in India where successful control measures TTT (tracking, testing and treatment) was adopted to curb the disease in the very early phase of the spread of the disease in India. Local and global asymptotic stability is established for endemic equilibrium. Extensive numerical simulations with real parametric values are performed to validate the analytical results. Trend analysis of fatality rate, infection rate, and impact of lockdown is performed for USA, European countries, Russia, Iran, China, Japan, S. Korea with a comparative assessment by India. Kruskal - Wallis test is performed to test the null hypothesis for infected cases during the four lockdown phases in India. It has been observed that there is a significant difference at both 95% and 99% confidence interval in the infected cases, recovered cases and the case fatality rate during all the four phases of the lockdown.

Meta-QTLs, ortho-MQTLs and candidate genes for the traits contributing to salinity stress tolerance in common wheat (Triticum aestivum L.).

A meta-analysis of QTLs associated with the traits contributing to salinity tolerance was undertaken in wheat to detect consensus and robust meta-QTLs (MQTLs) using 844 known QTLs retrieved from 26 earlier studies. A consensus map with a total length of 4621.56 cM including 7710 markers was constructed using 21 individual linkage maps and three previously published integrated genetic maps. Out of 844 QTLs, 571 QTLs were projected on the consensus map which gave origin to 100 MQTLs. Interestingly, 49 MQTLs were co-located with marker-trait associations reported in wheat genome-wide association studies for the traits contributing to salinity stress tolerance. Five potential MQTLs associated with the major salinity-responsive traits were also identified to be utilized in the breeding programme. In the resulted MQTLs, the average confidence interval (CI, 3.58 cM) was reduced up to 4.16 folds compared to the mean CI of the initial QTLs. Furthermore, as many as 617 gene models including 81 most likely candidate genes (CGs) were identified in the high confidence MQTL regions. These most likely CGs encoded proteins mainly belonging to the following families: B-box-type zinc finger, cytochrome P450 protein, pentatricopeptide repeat, phospholipid/glycerol acyltransferase, F-box protein, small auxin-up RNA, UDP-glucosyltransferase, glutathione S-transferase protein, etc. In addition, ortho-MQTL analysis based on synteny among wheat, rice and barley was also performed which permitted the identification of six ortho-MQTLs among these three cereals. This meta-analysis defines a genome-wide landscape on the most stable and consistent loci associated with reliable molecular markers and candidate genes for salinity tolerance in wheat. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12298-021-01112-0.

Meta-QTLs, ortho-MQTLs and candidate genes for nitrogen use efficiency and root system architecture in bread wheat (Triticum aestivum L.).

In wheat, meta-QTLs (MQTLs), ortho-MQTLs, and candidate genes (CGs) were identified for nitrogen use efficiency and root system architecture. For this purpose, 1788 QTLs were available from 24 studies published during 2006-2020. Of these, 1098 QTLs were projected onto the consensus map resulting in 118 MQTLs. The average confidence interval (CI) of MQTLs was reduced up to 8.56 folds in comparison to the average CI of QTLs. Of the 118 MQTLs, 112 were anchored to the physical map of the wheat reference genome. The physical interval of MQTLs ranged from 0.02 to 666.18 Mb with a mean of 94.36 Mb. Eighty-eight of these 112 MQTLs were verified by marker-trait associations (MTAs) identified in published genome-wide association studies (GWAS); the MQTLs that were verified using GWAS also included 9 most robust MQTLs, which are particularly useful for breeders; we call them 'Breeder's QTLs'. Some selected wheat MQTLs were further utilized for the identification of ortho-MQTLs for wheat and maize; 9 such ortho-MQTLs were available. As many as 1991 candidate genes (CGs) were also detected, which included 930 CGs with an expression level of > 2 transcripts per million in relevant organs/tissues. Among the CGs, 97 CGs with functions previously reported as important for the traits under study were selected. Based on homology analysis and expression patterns, 49 orthologues of 35 rice genes were also identified in MQTL regions. The results of the present study may prove useful for the improvement of selection strategy for yield potential, stability, and performance under N-limiting conditions. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12298-021-01085-0.

Enhancing production of microalgal biopigments through metabolic and genetic engineering.

The versatile use of biopigments in food, feed, cosmetic, pharmaceutical and analytical industries emphasized to find different and renewable sources of biopigments. Microalgae, including cyanobacteria, are becoming a potential candidate for pigment production as these have fast-growing ability, high pigment content, highly variable and also have "Generally recognized as safe" status. These algal groups are known to produce different metabolites that include hormones, vitamins, biopolythene and biochemicals. We discuss here the potential use of microalgal biopigments in our daily life as well as in food and cosmetic industries. Pigment like carotenoids has many health benefits such as antioxidant, anti-inflammatory properties and also provide photo-protection against UV radiation. This review details the effect of various abiotic and biotic factors such as temperature, light, nutrition on maximizing the pigment content in the microalgal cell. This review also highlights the potential of microalgae, whether in present native or engineered strain including the many metabolic strategies which are used or can be used to produce a higher amount of these valuable biopigments. Additionally, future challenges in the context of pigment production have also been discussed.

COVID-19 created chaos across the globe: Three novel quarantine epidemic models.

The latest version of human coronavirus said to be COVID-19 came out as a sudden pandemic disease within human population and in the absence of vaccination and proper treatment till date, it daunting threats heavily to human lives, infecting more than 12, 11, 214 people and death more than 67, 666 people in 208 countries across the globe as on April 06, 2020, which is highly alarming. When no treatment or vaccine is available till date and to avoid COVID-19 to be transmitted in the community, social distancing is the only way to prevent the disease, which is well taken into account in our novel epidemic models as a special compartment, that is, home isolation. Based on the transmitting behavior of COVID-19 in the human population, we develop three quarantine models of this pandemic taking into account the compartments: susceptible population, immigrant population, home isolation population, infectious population, hospital quarantine population, and recovered population. Local and global asymptotic stability is proved for all the three models. Extensive numerical simulations are performed to establish the analytical results with suitable examples. Our research reveals that home isolation and quarantine to hospitals are the two pivot force-control policies under the present situation when no treatment is available for this pandemic.

Manipulating epigenetic diversity in crop plants: Techniques, challenges and opportunities.

Epigenetic modifications act as conductors of inheritable alterations in gene expression, all while keeping the DNA sequence intact, thereby playing a pivotal role in shaping plant growth and development. This review article presents an overview of techniques employed to investigate and manipulate epigenetic diversity in crop plants, focusing on both naturally occurring and artificially induced epialleles. The significance of epigenetic modifications in facilitating adaptive responses is explored through the examination of how various biotic and abiotic stresses impact them. Further, environmental chemicals are explored for their role in inducing epigenetic changes, particularly focusing on inhibitors of DNA methylation like 5-AzaC and zebularine, as well as inhibitors of histone deacetylation including trichostatin A and sodium butyrate. The review delves into various approaches for generating epialleles, including tissue culture techniques, mutagenesis, and grafting, elucidating their potential to induce heritable epigenetic modifications in plants. In addition, the ground breaking CRISPR/Cas is emphasized for its accuracy in targeting specific epigenetic changes. This presents a potent tools for deciphering the intricacies of epigenetic mechanisms. Furthermore, the intricate relationship between epigenetic modifications and non-coding RNA expression, including siRNAs and miRNAs, is investigated. The emerging role of exo-RNAi in epigenetic regulation is also introduced, unveiling its promising potential for future applications. The article concludes by addressing the opportunities and challenges presented by these techniques, emphasizing their implications for crop improvement. Conclusively, this extensive review provides valuable insights into the intricate realm of epigenetic changes, illuminating their significance in phenotypic plasticity and their potential in advancing crop improvement.