The current perspective of increasing global temperature makes heat stress as a major threat to wheat production worldwide. In order to identify quantitative trait loci (QTLs) associated with heat ...tolerance, 251 recombinant inbred lines (RILs) derived from a cross between HD2808 (heat tolerant) and HUW510 (heat susceptible) were evaluated under timely sown (normal) and late sown (heat stress) conditions for two consecutive crop seasons; 2013-14 and 2014-15. Grain yield (GY) and its components namely, grain weight/spike (GWS), grain number/spike (GNS), thousand grain weight (TGW), grain filling rate (GFR) and grain filling duration (GFD) were recorded for both conditions and years. The data collected for both timely and late sown conditions and heat susceptibility index (HSI) of these traits were used as phenotypic data for QTL identification. The frequency distribution of HSI for all the studied traits was continuous during both the years and also included transgressive segregants. Composite interval mapping identified total 24 QTLs viz., 9 (timely sown traits), 6 (late sown traits) and 9 (HSI of traits) mapped on linkage groups 2A, 2B, and 6D during both the crop seasons 2013-14 and 2014-15. The QTLs were detected for GWS (6), GNS (6), GFR (4), TGW (3), GY (3) and GFD (2). The LOD score of identified QTLs varied from 3.03 (Qtgns.iiwbr-6D) to 21.01 (Qhsitgw.iiwbr-2A) during 2014-15, explaining 11.2 and 30.6% phenotypic variance, respectively. Maximum no of QTLs were detected in chromosome 2A followed by 6D and 2B. All the QTL detected under late sown and HSI traits were identified on chromosome 2A except for QTLs associated with GFD. Fifteen out of 17 QTL detected on chromosome 2A were clustered within the marker interval between gwm448 and wmc296 and showed tight linkage with gwm122 and these were localized in 49-52 cM region of Somers consensus map of chromosome 2A i.e. within 18-59.56 cM region of chromosome 2A where no QTL related to heat stress were reported earlier. Besides, three consistent QTLs, Qgws.iiwbr-2A, Qgns.iiwbr-2A and Qgns.iiwbr-2A were also detected in all the environments in this region. The nearest QTL detected in earlier studies, QFv/Fm.cgb-2A was approximately 6cM below the presently identified QTLs region, respectively Additionally, QTLs for physiological and phenological traits and plant height under late sown and HSI of these traits were also detected on chromosome 2A. QTL for HSI of plant height and physiological maturity were located in the same genomic region of chromosome 2Awhereas QTLs for physiological and phonological traits under late sown were located 8cM and 33.5 cM below the genomic location associated with grain traits, respectively in consensus map of Somers. This QTL hot-spot region with consistent QTLs could be used to improve heat tolerance after validation.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
In bread wheat, QTL interval mapping was conducted for nine important drought responsive agronomic traits. For this purpose, a doubled haploid (DH) mapping population derived from Kukri/Excalibur was ...grown over three years at four separate locations in India, both under irrigated and rain-fed environments. Single locus analysis using composite interval mapping (CIM) allowed detection of 98 QTL, which included 66 QTL for nine individual agronomic traits and 32 QTL, which affected drought sensitivity index (DSI) for the same nine traits. Two-locus analysis allowed detection of 19 main effect QTL (M-QTL) for four traits (days to anthesis, days to maturity, grain filling duration and thousand grain weight) and 19 pairs of epistatic QTL (E-QTL) for two traits (days to anthesis and thousand grain weight). Eight QTL were common in single locus analysis and two locus analysis. These QTL (identified both in single- and two-locus analysis) were distributed on 20 different chromosomes (except 4D). Important genomic regions on chromosomes 5A and 7A were also identified (5A carried QTL for seven traits and 7A carried QTL for six traits). Marker-assisted recurrent selection (MARS) involving pyramiding of important QTL reported in the present study, together with important QTL reported earlier, may be used for improvement of drought tolerance in wheat. In future, more closely linked markers for the QTL reported here may be developed through fine mapping, and the candidate genes may be identified and used for developing a better understanding of the genetic basis of drought tolerance in wheat.
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The study was conducted to identify novel simple sequence repeat (SSR) markers associated with resistance to corn aphid (CLA), Rhopalosiphum maidis L. in 48 selected bread wheat (Triticum aestivum ...L.) and wild wheat (Aegilops spp. & T. dicoccoides) genotypes during two consecutive cropping seasons (2018-19 and 2019-20). A total of 51 polymorphic markers containing 143 alleles were used for the analysis. The frequency of the major allele ranged from 0.552 (Xgwm113) to 0.938 (Xcfd45, Xgwm194 and Xgwm526), with a mean of 0.731. Gene diversity ranged from 0.116 (Xgwm526) to 0.489 (Xgwm113), with a mean of 0.354. The polymorphic information content (PIC) value for the SSR markers ranged from 0.107 (Xgwm526) to 0.370 (Xgwm113) with a mean of 0.282. The results of the STRUCTURE analysis revealed the presence of four main subgroups in the populations. Analysis of molecular variance (AMOVA) showed that the between-group difference was around 37 per cent of the total variation contributed to the diversity by the whole germplasm, while 63 per cent of the variation was attributed between individuals within the group. A general linear model (GLM) was used to identify marker-trait associations, which detected a total of 23 and 27 significant new marker-trait associations (MTAs) at the p < 0.01 significance level during the 2018-19 and 2019-20 crop seasons, respectively. The findings of this study have important implications for the identification of molecular markers associated with CLA resistance. These markers can increase the accuracy and efficiency of aphid-resistant germplasm selection, ultimately facilitating the transfer of resistance traits to desirable wheat genotypes.
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Wheat is an important dietary source of zinc (Zn) and other mineral elements in many countries. Dietary Zn deficiency is widespread, especially in developing countries, and breeding (genetic ...biofortification) through the HarvestPlus programme has recently started to deliver new wheat varieties to help alleviate this problem in South Asia. To better understand the potential of wheat to alleviate dietary Zn deficiency, this study aimed to characterise the baseline effects of genotype (G), site (E), and genotype by site interactions (GxE) on grain Zn concentration under a wide range of soil conditions in India. Field experiments were conducted on a diverse panel of 36 Indian-adapted wheat genotypes, grown on a range of soil types (pH range 4.5-9.5), in 2013-14 (five sites) and 2014-15 (six sites). Grain samples were analysed using inductively coupled plasma-mass spectrometry (ICP-MS). The mean grain Zn concentration of the genotypes ranged from 24.9-34.8 mg kg-1, averaged across site and year. Genotype and site effects were associated with 10% and 6% of the overall variation in grain Zn concentration, respectively. Whilst G x E interaction effects were evident across the panel, some genotypes had consistent rankings between sites and years. Grain Zn concentration correlated positively with grain concentrations of iron (Fe), sulphur (S), and eight other elements, but did not correlate negatively with grain yield, i.e. no yield dilution was observed. Despite a relatively small contribution of genotype to the overall variation in grain Zn concentration, due to experiments being conducted across many contrasting sites and two years, our data are consistent with reports that biofortifying wheat through breeding is likely to be effective at scale given that some genotypes performed consistently across diverse soil types. Notably, all soils in this study were probably Zn deficient and interactions between wheat genotypes and soil Zn availability/management (e.g. the use of Zn-containing fertilisers) need to be better-understood to improve Zn supply in food systems.
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Abstract
Rice–wheat production in the Indo-gangetic plains (IGPs) of India faces major concerns such as depleting resources, rice residue burning, excessive fertilizer use, and decreasing nitrogen ...use efficiency. These issues threaten sustainable crop production in the future. Therefore, a field study was conducted during the winter seasons of 2020–21 and 2021–22 to evaluate the effect of combined conventional and nano fertilizers on nitrogen application just before or after irrigation to improve wheat productivity, profitability and NUE under conservation tillage. The study evaluated eight treatment combinations of nitrogen application through conventionally applied urea (46% N) and foliar applied nano urea (4% N) under zero tillage with rice residue retention. Results revealed that growth, physiological indices, yield, and quality parameters were enhanced with the application of 150 kg N/ha in three equal splits as basal and just before 1st and 2nd irrigation alone (T2) or along with a spray of nano urea (T5) compared to other treatments. T5 recorded 7.2%, 8.5%, and 7.8% more plant dry matter, number of tillers, and grain yield, respectively, over the conventional practice of applying 150 kg N/ha in three equal splits as basal and 7–10 days after 1st and 2nd irrigation (T3, farmers practice). Although, T2 showed similar results to T5, T5 recorded significantly higher gross ($2542/ha) and net returns ($1279/ha) than the other treatments. However, the benefit–cost ratio of T2 and T5 was same (2.01). A significant and positive correlation coefficient between grain yield and physiological parameters such as CCI and NDVI confirmed that increasing the nitrogen dose enhanced the chlorophyll content, greenness, and plant vigor. Based on the results, it can be concluded that applying 150 kg N/ha in three equal splits as basal and just before 1st and 2nd irrigation under conservation agriculture, along with a single spray of nano urea (4% N) at 60–65 days after sowing, can improve growth, yield attributes, wheat yield, and NUE compared to farmers practice (T3) in India.
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Heat stress is a genetically complex and physiologically diverse phenomenon. To overcome the effect of heat stress identification of genomic locations associated with heat stress tolerance is ...essential. This article provides the dataset of phenotyping used in the research article entitled “Mapping QTLs for grain yield components in wheat under heat stress”. The presented data included the phenotyping of the 249 RIL population of F8 and F9 generations under timely and late sown conditions during the 2013-14 and 2014-15 crop seasons, respectively. The RIL population was derived from the cross between HUW510 and HD2808 wheat genotypes. A total of eight agronomic traits were subjected to phenotype and the heat susceptibility index (HSI) of these traits was estimated to identify the effect of heat stress on the parents and RIL population. The presented dataset could be utilized to understand the genetic basis for heat stress tolerance in wheat.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Poor understanding of the genetic and molecular basis of heat tolerance component traits is a major bottleneck in designing heat tolerant wheat cultivars. The impact of terminal heat stress is ...generally reported in the case of late sown wheat. In this study, our aim was to identify genomic regions for various agronomic traits under late sown conditions by using genome-wide association approach. An association mapping panel of 205 wheat accessions was evaluated under late sown conditions at three different locations in India. Genotyping of the association panel revealed 15,886 SNPs, out of which 11,911 SNPs with exact physical locations on the wheat reference genome were used in association analysis. A total of 69 QTLs (10 significantly associated and 59 suggestive) were identified for ten different traits including productive tiller number (17), grain yield (14), plant height (12), grain filling rate (6), grain filling duration (5), days to physiological maturity (4), grain number (3), thousand grain weight (3), harvest index (3), and biomass (2). Out of these associated QTLs, 17 were novel for traits, namely PTL (3), GY (2), GFR (6), HI (3) and GNM (3). Moreover, five consistent QTLs across environments were identified for GY (4) and TGW (1). Also, 11 multi-trait SNPs and three hot spot regions on Chr1Ds, Chr2BS, Chr2DS harboring many QTLs for many traits were identified. In addition, identification of heat tolerant germplasm lines based on favorable alleles HD2888, IC611071, IC611273, IC75240, IC321906, IC416188, and J31-170 would facilitate their targeted introgression into popular wheat cultivars. The significantly associated QTLs identified in the present study can be further validated to identify robust markers for utilization in marker-assisted selection (MAS) for development of heat tolerant wheat cultivars.
Intensive crop breeding has increased wheat yields and production in India. Wheat improvement in India typically involves selecting yield and component traits under non-hostile soil conditions at ...regional scales. The aim of this study is to quantify G*E interactions on yield and component traits to further explore site-specific trait selection for hostile soils. Field experiments were conducted at six sites (pH range 4.5-9.5) in 2013-14 and 2014-15, in three agro-climatic regions of India. At each site, yield and component traits were measured on 36 genotypes, representing elite varieties from a wide genetic background developed for different regions. Mean grain yields ranged from 1.0 to 5.5 t ha-1 at hostile and non-hostile sites, respectively. Site (E) had the largest effect on yield and component traits, however, interactions between genotype and site (G*E) affected most traits to a greater extent than genotype alone. Within each agro-climatic region, yield and component traits correlated positively between hostile and non-hostile sites. However, some genotypes performed better under hostile soils, with site-specific relationships between yield and component traits, which supports the value of ongoing site-specific selection activities.
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Drought is one of the major constraints in wheat production and causes a huge loss at grain-filling stage. In this study we highlighted the response of different wheat genotypes under drought stress ...at the grain-filling stage. Field experiments were conducted to evaluate 72 wheat (
Triticum aestivum
L.) genotypes under two water regimes: irrigated and drought condition. Four wheat genotypes, two each of drought tolerant (IC36761A, IC128335) and drought-susceptible category (IC335732 and IC138852) were selected on the basis of agronomic traits and drought susceptibility index (DSI), to understand their morphological, biochemical and molecular basis of drought stress tolerance. Among agronomic traits, productive tillers followed by biomass had high percent reduction under drought stress, thus drought stress had a great impact. Antioxidant activity (AO), total phenolic and proline content were found to be significantly higher in IC128335 genotype. Differential expression pattern of transcription factors of ten genes revealed that transcription factor qTaWRKY2 followed by qTaDREB, qTaEXPB23 and qTaAPEX might be utilized for developing wheat varieties resistant to drought stress. Understanding the role of TFs would be helpful to decipher the molecular mechanism involved in drought stress. Identified genotypes (IC128335 and IC36761A) may be useful as parental material for future breeding program to generate new drought-tolerant varieties.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OBVAL, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
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