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.
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.
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.
Heat stress greatly limits the productivity of wheat in many regions. Knowledge on the degree of genetic diversity of wheat varieties along with their selective traits will facilitate the development of high yielding, stress-tolerant wheat cultivar. The objective of this study were to determine genetic variation in morpho-physiological traits associated with heat tolerance in 30 diverse wheat genotypes and to examine genetic diversity and relationship among the genotypes varying heat tolerance using molecular markers. Phenotypic data of 15 traits were evaluated for heat tolerance under non-stress and stress conditions for two consecutive years. A positive and significant correlation among cell membrane stability, canopy temperature depression, biomass, susceptibility index and grain yield was shown. Genetic diversity assessed by 41 polymorphic simple sequence repeat (SSR) markers was compared with diversity evaluated for 15 phenotypic traits averaged over stress and non-stress field conditions. The mean polymorphic information content for SSR value was 0.38 with range of 0.12–0.75. Based on morpho-physiological traits and genotypic data, three groups were obtained based on their tolerance (HHT, MHT and LHT) levels. Analysis of molecular variance explained 91.7% of the total variation could be due to variance within the heat tolerance genotypes. Genetic diversity among HHT was higher than LHT genotypes and HHT genotypes were distributed among all cluster implied that genetic basis of heat tolerance in these genotypes was different thereby enabling the wheat breeders to combine these diverse sources of genetic variation to improve heat tolerance in wheat breeding programme.
The rising population entails enhancement in wheat productivity to ensure substantial food supply which often get hindered by various biotic and abiotic stresses. Lodging, due to rain and high velocity wind causes significant economic and yield losses in cereals. Hence, lodging is emerging as a major hurdle to achieve the required yield targets. Various morphological, biochemical, anatomical and genetic traits contribute to produce a plant competent enough to bear lodging stress. Hence, in this review, we intend to elaborate the cause and impact relationship of lodging and tried to link lodging tolerance traits to field practices to minimize the losses. Because of the complex nature of lodging phenomenon, it is still obscure to identify best correlated traits to screen genotype in breeding programmes. However, the genotypes with best correlated traits like plant height, culm wall thickness should be introduced/selected in breeding programmes to inculcate lodging tolerance in a high yielding variety as in recent era lodging tolerance is a key factor to enhance productivity and farmer's income as well.
A set of thirty-six wheat cultivars were grown for two consecutive years under low and high nitrogen conditions. The interactions of cultivars with different environmental factors were shown to be highly significant for most of the studied traits, suggesting the presence of wider genetic variability which may be utilized for the genetic improvement of desired trait(s). Three cultivars, i.e., RAJ 4037, DBW 39 and GW 322, were selected based on three selection indices, i.e., tolerance index (TOL), stress susceptibility index (SSI), and yield stability index (YSI), while two cultivars, HD 2967 and MACS 6478, were selected based on all four selection indices which were common in both of the study years. According to Kendall’s concordance coefficient, the consistency of geometric mean productivity (GMP) was found to be highest (0.778), followed by YSI (0.556), SSI (0.472), and TOL (0.200). Due to the high consistency of GMP followed by YSI and SSI, the three selection indices could be utilized as a selection tool in the identification of high-yielding genotypes under low nitrogen conditions. The GMP and YSI selection indices had a positive and significant correlation with grain yield, whereas TOL and SSI exhibited a significant but negative correlation with grain yield under both high and low nitrogen conditions in both years. The common tolerant genotypes identified through different selection indices could be utilized as potential donors in active breeding programs to incorporate the low nitrogen tolerant genes to develop high-yielding wheat varieties for low nitrogen conditions. The study also helps in understanding the physiological basis of tolerance in high-yielding wheat genotypes under low nitrogen conditions.
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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