Identification of wheat cultivars for low nitrogen tolerance using multivariable screening approaches

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). 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

Ensuring nutritional security in India through wheat biofortification: A review

Undernourishment of nutrients, also known as hidden hunger, affects over 2 billion populace globally. Even though stunting among children below five years of age has decreased in India in the last ten years, India is home to roughly thirty percent of the world's population of stunted pre-schoolers. A significant improvement has been witnessed in the targeted development and deployment of biofortified crops; approximately 20 million farm households from developing counties benefit from cultivating and consuming biofortified crops. There is ample scope for including biofortified varieties in the seed chain, ensuring nutritional security. Wheat is a dietary staple in India, typically consumed as wholemeal flour in the form of flatbreads such as chapatti and roti. Wheat contributes to nearly one fifth of global energy requirements and can also provide better amounts of iron (Fe) and zinc (Zn). As a result, biofortified wheat can serve as a medium for delivery of essential micronutrients such as Fe and Zn to end users. This review discusses wheat biofortification components such as Fe and Zn dynamics, its uptake and movement in plants, the genetics of their buildup, and the inclusion of biofortified wheat varieties in the seed multiplication chain concerning India

QTL mapping for seedling and adult plant resistance to stripe and leaf rust in two winter wheat populations

The two recombinant inbred line (RIL) populations developed by crossing Almaly × Avocet S (206 RILs) and Almaly × Anza (162 RILs) were used to detect the novel genomic regions associated with adult plant resistance (APR) and seedling or all-stage resistance (ASR) to yellow rust (YR) and leaf rust (LR). The quantitative trait loci (QTLs) were detected through multi-year phenotypic evaluations (2018–2020) and using high-throughput DArTseq genotyping technology. RILs exhibited significant genetic variation with p < 0.001, and the coefficient of variation ranged from 9.79% to 47.99% for both LR and YR in all Environments and stages of evaluations. The heritability is quite high and ranged between 0.47 and 0.98. We identified nine stable QTLs for YR APR on chromosomes 1B, 2A, 2B, 3D, and 4D and four stable QTLs for LR APR on chromosomes 2B, 3B, 4A, and 5A. Furthermore, in silico analysis revealed that the key putative candidate genes such as cytochrome P450, protein kinase-like domain superfamily, zinc-binding ribosomal protein, SANT/Myb domain, WRKY transcription factor, nucleotide sugar transporter, and NAC domain superfamily were in the QTL regions and probably involved in the regulation of host response toward pathogen infection. The stable QTLs identified in this study are useful for developing rust-resistant varieties through marker-assisted selection (MAS)

Genome-wide association study identifies loci and candidate genes for grain micronutrients and quality traits in wheat (Triticum aestivum L.)

Malnutrition due to micronutrients and protein deficiency is recognized among the major global health issues. Genetic biofortification of wheat is a cost-effective and sustainable strategy to mitigate the global micronutrient and protein malnutrition. Genomic regions governing grain zinc concentration (GZnC), grain iron concentration (GFeC), grain protein content (GPC), test weight (TW), and thousand kernel weight (TKW) were investigated in a set of 184 diverse bread wheat genotypes through genome-wide association study (GWAS). The GWAS panel was genotyped using Breeders' 35 K Axiom Array and phenotyped in three different environments during 2019–2020. A total of 55 marker-trait associations (MTAs) were identified representing all three sub-genomes of wheat. The highest number of MTAs were identified for GPC (23), followed by TKW (15), TW (11), GFeC (4), and GZnC (2). Further, a stable SNP was identified for TKW, and also pleiotropic regions were identified for GPC and TKW. In silico analysis revealed important putative candidate genes underlying the identified genomic regions such as F-box-like domain superfamily, Zinc finger CCCH-type proteins, Serine-threonine/tyrosine-protein kinase, Histone deacetylase domain superfamily, and SANT/Myb domain superfamily proteins, etc. The identified novel MTAs will be validated to estimate their effects in different genetic backgrounds for subsequent use in marker-assisted selection

Genome-Wide Association Study for Grain Protein, Thousand Kernel Weight, and Normalized Difference Vegetation Index in Bread Wheat (Triticum aestivum L.)

Genomic regions governing grain protein content (GPC), 1000 kernel weight (TKW), and normalized difference vegetation index (NDVI) were studied in a set of 280 bread wheat genotypes. The genome-wide association (GWAS) panel was genotyped using a 35K Axiom array and phenotyped in three environments. A total of 26 marker-trait associations (MTAs) were detected on 18 chromosomes covering the A, B, and D subgenomes of bread wheat. The GPC showed the maximum MTAs (16), followed by NDVI (6), and TKW (4). A maximum of 10 MTAs was located on the B subgenome, whereas, 8 MTAs each were mapped on the A and D subgenomes. In silico analysis suggest that the SNPs were located on important putative candidate genes such as NAC domain superfamily, zinc finger RING-H2-type, aspartic peptidase domain, folylpolyglutamate synthase, serine/threonine-protein kinase LRK10, pentatricopeptide repeat, protein kinase-like domain superfamily, cytochrome P450, and expansin. These candidate genes were found to have different roles including regulation of stress tolerance, nutrient remobilization, protein accumulation, nitrogen utilization, photosynthesis, grain filling, mitochondrial function, and kernel development. The effects of newly identified MTAs will be validated in different genetic backgrounds for further utilization in marker-aided breeding

QTL mapping for seedling and adult plant resistance to stripe and leaf rust in two winter wheat populations

<p><span>The two recombinant inbred lines (RIL) populations developed by crossing Almaly × Avocet S (206 RILs) and Almaly × Anza (162 RILs) were used to detect the novel genomic regions associated with adult plant resistance (APR) and seedling or all-stage resistance (ASR) to yellow rust (YR) and leaf rust (LR). Both the populations were evaluated for YR APR in two environments (2018 and 2019) and LR APR in three environments (2018, 2019, and 2020) in the Anza population and two environments (2018 and 2019) in the Avocet population; both the populations were phenotyped for one environment during 2020 for LR and YR ASR and genotyped using high throughput DArTseq technology. A set of 51 QTLs including 22 for YR APR, nine for LR APR, nine for YR ASR, and 11 for LR ASR were identified. Also, a set of 13 stable QTLs including nine QTLs (<em>QYR-APR-2A.1, QYR-APR-2A.2, QYR-APR-4D.2, QYR-APR-1B, QYR-APR-2B.1, QYR-APR-2B.2, QYR-APR-3D, QYR-APR-4D.1, </em>and<em> QYR-APR-4D.2</em>) for YR APR and four QTLs (<em>QLR-APR-4A, QLR-APR-2B, QLR-APR-3B, </em>and<em> </em></span><em>QLR-APR-5A.2</em>) <span>for LR APR were identified. </span><span>In silico analysis revealed that the key putative candidate genes such as <em>Cytochrome P450</em></span><em><span>, Protein kinase-like domain superfamily</span><span>, Zinc-binding ribosomal protein</span><span>, SANT/Myb domain</span><span>, WRKY transcription factor</span><span>, Nucleotide-sugar transporter,</span></em><span> and <em>NAC</em> </span><em><span>domain superfamily</span></em><span> were in the QTL regions and involved in the regulation of host response towards the pathogen infection. </span><span>The stable QTLs identified in this study are useful for developing rust-resistant varieties through marker-assisted selection (MAS).</span></p><p>Funding provided by: The Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan*<br>Crossref Funder Registry ID: <br>Award Number: AP09258991</p><p>Funding provided by: The Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan*<br>Crossref Funder Registry ID: <br>Award Number: BR18574099</p><h3><span>Phenotypic dataset</span></h3> <h4><span>Seedling resistance in greenhouse</span></h4> <p><span>The<em> P. striiformis</em> races were differentiated in 2020  using a set of 12 wheat lines developed in the Avocet wheat background and on nine supplemental wheat differentials using a method developed by Johnson et al. 1972. Determination of the type of plant reaction was carried out twice within 14–20 days after infection according to the Gassner and Straib accounting scale (Gassner and Straib, 1932). At the same time, reactions of 0, 1, and 2 points were assigned to the resistant type R (Resistant), and 3 and 4 points were assigned to the susceptible type S (Susceptible). The <em>P. triticina</em> races were also differentiated during 2020 using 20 near-isogenic lines (NILs) developed in Thatcher background each carrying one of the LR-resistant genes. (Kolmer et al., 2014; Schachtel et al., 2012; Kolmer and Ordonez, 2007). The virulence of the phenotypes was determined on these 20 differential lines and encoded with 0 and 1 for avirulence and virulence, respectively (Kolmer and Ordonez, 2007; Long and Kolmer, 1989). The Virulence Analysis Tools (Schachtel et al., 2012) was used for the nomenclature of<em> P. triticina</em> races. The type of response to leaf rust was determined twice within 14-20 days after infection according to the scale of Mains and Jackson (1926). Reactions of 0, 1, and 2 points were assigned to the resistant type R (Resistant), and 3 and 4 points were assigned to the susceptible type S (Susceptible).</span></p> <p><span>The seedlings of the RIL population from Almaly × Avocet S cross along with the parents were inoculated with two races of<em> P. striiformis</em> i.e., 108E187 (Pst_1) and 110E191 (Pst_2) and two races of <em>P. triticina</em> i.e., MLTTH and TLTTR to determine the race-specific resistance. Similarly, the RIL population from Almaly × Anza cross along with parents were inoculated with two races of <em>P. striiformis </em>i.e., 108E187 (Pst_1) and 101E191 (Pst_3) and four races of <em>P. triticina</em> i.e., THTTQ,</span> <span>TCTTR, TCPTQ, and THTTR. The plants were infected with spores at 3-leaf stage and humid chamber was created for 24 hours. The seedling infection type of RIL was scored using the same approach as for rases differentiation. </span></p> <h4><span>Phenotyping for Adult Plant Resistance in Field </span></h4> <p><span>The field phenotyping for YR and LR APR was done during 2018 and 2019 for both the populations and an additional year during 2020 for LR APR for the Anza population at Kazakh Research Institute of Agriculture and Crop Production (KazNIIZiR), Almalybak. Pathogen racial mixtures from the local population were used to inoculate the mapping populations. The method of Roelfs et al. (1992) was followed for spore sampling, storage, and propagation. The pathogen was propagated in a greenhouse on the susceptible wheat variety, Morocco. The experimental wheat material was inoculated with a mixture of spores and talc in the ratio of 1:100 by spraying with an aqueous suspension of spores with 0.001% Tween-80 at a stem elongation stages (Z21-32). After infection, the plots were wrapped with plastic cover for 16-18 hours to create high humidity. </span><span>After the manifestation of diseases on susceptible control varieties, an assessment (2–3 times) of rust resistance was carried out. Leaf and yellow rust resistance of wheat accessions was evaluated using the modified Cobb scale </span><span>(Peterson</span><span> et al., 1948; McIntosh et al., 1995).  </span><span> <span>The scoring was based both on disease severity (proportion of leaf area infected) and on the plant response to infection (reaction type). Plant responses were recorded as resistant (R), moderately resistant (MR), moderately susceptible (MS), and susceptible (S) reactions.</span></span></p> <h3><span>Genotypic dataset</span></h3> <p>The genomic DNA was extracted from parents and each RIL from both populations following the modified <span>CTAB (cetyltrimethylammonium bromide) method</span> (<span>Dreisigacker et al., 2012</span><span>)</span>. The <span>DArTseq technology </span>was used for genotyping of both the RILs in<span> Genetic Analysis and Service for Agriculture (SAGA) lab</span> based<span> in Mexico</span> (Edet et al., 2018). <span>Briefly, the </span>sequencing of mapping populations was carried out <span>at 192-plexing on Illumina HiSeq2500 with 1 × 77-bp reads. </span>A<span>llele calls for SNP</span>s were generated through <span>proprietary analytical pipeline developed by DArT P/L</span> <span>(Sansaloni et al., 2011).</span> Further, genetic locations of the SNPs were identified by using <span>100K consensus map </span>given<span> by SAGA (Sansaloni et al. unpublished).  </span></p> <p><span><span>The markers were filtered and removed the monomorphic markers, markers with >30% missing data, high heterozygosity percentage (>30%), low allele frequency (<5%) using MS Excel. The BIN functionality in IciMapping 4.2 QTL software was used to remove redundant markers. A filtered set of 1293 and 1127 high-quality SNPs were finally used for QTL analysis in Anza and Avocet populations.</span></span></p&gt

Relevance of yield-related growth parameters in protein, iron and zinc and the prospects of their utilization for wheat (Triticum aestivum L.) improvement

Trial data for the period of 2005–2021 have been utilized to assess the inter-relationship of grain protein, iron, and zinc with different plant growth parameters in 10 agro-climatically diverse production environments. Protein registered strong association with the micronutrients. Iron increased with zinc until iron exceeded 50 ppm and zinc was more than 40 ppm. Barring the hills, iron content in late-sown wheat was higher than the timely-sown. Late-sown wheat also registered higher zinc content in four out of the five zones. Iron content was more variable in the late planted short duration genotypes. Analogy was observed between the three quality attributes for the response of plant height, pre- and post-anthesis durations. Comparison of different environments underlined that protein, iron and zinc contents are generally high in the environments where plants are not tall, pre-anthesis period is short and duration of the crop is not prolonged. Significance of the yield governing components was production environment specific. This Relationship was stronger in the late-sown wheat in comparison to the wheat varieties planted under timely sown condition especially in protein and iron. Collective contribution of these descriptors was highly significant but the major driving force differed and out of seven, only 1–2 plant growth parameters could be rated highly significant contributor. Selection criteria based upon only three parameters, i.e., height, heading and maturity have been suggested for certain situations. With some premium on yield, this simple methodology can surely be a way out to enhance grain nutrition properties of wheat

Mapping QTL for Phenological and Grain-Related Traits in a Mapping Population Derived from High-Zinc-Biofortified Wheat

Genomic regions governing days to heading (DH), days to maturity (DM), plant height (PH), thousand-kernel weight (TKW), and test weight (TW) were investigated in a set of 190 RILs derived from a cross between a widely cultivated wheat-variety, Kachu (DPW-621-50), and a high-zinc variety, Zinc-Shakti. The RIL population was genotyped using 909 DArTseq markers and phenotyped in three environments. The constructed genetic map had a total genetic length of 4665 cM, with an average marker density of 5.13 cM. A total of thirty-seven novel quantitative trait loci (QTL), including twelve for PH, six for DH, five for DM, eight for TKW and six for TW were identified. A set of 20 stable QTLs associated with the expression of DH, DM, PH, TKW, and TW were identified in two or more environments. Three novel pleiotropic genomic-regions harboring co-localized QTLs governing two or more traits were also identified. In silico analysis revealed that the DArTseq markers were located on important putative candidate genes such as MLO-like protein, Phytochrome, Zinc finger and RING-type, Cytochrome P450 and pentatricopeptide repeat, involved in the regulation of pollen maturity, the photoperiodic modulation of flowering-time, abiotic-stress tolerance, grain-filling duration, thousand-kernel weight, seed morphology, and plant growth and development. The identified novel QTLs, particularly stable and co-localized QTLs, will be validated to estimate their effects in different genetic backgrounds for subsequent use in marker-assisted selection (MAS)

Grain weight predictors in wheat and the prospects of their utilization in different production environments

Prospect of grain weight improvement in bread wheat has been reviewed by analysing 30-year data of the released and pre-released varieties evaluated in India during 1992–2021 under 10 diverse environments in five zones under timely and late sown conditions. The study reveals that enlargement of the reproductive phase by early heading and late maturity is feasible only in the environments where vegetative phase is large. This strategy of grain weight improvement can be effective in the northern hills of India for both timely sown (second fortnight of October) and late sown (first fortnight of December) wheat and it causes no reduction in the yield. This approach is also applicable in the agro-ecologies highly congenial for wheat growth as noticed in timely sown wheat varieties of western Indo-Gangetic plains. In areas where crop duration is short due to warmer climatic conditions like central and peninsular India, early heading can be effective for increasing grain weight, but it affects yield. Besides phenology, height can also be crucial in certain environments as it is closely related with grain weight in timely sown wheat of eastern as well as western Indo-Gangetic plains and late sown wheat of the northern hills. Height can also enhance the effect of phenological attributes in some environments. Under late heat-stressed environments, none of these parameters can be helpful in grain weight improvement. The study suggests that simultaneous improvement in grain weight and grain yield of wheat by the adoption of these predictors individually or in combination is possible in certain agro-ecologies of India

Wheat quality index: new holistic approach to identify quality superior genotypes

Ranking test entries or test sites on a quality basis is very difficult in wheat as value addition is perceived by several grain properties and the quality of the end-products. Selection of elite lines is easy when a single quality trait is under consideration and unmanageable for multiple quality traits. Here, a novel approach has been developed and tested by deriving wheat quality index based on principal component analysis of 13 physico-chemical grain parameters and 3 end products of 45 wheat varieties. This novel approach has been developed to distinguish an array of high-yielding wheat varieties based on their overall quality status. Depending upon the wheat quality index range (0.15–0.71), the cultivars were assorted into three distinct categories, i.e. elite, moderate and poor. The top group ascertained genotypes with high-quality standards suited for bread and chapati, whereas the bottom group assured varieties suited for good quality biscuits. This technique was also tested to differentiate quality enriched test sites within a wheat-growing zone to demarcate the most suited production environments to harness good quality wheat. The index will have an implication on the farmers (premium price for varietal segregation), industry (product-specific quality cultivars), and consumers (superior quality products)