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

Singh, Kalpana; Saini, Dinesh Kumar; Saripalli, Gautam; Batra, Renu; Gautam, Tinku; Singh, Ravinder; Pal, Saikat; Kumar, Manoj; Jan, Irfat; Singh, S.R.; Kumar, Anuj; Sharma, Hemant; Chaudhary, Jyoti; Kumar, Kuldeep; Kumar, Sachin; Singh, Vikas Kumar; Singh, Vivudh Pratap; Kumar, Deepak; Sharma, Shiveta; Kumar, Sachin; Kumar, Rahul; Sharma, Shailendra; Gaurav, Shailendra Singh; Sharma, Pradeep; Balyan, H. S.; Gupta, P. K.

doi:10.1007/s11032-022-01329-1

Cited by 26 publications

(14 citation statements)

References 2 publications

(2 reference statements)

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“…QTL mapping is a powerful technique for the genetic dissection of complex traits, including quantitative disease resistance, in crop plants [ 20 , 21 ]. Since the publication of the first study on QTL mapping for stripe rust resistance in wheat in 2000 [ 22 ], a large number of QTL mapping studies for stripe rust resistance in wheat have been published ( http://wheatqtldb.net/ , [ 23 ]). However, the deployment of these resistance QTLs in wheat breeding programs has been minimal for several reasons, a few of which are as follows: inconsistencies between the QTLs reported in different studies, use of different types of markers, population types and sizes, and phenotyping environments.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

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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.

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Section: Introductionmentioning

confidence: 99%

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

et al. 2023

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“…Further, the effectiveness of GWAS has already been established to capture genetic factors for various traits in wheat including agronomic (Sukumaran et al., 2015; Sun et al., 2017), disease resistance (Arruda et al., 2016; Halder et al., 2019; Juliana et al., 2018; Zhang et al., 2022), and end‐use quality (Chen et al., 2019). Likewise, these approaches have been used to identify significant QTL for several yield‐related traits in wheat (Singh et al., 2022). Quantitative trait loci and genes associated with spike‐ (Cui et al., 2014; Gao et al., 2015; Guo et al., 2017; Huang et al., 2006; Liu et al., 2017b; Naruoka et al., 2011; Pang et al., 2020; Ward et al., 2019) and kernel‐related traits (Chen et al., 2016; Jaiswal et al., 2015; Liu et al., 2018b; Zhang et al., 2012) have been identified on all the wheat chromosomes.…”

Section: Introductionmentioning

confidence: 99%

Genome‐wide association analysis of spike and kernel traits in the U.S. hard winter wheat

et al. 2023

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A better understanding of the genetic control of spike and kernel traits that have higher heritability can help in the development of high‐yielding wheat varieties. Here, we identified the marker‐trait associations (MTAs) for various spike‐ and kernel‐related traits in winter wheat (Triticum aestivum L.) through genome‐wide association studies (GWAS). An association mapping panel comprising 297 hard winter wheat accessions from the U.S. Great Plains was evaluated for eight spike‐ and kernel‐related traits in three different environments. A GWAS using 15,590 single‐nucleotide polymorphisms (SNPs) identified a total of 53 MTAs for seven spike‐ and kernel‐related traits, where the highest number of MTAs were identified for spike length (16) followed by the number of spikelets per spike (15) and spikelet density (11). Out of 53 MTAs, 14 were considered to represent stable quantitative trait loci (QTL) as they were identified in multiple environments. Five multi‐trait MTAs were identified for various traits including the number of spikelets per spike (NSPS), spikelet density (SD), kernel width (KW), and kernel area (KA) that could facilitate the pyramiding of yield‐contributing traits. Further, a significant additive effect of accumulated favorable alleles on the phenotype of four spike‐related traits suggested that breeding lines and cultivars with a higher number of favorable alleles could be a valuable resource for breeders to improve yield‐related traits. This study improves the understanding of the genetic basis of yield‐related traits in hard winter wheat and provides reliable molecular markers that will facilitate marker‐assisted selection (MAS) in wheat breeding programs.

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“…The medicinal properties, such as anti‐inflammatory, antifungal, and antioxidant have been reported for Moringa gum [25] . Singh et al [26] have developed a hydrogel dressing based on Moringa gum‐Sterculia gum‐polyacrylamide using an NNMBA crosslinker. Parwani et al [27] have synthesized Moringa oleifera /PVA composite hydrogel dressing using glutaraldehyde a crosslinker.…”

Section: Introductionmentioning

confidence: 99%

Self‐Healing, Biocompatible Injectable Hydrogel Based on Multialdehyde Moringa oleifera Gum and Carboxymethyl Chitosan: A Suitable Platform for Drug Delivery in Wound Healing Application

Kumar,

Purwar

2024

ChemistrySelect

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This study focuses on synthesizing an injectable hydrogel (IH) using Moringa oleifera gum (MOG) with multi‐aldehyde groups (MOG‐CHO) and carboxymethyl chitosan (CMCh) through a Schiff base reaction at 37 °C. The study includes the optimal concentration of the oxidizing agent to achieve the best content of aldehydes in Moringa oleifera gum. Furthermore, the best oxidized Moringa oleifera gum decides the least gelation time. The physical properties of the optimized IH (IH‐3‐7) are assessed, including color, gel content, porosity, load‐bearing capacity, syringeability, and self‐healing capability. Structural characterization is performed using Fourier transform Infrared spectroscopy (FT‐IR) and nuclear magnetic spectroscopy (1H NMR), while scanning electron microscope (SEM) and rheometer are used to analyze surface morphology and rheological properties. IH‐3‐7 exhibits a porous structure with 35 % porosity and interconnected holes of 1.45 to 4 μm diameter. Higher swelling at pH 5.5 is shown by swelling ratio test conducted in phosphate buffer saline (PBS) at pH 7.4 and pH 5.5. Ciprofloxacin HCl (Cipro) is loaded into IH for release assays, revealing pH‐dependent drug release. In vitro, cytotoxicity assessments on L‐929 fibroblast cell lines show IH′s biocompatibility. Hydrolytic degradation of IH in PBS at pH 7.4 is also evaluated.

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

Cited by 26 publications

References 2 publications

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

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

Genome‐wide association analysis of spike and kernel traits in the U.S. hard winter wheat

Self‐Healing, Biocompatible Injectable Hydrogel Based on Multialdehyde Moringa oleifera Gum and Carboxymethyl Chitosan: A Suitable Platform for Drug Delivery in Wound Healing Application

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