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

Pal, Neeraj; Jan, Irfat; Saini, Dinesh Kumar; Kumar, Kuldeep; Kumar, Anuj; Sharma, Pradeep; Kumar, Sunil; Balyan, H. S.; Gupta, P. K.

doi:10.1007/s00122-022-04119-7

Cited by 30 publications

(31 citation statements)

References 122 publications

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“…These ortho-MQTL regions were identified by integrating the two different approaches utilized by Saini et al 26 and Sandhu et al 20 , respectively. Ortho-MQTL analyses have recently been conducted in wheat for different traits including quality traits 28 , HS 33 , SS 30 , nitrogen use efficiency 24 , grain yield and component traits 22 . The discovery of ortho-MQTLs in closely related species shows their relevance and stability, as well as the reliability of associated genes 56 .…”

Section: Discussionmentioning

confidence: 99%

Consensus genomic regions associated with multiple abiotic stress tolerance in wheat and implications for wheat breeding

Tanin

Saini

Sandhu

et al. 2022

Sci Rep

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In wheat, a meta-analysis was performed using previously identified QTLs associated with drought stress (DS), heat stress (HS), salinity stress (SS), water-logging stress (WS), pre-harvest sprouting (PHS), and aluminium stress (AS) which predicted a total of 134 meta-QTLs (MQTLs) that involved at least 28 consistent and stable MQTLs conferring tolerance to five or all six abiotic stresses under study. Seventy-six MQTLs out of the 132 physically anchored MQTLs were also verified with genome-wide association studies. Around 43% of MQTLs had genetic and physical confidence intervals of less than 1 cM and 5 Mb, respectively. Consequently, 539 genes were identified in some selected MQTLs providing tolerance to 5 or all 6 abiotic stresses. Comparative analysis of genes underlying MQTLs with four RNA-seq based transcriptomic datasets unravelled a total of 189 differentially expressed genes which also included at least 11 most promising candidate genes common among different datasets. The promoter analysis showed that the promoters of these genes include many stress responsiveness cis-regulatory elements, such as ARE, MBS, TC-rich repeats, As-1 element, STRE, LTR, WRE3, and WUN-motif among others. Further, some MQTLs also overlapped with as many as 34 known abiotic stress tolerance genes. In addition, numerous ortho-MQTLs among the wheat, maize, and rice genomes were discovered. These findings could help with fine mapping and gene cloning, as well as marker-assisted breeding for multiple abiotic stress tolerances in wheat.

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Section: Discussionmentioning

confidence: 99%

Consensus genomic regions associated with multiple abiotic stress tolerance in wheat and implications for wheat breeding

Tanin

Saini

Sandhu

et al. 2022

Sci Rep

Self Cite

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“…These ortho-MQTL regions were identified by integrating the two different approaches utilized by Saini et al 26 and Sandhu et al 20 , respectively. Ortho-MQTLs have recently been conducted in wheat for different traits including quality traits 28 , thermotolerance 33 , salinity stress tolerance 30 , nitrogen use efficiency 24 , grain yield and component traits 22 . The discovery of ortho-MQTLs in closely related species shows their relevance and stability, as well as the reliability of associated genes 54 .…”

Section: Discussionmentioning

confidence: 99%

“…The decrease of confidence intervals (CIs) in MQTLs, which leads to increased genetic resolution for marker-assisted breeding (MAB) and the identification of high-confidence candidate genes (CGs), is another advantage of this approach. For instance, meta-analysis of QTLs associated with several traits has already been conducted in wheat, for instance, disease resistance 21,22 23 ,. nitrogen use efficiency and root related traits 24,25 , yield and associated traits 26,27 , and quality traits 28,29 .…”

Section: Introductionmentioning

confidence: 99%

Consensus genomic regions associated with multiple abiotic stress tolerance in wheat and implications for wheat breeding

Tanin

Saini

Sandhu

et al. 2022

Preprint

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In wheat, a meta-analysis was performed using previously identified QTLs associated with drought stress, heat stress, salinity stress, water-logging stress, pre-harvest sprouting, and aluminium stress which predicted a total of 134 meta-QTLs (MQTLs) that involved at least 28 consistent and stable MQTLs conferring tolerance to five or all six abiotic stresses under study. Seventy-six MQTLs out of the 132 physically anchored MQTLs were also verified with genome-wide association studies. Around 43% of MQTLs had genetic and physical confidence intervals of less than 1 cM and 5 Mb, respectively. Consequently, 539 genes were identified in some selected MQTLs providing tolerance to 5 or all 6 abiotic stresses. Comparative analysis of genes underlying MQTLs with four RNA-seq based transcriptomic datasets unravelled a total of 191 differentially expressed genes which also included at least 11 most promising candidate genes common among different datasets. The promoter analysis showed that the promoters of these genes include many stress responsiveness cis-regulatory elements, such as ARE, MBS, TC-rich repeats, As-1 element, STRE, LTR, WRE3, and WUN-motif among others. Further, some MQTLs also overlapped with as many as 34 known abiotic stress tolerance genes. In addition, numerous ortho-MQTLs among the wheat, maize, and rice genomes were discovered. These findings could help with fine mapping and gene cloning, as well as marker-assisted breeding for multiple abiotic stress tolerances in wheat.

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“…The MQTL analysis has been considered as the most important strategy for integrating QTLs in order to overcome heterogeneity present in different studies (Go net and Gerber 2000). It has a great capability of identifying stable and reliable MQTLs by compiling information from several mapping studies involving diverse genetic backgrounds and multiple environments (Chardon et al 2004;Welcker et al 2011;Kumar et al 2021;Pal et al 2021Pal et al , 2022Saini et al 2021Saini et al , 2022aGudi et al 2022).…”

Section: Discussionmentioning

confidence: 99%

“…Meta analysis can be performed using either Meta-QTL or BioMercator software by formulating and inserting particular sets of algorithms for accurate estimation and recalculation of the genetic position (in cM) for a speci ed set of QTLs (Wang et al 2014). The MQTL analysis has already been conducted in different crops including wheat (Kumar et al 2021;Pal et al 2021Pal et al , 2022Saini et al 2021Saini et al , 2022aGudi et al 2022), rice (Sandhu et al 2021), maize (Kaur et al 2021), sorghum (Aquib and Na s 2022), barley (Zhang et al 2017), and tomato (Ayenan et al 2018). Furthermore, MQTL analysis has also been conducted in some legume crops for different traits such Fe and Zn content and resistance to white mold in french bean (Vasconcellos et al 2017;Izquierdo et al 2018), yield-related traits, seed protein content and partial resistance to Aphanomyces root rot in pea (Hamon et al 2013;Klein et al 2020), leaf spot resistance, plant height, seed number, and seed yield in peanut (Lu et al 2018;Lv et al 2018;Chen et al 2019), and major quality traits in soybean (Chen et al 2021).…”

Section: Introductionmentioning

confidence: 99%

Meta-analysis of the quantitative trait loci associated with agronomic traits, fertility restoration, disease resistance, and seed quality traits in pigeonpea (Cajanus cajan L.)

Halladakeri

Gudi

Singh

et al. 2022

Preprint

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A meta-analysis of quantitative trait loci (QTLs) associated with agronomic traits, fertility restoration, disease resistance, and seed quality traits, was conducted for the first time in pigeonpea. Data on 498 QTLs was collected from 9 linkage mapping studies (involving 21 bi-parental populations). Of these 498, 203 QTLs were projected onto "PigeonPea_ConsensusMap_2022," saturated with 10,522 markers, which resulted in the prediction of 34 meta-QTLs (MQTLs). The average confidence interval (CI) of these MQTLs (2.54 cM) was 3.37-times lower than the CI of the initial QTLs (8.56 cM). Of the 34 MQTLs, 12 high-confidence MQTLs with smaller CI (≤ 5 cM) and a greater number of initial QTLs (≥ 5) were utilized to extract 2,255 gene models, of which 105 were believed to be associated with different traits. The comparison of physical coordinates of MQTLs and marker trait associations reported from genome-wide association studies enabled the validation of eight MQTLs. Furthermore, synteny and ortho-MQTL analysis among pigeonpea and four related legumes crops such as chickpea, pea, cowpea, and french bean led to the identification 117 orthologous genes from 20 MQTL regions. Flanking markers of identified MQTLs can be employed for MQTL-assisted breeding as well as to improve the prediction accuracy of genomic selection in pigeonpea. Furthermore, genes identified in this study could be potential targets for positional cloning and functional analysis to unveil the molecular mechanisms underlying the target traits.

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

Cited by 30 publications

References 122 publications

Consensus genomic regions associated with multiple abiotic stress tolerance in wheat and implications for wheat breeding

Consensus genomic regions associated with multiple abiotic stress tolerance in wheat and implications for wheat breeding

Consensus genomic regions associated with multiple abiotic stress tolerance in wheat and implications for wheat breeding

Meta-analysis of the quantitative trait loci associated with agronomic traits, fertility restoration, disease resistance, and seed quality traits in pigeonpea (Cajanus cajan L.)

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