Meta-QTLs, ortho-MQTLs and candidate genes for the traits contributing to salinity stress tolerance in common wheat (Triticum aestivum L.)

Pal, Neeraj; Saini, Dinesh Kumar; Kumar, Sunil

doi:10.1007/s12298-021-01112-0

Cited by 35 publications

(26 citation statements)

References 82 publications

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“…Several meta-QTL analyses for various traits in cereal crops such as maize 39 , rice 41,46,20 , and barley have already been conducted 35 . In wheat, meta-QTL studies have been undertaken for four different abiotic stresses, including PHS 54 , SS 30 , DS 32 , and HS 33 , but no meta-analysis has been conducted for the traits contributing to WS and AS stresses. Furthermore, to our knowledge, no MAST meta-analysis has ever been performed on wheat.…”

Section: Discussionmentioning

confidence: 99%

“…This study’s consensus map is much better and more comprehensive than previous consensus maps used for MQTL for some of these abiotic stress tolerance traits 32,33,30 . The consensus map contained 100,614 markers (mostly SSR and SNPs) scattered throughout 6,647 cM.…”

Section: Discussionmentioning

confidence: 99%

“…nitrogen use efficiency and root related traits 24,25 , yield and associated traits 26,27 , and quality traits 28,29 . In addition, meta-analysis of QTLs associated with some of the individual abiotic stresses has also been conducted previously, for instance, (i) salinity stress 30 , (ii) drought stress 31,32 , heat stress 31,33 and pre-harvest sprouting 72 . However, no meta-analysis for QTLs associated with WS and AS has been reported in the literature.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

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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 (Cajanus cajan L.). Data on 498 QTLs was collected from 9 linkage mapping studies (involving 21 biparental 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 CI (≤5 cM) and a greater number of initial QTLs (≥5) were utilized to extract 2255 gene models, of which 105 were believed to be associated with different traits under study. Furthermore, eight of these MQTLs were observed to overlap with several marker‐trait associations or significant SNPs identified in previous genome‐wide association studies. Furthermore, synteny and ortho‐MQTL analyses among pigeonpea and four related legumes crops, such as chickpea, pea, cowpea, and French bean, led to the identification of 117 orthologous genes from 20 MQTL regions. Markers associated with MQTLs can be employed for MQTL‐assisted breeding as well as to improve the prediction accuracy of genomic selection in pigeonpea. Additionally, MQTLs may be subjected to fine mapping, and some of the promising candidate genes may serve as potential targets for positional cloning and functional analysis to elucidate the molecular mechanisms underlying the target traits.

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Meta-QTLs, ortho-MQTLs and candidate genes for the traits contributing to salinity stress tolerance in common wheat (Triticum aestivum L.)

Cited by 35 publications

References 82 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

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

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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