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

Abstract

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.

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

“…Some of the major differences between the previous study and the current study in terms of data used and results obtained are as follows- (i) the current study used 505 QTLs that were collected from 101 mapping studies, in contrast to the earlier study [ 54 ], which used only 353 QTLs that were collected from just 75 studies. The number of initial QTLs utilized for meta-QTL analysis has been found to be significantly and positively correlated with the accuracy of the statistical findings [ 43 , 44 ]; (ii) in contrast to the earlier study, where a consensus map was created using only 76,753 markers [ 54 ], the current study involved a dense consensus map involving 138,574 markers; (iii) in contrast to the previous study, where only 184 QTLs could be grouped into the MQTLs, the use of a dense consensus map during the present study enabled the inclusion of an increased number of QTLs (309 QTLs) into MQTLs; (iv) further, as many as 44 MQTLs predicted during the present study were validated with MTAs available from GWAS in contrast to the earlier study where no such efforts for validating MQTLs were made [ 54 ]. Validation of MQTLs with GWAS-based MTAs suggests that the impact of these genomic regions on stripe rust resistance may be less limited by genetic background; (v) we observed an average CI of MQTLs of 1.97 cM and a 6.89-fold reduction in CI of initial QTLs after meta-analysis in the current study; no such statistics were reported in the previous study [ 54 ]; (vi) we observed significant reduction (i.e., 5.67 fold) in the number of genomic regions associated with stripe rust resistance after meta-analysis, this is in contrast to earlier study [ 54 ], where only 3.5-fold reduction was observed; (vii) rather than analyzing all available MQTLs (irrespective of their importance) for CGs as done in previous study [ 54 ], we used a set of criteria to prioritize some hcMQTLs for CG mining, which enabled the identification of promising CGs; and (viii) in the current study, we examined the patterns of important genes (showing differential expression under disease infection) in wheat tissues at various developmental stages.…”

“…As a result, meta-analysis is able to identify the consensus QTLs associated with the targeted traits in multiple environments and diverse genetic backgrounds [ 39 ]. For instance, meta-analysis has already been conducted in wheat for a number of different traits [ 40 – 44 ] including resistance to different diseases such as leaf rust [ 45 , 46 ], stem rust [ 47 ], tanspot [ 48 ], Fusarium head blight [ 49 – 52 ] and powdery mildew [ 53 ]. Despite the fact that there are over 500 QTLs associated with stripe rust resistance, a recent study published in 2021 conducted a meta-analysis using only 184 QTLs and identified 61 MQTLs [ 54 ].…”

Delineating meta-quantitative trait loci for anthracnose resistance in common bean (Phaseolus vulgaris L.)

“…Meta-QTL (MQTL) analysis assembles information from multiple studies and refines QTL locations by narrowing down the confidence intervals obtained from individual studies and correlating them with each other ( Goffinet and Gerber, 2000 ). MQTL analysis involving known QTLs for any particular trait has been conducted in several crops for various traits, including yield-related traits ( Tyagi et al, 2015 ; Saini et al, 2022c ), stripe rust resistance ( Jan I. et al, 2021 ), multiple disease resistance ( Pal et al, 2022 ; Saini et al, 2022a ), thermo-tolerance ( Kumar et al, 2021 ), salinity stress ( Pal et al, 2021 ), multiple abiotic stress tolerance ( Tanin et al, 2022 ) in wheat, nitrogen use efficiency ( Sandhu et al, 2021 ), grain size and African gall midge resistance ( Yao et al, 2016 ; Daware et al, 2017 ) in rice and protein and oil content in soybean ( Van and McHale, 2017 ). However, only a very few reports are available for MQTL analysis in this important legume crop for different traits including seed Fe and Zn concentrations ( Izquierdo et al, 2018 ) and white mold resistance ( Vasconcellos et al, 2017 ).…”

Application of potassium nitrate and salicylic acid improves grain yield and related traits by delaying leaf senescence in Gpc-B1 carrying advanced wheat genotypes

“…In addition to significant effects on GPC, the Gpc-B1 is associated with the earlier initiation of senescence, shorter grain filling period, higher percentage of yellow peduncle, and more efficient nitrogen remobilization in wheat (Uauy et al, 2006). The SA and PN are known to play significant roles in delaying the leaf senescence and improving tolerance against biotic and abiotic stresses (El-Tayeb, 2005;Ibrahim et al, 2014;Jatana et al, 2021;Gudi et al, 2022b;Tanin et al, 2022a).…”