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

Abstract

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.

Genetic architecture of adult-plant resistance to stripe rust in bread wheat (Triticum aestivum L.) association panel

“…Even though it is challenging to compare the positions of QTLs from different studies because of the different mapping methodologies, marker systems, and mapping populations employed, some QTLs found in this study coincided with the mapping positions of previously reported yellow rust resistant genes in the literature. Similar to the current study, several earlier studies reported yellow rust-resistant QTLs on 1A ( Fu et al., 2019 ; Tehseen et al., 2020 ; Yao et al., 2021 ; Baranwal et al., 2022 ; Bouvet et al., 2022 ), on 1B ( Losert et al., 2017 ; Alemu et al., 2020 ; Baranwal et al., 2022 ), on 1D ( Long et al., 2019 ; Jia et al., 2020 ; Zhang et al, 2021 ; Baranwal et al., 2022 ; Kumar et al., 2023 ) on 2A ( Tehseen et al., 2020 ; Zhang et al., 2021 ; Zhang et al., 2021 ; Baranwal et al., 2022 ), on 2B ( Li et al., 2020 ), on 2D ( Mu et al., 2020 ; Baranwal et al., 2022 ), on 3A ( Tehseen et al., 2022 ), on 3B ( Zegeye et al., 2014 ; Kumar et al., 2020 ), on 3D ( Bouvet et al., 2022 ), on 4A ( Mu et al., 2020 ), on 4B ( Alemu et al., 2020 ), on 4D ( Rollar et al., 2021 ), on 5A ( Zhang et al., 2021 ), on 5B ( Lu et al., 2014 ), on 5D ( Zhang et al., 2021 ), on 6A ( Baranwal et al., 2022 ), on 6B ( Zhang et al., 2021 ) on 7A ( Yel et al., 2019 ; Alemu et al., 2020 ; Tehseen et al., 2020 ; Baranwal et al., 2022 ), and on 7D ( Long et al,. 2019 ; Rollar et al., 2021 ).…”

Unravelling the genetic framework associated with grain quality and yield-related traits in maize (Zea mays L.)

“…Meta-analyses of QTLs associated with a variety of traits have been recently conducted in different crops such as wheat ( Kumar et al, 2021 ; Kumar et al, 2022 A. C. ; Kumar et al, 2023 S. ; Saini et al, 2021 ; Saini et al, 2022 ; Tanin et al, 2022 ), rice ( Sandhu et al, 2021 ; Kumari et al, 2023 ), barley ( Akbari et al, 2022 ), common bean ( Shafi et al, 2022 ), pigeon pea ( Halladakeri et al, 2023 ), including maize ( Kaur et al, 2021 ; Sheoran et al, 2022 ; Wang et al, 2022 ; Gupta et al, 2023 ; Karnatam et al, 2023 ), for diverse traits, including both yield-related traits ( Semagn et al, 2013 ; Wang Y. et al, 2016 ; 2020 ; Chen et al, 2017 ; Zhou et al, 2020 ) and quality traits ( Jin et al, 2013 ; Dong et al, 2015 ). However, there is currently no comprehensive study on the genomic regions influencing both grain quality and yield in maize.…”

Genotype-Specific Expression of Selected Candidate Genes Conferring Resistance to Leaf Rust of Rye (Secale cereale L.)

“…Among these genes, TraesCS7D02G217700 has been proposed as a candidate gene conferring resistance to LR. Furthermore, Kumar et al [ 35 ] used high-confidence meta-QTLs to identify three GT-encoding genes ( TraesCS2B02G012000 , TraesCS5A02G305000 and TraesCS5A02G305100 ) in wheat that may be linked to stripe rust resistance. In a previous study [ 10 ], we showed that in rye inoculated with a semi-compatible Prs isolate, the protective role of GT may be related to the conversion of DIBOA to DIBOA glucoside, the content of which increased at 24 hpt and then decreased at 48 hpt.…”