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. 2023 May 12;24(1):259.
doi: 10.1186/s12864-023-09336-y.

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

Sandeep Kumar #  1 , Dinesh Kumar Saini #  2 , Farkhandah Jan #  1 , Sofora Jan  1 , Mohd Tahir  1 , Ivica Djalovic  3 , Dragana Latkovic  4 , Mohd Anwar Khan  1 , Sundeep Kumar  5 , V K Vikas  6 , Upendra Kumar  7 , Sundip Kumar  8 , Narendra Singh Dhaka  9 , Om Parkash Dhankher  10 , Sachin Rustgi  11 , Reyazul Rouf Mir  12
Affiliations

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

Sandeep Kumar et al. BMC Genomics. .

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.

Keywords: Candidate genes; Consensus map; MQTL; Meta-analysis; Stripe rust.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Basic characteristics of QTLs associated with yellow rust resistance (a) chromosome-wise distribution of QTLs, (b) LOD scores of QTLs, (c) PVE values of the QTLs
Fig. 2
Fig. 2
Basic characteristics of MQTLs associated with yellow rust resistance (a) chromosome wise distribution of MQTLs, (b) the number of QTLs involved in different MQTLs, (c) the number of QTL studies involved in different MQTLs, (d) fold reduction in confidence intervals of QTLs after meta-analysis
Fig. 3
Fig. 3
Circular diagram representing the features of QTLs and MQTLs associated with yellow rust resistance. The information projected includes, (moving inwards) the outermost ring represents consensus map, the positions of MQTLs on the chromosomes, and the innermost ring represents the frequency of QTLs involved in each identified MQTL
Fig. 4
Fig. 4
Distribution of MQTLs on different wheat chromosomes; MQTLs: green, GWAS validated MQTLs: purple, and QTL hotspots: black
See this image and copyright information in PMC

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