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Acta Agronomica Sinica ›› 2025, Vol. 51 ›› Issue (8): 2111-2127.doi: 10.3724/SP.J.1006.2025.41069

• CROP GENETICS & BREEDING·GERMPLASM RESOURCES·MOLECULAR GENETICS • Previous Articles     Next Articles

Meta-analysis of stripe rust resistance-associated traits and candidate gene identification in wheat

ZHANG Fei-Fei1(), HE Wan-Long1, JIAO Wen-Juan1, BAI Bin2, GENG Hong-Wei1, CHENG Yu-Kun1,*()   

  1. 1College of Agronomy, Xinjiang Agricultural University / Special High Quality Triticeae Crops Engineering and Technology Research Center, Xinjiang Agricultural University, Urumqi 830052, Xinjiang, China
    2Wheat Research Institute, Gansu Academy of Agricultural Sciences, Lanzhou 730070, Gansu, China
  • Received:2024-10-12 Accepted:2025-04-27 Online:2025-08-12 Published:2025-05-09
  • Contact: *E-mail: ykcheng@126.com
  • Supported by:
    Fundamental Research Funds for Universities of the Xinjiang Uygur Autonomous Region(XIEDU20241042);Key Research and Development Program Project of the Xinjiang Uygur Autonomous Region(2022B02015-3);China Postdoctoral Science Foundation(2021MD703887)

Abstract:

Wheat stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst), poses a serious threat to global wheat production. In this study, we performed a comprehensive meta-analysis of 480 published quantitative trait loci (QTL) and known resistance genes (Yr) associated with stripe rust resistance in wheat. These QTLs were projected onto a consensus genetic map, resulting in the identification of 90 meta-QTLs (MQTLs). Among these MQTLs, 16 were associated with disease severity (DS), 10 with infection type (IT), 7 with the area under the disease progress curve (AUDPC), and 3 with other resistance-related traits. Additionally, 19 MQTLs were associated with both DS and IT, 20 with DS and AUDPC, and 15 with IT and AUDPC. The MQTLs were unevenly distributed across the 21 wheat chromosomes, with several forming clusters. These MQTLs explained phenotypic variances ranging from 2.00% to 63.01%, with confidence intervals spanning 0.01 to 24.60 cM. Thirteen MQTLs co-localized with known resistance genes, including Yr5, Yr7, Yr17, Yr18, Yr28, Yr29, Yr30, Yr44, Yr48, Yr52, Yr54, Yr67, and Yr82. Furthermore, candidate gene (CG) analysis identified 72 genes within the MQTL regions. Functional annotation and expression profiling revealed that many of these CGs encode proteins involved in sugar transport or contain resistance-related domains such as NBS-LRR, WRKY, and F-box. Expression analysis across different leaf tissues further supported their potential roles in defense responses. These findings provide valuable molecular markers and candidate genes for the pyramiding of resistance QTLs/genes, offering a promising strategy for developing stripe rust-resistant wheat cultivars and contributing to global food security.

Key words: wheat, stripe rust, meta-analysis, MQTL

Fig. 1

Distribution of QTLs on 21 chromosomes"

Fig. 2

Major QTLs with different LOD values"

Fig. 3

Primary QTLs with different PVE values"

Fig. 4

Distribution of major QTLs based on their LOD scores"

Fig. 5

Number of MQTLs on different wheat chromosomes"

Fig. 6

Number of MQTLs with different amounts of initial QTLs"

Fig. 7

Fold reduction in confidence intervals of QTLs based on meta-analysis"

Fig. 8

Distribution of MQTLs on 21 chromosomes Green: MQTL; black: GWAS validated MQTLs; blue: MQTL hotspots; red: GWAS validated MQTLs and QTL hotspots."

Fig. 9

Homology map of the 21 wheat chromosomes based on MQTLs associated with stripe rust resistance trait"

Table 1

MQTLs co-location with known major rust resistance genes"

MQTLs 染色体
Chr.
共定位基因
Co-localized gene
MQTL1B.3 1B Yr29
MQTL2A.1 2A Yr17/Lr37
MQTL2B.1 2B Yr44
MQTL2B.3 2B Yr5/Yr7
MQTL2D.2 2D Yr54
MQTL3B.1 3B Yr82
MQTL3B.1 3B Yr30
MQTL3B.2 3B Yr30
MQTL4D.2 4D Yr28
MQTL5A.1 5A Yr48
MQTL7B.4 7B Yr52/Yr67
MQTL7D.1 7D Yr18

Fig. 10

Expression patterns of candidate genes in different tissues of wheat at different growth stages"

Fig. 11

Level 2 gene ontology (GO) terms from meta-quantitative trait locus (MQTL) region"

Fig. 12

KEGG enrichment pathway of candidate genes in MQTL region"

Table 2

Comparison of MQTL difference"

来源
Origin
时间
Time
标记数目
No. of markers
标记类型
Marker types
MQTLs 置信区间
CI
(cM)
平均置信区间
Mean CI
(cM)
QTL数量
No. of Yr genes and QTLs
映射数量
No. of mappings
映射成功文献数
No. of citations
程宇坤[42] 1997-2018 12,681 DArT, SSR, 55K SNP 12 0.03‒89.56 12.52 79Yr,342QTLs 194 58
Jan[43] 2000‒2020 76,753 DArT, SSR, 90K SNP 61 0.02‒11.47 9.78 353QTLs 184 70
Kumar[41] 2002‒2022 138,574 ITMI_SSR图谱
ITMI_SSR map
2004小麦SSR参考图谱
Wheat, Consensus SSR map, 2004
硬粒小麦参考图谱
an integrated map for durum wheat
9K SNP、55K SNP、90K SNP、660K SNP标记
wheat 9K 、55K、90K、660K SNP markers
67 0‒11.68 1.97 505QTLs 380 117
本研究 1997‒2024 138,574 ITMI_SSR图谱
ITMI_SSR map
2004小麦SSR参考图谱
Wheat, Consensus SSR map, 2004
硬粒小麦参考图谱
an integrated map for durum wheat
9K SNP、55K SNP、90K SNP、660K SNP标记
wheat 9K 、55K、90K、660K SNP markers
90 0.01‒24.60 7.09 1656QTLs 480 165
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