- Genome-wide association study reveals new loci for yield-related traits in Sichuan wheat germplasm under stripe rust stress. - Background: As one of the most important food crops in the world, increasing wheat ( Triticum aestivum L.) yield is an urgent task for global food security under the continuous threat of stripe rust (caused by Puccinia striiformis f. - Here, we identified loci associated to multi-environmental yield-related traits under stripe rust stress in 244 wheat accessions from Sichuan Province through genome-wide association study (GWAS) using 44,059 polymorphic markers from the 55 K single nucleotide polymorphism (SNP) chip.. - Results: A total of 13 stable quantitative trait loci (QTLs) were found to be highly associating to yield-related traits, including 6 for spike length (SL), 3 for thousand-kernel weight (TKW), 2 for kernel weight per spike (KWPS), and 2 for both TKW and KWPS, in at least two test environments under stripe rust stress conditions.. - Conclusions: QTLs and candidate genes detected in our study for yield-related traits under stripe rust stress will facilitate elucidating genetic basis of yield-related trait and could be used in marker-assisted selection in wheat yield breeding.. - Keywords: Wheat, 55 K SNP, Genome-wide association study, Yield-related traits, Stripe rust. - As one of the most destructive wheat diseases in the world, stripe rust that is caused by fungus Puccinia striiformis Westend. - Therefore, improving wheat yield under stripe rust stress is extremely urgent. - Thus, identifying loci associated with yield-related traits under stripe rust stress may provide favourable alleles and their useful markers for breeding wheat cultivars with high yield in combin- ation with stripe rust resistance.. - Based on multi-environmental yield-related traits data under stripe rust stress, a GWAS was con- ducted to identify the associated loci for yield-related traits, such as FTN, SL, SlPS, Kernel weight per spike (KWPS), TKW and SlC. - Phenotypic characterization of eight yield-related traits The yield-related traits were collected from Chongzhou in CZ17, CZ18), Mianyang in 2017 (MY17) under stripe rust stress, and Chongzhou in 2017, 2018. - The phenotypic variations of eight yield-related traits under stripe rust stress were determined based on their best linear unbiased prediction (BLUP) values (Table 1).. - Phenotypic differences between landraces and cultivars under stripe rust stress. - The t-test identified significant differences between landraces and cultivars in FTN, SlPS, KPSl, KWPS, TKW and SlC under stripe rust stress based on the BLUP values (Table 2). - Correlations among yield-related traits under stripe rust stress and stripe rust reaction. - Pearson correlation coefficient analysis among the yield components and stripe rust reaction measured as IT. - 1 The box plots of eight yield-related traits in multiple environments. - The impact of stripe rust on yield-related traits. - In order to further understand the stripe rust effects on yield traits, we compared the control plots (CZ17ck and CZ18ck) and the inoculated plots (MY17, CZ17, and CZ18) (Table 3). - Significant differ- ences were observed in the BLUP values of five yield- related traits (FTN, SlPS, KWPS, TKW and SlC) between two sub-populations. - Subsequently, GWAS was conducted to identify loci associated with yield-related traits in three environments under stripe rust stress based on the MLM model with Q + K as covariates. - Table 1 The phenotypic variations for 244 wheat accessions under stripe rust stress based on BLUP values. - Based on the Chinese Spring reference RefSeq v1.0 (IWGSC) and RefSeq Annotation v and 33 genes included in QSL.sicau-1AL, QTKW.- sicau-4AL and QKWPS.sicau-4AL.1 region were se- lected. - Characterization of yield-related traits under stripe rust stress. - We evaluated 244 wheat accessions in three field envi- ronments under stripe rust stress (CZ17, MY17 and CZ18) and two sites without inoculating Pst (CZ17ck. - 2 The correlations matrix and network analysis among eight yield-related traits and infection type (IT). - Table 3 The difference in yield-related traits between resistant and susceptible accessions with or without Pst inoculation. - percentage of yield-related traits under stripe rust stress. - The seven yield-related traits displayed significant differences between 2017 and 2018 except KWPS. - 3 Significant difference in five yield-related traits under stripe rust stress between two sub-populations based on Q-matrix. - Yield-related traits SlPS, TKW and SlC exhibited rela- tively higher broad-sense heritabilities, while FTN and KPSl showed lower broad-sense heritabilities (Table 1).. - Consistent with many Table 4 The details of QTLs associated with yield-related traits under stripe rust stress. - QSL.sicau-1AL AX- 110408975. - QKWPS.sicau-1BL AX- 109335890. - Zhang et al.. - QKWPS.sicau-2AS AX- 108919444. - QSL.sicau-2AL AX- 110079477. - QSL.sicau-2DS AX- 110647062. - QSL.sicau-4AS AX- 109296730. - QTKW.sicau-4AL AX- 109993853. - QSL.sicau-5AL.1 AX- 109624254. - QSL.sicau-5AL.2 AX- 110521338. - The further comparison between the non-inoculation control and Pst-inoculation indicated that many accessions exhibited significantly lower KPS, KWPS and TKW, but higher FTN under stripe rust stress. - However, stripe rust did not significantly affect SL, SlPS, KPSl and SlC. - The resistant accessions exhibited higher mean values of yield-related traits than susceptible accessions no matter inoculated or not inoculated indicating that stripe rust resistance protects most of the yield-related traits.. - Interestingly, both resistant and susceptible accessions under stripe rust stress exhibited higher FTN. - So, we speculated that the stripe rust should not have significant effects on FTN.. - There were many reports also demonstrated that the stripe rust didn’t affect tiller number [37, 38]. - The differ- ences in FTN between the control and Pst-inoculation fields could be due to other conditions such as weather, water, and soil fertility rather than stripe rust.. - There is no doubt that stripe rust can reduce yield, especially the KPS, KWPS and TKW [39 – 41]. - In the present study, the values of KPS, KWPS and TKW of resistant accessions under stripe rust stress were reduced by 1.5, 8.5 and 5.6%, while those of susceptible acces- sions under stripe rust stress were reduced 2.7, 11.8 and 10.2% separately. - Thus, susceptible accessions had more serious reduction by stripe rust than resistant accessions.. - In other words, resistance can effectively reduce the losses of KPS, KWPS and TKW under stripe rust stress.. - KWPS and TKW) [43]. - The accessions with favourable allele showed higher mean values of spike length, kernel weight per spike and thousand-kernel weight than that without favourable allele in three environments under stripe rust stress and BLU P values. - But most of all, we can be sure the infection of stripe rust can result in the decrease of KPS, KWPS and TWK in this study, which were the important compo- nents to results in the yield loss.. - The comparison analysis for the eight yield-related traits under stripe rust stress between landraces and cultivars showed the significant differences in FTN, SlPS, KPSl, KWPS, TKW and SlC (Table 2). - 5 The P values of associated loci with yield-related traits under stripe rust stress exhibited as Manhattan plots. - The represent study provides additional evidence for taking the advantages of landraces with favourable alleles for yield-related traits under stripe rust stress.. - Markers associated to yield-related traits. - The QTLs associated with SL, KWPS and TKW was named as QSL.sicau, QKWPS.- sicau and QTKW.sicau, respectively (Table 4). - They were QSL.sicau-1AL, QTKW.sicau- 4AL and QKWPS.sicau-4AL.1, which were located at dif- ferent physical positions from previously reported genes related to SL, TKW and KWPS.. - The six QTLs were identified associated with SL, in- cluding one potentially new (QSL.sicau-1AL) and five previously reported QTLs. - QSL.sicau-2AL was located around the position of 432.58 Mb at 2A, which was the same as QSl.sdau-2A [57]. - QSL.sicau-2DS overlapped with QPht/SL.cau-2D.2 [58], and the QSL.sicau-4AS was covered by QSl.sau-4A [59]. - One was QSL.sicau-5AL.1, which was the same as QSL.caas-5AL that was flanked by marker JD_c15758_. - QTKW.sicau-4AL and QKWPS.sicau-4AL.1 were potentially new based on their physical locations.. - QTKW.sicau-1BL.2 was located in the distal region of 1BL was covered by QTgw.ipk-1B-FS4 [61]. - QTKW.- sicau-2AS.1 was mapped on the short end of 2AS, which was overlapped with QTkw-2A.2 [9] and Qtkw2A-2 [62].. - QKWPS.sicau-4AL.2 associated with KWPS was a major. - QTKW.sicau-1BL.1 and QKWPS.sicau-1BL were located in the same region around 670 Mb. - In addition, QTKW.sicau-2AS.2 and QKWPS.- sicau-2AS were also mapped at the same position of 24.05 Mb, which was very close to Qtkw2A-1 [62].. - We identified Qyrsicau-1BL.1 around the position of 670 Mb that was associated with stripe rust IT and DS [27], which belonged to the same QTL block of both QTKW.sicau-1BL.1 and QKWPS.sicau-1BL. - These re- sults indicate that this QTL block around the position of 670 Mb on 1BL confers stripe rust resistance, and thus related to KWPS and TKW under stripe rust stress in the present study. - In addition, Qyrsicau-1BL.2 around the region of 681 Mb associated with stripe rust IT [27]. - was the same as QTKW.sicau-1BL.2 which was also associated with TKW in this study. - This is another QTL block conferring stripe rust resistance and thus associ- ated to TKW. - Thus, 1BL harbours numerous QTLs for stripe rust resistance and other traits.. - Of these genes, 11, 3 and 7 candidate genes were identified for QSL.sicau-1AL, QTKW.sicau-4AL and QKWPS.sicau- 4AL.1, respectively (Additional file 6). - There were three putative candidate genes for QTKW.sicau-4AL, TraesCS4A02G229100, TraesCS4A02 G229600, and TraesCS4A02G229700. - Evaluation of yield-related traits and stripe rust infection type. - The 244 wheat accessions were evaluated in two loca- tions in Sichuan with different years but all under stripe rust stress: Chongzhou N E, elevation 513 m) in 2017 (CZ17) and 2018 (CZ18);. - The rule of identification of infection type (IT) for stripe rust was the same as Ye et al. - Combining the yield-related traits under stripe rust stress with 44,059 effective SNP markers, GWAS analyses were performed on the 244 accessions using software TASSEL v5.2.38 based on the mixed linear model (MLM) with Q and K as covariates . - The associated loci with related traits were visualized with Manhattan plots with P values using the ggplot2 package in the R program [109].. - Analyses of high confidence significant associated loci There are many QTLs associated with yield-related traits previously reported. - Additional file 1: Pearson coefficient analysis for eight yield-related traits among multiple environments (XLSX 11 kb). - Molecular genetic analysis of five spike-related traits in wheat using RIL and immortalized F 2 populations. - A genome-wide association study of wheat spike related traits in China. - Genome-wide association mapping for seedling and adult plant resistance to stripe rust in synthetic hexaploid wheat. - Genome-wide association study for kernel weight-related traits using SNPs in a Chinese winter wheat population. - Genome-wide association study of resistance to stripe rust ( Puccinia striiformis f. - Effects of stripe rust on the wheat plant.. - Effect of stripe rust ( Puccinia striiformis west.) and irrigation on the yield and foliage temperature of wheat. - High-temperature, adult-plant resistance to wheat stripe rust and effects on yield components. - Reduction of winter wheat yield losses caused by stripe rust through fungicide management. - Stripe rust: understanding the disease in wheat.. - Stripe Rust: Stripe Rust Resistance. - Identification of quantitative trait loci controlling yield-related traits indicates breeding potential of Tibetan semiwild wheat ( Triticum aestivum ssp. - Unconditional and conditional QTL analysis of kernel weight related traits in wheat ( Triticum aestivum L.) in multiple genetic backgrounds
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