High-resolution genome-wide association study and genomic prediction for disease resistance and cold tolerance in wheat

Pang, Yongzhen; Wu, Yuye; Liu, Chunxia; Li, Wenhui; Amand, Paul St.; Bernardo, Amy; Wang, Danfeng; Dong, Lü; Yuan, Xiufang; Zhang, Huirui; Zhao, Meng; Li, Linzhi; Wang, Liming; He, Fang; Liang, Yunlong; Yan, Qiang; Lu, Yue; Su, Yu; Jiang, Haijun; Wu, Jiajie; Li, Anfei; Kong, Lingrang; Bai, Guihua; Liu, Shubing

doi:10.1007/s00122-021-03863-6

Cited by 19 publications

(14 citation statements)

References 71 publications

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“…It is also possible that limited statistical power of both Kandel et al (2018) (n = 192 lines) and our own study (n = 267 lines) make it difficult to compare results. As indicated above, the other two studies are field-based GWAS of traits related to freezing damage in large panels of lines mostly from China (Zhao et al, 2020 andPang et al, 2021), and neither study reports GWAS hits near the ones we found. This again suggests that the genetic basis of freezing tolerance in winter wheat is polygenic and may vary dramatically among broad geographic scales.…”

Section: Crop Sciencementioning

confidence: 65%

“…This suggests that this locus may play a major role in distinguishing differences in freezing tolerance between winter and spring cultivars, but that it is not responsible for variation in freezing tolerance within winter wheat, or at least within these two panels. However, this locus has been implicated as being important for cold stress in two recent field studies using mostly Chinese cultivars of winter wheat (Pang et al, 2021;Zhao et al, 2020), so the importance of Fr-A2 for freezing tolerance within winter wheat appears to vary across broad spatial scales.…”

Section: Crop Sciencementioning

confidence: 99%

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Genome‐wide association analysis of freezing tolerance in soft red winter wheat

et al. 2022

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Freezing tolerance is likely to be an important adaptation for both natural populations and crop cultivars like winter wheat (Triticum aestivum L.). In the United States, winter wheat represents 80% of the total wheat production. Understanding the genetic basis of freezing tolerance in wheat furthers our knowledge of abiotic stress tolerance in plants and may inform breeding programs aimed at adjusting the level of freezing tolerance for a given region. We examined freezing tolerance in a 267-line panel of elite soft red winter wheat that has previously been used for genome-wide association study (GWAS) on agronomically important traits. We were specifically interested in determining the extent of genetic variation for freezing tolerance within the panel, what the genetic basis of that variation is, and if there are correlations between freezing tolerance and other agronomically important traits. We found significant variation in freezing tolerance among the lines, measured as survival through three total days at −8 ˚C. We performed a GWAS on freezing tolerance and identified 13 candidate loci, with nearby candidate genes involved in different functions potentially associated with freezing tolerance. In addition, we found significant correlations between freezing tolerance and seven previously published yield related traits. In summary, we found considerable variation in freezing tolerance in this panel that is associated with yield related traits. Thus, these lines may be useful for breeding programs seeking to optimize freezing tolerance for present and future climatic conditions. INTRODUCTIONWheat (Triticum aestivum L.) is one of the most important crops worldwide. It is cultivated in ∼200 million ha (http: //www.fao.org/faostat/en/#data/QC/visualize) (Zhao et al., 2013) and provided about 20% of all dietary calories consumed globally (

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Section: Crop Sciencementioning

confidence: 65%

Section: Crop Sciencementioning

confidence: 99%

Genome‐wide association analysis of freezing tolerance in soft red winter wheat

et al. 2022

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“…The BayesB method used in the DHI population outperformed the rrBLUP method for DTF, HIR, and PBL, but not PHT (-2.3%) by 1.8%, 1.8%, and 2.2% of PA, respectively. The inclusion of major QTL detected in the de novo GWAS as a fixed effect in rrBLUP method of GP improves PA (Spindel et al, 2016;Herter et al, 2018a;Pang et al, 2021;. By employing ST_GWAS_rrBLUP method in this study, the PA of HIR was improved by 11.1%, but that of DTF, PHT, and PBL declined by 21.4%, 28.8% and 11.7%.…”

Section: Availability Of Gp Models Among Traitsmentioning

confidence: 75%

Multi-trait ridge regression BLUP with de novo GWAS improves genomic prediction for haploid induction ability and agronomic traits of haploid inducers in maize

Chen,

Frei,

Lübberstedt

2024

Preprint

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Key message Employing multi-trait and de novo GWAS in a ridge regression BLUP model increases the predictive ability of haploid induction rate of haploid inducers in maize. Ridge regression BLUP (rrBLUP) is a widely used model for genomic selection. Different genomic prediction (GP) models have their own niches depending on the genetic architecture of traits and computational complexity. Haploid inducers have unique trait performances, relevant for doubled haploid (DH) technology in maize (Zea mays L.). We herein compared the performance of single-trait (ST) and multi-trait (MT) GP models (rrBLUP, BayesB, Random Forest, and xGBoost) and employed multi-trait and de novo GWAS in the ridge regression BLUP model for four traits of interest (Days to flowering, DTF; haploid induction rate, HIR; plant height, PHT; primary branch length, PBL) of the multifamily DH inducers (DHIs), and next tested the GP models in multi-parent advanced generation inter-cross (MAGIC) DHIs. The average predictive abilities (PA) of different GP methods across traits were 0.44 to 0.65 in multifamily DHIs. ST/MT de novo GWAS rrBLUP methods increased PA of HIR when using five-fold cross-validation. In addition, MT GP models improved PA by 13% on average across traits relative to ST GP models in MAGIC DHIs. These results provide empirical evidence that employing multi-trait and de novo GWAS in rrBLUP model in genomic selection could benefit the genetic improvement of haploid inducers.

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“…Genome-wide markers covering the entire genome are critical in this method to attest inclusion of all major- and minor-effect QTL of a target trait (Desta and Ortiz 2014 ). However, the presence of several markers with no effect increases noise in genomic prediction models and reduces the accuracy as evidenced in previous studies (Meher et al 2022 ; Pang et al 2021 ; Schulz-Streeck et al 2011 ). Besides, studies have shown that increasing marker density could negatively affect the prediction accuracy of Bayesian-based models due to the slow or non-convergence of the Markov Chain Monte Carlo (MCMC) iterations (Zhang et al 2019 ).…”

Section: Introductionmentioning

confidence: 95%

Haplotype-tagged SNPs improve genomic prediction accuracy for Fusarium head blight resistance and yield-related traits in wheat

et al. 2023

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Key message Linkage disequilibrium (LD)-based haplotyping with subsequent SNP tagging improved the genomic prediction accuracy up to 0.07 and 0.092 for Fusarium head blight resistance and spike width, respectively, across six different models. Abstract Genomic prediction is a powerful tool to enhance genetic gain in plant breeding. However, the method is accompanied by various complications leading to low prediction accuracy. One of the major challenges arises from the complex dimensionality of marker data. To overcome this issue, we applied two pre-selection methods for SNP markers viz. LD-based haplotype-tagging and GWAS-based trait-linked marker identification. Six different models were tested with preselected SNPs to predict the genomic estimated breeding values (GEBVs) of four traits measured in 419 winter wheat genotypes. Ten different sets of haplotype-tagged SNPs were selected by adjusting the level of LD thresholds. In addition, various sets of trait-linked SNPs were identified with different scenarios from the training-test combined and only from the training populations. The BRR and RR-BLUP models developed from haplotype-tagged SNPs had a higher prediction accuracy for FHB and SPW by 0.07 and 0.092, respectively, compared to the corresponding models developed without marker pre-selection. The highest prediction accuracy for SPW and FHB was achieved with tagged SNPs pruned at weak LD thresholds (r2 < 0.5), while stringent LD was required for spike length (SPL) and flag leaf area (FLA). Trait-linked SNPs identified only from training populations failed to improve the prediction accuracy of the four studied traits. Pre-selection of SNPs via LD-based haplotype-tagging could play a vital role in optimizing genomic selection and reducing genotyping costs. Furthermore, the method could pave the way for developing low-cost genotyping methods through customized genotyping platforms targeting key SNP markers tagged to essential haplotype blocks.

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High-resolution genome-wide association study and genomic prediction for disease resistance and cold tolerance in wheat

Cited by 19 publications

References 71 publications

Genome‐wide association analysis of freezing tolerance in soft red winter wheat

Genome‐wide association analysis of freezing tolerance in soft red winter wheat

Multi-trait ridge regression BLUP with de novo GWAS improves genomic prediction for haploid induction ability and agronomic traits of haploid inducers in maize

Haplotype-tagged SNPs improve genomic prediction accuracy for Fusarium head blight resistance and yield-related traits in wheat

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