Multi-Locus Genome-Wide Association Study Reveals the Genetic Architecture of Stalk Lodging Resistance-Related Traits in Maize

Zhang, Yanling; Liu, Peng; Zhang, Xiaoxiang; Zheng, Qi; Chen, Min; Ge, Fei; Li, Zhaoling; Sun, Wenting; Guan, Zhongrong; Liang, Tianhu; Zheng, Yan; Tan, Xiaolong; Zou, Chaoying; Peng, Huanwei; Pan, Guangtang; Shen, Yaou

doi:10.3389/fpls.2018.00611

Cited by 80 publications

(70 citation statements)

References 47 publications

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“…According to the ANOVA results, RPR has relatively high broad-sense heritability, which is supported by several previous studies [1,19,20,26,27], illustrating that genetic effects can account for the most proportion of phenotypic variance in RPR, and that better selection of RPR can be achieved in early generations if target lines are used as parents to construct breeding populations to screen out varieties with high stalk strength. However, RPR, a complex quantitative trait, is controlled by multiple genes with minor effects, which has been discussed in previous studies [1,19,28,29]. The breeding scheme of population recurrent selection may be more efficient for the pyramid of favorable alleles related to RPR [8, 15,25].…”

Section: Discussionmentioning

confidence: 99%

Genetic mapping and genomic selection for maize stalk strength

Liu

Xiu

et al. 2019

Preprint

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Background Maize is one of the most important staple crops and is widely grown throughout the world. Stalk lodging can cause enormous yield losses in maize production. However, rind penetrometer resistance (RPR), which is recognized as a reliable measurement to evaluate stalk strength, has been shown to be efficient and useful for improving stalk lodging-resistance. Linkage mapping is an acknowledged approach for exploring the genetic architecture of target traits. In addition, genomic selection (GS) using whole genome markers enhances selection efficiency for genetically complex traits. In the present study, two recombinant inbred line (RIL) populations were utilized to dissect the genetic basis of RPR, which was evaluated in seven growth stages.Results The optimal stages to measure stalk strength are the silking phase and stages after silking. A total of 66 and 45 quantitative trait loci (QTL) were identified in each RIL population. Several potential candidate genes were predicted according to the maize gene annotation database and were closely associated with the biosynthesis of cell wall components. Moreover, analysis of gene ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway further indicated that genes related to cell wall formation were involved in the determination of RPR. In addition, a multivariate model of genomic selection efficiently improved the prediction accuracy relative to a univariate model and a model considering RPR-relevant loci as fixed effects.Conclusions The genetic architecture of RPR is highly genetically complex. Multiple minor effect QTL are jointly involved in controlling phenotypic variation in RPR. Several pleiotropic QTL identified in multiple stages may contain reliable genes and can be used to develop functional markers for improving the selection efficiency of stalk strength. The application of genomic selection to RPR may be a promising approach to accelerate breeding process for improving stalk strength and enhancing lodging-resistance.

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Section: Discussionmentioning

confidence: 99%

Genetic mapping and genomic selection for maize stalk strength

Liu

Xiu

et al. 2019

Preprint

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“…However, more effective methods are required for detecting QTLs. Multi-locus GWAS is a suitable method for detecting significant quantitative trait nucleotides (QTNs) and has been used in several studies (Hou et al, 2018;Zhang et al, 2018c).…”

Section: Introductionmentioning

confidence: 99%

Linkage Analysis and Multi-Locus Genome-Wide Association Studies Identify QTNs Controlling Soybean Plant Height

et al. 2020

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Plant height is an important target for soybean breeding. It is a typical quantitative trait controlled by multiple genes and is susceptible to environmental influences. Here, we carried out phenotypic analysis of 156 recombinant inbred lines derived from "Dongnong L13" and "Henong 60" in nine environments at four locations over 6 years using interval mapping and inclusive composite interval mapping methods. We performed quantitative trait locus (QTL) analysis by applying pre-built simple-sequence repeat maps. We detected 48 QTLs, including nine significant QTLs detected by multiple methods and in multiple environments. Meanwhile, genotyping of all lines using the SoySNP660k BeadChip produced 54,836 non-redundant single-nucleotide polymorphism (SNP) genotypes. We used five multi-locus genome-wide association analysis methods to locate 10 quantitative trait nucleotides (QTNs), four of which overlap with previously located QTLs. Five candidate genes related to plant height are predicted to lie within 200 kb of these four QTNs. We identified 19 homologous genes in Arabidopsis, two of which may be associated with plant height. These findings further our understanding of the multigene regulatory network and genetic determinants of soybean plant height, which will be important for breeding high-yielding soybean.

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“…By comparing four multi-locus methods i.e. FASTmrEMMA, mrMLM, pLARmEB, and ISIS EM-BLASSO methods, it was con rmed that ISIS EM-BLASSO was the most effective approach for QTL identi cation [76]. The combination of two SL-GWAS and ML-GWAS methods contributes e ciently to detection of signi cant loci associated with pre-harvest sprouting tolerance in wheat [77].…”

Section: Signi Cance Of Gwas Using High-density Genotypingmentioning

confidence: 95%

“…FarmCPU approach is more complicated and less obvious to user than mix linear model, and hence more care should be taken during using FarmCPU algorithm [18]. Y Zhang, P Liu, X Zhang, Q Zheng, M Chen, F Ge, Z Li, W Sun, Z Guan and T Liang [76] evaluated maize lines through a series of multi locus GWAS approaches to detect some novel loci responsible for lodging resistance. By comparing four multi-locus methods i.e.…”

Section: Signi Cance Of Gwas Using High-density Genotypingmentioning

confidence: 99%

Uncovering genomic regions controlling plant architectural traits in hexaploid wheat using GWAS

Wang¹,

Ali²,

Hu³

et al. 2020

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Background: Wheat is a staple food crop worldwide. Plant height is a key factor in plant architecture as it plays a crucial role in lodging and thus affects yield and quality. Genome-wide studies are mostly applied in crop plants, due to its advanced genotyping technologies, identification of novel loci, and improved statistical approaches. Results: In this study, the population was genotyped by using Illumina iSelect 90K single nucleotide polymorphism (SNP) assay and finally 22,905 high-quality SNPs were used to perform a genome-wide association study (GWAS) for plant architectural traits employing four multi-locus GWAS (ML-GWAS) and three single-locus GWAS (SL-GWAS) models. As a result, 174 and 97 significant SNPs controlling plant architectural traits were detected by four ML-GWAS and three SL-GWAS methods, respectively. Among these SNP makers, 43 SNPs were commonly detected, including seven across multiple environments and thirty-six across multiple methods. Interestingly, five most stable SNPs (Kukri_c34553_89, RAC875_c8121_1490, wsnp_Ex_rep_c66315_64480362, Ku_c5191_340, and tplb0049a09_1302) consistently detected across multiple environments and methods, possibly played a role in modulating plant height and flag leaf length. When comparing ML-GWAS methods, pLARmEB was the most powerful and accountable for the detection of 49 significant SNPs that mostly contributed to plant height (36 SNPs). However, in SL-GWAS the FarmCPU model detected most of the significant SNPs. Moreover, a total of 152 candidate genes were found that are likely to be involved in plant growth and development which may provide insightful information related to plant architectural traits.Conclusion: Altogether, our results reveal 174 and 97 significant SNPs controlling plant architectural traits using four ML-GWAS and three SL-GWAS methods, respectively. The detection of the stable loci across multiple environments and methods, possibly play a role in modulating plant architectural traits in hexaploid wheat, and finally will contribute to the discovery of valuable SNP loci for marker-assisted selection (MAS) in wheat molecular breeding.

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Multi-Locus Genome-Wide Association Study Reveals the Genetic Architecture of Stalk Lodging Resistance-Related Traits in Maize

Cited by 80 publications

References 47 publications

Genetic mapping and genomic selection for maize stalk strength

Genetic mapping and genomic selection for maize stalk strength

Linkage Analysis and Multi-Locus Genome-Wide Association Studies Identify QTNs Controlling Soybean Plant Height

Uncovering genomic regions controlling plant architectural traits in hexaploid wheat using GWAS

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