Genome-wide association study of pre-harvest sprouting tolerance using a 90K SNP array in common wheat (Triticum aestivum L.)

Zhu, Yanfang; Wang, Shengxing; Wei, Wenxin; Xie, Hong-Yong; Liu, Kai; Zhang, Can; Wu, Zeng-Yun; Jiang, Hao; Cao, Junhui; Zhao, Liangxia; Lu, Jie; Zhang, Haiping; Chang, Cheng; Xia, Xianchun; Xiao, Shuai; Ma, Chunyan

doi:10.1007/s00122-019-03398-x

Cited by 44 publications

(36 citation statements)

References 85 publications

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“…Among these, the 90K and 660K arrays are now widely applied, as compared to simple sequence repeat (SSR) markers, for the genetic improvement of wheat traits related to yield, quality, stress tolerance, and disease resistance [27][28][29][30]. Recently, the 90K array based on the Illumina platform can be found in bi-parental QTL mapping [31][32][33][34] and GWAS for yield-contributing traits in wheat [35][36][37]. Conventional QTL mapping can only reflect the genetic content between parents and focuses on specific traits [20].…”

Section: Introductionmentioning

confidence: 99%

Appraising the Genetic Architecture of Kernel Traits in Hexaploid Wheat Using GWAS

Ali

et al. 2020

IJMS

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Kernel morphology is one of the major yield traits of wheat, the genetic architecture of which is always important in crop breeding. In this study, we performed a genome-wide association study (GWAS) to appraise the genetic architecture of the kernel traits of 319 wheat accessions using 22,905 single nucleotide polymorphism (SNP) markers from a wheat 90K SNP array. As a result, 111 and 104 significant SNPs for Kernel traits were detected using four multi-locus GWAS models (mrMLM, FASTmrMLM, FASTmrEMMA, and pLARmEB) and three single-locus models (FarmCPU, MLM, and MLMM), respectively. Among the 111 SNPs detected by the multi-locus models, 24 SNPs were simultaneously detected across multiple models, including seven for kernel length, six for kernel width, six for kernels per spike, and five for thousand kernel weight. Interestingly, the five most stable SNPs (RAC875_29540_391, Kukri_07961_503, tplb0034e07_1581, BS00074341_51, and BobWhite_049_3064) were simultaneously detected by at least three multi-locus models. Integrating these newly developed multi-locus GWAS models to unravel the genetic architecture of kernel traits, the mrMLM approach detected the maximum number of SNPs. Furthermore, a total of 41 putative candidate genes were predicted to likely be involved in the genetic architecture underlining kernel traits. These findings can facilitate a better understanding of the complex genetic mechanisms of kernel traits and may lead to the genetic improvement of grain yield in wheat.

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Section: Introductionmentioning

confidence: 99%

Appraising the Genetic Architecture of Kernel Traits in Hexaploid Wheat Using GWAS

Ali

et al. 2020

IJMS

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“…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]. SU Khan, J Yangmiao, S Liu, K Zhang, MHU Khan, Y Zhai, A Olalekan, C Fan and Y Zhou [68] combined SL and ML-GWAS approaches and revealed that ML-GWAS methods are more effective with high robustness and power of QTN detection than SL-GWAS in the genetic dissection of yield related traits of rapeseed genotypes.…”

Section: Signi Cance Of Gwas Using High-density Genotypingmentioning

confidence: 93%

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

Wang¹,

Ali²,

Hu³

et al. 2020

Preprint

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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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“…One hundred ninety-two wheat varieties (WVs) grown in the 2015-2016, 2016-2017, and 2017-2018 cropping seasons, containing 159 cultivars, 11 advanced breeding lines, and 22 landraces, were chosen for a GWAS panel to validate QTLs identified by linkage analysis. The origin of these varieties has been described in detail by Zhu et al (2019). One hundred eighty Chinese mini-core collection accessions (CMCCs) planted in the 2014-2015, 2015-2016, and 2016-2017 cropping seasons comprising 74 cultivars, 11 advanced breeding lines, and 95 landraces were used to validate new stable loci for grain weight and size.…”

Section: Plant Materialsmentioning

confidence: 99%

“…Linkage disequilibrium and population structure were analyzed for 192 WVs following methods in our previous study (Zhu et al, 2019). The same K and Q matrix data were used in the present study to identify significant marker-trait associations (MTAs) for grain weight and size by the MLM.…”

Section: Genotyping and Association Analysis Of 192 Wvs Using 90k Snpmentioning

confidence: 99%

“…Zhang et al (2016) identified 63 and 16 additive QTLs for fiber strength of cotton using a composite interval mapping (CIM) method with WinQTLCartographer2.5 and an ICIM model by QTL IciMapping4.1, respectively, and four QTLs detected by both CIM and ICIM were considered as stable loci for the fiber strength of cotton. In our previous study, based on both single-and multi-locus MLMs, 23 and 39 MTAs for preharvest sprouting resistance were detected by GWAS, respectively, and 6 loci were jointly identified by the two models and were, thus, considered stable loci (Zhu et al, 2019).…”

Genome-wide association study of pre-harvest sprouting tolerance using a 90K SNP array in common wheat (Triticum aestivum L.)

Cited by 44 publications

References 85 publications

Appraising the Genetic Architecture of Kernel Traits in Hexaploid Wheat Using GWAS

Appraising the Genetic Architecture of Kernel Traits in Hexaploid Wheat Using GWAS

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

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