GWAS for main effects and epistatic interactions for grain morphology traits in wheat

Malik, Parveen; Kumar, Jitendra; Sharma, Shiveta; Meher, Prabina Kumar; Balyan, H. S.; Gupta, P. K.; Sharma, Shailendra

doi:10.1007/s12298-022-01164-w

Cited by 12 publications

(7 citation statements)

References 108 publications

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“…The genotyping data for 210 SNP markers (distributed on 21 chromosomes) suggested a low level of population structure in the association panel ( Figure 2 ), which is a desirable feature for GWAS, as also shown in our earlier studies involving the same association panel with minor differences ( Kumar et al., 2018 ; Gahlaut et al., 2019 ; Malik et al., 2021a ; Malik et al., 2021b ; Malik et al., 2022 ). In earlier studies involving different association panels also, the number of sub-populations ranged from three (for example, Wang et al., 2017 ; Rahimi et al., 2019 ) to six (for example, Li et al., 2016 ; Qaseem et al., 2018 ; Jamil et al., 2019 ), suggesting that in majority of studies in wheat, the level of population structure is low.…”

Section: Discussionsupporting

confidence: 73%

“…High level of variability (as revealed by descriptive statistics) for each of the three traits suggested that the panel was suitable for a study of the genetics of quantitative traits. The same panel was earlier utilized by us in GWAS for several other traits including the following: (i) yield related traits ( Sehgal et al., 2017 ; Malik et al., 2021a ; Malik et al., 2021b ; Malik et al., 2022 ); (ii) Fe, Zn, β-carotene, GPC content ( Kumar et al., 2018 ); and, (iii) drought tolerance ( Gahlaut et al., 2019 ).…”

Section: Discussionmentioning

confidence: 99%

“…Epistatic interactions are often ignored in GWAS, although recently studied for the following traits: (1) flowering time ( Reif et al., 2011 ; Langer et al., 2014 ), (2) stem rust resistance ( Yu et al., 2011 ), (3) agronomic traits ( Sehgal et al., 2017 ), (iv) yield related traits, micronutrients, and grain morphology ( Jaiswal et al., 2016 ; Kumar et al., 2018 ; Malik et al., 2021a , Malik et al., 2022 ). Epistatic interactions were also detected through interval mapping ( Li et al., 2011 ; Xu et al., 2012 ; Rouse et al., 2014 ; Boeven et al., 2020 ).…”

Section: Discussionmentioning

confidence: 99%

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Genetics of spot blotch resistance in bread wheat (Triticum aestivum L.) using five models for GWAS

Singh

Gaurav²,

Vasistha³

et al. 2023

Front. Plant Sci.

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Genetic architecture of resistance to spot blotch in wheat was examined using a Genome-Wide Association Study (GWAS) involving an association panel comprising 303 diverse genotypes. The association panel was evaluated at two different locations in India including Banaras Hindu University (BHU), Varanasi (Uttar Pradesh), and Borlaug Institute for South Asia (BISA), Pusa, Samastipur (Bihar) for two consecutive years (2017-2018 and 2018-2019), thus making four environments (E1, BHU 2017-18; E2, BHU 2018-19; E3, PUSA, 2017-18; E4, PUSA, 2018-19). The panel was genotyped for 12,196 SNPs based on DArT-seq (outsourced to DArT Ltd by CIMMYT); these SNPs included 5,400 SNPs, which could not be assigned to individual chromosomes and were therefore, described as unassigned by the vendor. Phenotypic data was recorded on the following three disease-related traits: (i) Area Under Disease Progress Curve (AUDPC), (ii) Incubation Period (IP), and (iii) Lesion Number (LN). GWAS was conducted using each of five different models, which included two single-locus models (CMLM and SUPER) and three multi-locus models (MLMM, FarmCPU, and BLINK). This exercise gave 306 MTAs, but only 89 MTAs (33 for AUDPC, 30 for IP and 26 for LN) including a solitary MTA detected using all the five models and 88 identified using four of the five models (barring SUPER) were considered to be important. These were used for further analysis, which included identification of candidate genes (CGs) and their annotation. A majority of these MTAs were novel. Only 70 of the 89 MTAs were assigned to individual chromosomes; the remaining 19 MTAs belonged to unassigned SNPs, for which chromosomes were not known. Seven MTAs were selected on the basis of minimum P value, number of models, number of environments and location on chromosomes with respect to QTLs reported earlier. These 7 MTAs, which included five main effect MTAs and two for epistatic interactions, were considered to be important for marker-assisted selection (MAS). The present study thus improved our understanding of the genetics of resistance against spot blotch in wheat and provided seven MTAs, which may be used for MAS after due validation.

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Section: Discussionsupporting

confidence: 73%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Genetics of spot blotch resistance in bread wheat (Triticum aestivum L.) using five models for GWAS

Singh

Gaurav²,

Vasistha³

et al. 2023

Front. Plant Sci.

Self Cite

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“…Yield-related traits are complex quantitative traits controlled by polygenes [9][10][11], which are strongly influenced by genotypic and environmental factors [12]. In the last two decades, a large number of QTLs underlying wheat kernel size-related traits have been successfully identified by traditional bi-parental linkage mapping [7,[9][10][11][13][14][15][16][17]] and genome-wide association studies (GWAS) [18][19][20][21][22][23]. However, due to the large and highly repetitive nature of the wheat genome, identifying stable and robust QTLs for kernel size-related and yield traits remains challenging in wheat breeding [24,25].…”

Section: Introductionmentioning

confidence: 99%

Identification of quantitative trait loci (QTL) and meta-QTL analysis for kernel size-related traits in wheat (Triticum aestivum L.)

Liu

Zhang³

et al. 2022

BMC Plant Biol

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Background Kernel size-related traits, including kernel length (KL), kernel width (KW), kernel diameter ratio (KDR) and kernel thickness (KT), are critical determinants for wheat kernel weight and yield and highly governed by a type of quantitative genetic basis. Genome-wide identification of major and stable quantitative trait loci (QTLs) and functional genes are urgently required for genetic improvement in wheat kernel yield. A hexaploid wheat population consisting of 120 recombinant inbred lines was developed to identify QTLs for kernel size-related traits under different water environments. The meta-analysis and transcriptome evaluation were further integrated to identify major genomic regions and putative candidate genes. Results The analysis of variance (ANOVA) revealed more significant genotypic effects for kernel size-related traits, indicating the moderate to high heritability of 0.61–0.89. Thirty-two QTLs for kernel size-related traits were identified, explaining 3.06%—14.2% of the phenotypic variation. Eleven stable QTLs were detected in more than three water environments. The 1103 original QTLs from the 34 previous studies and the present study were employed for the MQTL analysis and refined into 58 MQTLs. The average confidence interval of the MQTLs was 3.26-fold less than that of the original QTLs. The 1864 putative candidate genes were mined within the regions of 12 core MQTLs, where 70 candidate genes were highly expressed in spikes and kernels by comprehensive analysis of wheat transcriptome data. They were involved in various metabolic pathways, such as carbon fixation in photosynthetic organisms, carbon metabolism, mRNA surveillance pathway, RNA transport and biosynthesis of secondary metabolites. Conclusions Major genomic regions and putative candidate genes for kernel size-related traits in wheat have been revealed by an integrative strategy with QTL linkage mapping, meta-analysis and transcriptomic assessment. The findings provide a novel insight into understanding the genetic determinants of kernel size-related traits and will be useful for the marker-assisted selection of high yield in wheat breeding.

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“…These are complex quantitative traits controlled by polygenes [9][10][11] and are strongly in uenced by both genotypic and environmental factors [12]. In the last two decades, a large number of QTLs underlying wheat kernel size-related traits have been successfully identi ed by traditional bi-parental linkage mapping [7,[9][10][11][13][14][15][16][17] and genome-wide association studies (GWAS) [18][19][20][21][22][23]. However, due to the redundancy of functional genes in three sub-genomes A, B, and D of wheat and the highly repetitive nature of this genome, identifying stable and robust QTLs for kernel size-related and yield traits remains challenging [24,25].…”

Section: Introductionmentioning

confidence: 99%

Identification of quantitative trait loci (QTL) and meta-QTL analysis for kernel size-related traits in wheat (Triticum aestivum L.)

Liu

Zhang

et al. 2022

Preprint

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Background Kernel size-related traits, as critical determinants of wheat kernel weight and yield potential, are complex quantitative traits controlled by polygenes. Genome-wide identification of important and stable quantitative trait loci (QTL) and functional genes for these traits can advance molecular genetic improvement of wheat kernel yield. Results In this study, Thirty-two QTLs for kernel size-related traits, including kernel length (KL), kernel width (KW), kernel diameter ratio (KDR) and kernel thickness (KT), were discovered using a series of recombinant inbred lines (RILs) which explained 3.06–14.2% of the phenotypic variation. Of these, eleven QTLs were confirmed as stable QTLs in multiple environments. The 1103 original QTLs from 34 previous studies and the present study were employed for the MQTL analysis, in which 346 of the 1103 original QTLs were refined to 58 MQTLs. Compared to the original QTLs, the average confidence intervals of the target MQTLs decreased 3.26-fold, but the average genetic contribution rates to explain phenotypic variation increased 1.72-fold. Through comprehensive analysis of wheat publicly available transcriptome data, a total of 70 putative candidate genes for kernel size development were ultimately identified within the MQTL regions. Conclusions kernel size-related traits predominantly regulated by genetic factors and can be detected in different water conditions. Potential candidate genes expressed in spike and grain were identified through meta-QTL and in-silico expression analysis. The identification of stable QTLs and candidate genes for kernel size-related traits provides a novel insight to understand the genetic basis of kernel size-related traits in wheat.

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GWAS for main effects and epistatic interactions for grain morphology traits in wheat

Cited by 12 publications

References 108 publications

Genetics of spot blotch resistance in bread wheat (Triticum aestivum L.) using five models for GWAS

Genetics of spot blotch resistance in bread wheat (Triticum aestivum L.) using five models for GWAS

Identification of quantitative trait loci (QTL) and meta-QTL analysis for kernel size-related traits in wheat (Triticum aestivum L.)

Identification of quantitative trait loci (QTL) and meta-QTL analysis for kernel size-related traits in wheat (Triticum aestivum L.)

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