Genome-wide association study of six quality-related traits in common wheat (Triticum aestivum L.) under two sowing conditions

Lou, Hongyao; Zhang, Runqi; Liu, Yitong; Guo, Dandan; Zhai, Shanshan; Chen, Aiyan; Zhang, Yufeng; Xie, Chaojie; You, Minsheng; Peng, Huiru; Liang, Rongqi; Sun, Qixin; Li, Baoyun

doi:10.1007/s00122-020-03704-y

Cited by 27 publications

(30 citation statements)

References 75 publications

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“…Heritability of some traits increased and for some traits decrease under late sown which mainly attributed to change in the genetic variation among genotypes in two sowing dates. Change in heritability estimates under different sowing dates showed that selection criteria should be reevaluated for different environmental conditions which are supported by the findings of other researchers working on wheat (Eid 2009;Abou-Zaid et al 2017;Lou et al 2021).…”

Section: Discussionsupporting

confidence: 68%

“…4). These changes, attributed to the activation of different type of genes and change in the association of linked traits under different sowing dates (Abou-Zaid et al 2017;Agrawal et al 2018;Lou et al 2021). Agrawal et al (2018) showed that direct and indirect effects of phenotypic traits and their association changed under normal and late sown chickpea.…”

Section: Discussionmentioning

confidence: 99%

“…Likewise, in normal sowing number of tillers were strongly correlated with grain yield (r = 0.38; P < 0.01), while in late sowing association was non-significant. Differences in the association under different sowing dates attributed the genes which changes their expression under different environment (Eid 2009;Abou-Zaid et al 2017;Lou et al 2021).…”

Section: Discussionmentioning

confidence: 99%

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Assessment of Genetic Variability and Direct-Indirect Contribution of Post-Anthesis Traits to the Grain Yield in Bread Wheat (Triticum aestivum) at Different Sowing Dates

Ul‐Allah¹

2021

IJAB

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In cotton-wheat cropping system of Pakistan, sowing of wheat is delayed which results in a heavy loss to the farmers. To this end, a two-year field experiment was conducted to i) asses the performance of genotypes under late sowing ii) heritability and genetic association of different traits under normal and late sowing for their use in the breeding programs. Experimental factors included 20 diverse wheat genotypes (16 advance lines and 04 approved varieties) factorially combined with two sowing dates i.e., normal sowing (11 and 13 November in 2018 and 2019, respectively) and late sowing (09 and 11 December in 2018 and 2019, respectively). The results revealed a highly significant (P < 0.01) effect of genotype and sowing date on all the post-anthesis studied traits and the interaction of the two factors was also significant (P < 0.01) for all the traits except number of spikelets per spike. Contribution of sowing dates was more in overall variation of different post-anthesis traits than genotypes and their interaction. With respect to the performance, genotype 9725 performed best in both early and late sowing, but a reduction of 26% in the grain yield was observed due to late sowing, whereas the grain yield of Miraj-2008 was comparatively lower but showed a reduction of only 17% due to late sowing. Number of tillers per plant and biological yield showed a highly significant (P < 0.01) correlation (r= 0.38 and 0.72, respectively) with grain yield under normal conditions, but path analyses revealed that number of tillers per plant showed association indirectly through biological yield and its direct effect is only one third of the total effect. Under late sowing, association of number of tillers turned non-significant and direct effect become negative which showed complicated association of this trait under normal and late sowing. High broad sense heritability (H2 > 75%) of all the traits suggests a good genetic gain in breeding program by using these traits. As different genotypes performed best for different yield related traits, therefore a breeding program with a careful crossing plan should be developed to pyramid the genes for different traits for the development of genotypes adapted to late sowing. © 2021 Friends Science Publishers

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Section: Discussionsupporting

confidence: 68%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Assessment of Genetic Variability and Direct-Indirect Contribution of Post-Anthesis Traits to the Grain Yield in Bread Wheat (Triticum aestivum) at Different Sowing Dates

Ul‐Allah¹

2021

IJAB

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“…By comparing the QTL mapping results with QTLs reported in previous studies, we found that several QTLs were commonly detected in this study and previous studies (Table 3). The QTL cluster of 1DL.2 overlapped with a detected QTL for parameters Mixolab protein weakening torque (C2), Mixolab development time (DT), Mixograph midline peak time (MPT), Mixograph midline peak value (MPV), Mixograph midline 8 min band width (MTxW), and Mixolab stability time (ST) reported by Jin et al (2016), which also overlapped with a detected QTL for parameters dough development time of Farinograph, (Lou et al, 2020), whereas the QTL cluster of 6AS.3 in this study colocalized with a QTL for parameters ST and SV reported by Guo et al (2020). On the contrary, the other four QTL clusters of 1DS.1, 4AL.2, 4DS, and 7DS were not reported in previous studies, which were probable novel QTL clusters regulating the characteristics of gluten aggregation.…”

Section: Discussionmentioning

confidence: 69%

Genetic Basis of Gluten Aggregation Properties in Wheat (Triticum aestivum L.) Dissected by QTL Mapping of GlutoPeak Parameters

et al. 2021

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Bread wheat is one of the most important crops worldwide, supplying approximately one-fifth of the daily protein and the calories for human consumption. Gluten aggregation properties play important roles in determining the processing quality of wheat (Triticum aestivum L.) products. Nevertheless, the genetic basis of gluten aggregation properties has not been reported so far. In this study, a recombinant inbred line (RIL) population derived from the cross between Luozhen No. 1 and Zhengyumai 9987 was used to identify quantitative trait loci (QTL) underlying gluten aggregation properties with GlutoPeak parameters. A linkage map was constructed based on 8,518 SNPs genotyped by specific length amplified fragment sequencing (SLAF-seq). A total of 33 additive QTLs on 14 chromosomes were detected by genome-wide composite interval mapping (GCIM), four of which accounted for more than 10% of the phenotypic variation across three environments. Two major QTL clusters were identified on chromosomes 1DS and 1DL. A premature termination of codon (PTC) mutation in the candidate gene (TraesCS1D02G009900) of the QTL cluster on 1DS was detected between Luozhen No. 1 and Zhengyumai 9987, which might be responsible for the difference in gluten aggregation properties between the two varieties. Subsequently, two KASP markers were designed based on SNPs in stringent linkage with the two major QTL clusters. Results of this study provide new insights into the genetic architecture of gluten aggregation properties in wheat, which are helpful for future improvement of the processing quality in wheat breeding.

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“…Genetic mechanisms of starch metabolism have been well studied in major cereal crops, whereas little information is available in legume crops including peas. Quantitative trait locus (QTL) mapping and a genome-wide association study (GWAS) coupled with transcriptome profiling have been conducted in cereal crops to decipher the mechanisms regulating starch metabolism: starch content, composition, granule size, and functional properties [98][99][100][101][102][103][104][105][106][107][108][109][110]. In rice endosperms, different alleles at the waxy locus lead to various levels of GBSS and hence contribute to the variation of amylose content and starch properties [98,99,111].…”

Section: Genetic Mapping Of Starch Metabolismmentioning

confidence: 99%

Understanding Starch Metabolism in Pea Seeds towards Tailoring Functionality for Value-Added Utilization

Xiang

Mahfuz

et al. 2021

IJMS

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Starch is the most abundant storage carbohydrate and a major component in pea seeds, accounting for about 50% of dry seed weight. As a by-product of pea protein processing, current uses for pea starch are limited to low-value, commodity markets. The globally growing demand for pea protein poses a great challenge for the pea fractionation industry to develop new markets for starch valorization. However, there exist gaps in our understanding of the genetic mechanism underlying starch metabolism, and its relationship with physicochemical and functional properties, which is a prerequisite for targeted tailoring functionality and innovative applications of starch. This review outlines the understanding of starch metabolism with a particular focus on peas and highlights the knowledge of pea starch granule structure and its relationship with functional properties, and industrial applications. Using the currently available pea genetics and genomics knowledge and breakthroughs in omics technologies, we discuss the perspectives and possible avenues to advance our understanding of starch metabolism in peas at an unprecedented level, to ultimately enable the molecular design of multi-functional native pea starch and to create value-added utilization.

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Genome-wide association study of six quality-related traits in common wheat (Triticum aestivum L.) under two sowing conditions

Cited by 27 publications

References 75 publications

Assessment of Genetic Variability and Direct-Indirect Contribution of Post-Anthesis Traits to the Grain Yield in Bread Wheat (Triticum aestivum) at Different Sowing Dates

Assessment of Genetic Variability and Direct-Indirect Contribution of Post-Anthesis Traits to the Grain Yield in Bread Wheat (Triticum aestivum) at Different Sowing Dates

Genetic Basis of Gluten Aggregation Properties in Wheat (Triticum aestivum L.) Dissected by QTL Mapping of GlutoPeak Parameters

Understanding Starch Metabolism in Pea Seeds towards Tailoring Functionality for Value-Added Utilization

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