Mapping dynamic QTL dissects the genetic architecture of grain size and grain filling rate at different grain-filling stages in barley

Du, Binbin; Wang, Qifei; Sun, Genlou; Ren, Xifeng; Cheng, Yun; Wang, Yixiang; Gao, Song; Li, Chengdao; Sun, Dongfa

doi:10.1038/s41598-019-53620-5

Cited by 21 publications

(14 citation statements)

References 76 publications

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“…6). Our dynamic QTL analysis results were in line with the previous reports on several dynamic traits at different developmental stages in Arabidopsis, barley, wheat, upland cotton, maize, and B. napus (Liang et al 2014;Bac-Molenaar et al 2015;Wang et al 2015;Muraya et al 2017;Su et al 2018;Du et al 2019;Mohler and Stadlmeier 2019;Knoch et al 2020). For example, 35 dynamic conditional QTL which can enhance the number of roots were detected at different root development stages in upland cotton, suggesting the dynamic development of roots (Liang et al 2014).…”

Section: Two Types (Persistent and Stage-specific) Of Temporal Genetic Factors Controlling Root Development In B Napussupporting

confidence: 91%

Genome-Wide Association Study and Transcriptome Analysis Reveal Key Genes Affecting Root Growth Dynamics In Rapeseed

Wang

Kuang

et al. 2021

Preprint

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Root growth is a dynamic process mediated by interactive genetic factors and environmental cues. Seven root-related traits and two shoot-related traits in 280 Brassica napus accessions at five continuous vegetative stages were measured to establish the genetic basis of root growth in rapeseed. The persistent and stage-specific genetic mechanisms were revealed by root dynamic analysis. Sixteen persistent and 32 stage-specific quantitative trait loci (QTL) clusters were identified through genome-wide association study (GWAS). Root samples with contrasting (slow and fast) growth rates throughout investigated stages and those with obvious stage-specific changes in growth rates were subjected to transcriptome analysis. The former identified 367 differentially expressed genes (DEGs) with persistently differential expressions throughout root development that were significantly enriched in GO terms of energy metabolism and response to biotic or abiotic stress, whereas the later identified 485 stage-specific DEGs with different expressions at corresponding stage that were enriched in GO terms of nitrogen metabolism. By integrating GWAS, weighted gene co-expression network analysis (WGCNA), and differential expression analysis, four and eight candidate genes less than 100 kb away from peak SNPs of QTL clusters were identified as key persistent and stage-specific genetic factors, respectively. The homologs of 3 genes ( BnaA03g52990D , BnaA06g37280D, and BnaA09g07580D ) out of 12 candidate genes have been reported to regulate root development in previous studies. Our results provide new insights into the temporal genetic mechanisms of root growth by identifying key candidate QTL/genes in rapeseed.

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Section: Two Types (Persistent and Stage-specific) Of Temporal Genetic Factors Controlling Root Development In B Napussupporting

confidence: 91%

Genome-Wide Association Study and Transcriptome Analysis Reveal Key Genes Affecting Root Growth Dynamics In Rapeseed

Wang

Kuang

et al. 2021

Preprint

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“…Therefore, it could be supposed that high values of test weight might be determined by increasing GL and GW. The co-localization of QTL affecting different traits such as PH, GL and GW, implies closely linked genes involved in different biological processes related to yield [43]. In this QTL cluster, two candidate genes (TRITD6Bv1G005370 and TRITD6Bv1G005450) encoding the acid β-fructofuranosidase enzyme were found.…”

Section: Discussionmentioning

confidence: 94%

Candidate Genes and Quantitative Trait Loci for Grain Yield and Seed Size in Durum Wheat

Mangini

Blanco

Nigro

et al. 2021

Plants

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Grain yield (YLD) is affected by thousand kernel weight (TKW) which reflects the combination of grain length (GL), grain width (GW) and grain area (AREA). Grain weight is also influenced by heading time (HT) and plant height (PH). To detect candidate genes and quantitative trait loci (QTL) of yield components, a durum wheat recombinant inbred line (RIL) population was evaluated in three field trials. The RIL was genotyped with a 90K single nucleotide polymorphism (SNP) array and a high-density genetic linkage map with 5134 markers was obtained. A total of 30 QTL were detected including 23 QTL grouped in clusters on 1B, 2A, 3A, 4B and 6B chromosomes. A QTL cluster on 2A chromosome included a major QTL for HT co-located with QTL for YLD, TKW, GL, GW and AREA, respectively. The photoperiod sensitivity (Ppd-A1) gene was found in the physical position of this cluster. Serine carboxypeptidase, Big grain 1 and β-fructofuranosidase candidate genes were mapped in clusters containing QTL for seed size. This study showed that yield components and phenological traits had higher inheritances than grain yield, allowing an accurate QTL cluster detection. This was a requisite to physically map QTL on durum genome and to identify candidate genes affecting grain yield.

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“…The statistical significance of the QTL was assessed using permutation tests (1000 replications) for all traits. A logarithm of odds (LOD) of 3.0 was set through the permutation test to identify significant QTLs for the traits [59]. The additive effects and proportion of phenotypic variance explained (PVE) by each QTL were estimated using the "fitqtl" function of R version 3.3.4.…”

Section: Qtl Analysis and Candidate Gene Identificationmentioning

confidence: 99%

Identification of QTLs Controlling Resistance/Tolerance to Striga hermonthica in an Extra-Early Maturing Yellow Maize Population

et al. 2020

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Striga hermonthica parasitism is a major constraint to maize production in sub-Saharan Africa with yield losses reaching 100% under severe infestation. The application of marker-assisted selection is highly promising for accelerating breeding for Striga resistance/tolerance in maize but requires the identification of quantitative trait loci (QTLs) linked to Striga resistance/tolerance traits. In the present study, 194 F2:3 families of TZEEI 79 × TZdEEI 11 were screened at two Striga-endemic locations in Nigeria, to identify QTLs associated with S. hermonthica resistance/tolerance and underlying putative candidate genes. A genetic map was constructed using 1139 filtered DArTseq markers distributed across the 10 maize chromosomes, covering 2016 cM, with mean genetic distance of 1.70 cM. Twelve minor and major QTLs were identified for four Striga resistance/tolerance adaptive traits, explaining 19.4%, 34.9%, 14.2% and 3.2% of observed phenotypic variation for grain yield, ears per plant, Striga damage and emerged Striga plants, respectively. The QTLs were found to be linked to candidate genes which may be associated with plant defense mechanisms in S. hermonthica infested environments. The results of this study provide insights into the genetic architecture of S. hermonthica resistance/tolerance indicator traits which could be employed for marker-assisted selection to accelerate efficient transfer host plant resistance genes to susceptible genotypes.

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Mapping dynamic QTL dissects the genetic architecture of grain size and grain filling rate at different grain-filling stages in barley

Cited by 21 publications

References 76 publications

Genome-Wide Association Study and Transcriptome Analysis Reveal Key Genes Affecting Root Growth Dynamics In Rapeseed

Genome-Wide Association Study and Transcriptome Analysis Reveal Key Genes Affecting Root Growth Dynamics In Rapeseed

Candidate Genes and Quantitative Trait Loci for Grain Yield and Seed Size in Durum Wheat

Identification of QTLs Controlling Resistance/Tolerance to Striga hermonthica in an Extra-Early Maturing Yellow Maize Population

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