Identification and physical mapping of QTLs associated with flowering time in Brassica napus L.

Yu, Kunjiang; Wang, Xiaodong; Li, Wenjing; Sun, Lijie; Peng, Qi; Chen, Feng; Zhang, Wei; Guan, Rongzhan; Zhang, Jiefu

doi:10.1007/s10681-019-2480-8

Cited by 15 publications

(10 citation statements)

References 56 publications

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“…Some of the challenges in the use of both population-specific and consensus genetic maps for QTL detection in polyploid species, such as wheat, include (1) difficulty in establishing linkage groups and determining correct markers orders, (2) an increase in the number of linkage groups as compared with the number of chromosomes, which could be 2-3-fold [38], (3) collinearity among multiple markers due to relatively small population sizes that result to very small inter-marker interval (<1 cM) between multiple adjacent markers, and (4) the lack of consistent relationship between genetic and physical maps, which makes direct comparisons of QTL discovery results across multiple populations unreliable [39]. Although some studies have published physical maps of QTL associated with diverse traits [39][40][41][42][43][44], the method has not yet been widely used due to a lack of reliable physical maps. Recently, the International Wheat Genome Sequencing Consortium (IWGSC) has released the latest version of the bread wheat reference genome sequence (RefSeq v2.0) and physical map (http://wheat-urgi.versailles.inra.fr/; accessed on 22 April 2021).…”

Section: Introductionmentioning

confidence: 99%

Physical Mapping of QTL in Four Spring Wheat Populations under Conventional and Organic Management Systems. I. Earliness

Semagn

Iqbal

Chen

et al. 2021

Plants

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In previous studies, we reported quantitative trait loci (QTL) associated with the heading, flowering, and maturity time in four hard red spring wheat recombinant inbred line (RIL) populations but the results are scattered in population-specific genetic maps, which is challenging to exploit efficiently in breeding. Here, we mapped and characterized QTL associated with these three earliness traits using the International Wheat Genome Sequencing Consortium (IWGSC) RefSeq v2.0 physical map. Our data consisted of (i) 6526 single nucleotide polymorphisms (SNPs) and two traits evaluated at five conventionally managed environments in the ‘Cutler’ × ‘AC Barrie’ population; (ii) 3158 SNPs and two traits evaluated across three organic and seven conventional managements in the ‘Attila’ × ‘CDC Go’ population; (iii) 5731 SilicoDArT and SNP markers and the three traits evaluated at four conventional and organic management systems in the ‘Peace’ × ‘Carberry’ population; and (iv) 1058 SNPs and two traits evaluated across two conventionally and organically managed environments in the ‘Peace’ × ‘CDC Stanley’ population. Using composite interval mapping, the phenotypic data across all environments, and the IWGSC RefSeq v2.0 physical maps, we identified a total of 44 QTL associated with days to heading (11), flowering (10), and maturity (23). Fifteen of the 44 QTL were common to both conventional and organic management systems, and the remaining QTL were specific to either the conventional (21) or organic (8) management systems. Some QTL harbor known genes, including the Vrn-A1, Vrn-B1, Rht-A1, and Rht-B1 that regulate photoperiodism, flowering time, and plant height in wheat, which lays a solid basis for cloning and further characterization.

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Section: Introductionmentioning

confidence: 99%

Physical Mapping of QTL in Four Spring Wheat Populations under Conventional and Organic Management Systems. I. Earliness

Semagn

Iqbal

Chen

et al. 2021

Plants

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“…The genotypes of this population and two parents were analyzed using simple sequence repeat (SSR) markers and the Illumina (San Diego, CA, USA) Brassica 60 K Infinium array by DNA Landmarks (Saint-Jean-sur-Richelieu, QC, Canada) in a previous study [ 75 ]. A high-density single nucleotide polymorphism (SNP) linkage map incorporating 2755 bins (involving 11,458 SNPs) and 57 SSRs, covering 2027.53 cM with an average marker interval of 0.72 cM was developed for QTL mapping of the apetalous trait [ 75 , 76 ], seed fatty acid composition [ 77 ], flowering time [ 78 ], and stem mechanical strength [ 79 ].…”

Section: Methodsmentioning

confidence: 99%

Quantitative Trait Locus Mapping Combined with RNA Sequencing Reveals the Molecular Basis of Seed Germination in Oilseed Rape

et al. 2021

Biomolecules

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Rapid and uniform seed germination improves mechanized oilseed rape production in modern agricultural cultivation practices. However, the molecular basis of seed germination is still unclear in Brassica napus. A population of recombined inbred lines of B. napus from a cross between the lower germination rate variety ‘APL01’ and the higher germination rate variety ‘Holly’ was used to study the genetics of seed germination using quantitative trait locus (QTL) mapping. A total of five QTLs for germination energy (GE) and six QTLs for germination percentage (GP) were detected across three seed lots, respectively. In addition, six epistatic interactions between the QTLs for GE and nine epistatic interactions between the QTLs for GP were detected. qGE.C3 for GE and qGP.C3 for GP were co-mapped to the 28.5–30.5 cM interval on C3, which was considered to be a novel major QTL regulating seed germination. Transcriptome analysis revealed that the differences in sugar, protein, lipid, amino acid, and DNA metabolism and the TCA cycle, electron transfer, and signal transduction potentially determined the higher germination rate of ‘Holly’ seeds. These results contribute to our knowledge about the molecular basis of seed germination in rapeseed.

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“…Genetic and physical maps have shown low to moderate correlations, which makes direct comparisons of mapping results from independent studies unreliable (Dossa, 2016;Semagn et al, 2021b). Some studies have published physical maps of QTLs associated with diverse traits (Dossa, 2016;Lecouls et al, 2002;Okada et al, 2019; Crop Science Schönhals et al, 2017;Wang et al, 2005;Yu et al, 2019), but most of those studies focused on a specific chromosome (but not the whole genome) and older versions of the reference sequences. Nearly all studies that used array-based genotyping platforms, such as the Wheat 90K SNP array are based on consensus linkage maps that are often erroneous.…”

Section: Core Ideasmentioning

confidence: 99%

Genome‐wide association mapping of agronomic traits and grain characteristics in spring wheat under conventional and organic management systems

et al. 2022

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Previous genome‐wide association studies in Canadian spring wheat (Triticum aestivum L.) cultivars were based on consensus linkage maps of small population sizes. Here, we used a panel of 192 spring wheat cultivars widely used in western Canada to (a) explore the allelic variation and effects of six known genes and (b) identify genomic regions associated with eight agronomic traits and grain characteristics using the International Wheat Genome Sequencing Consortium (IWGSC) RefSeq v2.0 map. The panel was evaluated for heading, maturity, plant height, lodging, grain yield, grain protein content, test weight, and kernel weight under four conventionally (high N) and three organically (low N) managed environments and genotyped with the wheat 90K single‐nucleotide polymorphism (SNP) array of which 23,342 SNPs were polymorphic. Using gene‐specific functional markers, the proportion of homozygous genotypes with favorable alleles was low for Fhb1 (7–8%), Rht‐D1 (14%), and Rht‐B1 (18%); moderate for Lr34/Yr18 (28–39%); and high for Glu‐A1 (78%) and Glu‐D1 (85%). We identified a total of 172 SNPs located at 94 genomic regions across 19 chromosomes of which 82 regions were associated with a single trait and 12 regions coincided with two to three traits. Twenty‐seven of the 94 regions (28%) were common between the conventional and organic managements systems; the remaining were management specific. Two of the identified regions on chromosome 5A (586.5–588.6 Mb) and 5B (575.8 Mb) coincided with the vernalization response Vrn‐A1 and Vrn‐B1 genes, respectively. The IWGSC RefSeq v2.0 physical positions presented here would serve as the basis for easily comparing independent discovery studies in the future.

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Identification and physical mapping of QTLs associated with flowering time in Brassica napus L.

Cited by 15 publications

References 56 publications

Physical Mapping of QTL in Four Spring Wheat Populations under Conventional and Organic Management Systems. I. Earliness

Physical Mapping of QTL in Four Spring Wheat Populations under Conventional and Organic Management Systems. I. Earliness

Quantitative Trait Locus Mapping Combined with RNA Sequencing Reveals the Molecular Basis of Seed Germination in Oilseed Rape

Genome‐wide association mapping of agronomic traits and grain characteristics in spring wheat under conventional and organic management systems

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