Physical mapping of QTL associated with agronomic and end-use quality traits in spring wheat under conventional and organic management systems

Semagn, Kassa; Iqbal, Muhammad; Chen, Hua; Perez-Lara, Enid; Bemister, Darcy H.; Xiang, Rongrong; Zou, Jun; Asif, Muhammad; Kamran, Atif; N’Diaye, Amidou; Randhawa, H. S.; Beres, Brian L.; Pozniak, Curtis; Spaner, Dean

doi:10.1007/s00122-021-03923-x

Cited by 26 publications

(35 citation statements)

References 136 publications

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“…Exploring the genetic basis of quality traits will accelerate the breeding of high-quality wheat varieties. Despite the complexity and limited genetic diversity of the wheat genome, there has been substantial molecular genetic research into wheat quality traits, which has resulted in the identification of some genes/QTL associated with quality traits through reverse genetics and traditional mapping methods ( Uauy et al, 2006 ; He et al, 2007 , 2009 ; Kumar et al, 2009 ; Crawford et al, 2011 ; Sun et al, 2016 ; Boehm et al, 2018 ; Jin et al, 2018 ; Goel et al, 2019 ; Semagn et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Genome-Wide Association Study Reveals the Genetic Basis of Five Quality Traits in Chinese Wheat

et al. 2022

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Bread wheat is a highly adaptable food crop grown extensively around the world and its quality genetic improvement has received wide attention. In this study, the genetic loci associated with five quality traits including protein content (PC), gluten content (GC), baking value (BV), grain hardness (HA), and sedimentation value (SV) in a population of 253 Chinese wheat grown in Inner Mongolia were investigated through genome wide association mapping. A total of 103 QTL containing 556 SNPs were significantly related to the five quality traits based on the phenotypic data collected from three environments and BLUP data. Of these QTL, 32 QTL were continuously detected under at least two experiments. Some QTL such as qBV3D.2/qHA3D.2 on 3D, qPC5A.3/qGC5A on 5A, qBV5D/qHA5D on 5D, qBV6B.2/qHA6B.3 on 6B, and qBV6D/qHA6D.1 on 6D were associated with multiple traits. In addition, distribution of favorable alleles of the stable QTL in the association panel and their effects on five quality traits were validated. Analysis of existing transcriptome data revealed that 34 genes were specifically highly expressed in grains during reproductive growth stages. The functions of these genes will be characterized in future experiments. This study provides novel insights into the genetic basis of quality traits in wheat.

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Section: Introductionmentioning

confidence: 99%

Genome-Wide Association Study Reveals the Genetic Basis of Five Quality Traits in Chinese Wheat

et al. 2022

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“…For such reasons, previous mapping studies used either population-specific or consensus genetic maps, but there is no direct linear relationship between the two types of maps. 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.…”

Section: Core Ideasmentioning

confidence: 99%

“…Although conventional breeding methods have a proven track record in developing improved cultivars with combinations of these desirable traits, the methods can be slow due to the polygenic nature of most traits, which require accumulating several quantitative trait loci (QTL) into an elite genetic background. To develop improved cultivars using marker‐assisted selection (MAS), there have been continuous efforts to map and characterize genes and QTL associated with multiple traits of economic importance using the conventional linkage‐based QTL mapping and linkage disequilibrium (LD) based genome‐wide association studies (GWAS) (Bernardo, 2008; Buerstmayr et al., 2009; Mellers et al., 2020; Mérida‐García et al., 2019; Sallam et al., 2020; Semagn et al., 2021b). Quantitative trait loci mapping depends on well‐defined biparental populations, such as F x derived families, backcross, near isogenic lines, doubled haploids, and recombinant inbred lines (RILs) (Collard et al., 2005; Gupta et al., 2019; Semagn et al., 2010).…”

Section: Introductionmentioning

confidence: 99%

“…Although QTL mapping in biparental populations is the most widely used method since the early 1990s, it has major drawbacks, including (a) the time and cost in developing the mapping populations, (b) low resolution due to a limited number of recombination events, (c) the use of old populations developed 5+ yr before the discovery studies, and (d) biparental mapping populations capture only alleles originated from their two parents. Besides, a substantial proportion of adjacent markers that differ by <1 cM are discarded due to the inability of QTL mapping software's in handling multicollinearity among tightly linked markers (Semagn et al., 2006; Wang et al., 2006), which can be avoided if physical maps are used instead of population‐specific or consensus genetic maps (Semagn et al., 2021b; Semagn et al., 2021c).…”

Section: Introductionmentioning

confidence: 99%

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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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“…Quantitative trait locus (QTL) mapping is a valuable approach for exploring specific genes underlying complex traits ( Miles and Wayne, 2008 ). This approach has been previously utilised for the study of growth-related traits in wheat ( Semagn et al, 2021 ), maize ( Guo et al, 2021 ), Arabidopsis thaliana ( Getahun et al, 2020 ), Populus trichocarpa ( Chhetri et al, 2019 ) and Eucalyptus ( Ammitzboll et al, 2020 ). A reasonable trade-off strategy for C distribution among organs or phenotypes optimises the resource acquisition, viability and adaptability of plants ( Poorter et al, 2012 ).…”

Section: Introductionmentioning

confidence: 99%

A Bivariate Mapping Model Identifies Major Covariation QTLs for Biomass Allocation Between Leaf and Stem Growth of Catalpa bungei

Zhang

Zhu

et al. 2021

Front. Genet.

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Biomass allocation plays a critical role in plant morphological formation and phenotypic plasticity, which greatly impact plant adaptability and competitiveness. While empirical studies on plant biomass allocation have focused on molecular biology and ecology approaches, detailed insight into the genetic basis of biomass allocation between leaf and stem growth is still lacking. Herein, we constructed a bivariate mapping model to identify covariation QTLs governing carbon (C) allocation between the leaves and stem as well as the covariation of traits within and between organs in a full-sib mapping population of C. bungei. A total of 123 covQTLs were detected for 23 trait pairs, including six leaf traits (leaf length, width, area, perimeter, length/width ratio and petiole length) and five stem traits (height, diameter at breast height, wood density, stemwood volume and stemwood biomass). The candidate genes were further identified in tissue-specific gene expression data, which provided insights into the genetic architecture underlying C allocation for traits or organs. The key QTLs related to growth and biomass allocation, which included UVH1, CLPT2, GAD/SPL, COG1 and MTERF4, were characterised and verified via gene function annotation and expression profiling. The integration of a bivariate Quantitative trait locus mapping model and gene expression profiling will enable the elucidation of genetic architecture underlying biomass allocation and covariation growth, in turn providing a theoretical basis for forest molecular marker-assisted breeding with specific C allocation strategies for adaptation to heterogeneous environments.

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Physical mapping of QTL associated with agronomic and end-use quality traits in spring wheat under conventional and organic management systems

Cited by 26 publications

References 136 publications

Genome-Wide Association Study Reveals the Genetic Basis of Five Quality Traits in Chinese Wheat

Genome-Wide Association Study Reveals the Genetic Basis of Five Quality Traits in Chinese Wheat

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

A Bivariate Mapping Model Identifies Major Covariation QTLs for Biomass Allocation Between Leaf and Stem Growth of Catalpa bungei

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