QTL Mapping and Validation for Kernel Area and Circumference in Common Wheat via High-Density SNP-Based Genotyping

As an important component, 1,000 kernel weight (TKW) plays a significant role in the formation of yield traits of wheat. Kernel size is significantly positively correlated to TKW. Although numerous loci for kernel size in wheat have been reported, our knowledge on loci for kernel area (KA) and kernel circumference (KC) remains limited. In the present study, a recombinant inbred lines (RIL) population containing 371 lines genotyped using the Wheat55K SNP array was used to map quantitative trait loci (QTLs) controlling the KA and KC in multiple environments. A total of 54 and 44 QTLs were mapped by using the biparental population or multienvironment trial module of the inclusive composite interval mapping method, respectively. Twenty-two QTLs were considered major QTLs. BLAST analysis showed that major and stable QTLs QKc.sau-6A.1 (23.12–31.64 cM on 6A) for KC and QKa.sau-6A.2 (66.00–66.57 cM on 6A) for KA were likely novel QTLs, which explained 22.25 and 20.34% of the phenotypic variation on average in the 3 year experiments, respectively. Two Kompetitive allele-specific PCR (KASP) markers, KASP-AX-109894590 and KASP-AX-109380327, were developed and tightly linked to QKc.sau-6A.1 and QKa.sau-6A.2, respectively, and the genetic effects of the different genotypes in the RIL population were successfully confirmed. Furthermore, in the interval where QKa.sau-6A.2 was located on Chinese Spring and T. Turgidum ssp. dicoccoides reference genomes, only 11 genes were found. In addition, digenic epistatic QTLs also showed a significant influence on KC and KA. Altogether, the results revealed the genetic basis of KA and KC and will be useful for the marker-assisted selection of lines with different kernel sizes, laying the foundation for the fine mapping and cloning of the gene(s) underlying the stable QTLs detected in this study.

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Identification of quantitative trait loci for leaf traits in rice

Genetika ◽

10.2298/gensr1602643j ◽

2016 ◽

Vol 48 (2) ◽

pp. 643-652 ◽

Cited By ~ 2

Author(s):

Baoyan Jia ◽

Xinhua Zhao ◽

Yang Qin ◽

Muhammad Irfan ◽

Tae-Heon Kim ◽

...

Keyword(s):

Recombinant Inbred Lines ◽

Leaf Traits ◽

Rice Cultivar ◽

Interval Mapping ◽

Mapping Method ◽

Japonica Rice ◽

Phenotypic Variance ◽

Simple Sequence ◽

Variance Explained ◽

Map Distance

A recombinant inbred lines (RILs) population of 90 lines were developed from a subspecies cross between an indica type cultivar, ?Cheongcheong?, and a japonica rice cultivar, ?Nagdong? was evaluated for leaf traits in 2009. A genetic linkage map consisting of 154 simple sequence repeat (SSR) markers was constructed, covering 1973.6 cM of 12 chromosomes with an average map distance of 13.9 cM between markers. By composite interval mapping method a total of 19 QTLs were identified for the leaf traits on 5 chromosomes (Chr.1, Chr.3, Chr.6, Chr.8 and Chr.11). The percentage of phenotypic variance explained by each QTL varied from 8.1% to 29.4%. Five pleiotropic effects loci were identified on chromosomes 1,6.

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Neighbor QTL: an interval mapping method for quantitative trait loci underlying plant neighborhood effects

10.1101/2020.05.20.089474 ◽

2020 ◽

Author(s):

Yasuhiro Sato ◽

Kazuya Takeda ◽

Atsushi J. Nagano

Keyword(s):

Qtl Mapping ◽

Neighborhood Effects ◽

Quantitative Trait ◽

Recombinant Inbred Lines ◽

Insect Herbivory ◽

R Package ◽

Interval Mapping ◽

Mapping Method ◽

Neighbor Effects ◽

Genotype Probabilities

AbstractPhenotypes of sessile organisms, such as plants, rely not only on their own genotype but also on the genotypes of neighboring individuals. Previously, we incorporated such neighbor effects into a single-marker regression using the Ising model of ferromagnetism. However, little is known about how to incorporate neighbor effects in quantitative trait locus (QTL) mapping. In this study, we propose a new method for interval QTL mapping of neighbor effects, named “Neighbor QTL”. The algorithm of neighbor QTL involves the following: (i) obtaining conditional self-genotype probabilities with recombination fraction between flanking markers, (ii) calculating neighbor genotypic identity using the self-genotype probabilities, and (iii) estimating additive and dominance deviation for neighbor effects. Our simulation using F2 and backcross lines showed that the power to detect neighbor effects increased as the effective range became smaller. The neighbor QTL was applied to insect herbivory on Col × Kas recombinant inbred lines of Arabidopsis thaliana. Consistent with previous evidence, the pilot experiment detected a self QTL effect on the herbivory at GLABRA1 locus. We also observed a weak QTL on chromosome 4 regarding neighbor effects on the herbivory. The neighbor QTL method is available as an R package (https://cran.r-project.org/package=rNeighborQTL), providing a novel tool to investigate neighbor effects in QTL studies.

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Construction of Consensus Genetic Map With Applications in Gene Mapping of Wheat (Triticum aestivum L.) Using 90K SNP Array

Frontiers in Plant Science ◽

10.3389/fpls.2021.727077 ◽

2021 ◽

Vol 12 ◽

Author(s):

Pingping Qu ◽

Jiankang Wang ◽

Weie Wen ◽

Fengmei Gao ◽

Jindong Liu ◽

...

Keyword(s):

Qtl Mapping ◽

Genetic Information ◽

Physical Map ◽

Recombinant Inbred Lines ◽

Snp Array ◽

Triticum Aestivum L ◽

Kernel Weight ◽

Consensus Map ◽

Phenotypic Data ◽

Putative Gene

Wheat is one of the most important cereal crops worldwide. A consensus map combines genetic information from multiple populations, providing an effective alternative to improve the genome coverage and marker density. In this study, we constructed a consensus map from three populations of recombinant inbred lines (RILs) of wheat using a 90K single nucleotide polymorphism (SNP) array. Phenotypic data on plant height (PH), spike length (SL), and thousand-kernel weight (TKW) was collected in six, four, and four environments in the three populations, and then used for quantitative trait locus (QTL) mapping. The mapping results obtained using the constructed consensus map were compared with previous results obtained using individual maps and previous studies on other populations. A simulation experiment was also conducted to assess the performance of QTL mapping with the consensus map. The constructed consensus map from the three populations spanned 4558.55 cM in length, with 25,667 SNPs, having high collinearity with physical map and individual maps. Based on the consensus map, 21, 27, and 19 stable QTLs were identified for PH, SL, and TKW, much more than those detected with individual maps. Four PH QTLs and six SL QTLs were likely to be novel. A putative gene called TraesCS4D02G076400 encoding gibberellin-regulated protein was identified to be the candidate gene for one major PH QTL located on 4DS, which may enrich genetic resources in wheat semi-dwarfing breeding. The simulation results indicated that the length of the confidence interval and standard errors of the QTLs detected using the consensus map were much smaller than those detected using individual maps. The consensus map constructed in this study provides the underlying genetic information for systematic mapping, comparison, and clustering of QTL, and gene discovery in wheat genetic study. The QTLs detected in this study had stable effects across environments and can be used to improve the wide adaptation of wheat cultivars through marker-assisted breeding.

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QTL mapping of agronomic traits in wheat using the UK Avalon × Cadenza reference mapping population grown in Kazakhstan

PeerJ ◽

10.7717/peerj.10733 ◽

2021 ◽

Vol 9 ◽

pp. e10733

Author(s):

Akerke Amalova ◽

Saule Abugalieva ◽

Vladimir Chudinov ◽

Grigoriy Sereda ◽

Laura Tokhetova ◽

...

Keyword(s):

Qtl Mapping ◽

Agronomic Traits ◽

Reference Population ◽

Interval Mapping ◽

Mapping Method ◽

Kernel Weight ◽

Snp Markers ◽

Potential Yield ◽

Dh Population ◽

The Uk

Background The success of wheat production is largely dependent on local breeding projects that focus on the development of high-yielding cultivars with the use of novel molecular tools. One strategy for improving wheat productivity involves the deployment of diverse germplasms with a high potential yield. An important factor for achieving success involves the dissection of quantitative trait loci (QTLs) for complex agronomic traits, such as grain yield components, in targeted environments for wheat growth. Methods In this study, we tested the United Kingdom (UK) spring set of the doubled haploid (DH) reference population derived from the cross between two British cultivars, Avalon (winter wheat) and Cadenza (spring wheat), in the Northern, Central, and Southern regions (Karabalyk, Karaganda, Kyzylorda) of Kazakhstan over three years (2013–2015). The DH population has previously been genotyped by UK scientists using 3647 polymorphic DNA markers. The list of tested traits includes the heading time, seed maturation time, plant height, spike length, productive tillering, number of kernels per spike, number of kernels per meter, thousand kernel weight, and yield per square meter. Windows QTL Cartographer was applied for QTL mapping using the composite interval mapping method. Results In total, 83 out of 232 QTLs were identified as stable QTLs from at least two environments. A literature survey suggests that 40 QTLs had previously been reported elsewhere, indicating that this study identified 43 QTLs that are presumably novel marker-trait associations (MTA) for these environments. Hence, the phenotyping of the DH population in new environments led to the discovery of novel MTAs. The identified SNP markers associated with agronomic traits in the DH population could be successfully used in local Kazakh breeding projects for the improvement of wheat productivity.

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QTL mapping for plant height and yield components in common wheat under water-limited and full irrigation environments

Crop and Pasture Science ◽

10.1071/cp14236 ◽

2015 ◽

Vol 66 (7) ◽

pp. 660 ◽

Cited By ~ 16

Author(s):

Xingmao Li ◽

Xianchun Xia ◽

Yonggui Xiao ◽

Zhonghu He ◽

Desen Wang ◽

...

Keyword(s):

Plant Height ◽

Yield Components ◽

Recombinant Inbred Lines ◽

Interval Mapping ◽

Wheat Breeding ◽

Kernel Weight ◽

Full Irrigation ◽

Inclusive Composite Interval Mapping ◽

Cropping Seasons ◽

Simple Sequence

Plant height (PH) and yield components are important traits for yield improvement in wheat breeding. In this study, 207 F2:4 recombinant inbred lines (RILs) derived from the cross Jingdong 8/Aikang 58 were investigated under limited and full irrigation environments at Beijing and Gaoyi, Hebei province, during the 2011–12 and 2012–13 cropping seasons. The RILs were genotyped with 149 polymorphic simple sequence repeat (SSR) markers, and quantitative trait loci (QTLs) for PH and yield components were analysed by inclusive composite interval mapping. All traits in the experiment showed significant genetic variation and interaction with environments. The range of broad-sense heritabilities of PH, 1000-kernel weight (TKW), number of kernels per spike (KNS), number of spikes per m2 (NS), and grain yield (GY) were 0.97–0.97, 0.87–0.89, 0.59–0.61, 0.58–0.68, and 0.23–0.48. The numbers of QTLs detected for PH, TKW, KNS, NS, and GY were 3, 10, 8, 7 and 9, respectively, across all eight environments. PH QTLs on chromosomes 4D and 6A, explaining 61.3–80.2% of the phenotypic variation, were stably expressed in all environments. QPH.caas-4D is assumed to be the Rht-D1b locus, whereas QPH.caas-6A is likely to be a newly discovered gene. The allele from Aikang 58 at QPH.caas-4D reduced PH by 11.5–18.2% and TKW by 2.6–3.8%; however, KNS increased (1.2–3.7%) as did NS (2.8–4.1%). The QPH.caas-6A allele from Aikang 58 reduced PH by 8.0–11.5% and TKW by 6.9–8.5%, whereas KNS increased by 1.2–3.6% and NS by 0.9–4.5%. Genotypes carrying both QPH.caas-4D and QPH.caas-6A alleles from Aikang 58 showed reduced PH by 28.6–30.6%, simultaneously reducing TKW (13.8–15.2%) and increasing KNS (3.4–4.9%) and NS (6.5–10%). QTKW.caas-4B and QTKW.caas-5B.1 were stably detected and significantly associated with either KNS or NS. Major KNS QTLs QKNS.caas-4B and QKNS.caas-5B.1 and the GY QTL QGY.caas-3B.2 were detected only in water-limited environments. The major TKW QTKW.caas-6D had no significant effect on either KNS or NS and it could have potential for improving yield.

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Mapping quantitative trait loci for yield-related traits and predicting candidate genes for grain weight in maize

Scientific Reports ◽

10.1038/s41598-019-52222-5 ◽

2019 ◽

Vol 9 (1) ◽

Author(s):

Yanming Zhao ◽

Chengfu Su

Keyword(s):

Quantitative Trait Loci ◽

Candidate Genes ◽

Quantitative Trait ◽

Composite Interval Mapping ◽

Developmental Stages ◽

Interval Mapping ◽

Kernel Weight ◽

Grain Weight ◽

Trait Loci ◽

Stable Qtls

Abstract Quantitative trait loci (QTLs) mapped in different genetic populations are of great significance for marker-assisted breeding. In this study, an F2:3 population were developed from the crossing of two maize inbred lines SG-5 and SG-7 and applied to QTL mapping for seven yield-related traits. The seven traits included 100-kernel weight, ear length, ear diameter, cob diameter, kernel row number, ear weight, and grain weight per plant. Based on an ultra-high density linkage map, a total of thirty-three QTLs were detected for the seven studied traits with composite interval mapping (CIM) method, and fifty-four QTLs were indentified with genome-wide composite interval mapping (GCIM) methods. For these QTLs, Fourteen were both detected by CIM and GCIM methods. Besides, eight of the thirty QTLs detected by CIM were identical to those previously mapped using a F2 population (generating from the same cross as the mapping population in this study), and fifteen were identical to the reported QTLs in other recent studies. For the fifty-four QTLs detected by GCIM, five of them were consistent with the QTLs mapped in the F2 population of SG-5 × SG-7, and twenty one had been reported in other recent studies. The stable QTLs associated with grain weight were located on maize chromosomes 2, 5, 7, and 9. In addition, differentially expressed genes (DEGs) between SG-5 and SG-7 were obtained from the transcriptomic profiling of grain at different developmental stages and overlaid onto the stable QTLs intervals to predict candidate genes for grain weight in maize. In the physical intervals of confirmed QTLs qKW-7, qEW-9, qEW-10, qGWP-6, qGWP-8, qGWP-10, qGWP-11 and qGWP-12, there were 213 DEGs in total. Finally, eight genes were predicted as candidate genes for grain size/weight. In summary, the stable QTLs would be reliable and the candidate genes predicted would be benefit for maker assisted breeding or cloning.

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QTL Analysis and Fine Mapping of a Major QTL Conferring Kernel Size in Maize (Zea mays)

Frontiers in Genetics ◽

10.3389/fgene.2020.603920 ◽

2020 ◽

Vol 11 ◽

Author(s):

Guiying Wang ◽

Yanming Zhao ◽

Wenbo Mao ◽

Xiaojie Ma ◽

Chengfu Su

Keyword(s):

Fine Mapping ◽

Interval Mapping ◽

Chromosome 9 ◽

Recurrent Parent ◽

Kernel Size ◽

Protein Coding ◽

Kernel Length ◽

Genetic Mechanisms ◽

Donor Parent ◽

Stable Qtls

Kernel size is an important agronomic trait for grain yield in maize. The purpose of this study is to map QTLs and predict candidate genes for kernel size in maize. A total of 199 F2 and its F2:3 lines from the cross between SG5/SG7 were developed. A composite interval mapping (CIM) method was used to detect QTLs in three environments of F2 and F2:3 populations. The result showed that a total of 10 QTLs for kernel size were detected, among which were five QTLs for kernel length (KL) and five QTLs for kernel width (KW). Two stable QTLs, qKW-1, and qKL-2, were mapped in all three environments. Three QTLs, qKL-1, qKW-1, and qKW-2, were overlapped with the QTLs identified from previous studies. In order to validate and fine map qKL-2, near-isogenic lines (NILs) were developed by continuous backcrossing between SG5 as the donor parent and SG7 as the recurrent parent. Marker-assisted selection was conducted from BC2F1 generation with molecular markers near qKL-2. A secondary linkage map with six markers around the qKL-2 region was developed and used for fine mapping of qKL-2. Finally, qKL-2 was confirmed in a 1.95 Mb physical interval with selected overlapping recombinant chromosomes on maize chromosome 9 by blasting with the Zea_Mays_B73 v4 genome. Transcriptome analysis showed that a total of 11 out of 40 protein-coding genes differently expressed between the two parents were detected in the identified qKL-2 interval. GRMZM2G006080 encoding a receptor-like protein kinase FERONIA, was predicted as a candidate gene to control kernel size. The work will not only help to understand the genetic mechanisms of kernel size of maize but also lay a foundation for further fine mapping and even cloning of the promising loci.

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Dissection of Genetic Basis Underpinning Kernel Weight-Related Traits in Common Wheat

Plants ◽

10.3390/plants10040713 ◽

2021 ◽

Vol 10 (4) ◽

pp. 713

Author(s):

Shunda Li ◽

Liang Wang ◽

Yaning Meng ◽

Yuanfeng Hao ◽

Hongxin Xu ◽

...

Keyword(s):

Recombinant Inbred Lines ◽

Wheat Yield ◽

Snp Array ◽

Kernel Weight ◽

Yield Potential ◽

Phenotypic Variance ◽

Genetic Dissection ◽

Single Nucleotide ◽

Grain Width ◽

Potential Improvement

Genetic dissection kernel weight-related traits is of great significance for improving wheat yield potential. As one of the three major yield components of wheat, thousand kernel weight (TKW) was mainly affected by grain length (GL) and grain width (GW). To uncover the key loci for these traits, we carried out a quantitative trait loci (QTL) analysis of an F6 recombinant inbred lines (RILs) population derived from a cross of Henong 5290 (small grain) and 06Dn23 (big grain) with a 50 K single nucleotide polymorphism (SNP) array. A total of 17 stable and big effect QTL, including 5 for TKW, 8 for GL and 4 for GW, were detected on the chromosomes 1B, 2A, 2B, 2D, 4B, 5A, 6A and 6D, respectively. Among these, there were two co-located loci for three traits that were mapped on the chromosome 4BS and 6AL. The QTL on 6AL was the most stable locus and explained 15.4–24.8%, 4.1–8.8% and 15.7–24.4% of TKW, GW and GL variance, respectively. In addition, two more major QTL of GL were located on chromosome arm 2BL and 2DL, accounting for 9.7–17.8% and 13.6–19.8% of phenotypic variance, respectively. In this study, we found one novel co-located QTL associated with GL and TKW in 2DL, QGl.haaf-2DL.2/QTkw.haaf-2DL.2, which could explain 13.6–19.8% and 9.8–10.7% phenotypic variance, respectively. Genetic regions and linked markers of these stable QTL will help to further refine mapping of the corresponding loci and marker-assisted selection (MAS) breeding for wheat grain yield potential improvement.

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Quantitative traits loci mapping and molecular marker development for total glutenin and glutenin fraction contents in wheat

BMC Plant Biology ◽

10.1186/s12870-021-03221-0 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Zhengfu Zhou ◽

Ziwei Zhang ◽

Annaliese S. Mason ◽

Lingzhi Chen ◽

Congcong Liu ◽

...

Keyword(s):

Genetic Basis ◽

Developmental Stages ◽

Recombinant Inbred Lines ◽

Genetic Locus ◽

Specific Pcr ◽

Qtl Cluster ◽

Ril Population ◽

End Use ◽

Molecular Marker Development ◽

Kasp Markers

Abstract Background Glutenin contents and compositions are crucial factors influencing the end-use quality of wheat. Although the composition of glutenin fractions is well known, there has been relatively little research on the genetic basis of glutenin fractions in wheat. Results To elucidate the genetic basis for the contents of glutenin and its fractions, a population comprising 196 recombinant inbred lines (RILs) was constructed from two parents, Luozhen No.1 and Zhengyumai 9987, which differ regarding their total glutenin and its fraction contents (except for the By fraction). Forty-one additive Quantitative Trait Loci (QTL) were detected in four environments over two years. These QTL explained 1.3% - 53.4% of the phenotypic variation in the examined traits. Forty-three pairs of epistatic QTL (E-QTL) were detected in the RIL population across four environments. The QTL controlling the content of total glutenin and its seven fractions were detected in clusters. Seven clusters enriched with QTL for more than three traits were identified, including a QTL cluster 6AS-3, which was revealed as a novel genetic locus for glutenin and related traits. Kompetitive Allele-Specific PCR (KASP) markers developed from the main QTL cluster 1DL-2 and the previously developed KASP marker for the QTL cluster 6AS-3 were validated as significantly associated with the target traits in the RIL population and in natural varieties. Conclusions This study identified novel genetic loci related to glutenin and its seven fractions. Additionally, the developed KASP markers may be useful for the marker-assisted selection of varieties with high glutenin fraction content and for identifying individuals in the early developmental stages without the need for phenotyping mature plants. On the basis of the results of this study and the KASP markers described herein, breeders will be able to efficiently select wheat lines with favorable glutenin properties and develop elite lines with high glutenin subunit contents.

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