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Klaus Pillen - One of the best experts on this subject based on the ideXlab platform.
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identification and verification of qtls for Agronomic Traits using wild barley introgression lines
Theoretical and Applied Genetics, 2009Co-Authors: Inga Schmalenbach, Jens Leon, Klaus PillenAbstract:A set of 39 wild barley introgression lines (hereafter abbreviated with S42ILs) was subjected to a QTL study to verify genetic effects for Agronomic Traits, previously detected in the BC2DH population S42 (von Korff et al. 2006 in Theor Appl Genet 112:1221-1231) and, in addition, to identify new QTLs and favorable wild barley alleles. Each line within the S42IL set contains a single marker-defined chromosomal introgression from wild barley (Hordeum vulgare ssp. spontaneum), whereas the remaining part of the genome is exclusively derived from elite spring barley (H. vulgare ssp. vulgare). Agronomic field data of the S42ILs were collected for seven Traits from three different environments during the 2007 growing season. For detection of putative QTLs, a two-factorial mixed model ANOVA and, subsequently, a Dunnett test with the recurrent parent as a control were conducted. The presence of a QTL effect on a wild barley introgression was accepted, if the trait value of a particular S42IL was significantly (P<0.05) different from the control, either across all environments and/or in a particular environment. A total of 47 QTLs were localized in the S42IL set, among which 39 QTLs were significant across all tested environments. For 19 QTLs (40.4%), the wild barley introgression was associated with a favorable effect on trait performance. Von Korff et al. (2006 in Theor Appl Genet 112:1221-1231) mapped altogether 44 QTLs for six Agronomic Traits to genomic regions, which are represented by wild barley introgressions of the S42IL set. Here, 18 QTLs (40.9%) revealed a favorable wild barley effect on the trait performance. By means of the S42ILs, 20 out of the 44 QTLs (45.5%) and ten out of the 18 favorable effects (55.6%) were verified. Most QTL effects were confirmed for the Traits days until heading and plant height. For the six corresponding Traits, a total of 17 new QTLs were identified, where at six QTLs (35.3%) the exotic introgression caused an improved trait performance. In addition, eight QTLs for the newly studied trait grains per ear were detected. Here, no QTL from wild barley exhibited a favorable effect. The introgression line S42IL-107, which carries an introgression on chromosome 2H, 17-42 cM is an example for S42ILs carrying several QTL effects simultaneously. This line exhibited improved performance across all tested environments for the Traits days until heading, plant height and thousand grain weight. The line can be directly used to transfer valuable Hsp alleles into modern elite cultivars, and, thus, for breeding of improved varieties.
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ab qtl analysis in spring barley ii detection of favourable exotic alleles for Agronomic Traits introgressed from wild barley h vulgare ssp spontaneum
Theoretical and Applied Genetics, 2006Co-Authors: M Von Korff, H Wang, Jens Leon, Klaus PillenAbstract:The objective of the present study was to identify favourable exotic Quantitative Trait Locus (QTL) alleles for the improvement of Agronomic Traits in the BC2DH population S42 derived from a cross between the spring barley cultivar Scarlett and the wild barley accession ISR42-8 (Hordeum vulgare ssp. spontaneum). QTLs were detected as a marker main effect and/or a marker × environment interaction effect (M × E) in a three-factorial ANOVA. Using field data of up to eight environments and genotype data of 98 SSR loci, we detected 86 QTLs for nine Agronomic Traits. At 60 QTLs the marker main effect, at five QTLs the M × E interaction effect, and at 21 QTLs both the effects were significant. The majority of the M × E interaction effects were due to changes in magnitude and are, therefore, still valuable for marker assisted selection across environments. The exotic alleles improved performance in 31 (36.0%) of 86 QTLs detected for Agronomic Traits. The exotic alleles had favourable effects on all analysed quantitative Traits. These favourable exotic alleles were detected, in particular on the short arm of chromosome 2H and the long arm of chromosome 4H. The exotic allele on 4HL, for example, improved yield by 7.1%. Furthermore, the presence of the exotic allele on 2HS increased the yield component Traits ears per m2 and thousand grain weight by 16.4% and 3.2%, respectively. The present study, hence, demonstrated that wild barley does harbour valuable alleles, which can enrich the genetic basis of cultivated barley and improve quantitative Agronomic Traits.
Manoj Prasad - One of the best experts on this subject based on the ideXlab platform.
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genome wide association study of major Agronomic Traits in foxtail millet setaria italica l using ddrad sequencing
Scientific Reports, 2019Co-Authors: Vandana Jaiswal, Sarika Gupta, Mehanathan Muthamilarasan, Vijay Gahlaut, Tirthankar Bandyopadhyay, Nirala Ramchiary, Manoj PrasadAbstract:Foxtail millet (Setaria italica), the second largest cultivated millet crop after pearl millet, is utilized for food and forage globally. Further, it is also considered as a model crop for studying Agronomic, nutritional and biofuel Traits. In the present study, a genome-wide association study (GWAS) was performed for ten important Agronomic Traits in 142 foxtail millet core eco-geographically diverse genotypes using 10 K SNPs developed through GBS-ddRAD approach. Number of SNPs on individual chromosome ranged from 844 (chromosome 5) to 2153 (chromosome 8) with an average SNP frequency of 25.9 per Mb. The pairwise linkage disequilibrium (LD) estimated using the squared-allele frequency correlations was found to decay rapidly with the genetic distance of 177 Kb. However, for individual chromosome, LD decay distance ranged from 76 Kb (chromosome 6) to 357 Kb (chromosome 4). GWAS identified 81 MTAs (marker-trait associations) for ten Traits across the genome. High confidence MTAs for three important Agronomic Traits including FLW (flag leaf width), GY (grain yield) and TGW (thousand-grain weight) were identified. Significant pyramiding effect of identified MTAs further supplemented its importance in breeding programs. Desirable alleles and superior genotypes identified in the present study may prove valuable for foxtail millet improvement through marker-assisted selection.
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population structure and association mapping of yield contributing Agronomic Traits in foxtail millet
Plant Cell Reports, 2014Co-Authors: Sarika Gupta, Kajal Kumari, Mehanathan Muthamilarasan, Swarup K Parida, Manoj PrasadAbstract:Key message Association analyses accounting for population structure and relative kinship identified eight SSR markers ( p < 0.01) showing significant association ( R 2 = 18 %) with nine Agronomic Traits in foxtail millet.
Wei Zhang - One of the best experts on this subject based on the ideXlab platform.
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genetic analysis of seedling root Traits reveals the association of root trait with other Agronomic Traits in maize
BMC Plant Biology, 2018Co-Authors: Wei Zhang, Ya Liu, Yufeng Gao, Xiaofan Wang, Jianbing Yan, Xiaohong R YangAbstract:Root systems play important roles in crop growth and stress responses. Although genetic mechanism of root Traits in maize (Zea mays L.) has been investigated in different mapping populations, root Traits have rarely been utilized in breeding programs. Elucidation of the genetic basis of maize root Traits and, more importantly, their connection to other Agronomic trait(s), such as grain yield, may facilitate root trait manipulation and maize germplasm improvement. In this study, we analyzed genome-wide genetic loci for maize seedling root Traits at three time-points after seed germination to identify chromosomal regions responsible for both seedling root Traits and other Agronomic Traits in a recombinant inbred line (RIL) population (Zong3 × Yu87–1). Eight seedling root Traits were examined at 4, 9, and 14 days after seed germination, and thirty-six putative quantitative trait loci (QTLs), accounting for 9.0–23.2% of the phenotypic variation in root Traits, were detected. Co-localization of root trait QTLs was observed at, but not between, the three time-points. We identified strong or moderate correlations between root Traits controlled by each co-localized QTL region. Furthermore, we identified an overlap in the QTL locations of seedling root Traits examined here and six other Traits reported previously in the same RIL population, including grain yield-related Traits, plant height-related Traits, and Traits in relation to stress responses. Maize chromosomal bins 1.02–1.03, 1.07, 2.06–2.07, 5.05, 7.02–7.03, 9.04, and 10.06 were identified QTL hotspots for three or four more Traits in addition to seedling root Traits. Our identification of co-localization of root trait QTLs at, but not between, each of the three time-points suggests that maize seedling root Traits are regulated by different sets of pleiotropic-effect QTLs at different developmental stages. Furthermore, the identification of QTL hotspots suggests the genetic association of seedling root Traits with several other Traits and reveals maize chromosomal regions valuable for marker-assisted selection to improve root systems and other Agronomic Traits simultaneously.
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qtl mapping of ten Agronomic Traits on the soybean glycine max l merr genetic map and their association with est markers
Theoretical and Applied Genetics, 2004Co-Authors: Wei Zhang, Yongju Wang, Guangzuo Luo, Jingfa Zhang, Junyi Gai, Shuiliang CheAbstract:A set of 184 recombinant inbred lines (RILs) derived from soybean vars. Kefeng No.1 × Nannong 1138-2 was used to construct a genetic linkage map. The two parents exhibit contrasting characteristics for most of the Traits that were mapped. Using restricted fragment length polymorphisms (RFLPs), simple sequence repeats (SSRs) and expressed sequence tags (ESTs), we mapped 452 markers onto 21 linkage groups and covered 3,595.9 cM of the soybean genome. All of the linkage groups except linkage group F were consistent with those of the consensus map of Cregan et al. (1999). Linkage group F was divided into two linkage groups, F1 and F2. The map consisted of 189 RFLPs, 219 SSRs, 40 ESTs, three R gene loci and one phenotype marker. Ten Agronomic Traits—days to flowering, days to maturity, plant height, number of nodes on main stem, lodging, number of pods per node, protein content, oil content, 100-seed weight, and plot yield—were studied. Using winqtlcart, we detected 63 quantitative trait loci (QTLs) that had LOD>3 for nine of the Agronomic Traits (only exception being seed oil content) and mapped these on 12 linkage groups. Most of the QTLs were clustered, especially on groups B1 and C2. Some QTLs were mapped to the same loci. This pleiotropism was common for most of the QTLs, and one QTL could influence at most five Traits. Seven EST markers were found to be linked closely with or located at the same loci as the QTLs. EST marker GmKF059a, encoding a repressor protein and mapped on group C2, accounted for about 20% of the total variation of days to flowering, plant height, lodging and nodes on the main stem, respectively.
Makoto Matsuoka - One of the best experts on this subject based on the ideXlab platform.
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genome wide association study using whole genome sequencing rapidly identifies new genes influencing Agronomic Traits in rice
Nature Genetics, 2016Co-Authors: Kenji Yano, Eiji Yamamoto, Koichiro Aya, Hideyuki Takeuchi, Masanori Yamasaki, Shinya Yoshida, Hidemi Kitano, Ko Hirano, Makoto MatsuokaAbstract:A genome-wide association study (GWAS) can be a powerful tool for the identification of genes associated with Agronomic Traits in crop species, but it is often hindered by population structure and the large extent of linkage disequilibrium. In this study, we identified Agronomically important genes in rice using GWAS based on whole-genome sequencing, followed by the screening of candidate genes based on the estimated effect of nucleotide polymorphisms. Using this approach, we identified four new genes associated with Agronomic Traits. Some genes were undetectable by standard SNP analysis, but we detected them using gene-based association analysis. This study provides fundamental insights relevant to the rapid identification of genes associated with Agronomic Traits using GWAS and will accelerate future efforts aimed at crop improvement.
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genome wide association study using whole genome sequencing rapidly identifies new genes influencing Agronomic Traits in rice
Nature Genetics, 2016Co-Authors: Kenji Yano, Eiji Yamamoto, Hideyuki Takeuchi, Masanori Yamasaki, Shinya Yoshida, Hidemi Kitano, Ko Hirano, Peiching Lo, Li Hu, Makoto MatsuokaAbstract:Makoto Matsuoka and colleagues use a whole-genome sequencing-based approach to perform genome-wide association analysis for important Agronomic Traits in rice. Using phenotypically diverse rice with low interrelationships, they rapidly identify novel genes associated with heading date, plant height and panicle number per plant, validating candidates with transgenic experiments.
Xiaohong R Yang - One of the best experts on this subject based on the ideXlab platform.
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genetic analysis of seedling root Traits reveals the association of root trait with other Agronomic Traits in maize
BMC Plant Biology, 2018Co-Authors: Wei Zhang, Ya Liu, Yufeng Gao, Xiaofan Wang, Jianbing Yan, Xiaohong R YangAbstract:Root systems play important roles in crop growth and stress responses. Although genetic mechanism of root Traits in maize (Zea mays L.) has been investigated in different mapping populations, root Traits have rarely been utilized in breeding programs. Elucidation of the genetic basis of maize root Traits and, more importantly, their connection to other Agronomic trait(s), such as grain yield, may facilitate root trait manipulation and maize germplasm improvement. In this study, we analyzed genome-wide genetic loci for maize seedling root Traits at three time-points after seed germination to identify chromosomal regions responsible for both seedling root Traits and other Agronomic Traits in a recombinant inbred line (RIL) population (Zong3 × Yu87–1). Eight seedling root Traits were examined at 4, 9, and 14 days after seed germination, and thirty-six putative quantitative trait loci (QTLs), accounting for 9.0–23.2% of the phenotypic variation in root Traits, were detected. Co-localization of root trait QTLs was observed at, but not between, the three time-points. We identified strong or moderate correlations between root Traits controlled by each co-localized QTL region. Furthermore, we identified an overlap in the QTL locations of seedling root Traits examined here and six other Traits reported previously in the same RIL population, including grain yield-related Traits, plant height-related Traits, and Traits in relation to stress responses. Maize chromosomal bins 1.02–1.03, 1.07, 2.06–2.07, 5.05, 7.02–7.03, 9.04, and 10.06 were identified QTL hotspots for three or four more Traits in addition to seedling root Traits. Our identification of co-localization of root trait QTLs at, but not between, each of the three time-points suggests that maize seedling root Traits are regulated by different sets of pleiotropic-effect QTLs at different developmental stages. Furthermore, the identification of QTL hotspots suggests the genetic association of seedling root Traits with several other Traits and reveals maize chromosomal regions valuable for marker-assisted selection to improve root systems and other Agronomic Traits simultaneously.