The quantitative trait loci （QTLs） for the dead leaf rate （DLR） and the dead seedling rate （DSR） at the different rice growing periods after transplanting under alkaline stress were identified using an F2：3 pop...The quantitative trait loci （QTLs） for the dead leaf rate （DLR） and the dead seedling rate （DSR） at the different rice growing periods after transplanting under alkaline stress were identified using an F2：3 population, which included 200 individuals and lines derived from a cross between two japonica rice cultivars Gaochan 106 and Changbai 9 with microsatellite markers. The DLR detected at 20 days to 62 days after transplanting under alkaline stress showed continuous normal or near normal distributions in F3 lines, which was the quantitative trait controlled by multiple genes. The DSR showed a continuous distribution with 3 or 4 peaks and was the quantitative trait controlled by main and multiple genes when rice was grown for 62 days after transplanting under alkaline stress. Thirteen QTLs associated with DLR were detected at 20 days to 62 days after transplanting under alkaline stress. Among these, qDLR9-2 located in RM5786-RM160 on chromosome 9 was detected at 34 days, 41 days, 48 days, 55 days, and 62 days, respectively; qDLR4 located in RM3524-RM3866 on chromosome 4 was detected at 34 days, 41 days, and 48 days, respectively; qDLR7-1 located in RM3859-RM320 on chromosome 7 was detected at 20 days and 27 days; and qDLR6-2 in RM1340-RM5957 on chromosome 6 was detected at 55 days and 62 days, respectively. The alleles of both qDLR9-2 and qDLR4 were derived from alkaline sensitive parent ＂Gaochanl06＂. The alleles of both qDLR7-1 and qDLR6-2 were from alkaline tolerant parent Changbai 9. These gene actions showed dominance and over dominance primarily. Six QTLs associated with DSR were detected at 62 days after transplanting under alkaline stress. Among these, qDSR6-2 and qDSR8 were located in RM1340-RM5957 on chromosome 6 and in RM3752-RM404 on chromosome 8, respectively, which were associated with DSR and accounted for 20.32% and 18.86% of the observed phenotypic variation, respectively; qDSR11-2 and qDSR11-3 were located in RM536-RM479 and RM2596-RM286 on chromosome 11, respectively, which were associated with DSR explaining 25.85% and 15.41% of the observed phenotypic variation, respectively. The marker flanking distances of these QTLs were quite far except that of qDSR6-2, which should be researched further.展开更多
Production of mutants with altered phenotypes is a powerful approach for determining the biological functions of genes in an organism. In this study, a high-grain-weight mutant line M8008 was identified from a library...Production of mutants with altered phenotypes is a powerful approach for determining the biological functions of genes in an organism. In this study, a high-grain-weight mutant line M8008 was identified from a library of mutants of the common wheat cultivar YN15 treated with ethylmethane sulfonate(EMS). F2 and F2:3generations produced from crosses of M8008 × YN15(MY) and M8008 × SJZ54(MS) were used for genetic analysis. There were significant differences between M8008 and YN15 in plant height(PH), spike length(SL),fertile spikelet number per spike(FSS), grain width(GW), grain length(GL), GL/GW ratio(GLW), and thousand-grain weight(TGW). Most simple correlation coefficients were significant for the investigated traits, suggesting that the correlative mutations occurred in M8008. Approximately 21% of simple sequence repeat(SSR) markers showed polymorphisms between M8008 and YN15, indicating that EMS can induce a large number of mutated loci. Twelve quantitative trait loci(QTLs) forming QTL clusters(one in MY and two in MS) were detected. The QTL clusters coinciding with(MY population) or near(MS population) the marker wmc41 were associated mainly with grain-size traits, among which the M8008 locus led to decreases in GW, factor form density(FFD), and TGW and to increases in GLW. The cluster in the wmc25–barc168 interval in the MS population was associated with yield traits, for which the M8008 locus led to decreased PH, spike number per plant(SN), and SL.展开更多
Favorable agronomic traits are important to improve productivity of popcorn. In this study, a recombinant inbred line（RIL） population consisting of 258 lines was evaluated to identify quantitative trait loci（QTLs）...Favorable agronomic traits are important to improve productivity of popcorn. In this study, a recombinant inbred line（RIL） population consisting of 258 lines was evaluated to identify quantitative trait loci（QTLs） for nine agronomic traits（plant height, ear height, top height（plant height subtracted ear height）, top height/plant height, number of leaves above the top ear, leaf area, stalk diameter, number of tassel branches and the length of tassel） under three environments. Meta-analysis was conducted then to integrate QTLs identified across three generations（RIL, F2：3 and BC2F2） developed from the same crosses. In total, 179 QTLs and 36 meta-QTLs（m QTL） were identified. The percentage of phenotypic variation（R2） explained by any single QTL varied from 3.86 to 28.4%, and 24 QTLs with contributions over 15%. Nine common QTLs located in the same or similar chromosome regions were detected across three generations. Five meta-QTLs were identified including QTLs in three independent studies. Seven important m QTLs were composed of 11–26 QTLs for 4–7 traits, respectively. Only 11 m QTLs were commonly identified in the same or similar chromosome regions across agronomic traits, popping characteristics（popping fold, popping volume and popping rate） and grain yield components（ear weight per plant, grain weight per plant, 100-grain weight, ear length, kernel number per row, ear diameter, row number per ear and kernel ratio） by meta-QTL analysis. In conclusion, we identified a list of QTLs, some of which with much higher contributions to agronomic traits should be valuable for further study in improving both popping characteristics and grain yield components in popcorn.展开更多
The study of yield traits can reveal the genetic architecture of grain yield for improving maize production.In this study, an association panel comprising 362 inbred lines and a recombinant inbred line population deri...The study of yield traits can reveal the genetic architecture of grain yield for improving maize production.In this study, an association panel comprising 362 inbred lines and a recombinant inbred line population derived from X178 × 9782 were used to identify candidate genes for nine yield traits. High-priority overlap(HPO) genes, which are genes prioritized in a genome-wide association study(GWAS), were investigated using coexpression networks. The GWAS identified 51 environmentally stable SNPs in two environments and 36 pleiotropic SNPs, including three SNPs with both attributes. Seven hotspots containing 41 trait-associated SNPs were identified on six chromosomes by permutation. Pyramiding of superior alleles showed a highly positive effect on all traits, and the phenotypic values of ear diameter and ear weight consistently corresponded with the number of superior alleles in tropical and temperate germplasm. A total of 61 HPO genes were detected after trait-associated SNPs were combined with the coexpression networks. Linkage mapping identified 16 environmentally stable and 16 pleiotropic QTL.Seven SNPs that were located in QTL intervals were assigned as consensus SNPs for the yield traits.Among the candidate genes predicted by our study, some genes were confirmed to function in seed development. The gene Zm00001 d016656 encoding a serine/threonine protein kinase was associated with five different traits across multiple environments. Some genes were uniquely expressed in specific tissues and at certain stages of seed development. These findings will provide genetic information and resources for molecular breeding of maize grain yield.展开更多
QTLs for quantitative traits are influenced by genetic background(GB) and environment.Identification of QTL with GB independency and environmental stability is prerequisite for effective marker-assisted selection(MAS)...QTLs for quantitative traits are influenced by genetic background(GB) and environment.Identification of QTL with GB independency and environmental stability is prerequisite for effective marker-assisted selection(MAS). In this study, QTLs and QTL × environment interactions affecting grain yield per plant(GY) and its component traits, filled grain number per panicle(FGN), panicle number per plant(PN) and 1000-grain weight(TGW) across six environments were dissected using two sets of reciprocal introgression lines(ILs) derived from the cross Lemont × Teqing and SNP genotypic data. ANOVA indicated that the differences among genotypes and environments within each set of ILs were highly significant for all traits. A total of 72 distinct QTLs for GY and its component traits including 15 for GY, 25 for FGN, 18 for PN, and 29 for TGW were detected over the six environments. Most QTLs(87.4%) showed significant QTL × environment interactions(QEIs) and appeared to be more or less environment-specific. Among 72 QTLs, 15(20.8%) QTLs and 12(16.7%) QEIs were commonly identified in both backgrounds, indicating QTL especially QEI for yield and its component traits had strong GB effects. Four QTL regions affecting GY and its component traits, including S1269707–S4288071, S16661497–S17511092, and S35861863–S36341768 on chromosome 3, and S4134205–S7643153 on chromosome 5, were detected in both backgrounds and coincided with cloned genes for yield-related traits. These regions can be the targeted in rice breeding for high yield potential through MAS. Application of QTL main effects and their environmental interaction effects in MAS was discussed in detail.展开更多
文摘The quantitative trait loci （QTLs） for the dead leaf rate （DLR） and the dead seedling rate （DSR） at the different rice growing periods after transplanting under alkaline stress were identified using an F2：3 population, which included 200 individuals and lines derived from a cross between two japonica rice cultivars Gaochan 106 and Changbai 9 with microsatellite markers. The DLR detected at 20 days to 62 days after transplanting under alkaline stress showed continuous normal or near normal distributions in F3 lines, which was the quantitative trait controlled by multiple genes. The DSR showed a continuous distribution with 3 or 4 peaks and was the quantitative trait controlled by main and multiple genes when rice was grown for 62 days after transplanting under alkaline stress. Thirteen QTLs associated with DLR were detected at 20 days to 62 days after transplanting under alkaline stress. Among these, qDLR9-2 located in RM5786-RM160 on chromosome 9 was detected at 34 days, 41 days, 48 days, 55 days, and 62 days, respectively; qDLR4 located in RM3524-RM3866 on chromosome 4 was detected at 34 days, 41 days, and 48 days, respectively; qDLR7-1 located in RM3859-RM320 on chromosome 7 was detected at 20 days and 27 days; and qDLR6-2 in RM1340-RM5957 on chromosome 6 was detected at 55 days and 62 days, respectively. The alleles of both qDLR9-2 and qDLR4 were derived from alkaline sensitive parent ＂Gaochanl06＂. The alleles of both qDLR7-1 and qDLR6-2 were from alkaline tolerant parent Changbai 9. These gene actions showed dominance and over dominance primarily. Six QTLs associated with DSR were detected at 62 days after transplanting under alkaline stress. Among these, qDSR6-2 and qDSR8 were located in RM1340-RM5957 on chromosome 6 and in RM3752-RM404 on chromosome 8, respectively, which were associated with DSR and accounted for 20.32% and 18.86% of the observed phenotypic variation, respectively; qDSR11-2 and qDSR11-3 were located in RM536-RM479 and RM2596-RM286 on chromosome 11, respectively, which were associated with DSR explaining 25.85% and 15.41% of the observed phenotypic variation, respectively. The marker flanking distances of these QTLs were quite far except that of qDSR6-2, which should be researched further.
基金supported by the National Natural Science Foundation of China (31271712)the National Key Technologies R&D Program of China (2013BAD01B02-8)
文摘Production of mutants with altered phenotypes is a powerful approach for determining the biological functions of genes in an organism. In this study, a high-grain-weight mutant line M8008 was identified from a library of mutants of the common wheat cultivar YN15 treated with ethylmethane sulfonate(EMS). F2 and F2:3generations produced from crosses of M8008 × YN15(MY) and M8008 × SJZ54(MS) were used for genetic analysis. There were significant differences between M8008 and YN15 in plant height(PH), spike length(SL),fertile spikelet number per spike(FSS), grain width(GW), grain length(GL), GL/GW ratio(GLW), and thousand-grain weight(TGW). Most simple correlation coefficients were significant for the investigated traits, suggesting that the correlative mutations occurred in M8008. Approximately 21% of simple sequence repeat(SSR) markers showed polymorphisms between M8008 and YN15, indicating that EMS can induce a large number of mutated loci. Twelve quantitative trait loci(QTLs) forming QTL clusters(one in MY and two in MS) were detected. The QTL clusters coinciding with(MY population) or near(MS population) the marker wmc41 were associated mainly with grain-size traits, among which the M8008 locus led to decreases in GW, factor form density(FFD), and TGW and to increases in GLW. The cluster in the wmc25–barc168 interval in the MS population was associated with yield traits, for which the M8008 locus led to decreased PH, spike number per plant(SN), and SL.
基金funded by the Plan for the Scientific Innovation Talent of Henan ProvinceChina(124200510003)+2 种基金the National High-Tech Research and Development Program of China(2012AA10A307)the Agricultural Science Creation in Henan Provincethe Modern Agricultural System in Industry and Technology of Henan Province,China(S2010-02-G01)
文摘Favorable agronomic traits are important to improve productivity of popcorn. In this study, a recombinant inbred line（RIL） population consisting of 258 lines was evaluated to identify quantitative trait loci（QTLs） for nine agronomic traits（plant height, ear height, top height（plant height subtracted ear height）, top height/plant height, number of leaves above the top ear, leaf area, stalk diameter, number of tassel branches and the length of tassel） under three environments. Meta-analysis was conducted then to integrate QTLs identified across three generations（RIL, F2：3 and BC2F2） developed from the same crosses. In total, 179 QTLs and 36 meta-QTLs（m QTL） were identified. The percentage of phenotypic variation（R2） explained by any single QTL varied from 3.86 to 28.4%, and 24 QTLs with contributions over 15%. Nine common QTLs located in the same or similar chromosome regions were detected across three generations. Five meta-QTLs were identified including QTLs in three independent studies. Seven important m QTLs were composed of 11–26 QTLs for 4–7 traits, respectively. Only 11 m QTLs were commonly identified in the same or similar chromosome regions across agronomic traits, popping characteristics（popping fold, popping volume and popping rate） and grain yield components（ear weight per plant, grain weight per plant, 100-grain weight, ear length, kernel number per row, ear diameter, row number per ear and kernel ratio） by meta-QTL analysis. In conclusion, we identified a list of QTLs, some of which with much higher contributions to agronomic traits should be valuable for further study in improving both popping characteristics and grain yield components in popcorn.
基金funded and supported by China Agriculture Research System of MOF and MARA,Sichuan Science and Technology Support Project(2021YFYZ0020,2021YFYZ0027,2021YFFZ0017)National Natural Science Foundation of China(31971955)Sichuan Science and Technology Program(2019YJ0418,2020YJ0138)。
文摘The study of yield traits can reveal the genetic architecture of grain yield for improving maize production.In this study, an association panel comprising 362 inbred lines and a recombinant inbred line population derived from X178 × 9782 were used to identify candidate genes for nine yield traits. High-priority overlap(HPO) genes, which are genes prioritized in a genome-wide association study(GWAS), were investigated using coexpression networks. The GWAS identified 51 environmentally stable SNPs in two environments and 36 pleiotropic SNPs, including three SNPs with both attributes. Seven hotspots containing 41 trait-associated SNPs were identified on six chromosomes by permutation. Pyramiding of superior alleles showed a highly positive effect on all traits, and the phenotypic values of ear diameter and ear weight consistently corresponded with the number of superior alleles in tropical and temperate germplasm. A total of 61 HPO genes were detected after trait-associated SNPs were combined with the coexpression networks. Linkage mapping identified 16 environmentally stable and 16 pleiotropic QTL.Seven SNPs that were located in QTL intervals were assigned as consensus SNPs for the yield traits.Among the candidate genes predicted by our study, some genes were confirmed to function in seed development. The gene Zm00001 d016656 encoding a serine/threonine protein kinase was associated with five different traits across multiple environments. Some genes were uniquely expressed in specific tissues and at certain stages of seed development. These findings will provide genetic information and resources for molecular breeding of maize grain yield.
基金funded by the National Natural Science Foundation (30570996)the Program of Introducing International Super Agricultural Science and Technology (from the Chinese Ministry of Agriculture (the "948" 483 Project, 2010-G2B), 484the Shenzhen Peacock Plan (20130415095710361)
文摘QTLs for quantitative traits are influenced by genetic background(GB) and environment.Identification of QTL with GB independency and environmental stability is prerequisite for effective marker-assisted selection(MAS). In this study, QTLs and QTL × environment interactions affecting grain yield per plant(GY) and its component traits, filled grain number per panicle(FGN), panicle number per plant(PN) and 1000-grain weight(TGW) across six environments were dissected using two sets of reciprocal introgression lines(ILs) derived from the cross Lemont × Teqing and SNP genotypic data. ANOVA indicated that the differences among genotypes and environments within each set of ILs were highly significant for all traits. A total of 72 distinct QTLs for GY and its component traits including 15 for GY, 25 for FGN, 18 for PN, and 29 for TGW were detected over the six environments. Most QTLs(87.4%) showed significant QTL × environment interactions(QEIs) and appeared to be more or less environment-specific. Among 72 QTLs, 15(20.8%) QTLs and 12(16.7%) QEIs were commonly identified in both backgrounds, indicating QTL especially QEI for yield and its component traits had strong GB effects. Four QTL regions affecting GY and its component traits, including S1269707–S4288071, S16661497–S17511092, and S35861863–S36341768 on chromosome 3, and S4134205–S7643153 on chromosome 5, were detected in both backgrounds and coincided with cloned genes for yield-related traits. These regions can be the targeted in rice breeding for high yield potential through MAS. Application of QTL main effects and their environmental interaction effects in MAS was discussed in detail.
