Genetic analysis of leaf morphology underlying the plant density response by QTL mapping in maize (<Emphasis Type="Italic">Zea mays</Emphasis> L.)

Maize yield increase has been strongly linked to plant population densities over time with changes in plant architecture, but the genetic basis for the plant architecture response to plant density is unknown, as is its stability across environments. To elucidate the genetic basis of the plant architecture response to density in maize, we mapped quantitative trait loci (QTLs) for leaf morphology-related traits in four sets of recombinant inbred line (RIL) populations under two plant density conditions. Forty-five QTLs for six traits were detected in both high and low plant density conditions. Thirty-seven QTLs were only detected when grown under high plant density, and 34 QTLs were only detected when grown under low plant density. Twenty-two meta-QTLs (mQTLs) were identified by meta-analysis, and mQTL1-1, mQTL3-2 and mQTL8 were identified when grown under high and low plant densities, with R ² of some initial QTLs > 10 %, suggesting the mQTLs might be hot spots of the important QTLs for the related traits under planting density stress conditions. The results presented here provide useful information for further research and the marker-assisted selection of varieties targeting increased plant density and will help to reveal the molecular mechanisms related to leaf morphology in response to density.     

QTL mapping for yield and yield contributing traits in two mapping populations of bread wheat

In bread wheat, single-locus and two-locus QTL analyses were conducted for seven yield and yield contributing traits using two different mapping populations (P I and P II). Single-locus QTL analyses involved composite interval mapping (CIM) for individual traits and multiple-trait composite interval mapping (MCIM) for correlated yield traits to detect the pleiotropic QTLs. Two-locus analyses were conducted to detect main effect QTLs (M-QTLs), epistatic QTLs (E-QTLs) and QTL × environment interactions (QE and QQE). Only a solitary QTL for spikelets per spike was common between the above two populations. HomoeoQTLs were also detected, suggesting the presence of triplicate QTLs in bread wheat. Relatively fewer QTLs were detected in P I than in P II. This may be partly due to low density of marker loci on P I framework map (173) than in P II (521) and partly due to more divergent parents used for developing P II. Six QTLs were important which were pleiotropic/coincident involving more than one trait and were also consistent over environments. These QTLs could be utilized efficiently for marker assisted selection (MAS).     

Mapping of QTLs governing agronomic and yield traits in chickpea

Chickpea is one of the most important leguminous cool season food crops, cultivated prevalently in South Asia and Middle East. The main objective of this study was to identify quantitative trait loci (QTLs) associated with seven agronomic and yield traits in two recombinant inbred line populations of chickpea derived from the crosses JG62 × Vijay (JV population) and Vijay × ICC4958 (VI population) from at least three environments. Single locus QTL analysis involved composite interval mapping (CIM) for individual traits and multiple-trait composite interval mapping (MCIM) for correlated traits to detect pleiotropic QTLs. Two-locus analysis was conducted to identify the main effect QTLs (M-QTLs), epistatic QTLs (E-QTLs) and QTL × environment interactions. Through CIM analysis, a total of 106 significant QTLs (41 in JV and 65 in VI populations) were identified for the seven traits, of which one QTL each for plant height and days to maturity was common in both the populations. Six pleiotropic QTLs that were consistent over the environments were also identified. LG2 in JV and LG1a in VI contained at least one QTL for each trait. Hence, concentrating on these LGs in molecular breeding programs is most likely to bring simultaneous improvement in these traits.     

Quantitative trait locus mapping of resistance to Aspergillus flavus infection using a recombinant inbred line population in maize

Aflatoxin contamination of maize (Zea mays L.) grain caused by Aspergillus flavus is a serious health hazard to animals and humans. Development of maize varieties resistant to A. flavus infection and/or aflatoxin production can reduce this contamination. This study was conducted to identify quantitative trait loci (QTL) associated with resistance to A. flavus infection. A recombinant inbred line population was developed derived from RA, a maize inbred line resistant to A. flavus infection, and M53, a susceptible inbred line. After inoculation with A. flavus under controlled conditions, the kernels from each plant line grown in three different environments were evaluated for infection level. Categorical inoculation data were collected for each plant line based on the percentage of the kernel surface covered by fungal conidia. Significant genotypic variation in infection level was observed in all environments. Based on a genetic map containing 916 polymorphic simple sequence repeat and single nucleotide polymorphism markers, the resistance QTL were initially analyzed by composite interval mapping (CIM) separately for each environment. One QTL in bin 5.03 was detected in all environments, and seven other QTL were identified in one environment. Next, a mixed model based on CIM (MCIM) was employed for QTL analysis using data from the three environments simultaneously. Significant epistasis and epistasis × environment interaction to A. flavus infection were revealed. The QTL in bin 5.03 was repeatedly detected by the MCIM. This QTL explained the largest phenotypic variation among all of the detected QTL and could be considered as a major QTL for use in breeding for A. flavus resistance.     

Quantitative trait loci analysis of plant height and ear height in maize (Zea mays L.)

Genetic map containing 103 microsatellite loci obtained on 200 F₂ plants derived from the cross R15 × 478 was used for quantitative trait loci (QTL) mapping in maize. QTLs were characterized in a population of 200 F_2:4 lines, derived from selfing the F₂ plants, and were evaluated with two replications in two environments. QTL determinations were made from the mean of these two environments. Plant height (PH) and ear height (EH) were measured. Using composite interval mapping (CIM) method, a total of 14 distinct QTLs were identified: nine for PH and five for EH. Additive, partial dominance, dominance, and overdominance actions existed among all detected QTLs affecting plant height and ear height. The QTLs explained 78.27% of the phenotypic variance of PH and 41.50% of EH. The 14 QTLs displayed mostly dominance or partial dominance gene action and mapped to chromosomes 2, 3, 4, 8, and 9. The text was submitted by the authors in English.     

Identification of quantitative trait loci controlling linolenic acid concentration in PI483463 (Glycine soja)

Key message

The QTLs controlling alpha-linolenic acid concentration from wild soybean were mapped on nine soybean chromosomes with various phenotypic variations. New QTLs for alpha-linolenic acid were detected in wild soybean.

Abstract

Alpha-linolenic acid (ALA) is a polyunsaturated fatty acid desired in human and animal diets. Some wild soybean (Glycine soja) genotypes are high in ALA. The objective of this study was to identify quantitative trait loci (QTLs) controlling ALA concentration in a wild soybean accession, PI483463. In total, 188 recombinant inbred lines of F_5:6, F_5:7, and F_5:8 generations derived from a cross of wild soybean PI483463 (~15 % ALA) and cultivar Hutcheson (~9 % ALA) were planted in four environments. Harvested seeds were used to measure fatty acid concentration. Single nucleotide polymorphism markers of the universal soybean linkage panel (USLP 1.0) and simple sequence repeat markers were used for molecular genotyping. Nine putative QTLs were identified that controlled ALA concentration by model-based composite interval mapping and mapped to different soybean chromosomes. The QTLs detected in four environments explained 2.4–7.9 % of the total phenotypic variation (PV). Five QTLs, qALA5_3, qALA6_1, qALA14_1, qALA15_1, and qALA17_1, located on chromosomes 5, 6, 14, 15, and 17 were identified by model-based composite interval mapping and composite interval mapping in two individual environments. Among them, qALA6_1 showed the highest contribution to the PV with 10.0–10.2 % in two environments. The total detected QTLs for additive and epistatic effects explained 52.4 % of the PV for ALA concentration. These findings will provide useful information for understanding genetic structure and marker-assisted breeding programs to increase ALA concentration in seeds derived from wild soybean PI483463.     

玉米雄穗分枝数与主轴长的QTL鉴定

在包含103个SSR标记的连锁图谱基础上, 运用复合区间作图法检测玉米组合(N87-1×9526 )F3家系在正常与干旱胁迫环境下的雄穗分枝数与主轴长性状QTL。雄穗分枝数在正常环境下被检测到2个QTL座位, 分别位于第5和7连锁群上; 在胁迫环境下被检测到4个QTL座位分别位于 2、5、7和10连锁群上, 其中位于第5和7连锁群上的QTL不仅具有一致性而且与本作图群体中曾检测到的耐旱相关性状QTL存在连锁。雄穗主轴长在正常环境下被检测到2个QTL位于第2和第6连锁群上, 在干旱胁迫环境下被检测到了3个QTL分别于第2、4和10连锁群上, 其中位于第2染色体上的QTL是两种环境下所共同检测到的QTL。分析QTL的遗传作用方式表明, 雄穗分枝数以部分加性效应为主, 而雄主轴长全部表现为显性和超显性。     

Two consensus quantitative trait loci clusters controlling anthesis–silking interval, ear setting and grain yield might be related with drought tolerance in maize

Unravelling the molecular basis of drought tolerance will provide novel opportunities for improving crop yield under water-limited conditions. The present study was conducted to identify quantitative trait loci (QTLs) controlling anthesis–silking interval (ASI), ear setting percentage (ESP) and grain yield (GY). The mapping population included 234 F₂ plants derived from the cross X178 (drought tolerant) × B73 (drought susceptible). The corresponding F_2:3 progenies, along with their parents, were evaluated for the above-mentioned traits under both well-watered and water-stressed field conditions in three different trials carried out in central and southern China. Interval mapping and composite interval mapping identified 45 and 65 QTLs for the investigated traits, respectively. Two QTL clusters influencing ASI and ESP on chromosomes 1 (bin 1.03) and 9 (bins 9.03–9.05) were identified in more than two environments, showing sizeable additive effects and contribution to phenotypic variance; these two QTL clusters influenced GY only in one environment. No significant interaction was detected between the two genomic regions. A comparative analysis of these two QTL clusters with the QTLs controlling maize drought tolerance previously described in three mapping populations confirmed and extended their relevance for marker-assisted breeding to improve maize production under water-limited conditions.     

Integrated multiple population analysis of leaf architecture traits in maize (Zea mays L.)

Leaf morphology in maize is regulated by developmental patterning along three axes: proximodistal, mediolateral, and adaxial-abaxial. Maize contains homologues of many genes identified as regulators of leaf development in other species, but their relationship to the natural variation of leaf shape remains unknown. In this study, quantitative trait loci (QTLs) for leaf angle, leaf orientation value, leaf length, and leaf width were mapped by a total of 256 F(2:3) families evaluated in three environments. Meta-analysis was used to integrate genetic maps and detect QTLs across several independent QTL studies, on the basis of the previously reported experimental results for leaf architecture traits. Candidate gene sequences for leaf architecture were mapped in the integrated consensus genetic map. In total, 21 QTLs and 17 meta-QTLs (mQTLs) were detected. Among these QTLs, qLA1-1 and qLA2 were consistently detected in five and three populations respectively, and six of seven QTLs with contributions (R(2)) >10% were integrated in mQTLs. Six key mQTLs (mQTL1-1, mQTL2-1, mQTL3-3, mQTL5-1, mQTL7-2, and mQTL8-1) with R(2) of some initial QTLs >10% included 4-6 initial QTLs associated with 2-4 traits. Therefore, the chromosome regions for six mQTLs with high QTL co-localization might be hot spots of the important QTLs for the associated traits. Fifteen key candidate genes controlling leaf architecture traits coincided with 11 corresponding mQTLs, namely DWARF4, KAN3, liguleless1, TAC1, ROT3, AS2/liguleless2, PFL2, yabby9/SE/LIC/yabby15, mwp1, CYCD3;2, and CYCB1. In particular, DWARF4, liguleless1, AS2/liguleless2, yabby9/SE/LIC/yabby15, and CYCD3;2 were mapped within the important mQTL1-1, mQTL2-1, mQTL3-3, mQTL5-1, and mQTL7-2 intervals, respectively. Fine mapping or construction of single chromosome segment lines for genetic regions of these five mQTLs is worth further study and could be put to use in marker-assisted breeding. In conclusion, the results provide useful information for further research and help to reveal the molecular mechanisms with regard to leaf architecture traits.     

Genetic analysis of shoot fresh weight in a cross of wild (<Emphasis Type="Italic">G. soja</Emphasis>) and cultivated (<Emphasis Type="Italic">G. max</Emphasis>) soybean

Shoot fresh weight (SFW) is one of the parameters, used to estimate the total plant biomass yield in soybean. In the present study, a total of 188 F_5:8 recombinant inbred lines (RIL) derived from an interspecific cross of PI 483463 (Glycine soja) and Hutcheson (Glycine max) were investigated for SFW variation in the field for three consecutive years. The parental lines and RILs were phenotyped in the field at the R6 stage by measuring total biomass in kg/plot to identify the QTLs for SFW. Three QTLs qSFW6_1, qSFW15_1, and qSFW19_1 influencing SFW were identified on chromosome 6, 15, and 19, respectively. The QTL qSFW19_1 flanked between the markers BARC-044913-08839 and BARC-029975-06765 was the stable QTL expressed in all the three environments. The phenotypic variation explained by the QTLs across all environments ranged from 6.56 to 21.32 %. The additive effects indicated contribution of alleles from both the parents and additive × environment interaction effects affected the expression of SFW QTL. Screening of the RIL population with additional SSRs from the qSFW19_1 region delimited the QTL between the markers SSR19-1329 and BARC-29975-06765. QTL mapping using bin map detected two QTLs, qSFW19_1A and qSFW19_1B. The QTL qSFW19_1A mapped close to the Dt1 gene locus, which affects stem termination, plant height, and floral initiation in soybean. Potential candidate genes for SFW were pinpointed, and sequence variations within their sequences were detected using high-quality whole-genome resequencing data. The findings in this study could be useful for understanding genetic basis of SFW in soybean.     

Identification and validation of quantitative trait loci for seed yield,oil and protein contents in two recombinant inbred line populations of soybean

Soybean seeds contain high levels of oil and protein, and are the important sources of vegetable oil and plant protein for human consumption and livestock feed. Increased seed yield, oil and protein contents are the main objectives of soybean breeding. The objectives of this study were to identify and validate quantitative trait loci (QTLs) associated with seed yield, oil and protein contents in two recombinant inbred line populations, and to evaluate the consistency of QTLs across different environments, studies and genetic backgrounds. Both the mapping population (SD02-4-59 × A02-381100) and validation population (SD02-911 × SD00-1501) were phenotyped for the three traits in multiple environments. Genetic analysis indicated that oil and protein contents showed high heritabilities while yield exhibited a lower heritability in both populations. Based on a linkage map constructed previously with the mapping population and using composite interval mapping and/or interval mapping analysis, 12 QTLs for seed yield, 16 QTLs for oil content and 11 QTLs for protein content were consistently detected in multiple environments and/or the average data over all environments. Of the QTLs detected in the mapping population, five QTLs for seed yield, eight QTLs for oil content and five QTLs for protein content were confirmed in the validation population by single marker analysis in at least one environment and the average data and by ANOVA over all environments. Eight of these validated QTLs were newly identified. Compared with the other studies, seven QTLs for seed yield, eight QTLs for oil content and nine QTLs for protein content further verified the previously reported QTLs. These QTLs will be useful for breeding higher yield and better quality cultivars, and help effectively and efficiently improve yield potential and nutritional quality in soybean.     

Genetic analysis and major QTL detection for maize kernel size and weight in multi-environments

Key Message

Twelve major QTL in five optimal clusters and several epistatic QTL are identified for maize kernel size and weight, some with pleiotropic will be promising for fine-mapping and yield improvement.

Abstract

Kernel size and weight are important target traits in maize (Zea mays L.) breeding programs. Here, we report a set of quantitative trait loci (QTL) scattered through the genome and significantly controlled the performance of four kernel traits including length, width, thickness and weight. From the cross V671 (large kernel) × Mc (small kernel), 270 derived F_2:3 families were used to identify QTL of maize kernel-size traits and kernel weight in five environments, using composite interval mapping (CIM) for single-environment analysis along with mixed linear model-based CIM for joint analysis. These two mapping strategies identified 55 and 28 QTL, respectively. Among them, 6 of 23 coincident were detected as interacting with environment. Single-environment analysis showed that 8 genetic regions on chromosomes 1, 2, 4, 5 and 9 clustered more than 60 % of the identified QTL. Twelve stable major QTLs accounting for over 10 % of phenotypic variation were included in five optimal clusters on the genetic region of bins 1.02–1.03, 1.04–1.06, 2.05–2.07, 4.07–4.08 and 9.03–9.04; the addition and partial dominance effects of significant QTL play an important role in controlling the development of maize kernel. These putative QTL may have great promising for further fine-mapping with more markers, and genetic improvement of maize kernel size and weight through marker-assisted breeding.     

Diurnal changes in leaflet gas exchange, water status and antioxidant responses in Carapa guianensis plants under water-deficit conditions

Young Carapa guianensis plants were examined under well-watered (control) and water-deficit conditions with the aim to evaluate possible relationship between diurnal changes in leaflet gas exchange with lipid peroxidation and adjustments in antioxidative responses. Treatment comparisons were assessed when leaflet water potential (Ψ_w) in water-stressed plants reached around ?2.5 ± 0.5 MPa at pre-dawn. Regardless of watering regime, the highest net CO₂ assimilation rate and stomatal conductance were recorded until 9:00 h. Control plants showed diurnal increases in transpiration, while it was strongly decreased in water-stressed plants. Diurnal decreases in intercellular to ambient CO₂ concentration ratio were just observed in stressed plants. Regardless of watering regime, non-significant changes (P > 0.05) in Ψ_w and relative water content were registered throughout the day; however, both variables were significantly lower (P < 0.05) in stressed plants. Malondialdehyde concentration did not vary throughout the day, but it was higher in stressed plants. Excepting for guaiacol-type peroxidase, the antioxidant enzyme activities varied throughout the day regardless of watering regimes. Nevertheless, increases in antioxidant enzymes were more expressive in water-stressed plants. Despite, a relationship between diurnal changes in A and g _s and lipid peroxidation or antioxidant enzymes was unclear regardless of watering regimes. Thus, we conclude that although plants from both watering regimes were able to adjust antioxidant enzymes activities throughout the day, the water-stressed plants were more susceptible to damages to net CO₂ assimilation and suffered more expressive oxidative damages to lipids than plants grown under well-watered conditions.     

Mapping of QTLs conferring resistance to bacterial leaf streak in rice

A large F₂ and a RI population were separately derived from a cross between two indica rice varieties, one of which was highly resistant to bacterial leaf streak (BLS) and the other highly susceptible. Following artificial inoculation of the RI population and over 2 years of testing, 11 QTLs were mapped by composite interval mapping (CIM) on six chromosomes. Six of the QTLs were detected in both seasons. Eight of the QTLs were significant following stepwise regression analysis, and of these, 5 with the largest effects were significant in both seasons. The detected QTLs explained 84.6% of the genetic variation in 1997. Bulked segregant analysis (BSA) of the extremes of the F₂ population identified 3 QTLs of large effect. The 3 QTLs were dentical to 3 of the 5 largest QTLs detected by CIM. The independent detection of the same QTLs using two methods of analysis in separate mapping populations verifies the existence of the QTLs for BLS and provides markers to ease their introduction into elite varieties. Received: 13 October 1999 / Accepted: 29 October 1999     

Study of strategies for selecting quantitative trait locus mapping procedures by computer simulation

Along with the development and integration of molecular genetics and quantitative genetics, many quantitative trait locus (QTL) mapping studies have been conducted using different mapping populations in various crop species. Existing QTLs can be used for marker-assisted breeding and map-based cloning, whereas the false-positive QTLs are no use. The purpose of this study is to evaluate the suitability of different mapping procedures for data from different genetic models. In this study, four types of recombinant inbred lines (RILs) with different genetic models, viz. additive QTLs (Model I), additive and epistatic QTLs (Model II), additive QTLs and QTL × environment interaction (Model III), additive, epistatic QTLs and QTL × environment interaction (Model IV), were simulated by computer. Six types of QTL mapping procedures, viz. CIM, MIMF, MIMR, ICIM, MQM and NWIM, on four kinds of QTL mapping software, viz. WinQTL Cartographer Version 2.5, IciMapping Version 2.0, MapQTL Version 5.0 and QTLnetwork Version 2.0, were used for screening QTLs of the simulated RILs. The results showed that different mapping procedures have different suitability for different genetic models. CIM and MQM can only screen Model I data. MIMR, MIMF and ICIM can only screen Model I and Model II data. NWIM can screen all four models’ data. It can be concluded that different genetic models’ data have different most suitable mapping procedures. In practical experiments where the genetic model of the data is unknown, a multiple model mapping strategy should be used, that is a full model scanning with complex model procedure followed by verification with other procedures corresponding to the scanning results.     

QTL detection for stover yield and quality traits using two connected populations in high-oil maize

Both yield and quality traits for stover portion were important for forage and biofuel production utility in maize. A high-oil maize inbred GY220 was crossed with two normal-oil dent maize inbred lines 8984 and 8622 to generate two connected F_2:3 populations with 284 and 265 F_2:3 families. Seven yield and quality traits were evaluated under two environments. The variance components of genotype (σ_g²), environment (σ_e²) and genotype × environment interactions (σ_ge²) were all significant for most traits in both populations. Different levels of correlations were observed for all traits. QTL mapping was conducted using composite interval mapping (CIM) for data under each environment and in combined analysis in both populations. Totally, 45 and 42 QTL were detected in the two populations. Only five common QTL across the two populations, and one and three common QTL across the two environments in the two populations were detected, reflecting substantial influence of genetic backgrounds and environments on the results of QTL detection for stover traits. Combined analysis across two environments failed to detect most QTL mapped using individual environmental data in both populations. Few of the detected QTL displayed digenic epistatic interactions. Common QTL among all traits were consistent with their correlations. Some QTL herein have been detected in previous researches, and linked with candidate genes for enzymes postulated to have direct and indirect roles in cell wall components biosynthesis.     

Genetic mapping of QTLs for sugar-related traits in a RIL population of Sorghum bicolor L. Moench

The productivity of sorghum is mainly determined by quantitative traits such as grain yield and stem sugar-related characteristics. Substantial crop improvement has been achieved by breeding in the last decades. Today, genetic mapping and characterization of quantitative trait loci (QTLs) is considered a valuable tool for trait enhancement. We have investigated QTL associated with the sugar components (Brix, glucose, sucrose, and total sugar content) and sugar-related agronomic traits (flowering date, plant height, stem diameter, tiller number per plant, fresh panicle weight, and estimated juice weight) in four different environments (two locations) using a population of 188 recombinant inbred lines (RILs) from a cross between grain (M71) and sweet sorghum (SS79). A genetic map with 157 AFLP, SSR, and EST-SSR markers was constructed, and several QTLs were detected using composite interval mapping (CIM). Further, additive × additive interaction and QTL × environmental interaction were estimated. CIM identified more than five additive QTLs in most traits explaining a range of 6.0–26.1% of the phenotypic variation. A total of 24 digenic epistatic locus pairs were identified in seven traits, supporting the hypothesis that QTL analysis without considering epistasis can result in biased estimates. QTLs showing multiple effects were identified, where the major QTL on SBI-06 was significantly associated with most of the traits, i.e., flowering date, plant height, Brix, sucrose, and sugar content. Four out of ten traits studied showed a significant QTL × environmental interaction. Our results are an important step toward marker-assisted selection for sugar-related traits and biofuel yield in sorghum.     

Influence of dent corn genetic backgrounds on QTL detection for plant-height traits and their relationships in high-oil maize

QTL mapping for plant-height traits has not been hitherto reported in high-oil maize. A high-oil maize inbred ‘GY220’ was crossed with two dent maize inbreds (‘8984’ and ‘8622’) to generate two connected F_2:3 populations. Four plant-height traits were evaluated in 284 and 265 F_2:3 families. Single-trait QTL mapping and multiple-trait joint QTL mapping was used to detect QTLs for the traits and the genetic relationship between plant height (PH) and two other plant-height traits. A total of 28 QTLs and 12 pairs of digenic interactions among detected QTLs for four traits were detected in the two F_2:3 families. Only one marker was shared between the two populations. Joint analysis of PH with ear height (EH) and PH with top height (TH) detected 32 additional QTLs. Our results showed that QTL detection for PH was dependent on the genetic background of dent corn inbreds. Multiple-trait joint QTL analysis could increase the number of detected QTLs.     

Genetic Analysis and Major Quantitative Trait Locus Mapping of Leaf Widths at Different Positions in Multiple Populations

Background

Leaf width is an important agricultural trait in maize. Leaf development is dependent on cell proliferation and expansion, and these processes exhibit polarity with respect to the longitudinal and transverse axes of the leaf. However, the molecular mechanism of the genetic control of seed vigor remains unknown in maize, and a better understanding of this mechanism is required.

Methodology/Principal Findings

To reveal the genetic architecture of leaf width, a comprehensive evaluation using four RIL populations was performed, followed by a meta-analysis. Forty-six QTLs associated with the widths of leaves at different positions above the uppermost ear were detected in the four RIL populations in three environments. The individual effects of the QTLs ranged from 4.33% to 18.01% of the observed phenotypic variation, with 14 QTLs showing effects of over 10%. We identified three common QTLs associated with leaf width at all of the examined positions, in addition to one common QTL associated with leaf width at three of the positions and six common QTLs associated with leaf width at two of the positions. The results indicate that leaf width at different leaf positions may be affected by one QTL or several of the same QTLs. Such traits may also be regulated by many different QTLs. Thirty-one of the forty-six initial QTLs were integrated into eight mQTLs through a meta-analysis, and 10 of the 14 initial QTLs presenting an R2>10% were integrated into six mQTLs.

Conclusions/Significance

mQTL1-2, mQTL3-1, mQTL7, and mQTL8 were composed of the initial QTLs showing an R2>10% and included four to six of the initial QTLs that were associated with two to four positions in a single population. Therefore, these four chromosome regions may be hot spots for important QTLs for these traits. Thus, they warrant further studies and may be useful for marker-assisted breeding.     

Comparative QTL mapping of resistance to Ustilago maydis across four populations of European flint-maize

 We mapped and characterized quantitative trait loci (QTLs) for resistance to Ustilago maydis and investigated their consistency across different flint-maize populations. Four independent populations, comprising 280 F₃ lines (A×B^I), 120 F₅ lines (A×B^II), 131 F₄ lines (A×C) and 133 F₄ lines (C×D), were produced from four European elite flint inbreds (A, B, C, D) and genotyped at 89, 151, 104, and 122 RFLP marker loci, respectively. All F_n lines were evaluated in field trials with two replications in five German environments. Genotypic variances were highly significant for the percentage of U. maydis infected plants (UST) in all populations, and heritabilities exceeded 0.69. Between five and ten QTLs were detected in individual populations by composite interval mapping, explaining between 39% and 58% of the phenotypic variance. These 19 different QTLs were distributed over all ten chromosomes without any clustering on certain chromosomes. In most cases, gene action was dominant or overdominant. Fourteen pairs of the detected QTLs for UST displayed significant digenic epistatic interactions, but only two of them did so after arcsin √UST/100 transformation. Significant QTL× environment interactions occurred frequently. Between two to four QTLs were common between pairs of populations. Population C×D was also grown in Chartres, a location with a high U. maydis incidence. Two out of six QTLs identified for Chartres were in common with QTLs detected across five German environments for C×D. Consequently, marker-assisted or phenotypic selection based on results from natural infection seem to be suitable breeding strategies for improving the resistance of maize to U. maydis. Received: 3 July 1998 / Accepted: 24 July 1998