Mapping of quantitative trait loci determining agronomic important characters in hexaploid wheat ( Triticum aestivum L.)

A set of 114 recombinant inbred lines of the 'International Triticeae Mapping Initiative' mapping population was grown during the seasons 1997, 1998, 1999 and 2000 under several environments. Twenty morphological (glume colour, awn colour, waxiness, leaf erectness, peduncle length), agronomical (ear emergence time, flowering time, grain filling time, ear length, plant height, lodging, grain number, thousand-grain-weight, grain weight per ear, grain protein content, winter hardiness) and disease resistance (powdery mildew, yellow rust, leaf rust, fusarium) traits were studied. Not all traits were scored in each experiment. In total 210 QTLs with a LOD threshold of >2.0 (minor QTLs) were detected of which 64 reached a LOD score of >3.0 (major QTLs). Often QTLs were detected in comparable positions in different experiments. Homologous and homoeologous relationships of the detected QTLs, and already described major genes or QTLs determining the same traits in wheat or other Triticeae members, are discussed.     

Genomic dissection of drought resistance in durum wheat × wild emmer wheat recombinant inbreed line population

Drought is the major factor limiting wheat productivity worldwide. The gene pool of wild emmer wheat, Triticum turgidum ssp. dicoccoides , harbours a rich allelic repertoire for morpho-physiological traits conferring drought resistance. The genetic and physiological bases of drought responses were studied here in a tetraploid wheat population of 152 recombinant inbreed lines (RILs), derived from a cross between durum wheat (cv. Langdon) and wild emmer (acc# G18-16), under contrasting water availabilities. Wide genetic variation was found among RILs for all studied traits. A total of 110 quantitative trait loci (QTLs) were mapped for 11 traits, with LOD score range of 3.0–35.4. Several QTLs showed environmental specificity, accounting for productivity and related traits under water-limited (20 QTLs) or well-watered conditions (15 QTLs), and in terms of drought susceptibility index (22 QTLs). Major genomic regions controlling productivity and related traits were identified on chromosomes 2B, 4A, 5A and 7B. QTLs for productivity were associated with QTLs for drought-adaptive traits, suggesting the involvement of several strategies in wheat adaptation to drought stress. Fifteen pairs of QTLs for the same trait were mapped to seemingly homoeologous positions, reflecting synteny between the A and B genomes. The identified QTLs may facilitate the use of wild alleles for improvement of drought resistance in elite wheat cultivars.     

Genetic basis of pre-harvest sprouting tolerance using single-locus and two-locus QTL analyses in bread wheat

Quantitative trait loci (QTL) analysis for pre-harvest sprouting tolerance (PHST) in bread wheat was conducted following single-locus and two-locus analyses, using data on a set of 110 recombinant inbred lines (RILs) of the International Triticeae Mapping Initiative population grown in four different environments. Single-locus analysis following composite interval mapping (CIM) resolved a total of five QTLs with one to four QTLs in each of the four individual environments. Four of these five QTLs were also detected following two-locus analysis, which resolved a total of 14 QTLs including 8 main effect QTLs (M-QTLs), 8 epistatic QTLs (E-QTLs) and 5 QTLs involved in QTL × environment (QE) or QTL × QTL × environment (QQE) interactions, some of these QTLs being common. The analysis revealed that a major fraction (76.68%) of the total phenotypic variation explained for PHST is due to M-QTLs (47.95%) and E-QTLs (28.73%), and that only a very small fraction of variation (3.24%) is due to QE and QQE interactions. Thus, more than three-quarters of the genetic variation for PHST is fixable and would contribute directly to gains under selection. Two QTLs that were detected in more than one environment and at LOD scores above the threshold values were located on 3BL and 3DL presumably in the vicinity of the dormancy gene TaVp1. Another QTL was found to be located on 3B, perhaps in close proximity to the R gene for red grain colour. However, these associations of QTLs for PHST with genes for dormancy and grain colour are only suggestive. The results obtained in the present study suggest that PHST is a complex trait controlled by large number of QTLs, some of them interacting among themselves or with the environment. These QTLs can be brought together through marker-aided selection, leading to enhanced PHST.     

Identification of quantitative trait loci for cadmium tolerance and accumulation in wheat

Quantitative trait loci (QTL) for Cadmium (Cd) tolerance and accumulation in wheat (Triticum aestivum L.) were identified, using 103 recombinant inbred lines (RILs) derived from a cross of Ch × Sh at germination and seedling stages. The traits of germination, growth and physiology were measured. Cd tolerance indexes (TI) were calculated for plants under Cd stress relative to control conditions. Cd concentrations in both root and shoot were determined and the amount of Cd accumulation and translocation calculated. The phenotypic variation of the above traits showed a continuous distribution pattern among the RILs. Twenty-six QTLs were detected, (16 of which were designated for the traits under the control and Cd stress, 8 for Cd tolerance and 2 for root Cd accumulation). These 26 QTLs individually could explain 7.97–60.16% of the phenotypic variation. Fourteen QTLs were positive (with the additive effects coming from Ch) while the remaining 12 QTLs were negative (with the additive effects contributed by Sh). No QTL were detected in the same region on the chromosomes of wheat. The results indicated that genetic mechanisms controlling the traits of Cd tolerance were independent from each other. Therefore, in this study, the properties of Cd tolerance and accumulation showed to be independent traits in wheat.     

Mapping of post-flowering drought resistance traits in grain sorghum: association between QTLs influencing premature senescence and maturity

The identification of genetic factors underlying the complex responses of plants to drought stress provides a solid basis for improving drought resistance. The stay-green character in sorghum (Sorghum bicolor L. Moench) is a post-flowering drought resistance trait, which makes plants resistant to premature senescence under drought stress during the grainfilling stage. The objective of this study was to identify quantitative trait loci (QTLs) that control premature senescence and maturity traits, and to investigate their association under post-flowering drought stress in grain sorghum. A genetic linkage map was developed using a set of recombinant inbred lines (RILs) obtained from the cross B35 × Tx430, which were scored for 142 restriction fragment length polymorphism (RFLP) markers. The RILs and their parental lines were evaluated for post-flowering drought resistance and maturity in four environments. Simple interval mapping identified seven stay-green QTLs and two maturity QTLs. Three major stay-green QTLs (SGA, SGD and SGG) contributed to 42% of the phenotypic variability (LOD 9.0) and four minor QTLs (SGB, SGI.1, SGI.2, and SGJ) significantly contributed to an additional 25% of the phenotypic variability in stay-green ratings. One maturity QTL (DFB) alone contributed to 40% of the phenotypic variability (LOD 10.0), while the second QTL (DFG) significantly contributed to an additional 17% of the phenotypic variability (LOD 4.9). Composite interval mapping confirmed the above results with an additional analysis of the QTL × Environment interaction. With heritability estimates of 0.72 for stay-green and 0.90 for maturity, the identified QTLs explained about 90% and 63% of genetic variability for stay-green and maturity traits, respectively. Although stay-green ratings were significantly correlated (r=0.22, P ≤ 0.05) with maturity, six of the seven stay-green QTLs were independent of the QTLs influencing maturity. Similarly, one maturity QTL (DFB) was independent of the stay-green QTLs. One stay-green QTL (SGG), however, mapped in the vicinity of a maturity QTL (DFG), and all markers in the vicinity of the independent maturity QTL (DFB) were significantly (P ≤ 0.1) correlated with stay-green ratings, confounding the phenotyping of stay-green. The molecular genetic analysis of the QTLs influencing stay-green and maturity, together with the association between these two inversely related traits, provides a basis for further study of the underlying physiological mechanisms and demonstrates the possibility of improving drought resistance in plants by pyramiding the favorable QTLs. Received: 10 October 1998 / Accepted: 12 July 1999     

QTL analysis for grain protein content using SSR markers and validation studies using NILs in bread wheat

QTL interval mapping for grain protein content (GPC) in bread wheat was conducted for the first time, using a framework map based on a mapping population, which was available in the form of 100 recombinant inbred lines (RILs). The data on GPC for QTL mapping was recorded by growing the RILs in five different environments representing three wheat growing locations from Northern India; one of these locations was repeated for 3 years. Distribution of GPC values followed normal distributions in all the environments, which could be explained by significant g x e interactions observed through analyses of variances, which also gave significant effects due to genotypes and environments. Thirteen (13) QTLs were identified in individual environments following three methods (single-marker analysis or SMA, simple interval mapping or SIM and composite interval mapping or CIM) and using LOD scores that ranged from 2.5 to 6.5. Threshold LOD scores (ranging from 3.05 to 3.57), worked out and used in each case, however, detected only seven of the above 13 QTLs. Only four (QGpc.ccsu-2B.1; QGpc.ccsu-2D.1; QGpc.ccsu-3D.1 and QGpc.ccsu-7A.1) of these QTLs were identified either in more than one location or following one more method other than CIM; another QTL (QGpc.ccsu-3D.2), which was identified using means for all the environments, was also considered to be important. These five QTLs have been recommended for marker-assisted selection (MAS). The QTLs identified as above were also validated using ten NILs derived from three crosses. Five of the ten NILs possessed 38 introgressed segments from 16 chromosomes and carried 42 of the 173 markers that were mapped. All the seven QTLs were associated with one or more of the markers carried by the above introgressed segments, thus validating the corresponding markers. More markers associated with many more QTLs to be identified should become available in the future by effective MAS for GPC improvement.     

QTL analyses of heterosis for grain yield and yield-related traits in indica-japonica crosses of rice (Oryza sativa L.)

Two sets of rice materials, 166 RILs derived from a cross between Milyang 23 (Korean indica-type rice) and Tong 88-7 (japonica Rice), and BC₁F₁ hybrids derived from crosses between the RILs and the female parent, Milyang 23, were produced to identify QTLs for heterosis of yield and yield-related traits. The QTLs were detected from three different phenotype data sets including the RILs, BC1F1 hybrids, and mid-parental heterosis data set acquired from the definition of mid-parental heterosis. A total of 57 QTLs were identified for nine traits. Of eight QTLs detected for yield heterosis, five overlapped with other heterosis QTLs for yield-related traits such as spikelet number per panicle, days to heading, and spikelet fertility. Four QTLs for yield heterosis, gy1.1, py6, gy10, and py11, were newly identified in this study. We identified a total of 17 EpQTLs for yield heterosis that explain 21.4 ?? 59.0 % of total phenotypic variation, indicating that epistatic interactions may play an important role in heterosis.     

Genetic Control of the Leaf Angle and Leaf Orientation Value as Revealed by Ultra-High Density Maps in Three Connected Maize Populations

Plant architecture is a key factor for high productivity maize because ideal plant architecture with an erect leaf angle and optimum leaf orientation value allow for more efficient light capture during photosynthesis and better wind circulation under dense planting conditions. To extend our understanding of the genetic mechanisms involved in leaf-related traits, three connected recombination inbred line (RIL) populations including 538 RILs were genotyped by genotyping-by-sequencing (GBS) method and phenotyped for the leaf angle and related traits in six environments. We conducted single population quantitative trait locus (QTL) mapping and joint linkage analysis based on high-density recombination bin maps constructed from GBS genotype data. A total of 45 QTLs with phenotypic effects ranging from 1.2% to 29.2% were detected for four leaf architecture traits by using joint linkage mapping across the three populations. All the QTLs identified for each trait could explain approximately 60% of the phenotypic variance. Four QTLs were located on small genomic regions where candidate genes were found. Genomic predictions from a genomic best linear unbiased prediction (GBLUP) model explained 45±9% to 68±8% of the variation in the remaining RILs for the four traits. These results extend our understanding of the genetics of leaf traits and can be used in genomic prediction to accelerate plant architecture improvement.     

Mapping QTLs of yield-related traits using RIL population derived from common wheat and Tibetan semi-wild wheat

Key message

QTLs controlling yield-related traits were mapped using a population derived from common wheat and Tibetan semi-wild wheat and they provided valuable information for using Tibetan semi-wild wheat in future wheat molecular breeding.

Abstract

Tibetan semi-wild wheat (Triticum aestivum ssp tibetanum Shao) is a kind of primitive hexaploid wheat and harbors several beneficial traits, such as tolerance to biotic and abiotic stresses. And as a wild relative of common wheat, heterosis of yield of the progeny between them was significant. This study focused on mapping QTLs controlling yield-related traits using a recombined inbred lines (RILs) population derived from a hybrid between a common wheat line NongDa3331 (ND3331) and the Tibetan semi-wild wheat accession Zang 1817. In nine location–year environments, a total of 148 putative QTLs controlling nine traits were detected, distributed on 19 chromosomes except for 1A and 2D. Single QTL explained the phenotypic variation ranging from 3.12 to 49.95 %. Of these QTLs, 56 were contributed by Zang 1817. Some stable QTLs contributed by Zang 1817 were also detected in more than four environments, such as QPh-3A1, QPh-4B1 and QPh-4D for plant height, QSl-7A1 for spike length, QEp-4B2 for ears per plant, QGws-4D for grain weight per spike, and QTgw-4D for thousand grain weight. Several QTL-rich Regions were also identified, especially on the homoeologous group 4. The TaANT gene involved in floral organ development was mapped on chromosome 4A between Xksm71 and Xcfd6 with 0.8 cM interval, and co-segregated with the QTLs controlling floret number per spikelet, explaining 4.96–11.84 % of the phenotypic variation. The current study broadens our understanding of the genetic characterization of Tibetan semi-wild wheat, which will enlarge the genetic diversity of yield-related traits in modern wheat breeding program.     

Identification of quantitative trait loci for agronomically important traits and their association with genic-microsatellite markers in sorghum

The identification of quantitative trait loci (QTLs) affecting agronomically important traits enable to understand their underlying genetic mechanisms and genetic basis of their complex interactions. The aim of the present study was to detect QTLs for 12 agronomic traits related to staygreen, plant early development, grain yield and its components, and some growth characters by analyzing replicated phenotypic datasets from three crop seasons, using the population of 168 F₇ RILs of the cross 296B × IS18551. In addition, we report mapping of a subset of genic-microsatellite markers. A linkage map was constructed with 152 marker loci comprising 149 microsatellites (100 genomic- and 49 genic-microsatellites) and three morphological markers. QTL analysis was performed by using MQM approach. Forty-nine QTLs were detected, across environments or in individual environments, with 1–9 QTLs for each trait. Individual QTL accounted for 5.2–50.4% of phenotypic variance. Several genomic regions affected multiple traits, suggesting the phenomenon of pleiotropy or tight linkage. Stable QTLs were identified for studied traits across different environments, and genetic backgrounds by comparing the QTLs in the study with previously reported QTLs in sorghum. Of the 49 mapped genic-markers, 18 were detected associating either closely or exactly as the QTL positions of agronomic traits. EST marker Dsenhsbm19, coding for a key regulator (EIL-1) of ethylene biosynthesis, was identified co-located with the QTLs for plant early development and staygreen trait, a probable candidate gene for these traits. Similarly, such exact co-locations between EST markers and QTLs were observed in four other instances. Collectively, the QTLs/markers identified in the study are likely candidates for improving the sorghum performance through MAS and map-based gene isolations.     

Novel pleiotropic loci controlling panicle architecture across environments in japonica rice （Oryza sativa L.）

To identify quantitative trait loci (QTLs) controlling panicle architecture in japonica rice, a genetic map was constructed based on simple sequence repeat (SSR) markers and 254 recombinant inbred lines (RILs) derived from a cross between cultivars Xiushui 79 and C Bao. Seven panicle traits were investigated under three environments. Single marker analysis indicated that a total of 27 SSR markers were highly associated with panicle traits in all the three environments. Percentage of phenotypic variation explained by single locus varied from 2% to 35%. Based on the mixed linear model, a total of 40 additive QTLs for seven panicle traits were detected by composite interval mapping, explaining 1.2%-35% of phenotypic variation. Among the 9 QTLs with more than 10% of explained phenotypic variation, two QTLs were for the number of primary branches per panicle (NPB), two for panicle length (PL), two for spikelet density (SD), one for the number of secondary branches per panicle (NSB), one for secondary branch distribution density (SBD), and one for the number of spikelets per panicle (NS), respectively. qPLSD-9-1 and qPLSD-9-2 were novel pleiotropic loci, showing effects on PL and SD simultaneously. qPLSD-9-1 explained 34.7% of the phenotypic variation for PL and 25.4% of the phenotypic variation for SD, respec- tively. qPLSD-9-2 explained 34.9% and 24.4% of the phenotypic variation for PL and SD, respectively. The C Bao alleles at the both QTLs showed positive effects on PL, and the Xiushui 79 alleles at the both QTLs showed positive effects on SD. Genetic variation of panicle traits are mainly attributed to additive effects. QTL × environment interactions were not significant for additive QTLs and additive × additive QTL pairs.     

Genetic control of early events in protoplast division and regeneration pathways in sunflower

Experiments were conducted to identify the genetic factors controlling protoplast division and to determine eventual relations between genetic factors involving organogenesis, somatic embryogenesis and protoplast division in sunflower. The present study involved protoplast culture and two traits: total division per 100 protoplasts (TOTD) and asymmetric division per 100 protoplasts (ASYD) were scored in 52 recombinant inbred lines (RILs) from a cross between PAC-2 and RHA-266. Asymmetric division is an early event in the formation of embryoids from protoplasts. Analysis of variance indicated the existence of highly significant differences among parental genotypes and their RILs. Heritability for the two protoplast division parameters (TOTD and ASYD) was high (0.87 and 0.89, respectively) and genetic gain expressed as percentage of the best parent for 10% of the selected RILs was significant. Twelve putative loci associated with total division per 100 protoplasts were identified. Eleven QTLs were also detected for asymmetric division per 100 protoplasts. The QTLs present high significant LOD scores and sum to a high percentage of phenotypic variance. The percentage of phenotypic variation explained by each QTL ranged from 2% to 24%. Some segments of the linkage groups I, XV and XVII are likely to contain genes important for organogenesis, somatic embryogenesis and protoplast division, as clustering of QTLs for these characters were described. The QTLs identified in these three linkage groups should be involved in cell division and in early events associated with cell differenciation. Received: 15 December 1999 / Accepted: 30 December 1999     

Genetic mapping of yield traits using RIL population derived from Fuchuan Dahuasheng and ICG6375 of peanut (<Emphasis Type="Italic">Arachis hypogaea</Emphasis> L.)

The genetic architecture determinants of yield traits in peanut (Arachis hypogaea L.) are poorly understood. In the present study, an effort was made to map quantitative trait loci (QTLs) for yield traits using recombinant inbred lines (RIL). A genetic linkage map was constructed containing 609 loci, covering a total of 1557.48 cM with an average distance of 2.56 cM between adjacent markers. The present map exhibited good collinearity with the physical map of diploid species of Arachis. Ninety-two repeatable QTLs were identified for 11 traits including height of main stem, total branching number, and nine pod- and seed-related traits. Of the 92 QTLs, 15 QTLs were expressed across three environments and 65 QTLs were newly identified. Twelve QTLs for the height of main stem and the pod- and seed-related traits explaining more than 10 % of phenotypic variation showed a great potential for marker-assisted selection in improving these traits. 相似文献   

Identification of quantitative trait loci influencing aerial morphogenesis in the model legume <Emphasis Type="Italic">Medicago truncatula</Emphasis>

In many legume crops, especially in forage legumes, aerial morphogenesis defined as growth and development of plant organs, is an essential trait as it determines plant and seed biomass as well as forage quality (protein concentration, dry matter digestibility). Medicago truncatula is a model species for legume crops. A set of 29 accessions of M. truncatula was evaluated for aerial morphogenetic traits. A recombinant inbred lines (RILs) mapping population was used for analysing quantitative variation in aerial morphogenetic traits and QTL detection. Genes described to be involved in aerial morphogenetic traits in other species were mapped to analyse co-location between QTLs and genes. A large variation was found for flowering date, morphology and dynamics of branch elongation among the 29 accessions and within the RILs population. Flowering date was negatively correlated to main stem and branch length. QTLs were detected for all traits, and each QTL explained from 5.2 to 59.2% of the phenotypic variation. A QTL explaining a large part of genetic variation for flowering date and branch growth was found on chromosome 7. The other chromosomes were also involved in the variation detected in several traits. Mapping of candidate genes indicates a co-location between a homologue of Constans gene or a flowering locus T (FT) gene and the QTL of flowering date on chromosome 7. Other candidate genes for several QTLs are described. Electronic supplementary material The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Premature progression of anther early developmental programs accompanied by comprehensive alterations in transcription during high-temperature injury in barley plants

Kernel number per spike is one of the most important yield components of wheat. To map QTLs related to kernel number including spike length (SPL), spikelet number per spike (SPN), fertile spikelet number (FSPN), sterile spikelet number (SSPN) and compactness, and to characterize the inheritance modes of the QTLs and two-locus interactions, 136 recombinant inbred lines (RILs) derived from ‘Nanda2419’ x ‘Wangshuibai’ and an immortalized F₂ population (IF₂) generated by randomly permutated intermating of these RILs were investigated. QTL mapping made use of the previously constructed over 3300 cM linkage map of the RIL population. Three, five, two, two and six chromosome regions were identified, respectively, for their association with SPL, SPN, FSPN, SSPN, and compactness in at least two of the three environments examined. All compactness QTLs but one shared the respective intervals of QSpn.nau-5A and the SPL QTLs. Xcfd46–Xwmc702 interval on chromosome 7D was related to all traits but SSPN and had consistently the largest effects. The fact that not all the compactness QTL intervals were related to both SPL and SPN indicates that compactness is regulated by different mechanisms. Interval coincidence between QTLs of SPL and SPN and between QTLs of FSPN and SSPN was minimal. For all the traits, favorable alleles exist in both parents. Inheritance modes from additiveness to overdominance of the QTLs were revealed and two-locus interactions were detected, implying that the traits studied are under complex genetic control. The results could contribute to wheat yield improvement and better use of Wangshuibai and Nanda2419 the two special germplasms in wheat breeding program.     

High-Density Genetic Linkage Map Construction and QTL Mapping of Grain Shape and Size in the Wheat Population Yanda1817 × Beinong6

High-density genetic linkage maps are necessary for precisely mapping quantitative trait loci (QTLs) controlling grain shape and size in wheat. By applying the Infinium iSelect 9K SNP assay, we have constructed a high-density genetic linkage map with 269 F ₈ recombinant inbred lines (RILs) developed between a Chinese cornerstone wheat breeding parental line Yanda1817 and a high-yielding line Beinong6. The map contains 2431 SNPs and 128 SSR & EST-SSR markers in a total coverage of 3213.2 cM with an average interval of 1.26 cM per marker. Eighty-eight QTLs for thousand-grain weight (TGW), grain length (GL), grain width (GW) and grain thickness (GT) were detected in nine ecological environments (Beijing, Shijiazhuang and Kaifeng) during five years between 2010–2014 by inclusive composite interval mapping (ICIM) (LOD≥2.5). Among which, 17 QTLs for TGW were mapped on chromosomes 1A, 1B, 2A, 2B, 3A, 3B, 3D, 4A, 4D, 5A, 5B and 6B with phenotypic variations ranging from 2.62% to 12.08%. Four stable QTLs for TGW could be detected in five and seven environments, respectively. Thirty-two QTLs for GL were mapped on chromosomes 1B, 1D, 2A, 2B, 2D, 3B, 3D, 4A, 4B, 4D, 5A, 5B, 6B, 7A and 7B, with phenotypic variations ranging from 2.62% to 44.39%. QGl.cau-2A.2 can be detected in all the environments with the largest phenotypic variations, indicating that it is a major and stable QTL. For GW, 12 QTLs were identified with phenotypic variations range from 3.69% to 12.30%. We found 27 QTLs for GT with phenotypic variations ranged from 2.55% to 36.42%. In particular, QTL QGt.cau-5A.1 with phenotypic variations of 6.82–23.59% was detected in all the nine environments. Moreover, pleiotropic effects were detected for several QTL loci responsible for grain shape and size that could serve as target regions for fine mapping and marker assisted selection in wheat breeding programs.     

Quantitative trait loci for yield and related traits in the wheat population Ning7840 × Clark

Grain yield and associated agronomic traits are important factors in wheat (Triticum aestivum L.) improvement. Knowledge regarding the number, genomic location, and effect of quantitative trait loci (QTL) would facilitate marker-assisted selection and the development of cultivars with desirable characteristics. Our objectives were to identify QTLs directly and indirectly affecting grain yield expression. A population of 132 F₁₂ recombinant inbred lines (RILs) was derived by single-seed descent from a cross between the Chinese facultative wheat Ning7840 and the US soft red winter wheat Clark. Phenotypic data were collected for 15 yield and other agronomic traits in the RILs and parental lines from three locations in Oklahoma from 2001 to 2003. Twenty-nine linkage groups, consisting of 363 AFLP and 47 SSR markers, were identified. Using composite interval mapping (CIM) analysis, 10, 16, 30, and 14 QTLs were detected for yield, yield components, plant adaptation (shattering and lodging resistance, heading date, and plant height), and spike morphology traits, respectively. The QTL effects ranged from 7 to 23%. Marker alleles from Clark were associated with a positive effect for the majority of QTLs for yield and yield components, but gene dispersion was the rule rather than the exception for this RIL population. Often, QTLs were detected in proximal positions for different traits. Consistent, co-localized QTLs were identified in linkage groups 1AL, 1B, 4B, 5A, 6A, and 7A, and less consistent but unique QTLs were identified on 2BL, 2BS, 2DL, and 6B. Results of this study provide a benchmark for future efforts on QTL identification for yield traits.     

Quantitative trait loci for lodging resistance,plant height and partial resistance to mycosphaerella blight in field pea (Pisum sativum L.)

With the development of genetic maps and the identification of the most-likely positions of quantitative trait loci (QTLs) on these maps, molecular markers for lodging resistance can be identified. Consequently, marker-assisted selection (MAS) has the potential to improve the efficiency of selection for lodging resistance in a breeding program. This study was conducted to identify genetic loci associated with lodging resistance, plant height and reaction to mycosphaerella blight in pea. A population consisting of 88 recombinant inbred lines (RILs) was developed from a cross between Carneval and MP1401. The RILs were evaluated in 11 environments across the provinces of Manitoba, Saskatchewan and Alberta, Canada in 1998, 1999 and 2000. One hundred and ninety two amplified fragment length polymorphism (AFLP) markers, 13 random amplified polymorphic DNA (RAPD) markers and one sequence tagged site (STS) marker were assigned to ten linkage groups (LGs) that covered 1,274 centi Morgans (cM) of the pea genome. Six of these LGs were aligned with the previous pea map. Two QTLs were identified for lodging resistance that collectively explained 58% of the total phenotypic variation in the mean environment. Three QTLs were identified each for plant height and resistance to mycosphaerella blight, which accounted for 65% and 36% of the total phenotypic variation, respectively, in the mean environment. These QTLs were relatively consistent across environments. The AFLP marker that was associated with the major locus for lodging resistance was converted into the sequence-characterized amplified-region (SCAR) marker. The presence or absence of the SCAR marker corresponded well with the lodging reaction of 50 commercial pea varieties.Communicated by H. F. Linskens     

QTL analysis of resistance to Fusarium head blight in Swiss winter wheat (<Emphasis Type="Italic">Triticum aestivum</Emphasis> L.)

Fusarium head blight (FHB) of wheat is a widespread and destructive disease which occurs in humid and semi-humid areas. FHB epidemics can cause serious yield and quality losses under favorable climatic conditions, but the major concern is the contamination of grains with mycotoxins. Resistance to FHB is quantitatively inherited and greatly influenced by the environment. Its evaluation is costly and time-consuming. The genetic basis of FHB resistance has mainly been studied in spring wheat. The objective of this study was to map quantitative trait loci (QTLs) for resistance to FHB in a population of 240 recombinant inbred lines (RILs) derived from a cross between the two Swiss winter wheat cultivars Arina (resistant) and Forno (susceptible). The RILs were genotyped with microsatellite and RFLP markers. The resulting genetic map comprises 380 loci and spans 3,086 cM. The 240 RILs were evaluated for resistance to FHB in six field trials over 3 years. Composite interval mapping (CIM) analyses carried out on FHB AUDPC (i.e. mean values across six environments) revealed eight QTLs which altogether explained 47% of the phenotypic variance. The three main QTLs were mapped on the long arms of chromosomes 6D (R²=22%), 5B (R²=14%) and 4A (R²=10%). The QTL detected on 5B originated from the susceptible parent Forno. Other QTLs with smaller effects on FHB resistance were detected on chromosomes 2AL, 3AL, 3BL, 3DS and 5AL.Communicated by H.C. Becker     

Quantitative trait locus analysis of lateral branch– related traits in cucumber (Cucumis sativus L.) using recombinant inbred lines

A group of 224 recombinant inbred lines (RILs) was derived from a narrow cross between 2 cucumber (Cucumis sativus L.) lines, namely, S94 (Northern China type with weak lateral branch growth potential and early lateral branch sprouting time) and S06 (Northern European type with strong lateral branch growth potential and late lateral branch sprouting time). These lines were then used for investigating lateral branch-related traits. A total of 36 quantitative trait loci (QTLs) were detected for the following 4 lateral branch-related traits: lateral branch average length (LBAL), lateral branch total length (LBTL), lateral branch number (LBN), and first lateral branch node (FLBN). Further, each QTL explained 3.1% (lbtl2.1, spring) to 32.3% (lbn2.3, spring) of the observed phenotypic variance. Eleven QTLs (lbal1.1, lbtl1.1, lbn1.2, flbn1.2, etc.) for different traits were found to be clustered on the e23m18d-ME23EM6c section (7.4 cM) of linkage group (LG) 1; further, 15 QTLs (lbal2.1, lbtl2.1, lbn2.1, flbn2.1, etc.) were found to be clustered on the S94A1-ME4SA4a section (13.9 cM) of LG2. Twenty-one QTLs explained more than 10% of the phenotypic variance. Moreover, lbtl1.3 (autumn, 26.2%, logarithm of odds (LOD) = 17.4; spring, 26.9%, LOD = 17.9) had stable position and contribution in both seasons. Several se-quence-anchor markers (CMBR40, F, CS30, S94A1, CSWTA11B, etc.) were closely linked with some QTLs for LBAL, LBTL, LBN, and FLBN, which can be used for the marker-assisted selection to improve the plant architecture in cucumber breeding.