Genetic control of soybean seed oil: I. QTL and genes associated with seed oil concentration in RIL populations derived from crossing moderately high-oil parents

Soybean seed is a major source of oil for human consumption worldwide and the main renewable feedstock for biodiesel production in North America. Increasing seed oil concentration in soybean [Glycine max (L.) Merrill] with no or minimal impact on protein concentration could be accelerated by exploiting quantitative trait loci (QTL) or gene-specific markers. Oil concentration in soybean is a polygenic trait regulated by many genes with mostly small effects and which is negatively associated with protein concentration. The objectives of this study were to discover and validate oil QTL in two recombinant inbred line (RIL) populations derived from crosses between three moderately high-oil soybean cultivars, OAC Wallace, OAC Glencoe, and RCAT Angora. The RIL populations were grown across several environments over 2 years in Ontario, Canada. In a population of 203 F_3:6 RILs from a cross of OAC Wallace and OAC Glencoe, a total of 11 genomic regions on nine different chromosomes were identified as associated with oil concentration using multiple QTL mapping and single-factor ANOVA. The percentage of the phenotypic variation accounted for by each QTL ranged from 4 to 11 %. Of the five QTL that were tested in a population of 211 F_3:5 RILs from the cross RCAT Angora × OAC Wallace, a “trait-based” bidirectional selective genotyping analysis validated four QTL (80 %). In addition, a total of seven two-way epistatic interactions were identified for oil concentration in this study. The QTL and epistatic interactions identified in this study could be used in marker-assisted introgression aimed at pyramiding high-oil alleles in soybean cultivars to increase oil concentration for biodiesel as well as edible oil applications.     

Identification of a major QTL allele from wild soybean (Glycine soja Sieb. & Zucc.) for increasing alkaline salt tolerance in soybean

Salt-affected soils are generally classified into two main categories, sodic (alkaline) and saline. Our previous studies showed that the wild soybean accession JWS156-1 (Glycine soja) from the Kinki area of Japan was tolerant to NaCl salt, and the quantitative trait locus (QTL) for NaCl salt tolerance was located on soybean linkage group N (chromosome 3). Further investigation revealed that the wild soybean accession JWS156-1 also had a higher tolerance to alkaline salt stress. In the present study, an F₆ recombinant inbred line mapping population (n = 112) and an F₂ population (n = 149) derived from crosses between a cultivated soybean cultivar Jackson and JWS156-1 were used to identify QTL for alkaline salt tolerance in soybean. Evaluation of soybean alkaline salt tolerance was carried out based on salt tolerance rating (STR) and leaf chlorophyll content (SPAD value) after treatment with 180 mM NaHCO₃ for about 3 weeks under greenhouse conditions. In both populations, a significant QTL for alkaline salt tolerance was detected on the molecular linkage group D2 (chromosome 17), which accounted for 50.2 and 13.0% of the total variation for STR in the F₆ and the F₂ populations, respectively. The wild soybean contributed to the tolerance allele in the progenies. Our results suggest that QTL for alkaline salt tolerance is different from the QTL for NaCl salt tolerance found previously in this wild soybean genotype. The DNA markers closely associated with the QTLs might be useful for marker-assisted selection to pyramid tolerance genes in soybean for both alkaline and saline stresses.     

Mapping of QTL associated with chilling tolerance during reproductive growth in soybean

Low temperatures in summer bring about drastic reduction in seed yield of soybean [Glycine max (L.) Merr.]. To identify quantitative trait loci (QTL) associated with chilling tolerance during the reproductive growth in soybean, a recombinant inbred line (RIL) population consisting of 104 F₆-derived lines was created from a cross between two cultivars, chilling-tolerant Hayahikari and chilling-sensitive Toyomusume. The RIL were genotyped with 181 molecular and phenotypic markers and were scored with regard to chilling tolerance, which was evaluated by comparison of seed-yielding abilities in two artificial climatic environments at chilling and usual temperatures. Three QTL were detected for chilling tolerance in seed-yielding ability. Two of them, qCTTSW1 and qCTTSW2, were mapped near QTL for flowering time, and the latter had an epistatic interaction with a marker locus located near another QTL for flowering time, where no significant QTL for chilling tolerance was detected. The analysis of an F₂ population derived from the cross between Hayahikari and an RIL of the Hayahikari genotype at all QTL for flowering time confirmed the effect of the third QTL, qCTTSW3, on chilling tolerance and suggested that qCTTSW1 was basically independent of the QTL for flowering time. The findings and QTL found in this study may provide useful information for marker-assisted selection (MAS) and further genetic studies on soybean chilling tolerance.     

QTL analyses of seed weight during the development of soybean (Glycine max L. Merr.)

At harvest traits such as seed weight are the sum of development and responses to stresses over the growing season and particularly during the reproductive phase of growth. The aim here was to measure quantitative trait loci (QTL) underlying the seed weight from early development to drying post harvest. One hundred forty-three F(5) derived recombinant inbred lines (RILs) developed from the cross of soybean cultivars 'Charleston' and 'Dongnong 594' were used for the analysis of QTL underlying mean 100-seed weight at six different developmental stages. QTL x Environment interactions (QE) were analyzed by a mixed genetic mode based on 3 years' data. At an experiment-wise threshold of a=0.05 and by single-point analysis 94 QTL unaffected by QE underlay the mean seed weight at different developmental stages. Sixty-eight QTL affected by QE that also underlay mean seed weight were identified. From the 162 QTL 42 could be located on 12 linkage groups by composite interval mapping (LOD>2.0). The numbers, locations and types of the QTL and the genetic effects were different at each developmental stage. On linkage group C2 the distantly linked QTL swC2-1, swC2-2 and swC2-3 each affected mean seed weight throughout the different developmental stages. The DNA markers linked to the QTL possessed potential for use in marker-assisted selection for soybean seed size. The identification of QTL with genetic main effects and QE interaction effects suggested that such interactions might significantly alter seed weight during seed development.     

QTL Analysis of Na+ and K+ Concentrations in Roots and Shoots under Different Levels of NaCl Stress in Rice (Oryza sativa L.)

The key to plant survival under NaCl salt stress is maintaining a low Na⁺ level or Na⁺/K⁺ ratio in the cells. A population of recombinant inbred lines (RILs, F_2∶9) derived from a cross between the salt-tolerant japonica rice variety Jiucaiqing and the salt-sensitive indica variety IR26, was used to determine Na⁺ and K⁺ concentrations in the roots and shoots under three different NaCl stress conditions (0, 100 and 120 mM NaCl). A total of nine additive QTLs were identified by QTL Cartographer program using single-environment phenotypic values, whereas eight additive QTLs were identified by QTL IciMapping program. Among these additive QTLs, five were identified by both programs. Epistatic QTLs and QTL-by-environment interactions were detected by QTLNetwork program in the joint analyses of multi-environment phenotypic values, and one additive QTL and nine epistatic QTLs were identified. There were three epistatic QTLs identified for Na⁺ in roots (RNC), three additive QTLs and two epistatic QTLs identified for Na⁺ in shoots (SNC), four additive QTLs identified for K⁺ in roots (RKC), four additive QTLs and three epistatic QTLs identified for K⁺ in shoots (SKC) and one additive QTL and one epistatic QTL for salt tolerance rating (STR). The phenotypic variation explained by each additive, epistatic QTL and QTL×environment interaction ranged from 8.5 to 18.9%, 0.5 to 5.3% and 0.7 to 7.5%, respectively. By comparing the chromosomal positions of these additive QTLs with those previously identified, five additive QTLs, qSNC9, qSKC1, qSKC9, qRKC4 and qSTR7, might represent novel salt tolerance loci. The identification of salt tolerance in selected RILs showed that a major QTL qSNC11 played a significant role in rice salt tolerance, and could be used to improve salt tolerance of commercial rice varieties with marker-assisted selection (MAS) approach.     

A major QTL conditioning salt tolerance in S-100 soybean and descendent cultivars

Deployment of salt tolerant cultivars is an effective approach to minimize yield loss in a saline soil. In soybean, Glycine max (L.) Merr., substantial genetic variation exists for salt response. However, breeding for salt tolerance is hampered because no economically viable screening method has been developed for practical breeding. To facilitate the development of an effective screening method for salt tolerance in soybean, the present study was conducted to determine the heritability of salt tolerance and to identify associated quantitative trait loci (QTL). F_2:5 lines from the cross of S-100 (salt tolerant) × Tokyo (salt sensitive) were evaluated in a saline field in Hyde County, N.C., USA, in 1999 and in a greenhouse located in Raleigh, N.C., USA, in 2001. S-100 and Tokyo are ancestors of popular soybean cultivars released for the southern USA. The visual salt tolerance ratings of the F_2:5 lines ranged from 0 (complete death) to 5 (normal healthy appearance). The entry-mean heritability for salt tolerance was 0.85, 0.48, and 0.57 in the field (four replications), greenhouse (two replications), and combined environments, respectively. The genotypic correlation between field and greenhouse ratings was 0.55, indicating reasonably good agreement between the two screening environments. To identify QTL associated with salt tolerance, each line was characterized with RFLP markers and an initial QTL single-factor analysis was completed. These results were used to identify genomic regions associated with the trait and to saturate the selected genomic regions with SSR markers to improve mapping precision. Subsequently, a major QTL for salt tolerance was discovered near the Sat_091 SSR marker on linkage group (LG) N, accounting for 41, 60, and 79% of the total genetic variation for salt tolerance in the field, greenhouse, and combined environments, respectively. The QTL allele associated with tolerance was derived from S-100. Pedigree tracking was used to examine the association between the salt tolerance QTL and flanking SSR marker alleles in U.S. cultivars descended from S-100 or Tokyo through 60 years of breeding. The presence of alleles from S-100 at the Sat_091 and Satt237 marker loci was always associated with salt tolerance in descendants. Alleles from Tokyo for these same markers were generally associated with salt sensitivity in descendent cultivars. The strong relationship between the SSR marker alleles and salt tolerance suggests that these markers could be used for marker-assisted selection in commercial breeding.An erratum to this article can be found at     

Validation and high-resolution mapping of a major quantitative trait locus for alkaline salt tolerance in soybean using residual heterozygous line

Alkaline soil restricts soybean plant growth and yield. In our previous study, a major alkaline salt tolerance quantitative trait locus (QTL) was identified in soybean on chromosome 17. In this study, the residual heterozygous line (RHL46), which was selected from a population of F6 recombinant inbred lines (RILs) derived from a cross between an alkaline salt-sensitive soybean cultivar Jackson and a tolerant wild soybean accession JWS156-1, was used for validation and high-resolution mapping of the QTL. In a large segregating population (n = 1,109), which was produced by self-pollinating heterozygotes of RHL46, segregation of alkaline salt tolerance showed a continuous distribution, and the tolerant plants were predominant. Linkage mapping analysis revealed a major QTL with a large dominant effect for alkaline salt tolerance, and the highest LOD score was detected between the single sequence repeat (SSR) markers GM17-12.2 and Satt447. Furthermore, 10 fixed recombinant lines carrying chromosome fragments of different lengths in the QTL region were selected from the RHL46 progeny. Phenotype evaluation and SSR marker analysis of the recombinant lines narrowed down the QTL to a 3.33-cM interval region between the markers GM17-11.6 and Satt447 with a physical map length of approximately 771 kb. High-resolution mapping of the alkaline salt tolerance QTL will be useful not only for marker-assisted selection in soybean breeding programs but also for map-based cloning of the alkaline salt tolerance gene in order to understand alkaline salt tolerance in soybean and other plant species.     

Mapping QTLs with epistatic effects and QTL×environment interactions by mixed linear model approaches

A new methodology based on mixed linear models was developed for mapping QTLs with digenic epistasis and QTL×environment (QE) interactions. Reliable estimates of QTL main effects (additive and epistasis effects) can be obtained by the maximum-likelihood estimation method, while QE interaction effects (additive×environment interaction and epistasis×environment interaction) can be predicted by the-best-linear-unbiased-prediction (BLUP) method. Likelihood ratio and t statistics were combined for testing hypotheses about QTL effects and QE interactions. Monte Carlo simulations were conducted for evaluating the unbiasedness, accuracy, and power for parameter estimation in QTL mapping. The results indicated that the mixed-model approaches could provide unbiased estimates for both positions and effects of QTLs, as well as unbiased predicted values for QE interactions. Additionally, the mixed-model approaches also showed high accuracy and power in mapping QTLs with epistatic effects and QE interactions. Based on the models and the methodology, a computer software program (QTLMapper version 1.0) was developed, which is suitable for interval mapping of QTLs with additive, additive×additive epistasis, and their environment interactions. Received: 23 October 1998 / Accepted: 11 May 1999     

Genetic control of soybean seed oil: II. QTL and genes that increase oil concentration without decreasing protein or with increased seed yield

Soybean [Glycine max (L.) Merrill] seed oil is the primary global source of edible oil and a major renewable and sustainable feedstock for biodiesel production. Therefore, increasing the relative oil concentration in soybean is desirable; however, that goal is complex due to the quantitative nature of the oil concentration trait and possible effects on major agronomic traits such as seed yield or protein concentration. The objectives of the present study were to study the relationship between seed oil concentration and important agronomic and seed quality traits, including seed yield, 100-seed weight, protein concentration, plant height, and days to maturity, and to identify oil quantitative trait loci (QTL) that are co-localized with the traits evaluated. A population of 203 F_4:6 recombinant inbred lines, derived from a cross between moderately high oil soybean genotypes OAC Wallace and OAC Glencoe, was developed and grown across multiple environments in Ontario, Canada, in 2009 and 2010. Among the 11 QTL associated with seed oil concentration in the population, which were detected using either single-factor ANOVA or multiple QTL mapping methods, the number of QTL that were co-localized with other important traits QTL were six for protein concentration, four for seed yield, two for 100-seed weight, one for days to maturity, and one for plant height. The oil-beneficial allele of the QTL tagged by marker Sat_020 was positively associated with seed protein concentration. The oil favorable alleles of markers Satt001 and GmDGAT2B were positively correlated with seed yield. In addition, significant two-way epistatic interactions, where one of the interacting markers was solely associated with seed oil concentration, were identified for the selected traits in this study. The number of significant epistatic interactions was seven for yield, four for days to maturity, two for 100-seed weight, one for protein concentration, and one for plant height. The identified molecular markers associated with oil-related QTL in this study, which also have positive effects on other important traits such as seed yield and protein concentration, could be used in the soybean marker breeding programs aimed at developing either higher seed yield and oil concentration or higher seed protein and oil concentration per hectare. Alternatively, selecting complementary parents with greater breeding values due to positive epistatic interactions could lead to the development of higher oil soybean cultivars.     

Dynamic QTL analysis of linolenic acid content in different developmental stages of soybean seed

Linolenic acid (LN) in soybean (Glycine max L. Merr.) seed mainly contributes to the undesirable odors and flavors commonly associated with poor oil quality. LN deposition at various stages of soybean seed development had not been reported by 2010. The objects of this study were (1) to identify and measure quantitative trait loci (QTL) underlying LN content and (2) to estimate the QTL effects expressed from earlier seed developmental stages to drying seed of soybean. One hundred and twenty-five F_5:8 and F_5:9 recombinant inbred lines derived from the cross of soybean cultivars ‘Hefeng 25’ and ‘Dongnong L5’ were used for the identification of QTL underlying LN content from the 37 day (D) to 86D stages after flowering, at Harbin in 2008 and 2009. QTL × Environment interactions (QE) effects were evaluated using a mixed genetic model (Zhu in J Zhejiang Univ (Natural Science) 33:327–335, 1999). Twelve unconditional QTL and 12 conditional QTL associated with LN content were identified at different developmental stages. Most of the QTL explained <10% of phenotypic variation of LN content. Unconditional QTL QLNF-1, QLNC2-1, QLND1b-1, QLNA2-1 and QLNH-1 influenced LN content across different development stages and environments. Conditional QTL QLNF-1, QLNC2-1 and QLNH-1 were identified in multiple developmental stages and environments. Conditional and unconditional QTL clustered in neighboring intervals on linkage groups A2, C2 and D1b. Ten QTL with conditional additive main effects (a) and/or conditional additive × environment interaction effects (ae) at specific developmental stage were identified on nine linkage groups. Of them, six QTL only possessed additive main effects and seven QTL had significant ae effects in different developmental stages. A total of 13 epistatic pairwise QTL were identified by conditional mapping in different developmental stages. Two pairs of QTL only showed aa effects and five pairs of QTL only showed aae effects at different developmental stages. QTL with aa effects, as well as their environmental interaction effects, appeared to vary at different developmental stages.     

水稻株高上位性效应和ＱＥ互作效应的ＱＴＬ遗传研究

利用基因混合模型的ＱＴＬ定位方法研究了由籼稻品种ＩＲ６４和粳稻品种Ａzucena杂交衍生的ＤＨ群体在４个环境中的ＱＴＬ上位性效应和环境互作效应,结果表明,上位性是数量性状的重要遗传基础,并揭示了上位性的几个重要特点,所有的ＱＴＬ都参与了上位性效应的形成,６４％的ＱＴＬ还具有本身的加性效应,因此传统方法对ＱＴＬ加性效应的估算会由于上位性的影响而有偏,其他３６％的ＱＴＬ没有本身的加性效应,却参与了４８％的上位性互作用,这些位点可能通过诱发和修饰其他位点而起作用,上位性的特点还包括,经常发现了一个ＱＴＬ与多个ＱＴＬ发生互作;大效应的ＱＴＬ也参与上位性互作;上位性互作受环境影响,ＱＴＬ与环境的互效应比ＱＴＬ的主效应更多地被检测到,表明数量性状基因的表达易受环境影响。     

QTL for fibre-related traits in grain × sweet sorghum as a tool for the enhancement of sorghum as a biomass crop

Compared to maize and temperate grasses, sorghum has received less attention in terms of improving cell wall components. The objectives of this study were to identify quantitative trait loci (QTL) with main effects, epistatic and pleiotropic effects along with QTL × environment (QE) interactions controlling fibre-related traits in sorghum. Neutral detergent fibre (NDF), acid detergent fibre (ADF), acid detergent lignin (ADL), cellulose, hemicellulose, fresh leaf mass, stripped stalk mass, dry stalk mass, fresh biomass and dry biomass were analysed from a population of 188 grain × sweet sorghum recombinant inbred lines. A genetic map consisting of 157 DNA markers was constructed, and QTL were detected using composite interval mapping (CIM). CIM detected more than 5 additive QTL per trait explaining 7.1–24.7% of the phenotypic variation. Abundant co-localization of these QTL was observed across all chromosomes, and the highest cluster was identified on chromosome 6. Searching for candidate genes using the confidence interval of our QTL clusters reveals that these clusters might comprise a set of genes that are tightly linked. Some QTL showed multiple effects; however, the allele for each trait was favouring the parent with the increasing effect. QE interactions were observed for QTL showing multiple effects. Additive × additive interaction was observed for 7 out of 10 traits, indicating the importance of epistatic analysis. However, the phenotypic variation explained by digenic interactions was lower compared to the individual QTL. Our results indicate that various genetic components contribute to fibre-related traits and should be considered during the enhancement of sorghum for lignocellulosic biomass.     

Identification of main effect and epistatic quantitative trait loci for morphological and yield-related traits in peanut (<Emphasis Type="Italic">Arachis hypogaea</Emphasis> L.)

An effort was made in the present study to identify the main effect and epistatic quantitative trait locus (QTL) for the morphological and yield-related traits in peanut. A recombinant inbred line (RIL) population derived from TAG 24 × GPBD 4 was phenotyped in seven environments at two locations. QTL analysis with available genetic map identified 62 main-effect QTLs (M-QTLs) for ten morphological and yield-related traits with the phenotypic variance explained (PVE) of 3.84–15.06%. Six major QTLs (PVE >?10%) were detected for PLHT, PPP, YPP, and SLNG. Stable M-QTLs appearing in at least two environments were detected for PLHT, LLN, YPP, YKGH, and HSW. Five M-QTLs governed two traits each, and 16 genomic regions showed co-localization of two to four M-QTLs. Intriguingly, a major QTL reported to be linked to rust resistance showed pleiotropic effect for yield-attributing traits like YPP (15.06%, PVE) and SLNG (13.40%, PVE). Of the 24 epistatic interactions identified across the traits, five interactions involved six M-QTLs. Three interactions were additive × additive and remaining two involved QTL × environment (QE) interactions. Only one major M-QTL governing PLHT showed epistatic interaction. Overall, this study identified the major M-QTLs for the important productivity traits and also described the lack of epistatic interactions for majority of them so that they can be conveniently employed in peanut breeding.     

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 major QTLs and epistatic interactions for seed protein concentration in soybean under multiple environments based on a high-density map

The concentration of protein in soybean is an important trait that drives successful soybean quality. A recombinant inbred line derived from a cross between the Charleston and Dongnong594 cultivars was planted in one location across 10 years and two locations across 5 years in China (20 environments in total), and the genetic effects were partitioned into additive main effects, epistatic main effects and their environmental interaction effects using composite interval mapping and inclusive composite interval mapping models based on a high-density genetic map. Ten main-effect quantitative trait loci (QTLs) were identified on chromosomes 3, 6, 7, 13, 15 and 20 and detected in more than three environments, with each of the main-effect QTLs contributing a phenotypic variation of around 10 %. Between the intervals of the main-effect QTLs, 93 candidate genes were screened for their involvement in seed protein storage and/or amino acid biosynthesis and metabolism processes based on gene ontology and annotation information. Furthermore, an analysis of epistatic interactions showed that three epistatic QTL pairs were detected, and could explain approximately 50 % of the phenotypic variation. The additive main-effect QTLs and epistatic QTL pairs contributed to high phenotypic variation under multiple environments, and the results were also validated and corroborated with previous research, indicating that marker-assisted selection can be used to improve soybean protein concentrations and that the candidate genes can also be used as a foundation data set for research on gene function.     

Using a physiological framework for improving the detection of quantitative trait loci related to nitrogen nutrition in <Emphasis Type="Italic">Medicago truncatula</Emphasis>

Medicago truncatula is used as a model plant for exploring the genetic and molecular determinants of nitrogen (N) nutrition in legumes. In this study, our aim was to detect quantitative trait loci (QTL) controlling plant N nutrition using a simple framework of carbon/N plant functioning stemming from crop physiology. This framework was based on efficiency variables which delineated the plant’s efficiency to take up and process carbon and N resources. A recombinant inbred line population (LR4) was grown in a glasshouse experiment under two contrasting nitrate concentrations. At low nitrate, symbiotic N₂ fixation was the main N source for plant growth and a QTL with a large effect located on linkage group (LG) 8 affected all the traits. Significantly, efficiency variables were necessary both to precisely localize a second QTL on LG5 and to detect a third QTL involved in epistatic interactions on LG2. At high nitrate, nitrate assimilation was the main N source and a larger number of QTL with weaker effects were identified compared to low nitrate. Only two QTL were common to both nitrate treatments: a QTL of belowground biomass located at the bottom of LG3 and another one on LG6 related to three different variables (leaf area, specific N uptake and aboveground:belowground biomass ratio). Possible functions of several candidate genes underlying QTL of efficiency variables could be proposed. Altogether, our results provided new insights into the genetic control of N nutrition in M. truncatula. For instance, a novel result for M. truncatula was identification of two epistatic interactions in controlling plant N₂ fixation. As such this study showed the value of a simple conceptual framework based on efficiency variables for studying genetic determinants of complex traits and particularly epistatic interactions.     

Dissection of QTL effects for root traits using a chromosome arm-specific mapping population in bread wheat

A high-resolution chromosome arm-specific mapping population was used in an attempt to locate/detect gene(s)/QTL for different root traits on the short arm of rye chromosome 1 (1RS) in bread wheat. This population consisted of induced homoeologous recombinants of 1RS with 1BS, each originating from a different crossover event and distinct from all other recombinants in the proportions of rye and wheat chromatin present. It provides a simple and powerful approach to detect even small QTL effects using fewer progeny. A promising empirical Bayes method was applied to estimate additive and epistatic effects for all possible marker pairs simultaneously in a single model. This method has an advantage for QTL analysis in minimizing the error variance and detecting interaction effects between loci with no main effect. A total of 15 QTL effects, 6 additive and 9 epistatic, were detected for different traits of root length and root weight in 1RS wheat. Epistatic interactions were further partitioned into inter-genomic (wheat and rye alleles) and intra-genomic (rye–rye or wheat–wheat alleles) interactions affecting various root traits. Four common regions were identified involving all the QTL for root traits. Two regions carried QTL for almost all the root traits and were responsible for all the epistatic interactions. Evidence for inter-genomic interactions is provided. Comparison of mean values supported the QTL detection.     

Genetic characterization of QTL associated with resistance to Fusarium head blight in a doubled-haploid spring wheat population.

Fusarium head blight (FHB) is one of the most important fungal wheat diseases worldwide. Understanding the genetics of FHB resistance is the key to facilitating the introgression of different FHB resistance genes into adapted wheat. The objectives of the present study were to detect and map quantitative trait loci (QTL) associated with FHB resistance genes and characterize the genetic components of the QTL in a doubled-haploid (DH) spring wheat population using both single-locus and two-locus analysis. A mapping population, consisting of 174 DH lines from the cross between DH181 (resistant) and AC Foremost (susceptible), was evaluated for type I resistance to initial infection during a 2-year period in spray-inoculated field trials, for Type II resistance to fungal spread within the spike in 3 greenhouse experiments using single-floret inoculation, and for resistance to kernel infection in a 2001 field trial. One-locus QTL analysis revealed 7 QTL for type I resistance on chromosome arms 2DS, 3AS, 3BS, 3BC (centromeric), 4DL, 5AS, and 6BS, 4 QTL for type II resistance on chromosomes 2DS, 3BS, 6BS, and 7BL, and 6 QTL for resistance to kernel infection on chromosomes 1DL, 2DS, 3BS, 3BC, 4DL, and 6BS. Two-locus QTL analysis detected 8 QTL with main effects and 4 additive by additive epistatic interactions for FHB resistance and identified novel FHB resistance genes for the first time on chromosomes 1DL, 4AL, and 4DL. Neither significant QTL by environment interactions nor epistatic QTL by environment interactions were found for either type I or type II resistance. The additive effects of QTL explained most of the phenotypic variance for FHB resistance. Marker-assisted selection for the favored alleles at multiple genomic regions appears to be a promising tool to accelerate the introgression and pyramiding of different FHB resistance genes into adapted wheat genetic backgrounds.     

Identification of quantitative trait loci and environmental interactions for accumulation and remobilization of water-soluble carbohydrates in wheat (Triticum aestivum L.) stems

Genetic analyses of nine traits associated with stem water-soluble carbohydrate (SWSC) accumulation and remobilization at grain-filling period under drought stress (DS) and well-watered (WW) conditions were undertaken using doubled haploid lines (DHLs) derived from two Chinese common wheat cultivars. Some significantly and very significantly positive correlation was observed among nine traits associated with SWSC. Higher phenotypic values for most traits were detected under DS. Broad sense heritabilities (h(B)(2)) of the traits showed wide fluctuations between two water treatments. A total of 48 additive and 62 pairs of epistatic QTL for nine traits were identified as distributing on all 21 chromosomes. A majority of QTL involved significant additive and epistatic effects with interactions of QTL and environments (QEIs). Two additive and two pairs of epistatic loci involved only QEIs without corresponding significant additive or epistatic effects. The contributions of the additive QEIs were two- to fourfolds higher than those of their corresponding additive QTL. Most of the additive QEIs for traits associated with SWSC interacted with DS. In addition, some QTL for the grain-filling efficiencies and thousand-grain weight were colocated in the same or adjacent chromosome intervals with QTL for accumulation and remobilization efficiency of SWSC before 14 days after flowering.     

The genetic basis of flowering time and photoperiod sensitivity in rapeseed <Emphasis Type="Italic">Brassica napus</Emphasis> L.

The objective of this study was to dissect the genetic control of days to flowering (DTF) and photoperiod sensitivity (PS) into the various components including the main-effect quantitative trait loci (QTLs), epistatic QTLs and QTL-by-environment interactions (QEs). Doubled haploid (DH) lines were produced from an F₁ between two spring Brassica napus cultivars Hyola 401 and Q2. DTF of the DH lines and parents were investigated in two locations, one location with a short and the other with a long photoperiod regime over two years. PS was calculated by the delay in DTF under long day as compared to that under short day. A genetic linkage map was constructed that comprised 248 marker loci including SSR, SRAP, and AFLP markers. Further QTL analysis resolved the genetic components of flowering time and PS into the main-effect QTLs, epistatic QTLs, and QEs. A total of 7 main-effect QTLs and 11 digenic interactions involving 21 loci located on 13 out of the 19 linkage groups were detected for the two traits. Three main-effect QTLs and four pairs of epistatic QTLs were involved in QEs conferring DTF. One QTL on linkage group (LG) 18 was revealed to simultaneously affect DTF and PS and explain for the highest percentage of the phenotypic variation. The implications of the results for B. napus breeding have been discussed. The text was submitted by the authors in English.