Mixed linear model approach for mapping quantitative trait loci underlying crop seed traits

The crop seed is a complex organ that may be composed of the diploid embryo, the triploid endosperm and the diploid maternal tissues. According to the genetic features of seed characters, two genetic models for mapping quantitative trait loci (QTLs) of crop seed traits are proposed, with inclusion of maternal effects, embryo or endosperm effects of QTL, environmental effects and QTL-by-environment (QE) interactions. The mapping population can be generated either from double back-cross of immortalized F₂ (IF₂) to the two parents, from random-cross of IF₂ or from selfing of IF₂ population. Candidate marker intervals potentially harboring QTLs are first selected through one-dimensional scanning across the whole genome. The selected candidate marker intervals are then included in the model as cofactors to control background genetic effects on the putative QTL(s). Finally, a QTL full model is constructed and model selection is conducted to eliminate false positive QTLs. The genetic main effects of QTLs, QE interaction effects and the corresponding P-values are computed by Markov chain Monte Carlo algorithm for Gaussian mixed linear model via Gibbs sampling. Monte Carlo simulations were performed to investigate the reliability and efficiency of the proposed method. The simulation results showed that the proposed method had higher power to accurately detect simulated QTLs and properly estimated effect of these QTLs. To demonstrate the usefulness, the proposed method was used to identify the QTLs underlying fiber percentage in an upland cotton IF₂ population. A computer software, QTLNetwork-Seed, was developed for QTL analysis of seed traits.     

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     

上位性和QTL×环境互作对水稻(Oryza sativa L.)抽穗期的影响

水稻抽穗期是重要的农艺性状之一,对水稻品种的地理分布和适应性起到关键性作用。适宜的抽穗期是获得高产的前提。因此确定水稻抽穗期的遗传基础在育种计划中具有重要的意义。本研究用一套来源于亲本IR64/Azucena的双单倍体(DH)群体在两个种植季节的试验资料,用基于混合线性模型的复合区间作图方法,对水稻抽穗期QTL的加性、上位性及其与环境互作效应进行了研究。结果表明共有14个QTL影响水稻抽穗期,它们分布在除第5和第9条染色体外的10条染色体上,有8个位点携带单位点效应,5对位点携带双位点互作效应,2个单位点和1对双位点存在与环境的互作,所有效应值介于1.179～2.549天之间,相应的贡献率为1.04%～4.84%。基于所估算的QTL效应值,本研究预测了两个亲本和两个极端型品系的遗传效应值,并讨论了影响遗传效应值与实际观测值偏差的可能原因,以及研究群体所具有的遗传潜力。对水稻抽穗期QTL的定位结果与前人研究基本一致,并进一步证实了上位性和QE互作效应是水稻抽穗期的重要遗传基础。     

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。     

上位性和QTL×环境互作对水稻(Oryza sativa L.)抽穗期的影响

水稻抽穗期是重要的农艺性状之一,对水稻品种的地理分布和适应性起到关键性作用。适宜的抽穗期是获得高产的前提。因此确定水稻抽穗期的遗传基础在育种计划中具有重要的意义。本研究用一套来源于亲本IR64/Azucena的双单倍体（DH）群体在两个种植季节的试验资料,用基于混合线性模型的复合区间作图方法,对水稻抽穗期QTL的加性、上位性及其与环境互作效应进行了研究。结果表明共有14个QTL影响水稻抽穗期,它们分布在除第5和第9条染色体外的10条染色体上,有8个位点携带单位点效应,5对位点携带双位点互作效应,2个单位点和1对双位点存在与环境的互作,所有效应值介于1.179～2.549天之间,相应的贡献率为1.04%～4.84%。基于所估算的QTL效应值,本研究预测了两个亲本和两个极端型品系的遗传效应值,并讨论了影响遗传效应值与实际观测值偏差的可能原因,以及研究群体所具有的遗传潜力。对水稻抽穗期QTL的定位结果与前人研究基本一致,并进一步证实了上位性和QE互作效应是水稻抽穗期的重要遗传基础。     

Molecular breeding for grain yield in barley: an evaluation of QTL effects in a spring barley cross

 We report results from a breeding strategy designed to accumulate favorable QTL alleles for grain yield identified in the SteptoeבMorex’ (SM) barley germplasm. Two map lines (SM73 and SM145) from the original mapping population were selected based on their marker genotype and QTL structure. When crossed, these lines would be expected to produce progeny with most favorable QTL alleles. One hundred doubled haploid (DH) lines from the F₁ hybrid of this cross were genotyped with ten RFLP markers and one morphological marker defining grain yield to monitor QTL segregation. A subset of 24 lines representing various combinations of putatively favorable and unfavorable QTL alleles, together with Steptoe, ‘Morex’, SM73, and SM145, were phenotyped for grain yield in five environments. Multiple regression procedures were used to explore phenotype and genotype relationships. Most target QTLs showed significant effects. However, significance and magnitude of QTL effects and favorable QTL allele phase varied across environments. All target QTLs showed significant QTL-by-environment interaction (QTL×E), and the QTL on chromosome 2 expressed alternative favorable QTL alleles in different environments. Digenic epistatic effects were also detected between some QTL loci. For traits such as grain yield, marker-assisted selection efforts may be better targeted at determining optimum combinations of QTL alleles rather than pyramiding alleles detected in a reference mapping population. Received: 2 June 1998 / Accepted: 17 September 1998     

Impact of epistasis and QTL×environment interaction on the developmental behavior of plant height in rice (Oryza sativa L.)

QTLs with epistatic effects and environmental interaction effects for the developmental behavior of plant height in rice were studied by conventional and conditional methods for quantitative trait loci (QTLs) by mapping with a doubled-haploid population of 123 lines from IR64/Azucena in three environments. The results showed that epistatic effects were important and most epistasis could be detected only by conditional QTL mapping, while most non–epistatic QTLs could be detected by both conventional and conditional methods. Many modificative QTLs showed only epistatic effects without their own additive effects at some stages. QTL×environment (QE) interaction effects were detected more often than QTL main effects for plant-height behavior, which might indicate that gene expression could be greatly affected by the environment. No QTLs had effects during the whole of ontogeny. Conditional QTL mapping might be a valid way to reveal dynamic gene expression for the development of quantitative traits, especially for epistatic effects. Received: 19 May 2000 / Accepted: 27 October 2000     

Identification of quantitative trait loci controlling sucrose content in soybean (Glycine max)

Sucrose is a primary constituent of soybean (Glycine max) seed; however, little information concerning the inheritance of seed sucrose in soybean is available. The objective of this research was to use molecular markers to identify genomic regions significantly associated with quantitative trait loci (QTL) controlling sucrose content in a segregating F₂ population. DNA samples from 149 F₂ individuals were analyzed with 178 polymorphic genetic markers, including RFLPs, SSRs, and RAPDs. Sucrose content was measured on seed harvested from each of 149 F_2:3 lines from replicated field experiments in 1993 and 1995. Seventeen marker loci, mapping to seven different genomic regions, were significantly associated with sucrose variation at P<0.01. Individually, these markers explained from 6.1% to 12.4% of the total phenotypic variation for sucrose content in this population. In a combined analysis these genomic regions; explained 53% of total variation for sucrose content. No significant evidence of epistasis among QTLs was observed. Comparison of our QTL mapping results for sucrose content and those previously reported for protein and oil content (the other major seed constituents in soybean), suggests that seed quality traits are inherited as clusters of linked loci or that `major' QTLs with pleiotropic effects may control all three traits. Of the seven genomic regions having significant effects on sucrose content, three were associated with significant variation for protein content and three were significantly associated with oil content.     

应用中国和欧洲油菜建立的双二倍体群体对3个重要农艺性状的QTL定位以及与环境的互作分析

以中国的高油分自交系“高油”和欧洲高含油量品种“Sollux”的F1产生的282个株系组成的双二倍体(DH)群体为材料,在125个SSR标记座位构建的连锁图谱基础上,根据在中国和欧洲四个不同环境下的表型鉴定结果,采用混合线性模型基础上的QTL分析软件,对油菜3个重要农艺性状:株高,开花期和成熟期进行了数量性状基因座位(QTL)的联合定位分析,估测了这些QTL的加性、上位性以及与环境的互作效应。结果表明各性状均受多个加性、加加上位以及与环境互作的QTL控制。株高受多个QTL影响(12个位点具有加性或兼有环境互作效应,5个位点具有互作效应),以加性效应为主,加性效应总和可解释定位群体表型变异的75%左右,并多兼有上位性效应。12个主效QTL中,9个是“高油”等位基因相对“Sollux”有降低株高的作用,大多数加性×环境互作QTL的有效等位基因具有环境选择特异性。7个ae基因座位中,5个“高油”等位基因在杭州种植环境下,除一例外所有在德国环境下的互作基因座中,“Sollux”等位基因起着增加株高的作用,加加上位性主效总和为加性主效总和的三分之一。7个控制花期和8个控制成熟期的主效QTL中,分别有6个和5个是来自“高油”的等位基因相对“Sollux”具有提前开花和成熟的效应,这些QTL的效应总和占到性状表型变异的60%左右。5个位于第2和第12连锁群中的2个大效应QTL可能和已多次报导的VFN1和VFN3基因相近或相同。开花期和成熟期两性状均检测到显著的ae互作效应,双亲等位基因的效应在各环境下呈离散分布。位于14和19连锁群上的两个主效株高QTL同时也是控制开花期和油分含量的基因位点,因而利用这两个位点进行标记辅助筛选时要考虑到对油分含量的影响。控制成熟期的8个主效QTL中有3个同时也是控制开花期的基因座位,证实了开花期和成熟期高度正相关的遗传基础,两个生育性状均表现有较弱的QTL间加加上位互作,但以主效QTL的作用为主。     

Mapping of QTL for tiller number at different stages of growth in wheat using double haploid and immortalized F<Subscript>2</Subscript> populations

Effective tiller number is one of the most important traits for wheat (Triticum aestivum L.) yield, but the inheritance of tillering is poorly understood. A set of 168 doubled haploid (DH) lines derivatives of a cross between two winter wheat cultivars (Huapei 3 and Yumai 57), and an immortalized F₂ (IF₂) population generated by randomly permutated intermating of these DHs were investigated, and QTLs of tillering related to the maximum tillering of pre-winter (MTW), maximum tillering in spring (MTS), and effective tillering in harvest (ETH) were mapped. Phenotypic data were collected for the two populations from two different environments. Using inclusive composite interval mapping (ICIM), a total of 9 and 18 significant QTL were detected across environments for tillering in the DH and IF₂ populations, respectively. Four QTLs were common between two populations. A major QTL located on the 5D chromosome with the allele originating from Yumai 57 was detected and increased 1.92 and 3.55 tillers in MTW and MTS, respectively. QTLs (QMts6D, QEth6D) having a neighbouring marker interval at Xswes679.1 and Xcfa2129 on chromosome 6D was detected in MTS and ETH. These results provide a better understanding of the genetic factors for selectively expressing the control of tiller number in different growth stages and facilitate marker-assisted selection strategy in breeding.     

Genetic basis and mapping of the resistance to rice yellow mottle virus. II. Evidence of a complementary epistasis between two QTLs

 The genetic basis of resistance to rice yellow mottle virus (RYMV) was studied in a doubled-haploid (DH) population derived from a cross between the very susceptible indica variety ‘IR64’ and the resistant upland japonica variety Azucena. As a quantitative trait locus (QTL) involved in virus content estimated with an ELISA test has been previously identified on chromosome 12, we performed a wide search for interactions between this QTL and the rest of the genome, and between this QTL and morphological traits segregating in the population. Multiple regression with all identified genetic factors was used to validate the interactions. Significant epistasis accounting for a major part of the total genetic variation was observed. A complementary epistasis between the QTL located on chromosome 12 and a QTL located on chromosome 7 could be the major genetic factor controlling the virus content. Resistance was also affected by a morphology-dependent mechanism since tillering was interfering with the resistance mechanism conditioned by the epistasis between the two QTLs. Marker-assisted backcross breeding was developed to introgress the QTLs of chromosome 7 and chromosome 12 in the susceptible ‘IR64’ genetic background. First results confirmed that if both QTLs do not segregate in a backcross-derived F₂ population, then the QTL of chromosome 12 cannot explain differences in virus content. A near-isogenic line (NIL) approach is currently being developed to confirm the proposed genetic model of resistance to RYMV. Received: 20 April 1990 / Accepted: 30 April 1998     

Mapping quantitative trait loci controlling silking date in a diallel cross among four lines of maize

 We describe and apply an interval mapping method for quantitative trait locus (QTL) detection using F₃ and testcross progenies derived from F₂ populations obtained from a diallel cross among four elite lines of maize. Linear model-based procedures were used for the test and estimation of putative QTL effects together with genetic interactions including epistasis. We mapped QTL associated with silking date and explored their genetic effects. Ten QTL were detected, and these explained more than 40% of the phenotypic variance. Most of these QTL had consistent and stable effects among genetic backgrounds and did not show significant epistasis. QTL-by-environment interaction was important for four QTL and was essentially due to changes in magnitude of allelic effects. These results show the efficiency of our method in several genetic situations as well as the power of the diallel design in detecting QTL simultaneously over several populations. Received: 2 September 1996 / Accepted: 20 December 1996     

利用双单倍体群体剖析水稻产量及其相关性状的遗传基础

主效QTL、上位性效应和它们与环境的互作(QE)都是数量性状的重要遗传因素。利用籼粳交珍汕97／武育粳2号F1植株上的花药进行组织培养得到的190个双单倍体群体和179个微卫星标记,通过两年两重复田间试验,采用混合线性模型方法分析了9个控制水稻产量及其相关性状的遗传效应,得到57个主效QTL,41对上位性互作,8对QTL与环境的互作和7对上位性效应与环境的互作。单个主效QTL解释这些性状1．3％～25．8％的表型方差。各性状QTL的累积表型贡献率达11．5％～66．8％。大多数性状之间具有显著的表型相关性,相关性较高的性状之间常具有较多共同或紧密连锁的QTL。结果表明,基因的多效性或紧密连锁可能是性状相关的重要遗传基础。     

Detection of QTL for six yield-related traits in oilseed rape (<Emphasis Type="Italic">Brassica napus</Emphasis>) using DH and immortalized F<Subscript>2</Subscript> populations

The inheritance of yield-related traits in rapeseed (Brassica napus) is poorly understood, and the investigations on mapping of quantitative trait loci (QTL) for such traits are only few. QTL related to six traits were mapped which include plant height (PH), height of lowest primary effective branch (HPB), length of main inflorescence (LMI), silique length (SL), number of primary branches (FB) and silique density (SD). A set of 258 doubled haploid (DH) lines derivatives of a cross between a canola variety Quantum and a resynthesized B. napus line No.2127-17, and a fixed immortalized F₂ (designated as IF₂) population generated by randomly permutated intermating of these DHs were investigated. A genetic linkage map was constructed using 208 SSR and 189 SRAP markers for the DH population. Phenotypic data were collected from three environments for the two populations. Using composite interval mapping analyses, 30 and 22 significant QTL were repeatedly detected across environments for the six traits in the DH and IF₂ populations, respectively. Twenty-nine QTL were common between the two populations. The directions of parental contribution for all common QTL were the same, showing a great potential for marker-assisted selection in improving these traits. Some chromosomal regions harbor QTL for multiple traits, which were consistent with significant phenotypic correlations observed among traits. The results provided a better understanding of the genetic factors controlling yield-related traits in rapeseed. Electronic supplementary material The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Mapping multiple QTL of different effects: comparison of a simple sequential testing strategy and multiple QTL mapping

The aim of this study was to explore, by computer simulation, the mapping of QTLs in a realistic but complex situation of many (linked) QTLs with different effects, and to compare two QTL mapping methods. A novel method to dissect genetic variation on multiple chromosomes using molecular markers in backcross and F₂ populations derived from inbred lines was suggested, and its properties tested using simulations. The rationale for this sequential testing method was to explicitly test for alternative genetic models. The method consists of a series of four basic statistical tests to decide whether variance was due to a single QTL, two QTLs, multiple QTLs, or polygenes, starting with a test to detect genetic variance associated with a particular chromosome. The method was able to distinguish between different QTL configurations, in that the probability to `detect' the correct model was high, varying from 0.75 to 1. For example, for a backcross population of 200 and an overall heritability of 50%, in 78% of replicates a polygenic model was detected when that was the underlying true model. To test the method for multiple chromosomes, QTLs were simulated on 10 chromosomes, following a geometric series of allele effects, assuming positive alleles were in coupling in the founder lines For these simulations, the sequential testing method was compared to the established Multiple QTL Mapping (MQM) method. For a backcross population of 400 individuals, power to detect genetic variance was low with both methods when the heritability was 0.40. For example, the power to detect genetic variation on a chromosome on which 6 QTLs explained 12.6% of the genetic variance, was less than 60% for both methods. For a large heritability (0.90), the power of MQM to detect genetic variance and to dissect QTL configurations was generally better, due to the simultaneous fitting of markers on all chromosomes. It is concluded that when testing different QTL configurations on a single chromosome using the sequential testing procedure, regions of other chromosomes which explain a significant amount of variation should be fitted in the model of analysis. This study reinforces the need for large experiments in plants and other species if the aim of a genome scan is to dissect quantitative genetic variation.     

Quantitative genetic dissection of complex traits in a QTL-mapping pedigree

This paper summarizes and modifies quantitative genetic analyses on a pedigree used to map genetic factors (i.e., QTLs) underlying a complex trait. The total genetic variance can be exactly estimated based on the F₂ family derived from two homozygous parents for alternative alleles at all QTLs of interest. The parents, F₁ hybrids, and two backcrosses are combined to each parent, and the total number of QTLs and the number of dominant QTLs are estimated under the assumptions of gene association with the two parents, equal gene effect, no linkage, and no epistasis among QTLs. Further relaxation for each of the assumptions are made in detail. The biometric estimator for the QTL number and action mode averaged over the entire genome could provide some basic and complementary information to QTL mapping designed to detect the effect and location of specific genetic factors.     

Detection of putative quantitative trait loci in line crosses under infinitesimal genetic models

Quantitative trait locus (QTL) mapping studies often employ segregating generations derived from a cross between genetically divergent inbred lines. In the analysis of such data it is customary to fit a single QTL and use a null hypothesis which assumes that the genomic region under study contributes no genetic variance. To explore the situation in which multiple linked genes contribute to the genetic variance, we simulated an F₂-mapping experiment in which the genetic difference between the two original inbred strains was caused by a large number of loci, each having equal effect on the quantitative trait. QTLs were either in coupling, dispersion or repulsion phase in the base population of inbred lines, with the expected F₂ genetic variance explained by the QTLs being equivalent in the three models. Where QTLs were in coupling phase, one inbred line was fixed for all plus alleles, and the other line was fixed for minus alleles. Where QTLs were in dispersion phase, they were assumed to be randomly fixed for one or other allele (as if the inbred lines had evolved from a common ancestor by random drift). Where QTLs were in repulsion phase alleles within an inbred line were alternating plus and minus at adjacent loci, and alternative alleles were fixed in the two inbred lines. In all these genetic models a standard interval mapping test statistic used to determine whether there is a QTL of large effect segregating in the population was inflated on average. Furthermore, the use of a threshold for QTL detection derived under the assumption that no QTLs were segregating would often lead to spurious conclusions regards the presence of genes of large effects (i.e. type I errors). The employment of an alternative model for the analysis, including linked markers as cofactors in the analysis of a single interval, reduced the problem of type I error rate, although test statistics were still inflated relative to the case of no QTLs. It is argued that in practice one should take into account the difference between the strains or the genetic variance in the F₂ population when setting significance thresholds. In addition, tests designed to probe the adequacy of a single-QTL model or of an alternative infinitesimal coupling model are described. Such tests should be applied in QTL mapping studies to help dissect the true nature of genetic variation.     

Analyzing quantitative trait loci for yield using a vegetatively replicated F2 population from a cross between the parents of an elite rice hybrid

Although F₂s are the most informative populations for genetic analysis, it has been difficult to use F₂ populations directly for QTL analysis because it is usually difficult to assess the reliability of the data, due to an inability to estimate the experimental errors. In this study, we performed a QTL analysis for yield and yield-component traits of an F₂ population based on data from replicated field trials over 2 years using vegetative shoots of ratooned plants, making use of the ratooning habit of rice. The objective of this study was to explore the possibility of conducting QTL analyses directly based on an F₂ population by means of ratooning plants. The experimental population was from a cross between ’Zhenshan 97’ and ’Minghui 63’, the parents of ’Shanyou 63’, an elite rice hybrid widely grown in China. A genetic linkage map containing 151 molecular markers was constructed for QTL mapping. A total of 20 distinct QTLs were detected; eight of these were detected in both years and remaining 12 in only 1 year. Compared with the results of our previous analysis of the F_2:3 families from the same cross, it was shown that most of the QTLs detected in the ratooned F₂ population were also detected in the F_2:3 population. However, the estimates of both additive and dominant types of genetic effects for many of the QTLs based on F₂ ratoons were substantially larger than those based on F_2:3 families. The results indicate that vegetatively ratooned F₂ populations may have considerable utility in the mapping of QTLs, especially if dominant types of gene actions are of concern, although there were certain technical limitations in making use of such populations in the experiments. Received: 11 November 1999 / Accepted: 24 November 1999     

QTL x environment interactions in rice. I. heading date and plant height

One hundred twenty six doubled-haploid (DH) rice lines were evaluated in nine diverse Asian environments to reveal the genetic basis of genotype × environment interactions (GEI) for plant height (PH) and heading date (HD). A subset of lines was also evaluated in four water-limited environments, where the environmental basis of G × E could be more precisely defined. Responses to the environments were resolved into individual QTL × environment interactions using replicated phenotyping and the mixed linear-model approach. A total of 37 main-effect QTLs and 29 epistatic QTLs were identified. On average, these QTLs were detectable in 56% of the environments. When detected in multiple environments, the main effects of most QTLs were consistent in direction but varied considerably in magnitude across environments. Some QTLs had opposite effects in different environments, particularly in water-limited environments, indicating that they responded to the environments differently. Inconsistent QTL detection across environments was due primarily to non- or weak-expression of the QTL, and in part to significant QTL × environment interaction effects in the opposite direction to QTL main effects, and to pronounced epistasis. QTL × environment interactions were trait- and gene-specific. The greater GEI for HD than for PH in rice were reflected by more environment-specific QTLs, greater frequency and magnitude of QTL × environment interaction effects, and more pronounced epistasis for HD than for PH. Our results demonstrated that QTL × environment interaction is an important property of many QTLs, even for highly heritable traits such as height and maturity. Information about QTL × environment interaction is essential if marker-assisted selection is to be applied to the manipulation of quantitative traits.Communicated by G. Wenzel     

QTL analysis reveals context-dependent loci for seed glucosinolate trait in the oilseed <Emphasis Type="Italic">Brassica juncea</Emphasis>: importance of recurrent selection backcross scheme for the identification of ‘true’ QTL

Seed glucosinolate content in Brassica juncea is a complex quantitative trait. A recurrent selection backcross (RSB) method with a doubled haploid (DH) generation interspersing backcross generations was used for the introgression of low glucosinolate alleles from an east European gene pool B. juncea line, Heera into an Indian gene pool variety, Varuna. Phenotypic comparisons among the DH populations derived from early to advanced backcrosses revealed a shift in the mean values for various glucosinolates with the advancement of backcrossing, indicating a change in the selective values of the alleles with change in the genetic background due to the existence of epistasis and context dependencies. QTL mapping for various seed glucosinolates from early (F₁DH) and advanced generation (BC₄DH) populations confirmed the presence of epistasis and context dependency. The common QTL detected in both F₁DH and BC₄DH changed their R ² values from the former to the later generation. Some of the QTL detected in the F₁DH became irrelevant in the BC₄DH population. Further, new QTL were detected in the BC₄DH population for various glucosinolates. A validation study on a population of low glucosinolate DH lines derived from all the backcross generations of the RSB breeding programme revealed that the QTL detected in BC₄DH were the ‘true’ QTL. Using glucosinolate as an example, the study provides strong evidence for the importance of the RSB method for the identification of the ‘true’ QTL which would be significant for marker assisted introgression of a complex quantitative trait whose expression is influenced by epistatic interactions. Electronic supplementary material The online version of this article (doi:) contains supplementary material, which is available to authorized users. Authors N. Ramchiary, N. C. Bisht, V. Gupta, A. Mukhopadhyay and N. Arumugam have contributed equally to this work.