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

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

Quantitative trait loci mapping and genetic dissection for lint percentage in upland cotton (Gossypium hirsutum)

Lint percentage is an important character of cotton yield components and it is also correlated with cotton fibre development. In this study, we used a high lint percentage variety, Baimian1, and a low lint percentage, TM-1 genetic standard for Gossypium hirsutum, as parents to construct a mapping populations in upland cotton (G. hirsutum). A quantitative trait locus/loci (QTL) analysis of lint percentage was performed by using two mapping procedures; composite interval mapping (CIM), inclusive composite interval mapping (ICIM) and the F_2:3 populations in 2 years. Six main-effect QTL (M-QTL) for lint percentage (four significant and two suggestive) were detected in both years by CIM, and were located on chr. 3, chr. 19, chr. 26 and chr. 5/chr. 19. Of the six QTL, marker intervals and favourable gene sources of the significant M-QTL, qLP-3(2010) and qLP-3(2011) were consistent. These QTL were also detected by ICIM, and therefore, should preferentially be used for marker-assisted selection (MAS) of lint percentage. Another M-QTL, qLP-19(2010), was detected by two mapping procedures, and it could also be a candidate for MAS. We detected the interaction between two M-QTL and environment, and 11 epistatic QTL (E-QTL) and their interaction with environment by using ICIM. The study also found two EST-SSRs, NAU1187 and NAU1255, linked to M-QTL for lint percentage that could be candidate markers affecting cotton fibre development.     

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

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

The QTL analysis on maternal and endosperm genome and their environmental interactions for characters of cooking quality in rice (<Emphasis Type="Italic">Oryza sativa</Emphasis> L<Emphasis Type="Italic">.</Emphasis>)

Investigations to identify quantitative trait loci (QTLs) governing cooking quality traits including amylose content, gel consistency and gelatinization temperature (expressed by the alkali spread value) were conducted using a set of 241 RIL populations derived from an elite hybrid cross of “Zhenshan 97” × “Minghui 63” and their reciprocal backcrosses BC₁F₁ and BC₂F₁ populations in two environments. QTLs and QTL × environment interactions were analyzed by using the genetic model with endosperm and maternal effects and environmental interaction effects on quantitative traits of seed in cereal crops. The results suggested that a total of seven QTLs were associated with cooking quality of rice, which were subsequently mapped to chromosomes 1, 4 and 6. Six of these QTLs were also found to have environmental interaction effects.     

QTL identification of grain protein concentration and its genetic correlation with starch concentration and grain weight using two populations in maize (<Emphasis Type="Italic">Zea mays</Emphasis> L.)

Protein is one of the three main storage chemical components in maize grains, and is negatively correlated with starch concentration (SC). Our objective was to analyse the influence of genetic backgrounds on QTL detection for protein concentration (PC) and to reveal the molecular genetic associations between PC and both SC and grain weight (GWP). Two hundred and eighty-four (Pop1) and 265 (Pop2) F_2:3 families were developed from two crosses between one high-oil maize inbred GY220 and two normal maize inbreds 8984 and 8622 respectively, and were genotyped with 185 and 173 pairs of SSR markers. PC, SC and GWP were evaluated under two environments. Composite interval mapping (CIM) and multiple interval mapping (MIM) methods were used to detect single-trait QTL for PC, and multiple-trait QTL for PC with both SC and GWP. No common QTL were shared between the two populations for their four and one PC QTL. Common QTL with opposite signs of effects for PC and SC/GWP were detected on three marker intervals at bins 6.07–6.08, 8.03 and 8.03–8.04. Multiple-traits QTL mapping showed that tightly-linked QTL, pleiotropic QTL and QTL having effects with opposite directions for PC and SC/GWP were all observed in Pop1, while all QTL reflected opposite effects in Pop2.     

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 analysis of yield traits in an advanced backcross population derived from a cultivated Andean × wild common bean (Phaseolus vulgaris L.) cross

Advanced backcross QTL analysis was used to identify quantitative trait loci (QTL) for agronomic performance in a population of BC₂F_3:5 introgression lines created from the cross of a Colombian large red-seeded commercial cultivar, ICA Cerinza, and a wild common bean accession, G24404. A total of 157 lines were evaluated for phenological traits, plant architecture, seed weight, yield and yield components in replicated trials in three environments in Colombia and genotyped with microsatellite, SCAR, and phaseolin markers that were used to create a genetic map that covered all 11 linkage groups of the common bean genome with markers spaced at an average distance of every 10.4 cM. Segregation distortion was most significant in regions orthologous for a seed coat color locus (R-C) on linkage group b08 and two domestication syndrome genes, one on linkage group b01 at the determinacy (fin) locus and the other on linkage group b02 at the seed-shattering (st) locus. Composite interval mapping analysis identified a total of 41 significant QTL for the eight traits measured of which five for seed weight, two for days to flowering, and one for yield were consistent across two or more environments. QTL were located on every linkage group with b06 showing the greatest number of independent loci. A total of 13 QTL for plant height, yield and yield components along with a single QTL for seed size showed positive alleles from the wild parent while the remaining QTL showed positive alleles from the cultivated parent. Some QTL co-localized with regions that had previously been described to be important for these traits. Compensation was observed between greater pod and seed production and smaller seed size and may have resulted from QTL for these traits being linked or pleiotropic. Although wild beans have been used before to transfer biotic stress resistance traits, this study is the first to attempt to simultaneously obtain a higher yield potential from wild beans and to analyze this trait with single-copy markers. The wild accession was notable for being from a unique center of diversity and for contributing positive alleles for yield and other traits to the introgression lines showing the potential that advanced backcrossing has in common bean improvement.     

QTL consistency and meta-analysis for grain yield components in three generations in maize

Grain yield is the most important and complex trait in maize. In this study, a total of 258 F₉ recombinant inbred lines (RIL), derived from a cross between dent corn inbred Dan232 and popcorn inbred N04, were evaluated for eight grain yield components under four environments. Quantitative trait loci (QTL) and their epistatic interactions were detected for all traits under each environment and in combined analysis. Meta-analysis was used to integrate genetic maps and detected QTL across three generations (RIL, F_2:3 and BC₂F₂) derived from the same cross. In total, 103 QTL, 42 pairs of epistatic interactions and 16 meta-QTL (mQTL) were detected. Twelve out of 13 QTL with contributions (R ²) over 15% were consistently detected in 3–4 environments (or in combined analysis) and integrated in mQTL. Only q100GW-7-1 was detected in all four environments and in combined analysis. 100qGW-1-1 had the largest R ² (19.3–24.6%) in three environments and in combined analysis. In contrast, 35 QTL for 6 grain yield components were detected in the BC₂F₂ and F_2:3 generations, no common QTL across three generations were located in the same marker intervals. Only 100 grain weight (100GW) QTL on chromosome 5 were located in adjacent marker intervals. Four common QTL were detected across the RIL and F_2:3 generations, and two between the RIL and BC₂F₂ generations. Each of five important mQTL (mQTL7-1, mQTL10-2, mQTL4-1, mQTL5-1 and mQTL1-3) included 7–12 QTL associated with 2–6 traits. In conclusion, we found evidence of strong influence of genetic structure and environment on QTL detection, high consistency of major QTL across environments and generations, and remarkable QTL co-location for grain yield components. Fine mapping for five major QTL (q100GW-1-1, q100GW-7-1, qGWP-4-1, qERN-4-1 and qKR-4-1) and construction of single chromosome segment lines for genetic regions of five mQTL merit further studies and could be put into use in marker-assisted breeding.     

Mapping of QTLs governing agronomic and yield traits in chickpea

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

Identification of agronomically favorable quantitative trait loci alleles from a dent corn inbred Dan232 using advanced backcross QTL analysis and comparison with the F<Subscript>2:3</Subscript> population in popcorn

Specific traits are an important consideration in plant breeding. In popcorn, inferior agronomic traits could be improved using dent or flint corn backcrossed with popcorn. In this study, we used advanced backcross quantitative trait locus (AB-QTL) analysis to identify trait-improving QTL alleles from a dent maize inbred Dan232, and compared the detection of QTL in the BC₂S₁ population with QTL results using F_2:3 families of the same population. Two hundred and twenty BC₂S₁ families developed from a cross between Dan232 and an elite popcorn inbred N04 were evaluated for nine plant traits in replicated field trials under two environments. Using composite interval mapping (CIM), a total of 28 significant QTL were detected, and of these, 23 (82.14%) had favorable alleles contributed by the dent corn parent Dan232. Nine QTL (32.14%) detected in the BC₂S₁ population were also located in or near the same chromosome intervals in the F_2:3 population. All of the favorable QTL alleles from Dan232 could be used in marker-assisted selection (MAS) to improve the respective plant traits in popcorn breeding. In addition, their near isogenic lines (QTL-NILs) could be obtained through selfing or another 1–2 backcross with N04. Also, N04 improved for the studied plant traits could be developed from the BC₂S₁ families used in this study. This study demonstrated that the AB-QTL method can be applied to identify favorable QTL from dent corn inbred in popcorn breeding and, once identified, the alleles could be used in marker-assisted selection to improve the respective plant traits.     

Construction of a Genetic Linkage Map and Identification of QTLs for Seed Weight and Seed Size Traits in Lentil (Lens culinaris Medik.)

Seed weight and seed size both are quantitative traits and have been considered as important components of grain yield, thus identification of quantitative trait loci (QTL) for seed traits in lentil (Lens culinaris) would be beneficial for the improvement of grain yield. Hence the main objective of this study was to identify QTLs for seed traits using an intraspecific mapping population derived from a cross between L. culinaris cv. Precoz (seed weight-5.1g, seed size-5.7mm) and L. culinaris cv. L830 (seed weight-2.2g, seed size-4mm) comprising 126 F₈-RILs. For this, two microsatellite genomic libraries enriched for (GA/CT) and (GAA/CTT) motif were constructed which resulted in the development of 501 new genomic SSR markers. Six hundred forty seven SSR markers (including 146 previously published) were screened for parental polymorphism and 219 (33.8%) were found to be polymorphic among the parents. Of these 216 were mapped on seven linkage groups at LOD4.0 spanning 1183.7cM with an average marker density of 5.48cM. Phenotypic data from the RILs was used to identify QTLs for the seed weight and seed size traits by single marker analysis (SMA) followed by composite interval mapping (CIM) which resulted in one QTL each for the 2 traits (qSW and qSS) that were co-localized on LG4 and explained 48.4% and 27.5% of phenotypic variance respectively. The current study would serve as a strong foundation for further validation and fine mapping for utilization in lentil breeding programs.     

Interactions between markers can be caused by the dominance effect of quantitative trait loci

F₂ populations are commonly used in genetic studies of animals and plants. For simplicity, most quantitative trait locus or loci (QTL) mapping methods have been developed on the basis of populations having two distinct genotypes at each polymorphic marker or gene locus. In this study, we demonstrate that dominance can cause the interactions between markers and propose an inclusive linear model that includes marker variables and marker interactions so as to completely control both additive and dominance effects of QTL. The proposed linear model is the theoretical basis for inclusive composite-interval QTL mapping (ICIM) for F₂ populations, which consists of two steps: first, the best regression model is selected by stepwise regression, which approximately identifies markers and marker interactions explaining both additive and dominance variations; second, the interval mapping approach is applied to the phenotypic values adjusted by the regression model selected in the first step. Due to the limited mapping population size, the large number of variables, and multicollinearity between variables, coefficients in the inclusive linear model cannot be accurately determined in the first step. Interval mapping is necessary in the second step to fine tune the QTL to their true positions. The efficiency of including marker interactions in mapping additive and dominance QTL was demonstrated by extensive simulations using three QTL distribution models with two population sizes and an actual rice F₂ population.     

Quantitative trait loci analysis of phenotypic traits and principal components of maize tassel inflorescence architecture

Maize tassel inflorescence architecture is relevant to efficient production of F₁ seed and yield performance of F₁ hybrids. The objectives of this study were to identify genetic relationships among seven measured tassel inflorescence architecture traits and six calculated traits in a maize backcross population derived from two lines with differing tassel architectures, and identify Quantitative Trait Loci (QTL) involved in the inheritance of those tassel inflorescence architecture traits. A Principal Component (PC) analysis was performed to examine relationships among correlated traits. Traits with high loadings for PC1 were branch number and branch number density, for PC2 were spikelet density on central spike and primary branch, and for PC3 were lengths of tassel and central spike. We detected 45 QTL for individual architecture traits and eight QTL for the three PCs. For control of inflorescence architecture, important QTL were found in bins 7.02 and 9.02. The interval phi034—ramosa1 (ral) in bin 7.02 was associated with six individual architecture trait QTL and explained the largest amount of phenotypic variation (17.3%) for PC1. Interval bnlg344–phi027 in bin 9.02 explained the largest amount of phenotypic variation (14.6%) for PC2. Inflorescence architecture QTL were detected in regions with candidate genes fasciated ear2, thick tassel dwarf1, and ral. However, the vast majority of QTL mapped to regions without known candidate genes, indicating positional cloning efforts will be necessary to identify these genes.     

A multi‐parent advanced generation inter‐cross (MAGIC) population for genetic analysis and improvement of cowpea (Vigna unguiculata L. Walp.)

Multi‐parent advanced generation inter‐cross (MAGIC) populations are an emerging type of resource for dissecting the genetic structure of traits and improving breeding populations. We developed a MAGIC population for cowpea (Vigna unguiculata L. Walp.) from eight founder parents. These founders were genetically diverse and carried many abiotic and biotic stress resistance, seed quality and agronomic traits relevant to cowpea improvement in the United States and sub‐Saharan Africa, where cowpea is vitally important in the human diet and local economies. The eight parents were inter‐crossed using structured matings to ensure that the population would have balanced representation from each parent, followed by single‐seed descent, resulting in 305 F₈ recombinant inbred lines each carrying a mosaic of genome blocks contributed by all founders. This was confirmed by single nucleotide polymorphism genotyping with the Illumina Cowpea Consortium Array. These lines were on average 99.74% homozygous but also diverse in agronomic traits across environments. Quantitative trait loci (QTLs) were identified for several parental traits. Loci with major effects on photoperiod sensitivity and seed size were also verified by biparental genetic mapping. The recombination events were concentrated in telomeric regions. Due to its broad genetic base, this cowpea MAGIC population promises breakthroughs in genetic gain, QTL and gene discovery, enhancement of breeding populations and, for some lines, direct releases as new varieties.     

Connected populations for detecting quantitative trait loci and testing for epistasis: an application in maize

Quantitative trait loci (QTL) detection experiments have often been restricted to large biallelic populations. Use of connected multiparental crosses has been proposed to increase the genetic variability addressed and to test for epistatic interactions between QTL and the genetic background. We present here the results of a QTL detection performed on six connected F₂ populations of 150 F_2:3 families each, derived from four maize inbreds and evaluated for three traits of agronomic interest. The QTL detection was carried out by composite interval mapping on each population separately, then on the global design either by taking into account the connections between populations or not. Epistatic interactions between loci and with the genetic background were tested. Taking into account the connections between populations increased the number of QTL detected and the accuracy of QTL position estimates. We detected many epistatic interactions, particularly for grain yield QTL (R ² increase of 9.6%). Use of connections for the QTL detection also allowed a global ranking of alleles at each QTL. Allelic relationships and epistasis both contribute to the lack of consistency for QTL positions observed among populations, in addition to the limited power of the tests. The potential benefit of assembling favorable alleles by marker-assisted selection are discussed.     

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

Key Message

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

Abstract

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

Composite interval mapping and mixed models reveal QTL associated with performance and carcass traits on chicken chromosomes 1, 3, and 4

Interval mapping (IM) implemented in QTL Express or GridQTL is widely used, but presents some limitations, such as restriction to a fixed model, risk of mapping two QTL when there may be only one and no discrimination of two or more QTL using both cofactors located on the same and other chromosomes. These limitations were overcome with composite interval mapping (CIM). We reported QTL associated with performance and carcass traits on chicken chromosomes 1, 3, and 4 through implementation of CIM and analysis of phenotypic data using mixed models. Thirty-four microsatellite markers were used to genotype 360 F₂ chickens from crosses between males from a layer line and females from a broiler line. Sixteen QTL were mapped using CIM and 14 QTL with IM. Furthermore, of those 30 QTL, six were mapped only when CIM was used: for body weight at 35 days (first and third peaks on GGA4), body weight at 41 days (GGA1B and second peak on GGA4), and weights of back and legs (both on GGA4). Three new regions had evidence for QTL presence: one on GGA1B associated with feed intake 35–41 d at 404 cM (LEI0107-ADL0183) and two on GGA4 associated with weight of back at 163 cM (LEI0076-MCW0240) and weight gain 35–41 d, feed efficiency 35–41 d and weight of legs at 241 cM (LEI0085-MCW0174). We dissected one more linked QTL on GGA4, where three QTL for BW35 and two QTL for BW41 were mapped. Therefore, these new regions mapped here need further investigations using high-density SNP to confirm these QTL and identify candidate genes associated with those traits.     

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.     

Genetic analysis of rice CMS-WA fertility restoration based on QTL mapping

 The inheritance of fertility restoration of rice cytoplasmic male sterility of the wild abortive type was studied by means of QTL mapping. The two segregating populations examined showed high frequencies of highly sterile and highly fertile progenies, but a low frequency of partially sterile and partially fertile progenies. The distributions suggested that fertility restoration was mainly controlled by major genes. Based on a linkage map constructed with 57 RFLP and 61 AFLP markers on a B₁F₁ population, composite interval mapping (CIM) revealed that the fertility was restored by the additive effects of two restorer loci located on chromosome 10. One QTL, tightly linked to RFLP marker C1361 in the middle of the long arm of chromosome 10, explained 71.5% of the phenotypic variance. The second QTL was located between RFLP markers R2309 and RG257 on the short arm and explained 27.3% of the phenotypic variance. Similar results were obtained using the simple interval mapping (SIM) methods. Recived: 8 January 1998/Accepted: 22 April 1998     

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.