Main-effect Quantitative Trait Loci Research Articles

Mapping quantitative trait loci for egg production traits in an F2 intercross of Oh-Shamo and White Leghorn chickens

We performed quantitative trait locus (QTL) analyses for egg production traits, including age at first egg (AFE) and egg production rates (EPR) measured every 4 weeks from 22 to 62 weeks of hen age, in a population of 421 F(2) hens derived from an intercross between the Oh-Shamo (Japanese Large Game) and White Leghorn breeds of chickens. Simple interval mapping revealed a main-effect QTL for AFE on chromosome 1 and four main-effect QTL for EPR on chromosomes 1 and 11 (three on chromosome 1 and one on chromosome 11) at the genome-wide 5% levels. Among the three EPR QTL on chromosome 1, two were identified at the early stage of egg laying (26-34 weeks of hen age) and the remaining one was discovered at the late stage (54-58 weeks). The alleles at the two EPR QTL derived from the Oh-Shamo breed unexpectedly increased the trait values, irrespective of the Oh-Shamo being inferior to the White Leghorn in the trait. This suggests that the Oh-Shamo, one of the indigenous Japanese breeds, is an untapped resource that is important for further improvement of current elite commercial laying chickens. In addition, six epistatic QTL were identified on chromosomes 2, 4, 7, 8, 17 and 19, where none of the above main-effect QTL were located. This is the first example of detection of epistatic QTL affecting egg production traits. The main and epistatic QTL identified accounted for 4-8% of the phenotypic variance. The total contribution of all QTL detected for each trait to the phenotypic and genetic variances ranged from 4.1% to 16.9% and from 11.5% to 58.5%, respectively.

Variation in feed quality traits for beef cattle in Steptoe × Morex barley population

Barley is a major feed source for livestock in the western regions of North America. Feed quality of beef cattle has been neglected as a selection criterion because of lack of understanding of the feed characteristics that could be responsive to selection and would improve feedlot performance. A Steptoe × Morex population was planted in irrigated and rain-fed environments, and collected data were used to evaluate the genetic variation in dry matter and starch digestibilities, acid detergent fiber, protein and starch contents, and to map quantitative trait loci controlling the variation in these traits. Transgressive genotypes indicate the wide genetic variation of these traits. High heritability estimates for these traits suggest that early selection for these traits during breeding would be achievable. A total of 32 main effect QTL and five epistatic QTL were identified which conditioned feed traits on different barley chromosomes. QTL for acid detergent fiber and starch contents overlapped on chromosome 6H at the Nar7 locus. Tight negative correlation between the two traits suggest the usability of Nar7 as anchor marker in marker-assisted selection programs to develop barley with low acid detergent fiber and high starch content.

Epistatic Association Mapping in Homozygous Crop Cultivars

The genetic dissection of complex traits plays a crucial role in crop breeding. However, genetic analysis and crop breeding have heretofore been performed separately. In this study, we designed a new approach that integrates epistatic association analysis in crop cultivars with breeding by design. First, we proposed an epistatic association mapping (EAM) approach in homozygous crop cultivars. The phenotypic values of complex traits, along with molecular marker information, were used to perform EAM. In our EAM, all the main-effect quantitative trait loci (QTLs), environmental effects, QTL-by-environment interactions and QTL-by-QTL interactions were included in a full model and estimated by empirical Bayes approach. A series of Monte Carlo simulations was performed to confirm the reliability of the new method. Next, the information from all detected QTLs was used to mine novel alleles for each locus and to design elite cross combination. Finally, the new approach was adopted to dissect the genetic basis of seed length in 215 soybean cultivars obtained, by stratified random sampling, from 6 geographic ecotypes in China. As a result, 19 main-effect QTLs and 3 epistatic QTLs were identified, more than 10 novel alleles were mined and 3 elite parental combinations, such as Daqingdou and Zhengzhou790034, were predicted.

QTL Analysis on Yield and Its Components in Recombinant Inbred Lines of Upland Cotton

To facilitate the application of simple sequence repeat (SSR) markers in molecular marker-assisted selection (MAS) of upland cotton ( Gossypium hirsutum L.), a linkage map harboring 192 SSRs was constructed from the CRI-G6 population (Acala 1517 × Dezhou 047) using mixed-model-based composite interval mapping (MCIM) method. This map was used to detect quantitative trait loci (QTLs) associated with cotton yield traits in 3 environments. In the separate analysis, 24 main-effect QTLs were identified including each one stable QTL for seed yield, lint yield, lint percentage, seed index, and boll number per plant. In the joint analysis, 14 main-effect QTLs and 20 pairs of additive–additive epistatic QTLs were detected including 6 main-effect and 7 pairs of epistatic QTLs with environmental interactions. Eight main-effect QTLs were mapped in the same regions using both methods. Some main-effect QTLs with stability in different growing environments had phenotypic contributions larger than 10%, and these QTLs are candidate genes/loci in MAS of upland cotton.

A new mapping method for quantitative trait loci of silkworm

BackgroundSilkworm is the basis of sericultural industry and the model organism in insect genetics study. Mapping quantitative trait loci (QTLs) underlying economically important traits of silkworm is of high significance for promoting the silkworm molecular breeding and advancing our knowledge on genetic architecture of the Lepidoptera. Yet, the currently used mapping methods are not well suitable for silkworm, because of ignoring the recombination difference in meiosis between two sexes.ResultsA mixed linear model including QTL main effects, epistatic effects, and QTL × sex interaction effects was proposed for mapping QTLs in an F2 population of silkworm. The number and positions of QTLs were determined by F-test and model selection. The Markov chain Monte Carlo (MCMC) algorithm was employed to estimate and test genetic effects of QTLs and QTL × sex interaction effects. The effectiveness of the model and statistical method was validated by a series of simulations. The results indicate that when markers are distributed sparsely on chromosomes, our method will substantially improve estimation accuracy as compared to the normal chiasmate F2 model. We also found that a sample size of hundreds was sufficiently large to unbiasedly estimate all the four types of epistases (i.e., additive-additive, additive-dominance, dominance-additive, and dominance-dominance) when the paired QTLs reside on different chromosomes in silkworm.ConclusionThe proposed method could accurately estimate not only the additive, dominance and digenic epistatic effects but also their interaction effects with sex, correcting the potential bias and precision loss in the current QTL mapping practice of silkworm and thus representing an important addition to the arsenal of QTL mapping tools.

A functional-structural model of rice linking quantitative genetic information with morphological development and physiological processes.

Although quantitative trait loci (QTL) analysis of yield-related traits for rice has developed rapidly, crop models using genotype information have been proposed only relatively recently. As a first step towards a generic genotype-phenotype model, we present here a three-dimensional functional-structural plant model (FSPM) of rice, in which some model parameters are controlled by functions describing the effect of main-effect and epistatic QTLs. The model simulates the growth and development of rice based on selected ecophysiological processes, such as photosynthesis (source process) and organ formation, growth and extension (sink processes). It was devised using GroIMP, an interactive modelling platform based on the Relational Growth Grammar formalism (RGG). RGG rules describe the course of organ initiation and extension resulting in final morphology. The link between the phenotype (as represented by the simulated rice plant) and the QTL genotype was implemented via a data interface between the rice FSPM and the QTLNetwork software, which computes predictions of QTLs from map data and measured trait data. Using plant height and grain yield, it is shown how QTL information for a given trait can be used in an FSPM, computing and visualizing the phenotypes of different lines of a mapping population. Furthermore, we demonstrate how modification of a particular trait feeds back on the entire plant phenotype via the physiological processes considered. We linked a rice FSPM to a quantitative genetic model, thereby employing QTL information to refine model parameters and visualizing the dynamics of development of the entire phenotype as a result of ecophysiological processes, including the trait(s) for which genetic information is available. Possibilities for further extension of the model, for example for the purposes of ideotype breeding, are discussed.

Molecular mapping of quantitative trait loci for kernel morphology traits in a non-1BL.1RS×1BL.1RS wheat cross

The improvement of kernel morphology traits is an important goal in common wheat (Triticum aestivum L.) breeding programs because of their close relationship with grain yield and milling quality. The aim of this study was to map quantitative trait loci (QTL) for kernel morphology traits using 240 recombinant inbred lines derived from a cross between the non-1BL.1RS translocation cv. PH 82-2 and the 1BL.1RS translocation cv. Neixiang 188, grown in six environments in China. Inclusive composite interval mapping identified 71 main-effect QTL on 16 chromosomes for seven kernel morphology traits measured by digital imaging, viz. kernel length, width, perimeter, area, shape factor, factor form-density and width/length ratio. Each of these loci explained from 2.6 to 28.2% of the phenotypic variation. Eight QTL clusters conferring the largest effects on kernel weight and kernel morphology traits were detected on chromosomes 1BL.1RS (2), 2A, 4A, 4B, 6B, 6D and 7A. Fourteen epistatic QTL were identified for all kernel morphology traits except kernel width/length ratio, involving 24 main-effect QTL distributed on 13 chromosomes, and explaining 2.5–8.3% of the phenotypic variance. Five loci, viz. Sec-1 on 1BL.1RS, Glu-B1 on 1BL, Xcfe53 on 2A, Xwmc238 on 4B, and Xbarc174 on 7A, were detected consistently across environments, and their linked DNA markers may be used for marker-assisted selection in breeding for improved wheat kernel traits and grain yield.

Identification of several small main-effect QTLs and a large number of epistatic QTLs for drought tolerance related traits in groundnut (Arachis hypogaea L.)

Cultivated groundnut or peanut (Arachis hypogaea L.), an allotetraploid (2n = 4x = 40), is a self pollinated and widely grown crop in the semi-arid regions of the world. Improvement of drought tolerance is an important area of research for groundnut breeding programmes. Therefore, for the identification of candidate QTLs for drought tolerance, a comprehensive and refined genetic map containing 191 SSR loci based on a single mapping population (TAG 24 × ICGV 86031), segregating for drought and surrogate traits was developed. Genotyping data and phenotyping data collected for more than ten drought related traits in 2–3 seasons were analyzed in detail for identification of main effect QTLs (M-QTLs) and epistatic QTLs (E-QTLs) using QTL Cartographer, QTLNetwork and Genotype Matrix Mapping (GMM) programmes. A total of 105 M-QTLs with 3.48–33.36% phenotypic variation explained (PVE) were identified using QTL Cartographer, while only 65 M-QTLs with 1.3–15.01% PVE were identified using QTLNetwork. A total of 53 M-QTLs were such which were identified using both programmes. On the other hand, GMM identified 186 (8.54–44.72% PVE) and 63 (7.11–21.13% PVE), three and two loci interactions, whereas only 8 E-QTL interactions with 1.7–8.34% PVE were identified through QTLNetwork. Interestingly a number of co-localized QTLs controlling 2–9 traits were also identified. The identification of few major, many minor M-QTLs and QTL × QTL interactions during the present study confirmed the complex and quantitative nature of drought tolerance in groundnut. This study suggests deployment of modern approaches like marker-assisted recurrent selection or genomic selection instead of marker-assisted backcrossing approach for breeding for drought tolerance in groundnut.Electronic supplementary materialThe online version of this article (doi:10.1007/s00122-010-1517-0) contains supplementary material, which is available to authorized users.

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.

QTL mapping for quantities of protein fractions in bread wheat (Triticum aestivum L.)

One of the key targets of breeding programs in bread wheat is to improve the end-use quality. The relationships between quantities of protein fractions and dough rheological characters have been well established, but there is little information on the genetic control of quantities of protein fractions. Two hundred and forty F(6) recombinant inbred lines derived from a cross between two Chinese wheat cultivars, PH82-2 and Neixiang 188, were sown at Jiaozuo in Henan province in the 2005-2006 and 2006-2007 cropping seasons, and inclusive composite interval mapping was used to dissect main effect quantitative trait loci (M-QTLs) and digenic epistatic QTLs (E-QTLs) for quantities of protein fractions. A total of 55 M-QTLs and 77 pairs of E-QTLs affecting the quantities of protein fractions including GLU-A1 (QGA1), GLU-B1 (QGB1), GLU-D1 (QGD1), HMW-GS (QHMW), GLU-A3 (QGA3), GLU-B3 (QGB3), LMW-GS (QLMW), glutenin (QGLU) and the ratio of the quantity of glutenin to those of gliadin were identified, with M-QTLs contributing 39.3-95.6% of the phenotypic variance explained (PVE), and E-QTLs accounting for 1.4-33.5% of the PVE. Among the M-QTLs, 33 were consistent in two seasons and in the mean value of two seasons with similar effects in both magnitude and direction, including major genes on HMW and LMW glutenin loci linked to Sec1 and Glu-B1c, Glu-D1d, Glu-A3a, and grain hardness locus Ha, indicating that these genes were the most important determinants of gluten strength, and they might have significant effects on dough properties not only through effects on allelic composition, but also by influencing quantities of protein fractions. The effects of E-QTLs were more influenced by environments, compared with those of M-QTLs, with only two pairs of E-QTLs consistent in two seasons and in the mean value of two seasons. The M-QTLs were detected in 12 marker intervals, all of which involved E-QTLs on quantities of protein fractions, whereas only 40 of 77 pairs of E-QTLs involved intervals in which M-QTLs were detected. The results indicated that besides main effects, epistatic effects were also important factors in determining quantities of protein fractions in wheat.

Association mapping for quality traits in soft winter wheat

Improvement of end-use quality in bread wheat (Triticum aestivum L.) depends on a thorough understanding of the genetic basis of important quality traits. The main goal of our study was to investigate the genetic basis of 1,000-kernel weight, protein content, sedimentation volume, test weight, and starch concentration using an association mapping approach. We fingerprinted 207 diverse European elite soft winter wheat lines with 115 SSR markers and evaluated the genotypes in multi-environment trials. The principal coordinate analysis revealed absence of a clear population but presence of a family structure. Therefore, we used linear mixed models and marker-based kinship matrices to correct for family structure. In genome-wide scans, we detected main effect QTL for all five traits. In contrast, epistatic QTL were only observed for sedimentation volume and test weight explaining a small proportion of the genotypic variation. Consequently, our findings suggested that integrating epistasis in marker-assisted breeding will not lead to substantially increased selection gain for quality traits in soft winter wheat.

Comparison and analysis of main effects, epistatic effects, and QTL × environment interactions of QTLs for agronomic traits using DH and RILs populations in rice

Two genetic linkage maps based on doubled haploid (DH) and recombinant inbred lines (RILs) populations, derived from the same indica-japonica cross ‘Samgang × Nagdong’, were constructed to analyze the quantitative trait loci (QTLs) affecting agronomic traits in rice. The segregations of agronomic traits in RILs population showed larger variations than those in DH population. A total of 10 and 12 QTLs were identified on six chromosomes using DH population and seven chromosomes using RILs population, respectively. Three stable QTLs including pl9.1, ph1.1, and gwp11.1 were detected through different years. The percentages of phenotypic variation explained by individual QTLs ranged from 8 to 18% in the DH population and 9 to 33% in the RILs population. Twenty-three epistatic QTLs were identified in the DH population, while 21 epistatic QTLs were detected in the RILs population. Epistatic interactions played an important role in controlling the agronomic traits genetically. Four significant main-effect QTLs were involved in the digenic interactions. Significant interactions between QTLs and environments (QE) were identified in two populations. The QTLs affecting grain weight per panicle (GWP) were more sensitive to the environmental changes. The comparison and QTLs analysis between two populations across different years should help rice breeders to comprehend the genetic mechanisms of quantitative traits and improve breeding programs in marker-assisted selection (MAS).

Genetic epistasis analysis of 10 peroxisome proliferator-activated receptor γ-correlated genes in broiler lines divergently selected for abdominal fat content

Chicken peroxisome proliferator-activated receptor γ (PPARγ), which is highly expressed in adipose tissues, is a key factor in fat accumulation in the abdominal fat pad. In this study, association and pairwise epistasis analyses were performed for all the polymorphisms detected in PPARγ and for 9 genes from PPARγ-correlated lipid metabolic pathways for abdominal fat weight (AFW) in 10th-generation populations of Northeast Agricultural University broiler lines divergently selected for abdominal fat content. Epistatic networks were then reconstructed with the identified epistatic effects. Single-marker association analyses showed that 5 of the 20 screened polymorphisms were significantly associated with AFW (P < 0.05), and CCAAT/enhancer-binding protein α (C/EBPα) c.552G>A was 1 of the 5 significant loci. Pairwise interaction analyses showed that 15 pairs of polymorphisms reached a significance level of P < 2.64 × 10(-4) (adjusted by Bonferroni correction) in the lean line, 41 pairs reached significance in the fat line, and 7 pairs reached significance in both lines. Interestingly, many other loci interacted with C/EBPα c.552G>A in both lines. In epistatic network analyses, C/EBPα c.552G>A seemed to behave as a hub for the epistatic network in both lines. All these results revealed that the genetic architecture of C/EBPα c.552G>A for AFW seemed to be an apparent individual main-effect QTL but that it could be dissected into a genetic epistatic network. Our results suggest that C/EBPα c.552G>A might be the most important locus contributing to phenotypic variation in AFW among all the polymorphisms detected in this study.

Mapping quantitative trait loci for seed size traits in soybean (Glycine max L. Merr.)

Seed size traits in soybean--length, width and thickness--and their corresponding ratios--length-to-width, length-to-thickness and width-to-thickness--play a crucial role in determining seed appearance, quality and yield. In this study, an attempt was made to detect quantitative trait loci (QTL) for the aforementioned seed size traits in F(2:3), F(2:4) and F(2:5) populations from the direct and reciprocal crosses of Lishuizhongzihuang with Nannong 493-1, using multi-QTL joint analysis (MJA) along with composite interval mapping (CIM). A total of 121 main-effect QTL (M-QTL), six environmental effects, eight environment-by-QTL interactions, five cytoplasmic effects and 92 cytoplasm-by-QTL interactions were detected. Fifty-two common M-QTL across MJA and CIM, 21 common M-QTL in more than two populations and 5 M-QTL in all three populations showed the stability of the results. Five M-QTL had higher heritability, greater than 20%. In addition, 28 cytoplasm-by-QTL and 4 environment-by-QTL interactions were confirmed by CIM. Most M-QTL were clustered in eight chromosomal regions. Our results provide a good foundation for fine mapping, cloning and designed molecular breeding of favorable genes related to soybean seed size traits.

Gene actions at loci underlying several quantitative traits in two elite rice hybrids

To understand the gene activities controlling nine important agronomic quantitative traits in rice, we applied a North Carolina design 3 (NC III design) analysis to recombinant inbred lines (RILs) in highly heterotic inter- (IJ) and intra-subspecific (II) hybrids by performing the following tasks: (1) investigating the relative contribution of additive, dominant, and epistatic effects for performance traits by generation means analysis and variance component estimates; (2) detecting the number, genomic positions, and genetic effects of QTL for phenotypic traits; and (3) characterizing their mode of gene action. Under an F∞-metric, generation means analysis and variance components estimates revealed that epistatic effects prevailed for the majority of traits in the two hybrids. QTL analysis identified 48 and 66 main-effect QTL (M-QTL) for nine traits in IJ and II hybrids, respectively. In IJ hybrids, 20 QTL (41.7%) showed an additive effect of gene actions, 20 (41.7%) showed partial-to-complete dominance, and 8 (16.7%) showed overdominance. In II hybrids, 34 QTL (51.5%) exhibited additive effects, 14 (21.2%) partial-to-complete dominance, and 18 (27.3%) overdominance. There were 153 digenic interactions (E-QTL) in the IJ hybrid and 252 in the II hybrid. These results suggest that additive effects, dominance, overdominance, and particularly epistasis attribute to the genetic basis of the expression of traits in the two hybrids. Additionally, we determined that the genetic causes of phenotypic traits and their heterosis are different. In the plants we studied, the phenotypic traits investigated and their heterosis were conditioned by different M-QTL and E-QTL, respectively, and were mainly due to non-allelic interactions (epistasis).

Identification and characterization of quantitative trait loci for grain yield and its components under different nitrogen fertilization levels in rice (Oryza sativa L.)

A set of rice (Oryza sativa L.) recombinant inbred lines from a cross between Zhenshan 97 (indica) and HR5 (indica) was planted for four different growing seasons in two locations at three nitrogen (N) fertilization levels (N300, 300 kg urea/ha; N150, 150 kg urea/ha; and N0, 0 kg urea/ha). Grain yield and its components were evaluated, including grain yield per plant (GYPP), panicle number per plant (PNPP), grain number per panicle (GNPP), filled grains per panicle (FGPP), spikelet fertility percentage (SFP) and 100-grain weight (HGW). Correlation and path analysis indicated that SFP had the greatest contribution to GYPP at the N300 and N150 levels, but FGPP contributed the most to GYPP at the N0 level. Quantitative trait loci (QTL) were mapped based on a mixed linear model; genetic components (main effects, epistatic effects and QTL-by-environment interactions) were estimated separately. Six to 15 QTL with main effects were detected for each trait except SFP. Clusters of main-effect QTL associated with PNPP, GNPP, SFP and HGW were observed in regions on chromosomes 1, 2, 3, 5, 7 and 10. The main-effect QTL (qGYPP-4b and qGNPP-12) were only detected at the N0 level and explained 10.9 and 10.2% of the total phenotypic variation, respectively. A total of 33 digenic interactions among grain yield and its components were also identified. The identification of genomic regions associated with yield and its components at different nitrogen levels will be useful in marker-assisted selection for improving the nitrogen use efficiency of rice.

Mapping QTL main and interaction influences on milling quality in elite US rice germplasm

Rice (Oryza sativa L.) head-rice yield (HR) is a key export and domestic quality trait whose genetic control is poorly understood. With the goal of identifying genomic regions influencing HR, quantitative-trait-locus (QTL) mapping was carried out for quality-related traits in recombinant inbred lines (RILs) derived from crosses of common parent Cypress, a high-HR US japonica cultivar, with RT0034, a low-HR indica line (129 RILs) and LaGrue, a low-HR japonica cultivar (298 RILs), grown in two US locations in 2005-2007. Early heading increased HR in the Louisiana (LA) but not the Arkansas (AR) location. Fitting QTL-mapping models to separate QTL main and QTL × environment interaction (QEI) effects and identify epistatic interactions revealed six main-effect HR QTLs in the two crosses, at four of which Cypress contributed the increasing allele. Multi-QTL models accounted for 0.36 of genetic and 0.21 of genetic × environment interaction of HR in MY1, and corresponding proportions of 0.25 and 0.37 in MY2. The greater HR advantage of Cypress in LA than in AR corresponded to a genomewide pattern of opposition of HR-increasing QTL effects by AR-specific effects, suggesting a selection strategy for improving this cultivar for AR. Treating year-location combinations as independent environments resulted in underestimation of QEI effects, evidently owing to lower variation among years within location than between location. Identification of robust HR QTLs in elite long-grain germplasm is suggested to require more detailed attention to the interaction of plant and grain development parameters with environmental conditions than has been given to date.

Intricate environment-modulated genetic networks control isoflavone accumulation in soybean seeds

BackgroundSoybean (Glycine max [L] Merr.) seed isoflavones have long been considered a desirable trait to target in selection programs for their contribution to human health and plant defense systems. However, attempts to modify seed isoflavone contents have not always produced the expected results because their genetic basis is polygenic and complex. Undoubtedly, the extreme variability that seed isoflavones display over environments has obscured our understanding of the genetics involved.ResultsIn this study, a mapping population of RILs with three replicates was analyzed in four different environments (two locations over two years). We found a total of thirty-five main-effect genomic regions and many epistatic interactions controlling genistein, daidzein, glycitein and total isoflavone accumulation in seeds. The use of distinct environments permitted detection of a great number of environment-modulated and minor-effect QTL. Our findings suggest that isoflavone seed concentration is controlled by a complex network of multiple minor-effect loci interconnected by a dense epistatic map of interactions. The magnitude and significance of the effects of many of the nodes and connections in the network varied depending on the environmental conditions. In an attempt to unravel the genetic architecture underlying the traits studied, we searched on a genome-wide scale for genomic regions homologous to the most important identified isoflavone biosynthetic genes. We identified putative candidate genes for several of the main-effect and epistatic QTL and for QTL reported by other groups.ConclusionsTo better understand the underlying genetics of isoflavone accumulation, we performed a large scale analysis to identify genomic regions associated with isoflavone concentrations. We not only identified a number of such regions, but also found that they can interact with one another and with the environment to form a complex adaptable network controlling seed isoflavone levels. We also found putative candidate genes in several regions and overall we advanced the knowledge of the genetics underlying isoflavone synthesis.

Keep on growing under drought: genetic and developmental bases of the response of rosette area using a recombinant inbred line population

Variation in leaf development caused by water deficit was analysed in 120 recombinant inbred lines derived from two Arabidopsis thaliana accessions, Ler and An-1. Main effect quantitative trait loci (QTLs) and QTLs in epistatic interactions were mapped for the responses of rosette area, leaf number and leaf 6 area to water deficit. An epistatic interaction between two QTLs affected the response of whole rosette area and individual leaf area but only with effects in well-watered condition. A second epistatic interaction between two QTLs controlled the response of rosette area and leaf number with specific effects in the water deficit condition. These effects were validated by generating and phenotyping new appropriate lines. Accordingly, a low reduction of rosette area was observed for lines with a specific allelic combination at the two interacting QTLs. This low reduction was accompanied by an increase in leaf number with a lengthening of the vegetative phase and a low reduction in individual leaf area with low reductions in epidermal cell area and number. Statistical analyses suggested that responses of epidermal cell area and number to water deficit in individual leaves were partly caused by delay in flowering time and reduction in leaf emergence rate, respectively.

Identification of main effects, epistatic effects and their environmental interactions of QTLs for yield traits in rice

Quantitative trait loci (QTLs) controlling yield and yield components were identified by using a doubled haploid (DH) population of 120 lines from a sub-specific cross between ‘Samgang’ (Indica) and ‘Nagdong’ (Japonica). Main effects, epistatic effects, their environment interactions of QTLs were analyzed via mixed linear model approach across different environments. A total of 17 putative QTLs were identified on 8 chromosomes and five QTLs were detected over two years. 7 QTLs of main effects and 23 epistatic interactions were observed for five traits. Epistatic interactions played an important role in controlling the expression of yield related traits. The epistatic effects explained higher percentages of phenotype variation for panicles per plant, seed set percentage and yield. Significant QTL×environment (QE) interactions effects were identified for all traits, including 5 main effect QTLs. However, the present study failed to identify the significant interactions between epistatic loci containing main effect QTLs and the environment. The information provided in the present study could be used in the marker-assisted selection to enhance selection efficiency and to improve yield in rice.