Grain of environment explains variation in the strength of genotype × environment interaction

Theory predicts that genetic variation in phenotypic plasticity (genotype × environment interaction or G × E) should be eroded by selection acting across environments. However, it appears that G × E is often maintained under selection, although not universally. This variation in the presence and strength of G × E requires explanation. Here I ask whether the explanation may lie in the grain of the environment at which G × E is expressed. The grain (or grain size) of the environment refers to the scale of environmental heterogeneity relative to generation time – that is, relative to the window of operation of selection – with higher rates of heterogeneity occurring in finer‐grained environments. The hypothesis that the grain of the environment explains variation in the expression of G × E encapsulates variation in the power of selection to shape reaction norms: selection should be able to erode G × E in fine‐grained environments but lose its power as the grain becomes coarser. I survey studies of G × E in sexual traits and demonstrate that the strength of G × E varies with the grain of the environment across which it is expressed, with G × E being stronger in coarser‐grained environments. This result elucidates when G × E is most likely to be sustained in the reaction norms of fitness‐related traits and when its evolutionary consequences will be most pronounced.     

Environment characterisation for the interpretation of environmental effect and genotype × environment interaction

Increasing attention is being paid to environment characterisation as a means of identifying the environmental factors determining grain protein content (GPC) in durum wheat. New insights in crop physiology and agronomy have led to the development of crop simulation models. Those models can reconstruct plant development for past cropping seasons. One major advantage of these models is that they can also indicate the intensity of limiting factors affecting plants during particular developmental stages. The main environmental factors determining GPC in durum wheat can be investigated by introducing the intensity of limiting factors into genotype × environment (G×E) models. In our case, limiting factors corresponding to water deficit and nitrogen availability were calculated for the development period between booting and heading. These variables were then introduced into a clustering model. This model is an extension of factorial regression applied to discrete environment and genotypic variables. This procedure effectively described the environment main effect: around 30.9% of the sum of squares of the environment main effect was accounted for, using less than 33% of the degrees of freedom. It also partially accounted for G×E interaction. Our methodology, coupling the use of crop simulation and G×E analysis models, is of potential value for improving our understanding of the main development stages and identification of environmental limiting factors for the development of GPC.     

Mapping QTL associated with photoperiod sensitivity and assessing the importance of QTL×environment interaction for flowering time in maize

Background

An understanding of the genetic determinism of photoperiod response of flowering is a prerequisite for the successful exchange of germplasm across different latitudes. In order to contribute to resolve the genetic basis of photoperiod sensitivity in maize, a set of 201 recombinant inbred lines (RIL), derived from a temperate and tropical inbred line cross were evaluated in 5 field trials spread in short- and long-day environments.

Methodology/Principal Findings

Firstly, QTL analyses for flowering time and photoperiod sensitivity in maize were conducted in individual photoperiod environments separately, and then, the total genetic effect was partitioned into additive effect (A) and additive-by-environment interaction effect (AE) by using a mixed-model-based composite interval mapping (MCIM) method.

Conclusions/Significance

Seven putative QTL were found associated with DPS thermal time based on the data estimated in individual environments. Nine putative QTL were found associated with DPS thermal time across environments and six of them showed significant QTL×enviroment (QE) interactions. Three QTL for photoperiod sensitivity were identified on chromosome 4, 9 and 10, which had the similar position to QTL for DPS thermal time in the two long-day environment. The major photoperiod sensitive loci qDPS10 responded to both short and long-day photoperiod environments and had opposite effects in different photoperiod environment. The QTL qDPS3, which had the greatest additive effect exclusively in the short-day environment, were photoperiod independent and should be classified in autonomous promotion pathway.     

Genomic reaction norm models exploiting genotype × environment interaction on sexual precocity indicator traits in Nellore cattle

Brazilian beef cattle are raised predominantly on pasture in a wide range of environments. In this scenario, genotype by environment (G×E) interaction is an important source of phenotypic variation in the reproductive traits. Hence, the evaluation of G×E interactions for heifer’s early pregnancy (HP) and scrotal circumference (SC) traits in Nellore cattle, belonging to three breeding programs, was carried out to determine the animal’s sensitivity to the environmental conditions (EC). The dataset consisted of 85 874 records for HP and 151 553 records for SC, from which 1800 heifers and 3343 young bulls were genotyped with the BovineHD BeadChip. Genotypic information for 826 sires was also used in the analyses. EC levels were based on the contemporary group solutions for yearling body weight. Linear reaction norm models (RNM), using pedigree information (RNM_A) or pedigree and genomic information (RNM_H), were used to infer G×E interactions. Two validation schemes were used to assess the predictive ability, with the following training populations: (a) forward scheme—dataset was split based on year of birth from 2008 for HP and from 2011 for SC; and (b) environment-specific scheme—low EC (−3.0 and −1.5) and high EC (1.5 and 3.0). The inclusion of the H matrix in RNM increased the genetic variance of the intercept and slope by 18.55 and 23.00% on average respectively, and provided genetic parameter estimates that were more accurate than those considering pedigree only. The same trend was observed for heritability estimates, which were 0.28–0.56 for SC and 0.26–0.49 for HP, using RNM_H, and 0.26–0.52 for SC and 0.22–0.45 for HP, using RNM_A. The lowest correlation observed between unfavorable (−3.0) and favorable (3.0) EC levels were 0.30 for HP and −0.12 for SC, indicating the presence of G×E interaction. The G×E interaction effect implied differences in animals’ genetic merit and re-ranking of animals on different environmental conditions. SNP marker–environment interaction was detected for Nellore sexual precocity indicator traits with changes in effect and variance across EC levels. The RNM_H captured G×E interaction effects better than RNM_A and improved the predictive ability by around 14.04% for SC and 21.31% for HP. Using the forward scheme increased the overall predictive ability for SC (20.55%) and HP (11.06%) compared with the environment-specific scheme. The results suggest that the inclusion of genomic information combined with the pedigree to assess the G×E interaction leads to more accurate variance components and genetic parameter estimates.     

Detection of QTLs for genotype × environment interactions in tomato seeds and seedlings

Seed quality and seedling establishment are the most important factors affecting successful crop development. They depend on the genetic background and are acquired during seed maturation and therefor, affected by the maternal environment under which the seeds develop. There is little knowledge about the genetic and environmental factors that affect seed quality and seedling establishment. The aim of this study is to identify the loci and possible molecular mechanisms involved in acquisition of seed quality and how these are controlled by adverse maternal conditions. For this, we used a tomato recombinant inbred line (RIL) population consisting of 100 lines which were grown under two different nutritional environmental conditions, high phosphate and low nitrate. Most of the seed germination traits such as maximum germination percentage (G_max), germination rate (t₅₀) and uniformity (U₈₄₁₆) showed ample variation between genotypes and under different germination conditions. This phenotypic variation leads to identification of quantitative trait loci (QTLs) which were dependent on genetic factors, but also on the interaction with the maternal environment (QTL × E). Further studies of these QTLs may ultimately help to predict the effect of different maternal environmental conditions on seed quality and seedling establishment which will be very useful to improve the production of high-performance seeds.     

Assessing the importance of genotype × environment interaction for root traits in rice using a mapping population II: conventional QTL analysis

Modifying plant root systems is considered a means of crop improvement targeted to low-resource environments, particularly low nutrient and drought-prone agriculture. The identification of quantitative trait loci (QTLs) for root traits has stimulated marker-assisted breeding to this end, but different QTLs have been detected in different populations of the same species, and importantly, in the same population when grown in different experimental environments. The presence of QTL × environment interaction is implicated, and this must be characterised if the utility of the target QTLs is to be realised. Previous attempts to do this suffer from a lack of control over replicate environments and inadequate statistical rigour. The Bala × Azucena mapping population was grown in two replicate experiments of four treatment environments, a control, a low light, a low soil nitrogen and a low soil water treatment. After a 4 weeks growth, maximum root length, maximum root thickness, root mass below 50 cm, total plant dry mass, % root mass and shoot length were measured. A summary of the overall results is presented in an accompanying paper. Here, QTL analysis by composite interval mapping is presented. A total of 145 QTLs were detected, mapping to 37 discrete loci on all chromosomes. Superficial evidence of QTL × E (great difference in LOD score) was tested by single-marker analysis which confirmed QTL × E for five loci representing only five individual trait-loci interactions. Some loci appeared to be stable across environments. Some QTLs were clearly more or less active under low light, low nitrogen or drought. A few notable loci on chromosomes 1, 2, 3, 5, 7 and 9 are briefly discussed. Also discussed are some remaining statistical shortcomings that will be addressed in another companion paper.     

Analysis of QTL×environment interaction for yield components and plant height in rice

 An F₂ and two equivalent F₃ populations of an indica-indica cross of rice, Tesanai 2/CB, were constructed and grown in different environments. The identification of quantitative trait loci (QTL) for yield components and plant height and an analysis of QTL×environment interaction were conducted for three trials. Interval mapping of QTL for eight traits was employed with a threshold of LOD=2 using the computer package MAPMAKER/QTL. A total of 44 QTL were detected in 18 intervals of nine chromosomes, including 3 for the number of panicles (NP), 5 for the number of filled grains (NFG), 6 for total number of spikelets (TNS), 3 for spikelet fertility (SF), 7 for 1000-grain weight (TGWT), 5 for grain weight per plant (GWT), 8 for plant height (PH) and 7 for panicle length (PL). The numbers of QTL detected in two or three trials were 1 for NP, 1 for NFG, 1 for TNS, none for SF, 4 for TGWT, 3 for GWT, 2 for PH and 5 for PL, making a total of 17. When a QTL was detected in more than one trial the direction and magnitude of its additive effect, the dominance effect and the degree of dominance were generally in good agreement. In all three trials, QTL were frequently detected for related traits in the same intervals. The directions of additive effect of QTL for related traits in a given interval were in agreement with few exceptions, no matter whether they were detected in the same trial or not. This result suggested that pleiotropism rather than close linkage of different QTL was the major reason why QTL for different traits were frequently detected in the same intervals. When gene pleiotropism was considered, 23 of the 29 QTL for yield and its components and 9 of the 15 QTL for plant stature were detected in more than one trial. This indicated that the detection of chromosomal segments harboring QTL was hardly affected by environmental factors. Received: 30 January 1997 / Accepted: 21 March 1997     

QTLs for resistance to Setosphaeria turcica in an early maturing Dent×Flint maize population

Quantitative trait loci (QTLs) for resistance to the fungal pathogen Setosphaeria turcica, the cause of northern corn leaf blight (NCLB), were mapped in a population of 220 F₃ families derived from a cross between two moderately resistant European inbred lines, D32 (dent) and D145 (flint). The population was genotyped with 87 RFLP and 7 SSR markers. Trials were conducted in the field in Switzerland, and in the greenhouse with selected F₃ families in Germany. The F₃ population segregated widely for resistance with transgression of the parents. By composite interval mapping, a total of 13 QTLs were detected with two disease ratings (0 and 3 weeks after flowering). Together these QTLs explained 48% and 62% of the phenotypic variation. Gene action at most QTLs was partially dominant. Eight out of the 13 QTL alleles for resistance were contributed by the more-resistant parent, D145. On chromosomes 3, 5 and 8, QTLs were located in the same chromosomal regions as QTLs in tropical and U.S. Corn Belt germplasm. Some QTLs affected NCLB, head smut and common rust at the same time, with alleles at these loci acting isodirectionally. Received: 25 January 1999 / Accepted: 20 Februar 1999     

Genotype × environment interaction patterns for grain yield of spring barley in different regions of Kazakhstan

Barley plays an important role in agricultural sector of Kazakhstan and it is grown in many different climate zones over 1.5 mln hectares annually. Therefore development of optimal cultivars for specific environments is a major challenge for barley breeding community in Kazakhstan. One of the approaches to address this question is to test large collection of commercial cultivars and advanced lines over a number of environmental sites that reflect major spatial and temporal climate variations in the country. In this work 103 cultivars and advanced lines of spring barley bred in six different breeding stations of Kazakhstan were grown in different testing sites in seven regions over 2009–2011 years. The major tasks of this research were to evaluate genotype × environment interactions and assess grain yield in associations with developmental stages of barley, such as heading date and seed maturation date. The results suggest that (i) heading and seed maturation dates are significantly correlated with grain yield in specific regions and may have opposite correlation indexes in response to environmental conditions; (ii) accessions of different bred origin vary in their ability to exhibit environmentally-dependent plastic responses; (iii) spatial variation was more important than temporal variation in GxE interactions; (iv) biplot analysis is effective approach in identification of best suitable and stable accessions for both broad and narrow environments. The obtained results are further contribution to understanding of complex mechanisms of genotype x environment interactions.     

Mapping QTLs and QTL × environment interaction for CIMMYT maize drought stress program using factorial regression and partial least squares methods

The study of QTL × environment interaction (QEI) is important for understanding genotype × environment interaction (GEI) in many quantitative traits. For modeling GEI and QEI, factorial regression (FR) models form a powerful class of models. In FR models, covariables (contrasts) defined on the levels of the genotypic and/or environmental factor(s) are used to describe main effects and interactions. In FR models for QTL expression, considerable numbers of genotypic covariables can occur as for each putative QTL an additional covariable needs to be introduced. For large numbers of genotypic and/or environmental covariables, least square estimation breaks down and partial least squares (PLS) estimation procedures become an attractive alternative. In this paper we develop methodology for analyzing QEI by FR for estimating effects and locations of QTLs and QEI and interpreting QEI in terms of environmental variables. A randomization test for the main effects of QTLs and QEI is presented. A population of F₂ derived F₃ families was evaluated in eight environments differing in drought stress and soil nitrogen content and the traits yield and anthesis silking interval (ASI) were measured. For grain yield, chromosomes 1 and 10 showed significant QEI, whereas in chromosomes 3 and 8 only main effect QTLs were observed. For ASI, QTL main effects were observed on chromosomes 1, 2, 6, 8, and 10, whereas QEI was observed only on chromosome 8. The assessment of the QEI at chromosome 1 for grain yield showed that the QTL main effect explained 35.8% of the QTL + QEI variability, while QEI explained 64.2%. Minimum temperature during flowering time explained 77.6% of the QEI. The QEI analysis at chromosome 10 showed that the QTL main effect explained 59.8% of the QTL + QEI variability, while QEI explained 40.2%. Maximum temperature during flowering time explained 23.8% of the QEI. Results of this study show the possibilities of using FR for mapping QTL and for dissecting QEI in terms of environmental variables. PLS regression is efficient in accounting for background noise produced by other QTLs.     

Use of artificial environments to reproduce and exploit genotype × location interaction for lucerne in northern Italy

Genotype × environment interaction effects can be exploited by breeding for specific adaptation to well-defined subregions within a target region. Previous work showed that genotype × location interaction for dry matter (DM) yield of lucerne (Medicago sativa L. subsp. sativa) cultivars in northern Italy is large and associated with soil type and level of summer drought stress of locations, suggesting the presence of two contrasting subregions. Thirteen farm landraces collected across the region and four control varieties were evaluated for DM yield in four artificial environments created at one site by the factorial combination of soil type (sandy loam or silty clay) and drought stress level (almost nil or high) for: (1) exploring the possibility to reproduce in artificial environments the adaptation patterns occurring across the region; (2) investigating the adaptation pattern of landraces and its relationship with environmental factors at collecting sites; and (3) providing a preliminary comparison of wide- versus specific-adaptation strategies based on yield gains predicted from selection of populations. Different soils filled large (24.0×1.6×0.8-m deep), bottomless containers in concrete. Water amounts were controlled by irrigation under a moving rain shelter. Cultivars varied largely for adaptation pattern across the artificial environments, mainly due to cultivar × stress interaction. Better response to stress conditions of landraces was closely associated with the level of summer drought at collecting sites (r=0.82), highlighting the importance of evolutionary adaptation. The additive main effects and multiplicative interaction-modelled responses of control cultivars successfully reproduced those observed across locations, candidating the artificial environments as a cheaper alternative to more selection locations when breeding for wide or specific adaptation. The latter implied about 40–50% greater estimated gains relative to breeding for wide adaptation.     

How effective are Mesocyclops aspericornis (Copepoda: Cyclopoida) in controlling mosquito immatures in the environment with an application of phytochemicals?

We investigated the susceptibility of Mesocyclops aspericornis and Aedes aegypti larvae to mosquito larvicidal phytochemicals: piperine and eugenol and the predation efficiency of the adult female M. aspericornis on Ae. aegypti larval instar-I and late II at the sublethal concentration of piperine and eugenol. Both prey and predator were susceptible to both the phytochemicals; however, the lethal concentration of either phytochemicals recorded for Ae. aegypti did not exert mortality on the copepods. The predator’s latent time was significantly longer in the phytochemical medium than control. The encounter frequency was significantly higher in control than in the phytochemical medium. For instar-I larvae, the post-encounter attack probabilities were not affected by the tested phytochemicals. However, for instar-II, the post-encounter attack probability was lower in eugenol treatment than in either the control or piperine. The escape and post-escape survival probabilities of larvae were substantially lower in phytochemical treatments than in the control. However, the overall copepod-imposed mortality did not differ significantly between the control and the phytochemical medium. In conclusion, both the phytochemicals modify the copepod feeding behavior without affecting the copepod incurred total larval mortality. Therefore, application of these phytochemicals along with M. aspericornis for mosquito control is advisable.     

Use of trial clustering to study QTL × environment effects for grain yield and related traits in maize

A population of 300 F_3:4 lines derived from the cross between maize inbred lines F2 and F252 was evaluated for testcross value in a large range of environmental conditions (11 different locations in 2 years: 1995 and 1996) in order to study (1) the magnitude of genotype × environment and (2) the stability of quantitative trait loci (QTL) effects. Several agronomic traits were measured: dry grain yield (DGY), kernel weight, average number of kernels per plant, silking date (SD) and grain moisture at harvest. A large genotype × environment interaction was found, particularly for DGY. A hierarchical classification of trials and an additive main effects and multiplicative interaction (AMMI) model were carried out. Both methods led to the conclusion that trials could be partitioned into three groups consistent with (1) the year of experiment and (2) the water availability (irrigated vs non-irrigated) for the trials sown in 1995. QTL detection was carried out for all the traits in the different groups of trials. Between 9 and 15 QTL were detected for each trait. QTL × group and QTL × trial effects were tested and proved significant for a large proportion of QTL. QTL detection was also performed on coordinates on the first two principal components (PC) of the AMMI model. PC QTL were generally detected in areas where QTL × group and QTL × trial interactions were significant. A region located on chromosome 8 near an SD QTL seemed to play a key role in DGY stability. Our results confirm the key role of water availability and flowering earliness on grain yield stability in maize.     

Metal interaction with redox regulation: an integrating concept in metal carcinogenesis?

The carcinogenicity of cadmium, arsenic, and chromium(VI) compounds has been recognized for some decades. However, the underlying molecular mechanisms seem to be complex and are not completely understood at present. Although, with the exception of chromium(VI), direct DNA damage seems to be of minor importance, interactions with DNA repair processes, tumor suppressor functions, and signal transduction pathways have been described in diverse biological systems. In addition to the induction of damage to cellular macromolecules by reactive oxygen species, the interference with cellular redox regulation by reaction with redox-sensitive protein domains or amino acids may provide one plausible mechanism involved in metal carcinogenicity. Consequences are the distortion of zinc-binding structures and the activation or inactivation of redox-regulated signal transduction pathways, provoking metal-induced genomic instability. Nevertheless, the relevance of the respective mechanisms depends on the actual metal or metal species under consideration and more research is needed to further strengthen this hypothesis.     

The consequence of genotype × environment interaction on high essential oil yield and its composition in clove basil (Ocimum gratissimum L.)

Clove basil (Ocimum gratissimum L.) is an aromatic, perennial herb belonging to the family Lamiaceae. The plant is indigenous to tropical areas especially India and West Africa. In Nigeria, it is found in the Savannah and coastal areas. The whole herbs of the plant contain essential oils and it is cultivated for various purposes. The plant is a rich source of eugenol in its essential oil. It is also used in the preparation of tea and infusions. It is also used in the treatment of fungal infections, fever, cold, and cough. Eugenol (4-allyl-2-methoxy phenol) is a phenolic compound from the class of phenylpropanoids. It is used in the food industry as a preservative, mainly due to its antioxidant property, and flavoring agent. The fresh herb × winter season/environment produced the highest essential oil 2.07 mL/plot with essential oil content 0.41% followed by fresh herb × summer season/environment (M1S2) = 1.68 mL. For the eugenol content, the autumn season/environment was found highly favorable = 74.52% (leaves = 69.024, stem = 74.531, and in inflorescence = 80.012) followed winter season/environment 72.30% (leaves = 71.841, stem 69.389, inflorescence 76.042, mean = 72.42%). These seasons are recommended for harvesting to obtain the optimum benefit for the quality essential oil yield.     

Assessing the importance of genotype x environment interaction for root traits in rice using a mapping population. I: a soil-filled box screen

Altering root system architecture is considered a method of improving crop water and soil nutrient capture. The analysis of quantitative trait loci (QTLs) for root traits has revealed inconsistency in the same population evaluated in different environments. It must be clarified if this is due to genotype × environment interaction or considerations of statistics if the value of QTLs for marker-assisted breeding is to be estimated. A modified split-plot design was used where a main plot corresponded to a separate experiment. The main plot factor had four treatments (environments), which were completely randomized among eight trials, so that each treatment was replicated twice. The sub-plot factor consisted of 168 recombinant inbreed lines of the Bala × Azucena rice mapping population, randomly allocated to the seven soil-filled boxes. The aim of the trial was to quantify QTL × environment interaction. The treatments were chosen to alter partitioning to roots; consisting of a control treatment (high-soil nitrogen, high light and high-water content) and further treatments where light, soil nitrogen or soil water was reduced singly. After 4 weeks growth, maximum root length (MRL), maximum root thickness, root mass below 50 cm, total plant dry mass (%), root mass and shoot length were measured. The treatments affected plant growth as predicted; low nitrogen and drought increased relative root partitioning, low-light decreased it. The parental varieties Bala and Azucena differed significantly for all traits. Broad-sense heritability of most traits was high (57–86%). Variation due to treatment was the most important influence on the variance, while genotype was next. Genotype × environment interaction was detected for all traits except MRL, although the proportion of variation due to this interaction was generally small. It is concluded that genotype × environment interaction is present but less important than genotypic variation. A companion paper presents QTL × environment analysis of data.     

Safe space in the woods: Mechanistic spatial models for predicting risks of human–bear conflicts in India

Human–wildlife interactions can have negative consequences when they involve large carnivores. Spatial risk modelling could serve as a useful management approach for predicting and pre-emptively mitigating negative interactions. We present a mechanistic modelling framework and examine interactions between humans and sloth bears (Melursus ursinus) in a multi-use forest landscape of central India. We first assessed patterns and determinants of bear distribution across the landscape using indirect sign surveys. At the same spatial scale, we then estimated spatial probabilities of bear attacks on people using information from 675 interviews with local residents, incorporating estimates of distribution probabilities from the previous step. We found the average occupancy probability across 128 grid-cells to be 0.77 (SE = 0.03). Bear occupancy was influenced by terrain ruggedness, forest composition and configuration, vegetation productivity and size of human settlements. The average probability of a bear attack in any given grid-cell was 0.61 (SE = 0.03), mostly determined by bear occurrence patterns, forest cover, terrain ruggedness, and size of human settlements. Using spatial information on people's dependence on forest resources, we identified locations with the highest risk of bear attacks. Our study demonstrates that human attacks by bears—generally believed to be random or incidental—in fact showed deterministic patterns. Our framework can be applied to other scenarios involving human–wildlife conflicts. Based on our findings, we propose that a proactive co-management approach which involves collaboration between wildlife managers and local residents could help better manage human–bear conflicts in central India and elsewhere across the species' range.     

Identification of QTLs with main, epistatic and QTL?×?environment interaction effects for salt tolerance in rice seedlings under different salinity conditions

Salt tolerance of rice (Oryza sativa L.) at the seedling stage is one of the major determinants of its stable establishment in saline soil. One population of recombinant inbred lines (RILs, F (2:9)) derived from a cross between the salt-tolerant variety Jiucaiqing and the salt-sensitive variety IR26 was used to determine the genetic mechanism of four salt tolerance indices, seedling height (SH), dry shoot weight (DSW), dry root weight (DRW) and Na/K ratios (Na/K) in roots after 10 days in three salt concentrations (0.0, 0.5 and 0.7 % NaCl). The main effect QTLs (M-QTLs) and epistatic QTLs (E-QTLs) were detected by QTL IciMapping program using single environment phenotypic values. Eleven M-QTLs and 11 E-QTLs were identified for the salt tolerance indices. There were six M-QTLs and two E-QTLs identified for SH, three M-QTLs and five E-QTLs identified for DSW, two M-QTLs and one E-QTL identified for DRW, and three E-QTLs identified for Na/K. The phenotypic variation explained by each M-QTL and E-QTL ranged from 7.8 to 23.9 % and 13.3 to 73.7 %, respectively. The QTL-by-environment interactions were detected by QTLNetwork program in the joint analyses of multi-environment phenotypic values. Six M-QTLs and five E-QTLs were identified. The phenotypic variation explained by each QTL and QTL × environment interaction ranged from 0.95 to 6.90 % and 0.02 to 0.50 %, respectively. By comparing chromosomal positions of these M-QTLs with those previously identified, five M-QTLs qSH1.3, qSH12.1, qSH12.2, qDSW12.1 and qDRW11 might represent novel salt tolerance genes. Five selected RILs with high salt tolerance had six to eight positive alleles of the M-QTLs, indicating that pyramiding by marker-assisted selection (MAS) of M-QTLs can be applied in rice salt tolerance breeding programs.