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A total of 36 colocated QTLs associated with plant production traits were mapped using IR62266/Norungan recombinant inbred lines under drought and non-stress conditions in target populations of environment. Multi-model composite interval mapping and multi-environment analysis detected seven consistent QTLs for plant production traits on chromosomes 1, 2, 3, 6, 10 and 11. QTLs for plant height, number of tillers, biomass, spikelet fertility and grain yield under drought identified on chr. 1 (RM5389-RM11943-RM5794), chr. 2 (RM324-RM5390 and RM12460-RM423-RM5345), chr. 3 (RM6329-RM5475-RM16030), chr. 6 (RM508-RM585-RM217), chr. 10 (RM7361-RM8207) and chr. 11 (RM209-RM6499) in this study have been reported as meta-QTLs for yield under drought. Consistent positive allelic contribution from the landrace, Norungan was shown at the two QTLs on chr. 2, while IR62266 showed consistent positive allelic effect at the QTL on chr. 10. At other four regions the effect was confounded with the stress condition, for instance the region on chr. 3 identified for straw yield showed additive effect from Norungan in all stress trials, while in the non-stress trials the positive allele came from IR62266. Epistasis for grain yield was detected in the regions of non-additive effects and its relative contribution was small. Relatively small percentage of grain yield variation was explained by additive x environment interaction (0.28 - 1.77%), but was larger than that explained by additive effect QTL across environments (0.05 - 0.35%). These consistent QTLs may have genes evolutionarily conserved in response to drought and could be ideal candidates for rice yield improvement in water-limited environments.

Soybean plants have high protein content and can be used as a supplementary source of high-protein feed. To map quantitative trait loci (QTL) for the content of crude protein (CP), neutral detergent fibre (NDF), acid detergent fibre (ADF) and dry weight of plant (DWP) in R2 stage of soybean, two recombinant inbred lines, RIL3613 and RIL6013, containing 134 and 156 RILs, derived from the cross of Dongnong L13 × Heihe 36 and Dongnong L13 × Henong 60, were planted for two consecutive years. Based on a simple sequence repeat (SSR) linkage map, QTLs of CP, NDF, ADF and DWP were mapped by interval mapping (IM) and inclusive composite interval mapping method (ICIM) using additive effect, epistatic effect and environmental interaction model. The variance components of genotype, environment and genotype × environment (G × E) interaction for quality and yield traits in the two RIL populations were significant under multiple environmental conditions. Eighteen additive effect QTLs on 10 of 20 soybean chromosomes explained 7.02%–15.67% and 2.13%–11.42% of the phenotypic variation in RIL3613 and RIL6013, respectively. Three epistatic QTL pairs related to CP and six ones for DWP were identified. Eight additive effect QTLs for CP, eight ones for NDF, three ones for ADF, and another three ones for DWP were identified by genotype × environment interaction analysis. One epistatic QTL for CP, 11 epistatic QTL pairs for ADF and 38 ones for NDF were identified. These results can provide better understanding of the genetic basis of soybean feed quality and yield.

The QTL mapping for gynoecy in cucmber (Cucumis sativus L.)was carried out by using backcross populations (BC1F2 & BC1F3) of a cross using two inbred lines; one monoecious with multiple (≥ 5) later...

Biomass yield is an important trait for wheat breeding programs. Enhancing the yield of the aerial components of wheat cultivars will be an integral part of future wheat improvement. Aluminum (Al) toxicity is one of the main factors limiting wheat growth and production in acid soils, which occur on up to 50% of the arable lands of the world especially in tropical and subtropical regions. Our objective was to identify quantitative trait loci (QTL) of plant growth characteristics and yield in wheat. A recombinant inbred line (RIL) population consisting of 167 lines, derived from a cross between SeriM82 and Babax were evaluated under two Al treatments (+ Al, 800 µM of Al; −Al, 0 µM of Al) in the field based on an alpha lattice design with two replications for two consecutive crop seasons. A total of 40 QTLs including nine putative and 31 suggestive QTLs were found for all traits using the composite interval mapping (CIM) method. By mixed model-based composite interval mapping (MCIM) method, 42 additive QTLs and nine pairs of epistatic effects were detected for studied traits, of which 20 additive and six pairs of epistatic QTLs showed significant QTL × environment interactions. Most of the detected QTLs across environments were stable, and the highest number of stable QTLs was related to A genome. Co-localization of QTL was found on linkage groups (LGs) 2B, 4B, 6A-a, and 7A (CIM method) and 2A-d, and 6A-a (MCIM method). These results have implications for selection strategies in biomass yield and for increasing the yield of the aerial part of wheat following further evaluations in various genetic backgrounds and environments.

A late leaf spot resistant breeding line VG 9514 was bred through interspecific hybridization between Arachis hypogaea L. and Arachis cardenasii. Genetic study involving segregation for late leaf spot resistance in F2 and recombinant inbred line population of a cross between VG 9514 X TAG 24 revealed duplicate recessive resistance for the disease. Integration of newly developed SSR markers from A02 chromosome with existing linkage map generated a new genetic linkage map with 278 markers and 2679.1 cM map distances. QTL mapping involving this genetic linkage map and phenotypic field score of late leaf spot identified two major consensus additive QTLs in the A02 chromosome of cultivated groundnut. Epistatic interaction between these two major QTLs was also noticed through an epistatic QTL analysis in Ici-Mapping 4.1. In all the fourteen epistatic QTLs, a common component locus was remained within the major additive QTL at 90 cM in chromosome A02. Sequence analysis within the major additive QTL peaks revealed open reading frame of genes that code LRR domain containing proteins which are involved in disease resistance in crop plants.

Breeding new rice varieties with high Eating and Cooking Quality (ECQ) characteristics is an important goal in rice breeding programs. To this end, 242 Recombinant Inbred Lines (RILs) of two populations derived from Alikazemi/IR67017-180-2-1-2 and Alikazemi/Saleh crosses were evaluated in two environments. The main and epistatic QTLs and QTL × Environment (Q × E) interactions were analyzed using the Mixed-Model-based Composite Interval Mapping method. A total of 17 main QTLs with additive effects were mapped on 6 rice chromosomes, including 6 QTLs for Amylose Content, 5 QTLs for Gelatinization Temperature and 6 QTLs for Gel Consistency. Seven QTLs were found on chromosome 6 associated with a common RM4128 marker (close to the Alk locus) influencing the ECQ characteristics with favorable alleles from the common female parent. Five QTL clusters were identified on chromosome 6, 7, 8, and 9, suggesting that the co-located QTLs with pleiotropic effects may control ECQ traits. A major QTL cluster was found for AC and GC between RM4128 and RM549 markers close to the Alk locus on chromosome 6, which is in the vicinity of another QTL located in RM587-RM4128 interval. 13 QTL pairs were identified to have a significant epistatic, Additive × Additive effects involved in controlling ECQ traits. A small phenotypic variation was found for the epistatic QTLs and Q × E interactions, suggesting the complexity of ECQ inheritance traits in rice. Further comprehensive studies and validation of the detected QTLs could be useful for marker-assisted selection to improve grain quality in rice.

Seed weight and shape are important agronomic traits that affect soybean quality and yield. In the present study, we used image analysis software to evaluate 100-seed weight and seed shape traits (length, width, perimeter, projection area, length/width, and weight/projection area) of 155 novel recombinant inbred soybean lines (NJRISX) generated by crossing “Su88-M21” and “XYXHD”. We examined quantitative trait loci (QTLs) associated with the six traits (except seed weight per projection area), and identified 42 additive QTLs (5–8 QTLs per trait) accounting for 24.9–37.5% of the phenotypic variation (PV). Meanwhile, 2–4 epistatic QTL pairs per trait out of a total of 18 accounted for 2.5–7.2% of the PV; and unmapped minor QTLs accounted for the remaining 35.0–56.7% of the PV. A total of 28 additive and 11 epistatic QTL pairs were concentrated in nine joint QTL segments (JQSs), indicating that QTLs associated with seed weight and shape are closely related and interacted. An interaction was also detected between additive and epistatic QTL pairs and environment, which made significant contributions of 1.4–9.5% and 0.4–0.8% to the PV, respectively. We annotated 18 candidate genes in the nine JQSs, which were important for interpreting the close relationships among the six traits. These findings indicate that examining the interactions between closely related traits rather than only analyzing individual trait provides more useful insight into the genetic system of the interrelated traits for which there has been limited QTL information.

Seed size and shape are important traits determining yield and quality in soybean. However, the genetic mechanism and genes underlying these traits remain largely unexplored. In this regard, this study used two related recombinant inbred line (RIL) populations (ZY and K3N) evaluated in multiple environments to identify main and epistatic-effect quantitative trait loci (QTLs) for six seed size and shape traits in soybean. A total of 88 and 48 QTLs were detected through composite interval mapping (CIM) and mixed-model-based composite interval mapping (MCIM), respectively, and 15 QTLs were common among both methods, two of them were major (R2 &gt, 10%) and novel QTLs (viz., qSW-1-1ZN and qSLT-20-1K3N). Additionally, 51 and 27 QTLs were identified for the first time through CIM and MCIM methods, respectively. Colocalization of QTLs occurred in four major QTL hotspots/clusters, viz., &ldquo, QTL Hotspot A&rdquo, &ldquo, QTL Hotspot B&rdquo, QTL Hotspot C&rdquo, and &ldquo, QTL Hotspot D&rdquo, located on Chr06, Chr10, Chr13, and Chr20, respectively. Based on gene annotation, gene ontology (GO) enrichment, and RNA-Seq analysis, 23 genes within four &ldquo, QTL Hotspots&rdquo, were predicted as possible candidates, regulating soybean seed size and shape. Network analyses demonstrated that 15 QTLs showed significant additive x environment (AE) effects, and 16 pairs of QTLs showing epistatic effects were also detected. However, except three epistatic QTLs, viz., qSL-13-3ZY, qSL-13-4ZY, and qSW-13-4ZY, all the remaining QTLs depicted no main effects. Hence, the present study is a detailed and comprehensive investigation uncovering the genetic basis of seed size and shape in soybeans. The use of a high-density map identified new genomic regions providing valuable information and could be the primary target for further fine mapping, candidate gene identification, and marker-assisted breeding (MAB).

In present study, F14recombinant inbred line (RIL) population was backcrossed to paternal parent for a paternal backcross (BC/P) population, deriving from one Upland cotton hybrid. Three repetitive BC/P field trials and one BC/M field trial were performed including both two BC populations and the original RIL population. Totally, for fiber quality traits, 24 novel QTLs were detected and 13 QTLs validated previous results. And 19 quantitative trait loci (QTL) in BC/P populations explained 5.01% - 22.09% of phenotype variation (PV). Among the 19 QTLs, three QTLs were detected simultaneously in BC/M population. The present study provided novel alleles of male parent for fiber quality traits with positive genetic effects. Particularly,qFS-Chr3-1controlling fiber strength explained 22.09% of PV in BC/P population, which increased 0.48 cN/tex for fiber strength. A total of seven, two, eight, two and six QTLs explained over 10.00% of PV for fiber length, fiber uniformity, fiber strength, fiber elongation and fiber micronaire, respectively. In the RIL population, six common QTLs detected in more than one environment such asqFL-Chr1-2,qFS-Chr5-1,qFS-Chr9-1,qFS-Chr21-1,qFM-Chr9-1andqFM-Chr9-2. Two common QTLs ofqFE-Chr2-2(TMB2386-SWU12343) andqFM-Chr9-1(NAU2873-CGR6771) explained 22.42% and 21.91% of PV. In addition, a total of 142 and 46 epistatic QTLs and QTL × environments (E-QTLs and QQEs) were identified in RIL-P and BC/P populations, respectively.

Aluminum (Al) toxicity is one of the major environmental constraints to crop production in acidic soils. The objective of this study was to map quantitative trait loci (QTLs) of yield-related traits associated with tolerance to Al in wheat (Triticum aestivum L.). A recombinant inbred line population of wheat was evaluated under conditions of 800 µm aluminum chloride hexahydrate (AlCL3.6H2O) at tillering, stem-elongation and flowering stages. Single-locus analysis showed that 22 of 79 QTLs identified were putative. These QTLs explained 4.38–12.24% of the total variation of traits in two growing seasons. Based on two-locus analysis, 71 additive and 11 epistatic QTLs were identified, of which 34 additive and three epistatic QTLs had significant interaction with environmental effects. The greatest number of stable QTLs was related to the A genome. Stable QTLs associated with days to heading and grain yield, with no additive × environment interactions, were co-located with Al concentration (on the 2D linkage group) and 1000-grain weight (on the 6B linkage group). The markers linked to major and stable QTLs identified in the present study can be further assessed in other genetic backgrounds and environmental conditions in order to improve marker-assisted selection and Al tolerance in wheat.

Soybean is one of the most important oilseed crops in the world. The soybean oil contains various fatty acids. Their concentrations determine the quality and nutritional value of soybean oil. On the other hand, quantitative trait loci (QTL) mapping for the concentrations of soybean predominant fatty acids could provide the genetic basis for soybean fatty acid composition. In this study, a soybean genetic linkage map was constructed based on 161 polymorphic SSR markers in recombinant inbred lines (RILs) derived from a cross of cv. Luheidou2 × Nanhuizao. By using this map, 35 additive QTLs underlying individual fatty acid concentrations were identified in single environment, while 17 additive QTLs were identified underlying specific fatty acids across multiple environments or underlying multiple fatty acids. Fifteen of the 52 loci were found to be novel loci, explaining 5–24 % of phenotypic variation. Moreover, 25 epistatic QTLs were identified and explained a high phenotypic variation for the fatty acid concentrations, suggesting an essential role of epistatic effect for fatty acid concentrations. The identification of additive and epistatic QTLs suggested a complex network for soybean fatty acid concentrations, and will facilitate the understanding for fatty acid accumulation.

Low temperatures dramatically influence spikelets at the booting stage, which is an important factor that greatly reduces grain yield. Mining the genes for grain yield under low temperatures and elucidating the genetic mechanism is a key approach to breeding improved-yield crops. In this study, we generated 190 recombinant inbred lines with 155 SSR markers from a cross between Dongnong422 (a low-temperature-sensitive variety) and Kongyu131 (a low-temperature-tolerant variety). These lines were employed for linkage mapping for yield-component traits under low water temperature and control conditions in two consecutive years. As a result, 18 addition QTLs (A-QTLs) were detected using ICIM, other two analytical methods, CIM (22 A-QTLs) and MICM (8 A-QTLs) were used to demonstrate QTLs ICIM has detected, which showed that all three analytical methods gave a consistency in result. Among these QTLs, 17 QTLs (35.4%) were detected simultaneously under both cold-water-irrigated and control conditions, and these QTLs were most influential (adaptive effect) under the cold-water treatment. Five pleiotropic genomic regions controlling yield were mapped on chromosomes 6, 7, 8 and 11. Among these regions, one of the major regions located, the marker interval RM1306–RM70, was repeatedly detected for panicle length, grains per panicle, primary branch number, secondary branch number and grain density under low-water temperature and control conditions based on three analysis models. Multiple-environment joint analysis showed that nine A-QTLs were detected for seven traits. Thirteen pairs of epistatic QTLs (E-QTLs) were detected for seven traits, but for yield per plant, only seven pairs had significant environmental specificity. The QTLs detected using three analytical models under different environments showed that it is possible to use molecular breeding for cold tolerance, and the markers linked to the QTLs of stable expression could be applied to rice yield improvement in low temperatures.

Along with the development and integration of molecular genetics and quantitative genetics, many quantitative trait locus (QTL) mapping studies have been conducted using different mapping populations in various crop species. Existing QTLs can be used for marker-assisted breeding and map-based cloning, whereas the false-positive QTLs are no use. The purpose of this study is to evaluate the suitability of different mapping procedures for data from different genetic models. In this study, four types of recombinant inbred lines (RILs) with different genetic models, viz. additive QTLs (Model I), additive and epistatic QTLs (Model II), additive QTLs and QTL × environment interaction (Model III), additive, epistatic QTLs and QTL × environment interaction (Model IV), were simulated by computer. Six types of QTL mapping procedures, viz. CIM, MIMF, MIMR, ICIM, MQM and NWIM, on four kinds of QTL mapping software, viz. WinQTL Cartographer Version 2.5, IciMapping Version 2.0, MapQTL Version 5.0 and QTLnetwork Version 2.0, were used for screening QTLs of the simulated RILs. The results showed that different mapping procedures have different suitability for different genetic models. CIM and MQM can only screen Model I data. MIMR, MIMF and ICIM can only screen Model I and Model II data. NWIM can screen all four models’ data. It can be concluded that different genetic models’ data have different most suitable mapping procedures. In practical experiments where the genetic model of the data is unknown, a multiple model mapping strategy should be used, that is a full model scanning with complex model procedure followed by verification with other procedures corresponding to the scanning results.

Seed coat cracking (SCC) consists of irregular cracks on the surface of seed coat, giving rise to serious effects on the seed quality in soybeans. However, breeding to achieve resistance to SCC has been an arduous task due to the complicated genetic behavior and environmental interactions. Thus, this study aims to map quantitative trait loci (QTLs) controlling irregular SCC in soybeans and to study QTL-by-environment interactions. QTL analysis was conducted using an RIL population derived from Keunolkong × Sinpaldalkong. The SCC was determined at natural conditions and calculated three times by counting cracked seeds with a 100 seeds per plant basis. In the four environments, a total of 10 QTLs with additive effects were identified. Among them, three QTLs (qSCC2-1, qSCC9, and qSCC20) located on chromosomes 2, 9, and 20 showed main-additive effects explaining 9.5–19.4 % of phenotypic variances in two environments. Across environments, seven additive QTLs, four additive × environment (AE) interactions, and two pairs of epistatic QTLs and epistasis × environment interactions were identified for SCC. Similar to the results in individual environments, two major QTLs (qSCC2-1 and qSCC9) showed additive main effects explaining 10.5 and 6.5 % of phenotypic variances, respectively and significant AE interaction in 2006 Suwon. The total QTLs explained 48.0 % of the phenotypic variation, with additive effects of 33.8 %, epistatic effects of 7.6 %, and AE and AAE of 6.6 %. All in all, these results showed that both additive effects and AE interactions can serve as important genetic basis for SCC.

complex traits that are important in agriculture, including mineral content. In the present study, with a set of 241 recombinant-inbred lines (RILs) derived from a cross be- tween Zhenshan 97 and Minghui 63, mapping of main-effect QTLs and epistatic QTLs for the Fe, Ca, Zn, Mn and Cu content of rice grain was conducted based on field measure- ments.

Quantitative trait loci (QTLs) for adult-plant resistance (APR) to powdery mildew, lodging resistance (LDR), and the internode length below spike (ILBS) in wheat ( Triticum aestivum L.) were studied using a set of 168 doubled haploid (DH) lines derived from the cross between Huapei 3 and Yumai 57. A genetic linkage map was constructed using 305 simple sequence repeat (SSR) markers. The DH population and the parents were evaluated for APR to powdery mildew, LDR, and ILBS in 2005 and 2006 growing seasons in Tai'an, Shandong Province and in 2006 growing season in Suzhou, Anhui Province, China. QTL analysis was performed using the software of QTLNetwork version 2.0 based on the mixed linear model. A total of 12 additive QTLs and 10 pairs of epistatic QTLs were detected for APR to powdery mildew, LDR, and ILBS. QTL qApr4D was detected for APR to powdery mildew on chromosome 4D with an increased effect on resistance. The QTL was from Yumai 57, which explained 20.0% of the phenotypic variances without additive × environment (AE) interactions. QTL qIlbs7D was identified for ILBS on chromosome 7D, explaining 12.9% of the phenotypic variances without AE interactions. Both additive and epistatic effects are important genetic bases for APR to powdery mildew, LDR, and ILBS, and are sometimes subjected to environmental modifications. The 2 QTLs mentioned earlier are applicable in marker-assisted selection for APR to powdery mildew and ILBS in wheat breeding programs.

Yield under drought stress is a highly complex trait with large influence to even a minor fluctuation in the environmental conditions. Genomics-assisted breeding holds great promise for improving such complex traits more efficiently in less time, but requires markers associated with the trait of interest. In this context, a recombinant inbred line mapping population (TAG 24 × ICGV 86031) was used to identify markers associated with quantitative trait loci (QTLs) for yield and yield related traits at two important locations of West Africa under well watered and water stress conditions. Among the traits analyzed under WS condition, the harvest index (HI) and the haulm yield (HYLD) were positively correlated with the pod yield (PYLD) and showed intermediate broad sense heritability. QTL analysis using phenotyping and genotyping data resulted in identification of 52 QTLs. These QTLs had low phenotypic variance (

Wheat thousand kernel weight (TKW) is a complex trait, and is largely controlled by several kernel traits, including kernel length (KL) and kernel width (KW). In order to reveal the genetic relationship between TKW and these kernel traits (KW and KL) as accurate as possible, we applied both unconditional and conditional mapping analyses to three distinct genetic populations, one DH population and two RIL populations. This report describes the identifications of 36 unconditional and conditional additive QTLs and 30 pairs of unconditional and conditional epistatic QTLs, all of which are closely associated with TKW. While the conditional additive locus Qtkw1B, detected in the RIL2 population, exhibited the largest contribution, explaining 14.12 % of TKW variance, the unconditional epistatic QTLs Qtkw3A-2/Qtkw5B.1, detected in the DH population, accounted for 11.95 % of phenotypic variance. This study also showed that, compared with unconditional mapping, conditional mapping resulted in very different numbers and different extent of effects of additive and epistatic QTLs that were associated with TKW when TKW was conditioned on kernel traits (KW and KL). These data strongly suggest that KW and KL indeed play a significant role in determining TKW. Furthermore, we demonstrated that the effects of the 25 additive QTLs for TKW were either entirely or largely determined by KW, while the effects of the other 25 additive QTLs for TKW were either entirely or largely affected by KL. We conclude that the conditional mapping can be useful for a better understanding of the interrelationship between the yield contributing traits at the QTL level.

We report a second major QTL for root-knot nematode resistance in the highly resistant Upland cotton line M-120RNR and show epistasis between two resistant QTLs with different mechanisms conferring resistance. In an earlier study, we identified a major QTL on Chromosome 11 associated with resistance to root-knot nematode in the M-120 RNR Upland cotton line (Gossypium hirsutum L.) of the Auburn 623 RNR source. Herein, we re-evaluated the genetics of the resistance to root-knot nematode in the M-120 RNR × Pima S-6 population by linkage mapping using recently published SSR markers. The QTL analysis detected two regions significantly associated with the resistance phenotype. In addition to the QTL previously identified on Chromosome 11 (qMi-C11), a major QTL was identified on Chromosome 14 (qMi-C14). The resistance locus on qMi-C11 originated from the Clevewilt parent, while the qMi-C14 locus originated from the other resistant parent, Mexico Wild Jack Jones. The qMi-C14 locus had logarithms of odds score of 17 and accounted for 45 % of the total phenotype variation in egg production. It was also associated with galling index, but the percent variation explained was only 6 %, suggesting that the qMi-C11 locus had a much stronger effect on root gall suppression than egg production, while the qMi-C14 locus had a stronger effect on egg production than galling. The results also suggest that the transgressive segregation observed in the development of Auburn 623 RNR was due to the pyramiding of at least two main effect QTLs as well as an additive-by-additive epistatic effects between the two resistant loci. The SSRs markers tightly linked to the qMi-C11 and qMi-C14 loci will greatly facilitate the improvement of RKN resistance in cotton via marker-assisted breeding.

The key to plant survival under NaCl salt stress is maintaining a low Na(+) level or Na(+)/K(+) ratio in the cells. A population of recombinant inbred lines (RILs, F(2:9)) derived from a cross between the salt-tolerant japonica rice variety Jiucaiqing and the salt-sensitive indica variety IR26, was used to determine Na(+) and K(+) concentrations in the roots and shoots under three different NaCl stress conditions (0, 100 and 120 mM NaCl). A total of nine additive QTLs were identified by QTL Cartographer program using single-environment phenotypic values, whereas eight additive QTLs were identified by QTL IciMapping program. Among these additive QTLs, five were identified by both programs. Epistatic QTLs and QTL-by-environment interactions were detected by QTLNetwork program in the joint analyses of multi-environment phenotypic values, and one additive QTL and nine epistatic QTLs were identified. There were three epistatic QTLs identified for Na(+) in roots (RNC), three additive QTLs and two epistatic QTLs identified for Na(+) in shoots (SNC), four additive QTLs identified for K(+) in roots (RKC), four additive QTLs and three epistatic QTLs identified for K(+) in shoots (SKC) and one additive QTL and one epistatic QTL for salt tolerance rating (STR). The phenotypic variation explained by each additive, epistatic QTL and QTL×environment interaction ranged from 8.5 to 18.9%, 0.5 to 5.3% and 0.7 to 7.5%, respectively. By comparing the chromosomal positions of these additive QTLs with those previously identified, five additive QTLs, qSNC9, qSKC1, qSKC9, qRKC4 and qSTR7, might represent novel salt tolerance loci. The identification of salt tolerance in selected RILs showed that a major QTL qSNC11 played a significant role in rice salt tolerance, and could be used to improve salt tolerance of commercial rice varieties with marker-assisted selection (MAS) approach.

Quantitative trait loci (QTLs) for plant height in wheat (Triticum aestivum L.) were studied using a set of 168 doubled haploid (DH) lines, which were derived from the cross Huapei 3/Yumai 57. A genetic linkage map was constructed using 283 SSR and 22 EST-SSR markers. The DH population and the parents were evaluated for wheat plant height in 2005 and 2006 in Tai'an and 2006 in Suzhou. QTL analyses were performed using the software of QTLNetwork version 2.0 based on the mixed linear model. Four additive QTLs and five pairs of epistatic effects were detected, which were distributed on chromosomes 3A, 4B, 4D, 5A, 6A, 7B, and 7D. Among them, three additive QTLs and three pairs of epistatic QTLs showed QTL x environment interactions (QEs). Two major QTLs, Qph4B and Qph4D, which accounted for 14.51% and 20.22% of the phenotypic variation, were located similar to the reported locations of the dwarfing genes Rht1 and Rht2, respectively. The Qph3A-2 with additive effect was not reported in previous linkage mapping studies. The total QTL effects detected for the plant height explained 85.04% of the phenotypic variation, with additive effects 46.07%, epistatic effects 19.89%, and QEs 19.09%. The results showed that both additive effects and epistatic effects were important genetic bases of wheat plant height, which were subjected to environmental modifications, and caused dramatic changes in phenotypic effects. The information obtained in this study will be useful for manipulating the QTLs for wheat plant height by molecular marker-assisted selection (MAS).

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

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 material The online version of this article (doi:10.1007/s00122-010-1517-0) contains supplementary material, which is available to authorized users.

Background Our aim was to simulate the data for the QTLMAS2011 workshop following a pig-type family structure under an oligogenic model, each QTL being specific. Results The population comprised 3000 individuals issued from 20 sires and 200 dams. Within each family, 10 progenies belonged to the experimental population and were assigned phenotypes and marker genotypes and 5 belonged to the selection population, only known on their marker genotypes. A total of 10,000 SNPs carried by 5 chromosomes of 1 Morgan each were simulated. Eight QTL were created (1 quadri-allelic, 2 linked in phase, 2 linked in repulsion, 1 imprinted and 2 epistatic). Random noise was added giving an heritability of 0.30. The marker density, LD and MAF were similar to real life parameters.