Epistatic Quantitative Trait Loci Research Articles

Detection of main-effect and epistatic QTL for yield-related traits in rice under drought stress and normal conditions

Xing, W., Zhao, H. and Zou, D. 2014. Detection of main-effect and epistatic QTL for yield-related traits in rice under drought stress and normal conditions. Can. J. Plant Sci. 94: 633-641. Drought-resistant cultivars play an important role in maintaining high and stable crop yields under drought-stress conditions. However, the genetic mechanism of drought resistance must first be elucidated. Therefore, 220 recombinant inbred lines from a cross between Xiaobaijingzi (upland rice) and Kongyu 131 (Oryza sativa L.) were used to identify quantitative trait loci (QTLs) for yield and yield-component traits under drought stress and control conditions in Heilongjiang and Tieli. As a result, 23 main-effect QTLs and 11 digenic interactions were detected for four traits under the above two conditions. Of the main-effect QTLs, 10 and 8 were detected under control and drought-stress conditions, respectively; and five common QTLs were observed. In addition, five QTLs were found to be responsible for the difference acros...

Re-evaluation of the inheritance for root-knot nematode resistance in the Upland cotton germplasm line M-120 RNR revealed two epistatic QTLs conferring resistance.

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.

Identification and multiple comparisons of QTL and epistatic interaction conferring high yield under boron and phosphorus deprivation in Brassica napus

Boron (B) and phosphorus (P) are two essential nutrients for plants. To unravel the genetic basis of B and P efficiency in Brassica napus, quantitative trait locus (QTL) and epistatic interaction analysis for yield and yield-related traits under contrast B and P conditions were performed using two mapping populations across various environments. Main effect QTLs were detected by QTLNetwork and QTL Icimapping (ICIM), and were compared with our previously reported main effect QTLs identified by QTLCartographer. Epistatic QTLs were identified by QTLNetwork, ICIM and Genotype matrix mapping (GMM), and multiple comparisons of main effect QTLs and epistatic QTLs were conducted. For the two mapping populations, 51 main effect QTLs were identified by QTLNetwork, 106 by ICIM. Among them, 35 main effect QTLs were simultaneously identified by three programs. Moreover, 578, 18 and 62 epistatic QTLs were identified by GMM, QTLNetwork and ICIM, respectively. Interestingly, a total of 235 epistatic QTLs identified by GMM were associated with 50 main effect QTLs identified by three programs. However, only nine epistatic QTLs identified by QTLNetwork and ICIM were involved in main effect QTLs. Twenty-two main effect QTLs in the BERIL population overlapped with 20 main effect QTLs for the same traits in the BQDH population, but no main effect QTLs were detected both under P and B stress environments, indicating the genetic differences in B and P homeostasis in B. napus. By in silico mapping, 29 candidate genes were located in the consensus QTL intervals. This study suggested the availability of dissecting genetic basis for complex traits under B/P deficiency by analyzing main effect QTLs and epistatic QTLs using multiple programs across different environments. The robust main effect QTLs and epistatic QTLs associated could be useful in breeding B and P efficient cultivars of B. napus.

Potential Involvement of Maternal Cytoplasm in the Regulation of Flowering Time via Interaction with Nuclear Genes in Maize

ABSTRACTFlowering time is a complex trait that controls adaptation of plants to their local environment in the outcrossing species maize (Zea mays L.). Previous studies have identified numerous quantitative trait loci (QTL) and genetic sequences associated with flowering time. However, beyond the nuclear genome, the maternal cytoplasm may also be involved in the regulation of flowering time. The objectives of this study were to locate the associated QTL based on a special mating design and to verify the hypothesis that the maternal cytoplasm may also contribute to the variation in flowering time. In the present study, a reciprocal mating design was conducted to generate F2 mapping populations comprising 120 F2 plants from the direct cross (JB × Y53) and 120 F2 plants from the reciprocal cross (Y53 × JB). F2:3 mapping populations were further generated with 91 direct F2:3 families and 120 reciprocal F2:3 families (10 plants per family). The days to tassel (DTT) and the days to pollen shed (DPS) of the above F2 and F2:3 mapping populations were evaluated in the same field and at the same experimental station in 2007 and 2008. A genetic linkage map with 154 microsatellite markers was constructed that covered 1735 cM of the maize genome with an average marker spacing of 11.3 cM. A joint analysis method incorporating the cytonuclear interaction mapping approach was proposed and performed to detect the QTL affecting DTT and DPS. As a result, eight and 11 QTL were detected for DTT and DPS, respectively. Interestingly, although the cytoplasmic effects were not significant between the direct and reciprocal populations, the interactions among the cytoplasm and the QTL were detected for both DTT and DPS. In the present study, four and eight cytonuclear epistatic QTL significantly contributed to the variation in DTT and DPS, respectively. Importantly, most of these cytonuclear epistatic QTL cannot be detected using the interval mapping method. These results suggest that the maternal cytoplasm might also be involved in the regulation of flowering time by interacting with nuclear genes in maize.

Dynamic quantitative trait locus analysis of seed dormancy at three development stages in rice

In this study, a rice population of recombinant inbred lines (RILs) was used to determine the genetic characteristics of seed dormancy (SD) at 4 (early), 5 (middle) and 6 (late) weeks after heading stages. Dynamic analysis showed that the indica IR28 variety tended to have deeper dormancy than the japonica Daguandao at the middle and late development stages. The level of SD decreased with the process of seed development. The significant interaction between heading date (HD) and SD occurred only in those seeds collected at the early development stage. A total of nine additive quantitative trait loci (QTLs) and eight epistatic QTLs for SD were identified at three seed development stages. Of them, one additive and four epistatic QTLs were identified for the early stage, six additive and one epistatic QTL for the middle stage and two additive and three epistatic QTLs for the late stage. The phenotypic variation explained by each additive and epistatic QTL ranged from 5.8 to 30.6 % and from 3.8 to 13.1 %, respectively. Compared with the additive QTLs, epistatic interactions were much more important for SD at the early and late development stages. Two major additive QTLs, qSD3.1 and qSD4.1, were identified; each QTL could explain more than 20 % of the total phenotypic variance and each dormancy-enhancing allele could decrease the germination percentage by about 10 %. By comparing the chromosomal positions of these additive QTLs with those previously identified, five additive QTLs, qSD1.2, qSD2.1, qSD3.2, qSD4.1 and qSD9.1, might represent novel genes. One QTL identified here, qHD1, and nine QTLs identified in previous studies for HD were co-located with our QTLs for SD, which indicated that the significant correlation between SD and HD might be due to the linkage of QTLs for SD and HD. Four RILs with deep dormancy at development stages but non-dormancy after post-ripening under different germination conditions were selected. Using the selected RILs, three cross combinations of SD for the development of RIL populations were predicted. The selected RILs and the identified QTLs might be applicable for the improvement of pre-harvest sprouting tolerance by marker-assisted selection in rice.

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

Twelve major QTL in five optimal clusters and several epistatic QTL are identified for maize kernel size and weight, some with pleiotropic will be promising for fine-mapping and yield improvement. Kernel size and weight are important target traits in maize (Zea mays L.) breeding programs. Here, we report a set of quantitative trait loci (QTL) scattered through the genome and significantly controlled the performance of four kernel traits including length, width, thickness and weight. From the cross V671 (large kernel) × Mc (small kernel), 270 derived F2:3 families were used to identify QTL of maize kernel-size traits and kernel weight in five environments, using composite interval mapping (CIM) for single-environment analysis along with mixed linear model-based CIM for joint analysis. These two mapping strategies identified 55 and 28 QTL, respectively. Among them, 6 of 23 coincident were detected as interacting with environment. Single-environment analysis showed that 8 genetic regions on chromosomes 1, 2, 4, 5 and 9 clustered more than 60 % of the identified QTL. Twelve stable major QTLs accounting for over 10 % of phenotypic variation were included in five optimal clusters on the genetic region of bins 1.02-1.03, 1.04-1.06, 2.05-2.07, 4.07-4.08 and 9.03-9.04; the addition and partial dominance effects of significant QTL play an important role in controlling the development of maize kernel. These putative QTL may have great promising for further fine-mapping with more markers, and genetic improvement of maize kernel size and weight through marker-assisted breeding.

Identification of quantitative trait loci with main and epistatic effects for plant architecture traits in Upland cotton (Gossypium hirsutum L.)

AbstractPlant architecture is important for cotton cultivation and breeding. In this study, two mapping generations/populations F2 and F2:3 in Upland cotton (Gossypium hirsutum L.), derived from ‘Baimian1’ and TM‐1, were used to identify quantitative trait loci (QTLs) for 10 plant architecture traits. A total of 55 main‐effect QTLs (M‐QTLs) were detected. Four common M‐QTLs, qTFB‐10(F2/F2:3) for total fruit branches, qFBL‐26b(F2)/qFBL‐26(F2:3) for fruit branch length, qFBA‐5(F2/F2:3) for fruit branch angle and qFBN‐26b(F2)/qFBN‐26(F2:3) for fruit branch nodes, were found. The synergistic alleles and the negative alleles can be utilized in cotton plant architecture breeding programmes according to specific breeding objectives. Altogether 54 pairs of epistatic QTLs (E‐QTLs) exhibiting the interactions of additive‐by‐additive (AA), additive‐by‐dominant (AD), dominant‐by‐additive (DA) and dominant‐by‐dominant (DD) were detected. The epistasis appeared to be an important contributor to genetic variation in cotton plant architecture traits. Therefore, the identified markers associated with E‐QTLs as well as M‐QTLs will be of importance in future breeding programmes to develop cotton cultivars exhibiting desirable plant architecture.

Genetic analysis of single-locus and epistatic QTLs for seed traits in an adapted × nuña RIL population of common bean (Phaseolus vulgaris L.)

The QTLs analyses here reported demonstrate the significant role of both individual additive and epistatic effects in the genetic control of seed quality traits in the Andean common bean. Common bean shows considerable variability in seed size and coat color, which are important agronomic traits determining farmer and consumer acceptability. Therefore, strategies must be devised to improve the genetic base of cultivated germplasm with new alleles that would contribute positively to breeding programs. For that purpose, a population of 185 recombinant inbred lines derived from an Andean intra-gene pool cross, involving an adapted common bean (PMB0225 parent) and an exotic nuña bean (PHA1037 parent), was evaluated under six different--short and long-day--environmental conditions for seed dimension, weight, color, and brightness traits, as well as the number of seed per pod. A multi-environment Quantitative Trait Loci (QTL) analysis was carried out and 59 QTLs were mapped on all linkage groups, 18 of which had only individual additive effects, while 27 showed only epistatic effects and 14 had both individual additive and epistatic effects. Multivariate models that included significant QTL explained from 8 to 68 % and 2 to 15 % of the additive and epistatic effects, respectively. Most of these QTLs were consistent over environment, though interactions between QTLs and environments were also detected. Despite this, QTLs with differential effect on long-day and short-day environments were not found. QTLs identified were positioned in cluster, suggesting that either pleiotropic QTLs control several traits or tightly linked QTLs for different traits map together in the same genomic regions. Overall, our results show that digenic epistatic interactions clearly play an important role in the genetic control of seed quality traits in the Andean common bean.

Genetic variation underlying pod size and color traits of common bean depends on quantitative trait loci with epistatic effects

Common bean is an important vegetable legume in many regions of the world. Size and color of fresh pods are the key factors for deciding the commercial acceptance of bean as a fresh vegetable. The genetic basis of important horticultural traits of common bean is still poorly understood, which hinders DNA marker-assisted breeding in this crop. Here we report the identification of single-locus and epistatic quantitative trait loci (QTLs), as well as their environment interaction effects for six pod traits, namely width, thickness, length, size index, beak length and color, using an Andean intra-gene pool recombinant inbred line population from a cross between a cultivated common bean and an exotic nuna bean. The QTL analyses performed detected a total of 23 QTLs (single-locus QTLs and epistatic QTLs): five with only individual additive effects and six with only epistatic effects, while the remaining twelve showed both effects. These QTLs were distributed across linkage groups (LGs) 1, 2, 4, 6, 7, 8, 9, 10 and 11; particularly noteworthy are the QTLs for pod size co-located on LGs 1 and 4, indicative of tight linkage or genes with pleiotropic effects governing these traits. Overall, the results obtained showed that additive and epistatic effects are the major genetic basis of pod size and color traits. The mapping of QTLs including epistatic loci for the six pod traits evaluated provides support for implementing marker-assisted selection toward genetic improvement of common bean.

Identification of quantitative trait loci for bruchid (Caryedon serratus Olivier) resistance components in cultivated groundnut (Arachis hypogaea L.)

Groundnut bruchid (Caryedon serratus Olivier) is a major storage insect pest that significantly lowers the quality and market acceptance of the produce. Screening for resistance against groundnut bruchid in field conditions is difficult due to the variation in environmental factors and possible occurrence of biotypes. Hence, identification of tightly linked markers or quantitative trait loci (QTLs) is needed for selection and pyramiding of resistance genes for durable resistance. A population of recombinant inbred lines derived from a cross between VG 9514 (resistant) and TAG 24 (susceptible) was screened for five component traits of bruchid resistance in 2 years. The same population was genotyped with 221 polymorphic marker loci. A genetic linkage map covering 1,796.7 cM map distance was constructed with 190 marker loci in cultivated groundnut. QTL analysis detected thirteen main QTLs for four components of bruchid resistance in nine linkage groups and 31 epistatic QTLs for total developmental period (TDP). Screening in 2 years for bruchid resistance identified two common main QTLs. The common QTL for TDP, qTDP-b08, explained 57–82 % of phenotypic variation, while the other common QTL for adult emergence, qAE2010/11-a02, explained 13–21 % of phenotypic variation. Additionally, three QTLs for TDP, adult emergence and number of holes and one QTL for pod weight loss were identified which explained 14–39 % of phenotypic variation. This is the first report on identification of multiple main and epistatic loci for bruchid resistance in groundnut.

Epistatic Association Mapping for Alkaline and Salinity Tolerance Traits in the Soybean Germination Stage

Soil salinity and alkalinity are important abiotic components that frequently have critical effects on crop growth, productivity and quality. Developing soybean cultivars with high salt tolerance is recognized as an efficient way to maintain sustainable soybean production in a salt stress environment. However, the genetic mechanism of the tolerance must first be elucidated. In this study, 257 soybean cultivars with 135 SSR markers were used to perform epistatic association mapping for salt tolerance. Tolerance was evaluated by assessing the main root length (RL), the fresh and dry weights of roots (FWR and DWR), the biomass of seedlings (BS) and the length of hypocotyls (LH) of healthy seedlings after treatments with control, 100 mM NaCl or 10 mM Na2CO3 solutions for approximately one week under greenhouse conditions. A total of 83 QTL-by-environment (QE) interactions for salt tolerance index were detected: 24 for LR, 12 for FWR, 11 for DWR, 15 for LH and 21 for BS, as well as one epistatic QTL for FWR. Furthermore, 86 QE interactions for alkaline tolerance index were found: 17 for LR, 16 for FWR, 17 for DWR, 18 for LH and 18 for BS. A total of 77 QE interactions for the original trait indicator were detected: 17 for LR, 14 for FWR, 4 for DWR, 21 for LH and 21 for BS, as well as 3 epistatic QTL for BS. Small-effect QTL were frequently observed. Several soybean genes with homology to Arabidopsis thaliana and soybean salt tolerance genes were found in close proximity to the above QTL. Using the novel alleles of the QTL detected above, some elite parental combinations were designed, although these QTL need to be further confirmed. The above results provide a valuable foundation for fine mapping, cloning and molecular breeding by design for soybean alkaline and salt tolerance.

QTLs and epistasis for drought-tolerant physiological index in soybean (Glycine maxL.) across different environments

The additive and epistatic effects and their genotype × environment (GE) effects on percentage gain with physiological traits under different environments were analyzed in soybean. The genetic characters of physiological traits in soybean and characters of quantitative trait loci (QTL) detected in different environments are discussed. A backcross inbred line (BIL) population of soybean derived from the cross of strong drought tolerant wild soybean, “SNWS0048” as a recipient parent and drought-sensitive variety “Jinda73” as a donor parent was examined to identify the QTL and epistatic QTL and GE effects on drought-tolerant physiological traits in Shanxi, China, in 2008 and 2010. Six QTLs with additive (a) effects and/or additive × environment interaction (ae) effects and three pairs of QTLs with additive × epistatic main (aa) and/or epistasis × environment interaction (aae) effects were detected in different water environment over two years. Six QTLs with a effects and/or ae effects were mapped to linkage groups D2, G, M and N. Among them, there were two QTLs without ae effects, one with ae effects in all four environments, two QTLs with ae effects in two environments and one QTL with ae effects in only one environment. Among three pairs of QTLs, one pair was identified with aa effect and aae effect, one pair only exhibited aa effect, and one pair only had aae effect without aa effect. These indicated that different water environments could affect the expression of genes relevant to drought tolerant traits.

QTL Analysis of Yield Components in Rice Using a Cheongcheong/Nagdong Doubled Haploid Genetic Map

In this study, only two of 12 quantitative trait loci (QTLs) affecting yield and yield components were identified in a single year, indicating that individual QTLs are probably sensitive to the environment. A rice growth survey of “Cheongcheong” and “Nag dong” in a doubled haploid population in 2012 revealed that yield capacity was influenced by climate change. Analysis of yield and yield components indicated that five average traits are high in “Cheongcheong”. Frequency distribution tables indicated that panicles per plant (PPP), spike lets per panicle (SPP), and 1000-grain weight (TGW) were normally distributed. The strongest relationship was identified between SPP and seed set percentage (SSP) among phenotypic correlations related to yield and yield components found on chromosomes 2, 3, 6, 8 in 2012. SPP and SSP was a very relevant requisite about quantity. Analysis of QTL about quantity was total 9. In the present study, a doubled haploid population was used to analyze the epistatic effects on yield and yield components in rice. Although other epistatic QTLs were not included in any of the main-effect QTLs, they significantly influenced the traits. These results indicated that epistatic interaction plays an important role in controlling the expression of complex traits. Thus, the utilization of marker-assisted selection in rice breeding programs should take epistatic effects into consideration. Hence, the QTLs responsible for major effects are more suitable for marker-assisted selection programs to improve yield and related traits across different environments.

Epistatic Effects and QTL×Environment Interaction Effects of QTLs for Yield and Agronomic Traits in Soybean

Improving seed yield is an important goal of soybean breeding programs.In this investigation,a soybean SSR genetic linkage map constructed by a total of 447 recombinant inbred lines(RILs) derived from a cross between Jindou 23(cultivar,female parent) and ZDD2315(semi-wild,male parent) and the mixed linear model was used to identify the QTLs of yield and other QTLs for major agronomic traits in a two-year experiment.Nine QTLs bearing additive effects for pod position,plant height,node number on main stem,branch number,stem thickness and yield per plot were mapped in the linkage groups J_2,I,and M.The QTLs of yield per plot,stem thickness,plant height,branch number and node number on main stem showed positive additive effects donated by Jindou 23.Seven pairs of epistatic effects QTLs for pod position,plant height and stem thickness were detected,which had an interaction with environments.The results indicated that the epistatic effects and the environmental factors played an important role in yield per plot and agronomic traits in soybean.It will be very important to pay attention to not only QTLs with major effects but also those with epistatic effects in soybean molecular marker-assisted breeding in considering the stability expression and inheritance of the agronomic traits.

Mapping dynamic QTL for plant height in triticale.

BackgroundPlant height is a prime example of a dynamic trait that changes constantly throughout adult development. In this study we utilised a large triticale mapping population, comprising 647 doubled haploid lines derived from 4 families, to phenotype for plant height by a precision phenotyping platform at multiple time points.ResultsUsing multiple-line cross QTL mapping we identified main effect and epistatic QTL for plant height for each of the time points. Interestingly, some QTL were detected at all time points whereas others were specific to particular developmental stages. Furthermore, the contribution of the QTL to the genotypic variance of plant height also varied with time as exemplified by a major QTL identified on chromosome 6A.ConclusionsTaken together, our results in the small grain cereal triticale reveal the importance of considering temporal genetic patterns in the regulation of complex traits such as plant height.

Analysis of quantitative trait loci affecting chlorophyll content of rice leaves in a double haploid population and two backcross populations

Chlorophyll content, one of the most important physiological parameters related to plant photosynthesis, is usually used to predict yield potential. To map the quantitative trait loci (QTLs) underlying the chlorophyll content of rice leaves, a double haploid (DH) population was developed from an indica/japonica (Zhenshan 97/Wuyujing 2) crossing and two backcross populations were established subsequently by backcrossing DH lines with each of their parents. The contents of chlorophyll a and chlorophyll b were determined by using a spectrophotometer to directly measure the leaf chlorophyll extracts. To determine the leaf chlorophyll retention along with maturation, all measurements were performed on the day of heading and were repeated 30days later. A total of 60 QTLs were resolved for all the traits using these three populations. These QTLs were distributed on 10 rice chromosomes, except chromosomes 5 and 10; the closer the traits, the more clustering of the QTLs residing on common rice chromosomal regions. In general, the majority of QTLs that specify chlorophyll a content also play a role in determining chlorophyll b content. Strangely, chlorophyll content in this study was found mostly to be lacking or to have a negative correlation with yield. In both backcross F1 populations, overdominant (or underdominant) loci were more important than complete or partially dominant loci for main-effect QTLs and epistatic QTLs, thereby supporting previous findings that overdominant effects are the primary genetic basis for depression in inbreeding and heterosis in rice.

Genetic dissection of drought tolerance in chickpea (Cicer arietinum L.)

Key message Analysis of phenotypic data for 20 drought tolerance traits in 1–7 seasons at 1–5 locations together with genetic mapping data for two mapping populations provided 9 QTL clusters of which one present on CaLG04 has a high potential to enhance drought tolerance in chickpea improvement. Chickpea (Cicer arietinum L.) is the second most important grain legume cultivated by resource poor farmers in the arid and semi-arid regions of the world. Drought is one of the major constraints leading up to 50 % production losses in chickpea. In order to dissect the complex nature of drought tolerance and to use genomics tools for enhancing yield of chickpea under drought conditions, two mapping populations—ICCRIL03 (ICC 4958 × ICC 1882) and ICCRIL04 (ICC 283 × ICC 8261) segregating for drought tolerance-related root traits were phenotyped for a total of 20 drought component traits in 1–7 seasons at 1–5 locations in India. Individual genetic maps comprising 241 loci and 168 loci for ICCRIL03 and ICCRIL04, respectively, and a consensus genetic map comprising 352 loci were constructed (http://cmap.icrisat.ac.in/cmap/sm/cp/varshney/). Analysis of extensive genotypic and precise phenotypic data revealed 45 robust main-effect QTLs (M-QTLs) explaining up to 58.20 % phenotypic variation and 973 epistatic QTLs (E-QTLs) explaining up to 92.19 % phenotypic variation for several target traits. Nine QTL clusters containing QTLs for several drought tolerance traits have been identified that can be targeted for molecular breeding. Among these clusters, one cluster harboring 48 % robust M-QTLs for 12 traits and explaining about 58.20 % phenotypic variation present on CaLG04 has been referred as “QTL-hotspot”. This genomic region contains seven SSR markers (ICCM0249, NCPGR127, TAA170, NCPGR21, TR11, GA24 and STMS11). Introgression of this region into elite cultivars is expected to enhance drought tolerance in chickpea.Electronic supplementary materialThe online version of this article (doi:10.1007/s00122-013-2230-6) contains supplementary material, which is available to authorized users.

Conditional QTL Mapping of Sedimentation Volume on Seven Quality Traits in Common Wheat

To evaluate the possible genetic interrelationships between flour components and the sedimentation volume (SD), a doubled haploid (DH) population comprising 168 lines were used to identify the conditional quantitative trait loci (QTLs) for SD in three environments. Ten additive QTLs and 15 pairs of epistatic QTLs were detected for SD through unconditional and conditional QTL mapping. Three major additive QTLs were detected for SD conditioned on the seven quality traits. Two additive QTLs were found to be independent of these traits. Three additive QTLs were suppressed by three of the seven traits because of non-detection in unconditional mapping. Three pairs of epistatic QTLs were completely affected by the seven traits because of detection in unconditional mapping but no-detection in conditional mapping. Twelve pairs of epistatic QTLs were detected in conditional mapping. Our results indicated that conditional mapping could contribute to a better understanding of the interdependence of different and closely correlated traits at the QTL molecular level, especially some minor QTLs were found. The conditional mapping approach provides new insights that will make it possible to avoid the disadvantages of different traits by breeding through molecular design.

Dynamic Quantitative Trait Loci Analysis of Seed Reserve Utilization during Three Germination Stages in Rice

In this study, one rice population of recombinant inbred lines (RILs) was used to determine the genetic characteristics of seed reserve utilization during the early (day 6), middle (day 10) and late (day 14) germination stages. The seedling dry weight (SDW) and weight of the mobilized seed reserve (WMSR) were increased, while the seed reserve utilization efficiency (SRUE) decreased, during the process of seed germination. The SDW and WMSR were affected by the seed weight, while the SRUE was not affected by the seed weight. A total of twenty unconditional and twenty-one conditional additive QTLs and eight epistatic QTLs were identified at three germination stages, and the more QTLs were expressed at the late germination stage. Among them, twelve additive and three epistatic QTLs for SDW, eight additive and three epistatic QTLs for WMSR and thirteen additive and two epistatic QTLs for SRUE were identified, respectively. The phenotypic variation explained by each additive QTL, epistatic QTL and QTL × development interaction ranged from 6.10 to 23.91%, 1.79 to 6.88% and 0.22 to 2.86%, respectively. Two major additive QTLs qWMSR7.1 and qSRUE4.3 were identified, and each QTL could explain more than 20% of the total phenotypic variance. By comparing the chromosomal positions of these additive QTLs with those previously identified, eleven QTLs might represent novel genes. The best four cross combinations of each trait for the development of RIL populations were selected. The selected RILs and the identified QTLs might be applicable to improve rice seed reserve utilization by the marker-assisted selection approach.

Identification of QTLs with additive, epistatic and QTL × development interaction effects for seed dormancy in rice

Seed dormancy (SD) is an important agronomic trait affecting crop yield and quality. In this study, one rice population of recombinant inbred lines (RILs) was used to determine the genetic characteristics of SD at the early (4weeks after heading), middle (5weeks after heading) and late (6weeks after heading) development stages. The level of SD decreased with the process of seed development, and the SD was significantly affected by the heading date (HD) and temperature at the early and middle development stages. A total of eight additive quantitative trait loci (QTLs) for SD were identified at three development stages, and more QTLs were expressed at the early and late development stages. Among them, four, one and three additive QTLs were identified at the early, middle and late development stages, respectively. Epistatic QTLs and QTL-by-development interactions were detected by the joint analysis of multi-development phenotypic values, and one additive and two epistatic QTLs were identified. The phenotypic variation of SD explained by each additive, epistatic QTL and QTL×development interaction ranged from 8.0 to 13.5%, 0.7 to 3.9% and 1.3 to 2.8%, respectively. One major QTL qSD7.1 for SD was tightly linked to the major QTL qHD7.4 for HD, which might be applied to reveal the relationship of SD and HD. By comparing chromosomal positions of these additive QTLs with those previously identified, five additive QTLs qSD1.1, qSD2.1, qSD2.2, qSD4.1 and qSD4.2 might represent novel genes. The best three cross combinations for the development of RIL populations were predicted to improve SD. The selected RILs and the identified QTLs might be applicable to improve the rice pre-harvest sprouting tolerance by the marker-assisted selection approach.