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.     

Dynamic analysis of QTLs on tiller number in rice (Oryza sativa L.) with single segment substitution lines

Twelve single segment substitution lines (SSSLs) in rice, which contain quantitative trait loci (QTLs) for tiller number detected previously, were used to study dynamic expression of the QTLs in this study. These SSSLs and their recipient, Hua-Jing-Xian 74 (HJX74), were used to produce 78 crossing combinations first, and then these combinations and their parents were grown in two planting seasons with three cropping densities. Tiller number was measured at seven developmental stages. QTL effects including main effects (additive, dominance and epistasis), QTL?×?season and QTL?×?density interaction effects were analyzed at each measured stage. The additive, dominant and epistatic effects of the 12 QTLs as well as their interaction effects with the seasons and with the densities all display dynamic changes with the development. Eight QTLs are detected with significant additive effects and/or additive?×?season and/or additive?×?density interaction effects at least at one developmental stage, and all QTLs have significant dominant and epistatic effects and/or interaction effects involved in. For most of the QTLs dominant effects are much bigger than additive effects, showing overdominance. Each QTL interacts at least with eight other QTLs. Additive and dominant effects of these QTLs are mostly positive while epistatic effects are negative and minor. Most of the QTLs show significant interactions with planting seasons and cropping densities, but the additive effects of QTLs Tn3-1 and Tn3-2, the dominant effects of QTL Tn7 and Tn8, and the epistatic effects of 14 pairs of QTLs are stable across seasons and the dominant effect of QTL Tn3-3 and the epistatic effects of QTL pairs Tn2-1/Tn6-2, Tn2-1/Tn9 and Tn3-3/Tn6-3 are nearly consistent across cropping densities. This paper is the first report of dynamics on dominances and epistasis of QTLs for tiller number in rice and provides abundant information, which is useful to improve rice tiller number via heterosis and/or QTL pyramiding.     

Genetic dissection of developmental behavior of grain weight in wheat under diverse temperature and water regimes

As a quantitatively inherited trait related to high yield potential, grain weight (GW) development in wheat is constrained by abiotic stresses such as limited water supply and high temperature. Data from a doubled haploid population, derived from a cross of (Hanxuan 10?×?Lumai 14), grown in four environments were used to explore the genetic basis of GW developmental behavior in unconditional and conditional quantitative trait locus (QTL) analyses using a mixed linear model. Thirty additive QTLs and 41 pairs of epistatic QTLs were detected, and were more frequently observed on chromosomes 1B, 2A, 2D, 4A, 4B and 7B. No single QTL was continually active during all stages or periods of grain growth. The QTLs with additive effects (A-QTLs) expressed in the period S1|S0 (the period from the flowering to the seventh day after) formed a foundation for GW development. GW development at these stages can be used as an index for screening superior genotypes under diverse abiotic stresses in a wheat breeding program. One QTL, i.e. Qgw.cgb-6A.2, showed high adaptability for water-limited and heat-stress environments. Many A-QTLs interacted with more than one other QTL in the two genetic models, such as Qgw.cgb-4B.2 interacted with five QTLs, showing that the genetic architecture underlying GW development involves a collective expression of genes with additive and epistatic effects.     

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

Key message

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.

Abstract

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.     

QTL Analysis of Na+ and K+ Concentrations in Roots and Shoots under Different Levels of NaCl Stress in Rice (Oryza sativa L.)

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.     

家蚕茧质性状的QTL定位研究

采用QTLMapper 2．0 QTL作图软件,对F2群体的家蚕全茧量、茧层量、茧层率和蛹体重等性状进行了QTL定位分析,分别检测出7个、6个、2个、8个有显著效应分量的QTLs,分布于7个、5个、2个、7个不同的连锁群。控制全茧量、茧层量的QTLs一般存在复杂的上位性效应。对全茧量性状,有3对QTLs存在显著的加加上位性效应,其中1对还存在加显、显显互作;共有3个QTLs存在显著的显性效应,1个存在显著的加性效应。对茧层量QTLs,发现1对QTLs存在极显著的各项遗传效应,包括上位性效应;1对QTLs被检测到显著的显显互作,1个QTL具有显著的显性效应,并与另一个QTL存在显著的加加互作。茧层率、蛹体重主要受加性或显性的QTLs作用,没有发现茧层率QTLs的上位性效应,蛹体重的有效QTL大都呈现显著的负向显性效应,只有一对QTLs存在显著的加加上位性效应。第2、3、4、11、13、24、34、37、40连锁群是两个或多个性状QTLs分布的共同连锁群。全茧量和茧层量存在共同的QTL或染色体区域,育种上可通过适当选配,利用基因的互作效应,同步改良这两个性状。     

Bayesian model selection for genome-wide epistatic quantitative trait loci analysis

The problem of identifying complex epistatic quantitative trait loci (QTL) across the entire genome continues to be a formidable challenge for geneticists. The complexity of genome-wide epistatic analysis results mainly from the number of QTL being unknown and the number of possible epistatic effects being huge. In this article, we use a composite model space approach to develop a Bayesian model selection framework for identifying epistatic QTL for complex traits in experimental crosses from two inbred lines. By placing a liberal constraint on the upper bound of the number of detectable QTL we restrict attention to models of fixed dimension, greatly simplifying calculations. Indicators specify which main and epistatic effects of putative QTL are included. We detail how to use prior knowledge to bound the number of detectable QTL and to specify prior distributions for indicators of genetic effects. We develop a computationally efficient Markov chain Monte Carlo (MCMC) algorithm using the Gibbs sampler and Metropolis-Hastings algorithm to explore the posterior distribution. We illustrate the proposed method by detecting new epistatic QTL for obesity in a backcross of CAST/Ei mice onto M16i.     

Study of strategies for selecting quantitative trait locus mapping procedures by computer simulation

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.     

Genetic analysis and QTL mapping for grain chalkiness characteristics of brown rice (Oryza sativa L.)

Grain chalkiness is one of the important appearance qualities in rice marketing. But it is a complex trait, controlled by polygenes and easily influenced by the environment. Genetic analysis and QTL detection was carried out on six characteristics of grain chalkiness consisting of the percentage of chalkiness (PGC), white belly (PWB) and white core grains (PWC), and the area of chalkiness (CA), white belly (WBA) and white core (WCA) in brown rice. A total of 16 main-effect QTLs associated with chalkiness characteristics of brown rice were mapped on seven chromosomes over two years. Among them,qPGC7.1 andqPWB7.2 were simultaneously located on chromosome 7 flanked by 7038 and 7042 at LOD scores 4.34 and 3.76, whileqPWC2.1 andqWCA2.1 were simultaneously located on chromosome 2 flanked by RM492 and RM324 with LOD scores of 2.50 and 3.39. Twelve epistatic combinations were detected for five chalkiness characteristics except for CA. Results indicated that WBA was mainly influenced by the additive effects of main-effect QTLs. PGC and PWC were affected by the effects of epistatic QTLs and the interactions between additive-by-additive effects and the environment. The effects of epistatic QTLs and the main-effect QTLs played important roles on CA, PWB and WCA. For the genetic improvement of grain chalkiness in breeding system, more attention should be paid to epistatic effects and the additive effects of main-effect QTLs.     

Epistasis for fitness-related quantitative traits in Arabidopsis thaliana grown in the field and in the greenhouse

The extent to which epistasis contributes to adaptation, population differentiation, and speciation is a long-standing and important problem in evolutionary genetics. Using recombinant inbred (RI) lines of Arabidopsis thaliana grown under natural field conditions, we have examined the genetic architecture of fitness-correlated traits with respect to epistasis; we identified both single-locus additive and two-locus epistatic QTL for natural variation in fruit number, germination, and seed length and width. For fruit number, we found seven significant epistatic interactions, but only two additive QTL. For seed germination, length, and width, there were from two to four additive QTL and from five to eight epistatic interactions. The epistatic interactions were both positive and negative. In each case, the magnitude of the epistatic effects was roughly double that of the effects of the additive QTL, varying from -41% to +29% for fruit number and from -5% to +4% for seed germination, length, and width. A number of the QTL that we describe participate in more than one epistatic interaction, and some loci identified as additive also may participate in an epistatic interaction; the genetic architecture for fitness traits may be a network of additive and epistatic effects. We compared the map positions of the additive and epistatic QTL for germination, seed width, and seed length from plants grown in both the field and the greenhouse. While the total number of significant additive and epistatic QTL was similar under the two growth conditions, the map locations were largely different. We found a small number of significant epistatic QTL x environment effects when we tested directly for them. Our results support the idea that epistatic interactions are an important part of natural genetic variation and reinforce the need for caution in comparing results from greenhouse-grown and field-grown plants.     

Mapping quantitative trait loci with epistatic effects

Epistatic variance can be an important source of variation for complex traits. However, detecting epistatic effects is difficult primarily due to insufficient sample sizes and lack of robust statistical methods. In this paper, we develop a Bayesian method to map multiple quantitative trait loci (QTLs) with epistatic effects. The method can map QTLs in complicated mating designs derived from the cross of two inbred lines. In addition to mapping QTLs for quantitative traits, the proposed method can even map genes underlying binary traits such as disease susceptibility using the threshold model. The parameters of interest are various QTL effects, including additive, dominance and epistatic effects of QTLs, the locations of identified QTLs and even the number of QTLs. When the number of QTLs is treated as an unknown parameter, the dimension of the model becomes a variable. This requires the reversible jump Markov chain Monte Carlo algorithm. The utility of the proposed method is demonstrated through analysis of simulation data.     

Unconditional and conditional QTL analysis of kernel weight related traits in wheat (Triticum aestivum L.) in multiple genetic backgrounds

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.     

Dynamic Quantitative Trait Locus Analysis of Seed Vigor at Three Maturity Stages in Rice

Seed vigor is an important characteristic of seed quality. In this study, one rice population of recombinant inbred lines (RILs) was used to determine the genetic characteristics of seed vigor, including the germination potential, germination rate, germination index and time for 50% of germination, at 4 (early), 5 (middle) and 6 weeks (late) after heading in two years. A total of 24 additive and 9 epistatic quantitative trait loci (QTL) for seed vigor were identified using QTL Cartographer and QTLNetwork program respectively in 2012; while 32 simple sequence repeat (SSR) markers associated with seed vigor were detected using bulked segregant analysis (BSA) in 2013. The additive, epistatic and QTL × development interaction effects regulated the dry maturity developmental process to improve seed vigor in rice. The phenotypic variation explained by each additive, epistatic QTL and QTL × development interaction ranged from 5.86 to 40.67%, 4.64 to 11.28% and 0.01 to 1.17%, respectively. The QTLs were rarely co-localized among the different maturity stages; more QTLs were expressed at the early maturity stage followed by the late and middle stages. Twenty additive QTLs were stably expressed in two years which might play important roles in establishment of seed vigor in different environments. By comparing chromosomal positions of these stably expressed additive QTLs with those previously identified, the regions of QTL for seed vigor are likely to coincide with QTL for grain size, low temperature germinability and seed dormancy; while 5 additive QTL might represent novel genes. Using four selected RILs, three cross combinations of seed vigor for the development of RIL populations were predicted; 19 elite alleles could be pyramided by each combination.     

Use of randomization testing to detect multiple epistatic QTLs

Here, we describe a randomization testing strategy for mapping interacting quantitative trait loci (QTLs). In a forward selection strategy, non-interacting QTLs and simultaneously mapped interacting QTL pairs are added to a total genetic model. Simultaneous mapping of epistatic QTLs increases the power of the mapping strategy by allowing detection of interacting QTL pairs where none of the QTL can be detected by their marginal additive and dominance effects. Randomization testing is used to derive empirical significance thresholds for every model selection step in the procedure. A simulation study was used to evaluate the statistical properties of the proposed randomization tests and for which types of epistasis simultaneous mapping of epistatic QTLs adds power. Least squares regression was used for QTL parameter estimation but any other QTL mapping method can be used. A genetic algorithm was used to search for interacting QTL pairs, which makes the proposed strategy feasible for single processor computers. We believe that this method will facilitate the evaluation of the importance at epistatic interaction among QTLs controlling multifactorial traits and disorders.     

Analysis on additive effects and additive-by-additive epistatic effects of QTLs for yield traits in a recombinant inbred line population of rice

A linkage map consisting of 158 DNA markers were constructed by using a recombinant inbred line (RIL) population derived from the indica-indica rice cross Zhenshan 97B 2 Milyang 46. Quantitative trait loci (QTLs) conditioning grain yield and five yield component traits were determined at the one-locus and two-locus levels, and genotype-by-environment (GE) interactions were analyzed. Thirty-one QTLs were detected to have significant additive effects for yield traits, of which 12 also exhibited significant epistatic effects. Sixteen significant additive-by-additive (AA) interactions were detected, of which nine occurred between QTLs with own additive effects (M_epQTLs), four occurred between QTLs showing epistatic effects only (epQTLs), and three occurred between M_epQTLs and epQTLs. Significant GE interactions were found for six QTLs with additive effects and one AA interaction. Generally, the contributions to the phenotypic variation were higher due to QTL main effects than to epistatic effects. The detection of additive effects and AA effects of a QTL interfered with each other, indicating that the detection of QTLs with main effects, as well as the magnitude and directions of the additive effects, might vary depending on their interactions with other loci.     

Mapping QTLs with epistatic effects and QTL×environment interactions for plant height using a doubled haploid population in cultivated wheat

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×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).     

水稻株高上位性效应和ＱＥ互作效应的ＱＴＬ遗传研究

利用基因混合模型的ＱＴＬ定位方法研究了由籼稻品种ＩＲ６４和粳稻品种Ａzucena杂交衍生的ＤＨ群体在４个环境中的ＱＴＬ上位性效应和环境互作效应,结果表明,上位性是数量性状的重要遗传基础,并揭示了上位性的几个重要特点,所有的ＱＴＬ都参与了上位性效应的形成,６４％的ＱＴＬ还具有本身的加性效应,因此传统方法对ＱＴＬ加性效应的估算会由于上位性的影响而有偏,其他３６％的ＱＴＬ没有本身的加性效应,却参与了４８％的上位性互作用,这些位点可能通过诱发和修饰其他位点而起作用,上位性的特点还包括,经常发现了一个ＱＴＬ与多个ＱＴＬ发生互作;大效应的ＱＴＬ也参与上位性互作;上位性互作受环境影响,ＱＴＬ与环境的互效应比ＱＴＬ的主效应更多地被检测到,表明数量性状基因的表达易受环境影响。