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 Rhtl 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）.

QTL Mapping for Wheat Flour Color with Additive,Epistatic,and QTL×Environmental Interaction Effects

To investigate genetic factors affecting wheat flour color traits,a linkage map was constructed using a recombinant inbred line （RIL） population derived from Jing 771×Pm 97034.Main,epistatic and QTL×environment （QE） interaction effects of quantitative trait loci （QTLs） controlling wheat flour color were studied by the mixed linear modeling of data collected from wheat RIL plants under three different environmental conditions.13 QTLs with additive effects and 55 pairs of QTLs with epistatic effects were detected for wheat flour color traits.The additive-additive interactions （AA） involved all of the wheat chromosomes except 3D.Epistasis accounted for more of the observed phenotypic variation than did the main effect QTLs （M-QTLs）.Our results suggested that dual-locus interactions are widespread in the wheat genome and play a critical role in determining wheat flour color characteristics.In this study,3 QTLs were identified to have QE interaction effects,one of them showing significant QE interaction in E2 environment.

Genetic background effects on QTL and QTL × environment interaction for yield and its component traits as revealed by reciprocal introgression lines in rice

QTLs for quantitative traits are influenced by genetic background(GB) and environment.Identification of QTL with GB independency and environmental stability is prerequisite for effective marker-assisted selection(MAS). In this study, QTLs and QTL × environment interactions affecting grain yield per plant(GY) and its component traits, filled grain number per panicle(FGN), panicle number per plant(PN) and 1000-grain weight(TGW) across six environments were dissected using two sets of reciprocal introgression lines(ILs) derived from the cross Lemont × Teqing and SNP genotypic data. ANOVA indicated that the differences among genotypes and environments within each set of ILs were highly significant for all traits. A total of 72 distinct QTLs for GY and its component traits including 15 for GY, 25 for FGN, 18 for PN, and 29 for TGW were detected over the six environments. Most QTLs(87.4%) showed significant QTL × environment interactions(QEIs) and appeared to be more or less environment-specific. Among 72 QTLs, 15(20.8%) QTLs and 12(16.7%) QEIs were commonly identified in both backgrounds, indicating QTL especially QEI for yield and its component traits had strong GB effects. Four QTL regions affecting GY and its component traits, including S1269707–S4288071, S16661497–S17511092, and S35861863–S36341768 on chromosome 3, and S4134205–S7643153 on chromosome 5, were detected in both backgrounds and coincided with cloned genes for yield-related traits. These regions can be the targeted in rice breeding for high yield potential through MAS. Application of QTL main effects and their environmental interaction effects in MAS was discussed in detail.

Influence of Epistasis and QTL×Environment Interaction on Heading Date of Rice(Oryza sativa L.)

QTLs for heading date of rice （Oryza sativa L.） with additive, epistatic, and QTL × environment （QE） interaction effects were studied using a mixed-model-based composite interval mapping （MCIM） method and a double haploid （DH） population derived from IR64/Azucena in two crop seasons. Fourteen QTLs conferring heading date in rice, which were distributed on ten chromosomes except for chromosomes 5 and 9, were detected. Among these QTLs, eight had single-locus effects, five pairs had double-locus interaction effects, and two single-loci and one pair of double-loci showed QTL × environment interaction effects. All predicted values of QTL effects varied from 1.179 days to 2.549 days, with corresponding contribution ratios of 1.04%-4.84%. On the basis of the effects of the QTLs, the total genetic effects on rice heading date for the two parents and the two superior lines were predicted, and the putative reasons for discrepancies between predicted values and observed values, and the genetic potentiality in the DH population for improvement of heading date were discussed. These results are in agreement with previous results for heading date in rice, and the results provide further information, which indicate that both epistasis and QE interaction are important genetic basis for determining heading date in rice.

QTL Mapping and Q×E Interactions of Grain Cooking and Nutrient Qualities in Rice Under Upland and Lowland Environments

Grain cooking and nutrient qualities are the most important components of rice （Oryza sativa L.） quality. A doubled haploid （DH） population from a cross between two japonica cultivars was used to examine the phenotypic values and potential QTLs for the quality traits. The cooking and nutrient quality traits, including the amylose content （AC）, the gel consistency （CJC）, the gelatinization temperature （GT）, and the protein content （PC）, in rice grown under upland and lowland environments were evaluated. Significant differences for AC, GC, GT, and PC between upland and lowland environments were detected. The phenotypic values of all four traits were higher under upland environment than lowland environment. The value of PC under upland environment was significantly higher （by 37.9%） than that under lowland environment. This suggests that upland cultivation had large effect on both cooking and nutrient qualifies. A total of seven QTLs and twelve pairs of QTLs were detected to have significant additive and epistatic effects for the four traits. Significant Q x E interaction effects of two QTLs and two pairs of QTLs were also discovered. The general contribution of additive QTLs ranged from 1.91% to 19.77%. The Q × E interactions of QTLs QGt3 and QAc6 accounted for 8.99% and 47.86% of the phenotypic variation, respectively, whereas those of the 2 pairs of epistatic QTLs, QAc6-QAcllb and QAc8-QAc9, accounted for 32.54% and 11.82%, respectively. Five QTLs QGt6b, QGt8, QGt11, QGcl, and QPc2, which had relatively high general contribution and no Q x E interactions, were selected to facilitate the upland rice grain quality breeding.

QTL mapping and QTL × environment interaction analysis of multi-seed pod in cultivated peanut(Arachis hypogaea L.)

To dissect the genetic mechanism of multi-seed pod in peanut, we explored the QTL/gene controlling multi-seed pod and analyzed the interaction effect of QTL and environment. Two hundred and forty eight recombinant inbred lines(RIL) from cross Silihong × Jinonghei 3 were used as experimental materials planted in 8 environments from 2012 to 2017. Three methods of analysis were performed. These included individual environment analysis, joint analysis in multiple environments, and epistatic interaction analysis for multi-seed pod QTL. Phenotypic data and best linear unbiased prediction(BLUP) value of the ratio of multi-seed pods per plant(RMSP) were used for QTL mapping. Seven QTL detected by the individual environmental mapping analysis and were distributed on linkage groups 1, 6, 9, 14, 19(2), and 21. Each QTL explained 4.42%–11.51% of the phenotypic variation in multi-seed pod, and synergistic alleles of5 QTL were from the Silihong parent. One QTL, explaining 4.93% of the phenotypic variation was detected using BLUP data, and this QTL mapped in the same interval as q RMSP19.1 detected in the individual environment analysis. Seventeen additive QTL were identified by joint analysis across multiple environments. A total of 43 epistatic QTL were detected by ICIM-EPI mapping in the multiple environment trials(MET) module, and involved 57 loci. Two main-effect QTL related to multi-seed pod in peanut were filtered. We also found that RMSP had a highly significant positive correlation with pod yield per plant(PY), and epistatic effects were much more important than additive effects. These results provide theoretical guidance for the genetic improvement of germplasm resources and further fine mapping of related genes in peanut.

Genetic and Genotype × Environment Interaction Effects for Appearance Quality of Rice

This study was conducted to generate genetic information in rice varieties based on a complete diallel crosses over two years. The results indicated that genotype effect was significant for all traits. Genotype × environment interaction effects were significant only for cooked grain length （CGL） and cooked grain shape （CGSH）. General combining ability （GCA） and specific combining ability （SCA） effects were significant for entire traits, which indicated the important roles of both additive and non-additive gene actions. GCA x environment interaction effects were significant for CGL, CGSH and grain elongation index （GEI）. In the controlling of the inheritance of milled grain shape （GSH）, milled grain width （MGW）, GEI, milled grain length （MGL）, CGSH and cooked grain width （CGW）, the additive gene effects were more important than non-additive one. The average degree of dominance was within the range of partial dominance for all of the traits. The narrow-sense heritability was ranged from 0.65 （GSH） to 0.36 （CGL）. GCA effects were significant for all of the parents in milled grain length and it was significant for some of the parents in other traits. The crosses of Deilmani × IRFAON-215 exhibited significant SCA for GEI. The positive mean of heterosis was observed for CGW. The highest maximum values of heterosis were revealed in GEI, flowed by GSH, MGW and CGW. GCA and MPV were significantly and positively correlated together for all traits.

Mapping epistasis and environment × QTX interaction based on four-omics genotypes for the detected QTX loci controlling complex traits in tobacco

Using newly developed methods and software, association mapping was conducted for chromium content and total sugar in tobacco leaf, based on four-omics datasets. Our objective was to collect data on genotype and phenotype for 60 leaf samples at four developmental stages, from three plant architectural positions and for three cultivars that were grown in two locations. Association mapping was conducted to detect genetic variants at quantitative trait SNP(QTS) loci, quantitative trait transcript(QTT) differences,quantitative trait protein(QTP) variability, and quantitative trait metabolite(QTM) changes,which can be summarized as QTX locus variation. The total heritabilities of the four-omics loci for both traits tested were 23.60% for epistasis and 15.26% for treatment interaction.Epistasis and environment × treatment interaction had important impacts on complex traits at all-omics levels. For decreasing chromium content and increasing total sugar in tobacco leaf, six methylated loci can be directly used for marker-assisted selection, and expression of ten QTTs, seven QTPs and six QTMs can be modified by selection or cultivation.

The Roles of Biotic Interactions and Environmental Factors on Multispecies Dynamics

The distributions of species in their habitats are constantly changing. This phenomenon is thought to be determined by species’ environmental tolerance and biotic interactions for limited resources and space. Consequently, predicting the future distribution of species is a major challenge in ecology. To address this problem, we use mathematical model to study the combined effects of biotic interactions (e.g. competition) and environmental factors on multiple species community assembly in a heterogenous environment. To gain insights into the dynamics of this ecological system, we perform both analytical and numerical analyses of the range margins of the species. We observe that the range margins of the species can be influenced by biotic interactions combined with environmental factors. Depending on the strength of biotic interactions, our model exhibits coexistence of species and priority effects;mediated by weak and intense biotic interactions respectively. We also show the existence of bifurcation points (i.e. the threshold values of competition coefficient) which lead to the presence—absence of different species. Thus, we suggest that adequate knowledge of biotic interactions and changes in the environments is important for effective maintenance of biodiversity and conservation management.

Multi-Environment Evaluation and Genotype ×Environment Interaction Analysis of Sorghum [<i>Sorghum bicolor</i>(L.) Moench] Genotypes in Highland Areas of Ethiopia

Sorghum [Sorghum bicolor (L.) Moench] is a high-yielding, nutrient-use efficient, and drought tolerant crop that can be cultivated on over 80 per cent of the world’s agricultural land. However, a number of biotic and abiotic factors are limiting grain yield increase. Diseases (leaf and grain) are considered as one of the major biotic factors hindering sorghum productivity in the highland and intermediate altitude sorghum growing areas of Ethiopia. In addition, the yield performance of crop varieties is highly influenced by genotype × environment (G × E) interaction which is the major focus of researchers while generating improved varieties. In Ethiopia, high yielding and stable varieties that withstand biotic stress in the highland areas are limited. In line with this, the yield performance of 21 sorghum genotypes and one standard check were evaluated across 14 environments with the objectives of estimating magnitude G × E interaction for grain yield and to identify high yielder and stable genotypes across environments. The experiment was laid out using Randomized Complete Block Design with three replications in all environments. The combined analysis of variance across environments revealed highly significant differences among environments, genotypes and G × E interactions of grain yield suggesting further analysis of the G × E interaction. The results of the combined AMMI analysis of variance indicated that the total variation in grain yield was attributed to environments effects 71.21%, genotypes effects 4.52% and G × E interactions effects 24.27% indicating the major sources of variation. Genotypes 2006AN7010 and 2006AN7011 were high yielder and they were stable across environments and one variety has been released for commercial production and can be used as parental lines for genetic improvement in the sorghum improvement program. In general, this research study revealed the importance of evaluating sorghum genotypes for their yield and stability across diverse highland areas of Ethiopia before releasing for commercial production.

QTL mapping for leaf area in maize (Zea mays L.) under multienvironments

Leaves are the main organs of photosynthesis in green plants. Leaf area plays a vital role in dry matter accumulation and grain yield in maize （Zea mays L.）. Thus, investigating the genetic basis of leaf area will aid efforts to breed maize with high yield. In this study, a total of 150 F7 recombinant inbred lines （RILs） derived from a cross between the maize lines Xu 178 and K12 were used to evaluate three ear-leaves area （TELA） under multi-environments. Inclusive composite interval map- ping （ICIM） was used to identify quantitative trait loci （QTLs） for TELA under a single environment and estimated breeding value （EBV）. A total of eight QTLs were detected under a single environmental condition, and four QTLs were identified for EBV which also can be detected in single environment. This indicated that the EBV-detected QTLs have high genetic stability. A major QTL （qTELA_2-9） located in chromosome bin 2.04/2.05 could be detected in four environments and has a high phenotypic contribution rate （ranging from 10.79 to 16.51%） that making it a good target for molecular breeding. In addition, joint analysis was used to reveal the genetic basis of leaf area in six environments. In total, six QTLxenvironment interactions and nine epistatic interactions were identified. Our results reveal that the genetic basis of the leaf area is not only mainly determined by additive effects, but also affected by epistatic effects environmental interaction effects.

芥菜型油菜每角籽粒数QTL的上位性互作和环境互作分析

【目的】为揭示芥菜型油菜及芸薹属作物每角籽粒数形成的分子机理,提高和改良芥菜型油菜产量和育种工作奠定基础。【方法】研究以包含221个芥菜型油菜株系的重组自交系(recombinant inbred line,RIL)群体为材料,在5个环境条件下对每角籽粒数性状进行加性QTL、加性×加性上位互作及环境互作分析。【结果】(1)共检测到7个与每角籽粒数相关的加性QTL,主要分布在芥菜型油菜A02、A03、A05、A08、B02和B03等染色体上,其加性效应分布在(-11.6424)~4.5246之间,其中qSS2-71的加性效应和遗传率均最大,分别达到-11.6424和14.44%,其余6个加性QTL的加性效应和遗传率均较小;(2)检测到7对影响每角籽粒数的加性×加性QTL上位互作效应及其与环境的互作效应,上位性QTL互作效应值分布在(-4.9308)~4.1936之间,7对上位性QTL与不同环境互作的遗传力均接近0;(3)每角籽粒数性状的广义遗传率为80.98%,狭义遗传率为30.98%。【结论】综合分析,芥菜型油菜每角籽粒数受一定环境影响,但控制该性状的加性效应受环境影响较小,且其加性×加性上位性QTL互作效应不明显。

多环境下的稻谷粒厚QTL定位分析

【目的】粒厚性状是影响水稻产量和稻米食味品质的控制因子之一,运用籼爪交(V20B/CPSLO17)遗传背景的重组自交系(RIL)开展稻谷粒厚QTL定位分析,获得粒厚性状主效QTL,为新的粒厚基因挖掘和开发功能分子标记提供科学依据。【方法】在水稻高密度遗传连锁图谱和籼爪交遗传背景的RIL群体基础上,结合3种不同生态环境(2020年贵州贵阳、2021年贵州贵定、2021年海南三亚)下RIL群体的稻谷粒厚性表型数据,运用IciMapping 4.0软件的ICIM-ADD方法进行粒厚QTL定位及其遗传效应分析。【结果】稻谷粒厚性状在3种生态环境下均呈现连续单峰分布,其受种植环境因子影响不显著。3种不同生态环境的水稻中共检测到分布在第3、5、8和10号染色体上的5个稻谷粒厚QTL(qGT3-1、qGT5-1、qGT5-2、qGT8-1和qGT10-1),它们的增效等位基因均来自亲本V20B,LOD值在3.431~14.081,表型贡献率变幅为5.479%~26.483%。2个QTL(qGT5-1和qGT5-2)的表型贡献率超过10%,其中qGT5-2是唯一在2种生态环境(2020年贵州贵阳、2021年海南三亚)下被反复检测到的,分别解释群体表型变异率的26.483%和14.933%。QTL qGT5-2位点在染色体上的物理距离约为3.9 kb,仅有1个候选基因(LOC_Os05g07920);qGT8-1位点的物理距离约为2.3 kb,仅有1个候选基因(LOC_Os08g10360)。【结论】稻谷粒厚性状呈现出受多基因调控的数量性状遗传特性。qGT5-2是1个稳定遗传且贡献率高的稻谷粒厚主效QTL,对粒厚调控基因挖掘和优质丰产稻新品种培育具有重要的应用潜力。

Analysis on Interaction Effects Between Variety and Site of Silage Maize Regional Test in Guizhou Province

Taking the yield in the second group of Guizhou silage maize regional test in 2019 as data information, 8 experimental sites and 12 silage maize varieties as experimental objects, the interaction effect between gene and environment was analyzed by using AMMI model. The results showed that the average fresh weight yield of each variety was 3 199.5~3 976.6 kg/667m^(2), among them, 5 varieties had an increase in the yield. Variety variation accounted for 10.51% of the total variation;experimental site variation accounted for 63.22% of the total variation;interaction effect variation between gene and environment accounted for 26.28% of the total variation;IPCA1 and IPCA2 variation accounted for 50.7% and 31.2% of the interaction variation, respectively;IPCA3 variation accounted for 7.25% of the interaction variation. g_4, g_8, g_9, g_10, g_11 and g_12 had better adaptability to e_1, e_2, e_6 and e_7;while g_1, g_2, g_3, g_5, g_6 and g_7 had better adaptability to e_3, e_4, e_5 and e_8. In consideration of yield, g_1(Huinongqing 2) and g_9(Xinyu 666) were silage maize varieties with high and stable yield;g_3(Hemuyu 905), g_8(Wuhuayu 3) and g_11(Liangdu 191) had general stability, and their yield was higher than that of the control;g_12(Jinduyu 999) had the worst stability and low yield.

One compound approach combining factor-analytic model with AMMI and GGE biplot to improve multi-environment trials analysis

To improve multi-environmental trial(MET)analysis,a compound method—which combines factor analytic(FA)model with additive main effect and multiplicative interaction(AMMI)and genotype main effect plus genotype-by-environment interaction(GGE)biplot—was conducted in this study.The diameter at breast height of 36 open-pollinated(OP)families of Pinus taeda at six sites in South China was used as a raw dataset.The best linear unbiased prediction(BLUP)data of all individual trees in each site was obtained by fitting the spatial effects with the FA method from raw data.The raw data and BLUP data were analyzed and compared by using the AMMI and GGE biplot.BLUP results showed that the six sites were heterogeneous and spatial variation could be effectively fitted by spatial analysis with the FA method.AMMI analysis identified that two datasets had highly significant effects on the site,family,and their interactions,while BLUP data had a smaller residual error,but higher variation explaining ability and more credible stability than raw data.GGE biplot results revealed that raw data and BLUP data had different results in mega-environment delineation,test-environment evaluation,and genotype evaluation.In addition,BLUP data results were more reasonable due to the stronger analytical ability of the first two principal components.Our study suggests that the compound method combing the FA method with the AMMI and GGE biplot could improve the analysis result of MET data in Pinus teada as it was more reliable than direct AMMI and GGE biplot analysis on raw data.

Genetic relationship of rapeseed yield-related traits revealed by elite lines under multiple environments

To provide a theoretical basis for further improvement of Brassica napus yield, additive dominance with additive - by - additive epistatic effects （ ADAA） genetic model and a 6 X 8 partial dial- lel cross design were used to analyze the genetic effects and correlations of five yield related traits of 14 excellent Brassica napus parental lines and their 46 and F2 populations. The results showed that silique density （SD） , siliques per plant （SPP） , seeds per silique （SPS） and thousand - seed weight （TSW） exhibited not only additive and dominant effects, but also significant epistatic effects. The dominant effects of all five yield - related traits were obviously greater than their additive effects and epistatic effects. Yield per plant （YPP） showed significant genetic correlation with SD, SPP and SPS, and the main component of the genetic correlation was the dominance correlation. SPP and SPS both showed a significant negative correlation with TSW. The SD of rapeseed was genetically correlated with all three components of yield to a certain extent, and there were different components of genetic effects positively correlated with the three yield components, indicating that SD is a potential trait to reconcile the conflict between TSW and SPP as well as SPS.

城市人居环境与人口迁移互动效应研究

本文借鉴人类发展指数的构建方法,基于移民福祉角度,从经济环境、公共服务、居住环境、制度环境和城市现代化5个方面重新评估城市人居环境质量,并利用2期人口迁移数据,对中国279个地级市的人居环境进行互动效应分析,探索城市人居环境与人口迁移之间的动态响应机制。研究发现:(1)样本考察期内,中国城市人居环境有一定改善,城市建设水平差距缩小,空间上呈现出“东高西低”特征,中心城市集聚效应明显,东部地区城市出现“俱乐部趋同”现象,中西部内陆地区陷入“低值陷阱”。(2)全国人口净迁移格局呈现出“中间低、周围高”的凹形空间特征,迁移人口主要分布在人口规模超过500万人的特大和超大城市,但迁移人口增长主要动力点是中小规模城市。(3)城市人居环境的改善对提高人口迁移流动具有积极效应,经济发展差异和社会融合水平是引发人口迁移规模空间差异的核心因素,生活舒适性逐渐成为驱动人口迁移的重要来源,在省级尺度上各驱动要素体现出空间异质性。(4)人口迁移规模尚不足以成为城市人居环境变化的稳定主导因素之一。据此提出以增进福祉为要义的城市人居环境建设应适配于新发展阶段人口特征变化以推进新型城镇化高质量发展。

不同环境下多个玉米穗部性状的QTL分析

【目的】探讨穗部性状之间的相互关系及其遗传机制。【方法】以优良玉米自交系黄早四为共同亲本,分别与掖478和齐319杂交,构建两套F2:3群体为研究材料(分别缩写为Y/H和Q/H),在2007年和2008年分别在北京、河南、新疆等3个地点共6个环境下进行了穗长、穗粗、穗行数和穗粒重4个性状的表型鉴定,采用单环境分析和多年多点的联合分析方法对其进行了数量性状位点(QTL)分析。【结果】在单环境分析中,2个群体分别检测到33个QTL和46个QTL,主要分布在第4、5、6、7、10染色体上。进一步分析发现,在Y/H群体中共定位到4个环境钝感的QTL(即在2或2以上环境下均能被检测到的QTL,且在联合分析中与环境无互作效应),其中以位于第4、5染色体上的qGW1-4-1、qKRE1-5-1对表型的贡献率最大,在不同的环境中对表型的贡献率均大于10%;在Q/H群体中共定位到6个环境钝感的QTL,其中以qKRE2-3-2、qED2-2-1对表型的贡献率最大,分别解释7.23%—18.3%和7.1%—15.6%表型变异。通过多个环境的联合分析,Y/H和Q/H群体分别检测到2个和6个QTL与环境存在显著互作,且以穗粒重与环境互作的QTL最多,而其它性状的大部分QTL与环境的互作效应不显著。上位性分析结果表明,只有少数几个显著QTL位点参与上位性互作,而大部分上位性QTL为非显著位点间的互作,对表型的贡献率较小。比较分析2个群体的QTL定位结果,在2个群体间共检测到4对共有QTL,分别与穗粒重和穗行数相关,位于bin1.10、bin5.05、bin6.05和bin7.02。【结论】这些在不同环境或不同遗传背景下检测到的QTL,可作为穗部性状改良的候选染色体区段,用于分子标记辅助选择或图位克隆,但是同时也要注意上位性和环境对它们的影响。

水稻株高、抽穗期和有效穗数的QTL与环境的互作分析

株高、抽穗期和有效穗数是水稻的重要农艺性状 ,合适的株高、抽穗期和有效穗数对水稻的高产稳产是至关重要的。该实验应用中 15 6 谷梅 2号的重组自交系 (RIL)群体 ,建立由 16 8个DNA分子标记组成的遗传连锁图 ,以一年两季作为不同的环境效应 ,对水稻株高、抽穗期和有效穗数进行了非条件和条件QTL定位 ,在非条件QTL定位中共检测到 7个株高QTLs、5个抽穗期QTLs和 3个有效穗数QTLs和 10对加加上位性互作位点 ,条件QTL定位结果表明 ,抽穗期这一性状对株高和有效穗数QTLs的表达既有抑制作用 。

大豆百粒重QTL的上位效应和基因型×环境互作效应

基于Charleston×东农594重组自交系群体,采用完备区间作图和混合线性模型对2006-2010年连续5年的百粒重QTL进行定位,并进行基因×环境互作及上位性分析。结果定位了16个与大豆百粒重性状相关的QTL,其中有5个QTL分别与环境发生互作,互作贡献率在0.11%～0.52%之间;定位了8对上位互作位点,贡献率在1.15%～2.59%之间。