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）.

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.

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 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.

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.

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。

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.

考虑建成环境的交通事故严重程度致因交互效应研究

为探究考虑建成环境影响下各类因素对交通事故的作用机理,本文提出一种融合ADASYN(Adaptive Synthetic Sampling)平衡算法与CatBoost模型的方法,对沈阳市2015—2020年的道路交通事故进行研究,并解析事故致因的交互效应。首先,通过地理信息匹配的方法补充事故地点周围14项建成环境因子,构建多源数据集。其次,通过比较4种经典的机器学习模型,即CatBoost,Random Forest,XGBoost,LightGBM,并筛选出泛化能力最强的模型。随后,利用SHAP(Shapley Additive Explanation)归因方法对最优模型进行解释以揭示单个风险因素效应以及影响重要度排序。最后,基于单因素分析,探究建成环境与事故特征之间的交互效应。研究表明:相同的特征在单因素以及双因素交互分析中对事故影响机制存在差异。在单因素分析中,季节、交通方式这2项因素对致命事故具有显著的正向影响;而主干路密度、快速路密度、工业用地比例、现场形态、道路物理隔离这5项因素对致命事故有着显著的负向影响。在双因素交互分析中,高主路密度与秋冬季节交互以及低工业用地比例与春季交互等对致命事故具有正向影响;而高工业用地比例与行人交互则产生了负向影响。本文成果可为相关人员提供准确的影响交通事故严重程度的相关因素,为优化和建设城市交通系统提供一定的理论支撑。

GENETIC MODELS AND ANALYSIS METHODS FOR SEX-LINKED AND MATERNAL GENE EFFECTS

Genetic models are proposed for analyzing sex-linked and maternal effects as well as autosomal gene effects.For the model with no genotype×environment interaction,the total genetic effect is partitioned into direct additive （A）,direct dominance （D）,sexlinked （L）,maternal additive （Am） and maternal dominance （Dm） genetic components.For the model including genotype×environment interaction （GE）,GE can also be partitioned into components of direct additive by environment interaction （AE）,direct dominance by environment interaction （DE）,sex-linked by environment interaction （LE）,maternal additive by environment interaction （AmE ）,and maternal dominance by environment interaction （DmE）.Linear functions of genetic components are listed for parent,F1,and F2.A set of parents,their reciprocal F1’s and F2’s is applicable for efficient analysis.Variance and covariance components can be well mated by MINQUE（O/l） with the jackknife procedure.The t-test conducted by the jackknife procedure is applicable for detecting significance of variation.Adjusted Unbiased Prediction （AUP） method is suggested for predicting genetic effects.

基于文献计量的植物根系构型研究热点及趋势分析

【目的】通过检索分析根系构型相关研究数据库,以期为进一步研究提供新视野新思路。【方法】基于Web of Science数据库收录2011—2020年有关根系构型研究的相关文献753篇,采用Citespace信息可视化手段对根系构型研究年发文量、研究国家与机构、关键词、研究主旨进行分析,旨在归纳当前研究热点与趋势,总结研究现状,展望未来研究方向。【结果】结果表明,2011—2020年10 a间根系构型文献发表量呈快速上升趋势;主要学科涉及植物科学、农学、土壤科学。中国在该研究发文量占全球总的28.69%;主要关键词为根系生理、根系构型、植物类型等3个聚类,热点关键词为“干旱”和“耐受性”;研究方向可分为探测技术、环境影响、根系生长特性、自身调控等4个聚类,其中环境影响为热点研究方向。研究方向自探测技术和根系生长特性(2011—2013年)、植株自身调控及根系生长特性(2014—2017年)至环境影响(2018—2020年)逐步过渡。【结论】鉴于目前研究多集中于根系本身或根系周围环境对土壤微生物、土壤水分生态及土壤养分生态的影响,推断地上部响应根系构型变化以适应全球气候变化可能会成为未来研究热点。

大豆产量及主要农艺性状QTL的上位性互作和环境互作分析

以栽培大豆晋豆23为母本,半野生大豆灰布支黑豆ZDD2315为父本杂交衍生的F2:15和F2:16的447个RIL家系为遗传群体,绘制SSR遗传图谱,采用混合线性模型方法,对2年大豆小区产量及主要农艺性状进行加性QTL、加性×加性上位互作及环境互作分析。结果检测到9个与小区产量、茎粗、有效分枝、主茎节数、株高、结荚高度相关的QTL,分别位于J_2、I、M连锁群上,其中小区产量、茎粗、株高、有效分枝和主茎节数QTL的加性效应为正值,说明增加这些性状的等位基因来源于母本晋豆23。同时,检测到7对影响小区产量、茎粗、株高和结荚高度的加性×加性上位互作效应及环境互作效应的QTL,共发现14个与环境存在互作的QTL。上位效应和QE互作效应对大豆小区产量及主要农艺性状的遗传影响较大。大豆分子标记辅助育种中,既要考虑起主要作用的QTL,又要注重上位性QTL,才有利于性状的稳定表达和遗传。

大豆油分含量相关的QTL间的上位效应和QE互作效应

利用Charleston ×东农594重组自交系构建的SSR遗传图谱,及混合线性模型方法对2002年到2006年连续5年的大豆油分含量进行QTL定位,并作加性效应,加性×加性上位互作效应及环境互作效应分析。共检测到11个控制油分含量的QTL,分别位于第A1、A2、B1、C2、D1a、D1b、F、H和O连锁群上,其中2个表现为遗传正效应,9个表现为遗传负效应,另检测到15对影响油分含量的加性×加性上位互作效应的QTL,解释该性状总变异的17.84%。发现9个QTL与环境存在互作,贡献率达到5.76%。

水稻产量相关性状QTL定位

以来自泸恢99×日本晴F8代重组自交系的188个家系及双亲为研究材料,用在亲本间有多态性的207个DNA标记对群体进行基因型分析,构建了全长为2397cM,标记间平均距离为12.29cM,覆盖水稻基因组12条染色体的连锁图。于2011年正季分别在德阳和泸州两地种植于四川农业科学院水稻高粱研究所实验农场,考查了单株有效穗数、每穗颖花数、每穗实粒数、结实率、千粒重、单株产量、穗长和株高7个性状。用基于混合线性模型的QTL Network 2.0软件进行QTL定位、上位性分析及其与环境的互作分析。7个性状共检测到22个加性主效应QTL,位于除第6、11、12染色体外的9条染色体上,除每穗颖花数、每穗实粒数、结实率未检测到上位性效应外,其他5个性状共检测到7对上位性互作;另外只发现两个QTL与环境发生明显互作。所有加性×加性上位性互作的效应及贡献率均较小,未发现上位性互作效应与环境的显著互作。

大豆蛋白质含量相关QTL间的上位效应和QE互作效应

利用Charleston×东农594重组自交系构建的SSR遗传图谱及混合线性模型方法对2002—2006连续5年的大豆蛋白质含量进行QTL定位,并作加性效应,加性×加性上位互作效应及环境互作效应分析。共检测到10个控制蛋白质含量的QTL,分别位于第B2、C2、D1a、E和N连锁群,其中1个表现为遗传正效应,9个表现为遗传负效应,另检测到15对影响蛋白质含量的加性×加性上位互作效应的QTL,解释该性状总变异的13.75%。环境互作检测中,发现9个QTL与环境存在互作,贡献率达到4.47%。

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。

水稻株高上位性效应和QE互作效应的QTL遗传研究(英文)

利用基于混合模型的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，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与环境的互作效应比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。

多种环境下大豆单株粒重QTL的定位与互作分析

定位大豆单株粒重QTL、分析QTL间的上位效应及QTL与环境互作效应，有利于大豆单株粒重遗传机制的深入研究。利用147个F2：14-F2：18RIL群体，在5年2点多环境下以CIM和MIM方法同时定位大豆单株粒重QTL。检测到17个控制单株粒重的QTL，分别位于Dla、B1、B2、C2、F、G和A1连锁群上，贡献率为6．0％～47．9％：用2种方法同时检测到3个QTL，即qSWPP-DIa-3、qSWPP-F-1和qSWPP-Dla-5，贡献率为6．3％～38．3％；2年以上同时检测到4个QTL，即qSWPP-DIa-1、qSWPP-Dla-2、qSWPP-B1-1和qSWPP-G-1，贡献率为8．1％～47．9％；利用QTLMapper分析QE互作效应和QTL间上位效应，7种环境下的数据联合分析得到1个QE互作QTL和4对上位效应QTL，贡献率和加性效应都较小。在分子标记辅助育种中应该同时考虑主效QTL及各微效QTL之间的互作。