果树杂交亲本性状育种值的简易遗传估算法

以梨树亲本紧凑性状育种值的遗传估算为研究对象 。

双列杂交分析估算遗传参数两种方法的比较

本文就遗传参数的估算,对双列杂交分析中应用最广的Griffing法和Hayman法作了比较。结果表明,Hayman法估算非加性方差的结果比Griffing法小(p-1)/P^2_5~2, 而估算加性方差和遗传方差的结果则分别比Griffing法大2(p-1)/P^2_s^2和(p-1)/P^2_s^2。另外,本文还就造成两种方法分析结果不同的原因作了讨论。

家蚕遗传育种方法及策略

家蚕作为高价值天然纤维——蚕丝的主要来源,在农业、医药、食品等领域中扮演着重要角色。随着生物技术的迅猛发展,家蚕遗传育种研究取得了显著进展,为提高家蚕的生产性能、增强其抗病性和适应能力提供了全新的路径。文章系统探讨了家蚕遗传育种的主要目标及选择方法,包括个体选择、家系选择、指数选择和基因组选择,并重点介绍了遗传力的估算方法和杂交育种策略,旨在为家蚕遗传育种研究人员和实践者提供有力的指导,推动家蚕育种效率和效果的进一步提升,为蚕业发展奠定坚实的基础。

陆地棉黄萎病抗性遗传分析

以抗落叶型黄萎病棉花品系常抗棉,耐黄萎病品系中5173、河北抗黄、山东抗黄及感病品种TM 1、军棉1号、新陆早1号、感病1号8个材料进行8×8半双列杂交,对亲本及F1的黄萎病株率及病情指数等主要性状进行了研究,采用MINQUE(1)法估算方差分量,采用AUP法预测遗传效应值。遗传估算方差结果表明,在病圃人工接菌条件下,品种平均病指及收获期剖秆病指均以加性遗传效应为主。遗传力分析表明,黄萎病的广义和狭义遗传率均达极显著。基因效应预测值表明,抗及耐黄萎病品种与感病品种之间存在极显著差异。协方差分析表明,表示黄萎病抗性两个性状(成株期平均病指及收获期剖秆病指)之间存在极显著的正相关,而黄萎病与产量性状之间存在极显著负相关。

刺槐主要性状遗传参数估算

刺槐主要性状遗传参数估算①甘雨（河南省林科所）李柏兴（郑州市园林局）吉顺祥赵天顺（孟州市林科所）师平钧（嵩县林业局）树木表现的性状是其基因型和外界环境共同作用的结果。树木性状遗传参数估算是林木育种的基础，它直接影响着树木育种策略的制定，并对育种效果的...

辣椒果实性状的遗传分析

以6个辣椒品种并按(1/2)n(n-1)双列杂交法配制的15个杂交组合为材料,根据Hayman分析法估算了辣椒果实5个性状的遗传模型和遗传参数。结果显示,果长、果肉厚和结果数的遗传符合“加性-显性”模型,果宽和单果质量不符合“加性-显性”模型,还存在显著上位性效应。遗传参数估算表明,5个果实性状遗传是以加性效应为主,但不同果实性状显性效应有较大的差异,果肉厚的显性效应最大,其次是果长和结果数,果宽和单果质量最小。

辣椒产量和品质性状Hayman遗传分析

将辣椒(Capsicam annuumL.)6个亲本,按(1/2)n(n-1)双列杂交法配制15个杂交组合,用Hayman双列杂交分析法估算5个关于产量、品质性状的遗传参数。Wr对Vr的回归分析结果表明,单株产量、小区产量、辣椒素和干物质含量的遗传符合“加性—显性”模型;早期产量的遗传不符合“加性—显性”模型。Wr+Vr与Yr间相关分析表明,单株产量、小区产量、早期产量和干物质含量呈负相关,说明含有更多显性基因的亲本具有较小的Wr+Vr值;辣椒素含量呈正相关,说明含有较多辣椒素显性基因的亲本具有较大的Wr+Vr值。遗传参数估算表明,辣椒素和干物质含量的遗传是加性效应方差明显大于显性效应方差;单株产量和小区产量的遗传加性效应方差明显小于显性效应方差;早期产量的遗传是显性效应方差显著大于加性效应方差,同时还存在显著的、复杂的上位性效应。

家蚕茧质性状广义遗传分析

采用广义遗传模型分析得知，全茧量、蛹重的性连锁效应作用强度达５０％左右，加性效应与显性效应的作用强度相近，为１５％左右；茧层量、丝素量主要受加性效应控制，达５０％左右，显性效应为２５％左右，性连锁效应较弱，为５％左右；茧层率加性效应与性连锁效应的作用具同等强度，为３０％多；显性效应作用较弱，为１０％左右．母体效应在５个性状中的作用较弱，基因与环境互作效应对５个性状作用形式相类似．其中基因加性及显性与环境互作两项效应的作用略强．由本模型所得剩余机误方差很小，仅为５％左右，说明本模型比较合理．因此，在此基础上。

双列杂交分析中遗传参数H_2的计算

根据Hayman双列杂交分析法估算遗传参数H_2,在误差项问题上至今仍存在两种不同意见的分歧,本文对其计算公式作了说细推导,并指出了错误公式的症结所在。

辣椒6个植物学性状广义遗传力分析

利用数量遗传学原理,以父本SJ-05-46(Capsicum frutescens L.)、母本荷兰尖椒(C.annuum L.)及其F_1、F_2代为供试材料,研究其F2代植物学性状的遗传特征。结果表明,在6个数量性状中,广义遗传力从大到小依次为:二叉分枝以下长度(主茎)>叶宽>节间长>株高>叶柄长>叶长。试验结果为辣椒新品种的选育提供了参考。

米老排杂交子代苗期生长性状遗传变异及选择

为分析米老排杂交子代苗期苗高、地径的遗传变异规律,以析因交配获得的杂交子代为对象,通过方差分析、配合力遗传参数估算和个体育种值比较,筛选出优良亲本、杂交组合和个体。苗高、地径的变异系数均超过20%,各性状主要在母本、组合、父本之间存在极显著差异(P<0.01),在区组上无显著差异(P<0.05),杂交子代生长性状变异丰富,遗传改良潜力大。苗高和地径母本加性效应大于父本,单株遗传力分别为0.653、0.291,表明苗高和地径分别受高度和中度的遗传控制。苗高与地径呈极显著的遗传正相关与表型正相关,表明同步改良可行。联合苗高和地径的一般配合力与特殊配合力选出了两个优良母本、两个优良父本和6个优良杂交组合,入选组合的苗高和地径的遗传增益分别为17.93%和12.63%;比较个体育种值,筛选出22个优良个体,入选率为5.12%,入选个体的苗高和地径遗传增益分别为50.73%和12.84%。米老排杂交子代苗高和地径存在丰富的遗传变异,遗传改良效果明显。

闽楠子代幼林主要生长性状遗传分析及优良家系选择

为了解开化县林场闽楠家系子代测定林的生长情况,对其进行了生长性状的调查。通过选取保存完整的59个家系分析了其变异系数、遗传力、遗传增益等遗传参数,筛选优良家系。结果表明:家系间树高、胸径和单株材积的变异系数分别为16.71%,17.78%和39.10%,高径比和冠径比的变异系数分别为16.36%和16.79%。树高、胸径和单株材积的家系遗传力分别为32.75%、47.11%和52.71%;优选出8个生长表现好的家系;并初选出53个优良单株。优良家系的树高、胸径、单株材积平均值分别为4.14 m、5.37 cm和0.0052 m^(3),比群体平均值提高了16.29%、16.99%和44.44%;优良单株的树高、胸径、单株材积平均值分别为4.82 m、6.78 cm和0.0096 m^(3),遗传增益8.9%、21.09%、76.16%。此次优良家系及优良单株的选择,可为今后闽楠种子园建设和良种选育提供参考。

辣椒主要病害抗性双列杂交分析

本文用6个亲本,按(1/2)n(n-1)双列杂交法配制15个杂交组合,用Hayman双列杂交分析法对辣椒TMV、CMV、疫病和疮痂病抗性进行遗传参数估算。结果表明,TMV、CMV和疮痂病抗性遗传符合“加性-显性”模型,疫病抗性遗传不符合“加性-显性”模型,还存在显著上位性效应。F1代杂种抗性CMV是纯合的显性基因决定的,疮痂病是杂合的显性基因决定的;疫病抗性F1代杂种显性效应之和和控制性状表现显性的基因组数很少,几乎接近零。

辣椒植株性状Hayman双列杂交分析

用辣椒(Capsicam annuum L.)6个亲本,按(1/2)n(n-1)双列杂交法配制15个杂交组合,用Hayman双列杂交分析法估算了5个植株性状的遗传参数。Wr对Vr的回归分析结果表明,侧枝数、株高、株幅、主茎高和首花节位的遗传都符合加性-显性模型。Wr+Vr与Y r间相关分析表明,侧枝数、株高、株幅、主茎高呈负相关,说明含有更多显性基因的亲本具有较小的Wr+Vr值;首花节位呈正相关,且达到极显著水平,说明含有更多显性基因的亲本具有较大的Wr+Vr值。遗传参数估算表明侧枝数、株幅、主茎高和首花节位性状的遗传以加性效应为主,加性效应比显性效应更加重要。株高的遗传中加性效应和显性效应都很重要。

4个玉米早代材料应用潜力研究

通过Shukla稳定性分析方法对4个玉米早代材料即A037、A026、A025、A15M9系列测交种产量及其构成要素的遗传方差分量和F1杂种优势进行分析。联合方差分析和遗传方差分量估算结果表明:4个玉米早代材料40个测交种小区产量杂交优势明显,群体平均优势都为正值,其中A037×PQ47组合增幅最大,为42.17%,增幅最小组合是A15M9×桂M130,为6.21%,其中增幅达到20%以上的组合就有34个。株高受显性效应、加性效应和显性与环境互作效应的影响,其占总表型变异的比率分别为16.14%、20.65%、11.34%;穗位高受显性与环境互作效应的影响,比率为12.27%;穗长受加性效应和显性与环境互作效应的影响,比率分别为28.10%、31.15%;穗粗受显性与环境互作效应的影响,比率为17.65%;行粒数受加性效应、显性效应、加性与环境互作效应和显性与环境互作效应的影响,比率分别为34.07%、27.24%、62.06%、41.63%;百粒重受加性与环境互作效应和显性与环境互作效应的影响,比率分别为42.86%、66.00%;出籽率受加性与环境互作效应和显性与环境互作效应的影响,比率分别为121.86%、85.63%。通过遗传差异研究表明:4个亲本与10个测验系之间遗传关系的远近与F1基因型值具有复杂的非线性关系。

毛白杨苗期多性状选择研究

本研究以全国毛白杨优良基因资源为选择群体，从优树资源的生长变异特点入手，揭示了毛白杨苗期选择的可靠性。通过对１２个性状的遗传力估算，选出Ｈ、Ｄ１．３、单叶光合面积和叶柄长４个遗传力较高的性状作为苗期选择的性状，并以留种率１％、５％、１０％三个梯度分别计算各性状的育种值，且以此为指标选留无性系。由于苗期毛白杨各性状彼此正相关，适宜采取多性状独立体标准法进行选择。结果表明苗期选出的优良无性系与七省实测的前３０名有１８％～７５％是相同的，因此本研究适用于早期选择。

Prediction of resilient modulus for subgrade soils based on ANN approach

The resilient modulus(MR)of subgrade soils is usually used to characterize the stiffness of subgrade and is a crucial parameter in pavement design.In order to determine the resilient modulus of compacted subgrade soils quickly and accurately,an optimized artificial neural network(ANN)approach based on the multi-population genetic algorithm(MPGA)was proposed in this study.The MPGA overcomes the problems of the traditional ANN such as low efficiency,local optimum and over-fitting.The developed optimized ANN method consists of ten input variables,twenty-one hidden neurons,and one output variable.The physical properties(liquid limit,plastic limit,plasticity index,0.075 mm passing percentage,maximum dry density,optimum moisture content),state variables(degree of compaction,moisture content)and stress variables(confining pressure,deviatoric stress)of subgrade soils were selected as input variables.The MR was directly used as the output variable.Then,adopting a large amount of experimental data from existing literature,the developed optimized ANN method was compared with the existing representative estimation methods.The results show that the developed optimized ANN method has the advantages of fast speed,strong generalization ability and good accuracy in MR estimation.

A High Precision Comprehensive Evaluation Method for Flood Disaster Loss Based on Improved Genetic Programming

Precise comprehensive evaluation of flood disaster loss is significant for the prevention and mitigation of flood disasters. Here, one of the difficulties involved is how to establish a model capable of describing the complex relation between the input and output data of the system of flood disaster loss. Genetic programming (GP) solves problems by using ideas from genetic algorithm and generates computer programs automatically. In this study a new method named the evaluation of the grade of flood disaster loss (EGFD) on the basis of improved genetic programming (IGP) is presented (IGP-EGFD). The flood disaster area and the direct economic loss are taken as the evaluation indexes of flood disaster loss. Obviously that the larger the evaluation index value, the larger the corresponding value of the grade of flood disaster loss is. Consequently the IGP code is designed to make the value of the grade of flood disaster be an increasing function of the index value. The result of the application of the IGP-EGFD model to Henan Province shows that a good function expression can be obtained within a bigger searched function space; and the model is of high precision and considerable practical significance. Thus, IGP-EGFD can be widely used in automatic modeling and other evaluation systems.

Inverse procedure for determining model parameter of soils using real-coded genetic algorithm

The hybrid genetic algorithm is utilized to facilitate model parameter estimation.The tri-dimensional compression tests of soil are performed to supply experimental data for identifying nonlinear constitutive model of soil.In order to save computing time during parameter inversion,a new procedure to compute the calculated strains is presented by multi-linear simplification approach instead of finite element method(FEM).The real-coded hybrid genetic algorithm is developed by combining normal genetic algorithm with gradient-based optimization algorithm.The numerical and experimental results for conditioned soil are compared.The forecast strains based on identified nonlinear constitutive model of soil agree well with observed ones.The effectiveness and accuracy of proposed parameter estimation approach are validated.

The application of projection pursuit classification in the process of strategy selection and evaluation based on the real coded accelerating genetic algorithm

During the process of enterprises＇ strategy evaluation and selection, there are many evaluating indicators, and among them there are some potential correlations and conflicts. Thus it poses the problems to the decision-makers how to conduct correct evaluation on a business and how to make strategy adjustment and selection according to the evaluation. Based on the qualitative and quantitative method, the paper introduces the Projection Pursuit Classification （PPC） model based on the Real-coded Accelerating Genetic Algorithm （RAGA） into the process of enterprises＇ strategy evaluation and selection. The characteristic of PPC model is that it ultimately overcomes the influence of the proportion of subjectivity and avoids precocious convergence, thus providing a new objective method for strategy evaluation and selection by pursuing the most objective strategy evaluation to make the relatively sensible strategy portfolio and action.