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1.
提出了一种基于分子标记数据及数量性状基因型值构建作物种质资源核心种质库的方法.采用包括基因型与环境互作的遗传模型及相应的混合线性模型统计分析方法,无偏预测各材料的基因型值,分别用基因型值和分子标记数据计算个体间的相似系数,加权得到最终的相似距离.采用不加权类平均法(UPGMA)进行系统聚类,用多次聚类随机取样法构建核心种质库.以水稻DH群体111个基因型8个农艺性状、175个分子标记位点的数据为实例,按四种抽样比率(25%,20%,15%,10%)构建了四个核心种质库,比较了核心种质库与整个群体的分子标记多样性及数量性状的遗传变异,评价了所用方法的有效性。  相似文献   

2.
保留特殊种质材料的核心库构建方法   总被引:13,自引:1,他引:12  
本研究提出了能保留特殊种质材料的多次聚类构建种质资源核心库方法,对种质材料的表现型数据采用合适的遗传模型及混合线性模型统计分析方法无偏预测基因型值,用基因型值构建核心库,计算材料间的马氏距离,用不加权类平均法进行聚类,根据聚类图选取材料构建核心库时,优先保留特殊遗传材料,用方差F测验、均值t测验、极差比和变异系数评价核心库代表原有种质资源群体遗传多样性的程度,以168个棉花基因型的5个纤维性状构建核心库。  相似文献   

3.
基因型值多次聚类法构建作物种质资源核心库   总被引:22,自引:2,他引:20  
采用合适的遗传模型无偏预测基因型值,用基因型值进行聚类分析,采用马氏距离计算遗传材料间的遗传距离,并用不加权类平均法(UPGMA)进行聚类,根据树型图,从遗传变异相似的每组二个遗传材料中随机选取一个遗传材料,如组内只有一个遗传材料,则选取该遗传材料,对所取的所有遗传材料再次聚类、取样,直至所取遗传材料的数量为总遗传的20% ̄30%,这些遗传材料作迷为核心聚类。用方差同质性测验、均值t测验评核心资源  相似文献   

4.
小扁豆种质资源形态标记遗传多样性分析   总被引:12,自引:4,他引:8  
选取国家种质库保存的481份小扁豆种质资源进行形态标记遗传多样性分析,表明14个形态性状的平均变异类型达8.79个,平均遗传多样性指数(I)为1.8149。16个不同地理来源群体间显示出显著的形态标记遗传多样性差异,国外群体的遗传多样性水平略高于国内群体。国内山西小扁豆种质资源的,值(1.573)仅次于,值最高的国外ICARDA群体(1.683)。研究结果显示,西北部省份是我国小扁豆资源遗传多样性最丰富的地区,应加强该区域小扁豆资源的进一步搜集、保护和研究。Structure群体遗传结构分析将481份参试资源划分为6大组群,各组群特征表现各异,变化丰富。  相似文献   

5.
福建家鸭品种的分子遗传多样性   总被引:6,自引:0,他引:6  
通过筛选的28个多态性较好的微卫星标记检测了福建省金定鸭、莆田黑鸭、连城白鸭、山麻鸭4个家鸭品种的遗传多样性.利用等位基因频率计算了各群体的遗传参数、群体间的Nei氏标准遗传距离DS和DA遗传距离,并采用邻近法(NJ)和类平均法(UPGMA)进行聚类分析和比较.结果表明,福建省4个家鸭品种全部群体的平均杂合度为0.5353,遗传一致性较好,应加强各保种场(区)多样性的保护;各品种间的遗传距离远近顺序在两种遗传距离DS和DA的结果中完全一致,以DA和DS为基础分别得到的UPGMA和NJ的聚类结果完全相同,表明在应用微卫星标记分析品种的遗传多样性时,使用更多的微卫星位点,才可以获得更准确更具普遍性的结论.4个家鸭品种的聚类与各品种的经济类型、生态地域分布关系密切.  相似文献   

6.
【目的】研究旨在构建可靠的材用云南松核心种质,加强其种质资源选育、开发利用研究,解决其种质资源分布广、保存成本高、保存难度大等问题,促进云南松种质资源有效利用。【方法】以云南松26个天然居群的780株样株为原种质,以18个表型性状为原数据,利用2种不同构建策略(利用地理角度与改进的最小距离逐步取样法),探讨不同构建策略所构建的核心种质对原种质的代表性。【结果】结果表明:(1)地理角度构建出的包含219株样株的种质子集,其遗传多样性指数显著低于改进的最小距离逐步取样法构建的4个种质子集,略高于原种质;该种质子集与原种质的MD值为3.921%,VD值为83.33%,CR值为82.207%,VR值为99.48%;通过对原种质与该种质子集18个性状做主成分分析,累计贡献率分别为79.376%、82.163%,其种质子集样株分布相对集中。(2)改进的最小距离逐步取样法构建的4个抽样比例分别为10%、20%、30%和40%的种质子集中,其中20%抽样比例的种质子集效果最好,该种质子集的多样性指数极显著大于原种质;20%抽样比例种质子集与原种质的MD值为6.363%,VD值为83.33%,CR值为91.099%,VR值为124.448%;对20%抽样比例的种质子集进行主成分分析,累计贡献率为83.539%,且高于原种质,该种质子集样株分布范围覆盖了整个取样范围。【结论】研究表明,不同方法构建的核心种质均获得了原种质不同程度遗传多样性,这2种构建结果均可代表材用云南松种质资源核心种质;而地理角度构建的种质子集在种质资源采集、保存和更新方面更具有优势,可为材用云南松种质资源保存和优良种质选育提供科学方法,同时为其他种质资源的构建提供一种新的参考方法。  相似文献   

7.
利用荧光SSR分子标记,对新收集的苹果属栽培种楸子种质资源进行了遗传多样性和群体结构分析,明确群体内和群体间的遗传多样性和结构,为苹果属植物种质资源的收集保存和砧木育种亲本选择提供参考。利用荧光SSR构建研究材料的指纹数据,主要利用GenAlEx 6.501软件分析遗传多样性,利用POPULATION 1.2软件基于Nei遗传距离构建Neighbour-Joining(NJ)树,并利用STRUCTURE 2.3.4软件进行群体结构分析。结果表明:19对SSR引物共检测出390个多态性等位变异,平均多态性等位基因数为20.526,平均有效等位基因数为9.399,观察杂合度和期望杂合度的平均值分别为0.706和0.868,香农多样性指数为2.446,高于以往研究的苹果属植物的遗传多样性。基于Nei遗传距离的聚类分析,在遗传距离0.9167处155份材料可以分成3个类群,3个类群间的遗传距离较近,并没有完全按来源地划分为相应的类群。群体结构分析将155份材料划分成了2个稳定的群体,群体结构分组与NJ聚类有相似的结果,其中150份材料的Q值均大于0.6,血缘相对单一。  相似文献   

8.
通过对来自国内外的28份金针菇菌株资源的重测序共计检测到SNP位点1 241 583个,InDel位点623 670个。通过筛选分型,1 474个高质量SNP标记(多态信息含量指数PIC介于0.101-0.966之间)被用于金针菇资源群体多样性和结构分析。经计算,菌株间遗传距离在0.057-0.631之间。UPGMA进化树拓扑结构显示栽培菌株是其与野生菌株混合分支的一个亚支,自然栽培和工厂化栽培菌株可各自聚成一支,符合金针菇育种历史。群体结构结果显示金针菇种质资源包含5个亚群。主成分分析显示菌株在二主分之间的位置及互相间距离基本符合进化树分类、群体结构和遗传距离。本研究为金针菇分子标记和基因型的确定提供序列基础,也为后续资源保护利用、重要农艺性状的基因定位和基于分子标记的聚合育种提供理论依据。  相似文献   

9.
虾夷扇贝(Patinopecten yessoensis)5个群体的遗传多样性   总被引:21,自引:0,他引:21  
虾夷扇贝为20世纪80年代初从日本引入我国并逐渐开展养殖的双壳贝类,目前已在我国北方地区大面积养殖。实验采用微卫星分子遗传标记技术对大连獐子岛底播增殖放流群体(CC)、黄海北部海区采集的野生群体(HQ)、日本青森养殖群体(JX)、俄罗斯远东日本海沿岸养殖群体(RX)及大连大长山岛养殖上壳白化群体(ZB)等5个虾夷扇贝群体的遗传多样性进行研究。其中HQ群体为本课题组2005年在黄海北部采集的野生群体,本研究筛选出一个该群体的特异性遗传标记。用8个微卫星位点进行扩增,共获得45个等位基因,每个位点的等位基因数处于3—9之间,大小为100—340bp,平均有效等位基因数为3.1535,基因型数为3—21个,PIC(PolymorphismInformationContent)值处于0.0322-0.5944之间。5个群体的平均观测杂合度分别为0.3292、0.3048、0.3167、0.2708、0.3042,平均期望杂合度分别为0.4595、0.4002、0.3838、0.3620、0.3885,群体间的多态性差异不显著。根据群体间遗传相似性系数、遗传距离及UPGMA聚类分析发现,CC和HQ群体亲缘关系最近,JX和RX群体的亲缘关系较近,ZB群体与JX和RX群体的亲缘关系较近。通过Hardy—Weinberg平衡及F-检验发现,5个群体都不同程度的偏离平衡,表明各群体基因频率和基因型频率的稳定性较低,且5个群体均处于不同程度的杂合子缺失状态,群体间的遗传分化程度较高,但遗传变异主要来自群体内的个体间。  相似文献   

10.
利用SSR标记分析水稻亲本间遗传距离与杂种优势的关系   总被引:7,自引:0,他引:7  
利用5个光温敏核不育系与40个恢复系(品种)配制了200个组合,应用SSR标记估算了这5个不育系与40个恢复系之间的遗传距离,分析了遗传距离与杂种优势的关系。结果表明:(1)不同材料、不同遗传距离范围之间,遗传距离与单株产量以及有效穗数、穗长、每穗粒敷、着粒密度、结实率、千粒重、单株产量7个性状超亲优势的相关性有很大差别,表现出很复杂的关系。(2)田丰S与父本遗传距离在0.6286~2.5257之间时,F1单株产量及其超亲优势与遗传距离极显著相关;培矮64S与父本遗传距离在0.8247~1.5315之间时,F1单株产量与遗传距离显著相关。(3)所有两系组合亲本间遗传距离在0.5333~1.5之间时,F1单株产量超亲优势与遗传距离显著相关;遗传距离在0.5333~1.0之间时,F1单株产量与遗传距离显著相关,遗传距离分别在1.0~1.5、0.5333~1.5和0.5333~2.5257之间时极显著相关。(4)另外,F1单株产量与遗传距离的相关程度普遍高于其超亲优势与遗传距离的相关程度。  相似文献   

11.
In the present study, a strategy was proposed for constructing plant core subsets by clusters based on the combination of continuous data for genotypic values and discrete data for molecular marker InformaUon. A mixed linear model approach was used to predict genotyplc values for eliminating the environment effect. The "mixed genetic distance" was designed to solve the difficult problem of combining continuous and discrete data to construct a core subset by cluster. Four commonly used genetic distances for continuous data (Euclidean distance, standardized Euclidean distance, city block distance, and Mahalanobls distance) were used to assess the validity of the conUnuous data part of the mixed genetic distance; three commonly used genetic distances for discrete data (cosine distance, correlaUon distance, and Jaccard distance) were used to assess the validity of the discrete data part of the mixed genetic distance, A rice germplasm group with eight quantitative traits and information for 60 molecular markers was used to evaluate the validity of the new strategy. The results suggest that the validity of both parts of the mixed geneUc distance are equal to or higher than the common geneUc distance. The core subset constructed on the basis of a combination of data for genotyplc values and molecular marker information was more representative than that constructed on the basis of data from genotypic values or molecular marker informaUon alone. Moreover, the strategy of using combined data was able to treat dominant marker informaUon and could combine any other continuous data and discrete data together to perform cluster to construct a plant core subset.  相似文献   

12.
A new method for the choice of variables with the greatest discriminatory power in the location model for mixed variable discriminant analysis is presented in the paper. The procedure based on the multivariate discriminatory measure enables a simultaneous reduction of the number of discrete and continuous variables. The introduced criterion can be used for both optimal or step-wise selection of variable subset. As an example the results of the stepwise variable selection for some medical data are presented in the paper.  相似文献   

13.
A strategy was proposed for constructing core collections by least distance stepwise sampling (LDSS) based on genotypic values. In each procedure of cluster, the sampling is performed in the subgroup with the least distance in the dendrogram during constructing a core collection. Mean difference percentage (MD), variance difference percentage (VD), coincidence rate of range (CR) and variable rate of coefficient of variation (VR) were used to evaluate the representativeness of core collections constructed by this strategy. A cotton germplasm collection of 1,547 accessions with 18 quantitative traits was used to construct core collections. Genotypic values of all quantitative traits of the cotton collection were unbiasedly predicted based on mixed linear model approach. By three sampling percentages (10, 20 and 30%), four genetic distances (city block distance, Euclidean distance, standardized Euclidean distance and Mahalanobis distance) combining four hierarchical cluster methods (nearest distance method, furthest distance method, unweighted pair-group average method and Ward’s method) were adopted to evaluate the property of this strategy. Simulations were conducted in order to draw consistent, stable and reproducible results. The principal components analysis was performed to validate this strategy. The results showed that core collections constructed by LDSS strategy had a good representativeness of the initial collection. As compared to the control strategy (stepwise clusters with random sampling strategy), LDSS strategy could construct more representative core collections. For LDSS strategy, cluster methods did not need to be considered because all hierarchical cluster methods could give same results completely. The results also suggested that standardized Euclidean distance was an appropriate genetic distance for constructing core collections in this strategy.  相似文献   

14.
The selection of an appropriate sampling strategy and a clustering method is important in the construction of core collections based on predicted genotypic values in order to retain the greatest degree of genetic diversity of the initial collection. In this study, methods of developing rice core collections were evaluated based on the predicted genotypic values for 992 rice varieties with 13 quantitative traits. The genotypic values of the traits were predicted by the adjusted unbiased prediction (AUP) method. Based on the predicted genotypic values, Mahalanobis distances were calculated and employed to measure the genetic similarities among the rice varieties. Six hierarchical clustering methods, including the single linkage, median linkage, centroid, unweighted pair-group average, weighted pair-group average and flexible-beta methods, were combined with random, preferred and deviation sampling to develop 18 core collections of rice germplasm. The results show that the deviation sampling strategy in combination with the unweighted pair-group average method of hierarchical clustering retains the greatest degree of genetic diversities of the initial collection. The core collections sampled using predicted genotypic values had more genetic diversity than those based on phenotypic values.Communicated by D.J. Mackill  相似文献   

15.
16.
IBDSim is a package for the simulation of genotypic data under isolation by distance. It is based on a backward 'generation by generation' coalescent algorithm allowing the consideration of various isolation by distance models with discrete subpopulations as well as continuous populations. Many dispersal distributions can be considered as well as heterogeneities in space and time of the demographic parameters. Typical applications of our program include (i) the study of the effect of various sampling, mutational and demographic factors on the pattern of genetic variation; and (ii) the production of test data sets to assess the influence of these factors on inferential methods available to analyse genotypic data.  相似文献   

17.
玉米籽粒性状的遗传模型研究   总被引:7,自引:0,他引:7  
用10个遗传上和籽粒形态性状上具有差异的玉米自交系,依多种可能的交配方法获得亲本P1、P2、F1(P1× P2)、F2、B1(F1×P1)、B2(F1× P2)及其相应反交RF1、RF2、RB1、RB2共10个种子世代。种植2年。依广义遗传模型建立包括种子胚乳加性、胚乳显性、母体加性、母体显性和细胞质效应的遗传模型,运用种子数量性状的精细鉴别法[1]和混合模型分析法[2,3],对粒长、粒宽、粒长宽比、粒厚及百粒重作了性状表达遗传机制的鉴别与探讨。单个组合的遗传模型精细测验表明,5个籽粒性状的遗传主要受母体显性和胚乳基因型(包括加性和灵性)的控制,一个组合的粒宽、粒厚和百粒重上还检测到细胞质效应。对25对 F1正反交组合世代均值依MINQUE法分析的结果表明,5个籽粒性状的遗传方差中,母体遗传方差占60%以上,胚乳基因型方差低于40%,粒长和百粒重还有细胞质效应,约占10%~30%。可见,籽粒性状的遗传特点是受多套遗传系统控制,其中以母体基因型的作用最大。  相似文献   

18.
Summary A procedure for genetic evaluation with field data is proposed for situations in which there is mixed major gene and polygenic inheritance and the major genotype membership of some or of all individuals is unknown. Location parameters (fixed environmental, major genotype and polygenic effects), major genotype frequencies and variance components are estimated by the modal values of joint and marginal posterior distributions. The method is described for continuous and discontinuous data as well as for univariate and multivariate evaluations. Results from a simulation study are presented.Journal Paper No. J-12728 of the Iowa Agriculture and Home Economics Experiment Station, Ames, Iowa. Project No. 1901  相似文献   

19.
A genetic model with genotype×environment (GE) interactions for controlling systematical errors in the field can be used for predicting genotypic values by an adjusted unbiased prediction (AUP) method. Mahalanobis distance, calculated based on the genotypic values, is then applied to measure the genetic distance among accessions. The unweighted pair-group average, Ward’s and the complete linkage methods of hierarchical clustering combined with three sampling strategies are proposed to construct core collections in a procedure of stepwise clustering. A homogeneous test and t-tests are suggested for use in testing variances and means, respectively. The coincidence rate (CR%) for range and the variable rate (VR%) for the coefficient of variation are designed to evaluate the property of core collections. A worked example of constructing core collections in cotton with 21 traits was conducted. Random sampling can represent the genetic diversity structure of the initial collection. Preferred sampling can keep the accessions with special or valuable characteristics in the initial collection. Deviation sampling can retain the larger genetic variability of the initial collection. For better representation of the core collection, cluster methods should be combined with different sampling strategies. The core collections based on genotypic values retained larger genetic variability and had superior representatives than those based on phenotypic values. Received: 15 October 1999 / Accepted: 24 November 1999  相似文献   

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