甘蔗品种资源的表型遗传多样性

为了提高甘蔗品种资源的利用效率, 为甘蔗遗传育种亲本的选择、杂交组合配制及核心种质构建提供理论指导, 我们利用17个质量性状和5个数量性状分析了来自13个国家共20个地区的1,160份甘蔗品种资源的遗传变异、遗传结构和遗传距离等。结果表明: 5个数量性状在不同来源地品种群体之间的变异系数值(CV)变化较大, 说明不同来源地品种群体的数量遗传变异有较大差异, 其中来自海南的品种其群体遗传变异最丰富。质量性状的Shannon-Wiener多样性指数表明, 来自美国的品种群体遗传多样性最高, 其次为中国台湾, 第三为澳大利亚, 说明上述3个地区的甘蔗种质创新比较活跃, 在遗传育种中使用了更多表型性状多样化的亲本。不同来源地品种其群体的遗传分化系数(Gst)和基因流(Nm)显示甘蔗品种群体表型性状的遗传变异主要来自来源地内部, 且不同来源地品种群体之间存在较大的基因交流。遗传距离和UPGMA聚类分析结果表明, 各来源地品种群体之间遗传距离在0.0261–0.2945之间, 其中以福建和广东的品种最为相似, 其次为古巴和美国、广西和云南、澳大利亚和菲律宾、江西和四川、巴西和法国, 说明上述地区在杂交亲本的选择上比较相近。鉴于此, 在遗传育种中应加大利用具有丰富遗传多样性的品种材料并尽量避免选择同一组的品种相互杂交, 同时对于与其他来源地品种群体遗传距离较远的墨西哥品种群体在亲本选配时应给予更多关注。     

不同基因型甘蔗种质资源的表型遗传多样性

为探明甘蔗原种和地方种的遗传多样性和亲缘关系,以期筛选出优良甘蔗种质和优良杂交亲本.该研究对18份甘蔗原种和地方种的14个数量性状进行了表型遗传多样性分析.结果表明：通过14个数量性状的变异系数(coefficient of variance,CV)和性状之间的相关分析,18份甘蔗原种和地方种的数量性状遗传变异主要来自甘蔗蔗糖分、单茎重、叶宽、茎径和纤维分;对14个数量性状进行主成分分析提取获得了4个主成分因子,分别命名为“品质因子”、“生长因子”、“成熟度因子”和“光合因子”,主成分因子累积贡献率达83．482％;进一步通过对主成分因子开展综合评价分析,获得数量性状综合表型高于平均水平的10份材料,依次为 Sampana→甜圪塔→合庆草甘蔗→桂林竹蔗→坦桑尼亚→芒戈→古芝蔗→大岛再来→托江红→春尼;聚类分析基于不同的遗传距离可将18份种质聚为5个类别,潜在的优良杂交组合是 Sampana 和甜圪塔或 Sampana 和合庆草甘蔗,表明在甘蔗遗传育种亲本选择上既要考虑各性状主要因子的互补,又要保持一定的遗传距离.该研究认为,在甘蔗育种工作中,利用因子分析法进行表型遗传多样性分析,将更加有助于亲本和杂交组合的选择.     

广东甘蔗品种遗传多样性的AFLP分析

采用15对多态性较好的AFLP引物对广东育成的41个甘蔗品种的遗传多样性进行分析.结果表明:每对引物的多态性位点平均为61.5,多态性位点百分率为77.40%.41个甘蔗品种间的遗传相似系数为0.5210～0.9211,平均为0.6842,遗传多样性属中等水平.聚类分析把41个品种分为2大类,在系谱中亲缘关系密切的大多数品种,如粤农81-762与粤农85-1285和粤农86-295,粤糖57-423与粤糖85-5和粤糖85-177等都能分别聚在一起.但也有例外,如粤糖83-251和粤糖83-271,都是CP72-1210×华南56-12组合的后代,但没有归为同一类.遗传多样性指数分析显示,不同年代的品种多样性指数差异明显,最高是80年代,为0.3072,最低是60年代,为0.1162;不同单位选育的品种遗传多样性指数变化不大,为0.2756～0.3061.加强新种质利用是有效提高甘蔗品种遗传多样性的重要措施.     

云南茶树地方品种农艺性状与品质性状遗传多样性分析

为发掘具有优异农艺性状与品质性状的茶树品种资源,对51份云南茶树地方品种进行农艺和品质性状遗传多样性分析。结果表明,云南茶树地方品种农艺和品质性状存在丰富的遗传变异,农艺性状变异系数平均为25.84%,多样性指数平均为1.94;生化成分变异系数平均为16.53%,多样性指数平均为1.88;茶叶感官审评红茶品质总分达87.5分～94.2分,绿茶品质总分达78.8分～91.5分,茶树品种红、绿茶适制性的多样性指数平均为0.92和0.94;聚类分析将供试材料分为3类：第Ⅰ类品种主要适合制作红茶,第Ⅱ类品种主要适合制作绿茶,第Ⅲ类为生化成分特异性品种。并从中筛选出18份优异品种资源,为今后的生产和育种提供利用。     

贵州柿地方品种表型性状遗传多样性分析

采用文献查阅、实地踏查和异地保存相结合的方法,对88份贵州地方柿资源的34个表型性状进行了研究,并用主成分分析和聚类分析的方法对贵州地方柿资源的遗传多样性和亲缘关系进行分析,为地方优良资源的开发利用奠定基础。结果表明:(1)88份贵州地方柿种质资源的数量性状和质量性状均具有较大的变异,呈现出丰富的多样性。(2)主成分分析获得了一个16因子模型,可解释81.41%试验数据,其中前6个主因子的方差累积贡献率达50.57%,对应的16个性状依次为:着色期果面油渍、叶先端形状、叶基部形状、柿蒂形状、萼片形状、果实颜色、果实纵沟、果顶形状、蒂洼、皮孔密度、叶横径、叶柄长、叶片颜色、单果重、可滴定酸含量、可溶性总糖含量、糖酸比,这些性状可作为评价贵州柿资源多样性的主要指标。(3)88份资源的Shannon遗传多样性指数平均值为1.57,遗传多样性在地区间差异明显,根据遗传多样性指数可初步推断贵州地方柿资源是由黔中、黔南和黔西区域向黔北和黔东区域演化。(4)Q型系统聚类分析显示,表型性状遗传距离最远为13.76,说明贵州柿资源的多样性丰富;88份资源被聚为6个类群,不同类群间表型性状差异较大,表明不同类群的亲缘关系较远,各类群或亚群可能具有相互独立的遗传背景。     

三角梅品种的表型遗传多样性综合分析与评价

为了三角梅种质资源的创新利用和新品种选育提供科学参考依据,该研究以100份三角梅品种资源作为研究对象,通过观测7个数量性状和13个质量性状,采用变异系数、数量分级、遗传多样性指数、相关性分析、主成分分析和Q型聚类分析的数据分析方法,对供试三角梅品种的表型遗传多样性进行了综合分析与评价。结果表明:(1)7个数量性状的品种内变异幅度为7.52%～29.27%,其中2个小于10%,3个在10%至20%之间,2个大于20%; 品种间变异幅度为20.15%～41.08%,全部超过20%。这说明三角梅数量性状在品种内的变异程度属于中等水平,而在品种间的变异程度属于较高水平,利用品种间的数量性状差异鉴别品种的可能性较大。(2)概率分级法比传统的等距离分级法更具科学性和合理性,因此更适用于该研究的数量性状分级。(3)7个数量性状的遗传多样性指数全部大于1.00; 13个质量性状的遗传多样性指数为0.08～2.74,其中8个质量性状的多样性指数大于1.00,这说明三角梅表型的遗传多样性水平总体较高。(4)主成分分析可将20个表型性状简化为8个主成分因子,累计贡献率达78.689%。第1主成分由叶片宽度、叶片长度、叶柄长度决定,说明叶片的数量性状是区分三角梅品种的最主要性状指标。(5)Q型聚类分析主要依据叶片和苞片的大小将100份三角梅品种资源分为4类。综上认为,三角梅表型遗传多样性水平较高,叶片与苞片是三角梅品种的主要性状指标,同时运用科学合理的概率分级法,对鉴别品种、种质创新与育种有重要意义。     

紫薇品种表型多样性分析

对111个紫薇品种的17个表型分类性状多样性进行了研究。结果表明,紫薇品种具有丰富的表型多样性,平均遗传多样性指数为0.707。总体上是数量性状形态多样性指数大于质量性状,其中花色、瓣爪色2个质量性状和花径、着花数、花序长、花序宽、种子千粒重5个数量性状变异明显,其多样性指数分别大于1.2和1.4。不同紫薇品种群表型性状多样性差异明显,多样性指数由高到低依次为:红薇品种群(H'=0.838)、堇薇品种群(H'=0.823)、银薇品种群(H'=0.696)、矮生品种群(H'=0.604)、复色品种群(H'=0.573)。通过主成分分析,上述2个质量性状和5个数量性状主成分的贡献率为67.70%,包括花序长、花序宽、着花数、花色数、花色、种子千粒重、瓣爪色、叶色、小枝四棱、花香、花径等11个形态性状指标,代表了紫薇品种表型分类性状的综合特征。基于表型形态性状,111个紫薇品种可聚类为5大类群,其遗传聚类与花色及株型关系密切,4个以花色为主要分类性状的品种群总体演化趋势是:堇薇品种群→红薇品种群→银薇品种群→复色品种群。     

浙江省薄皮甜瓜地方品种的表型遗传多样性

利用表型性状探讨了浙江省沿海地区27份薄皮甜瓜地方品种资源的遗传多样性。结果表明48个质量性状遗传多样性指数在0.17–1.98之间;33个数量性状的变异系数在4.56%–83.50%,表明其丰富的遗传多样性。形态学聚类分析表明,在相似系数0.30处可以将27个资源分成两大类,这与按生育期长短分类结果完全一致;亚类的划分与果实形状、种子形状、叶片颜色、果皮颜色、覆纹颜色等质量性状具有一定的相关性,但其划分依据相对独立。本研究结果进一步丰富了甜瓜的评价体系,并为今后优异基因资源的挖掘与利用提供重要依据。     

数量性状水平上甘、青两省春小麦品种间的遗传多样性现状及演变趋势

在数量性状水平上,调查了43个春小麦品种的19个数量性状,利用主成分分析法计算品种间的欧氏平方遗传距离。并在此基础上用最小方差法做了聚类分析。发现43个品种间在数量性状水平上的遗传距离变异范围很大（0.926-67.942)。平均遗传距离为18.000,说明供试品种的19个考察性状上存在较大的表型变异,从聚类结果来看,地方品种基本上被聚在一起,说明地方品种同引进品种和育成品种至少在表型上存在较大差异。从20世纪40年代以来,在数量水平上甘,青两省春小麦地方品种间的遗传多样性水平最高（GD=31.389),其次是20世纪50年代引进品种（GD=26.308),而育成品种间的遗传多样性水平最低,总体上呈下降趋势,说明随着育种进程的深入,作为育种目标追求的经济性状趋于一致,其变异集中在一个狭小的范围之内。品种间的遗传多样性下降。     

云南甘蔗常用亲本资源遗传多样性的SSR分析

以63份云南育种机构常用的亲本资源为研究对象,10份原始亲本种为外群体,使用30对多态性较高,在甘蔗遗传连锁图谱上分布较为广泛的Genomic-SSR引物对云南常用亲本资源开展遗传多样性评价。结果表明：云南常用亲本在30个SSR位点上表现出丰富的多态性,共获得363个扩增条带,其中多态性条带数为352个,平均多态性条带比例和多态信息量分别为96.97%和0.9441;从常用亲本的来源地区来看,大陆亲本的多样性要高于国外亲本和台湾亲本;在Jaccard相似性系数方面,云南常用亲本材料之间的相似性系数范围在0.2804-0.7329之间,平均为0.4309,表现出较大的遗传差异,其中来自大陆地区的亲本遗传差异最大,其次为来自国外地区的亲本;UPGMA聚类分析将所有亲本材料分为一大一小两个类群, 而大类群又可分为2个亚类群,中国大陆地区一些即是主栽品种又是亲本的材料表现出较近的亲缘关系,而引自国外的大部分亲本和一些老的国内自育亲本也表现出较近的亲缘关系。以上结果将为上述亲本资源的高效利用奠定了良好的基础,并为全国育种机构在亲本选择和组合配置提供重要参考。     

Derivation of single-locus relationship coefficients conditional on marker information

The coefficient of relationship is defined as the correlation between the additive genetic values of two individuals. This coefficient can be defined specifically for a single quantitative trait locus (QTL) and may deviate considerably from the overall expectation if it is taken conditional on information from linked marker loci. Conditional halfsib correlations are derived under a simple genetic model with a biallelic QTL linked to a biallelic marker locus. The conditional relationship coefficients are shown to depend on the recombination rate between the marker and the QTL and the population frequency of the marker alleles, but not on parameters of the QTL, i.e. number and frequency of QTL alleles, degree of dominance etc., nor on the (usually unknown) QTL genotype of the sire. Extensions to less simplified cases (multiple alleles at the marker locus and the QTL, two marker loci flanking the QTL) are given. For arbitrary pedigrees, conditional relationship coefficients can also be derived from the conditional gametic covariance matrix suggested by Fernando and Grossman (1989). The connection of these two approaches is discussed. The conditional relationship coefficient can be used for marker-assisted genetic evaluation as well as for the detection of QTL and the estimation of their effects.     

Prediction of the power of detection of marker-quantitative trait locus linkages using analysis of variance

Analysis of variance can be used to detect the linkage of segregating quantitative trait loci (QTLs) to molecular markers in outbred populations. Using independent full-sib families and assuming linkage equilibrium, equations to predict the power of detection of a QTL are described. These equations are based on an hierarchical analysis of variance assuming either a completely random model or a mixed model, in which the QTL effect is fixed. A simple prediction of power from the mean squares is used that assumes a random model so that in the mixed-model situation this is an approximation. Simulation is used to illustrate the failure of the random model to predict mean squares and, hence, the power. The mixed model is shown to provide accurate prediction of the mean squares and, using the approximation, of power.     

A new approach for mapping quantitative trait loci using complete genetic marker linkage maps

A new approach based on nonlinear regression for the mapping of quantitative trait loci (QTLs) using complete genetic marker linkage maps is advanced in this paper. We call the approach joint mapping as it makes comprehensive use of the information from every marker locus on a chromosome. With this approach, both the detection of the existence of QTLs and the estimation of their positions, with corresponding confidence intervals, and effects can be realized simultaneously. This approach is widely applicable because only moments are used. It is simple and can save considerable computer time. It is especially useful when there are multiple QTLs and/or interactions between them on a chromosome.     

The effects of selective genotyping on estimates of proportion of recombination between linked quantitative trait loci

Selective genotyping is the marker assay of only the more extreme phenotypes for a quantitative trait and is intended to increase the efficiency of quantitative trait loci (QTL) mapping. We show that selective genotyping can bias estimates of the recombination frequency between linked QTLs — upwardly when QTLs are in repulsion phase, and downwardly when QTLs are in coupling phase. We examined these biases under simple models involving two QTLs segregating in a backcross or F₂ population, using both analytical models and computer simulations. We found that bias is a function of the proportion selected, the magnitude of QTL effects, distance between QTLs and the dominance of QTLs. Selective genotyping thus may decrease the power of mapping multiple linked QTLs and bias the construction of a marker map. We suggest a large proportion than previously suggested (50%) or the entire population be genotyped if linked QTLs of large effects (explain > 10% phenotypic variance) are evident. New models need to be developed to explicitly incorporate selection into QTL map construction.     

利用RFLP进行数量基因定位及效应分析的原理与方法

本文介绍了利用RFLP分子标记进行数量基因定位及效应分析的原理与方法,并讨论了其应用存在的问题．     

Optimum spacing of genetic markers for determining linkage between marker loci and quantitative trait loci

The cost of experiments aimed at determining linkage between marker loci and quantitative trait loci (QTL) was investigated as a function of marker spacing and number of individuals scored. It was found that for a variety of experimental designs, fairly wide marker spacings (ca. 50 cM) are optimum or close to optimum for initial studies of marker-QTL linkage, in the sense of minimizing overall cost of the experiment. Thus, even when large numbers of more or less evenly spaced markers are available, it will not always be cost effective to make full utilization of this capacity. This is particularly true when costs of rearing and trait evaluation per individual scored are low, as when marker data are obtained on individuals raised and evaluated for quantitative traits as part of existing programs. When costs of rearing and trait evaluation per individual scored are high, however, as in human family data collection carried out primarily for subsequent marker — QTL analyses, or when plants or animals are raised specifically for purposes of marker — QTL linkage experiments, optimum spacing may be rather narrow. It is noteworthy that when marginal costs of additional markers or individuals are constant, total resources allocated to a given experiment will determine total number of individuals sampled, but not the optimal marker spacing.     

Trait-based analyses for the detection of linkage between marker loci and quantitative trait loci in crosses between inbred lines

Summary Methods are presented for determining linkage between a marker locus and a nearby locus affecting a quantitative trait (quantitative trait locus=QTL), based on changes in the marker allele frequencies in selection lines derived from the F-2 of a cross between inbred lines, or in the high and low phenotypic classes of an F-2 or BC population. The power of such trait-based (TB) analyses was evaluated and compared with that of methods for determining linkage based on the mean quantitative trait value of marker genotypes in F-2 or BC populations [marker-based (MB) analyses]. TB analyses can be utilized for marker-QTL linkage determination in situations where the MB analysis is not applicable, including analysis of polygenic resistance traits where only a part of the population survives exposure to the Stressor and analysis of marker-allele frequency changes in selection lines. TB analyses may be a useful alternative to MB analyses when interest is centered on a single quantitative trait only and costs of scoring for markers are high compared with costs of raising and obtaining quantitative trait information on F-2 or BC individuals. In this case, a TB analysis will enable equivalent power to be obtained with fewer individuals scored for the marker, but more individuals scored for the quantitative trait. MB analyses remain the method of choice when more than one quantitative trait is to be analyzed in a given population.Contribution from the ARO, Bet Dagan, Israel. No. 1698-E, 1986 series     

RIL群体区间分子标记-QTL定位的相关方法

分析了RIL群体中以分子区间标记进行QTL定位的相关方法．通过对分子标记赋值可获得与数量性状表型值的简单相关系数．然后,在此基础上进行连锁检验．此外．在特定情况下利用R值,可以估计数量性状座位(QTL)和分子标记位点(ML)间的重组值．