应用混合遗传距离和基因型值遗传距离构建植物遗传资源核心子集

将连续性的基因型值数据和间断性的分子标记数据整合建立混合遗传距离,对比了应用混合遗传距离和单纯应用基因型遗传距离构建植物遗传资源核心子集的效果．应用混合线性模型中的调整无偏预测法（AUP）预测基因型值,结合不加权类平均法（UPGMA）逐步聚类构建遗传资源群体的核心子集,并检测一系列核心子集的代表性评价参数．采用包含8个农艺性状和60个SSR标记信息的水稻群体数据验证混合遗传距离的有效性．结果表明,采用混合数据构建的核心子集比单纯的基因型值数据构建的核心子集更有代表性．主成分分析结果验证了该结论的可知陛．     

A strategy on constructing core collections by least distance stepwise sampling

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.     

利用分子标记和数量性状基因型值构建作物核心种质库的研究

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。     

Methods of developing core collections based on the predicted genotypic value of rice (<Emphasis Type="Italic">Oryza sativa</Emphasis> L.)

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     

基于ISSR标记和数量性状的火龙果种质材料遗传多样性分析

为鉴定火龙果种质材料的亲缘关系,筛选优良亲本,提高育种效率,采用ISSR分子标记技术,对25份火龙果种质材料进行遗传背景研究,将植株及果实的20个数量性状数据标准化后,采用欧氏距离计算种质间遗传距离进行比较分析。结果显示,7条ISSR引物共检测到97个位点,其中多态性位点数为93个,多态性条带比例为95.88%。基于分子标记的UPGMA聚类分析,在阈值为0.54处可将25份种质材料分为6大组群,各种质材料的相似系数分布在0.41～0.86之间。20个数量性状的变异系数在12.35%～51.66%之间,Ward法聚类分析在欧式距离为5处,可将25份种质聚为6个组群。两种分类结果并不一致,但均显示出火龙果种质丰富的遗传多样性,可根据分类结果及育种目的筛选适宜亲本。     

Genetic distance sampling: a novel sampling method for obtaining core collections using genetic distances with an application to cultivated lettuce

This paper introduces a novel sampling method for obtaining core collections, entitled genetic distance sampling. The method incorporates information about distances between individual accessions into a random sampling procedure. A basic feature of the method is that automatically larger samples are obtained if accessions are further apart and smaller samples if accessions are closer together. Genetic distance sampling can be used in conjunction with predefined stratifications of the accessions. Sample sizes are determined automatically; they depend on the distances between accessions within strata. The method is applied to the collection of cultivated lettuce of the Centre for Genetic Resources, the Netherlands. In this paper, genetic distances between accessions are obtained using AFLP marker data. However, genetic distance sampling can be applied using any measure of genetic distance between accessions. Some properties of genetic distance sampling are discussed.     

Evaluation of the use of high-density SNP genotyping to implement UPOV Model 2 for DUS testing in barley

Developments in high-throughput genotyping provide an opportunity to explore the application of marker technology in distinctness, uniformity and stability (DUS) testing of new varieties. We have used a large set of molecular markers to assess the feasibility of a UPOV Model 2 approach: “Calibration of threshold levels for molecular characteristics against the minimum distance in traditional characteristics”. We have examined 431 winter and spring barley varieties, with data from UK DUS trials comprising 28 characteristics, together with genotype data from 3072 SNP markers. Inter varietal distances were calculated and we found higher correlations between molecular and morphological distances than have been previously reported. When varieties were grouped by kinship, phenotypic and genotypic distances of these groups correlated well. We estimated the minimum marker numbers required and showed there was a ceiling after which the correlations do not improve. To investigate the possibility of breaking through this ceiling, we attempted genomic prediction of phenotypes from genotypes and higher correlations were achieved. We tested distinctness decisions made using either morphological or genotypic distances and found poor correspondence between each method.     

A multi-marker test based on family data in genome-wide association study

Background

Requirements for successful implementation of multivariate animal threshold models including phenotypic and genotypic information are not known yet. Here simulated horse data were used to investigate the properties of multivariate estimators of genetic parameters for categorical, continuous and molecular genetic data in the context of important radiological health traits using mixed linear-threshold animal models via Gibbs sampling. The simulated pedigree comprised 7 generations and 40000 animals per generation. Additive genetic values, residuals and fixed effects for one continuous trait and liabilities of four binary traits were simulated, resembling situations encountered in the Warmblood horse. Quantitative trait locus (QTL) effects and genetic marker information were simulated for one of the liabilities. Different scenarios with respect to recombination rate between genetic markers and QTL and polymorphism information content of genetic markers were studied. For each scenario ten replicates were sampled from the simulated population, and within each replicate six different datasets differing in number and distribution of animals with trait records and availability of genetic marker information were generated. (Co)Variance components were estimated using a Bayesian mixed linear-threshold animal model via Gibbs sampling. Residual variances were fixed to zero and a proper prior was used for the genetic covariance matrix.

Results

Effective sample sizes (ESS) and biases of genetic parameters differed significantly between datasets. Bias of heritability estimates was -6% to +6% for the continuous trait, -6% to +10% for the binary traits of moderate heritability, and -21% to +25% for the binary traits of low heritability. Additive genetic correlations were mostly underestimated between the continuous trait and binary traits of low heritability, under- or overestimated between the continuous trait and binary traits of moderate heritability, and overestimated between two binary traits. Use of trait information on two subsequent generations of animals increased ESS and reduced bias of parameter estimates more than mere increase of the number of informative animals from one generation. Consideration of genotype information as a fixed effect in the model resulted in overestimation of polygenic heritability of the QTL trait, but increased accuracy of estimated additive genetic correlations of the QTL trait.

Conclusion

Combined use of phenotype and genotype information on parents and offspring will help to identify agonistic and antagonistic genetic correlations between traits of interests, facilitating design of effective multiple trait selection schemes.     

群体遗传学研究中的数据处理方法I．RAPD数据的AMOVA分析

近年来,RAPD数据和AMOVA分析广泛地应用于群体遗传学和保护遗传学研究。然而,由于RAPD标记具显性特点。加上目前进行AMOVA分析所依赖的RAPDistance软件不完善,使得对RAPD数据进行AMOVA分析时存在许多不足。本文介绍了AMOVA分析的基本过程,同时引入一个新的程序DCFA用以替代RADistance并详述了将DCFA与WINAMOVA联用,对RAPD数据进行AMOVA分析的具体步骤与注意事项,最后,以产自中国和巴西8个普通野生稻（Oryza furipogon)天然群体为例,演示了对RAPD表型数据进行AMOVA分析的过程,讨论了AMOVA分析结果在群体遗传结构上的意义。通过对AMOVA算法的分析,同时比较4种距离系数所得AMOVA结果,我们认为在进行AMOVA分析时选择NEI－LI距离和欧氏距离平方较为合适,而目前国内使用较多的JACCARD系数不适合AMOVA分析。     

Bayesian prediction of breeding values for multivariate binary and continuous traits in simulated horse populations using threshold-linear models with Gibbs sampling

Simulated data were used to determine the properties of multivariate prediction of breeding values for categorical and continuous traits using phenotypic, molecular genetic and pedigree information by mixed linear-threshold animal models via Gibbs sampling. Simulation parameters were chosen such that the data resembled situations encountered in Warmblood horse populations. Genetic evaluation was performed in the context of the radiographic findings in the equine limbs. The simulated pedigree comprised seven generations and 40 000 animals per generation. The simulated data included additive genetic values, residuals and fixed effects for one continuous trait and liabilities of four binary traits. For one of the binary traits, quantitative trait locus (QTL) effects and genetic markers were simulated, with three different scenarios with respect to recombination rate (r) between genetic markers and QTL and polymorphism information content (PIC) of genetic markers being studied: r = 0.00 and PIC = 0.90 (r0p9), r = 0.01 and PIC = 0.90 (r1p9), and r = 0.00 and PIC = 0.70 (r0p7). For each scenario, 10 replicates were sampled from the simulated horse population, and six different data sets were generated per replicate. Data sets differed in number and distribution of animals with trait records and the availability of genetic marker information. Breeding values were predicted via Gibbs sampling using a Bayesian mixed linear-threshold animal model with residual covariances fixed to zero and a proper prior for the genetic covariance matrix. Relative breeding values were used to investigate expected response to multi- and single-trait selection. In the sires with 10 or more offspring with trait information, correlations between true and predicted breeding values ranged between 0.89 and 0.94 for the continuous traits and between 0.39 and 0.77 for the binary traits. Proportions of successful identification of sires of average, favourable and unfavourable genetic value were 81% to 86% for the continuous trait and 57% to 74% for the binary traits in these sires. Expected decrease of prevalence of the QTL trait was 3% to 12% after multi-trait selection for all binary traits and 9% to 17% after single-trait selection for the QTL trait. The combined use of phenotype and genotype data was superior to the use of phenotype data alone. It was concluded that information on phenotypes and highly informative genetic markers should be used for prediction of breeding values in mixed linear-threshold animal models via Gibbs sampling to achieve maximum reduction in prevalences of binary traits.     

Spatial genetic structure of northern pike (Esox lucius) in the Baltic Sea

The genetic relationships among 337 northern pike (Esox lucius) collected from the coastal zone of the central Baltic region and the Finnish islands of Aland were analysed using five microsatellite loci. Spatial structure was delineated using both traditional F-statistics and individually based approaches including spatial autocorrelation analysis. Our results indicate that the observed genotypic distribution is incompatible with that of a single, panmictic population. Isolation by distance appears important for shaping the genetic structure of pike in this region resulting in a largely continuous genetic change over the study area. Spatial autocorrelation analysis (Moran's I) of individual pairwise genotypic data show significant positive genetic correlation among pike collected within geographical distances of less than c. 100-150 km (genetic patch size). We suggest that the genetic patch size may be used as a preliminary basis for identifying management units for pike in the Baltic Sea.     

IBDSim: a computer program to simulate genotypic data under isolation by distance

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.     

Methods of constructing core collections by stepwise clustering with three sampling strategies based on the genotypic values of crops

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     

Genetic diversity assessment of sesame core collection in China by phenotype and molecular markers and extraction of a mini-core collection

Sesame (Sesamum indicum L.) is one of the four major oil crops in China. A sesame core collection (CC) was established in China in 2000, but no complete study on its genetic diversity has been carried out at either the phenotypic or molecular level. To provide technical guidance, a theoretical basis for further collection, effective protection, reasonable application, and a complete analysis of sesame genetic resources, a genetic diversity assessment of the sesame CC in China was conducted using phenotypic and molecular data and by extracting a sesame mini-core collection (MC). Results from a genetic diversity assessment of sesame CC in China were significantly inconsistent at the phenotypic and molecular levels. A Mantel test revealed the insignificant correlation between phenotype and molecular marker information (r = 0.0043, t = 0.1320, P = 0.5525). The Shannon-Weaver diversity index (I) and Nei genetic diversity index (h) were higher (I = 0.9537, h = 0.5490) when calculated using phenotypic data from the CC than when using molecular data (I = 0.3467, h = 0.2218). A mini-core collection (MC) containing 184 accessions was extracted based on both phenotypic and molecular data, with a low mean difference percentage (MD, 1.64%), low variance difference percentage (VD, 22.58%), large variable rate of coefficient of variance (VR, 114.86%), and large coincidence rate of range (CR, 95.76%). For molecular data, the diversity indices and the polymorphism information content (PIC) for the MC were significantly higher than for the CC. Compared to an alternative random sampling strategy, the advantages of capturing genetic diversity and validation by extracting a MC using an advanced maximization strategy were proven. This study provides a comprehensive characterization of the phenotypic and molecular genetic diversities of the sesame CC in China. A MC was extracted using both phenotypic and molecular data. Low MD% and VD%, and large VR% and CR% suggested that the MC provides a good representation of the genetic diversity of the original CC. The MC was more genetically diverse with higher diversity indices and a higher PIC value than the CC. A MC may aid in reasonably and efficiently selecting materials for sesame breeding and for genotypic biological studies, and may also be used as a population for association mapping in sesame.     

Comparison of different methods to construct a core germplasm collection in woody perennial species with simple sequence repeat markers. A case study in cherimoya (Annona cherimola, Annonaceae), an underutilised subtropical fruit tree species

Although molecular markers are becoming the tool of choice to develop core collections in plants, the examples of their use in woody perennial species are very scarce. In this work, we used simple sequence repeat (SSR) marker data to develop a core collection in an underutilised subtropical fruit tree species, cherimoya ( Annona cherimola , Annonaceae), from an initial collection of 279 genotypes from different countries. We compared six alternative allocation methods to construct the core collection, four not based upon the similarity dendrogram [random sampling, maximisation strategy (M strategy) and simulated annealing algorithm maximising both genetic diversity and number of SSR alleles] and two based on dendrogram data (logarithmic strategy and stepwise clustering). The diversity maintained in each subset was compared with that present in the entire collection. The results obtained indicate that the use of SSRs together with the M strategy is the most efficient method to develop a core collection in cherimoya. In the best subset, with 40 accessions, all the SSR alleles present in the whole collection were recovered and no significant differences in frequency distribution of alleles for any of the loci studied or in variability parameters ( H _O, H _E) were recorded between the core and the whole collection.     

Optimizing the trade‐off between spatial and genetic sampling efforts in patchy populations: towards a better assessment of functional connectivity using an individual‐based sampling scheme

Genetic data are increasingly used in landscape ecology for the indirect assessment of functional connectivity, that is, the permeability of landscape to movements of organisms. Among available tools, matrix correlation analyses (e.g. Mantel tests or mixed models) are commonly used to test for the relationship between pairwise genetic distances and movement costs incurred by dispersing individuals. When organisms are spatially clustered, a population‐based sampling scheme (PSS) is usually performed, so that a large number of genotypes can be used to compute pairwise genetic distances on the basis of allelic frequencies. Because of financial constraints, this kind of sampling scheme implies a drastic reduction in the number of sampled aggregates, thereby reducing sampling coverage at the landscape level. We used matrix correlation analyses on simulated and empirical genetic data sets to investigate the efficiency of an individual‐based sampling scheme (ISS) in detecting isolation‐by‐distance and isolation‐by‐barrier patterns. Provided that pseudo‐replication issues are taken into account (e.g. through restricted permutations in Mantel tests), we showed that the use of interindividual measures of genotypic dissimilarity may efficiently replace interpopulation measures of genetic differentiation: the sampling of only three or four individuals per aggregate may be sufficient to efficiently detect specific genetic patterns in most situations. The ISS proved to be a promising methodological alternative to the more conventional PSS, offering much flexibility in the spatial design of sampling schemes and ensuring an optimal representativeness of landscape heterogeneity in data, with few aggregates left unsampled. Each strategy offering specific advantages, a combined use of both sampling schemes is discussed.     

保留特殊种质材料的核心库构建方法

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。     

The effect of using approximate gametic variance covariance matrices on marker assisted selection by BLUP

Under additive inheritance, the Henderson mixed model equations (HMME) provide an efficient approach to obtaining genetic evaluations by marker assisted best linear unbiased prediction (MABLUP) given pedigree relationships, trait and marker data. For large pedigrees with many missing markers, however, it is not feasible to calculate the exact gametic variance covariance matrix required to construct HMME. The objective of this study was to investigate the consequences of using approximate gametic variance covariance matrices on response to selection by MABLUP. Two methods were used to generate approximate variance covariance matrices. The first method (Method A) completely discards the marker information for individuals with an unknown linkage phase between two flanking markers. The second method (Method B) makes use of the marker information at only the most polymorphic marker locus for individuals with an unknown linkage phase. Data sets were simulated with and without missing marker data for flanking markers with 2, 4, 6, 8 or 12 alleles. Several missing marker data patterns were considered. The genetic variability explained by marked quantitative trait loci (MQTL) was modeled with one or two MQTL of equal effect. Response to selection by MABLUP using Method A or Method B were compared with that obtained by MABLUP using the exact genetic variance covariance matrix, which was estimated using 15 000 samples from the conditional distribution of genotypic values given the observed marker data. For the simulated conditions, the superiority of MABLUP over BLUP based only on pedigree relationships and trait data varied between 0.1% and 13.5% for Method A, between 1.7% and 23.8% for Method B, and between 7.6% and 28.9% for the exact method. The relative performance of the methods under investigation was not affected by the number of MQTL in the model.     

三点自交法和两点最大似然法构建水稻F2群体分子标记连锁图谱的比较

根据连锁遗传的原理,列出了三点自交法和两点自交最大似然(ML)法估算显性标记遗传距离的具体步骤和算法,将水稻F2群体含香味基因Aro及其连锁的RFLP数据转变为显性标记数据后,用上述两种方法构建的连锁图谱与用MAPMAKER软件计算共显性数据得到的图谱排序相同、标记间距离相近．但是标记数据存在较大程度偏分离时,由三点自交法构建的图谱中标记间图距有增大趋势．作者为提高作图精确性,简化计算过程,讨论了三点自交法对估算重组值的影响及其在分子标记作图中的应用价值,并建议将共显性标记转变为显性标记时进行两次自交ML法估算。