群体融合对选择有效性的影响

探讨了群体融合对选择有效性的影响⒚显性完全且选择不利于纯合子 ａａ 时，当群体中隐性基因的频率 ０≤ｑ＜ １／３，群体融合导致选择有效性增加；而当 １／３＜ ｑ≤１ 时，融合使选择有效性减小⒚无显性即选择不利于 Ａａ 和 ａａ 时，群体融合导致选择有效性减小⒚超显性即选择有利于杂合子 Ａａ 而不利于纯合子 Ａ Ａ 和 ａａ 时，当 ０≤ｑ＜ １／２，融合导致选择有效性增加；当 １／２＜ ｑ≤１，融合导致选择有效性减小⒚     

种子与花粉的随机迁移对植物群体遗传结构分化的影响

将Ｗｒｉｇｈｔ的经典岛屿模型拓广到植物群体上,同时考虑了含有花粉和种子随机迁移的影响。并给出了３种不同遗传方式的基因（双亲遗传,父本和母本遗传）频率的期望均值和方差。理论结果证明花粉或种子的随机迁移可增加基因频率方差,其幅度取决于迁移率和迁移基因频率的方差。同绝对迁移率一样,花粉和种子的迁移率方差及迁移基因频率的方差对群体遗传结构的分化有着同样的重要。一个重要结论就是花粉或种子的随机迁移率和随机迁     

随机交配群体中连锁对世代平均数和方差的影响及其测定

本文以黄花烟草N.rustica的两个品种V_2和V_(12)为材料,人工创造4个随机交配轮次不同的群体,对开花期(FT)和最后株高(FH)两个性状在理论上探讨了随机交配群体中连锁对世代平均数和方差的影响,并用上述材料进行验证。结果指出,在平均数和方差两种水平上都测定出连锁的存在;无论是加性方差还是显性方差,都随着交配轮次的增加而减少,这是相引连锁的表现。本文还讨论了基因连锁强度、基因联合程度对世代平均数和方差的影响。     

普通群体基因频率逐代演变的一个不变量与平衡区域

建立普通群体基因型频率逐代演变规律的数学模型,发现在各代中RR与rr的频率的差为不变量,它与平衡状态的Rr的频率一一对应。在育种工作中这个不变量是可观测的,作物选种即是人为改变群体基因频率,因此它有一定的参考意义。获得由第一代基因型频率计算平衡状态基因型频率的公式,推导出平衡状态基因型频率与异交率的换算方法。     

长顺绿壳蛋鸡绿壳基因的扩增与鉴定

长顺绿壳蛋鸡是贵州特有的一个地方品种,为了更好地保护和利用该品种,有必要对长顺绿壳蛋鸡进行提纯。本试验随机抽取735只长顺绿壳蛋鸡作为研究对象,以绿壳基因(oocyan,O)作为候选基因,设计3条特异性引物,利用多重PCR技术对长顺绿壳蛋鸡进行纯合基因型的筛选。结果显示,在试验群体中,显性纯合基因型个体为172,隐形纯合基因型个体为69,其余的基因型个体为杂合子(494)。对该基因位点进行遗传特性分析,发现该位点属中度多态,且该位点在本群体中未达到Hardy-Weinberg平衡状态,这可能跟本群体经过了一定的选育有关。群体杂合基因型偏高,占67.21%,杂合子会在后代出现性状分离,所以有必要提高群体纯合绿壳基因频率和纯合基因型频率,从而提高群体绿壳蛋率,增加经济效益。     

怎样分析人类中某一性状是由一对完全显性的常染色体基因所控制

怎样分析人类中某一性状是由一对完全显性的常染色体基因所控制。要解决这个问题,首先要证明 Snyder 公式。即在一个大的随机婚配的群体中,如果基因 A 对 a 为完全显性;基因 A 的频率为 p,a 的频率为 q,则在显性个体与显性个体的婚配中,子代中隐性个体数与子代个体总数之比为(q/(1+q))~2,在显性个体与隐性个体的婚配中,子代中隐性个体数与子代个体总数之比为 q/(1+q)。证明:因为在一个大的随机婚配的群体中,个体基因型 A A、Aa、aa 的频率则分别为p~2、2pq、q~2,群体中个体间婚配频率及子代基因型频率如下表:     

连锁情况下自交群体遗传组成的一般表达及紧密连锁基因的选择

根据基因连锁模型推导出了计算杂交后代群体中基因型频率的数学公式,以此为基础分析了紧密连锁基因的选择策略。当Aa和Bb紧密连锁时,重组基因型在F2的频率很小,但是,随着世代的递增而迅速增大。重组基因型频率的变化与连锁强度有密切的关系。连锁越紧密,加代对提高其频率的效果就越明显。在两个位点紧密连锁的情况下,在F2代对重组类型直接进行选择很困难。推迟选择世代可以大幅度地提高选择效率。如果把选择世代由F2推迟到F3~F7,当连锁强度为5%时,选择效率可以提高到F2代的20~68倍;当连锁强度为0.5%时,选择效率可以提高到F2代的200~708倍。本文中推导出的数学公式和原理适用于任何连锁基因的选择和群体组成分析。当P=0.5时,也可以用于独立基因的遗传分析和选择。     

对高中生物课中群体遗传的应用分析

群体 ,是指一群可以相互交配的生物个体。在一个群体中 ,有许多性状表现 ,但各种性状绝不会在同一个体中同时出现。尤其是相对性状 ,不同表现类型由于在不同个体中出现 ,因而 ,各自具有一定的出现频率。据孟德尔定律 ,控制性状的基因在配子中也有相应的出现频率。因而可以进行有关群体遗传的数量及频率的测定和预算。下面仅就一对相对性状遗传进行分析。例 1　已知兔子的脂肪有白色和淡黄色两种 ,是 1对相对性状 ,属常染色体遗传 ,白色 (B)对淡黄色 (b)为显性。基因型为 BB和 bb的个体杂交 ,预测 F3 及以后的群体中 ,淡黄色脂肪个体占群体…     

杂交群体动物基因聚合

基因聚合是通过优化设计杂交方案,选择利用目标基因或与其紧密连锁的分子标记,通过世代选择实现将来源于多个不同群体的优势目标基因或基因型聚合到同一个理想个体中,进而达到生产出超级经济性状个体的目的。针对聚合不同目标基因个数,设计4类杂交方案——两群体、三群体、四群体级联、四群体对称。在相同的杂交方案中,比较基因型选择和表型选择策略,分析不同杂交组合、性状遗传力、初始基因频率、基础群体规模对聚合设计的影响,并筛选出最佳的聚合方案。研究结果表明,在较大的基础群体规模和较高初始群体基因频率下,获得聚合多个目标基因的理想个体的可能性较大。在四群体杂交方案中,亲本的杂交次序对于级联杂交比对称杂交的影响较大。模拟结果表明,运用基因型选择进行聚合育种优于表型选择。文章所开发设计的聚合模拟育种的统计分析方法和相应软件为指导杂交育种方案和选择策略的设计提供理论参考,同时,为进一步设计开发聚合设计模拟育种平台奠定基础。     

自交作物群体的遗传组成分析

用数学方法推导出了自交作物群体遗传组成的分析公式。根据所推出的公式,可以计算出任何自交世代群体的基因型频率和同一表现型中各基因型的频率。对自交群体遗传组成的分析可用于质量性状的选择,确定选择时需要的有效群体大小和选出有效群体的大小,亦有助于确定选择的最佳世代。     

Small scale genotypic richness stabilizes plot biomass and increases phenotypic variance in the invasive grass Phalaris arundinacea

Aims We aim to understand how small-scale genotypic richness and genotypic interactions influence the biomass and potential invasiveness of the invasive grass, Phalaris arundinacea under two different disturbance treatments: intact plots and disturbed plots, where all the native vegetation has been removed. Specifically, we address the following questions (i) Does genotypic richness increase biomass production? (ii) Do genotypic interactions promote or reduce biomass production? (iii) Does the effect of genotypic richness and genotypic interactions differ in different disturbance treatments? Finally (iv) Is phenotypic variation greater as genotypic richness increases?Methods We conducted a 2-year common garden experiment in which we manipulated genotype richness using eight genotypes planted under both intact and disturbed conditions in a wetland in Burlington, Vermont (44°27′23″N, 73°11′29″W). The experiment consisted of a randomized complete block design of three blocks, each containing 20 plots (0.5 m 2) per disturbed treatment. We calculated total plot biomass and partitioned the net biodiversity effect into three components: dominance effect, trait-dependent complementarity and trait-independent complementarity. We calculated the phenotypic variance for each different genotype richness treatment under the two disturbance treatments.Important findings Our results indicate that local genotypic richness does not increase total biomass production of the invasive grass P. arundinacea in either intact or disturbed treatments. However, genotypic interactions underlying the responses showed very different patterns in response to increasing genotypic richness. In the intact treatment, genotypic interactions resulted in the observed biomass being greater than the predicted biomass from monoculture plots (e.g., overyielding) and this was driven by facilitation. However, facilitation was reduced as genotypic richness increased. In the disturbed treatment, genotypic interactions resulted in underyielding with observed biomass being slightly less than expected from the performance of genotypes in monocultures; however, underyielding was reduced as genotypic richness increased. Thus, in both treatments, higher genotypic richness resulted in plot biomass nearing the additive biomass from individual monocultures. In general, higher genotypic richness buffered populations against interactions that would have reduced biomass and potentially spread. Phenotypic variance also had contrasting patterns in intact and disturbed treatments. In the intact treatment, phenotypic variance was low across all genotypic richness levels, while in the disturbed treatment, phenotypic variance estimates increased as genotypic richness increased. Thus, under the disturbed treatment, plots with higher genotypic richness had a greater potential response to selection. Therefore, limiting the introduction of new genotypes, even if existing genotypes of the invasive species are already present, should be considered a desirable management strategy to limit the invasive behavior of alien species.     

A brief note on the resemblance between relatives in the presence of population stratification

Population stratification occurs when a study population is comprised of several sub-populations, and can result in increased false positive findings in genomewide-association studies. Recently published work shows that sub-population-specific positive assortative mating at the genotypic level results in population stratification. We show that if the allele frequency of a single nucleotide polymorphism responsible for a trait varies between sub-populations and there is no dominance variance, then the heritability of the trait increases, primarily due to an increase in the additive genetic variance of the trait.     

The additive genetic variance after bottlenecks is affected by the number of loci involved in epistatic interactions

Abstract We investigated the role of the number of loci coding for a neutral trait on the release of additive variance for this trait after population bottlenecks. Different bottleneck sizes and durations were tested for various matrices of genotypic values, with initial conditions covering the allele frequency space. We used three different types of matrices. First, we extended Cheverud and Routman's model by defining matrices of "pure" epistasis for three and four independent loci; second, we used genotypic values drawn randomly from uniform, normal, and exponential distributions; and third we used two models of simple metabolic pathways leading to physiological epistasis. For all these matrices of genotypic values except the dominant metabolic pathway, we find that, as the number of loci increases from two to three and four, an increase in the release of additive variance is occurring. The amount of additive variance released for a given set of genotypic values is a function of the inbreeding coefficient, independently of the size and duration of the bottleneck. The level of inbreeding necessary to achieve maximum release in additive variance increases with the number of loci. We find that additive-by-additive epistasis is the type of epistasis most easily converted into additive variance. For a wide range of models, our results show that epistasis, rather than dominance, plays a significant role in the increase of additive variance following bottlenecks.     

Change in Genetic Variance under Selection in a Self-Fertilizing Population

In this study we show how the genetic variance of a quantitative trait changes in a self-fertilizing population under repeated cycles of truncation selection, with the analysis based on the infinitesimal model in which it is assumed that the trait is determined by an infinite number of unlinked loci without epistasis. The genetic variance is reduced not as a consequence of the genotypic frequency change but due to the build-up of linkage disequilibrium under truncation selection in this model. We assume that the order of the genotypic contribution from each locus is n(-1/2), where n is the number of loci involved, and investigate the change in linkage disequilibrium resulting from selection and self-fertilization using genotypic frequency dynamics in order to analyze the change in the genetic variance. Our analysis gives recurrence relations of genetic variance among the succeeding generations for the three cases of gene action, i.e., purely additive action, pure dominance without additive effect and the presence of both additive effect and dominance, respectively. Numerical examples are also given as a check on the recurrence formulas.     

Quantitative Genetics of Sperm Precedence in Drosophila Melanogaster

To assess the genetic basis of sperm competition under conditions in which it occurs, I estimated additive, dominance, homozygous and environmental variance components, the effects of inbreeding, and the weighted average dominance of segregating alleles for two measures of sperm precedence in a large, outbred laboratory population. Both first and second male precedence show significant decline on inbreeding. Second male precedence demonstrates significant dominance variance and homozygous genetic variance, but the additive variance is low and not significantly different from zero. For first male precedence, the variance among homozygous lines is again significant, and dominance variance is larger than the additive variance, but is not statistically significant. In contrast, male mating success and other fitness components in Drosophila generally exhibit significant additive variance and little or no dominance variance. Other recent experiments have shown significant genotypic variation for sperm precedence and have associated it with allelic variants of accessory-gland proteins. The contrast between sperm precedence and other male fitness traits in the structure of quantitative genetic variation suggests that different mechanisms may be responsible for the maintenance of variation in these traits. The pattern of genetic variation and inbreeding decline shown in this experiment suggests that one or a few genes with major effects on sperm precedence may be segregating in this population.     

Refinements to the partitioning of the inbred genotypic variance

Natural gene flow is often localised because of gamete dispersal limitations, and the quantity and structure of the genotypic variance in such populations is a key to predicting the advance from selection, in both evolution and artificial breeding programmes. Earlier derivations of this variance have shown that the total dominance variance may increase with inbreeding despite the fact that heterozygosity is decreasing. This anomaly has been corrected following the de novo biometrical derivation presented in this paper. The whole population also subdivides into descendant lineages that differ in allele frequencies and means because of the dispersion caused by genetic drift and continuing localisation of gamodemes. The paper defines for the first time the among-line and within-line partitions of the dominance variance; and corrects anomalies in the total genic (additive genetic) variance, and its underlying inbred average alle-substitution effect. The revisions also clarify the connections between the Fisher-Falconer, Mather-Hayman, and Wright approaches to defining the inbred genotypic variance. Relationships are discussed between the population dispersion structure and genetic efficiency in selection.     

玉米籽粒性状的遗传模型研究

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。     

Epistasis and Its Contribution to Genetic Variance Components

We present a new parameterization of physiological epistasis that allows the measurement of epistasis separate from its effects on the interaction (epistatic) genetic variance component. Epistasis is the deviation of two-locus genotypic values from the sum of the contributing single-locus genotypic values. This parameterization leads to statistical tests for epistasis given estimates of two-locus genotypic values such as can be obtained from quantitative trait locus studies. The contributions of epistasis to the additive, dominance and interaction genetic variances are specified. Epistasis can make substantial contributions to each of these variance components. This parameterization of epistasis allows general consideration of the role of epistasis in evolution by defining its contribution to the additive genetic variance.     

Age at menarche as a fitness trait: nonadditive genetic variance detected in a large twin sample.

The etiological role of genotype and environment in recalled age at menarche was examined using an unselected sample of 1,177 MZ and 711 DZ twin pairs aged 18 years and older. The correlation for onset of menarche between MZ twins was .65 +/- .03, and that for DZ pairs was .18 +/- .04, although these differed somewhat between four birth cohorts. Environmental factors were more important in the older cohorts (perhaps because of less reliable recall). Total genotypic variance (additive plus nonadditive) ranged from 61% in the oldest cohort to 68% in the youngest cohort. In the oldest birth cohort (born before 1939), there was evidence of greater influence of environmental factors on age at menarche in the second-born twin, although there was no other evidence in the data that birth trauma affected timing. The greater part of the genetic variance was nonadditive (dominance or epistasis), and this is typical of a fitness trait. It appears that genetic nonadditivity is in the decreasing direction, and this is consistent with selection for early menarche during human evolution. Breakdown of inbreeding depression as a possible explanation for the secular decline in age at menarche is discussed.     

On the Additive and Dominant Variance and Covariance of Individuals Within the Genomic Selection Scope

Genomic evaluation models can fit additive and dominant SNP effects. Under quantitative genetics theory, additive or “breeding” values of individuals are generated by substitution effects, which involve both “biological” additive and dominant effects of the markers. Dominance deviations include only a portion of the biological dominant effects of the markers. Additive variance includes variation due to the additive and dominant effects of the markers. We describe a matrix of dominant genomic relationships across individuals, D, which is similar to the G matrix used in genomic best linear unbiased prediction. This matrix can be used in a mixed-model context for genomic evaluations or to estimate dominant and additive variances in the population. From the “genotypic” value of individuals, an alternative parameterization defines additive and dominance as the parts attributable to the additive and dominant effect of the markers. This approach underestimates the additive genetic variance and overestimates the dominance variance. Transforming the variances from one model into the other is trivial if the distribution of allelic frequencies is known. We illustrate these results with mouse data (four traits, 1884 mice, and 10,946 markers) and simulated data (2100 individuals and 10,000 markers). Variance components were estimated correctly in the model, considering breeding values and dominance deviations. For the model considering genotypic values, the inclusion of dominant effects biased the estimate of additive variance. Genomic models were more accurate for the estimation of variance components than their pedigree-based counterparts.