群体遗传惯性理论与选择极限

本文提出了一个新的选择理论—群体遗传惯性理论,试图依此解释并解决在选择理论和家畜育种实践中存在的诸如遗传进展、选择极限等问题,通过理论和计算机的模拟实验证明这种理论是成立的。按照群体遗传惯性理论,实际遗传进展等于期望选择反应与群体遗传惯性力之差,出现选择极限的根本原因是群体遗传惯性力无限增大。群体只有发生新的基因突变,才能打破这个极限。     

群体遗传惯性理论与选择极限 续

2 群体遗传惯性与选择极限的理论分析根据群体遗传惯性理论,我们可以把外部力量(主要是选择)对群体的作用力称为选择压(Se-lection Pression,Sp),由这种选择压的作用而引起群体期望能获得的遗传反应称为期望选择反应(Expected Selection Respones,ER),或者简称选择反应(Selection Respones,R)。而把群体实际所获得的遗传值的变化定义为遗传增量(GeneticGain,△G),或称为获得遗传反应(Observed Se-lection Respones,OR)。这样我们就把群体的实际遗传增量定义为选择反应与群体遗传惯性力之差,即:     

极限计算方法的探讨

极限是数学中的一个普通但非常重要的课题.在几何学、力学、物理学、化学以及其他领域中经常会用到这一思想.本文主要讨论的是关于极限的计算问题,旨在通过对相关知识的回顾和理解,从而总结出了9种关于计算极限的方法,并且通过举例分析,说明了如何利用各种不同的方法针对各种不同类型的极限来求解.     

一题多解探讨二重极限的计算

函数的极限求解是高等数学中比较重要的一个问题,相对于一元函数的极限问题,以二元函数为代表的多元函数的重极限,因其自变量的增加和极限趋近路径的任意性而使问题变得相对复杂,本文主要针对教学过程中遇到的一个二元函数的二重极限求解的典型例子结合相关极限理论给出五类不同的解法,更加灵活鲜明地对二元函数的极限求解方法做了相关的系统性讨论总结,拓宽了解题思路。     

一题多解探讨二重极限的计算

函数的极限求解是高等数学中比较重要的一个问题,相对于一元函数的极限问题,以二元函数为代表的多元函数的重极限,因其自变量的增加和极限趋近路径的任意性而使问题变得相对复杂,本文主要针对教学过程中遇到的一个二元函数的二重极限求解的典型例子结合相关极限理论给出五类不同的解法,更加灵活鲜明地对二元函数的极限求解方法做了相关的系统性讨论总结,拓宽了解题思路。     

如何购买种猪

引种对养猪企业来说是一件大事，笔者根据实际选种育种经验将引种时应该注意的诸多因素写出来，希望能对养猪界的朋友们有所帮助。１如何选择种猪来源１．１考察种猪健康状况。目前国内许多养猪企业面临最大的问题就是猪的健康问题，在某种程度上来说，决定着企业的成败。因此，引种时能否引入健康的种猪，必须倍加小心。可以从以下几点考虑：（１）种猪场是否远离人口与交通繁华的地带，能否确保种猪健康。（２）防疫程序是否严密，人员、车辆、饲料及环境是否消毒严格。（３）最近几年内是否发生过重大疫情。（４）种猪能否经得起猪瘟、伪…     

试论蜜蜂生态学

蜜蜂生态遗传学是蜜蜂生态学、蜜蜂遗传学交叉形成的一门边缘学科 ,它在实践和理论上都将有重要的意义 ,如引种、育种、种质资源的保存和开发利用、环境保护 ,以及抗性起源、适应、蜜蜂进化及未来学演变趋势等问题上 ,它都能发挥自己独特的作用。     

如何选择你所需要的饲用酶制剂

1.前言 几年前饲用酶对大多数从事饲料工业的人来说还是一个新名词,但现在在许多饲料中,如猪禽、反刍、水产等,都已广泛地添加了酶制剂,而且市场上可供选择的产品种类也很多。这就给我们的营养师和饲料原料采购者带来了一个问题:如何选择最有效的酶制剂。对于其它种类的饲料添加剂来说这个问题并不复杂,只要选择那些每单位货币能买到最高有效成份就可以了,但这一原则是否适用于饲用酶制剂呢?本文就如何选用不同的酶制剂和影响测定酶制剂活性的因素加以讨论,为我们的营养师和原料采购者在选择酶制剂时提供一些参考依据。     

巴西家禽基因组研究进展及应用前景

1 家禽遗传学和基因组学的背景 20世纪以来由于引入先进的统计遗传学程序可以更好地估计动物性状的遗传力和育种值,经典的育种方法得到了很大的提高。多年的选育使得鸡的性状有了很大提高,同时在产量和适应性方面也有所增强。除了选育带来的成功外,对蛋肉生产进行选育获得的遗传进展理论上在接下来的20年里达到其生物极限。     

浅析低压电网中无功补偿的意义

针对低压电网系统,如何有效保持其良好的工作状态,降低电能损失,越来越成为我们在用电时需要着重考虑的问题。因为这与电网是否能稳定工作、设备是否能安全运行、工厂是否能安全生产以及人民生活用电是否能得到保障都有直接影响。本文主要从分析无功补偿的作用和主要选择方法,结合应用实例说明采用无功补偿技术,提高低压电网和用电设备的功率因数进行分析和探讨。     

Estimation of additive and non-additive genetic effects for fertility and reproduction traits in North American Holstein cattle using genomic information

Non-additive genetic effects are usually ignored in animal breeding programs due to data structure (e.g., incomplete pedigree), computational limitations and over-parameterization of the models. However, non-additive genetic effects may play an important role in the expression of complex traits in livestock species, such as fertility and reproduction traits. In this study, components of genetic variance for additive and non-additive genetic effects were estimated for a variety of fertility and reproduction traits in Holstein cattle using pedigree and genomic relationship matrices. Four linear models were used: (a) an additive genetic model; (b) a model including both additive and epistatic (additive by additive) genetic effects; (c) a model including both additive and dominance effects; and (d) a full model including additive, epistatic and dominance genetic effects. Nine fertility and reproduction traits were analysed, and models were run separately for heifers (N = 5,825) and cows (N = 6,090). For some traits, a larger proportion of phenotypic variance was explained by non-additive genetic effects compared with additive effects, indicating that epistasis, dominance or a combination thereof is of great importance. Epistatic genetic effects contributed more to the total phenotypic variance than dominance genetic effects. Although these models varied considerably in the partitioning of the components of genetic variance, the models including a non-additive genetic effect did not show a clear advantage over the additive model based on the Akaike information criterion. The partitioning of variance components resulted in a re-ranking of cows based solely on the cows’ additive genetic effects between models, indicating that adjusting for non-additive genetic effects could affect selection decisions made in dairy cattle breeding programs. These results suggest that non-additive genetic effects play an important role in some fertility and reproduction traits in Holstein cattle.     

Extension of the concept of kinship, relationship, and inbreeding to account for linked epistatic complexes

Although epistatic effects are well defined and, in principle, can be exploited in quantitative-genetic selection theory, they often are ignored or even treated as nuisance parameters in practical applications. Traditionally, epistasis is considered as an interaction between genes at unspecified loci. Inspired by the observation that functional genes are often organised in physical clusters, we developed a model to combine additive effects and additive × additive interactions in linked gene clusters of defined length. Malécot's kinship concept is extended to identity by descent probabilities for chromosome segments of a given length in Morgan units, called epistatic kinship. Using the analogy of Malécot's kinship and Wright's relationship and inbreeding coefficients, epistatic relationship coefficients and epistatic inbreeding coefficients are defined. Simple rules are given to set up the epistatic numerator relationship matrix and its inverse directly from a pedigree list. The well-known single locus parameters and algorithms to set up the additive numerator relationship matrix and its inverse are a special case of the suggested methodology for a chromosome segment length of null Morgan. A proof of concept of the suggested method is given with a small simulation study. Assuming additive, linked epistatic and residual variance components, 100 replicated data sets for 1000 individuals are generated. From these data, residual maximum likelihood estimates of the variance components and of the chromosome segment size are obtained. Potential applications of the methodology are discussed. Given that a substantial variance component is attributed to this effect, the expected genetic gain can be increased on the short term if selection is on additive and epistatic effects, the latter comprising additive × additive interaction effect of loci in linkage disequilibrium. This extra benefit, however, will diminish through crossing over in subsequent generations. Despite some practical problems yet to be solved, the suggested model and algorithms open new perspectives to use a higher proportion of genetic variability in selection and breeding.     

Changes in heterosis under within-line selection or reciprocal recurrent selection: an experiment on early egg production in Japanese quail

In much animal production, commercial animals are crossbreds from closed lines or breeds under long-term directional selection. Therefore it is desirable to be able to predict heterosis gains over the generations as it is done for genetic progress under within-line selection. However, heterosis is the phenotypic expression of a complex phenomenon which may involve several types of genetic effects like dominance and epistasis. In animal breeding, basic quantitative genetics theory indicates that heterosis should be proportional to (squared) differences in gene frequency between populations (e.g. F alconer and M ac K ay 1996), and it has been found approximately correct, so it is commonly used for planning crosses. Under that type of heterosis, however, selection towards the same objective in two populations should bring gene frequencies closer, and therefore it should eventually decrease heterosis. On the other hand, reciprocal recurrent selection designed to increase genetic distance between lines should eventually achieve maximum heterosis (O llivier 1982). Some experiments reviewed by brun (1982) have already compared genetic progress under within-line and reciprocal recurrent selection, but they did not focus on comparing the trend of heterosis with generations between the two selection methods. Also, heterosis was monitored periodically in some selection experiments on poultry, and results were reviewed by F airfull (1990). They were somewhat contradictory, but they generally failed to relate genetic progress to loss of heterosis under within-line selection. Moreover, in commercial production, as purebreds and crossbreds are not contemporaries and are generally maintained under very different management systems, estimations of heterosis and of the evolution of crossbred advantage over the generations may be difficult to obtain. Using the Japanese quail as an experimental animal, the present work was initiated specifically to follow the changes in heterosis brought about by selection for a single heterotic trait, early egg production (M invielle et al. 1995). For that purpose, two selection methods expected to have opposite effects on heterosis, directional within-line (or individual) selection and reciprocal recurrent selection, were applied for 13 generations in four quail lines started from two different origins, and trends of heterosis in the selected character and in weight and egg traits were evaluated.     

Estimation of additive genetic variance when base populations are selected

A population of size 40 was simulated 1,000 times for 10 generations. Five out of twenty males were selected each generation, and each male was mated to four females to have two progeny. The additive genetic variance (sigma 2a) before selection was 10, and the initial heritability was .5. Due to covariances among animals, inbreeding and gametic disequilibrium, the genetic variance was reduced to 6.72 after 10 generations of selection. Reduction of variance was lower in another population simulated with size 400 and 10% of the males selected. Restricted Maximum Likelihood was used to estimate sigma 2a using an animal model. The estimate of sigma 2a was empirically unbiased when all data and all relationships were used. Omitting data from selected ancestors caused biased estimates of sigma 2a due to not accounting for all gametic disequilibrium. Including additional relationships between assumed base animals adjusted for inbreeding and for covariances. Bias from gametic disequilibrium decreased slightly with the use of more relationship information, and it was smaller in the small population and(or) when selection had been practiced for just a few generations.     

Factors affecting direct and correlated responses in a White Leghorn population under long term selection for egg number

1. Data on 13,773 pullets, the progeny of 30 to 50 sires and about 135 to 220 dams per generation of a strain of White Leghorn under long term selection for part period egg production over 16 generations, were examined. 2. Realised genetic gain per generation was highly significant for egg production (2.55 eggs), egg weight (- 0.51 g), age at sexual maturity (- 2.30 d) and 40 week body weight (- 11.73 g). 3. The significant genetic response of 4.46 to 4.72 eggs per generation was realised in the first 8 generations only, which was accompanied by a marked decline in egg weight (- 1.67 and- 0.79 g per generation) and in age at sexual maturity (- 4.63 and- 6.39 d per generation). The 20 and 40 week body weights showed significant improvement in the last 4 generations. 4. Realised heritabilities were much lower in the last 8 than in the first 8 generation. 5. The additive genetic variance for most traits, except 20 week body weight and genetic covariances of egg production with egg weight and age at sexual maturity, declined significantly. 6. Natural selection did not seem to play any major role. No appreciable change in fitness traits was observed in the selected line. 7. The time trends were non-significant for all the traits in the control line, indicating its stability over generations.     

Potential benefit from using the αs1-casein genotype information in a selection scheme for dairy goats

A stochastic approach is proposed to predict responses to selection when using α_s1‐casein genotype information in a selection scheme of a Spanish breed of dairy goats. Two independent selection objectives were considered: protein yield (PY), where the major additive gene CSN1S1, which codes for α_s1‐casein, has a small effect, and protein content (P%), where this gene has a large effect on performances. Significant differences in response between using and ignoring information on the major gene were observed only when the major gene has a large effect. The main result was in the case of P%, the total genetic gain obtained in the early generations of selection was maintained in the long‐term. Taking account of genotype information either in the evaluation model or in the selection criteria leads to a faster fixation of the favourable allele and a reduction of the total genetic variance over generations. The inbreeding rates varied across generations, the highest rates observed in later generations of selection and when the major gene has a large effect and its genotype was included in the genetic evaluation procedure. It is concluded that inclusion of the casein genotype as an additional selection criteria will improve gains for protein traits, in particular P%. Recommendations are also given in order to optimize the use of this molecular information in dairy goat selection programs.     

A review of theoretical aspects in the estimation of breeding values for multi-trait selection

The natural links between best linear unbiased prediction and selection indices are reviewed. These links allow a simple interpretation of the estimation equations and allow results from selection index theory to be used in investigating the accuracy of predictors. The gain in accuracy in prediction for one trait from using other correlated traits is partitioned into a direct gain from measuring other traits and a gain because fixed effects are estimated more precisely. There will be a loss of accuracy in predictors and loss of selection response when variance parameters are not known exactly. A regression argument is used to explain this phenomenon and formulae to quantify the loss of selection response are interpreted using canonical variates. Often predictors and variance parameters are derived from selected populations. The relevance of procedures developed for unselected populations is discussed and unsolved problems indicated.     

Linkage disequilibrium and selection response in two-stage marker-assisted selection of dairy cattle over several generations

A stochastic simulation was carried out to investigate the advantage of marker‐assisted selection (MAS) in comparison with traditional selection over several generations. The selection goal was a sex‐limited trait or a linear combination of traits with a polygenic component, two unlinked additive QTL and a non‐genetic component. The simulated QTL were moderate or large and the allele frequencies were varied. Two stages of selection among the male offspring were carried out. In the first stage marker information was used to select among full sibs (MAS) or one full sib was chosen at random. In the second stage young bulls were selected based on a progeny test. The response in total genetic gain was faster with MAS than with traditional selection and persisted over several generations. With a QTL of moderate size and initial allele frequencies of the favourable allele of 0.05 the response with MAS was 6% higher than with traditional selection in the sires selected after progeny test. MAS in a within‐family two‐stage selection scheme improved the genetic merit of selected bulls even when linkage disequilibrium between QTL and polygenes was initially increased.     

Choice of statistical model for estimating genetic parameters using restricted maximum likelihood in swine

Summary Restricted maximum likelihood (REML) was used to determine the choice of statistical model, additive genetic maternal and common litter effects and consequences of ignoring these effects on estimates of variance–covariance components under random and phenotypic selection in swine using computer simulation. Two closed herds of different size and two traits, (i) pre‐weaning average daily gain and (ii) litter size at birth, were considered. Three levels of additive direct and maternal genetic correlations (r_dm) were assumed to each trait. Four mixed models (denoted as GRM1 through GRM4) were used to generate data sets. Model GRM1 included only additive direct genetic effects, GRM2 included only additive direct genetic and common litter effects, GRM3 included only additive direct and maternal genetic effects and GRM4 included all the random effects. Four mixed animal models (defined as EPM1 through EPM4) were defined for estimating genetic parameters similar to GRM. Data from each GRM were fitted with EPM1 through EPM4. The largest biased estimates of additive genetic variance were obtained when EPM1 was fitted to data generated assuming the presence of either additive maternal genetic, common litter effects or a combination thereof. The bias of estimated additive direct genetic variance (VA_d) increased and those of recidual variance (VE) decreased with an increase in level of r_dm when GRM3 was used. EPM1, EPM2 and EPM3 resulted in biased estimation of the direct genetic variances. EPM4 was the most accurate in each GRM. Phenotypic selection substantially increased bias of estimated additive direct genetic effect and its mean square error in trait 1, but decreased those in trait 2 when ignored in the statistical model. For trait 2, estimates under phenotypic selection were more biased than those under random selection. It was concluded that statistical models for estimating variance components should include all random effects considered to avoid bias.     

Genetic parameters for fur quality graded on live animals and dried pelts of American mink (Neovison vison)

Fur quality and skin size are integral qualities in the mink industry and are main determinants of sales price and subsequent income for mink fur producers. Parental animals of future generations are selected based on quality grading from live animals, but selection response is obtained from dried skins sold after pelting. In this study, we evaluated traits assessed during live grading and pelt traits examined on dried skins to determine correlation between live and pelt traits. Grading traits and body weight were measured during live animal grading for 9,539 Brown American mink, and pelt quality traits and skin size were evaluated on 8,385 dried mink skins after pelting. Data were sampled from 2 yearly production cycles. Genetic parameters were estimated using the REML method implemented in the DMU package. Heritabilities and proportions of litter variance were calculated from estimated variance components for all traits, and genetic and phenotypic correlations between all traits were estimated in a series of bivariate analyses. Heritability estimates for live grading traits ranged from 0.06 to 0.28, heritability estimates for pelt quality traits ranged from 0.20 to 0.30, and finally heritability estimates for body size traits ranged from 0.43 to 0.48. Skin size and body weight were regarded as different traits for the two sexes and were therefore analysed for each sex separately. Genetic correlations between grading traits exhibited a range of 0.30–0.99 and genetic correlations between pelt quality traits ranged from 0.38 to 0.86. Genetic correlations between quality, wool density and silky appearance evaluated during live animal grading and on dried skin after pelting were 0.74, 0.41 and 0.33, respectively. Skin size and body weight were negatively correlated with pelt quality traits and ranged from −0.55 to −0.25. Using standard selection index theory and combined information from both live grading and skin evaluation increase of reliability of selection ranged from 0.6% to 14%. Due to moderate genetic correlations between traits evaluated during live grading and on dried skins, and negative correlations between pelt quality traits and body size, we concluded that traits should be selected simultaneously.