微卫星DNA标记与乌骨鸡屠宰性能的相关分析

微卫星(mierosatellite)DNA标记是由重复单位为2～6 bp的核苷酸序列所构成的串联重复序列,由于重复单位的重复数目不同,形成了微卫星标记的丰富多态性.微卫星标记普遍存在于真核生物基因组中,由于具有数量多、分布广、多态性丰富、呈共显性遗传方式及检测快速和方便等优点而成为当今包括人类在内的各物种基因作图的首选标记,并在基因诊断、物种的演变追踪、QTL分析、系谱的确定、群体遗传结构分析、标记辅助选择等方面显示出巨大的应用价值.     

分子标记与鸡生产性能的研究进展

随着基因组学时代的到来和各种生物技术的飞速发展 ,分子标记技术在鸡的育种和生产中愈来愈显示其重要性。近几年来 ,国内外学者对微卫星标记进行了广泛而深刻的研究 ,在基因作图、数量性状位点 ( QTL)定位、群体遗传距离测定、抗病品系的选育等方面得到了广泛应用。作为第三代分子标记的 SNPs,为探索鸡的某些疾病的产生机理和生产性能差异的遗传基础提供了新方法。1　微卫星标记微卫星是在生物体基因组以 2～ 6bp为其核心序列 ,头尾相连组成的串状重复序列。核心序列一般重复 1 0～ 2 0次 ,多态性的产生是因重复次数的不同造成的 ,通常…     

微卫星标记技术的研究进展及其在家兔研究中的应用

微卫星标记DNA是简单串联重复序列(simple sequence repeas,SSR),它的组成基元为1～6个核苷酸,例如(CA)n、(CGA)n、(GACG)n等.由于这些序列广泛存在于真核细胞的基因组中,且由于串联重复的数目是可变的而呈现高度的多态性,以及在单个微卫星点上可作共显性分析.近年来,微卫星序列作为比较理想的分子标记广泛应用于遗体图谱的构建、居群遗传学以及系统发育的研究.     

微卫星标记BMS2508在4个山羊品种中的遗传多样性研究

微卫星DNA又称简单序列重复、短串联重复序列和简单序列长度多态性,广泛存在于真核生物基因组中.微卫星多态性是由于减数分裂过程中不等交换造成变异而产生的,具有保守性,其核心序列为2～6 bp,重复约10～20次,属于等显性遗传.自1989年在人类基因组研究发现微卫星多态性后,目前在马、牛、猪、绵羊和鸡等动物基因组中也筛选出了大量的微卫星标记,但山羊等动物中则相对较少.     

微卫星DNA遗传标记的研究进展及应用

微卫星DNA又称为短串联序列重复(short tandem repeats,STRs)、简单序列重复(simple sequence repeat,SSR),其作为遗传标记在DNA指纹图、遗传连锁图、群体遗传结构及遗传关系、分析以及遗传育种分子标记辅助选择等领域拥有广阔的应用前景.对微卫星DNA进行了概述,并对其应用及展望进行介绍,以期为相关研究提供参考.     

荧光标记通用引物在银狐微卫星基因分型中的应用

微卫星又称短串联重复序列(STR)或简单重复序列(SSR),由侧翼序列和串联重复核心序列组成,一般其串联重复核心序列长度为1～6 bp[1]。由于微卫星遗传标记具有多态信息含量高、基因组内分布广泛及呈共显性遗传等优点,因而已被广泛应用于遗传图谱的构建[2-3]、QTL定位[4]、亲缘关系鉴定[5]及遗传多样性评估等[6]。微卫星标记的基因分型主要     

微卫星标记在动物遗传中的应用

正1微卫星的概念及特点微卫星也被称之为简单序列重复,具体指的是依靠较少数量的核苷酸作为重复单位而构成的简单多次串连重复序列,一般其长度不都在100bp以内。微卫星标记是由核心序列以及两侧保守的侧翼序列两个部分组合而成。在微卫星标记当中保守的两侧侧翼序列定位在染色体的某一区域之内,其中     

微卫星DNA及其在动物育种中的应用

微卫星DNA(简单重复序列,SSRs)是由长度为2～6个碱基组成的串联重复的DNA束,SSRs是继RFLP之后发展起来的一种新的分子遗传标记技术.自从20世纪70年代被发现以来,SSRs得到了迅速发展.在各种动植物品种中,研究人员陆续发现了很多微卫星标记,这些微卫星标记在动植物的育种与遗传评估中得到了广泛的应用.     

蓝狐和貉WT1基因第8内含子(GAAA)n微卫星序列的克隆及分析

微卫星又称简单序列重复(SSR)或短串联重复(STR),其核心序列一般由1～6个核苷酸组成[1],例如(CA)n、(GAAA)n或(AG)n。由于微卫星序列广泛分布于真核生物的基因组中,且具有多态性高、呈共显性遗传等特点,因而自M.Litt等[2]首次报道微卫星基因分型以来,很快被广泛应用到遗传图谱构建、数量性状基因座(QTL)定位、遗传多样性分析、亲     

中国5种家兔的微卫星DNA多态性研究

微卫星又称简单序列重复(SSR),是20世纪80年代末期发展起来的一种新型DNA分子标记,它因具有分布广泛,多态信息丰富,呈共显性遗传和检测快速、方便以及中性标记等优点,被广泛应用于遗传连锁图谱构建、系谱确认、数量性状位点定位及遗传多样性评估等方面。文章选用了10个微卫星标记,分析了我国5种家兔的DNA遗传多样性,为正确评价各品种遗传多样性的价值制定合理可行的保种方案,开展配合力测定,预测杂种优势等提供了理论依据。     

猪多经济性状的重建研究

本研究的目标是对猪多个经济性状进行性状重建,并对重建性状的遗传参数进行估计。本研究涉及性状重建的原始经济性状包括了生长、胴体和肉质3类共24个性状。性状重建利用主成分分析法,经统计分析后得到4个主成分作为新的重建性状。在对重建性状进行描述性统计处理基础上,进一步利用动物模型和DFREML算法估计了各性状的遗传力,得到第1、2、3和4主成分的遗传力分别为0.87、0.58、0.49和0.56。研究结果表明,通过性状重建不但可以实现多性状的降维处理,而且重建性状的遗传力有提高趋势,性状重建策略有望促进多性状育种的进程。     

综合杂种优势及其统计检验

一般有多个性状影响家畜生产性能,且各性状相对经济价值不同,所以按单性状杂种优势来评价某杂交组合的优劣是不全面的。本文提出了综合杂种优势的概念和单杂交试验中综合杂种优势的多元分析方法。记杂种ij第1性状的杂种优势为hijl,其相对经济价值为W_1,若育种目标性状共有L个,我们把品系i与品系j杂交产生的杂种的综合优势定义为: 本文根据线性模型的理论给出了Hij的最优线性无偏估计(BLUE)及其置信区间。     

An association study using imputed whole‐genome sequence data identifies novel significant loci for growth‐related traits in a Duroc × Erhualian F2 population

The average daily gain (ADG) and body weight (BW) are very important traits for breeding programs and for the meat production industry, which have attracted many researchers to delineate the genetic architecture behind these traits. In the present study, single‐ and multi‐trait genome‐wide association studies (GWAS) were performed between imputed whole‐genome sequence data and the traits of the ADG and BW at different stages in a large‐scale White Duroc × Erhualian F₂ population. A bioinformatics annotation analysis was used to assist in the identification of candidate genes that are associated with these traits. Five and seven genome‐wide significant quantitative trait loci (QTLs) were identified by single‐ and multi‐trait GWAS, respectively. Furthermore, more than 40 genome‐wide suggestive loci were detected. On the basis of the whole‐genome sequence association study and the bioinformatics analysis, NDUFAF6, TNS1 and HMGA1 stood out as the strongest candidate genes. The presented single‐ and multi‐trait GWAS analysis using imputed whole‐genome sequence data identified several novel QTLs for pig growth‐related traits. Integrating the GWAS with bioinformatics analysis can facilitate the more accurate identification of candidate genes. Higher imputation accuracy, time‐saving algorithms, improved models and comprehensive databases will accelerate the identification of causal genes or mutations, which will contribute to genomic selection and pig breeding in the future.     

湖北白猪活体测定性状与屠宰性状间的典型相关分析

通过对湖北白猪新系90头样本的话体测定性状的9个变量与屠宰测定性状的10个变量之间的典型相关分析,得出了决定两组性状间相关关系的三对典型变量.湖北白猪活体测定性状与屠宰测定性状之间的相关主要是由:腹围、臀宽及活体膘厚与板油率密切相关,胸围、臀宽、臀长、臀围与屠宰率及后腿比例密切相关,及体长、胸围与瘦肉率和肥肉率间的较强相关所决定的.分析表明在湖北白猪新品系选育中,要加强胸围选择,注意保持适宜的体长.可以依据三个活体性状典型变量的信息利用达到改良屠宰性状的目的.     

Estimation of direct and correlated responses to selection using univariate animal models.

Analytic results obtained using simple models show that estimates of selection response of univariate experiments using animal models are completely dependent on the heritability used as prior when fixed effects are nested within generations, and both on the prior and on the true heritability parameter when fixed effects overlap across generations. Univariate animal model estimators of correlated changes of a trait not selected directly are usually biased. The absolute value of the estimate of the correlated response is smaller than the true value when the traits are only genetically correlated and larger than the expected value of zero when they are only environmentally correlated. The validity of the results derived from the analysis of simple models is confirmed using computer simulations, which illustrate the magnitude of the bias. It is emphasized that use of univariate animal models to estimate response in breeding programs whose breeding objectives include several correlated traits may lead to erroneous conclusions.     

Genetic evaluation of dairy cattle for conformation traits using random regression models

Random regression models were applied to eight conformation traits (i.e. stature, rump angle, thurl width, rear leg set, rear udder width, rear udder height, udder depth, and fore udder attachment) of Holstein cows from the northeastern United States. Covariates for fixed and random regressions included age and age‐squared for six of the traits, and two additional covariates were included for rear udder width and rear udder height. Other effects in the model were herd—year‐classifier and months in milk. Fixed covariates were nested within year of birth of the cow. Variance components were estimated using Bayesian theory and Gibbs sampling procedure. Estimated breeding values from the random regression models were compared to two single trait models. The first model utilized only the first classification record of the cow in first lactation, and the second model utilized all classifications of the cow in a simple repeatability model. Additive genetic merit for conformation traits changed with the age of the animal. Some traits were affected by age more than others. The single trait, single record model and the simple repeatability model were not appropriate in predicting breeding values at mature ages for rear udder width and rear udder height.     

Breakeven prices for recording of indicator traits to reduce the environmental impact of milk production

A breeding scheme using genomic selection and an indicator trait for environmental impact (EI) was studied to find the most effective recording strategy in terms of annual monetary genetic gain and breakeven price for the recording of indicator traits. The breakeven price shows the investment space for developing a recording system for an indicator trait. The breeding goal consisted of three traits – milk production, functional trait and environmental impact – with economic values of €83, €82 and €?83, respectively. The first scenario included only breeding goal traits and no indicator traits (NoIT). The other scenarios included all three breeding goal traits and one indicator trait (IT) for EI. The indicator traits were recorded on a large scale (stayability after first lactation and stature), medium scale (live weight and greenhouse gases (GHG) measured in the breath of the cow during milking) or small scale (residual feed intake and total enteric methane measured in a respiration chamber). In the scenario with stayability, the genetic gain in EI was over 11% higher than it was in NoIT. The breakeven price of recording stayability was €8 per record. Stayability is easy to record in the national milk recording system, and its use as an indicator trait for EI would not generate any additional recording costs. Therefore, stayability would be a good indicator trait to use to mitigate EI. The highest genetic gain in EI (23% higher compared to NoIT) was achieved when the GHG measured in the breath of the cow was used as indicator trait. The breakeven price for this indicator trait was €29 per record in the reference population. Ideally the recording of a specific indicator trait for EI would take place when: (i) the genetic correlation between the IT and EI is high; and (ii) the number of phenotypic records for the indicator trait is high enough to achieve a moderately high reliability of direct genomic values.     

比值性状的指数选择方法探讨

以构成幽会性状的两个组分性状为目标性状,以两个组分性状和与之存在较大相关的其它经济性状为信息性状,通过约束比值性状的遗传进展,给出了用于改良比值性状的遗传进展,给出了用于改良幽会性状的选择指数制订方法。该方法避开了直接选择幽会性状而在统计和选择效率方面所遇到的诸多麻烦,从而解决了性状的综合选择问题。以提高饲料效率的选择试验为例,演示了方法的使用过程,验证了方法的作用效果。     

Investigations in the character of QTL affecting negatively correlated milk traits

The primary aim of this study was to investigate the quantitative trait loci (QTL) on BTA6 that affect negatively correlated milk traits, using bivariate covariance component analysis of milk yield and fat (or protein) content, protein yield and fat content, and fat yield and protein content. A set of five different genetic models was adapted to differentiate trait‐specific QTL in close linkage from pleiotropy. Using a grand‐daughter design consisting of five half‐sib families from the German Holstein population and 298 sons genotyped for 16 microsatellite markers on BTA6, we found significant trait‐specific QTL for fat content and protein yield, 24 cM apart. Markers BM1329 and FBN12 bracketed the QTL for fat content, and the region between TGLA37 and FBN13 most likely harbours a QTL for protein yield. The analysis based on the close linkage model fully confirmed this result. Despite the pure QTL findings confirming results from the literature, distinguishing pleiotropic and closely linked QTL for competitive traits is a new aspect. Our multivariate analysis results did not suggest a pleiotropic QTL for the investigated negatively correlated traits. The QTL‐based trait correlations were discussed as an important aspect of modelling that needs to be considered in the future.     

Overview of genetic evaluation in dairy cattle

It is costly and time‐consuming to carry out dairy cattle selection on a large experimental scale. For this reason, sire and cow evaluations are almost exclusively based on field data, which are highly affected by a large array of environmental factors. Therefore, it is crucial to adjust for those environmental effects in order to accurately estimate the genetic merits of sires and cows. Index selection is a simple extension of the ordinary least squares under the assumption that the fixed effects are assumed known without error. The mixed‐model equations (MME) of Henderson provide a simpler alternative to the generalized least squares procedure, which is computationally difficult to apply to large data sets. Solution to the MME yields the best linear unbiased estimator of the fixed effects and the best linear unbiased predictor (BLUP) of the random effects. In an animal breeding situation, the random effects such as sire or animal represent the animal's estimated breeding value, which provides a basis for selection decision. The BLUP procedure under sire model assumes random mating between sires and dams. The genetic evaluation procedure has progressed a long way from the dam‐daughter comparison method to animal model, from single trait to multiple trait analysis, and from lactational to test‐day model, to improve accuracy of evaluations. Multiple‐trait evaluation appears desirable because it takes into account the genetic and environmental variance‐covariance of all traits evaluated. For these reasons, multiple‐trait evaluation would reduce bias from selection and achieve a better accuracy of prediction as compared to single‐trait evaluation. The number of traits included in multiple‐trait evaluation should depend upon the breeding goal. Recent advances in molecular and reproductive technologies have created great potential for quantitative geneticists concerning genetic dissection of quantitative traits, and marker‐assisted genetic evaluation and selection.