世代重叠群体多个QTL最优化选择

一种扩展的方法能够在一个世代重叠的群体内对多个数量性状位点选择进行最优化,目的是为了在整个计划期内获得最大的累积反应加权和。该模型允许群体有多个性别年龄组、公母畜间有不同的年龄组数、各年龄组有不同的遗传贡献。整个最优化问题被描述成一个多阶段系统优化控制问题,通过一个向前和向后的迭代循环解决。用一个世代重叠的实际育种猪群的参数来评价该方法的选择效果,并和标准QTL选择和常规BLUP选择进行比较。模拟结果表明,优化选择要优于标准QTL选择和常规BLUP选择。群体结构对优化选择的影响比较明显。优化QTL选择和标准QTL选择在世代重叠的群体内比在世代离散的群体内的选择优势更明显,相对于常规BLUP选择,能够获得更大的选择优势。在世代重叠群体内随着2岁公畜遗传贡献的增大,优化选择相对于常规BLUP选择的优势越明显。     

性状遗传力与QTL方差对标记辅助选择效果的影响

在采用动物模型标记辅助最佳线性无偏预测方法对个体育种值进行估计的基础上,模拟了在一个闭锁群体内连续对单个性状选择10个世代的情形,并系统地比较了性状遗传力和QTL方差对标记辅助选择所获得的遗传进展、QTL增效基因频率和群体近交系数变化的影响。结果表明：在对高遗传力和QTL方差较小的性状实施标记辅助选择时,可望获得更大的遗传进展;遗传力越高,QTL方差越大,则QTL增效基因频率的上升速度越快;遗传力较高时,群体近交系数上升的速度较为缓慢,而QTL方差对群体近交系数上升速度的影响则不甚明显。结合前人关于标记辅助选择相对效率的研究结果,可以认为：当选择性状的遗传力和QTL方差为中等水平时,标记辅助选择可望获得理想的效果。     

不同QTL增效基因初始频率下标记辅助选择的效果

采用随机模拟方法模拟了在一个闭锁群体内连续对单个性状选择10个世代的情形。在假定选择性状受一个位于常染色体上的QTL和多基因共同控制的情况下,采用动物模型标记辅助最佳线性无偏预测方法估计个体育种值并据此进行种畜的选留,并在此基础上系统地比较了QTL增效基因初始频率对标记辅助选择效果的影响。结果表明：当群体中QTL增效基因的初始频率较低时,选择所获得的QTL基因型值的进展会更大,标记辅助选择在单位时间内可获得较大的遗传进展;此时,尽管QTL增效基因在群体中固定所需的世代数会更长一些,但其频率上升的速度却更快。而QTL增效基因初始频率的高低对群体近交增量的影响不是很大。     

结合MOET技术的奶牛标记辅助选择方案比较

应用计算机模拟的方法比较利用遗传标记信息和/或超数排卵和胚胎移植(MOET)技术的一系列奶牛育种方案相对于传统育种方案的优越性. 模拟性状为产奶量, QTL作为已知遗传标记. 8个奶牛育种方案分别是：传统的后裔测定方案用STANPT表示; GASPT方案在青年公牛进入后裔测定前通过其自身QTL基因型进行预选择; MOETPT方案应用MOET技术产生青年公牛, 青年公牛预选择为家系内全同胞随机选择; GAMOPT方案结合了QTL预选择及MOET技术; COMBPT方案在GAMOPT方案基础上, 在个体育种值的评估中加入了QTL基因型效应; 另外对应后裔测定体系中MOETPT,GAMOPT,COMBPT方案设计了3个非后裔测定方案, 分别命名为MOET方案、GAMO方案及COMB方案. 动物个体通过相对应的动物模型进行育种值评估, 模拟针对产奶性状进行重叠世代连续17年的选择. 针对现役公牛、泌乳母牛、公牛母亲、公牛父亲和青年公牛5个不同群体, 对应用QTL信息和MOET技术对有利QTL基因频率、真实育种值、多基因值和累积遗传进展优势率产生的影响进行评估. 结果表明, 结合QTL信息和MOET技术的方案, 其真实育种值遗传进展显著高于其他方案, 仅应用QTL信息或MOET技术的单一改进方案间差异不显著, STANPT方案效率最低. 应用MOET技术的方案在第17年获得的多基因选择反应更大. 不同育种方案有利QTL基因频率增加速度在3个公畜群体中差异远大于泌乳母牛群体. GASPT,MOETPT,GAMOPT,COMBPT,MOET,GAMO 和COMB 方案相对STANPT方案, 累积遗传进展优势率在现役公牛群体中分别为8.42%, 3.59%, 14.58%, 18.54%, 4.12%, 14.12%和16.50%, 在泌乳母牛群体中分别为2.70%, 5.00%, 11.05%, 12.78%, 7.51%, 17.12%和25.38%.     

基因组育种值估计的贝叶斯方法

基因组育种值估计是基因组选择的重要环节,基因组育种值的准确性是基因组选择成功应用的关键,而其准确性在很大程度上取决于估计方法。目前研究和应用最多的基因组育种值估计方法是贝叶斯(Bayes)和最佳线性无偏预测(BLUP)两大类方法。文章系统介绍了目前已提出的各种Bayes方法,并总结了该类方法的估计效果和各方面的改进。模拟数据和实际数据研究结果都表明,Bayes类方法估计基因组育种值的准确性优于BLUP类方法,特别对于存在较大效应QTL的性状其优势更明显。由于Bayes方法的理论和计算过程相对复杂,目前其在实际育种中的运用不如BLUP类方法普遍,但随着快速算法的开发和计算机硬件的改进,计算问题有望得到解决;另外,随着对基因组和性状遗传结构研究的深入开展,能为Bayes方法提供更为准确的先验信息,从而使Bayes方法估计基因组育种值准确性的优势更加突出,应用将会更加广泛。     

多个典范选择性状的综合优化研究

利用综合优化方法,研究了在育种目标约束下,选择性状表现型向量与目标性状基因型向量的典范相关,提出了综合典范性状对的数学模型。综合典范性状对是Ｓ个间撞遗传系数较在的典范选择－目标性状对,在育种目标约束下间接遗传系数极大化的线性组合。综合典范选择性状,作为间接选择的指标,有较好地满足多目标育种的要求。     

赤拟合盗模拟种公畜选择—常规与改进方法的比较

本实验用实验昆虫赤拟谷盗两个世代各３０个家系的８５９和７２６只桶的２３日龄蛹长和肾宽作为选择性状模拟种公畜选择,对常规方法和改进方法进行比较。以Ｈｅｎｄｅｒｓｏｎ方法ＩＩＩ和综合指数作为常规方法,用约束最大似然法和ＢＬＵＰ估计方差组分和育种值为改进方法。结果表明,常规法估计遗传参数在两世代中不稳定,而改进方法弥补此缺点,对两世代个体和家系育种值排队,结果表明两种方法在前几名无差别,说明在选择强度大     

动物模型及多性状BLUP在家禽遗传鉴定中的应用

利用最佳线性无偏预测法(BLUP)估计家畜的育种值,目前除家禽外已在其它各家畜中得到了广泛的应用。本文利用动物模型和多性状BLUP对“京白Ⅰ系”蛋鸡在1986—1987年24个家系的777个个体的系统分组资料进行了分析,估计出了所有个体的复合育种值。其中考虑了两个性状(40周产蛋数和36周蛋重)和两个固定效应(鸡舍-鸡笼效应和孵化批次效应)。同时还对混合模型方程组维数较大时如何在微机上实现进行了研究,即(1)利用磁盘存取系数矩阵的非零元素和中间计算结果;(2)简化了多性状BLUP的计算,利用乔列斯基(Cholesky)分解变换后,此法建立的方程数是常规算法方程数的1/q(q为性状数);(3)简化了方程组迭代求解的方法,即利用块迭代法,这样大大缩短了计算的机吋,节省了费用,使BLUP在家禽中的推广应用成为可能。     

多个主成分性状的综合优化研究

考虑育种目标的要求,对多个独立的主成分性状进行综合优化,建立了综合主成分性状的数学模型．综合主成分性状是S个表型方差（遗传方差）最大的主成分性状在育种目标约束下表型方差（或遗传方差）极大化下的线性组合,作为育种指标,选择效果好,预见性强．     

直播条件下水稻6个穗部性状的QTL分析

在大田直播条件下,利用来源于＂Lemont/特青＂的重组自交系群体,对水稻6个穗部性状及其相互间遗传相关的分子基础进行了QTL分析,共检测到19个QTL,各性状QTL数为2～4个,单个QTL贡献率为4%～22%。共检测到3个染色体区段能同时影响多个穗部性状,其中第1染色体RM212-RM104和第2染色体RM263-RM221区段的QTL能同时影响单株产量、每穗颖花数、着粒密度和二次枝梗数中的3个或4个性状,且这2个区段的QTL对各性状的效应方向相同,增效等位基因均来自‘特青’,为各性状间表型正相关提供了重要的遗传解释。第11染色体RG1022附近的QTL对着粒密度的效应值为负,来自‘特青’的等位基因增加性状值,而对穗长的效应值为正,来自‘特青’的等位基因降低性状值,为这2个性状间表型负相关也提供了一定的遗传解释。此外,对水稻穗部性状QTL在多种环境和遗传背景下的稳定表达及其在分子标记辅助育种中的应用进行了讨论。     

Optimizing Selection on Multiple Identified Quantitative Trait Loci in Population with Overlapping Generations

A method was developed to model and optimize selection on multiple identified quantitative trait loci (QTLs) and polygenic estimated breeding value, in order to maximize a weighted sum of cumulative response to selection over multiple years in a population with overlapping generations. The model allows for a population with multiple sex-age classes, different number of age class between sires and dams, and varied genetic contribution of the age class. The optimization problem was formulated as a multiple-stage optimal control problem and solved by a forward and backward iteration loop. The practical utility of this method was illustrated in an example of pig breeding population with overlapping generations. The selection response of this method was compared with standard QTL selection and conventional best linear unbiased prediction (BLUP) selection. Simulation results show that optimal selection achieved greater selection response than either standard QTL or conventional BLUP selections. The influence of population structure on optimal selection was significant. Optimal QTL selection and standard QTL selection were more favorable in a population with overlapping generations than discrete generations, and obtained more benefits relative to conventional BLUP selection in a population with overlapping generations. Optimal QTL selection relative to conventional BLUP selection is also more favorable following increase of genetic contribution of two-year-old boars and sows in a population with overlapping generations.     

Optimal selection on two quantitative trait loci with linkage

A mathematical approach to optimize selection on multiple quantitative trait loci (QTL) and an estimate of residual polygenic effects was applied to selection on two linked or unlinked additive QTL. Strategies to maximize total or cumulative discounted response over ten generations were compared to standard QTL selection on the sum of breeding values for the QTL and an estimated breeding value for polygenes, and to phenotypic selection. Optimal selection resulted in greater response to selection than standard QTL or phenotypic selection. Tight linkage between the QTL (recombination rate 0.05) resulted in a slightly lower response for standard QTL and phenotypic selection but in a greater response for optimal selection. Optimal selection capitalized on linkage by emphasizing selection on favorable haplotypes. When the objective was to maximize total response after ten generations and QTL were unlinked, optimal selection increased QTL frequencies to fixation in a near linear manner. When starting frequencies were equal for the two QTL, equal emphasis was given to each QTL, regardless of the difference in effects of the QTL and regardless of the linkage, but the emphasis given to each of the two QTL was not additive. These results demonstrate the ability of optimal selection to capitalize on information on the complex genetic basis of quantitative traits that is forthcoming.     

Marker assisted selection for the improvement of two antagonistic traits under mixed inheritance

A Monte Carlo simulation was used to investigate the potential of Marker Assisted Selection (MAS) in a multiple-trait situation. Only additive effects were considered. The base population was assumed to be in linkage equilibrium and, next, the population was managed over 15 discrete generations, 10 males and 50 females were chosen out of the 100 candidates of each sex. Performance for two traits was simulated with an overall heritability of a given trait equal to 0.25 or 0.10 and the overall genetic correlation between traits was generally equal to -0.4 except in one case where it was equal to 0. The model involved one biallelic QTL, accounting for 10 or 20% of the genetic variance of a given trait, plus polygenes. Initial allelic frequencies at the QTL were generally equal to 0.5 but in one case were equal to 0.1 and 0.9. A marker with 120 different alleles in the 60 founder parents was simulated in the vicinity of the QTL. Two values of the recombination rate between these two loci were considered, 0.10 and 0.02. The genetic evaluation was based on a multiple-trait BLUP animal model, accounting (MAS) or not (conventional BLUP) for marker information. Two sets of simulations were run: (1) a "missing data"case, with males having no record for one of the traits, and (2) a "secondary trait"case, with one trait having a weight in the aggregate genotype 4 times less than the other trait and the QTL acting only on this secondary trait. In the first set, evaluation methods were found to mainly affect the accuracy of overall genetic values prediction for the trait with missing data. In comparison with BLUP, MAS led to an extra overall genetic response for the trait with missing data, which was strongly penalised under the conventional BLUP, and to a deficit in response for the other trait. This more balanced evolution of the two traits was obtained, however, at the expense of the long-term overall cumulated response for the aggregate genotype, which was 1 to 2.5% lower than the one obtained under the conventional BLUP. In the second set of simulation, in the case of low initial frequency (0.1) of the QTL allele favourable to the secondary trait, MAS was found to be substantially more efficient to avoid losing this allele than BLUP only when the QTL had a large effect and the marker was close. More benefits should be expected from MAS with more specific applications, such as early selection of animals, or by applying dynamic procedures i.e. letting the respective weights to QTL and polygenic values in the selection criterion vary across generation.     

A method to optimize selection on multiple identified quantitative trait loci

A mathematical approach was developed to model and optimize selection on multiple known quantitative trait loci (QTL) and polygenic estimated breeding values in order to maximize a weighted sum of responses to selection over multiple generations. The model allows for linkage between QTL with multiple alleles and arbitrary genetic effects, including dominance, epistasis, and gametic imprinting. Gametic phase disequilibrium between the QTL and between the QTL and polygenes is modeled but polygenic variance is assumed constant. Breeding programs with discrete generations, differential selection of males and females and random mating of selected parents are modeled. Polygenic EBV obtained from best linear unbiased prediction models can be accommodated. The problem was formulated as a multiple-stage optimal control problem and an iterative approach was developed for its solution. The method can be used to develop and evaluate optimal strategies for selection on multiple QTL for a wide range of situations and genetic models.     

Multiple Trait Analysis of Genetic Mapping for Quantitative Trait Loci

We present in this paper models and statistical methods for performing multiple trait analysis on mapping quantitative trait loci (QTL) based on the composite interval mapping method. By taking into account the correlated structure of multiple traits, this joint analysis has several advantages, compared with separate analyses, for mapping QTL, including the expected improvement on the statistical power of the test for QTL and on the precision of parameter estimation. Also this joint analysis provides formal procedures to test a number of biologically interesting hypotheses concerning the nature of genetic correlations between different traits. Among the testing procedures considered are those for joint mapping, pleiotropy, QTL by environment interaction, and pleiotropy vs. close linkage. The test of pleiotropy (one pleiotropic QTL at a genome position) vs. close linkage (multiple nearby nonpleiotropic QTL) can have important implications for our understanding of the nature of genetic correlations between different traits in certain regions of a genome and also for practical applications in animal and plant breeding because one of the major goals in breeding is to break unfavorable linkage. Results of extensive simulation studies are presented to illustrate various properties of the analyses.     

Interval Mapping of Multiple Quantitative Trait Loci

The interval mapping method is widely used for the mapping of quantitative trait loci (QTLs) in segregating generations derived from crosses between inbred lines. The efficiency of detecting and the accuracy of mapping multiple QTLs by using genetic markers are much increased by employing multiple QTL models instead of the single QTL models (and no QTL models) used in interval mapping. However, the computational work involved with multiple QTL models is considerable when the number of QTLs is large. In this paper it is proposed to combine multiple linear regression methods with conventional interval mapping. This is achieved by fitting one QTL at a time in a given interval and simultaneously using (part of) the markers as cofactors to eliminate the effects of additional QTLs. It is shown that the proposed method combines the easy computation of the single QTL interval mapping method with much of the efficiency and accuracy of multiple QTL models.     

Factors Affecting Accuracy From Genomic Selection in Populations Derived From Multiple Inbred Lines: A Barley Case Study

We compared the accuracies of four genomic-selection prediction methods as affected by marker density, level of linkage disequilibrium (LD), quantitative trait locus (QTL) number, sample size, and level of replication in populations generated from multiple inbred lines. Marker data on 42 two-row spring barley inbred lines were used to simulate high and low LD populations from multiple inbred line crosses: the first included many small full-sib families and the second was derived from five generations of random mating. True breeding values (TBV) were simulated on the basis of 20 or 80 additive QTL. Methods used to derive genomic estimated breeding values (GEBV) were random regression best linear unbiased prediction (RR–BLUP), Bayes-B, a Bayesian shrinkage regression method, and BLUP from a mixed model analysis using a relationship matrix calculated from marker data. Using the best methods, accuracies of GEBV were comparable to accuracies from phenotype for predicting TBV without requiring the time and expense of field evaluation. We identified a trade-off between a method's ability to capture marker-QTL LD vs. marker-based relatedness of individuals. The Bayesian shrinkage regression method primarily captured LD, the BLUP methods captured relationships, while Bayes-B captured both. Under most of the study scenarios, mixed-model analysis using a marker-derived relationship matrix (BLUP) was more accurate than methods that directly estimated marker effects, suggesting that relationship information was more valuable than LD information. When markers were in strong LD with large-effect QTL, or when predictions were made on individuals several generations removed from the training data set, however, the ranking of method performance was reversed and BLUP had the lowest accuracy.     

Optimizing purebred selection for crossbred performance using QTL with different degrees of dominance

A method was developed to optimize simultaneous selection for a quantitative trait with a known QTL within a male and a female line to maximize crossbred performance from a two-way cross. Strategies to maximize cumulative discounted response in crossbred performance over ten generations were derived by optimizing weights in an index of a QTL and phenotype. Strategies were compared to selection on purebred phenotype. Extra responses were limited for QTL with additive and partial dominance effects, but substantial for QTL with over-dominance, for which optimal QTL selection resulted in differential selection in male and female lines to increase the frequency of heterozygotes and polygenic responses. For over-dominant QTL, maximization of crossbred performance one generation at a time resulted in similar responses as optimization across all generations and simultaneous optimal selection in a male and female line resulted in greater response than optimal selection within a single line without crossbreeding. Results show that strategic use of information on over-dominant QTL can enhance crossbred performance without crossbred testing.