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。     

影响动物模型MBLUP评定准确性的主要因素

标记辅助最佳线性无偏预测（marker-assisted best linear unbiased prediction,MBLUP）是对动物实施标记辅助选择（marker-assisted selection,MAS）的一种重要方法。通过计算机随机模拟研究了所选性状的遗传力、QTL方差和相邻两个标记间图距3个因素对动物模型MBLUP评定准确性的影响。结果表明,性状的遗传力越高、QTL方差和相邻两个标记间图距越小时,动物模型MBLUP评定的准确性越高;相反,当性状的遗传力较低、QTL方差和相邻两个标记间图距较大时,动物模型MBLUP评定的准确性则较低。     

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%.     

棉花数量性状基因定位研究进展

棉花的许多重要性状,包括产量、纤维品质、株型、抗病抗逆性、生理生化等都是数量性状,受遗传和环境因子的共同作用。近年来,随着分子生物学技术的进步,棉花基因组研究得到迅速发展,为棉花数量性状基因（quantitative trait locus,QTL）定位奠定了坚实的基础。概述了近十几年来棉花QTL定位研究及分子标记辅助选择的进展,结合研究实践指出了棉花QTL定位及标记辅助选择存在的问题,并对其发展方向做出了初步探讨。     

长期人工选择对群体遗传结构的影响——一项计算机模拟研究 Ⅱ.长期选择对群体遗传方差的作用

本文给出了显性与超显性模型下加性方差的分剖公式,为研究选择作用下基因间关系的变化提供了有力的方法。并模拟研究了群体大小、连锁强度与遗传力水平对遗传方差变化的影响。小群体中遗传方差在世代间波动很大;大群体中则稳定下降、波动较小。选择作用下平衡加性方差下降很快,特别是高遗传力性状。紧密连锁在小群体中一方面降低选择反应,一方面维持了更多的加性方差,从而使得预测长期选择反应甚为困难。     

水稻低温发芽性QTL的分子标记定位

利用1个粳／籼交来源的重组自交系群体,采用纸卷法在15℃低温条件下进行发芽试验,在发芽培养的6～14d中每天观测统计1次发芽率(％)。结合一张含有198个DNA标记的连锁图谱,用复合区间作图法定位水稻低温发芽性QTL。共检测到7个主效应QTL,分别位于水稻1、3、5、6和8号染色体上,单个QTL对性状的贡献率为5％～16％。其中,位于3号染色体标记区间RM148-RM85的qLTG-3-2和位于8号染色体标记区间RM223-RM210的qLTG-8-1对性状的贡献率最大,分别达16％和14％。QTL qLTG-3-2在发芽培养6～10d中表达,其效应由强渐弱,对性状的贡献率由发芽培养6d时的16．4％逐渐降低为发芽11d时的5．1％;而QTL qLTG-8-1则在发芽培养9～14d中起作用,其效应值由小逐渐增大,对性状的贡献率由发芽9d时的8．6％逐渐上升为发芽13～14d的14％。尽管这2个QTL加性效应的大小在低温发芽过程中按一定趋势变化,但加性效应的方向始终是一致的。QTL qLTG-3-2的增效基因来源于亲本特青,而QTL qLTG-8-1的增效基因来自于亲本Lemont。这2个QTL的增效等位基因有望作为分子标记辅助育种的操作对象,用于水稻品种低温发芽性的遗传改良。     

大豆遗传图谱的构建和若干农艺性状的QTL定位分析

大豆许多重要农艺性状都是由微效多基因控制的数量性状,对这些数量性状进行QTL定位是大豆数量性状遗传研究领域的一个重要内容.本研究利用栽培大豆科新3号为父本、中黄20为母本杂交得到含192个单株的F2分离群体,构建了含122 个SSR标记、覆盖1719.6cM、由33个连锁群组成的连锁遗传图谱.利用复合区间作图法,对该群体的株高、主茎节数、单株粒重和蛋白质含量等农艺性状的调查数据进行QTL分析,共找到两个株高QTL,贡献率分别为9.15%和6.08%;两个主茎节数QTL,贡献率分别为10. 1%和8.6%;一个蛋白质含量QTL,贡献率为9.8%;一个单株粒重QTL,贡献率为11.4% .通过遗传作图共找到与所定位的4个农艺性状QTL连锁的6个SSR标记,这些标记可以应用于大豆种质资源的分子标记辅助选择,从而为大豆分子标记辅助育种提供理论依据.     

奶牛重要经济性状全基因组关联分析研究进展

产奶性状是奶牛最重要的生产性状,随着平衡育种理念的提出和发展,繁殖性状、体型性状、健康性状和长寿性等功能性状也逐渐被重视并纳入育种规划中。鉴定产奶性状和功能性状主效基因或遗传标记并将之应用于奶牛标记辅助选择可望加快遗传进展。随着高密度SNP标记的高通量检测技术的发展,全基因组关联分析已成为鉴定畜禽重要经济性状基因的重要途径。文章对奶牛产奶性状和功能性状全基因组关联分析研究进展进行综述。     

奶牛重要经济性状全基因组关联分析研究进展

产奶性状是奶牛最重要的生产性状, 随着平衡育种理念的提出和发展, 繁殖性状、体型性状、健康性状和长寿性等功能性状也逐渐被重视并纳入育种规划中。鉴定产奶性状和功能性状主效基因或遗传标记并将之应用于奶牛标记辅助选择可望加快遗传进展。随着高密度SNP标记的高通量检测技术的发展, 全基因组关联分析已成为鉴定畜禽重要经济性状基因的重要途径。文章对奶牛产奶性状和功能性状全基因组关联分析研究进展进行综述。     

侧翼标记基因型方差组分随不同选择强度的变化

从实际角度看,选择在用于标记-QTL连锁分析的实验群体中是较普遍存在的。为获得QTL位置及其效应的无偏估计,有必要研究选择对连锁分析实验所造成的影响。该文在侧翼标记情况下利用广泛使用的半同胞设计提出了3种方差组分,即标记组间方差、组内多基因方差和组内重组方差及其构成。着重从理论上研究了各方差组分在不同选择强度下的变化规律,阐明了选择对各方差组分的效应。结果表明,各方差组分对选择强度的变化很敏感。随着选择强度的增加,所有方差组分都以二次曲线方式有不同程度的下降。比较而言,标记组间方差组分下降最快,标记组内多基因方差下降较慢,组内重组方差下降最慢。这意味着QTL连锁分析功效的降低。所以在实际群体中应采取必要措施尽量避免选择的发生,从而消除选择对QTL连锁分析的负面效应。     

Within-family marker-assisted selection for aquaculture species

A within-family marker-assisted selection scheme was designed for typical aquaculture breeding schemes, where most traits are recorded on sibs of the candidates. Here, sibs of candidates were tested for the trait and genotyped to establish genetic marker effects on the trait. BLUP breeding values were calculated, including information of the markers (MAS) or not (NONMAS). These breeding values were identical for all family members in the NONMAS schemes, but differed between family members in the MAS schemes, making within-family selection possible. MAS had up to twice the total genetic gain of the corresponding NONMAS scheme. MAS was somewhat less effective when heritability increased from 0.06 to 0.12 or when the frequency of the positive allele was < 0.5. The relative efficiency of MAS was higher for schemes with more candidates, because of larger fullsib family sizes. MAS was also more efficient when male:female mating ratio changed from 1:1 to 1:5 or when the QTL explained more of the total genetic variation. Four instead of two markers linked to the QTL increased genetic gain somewhat. There was no significant difference in polygenic genetic gain between MAS and NONMAS for most schemes. The rates of inbreeding were lower for MAS than NON-MAS schemes, because fewer full-sibs were selected by MAS.     

Genetic response from marker assisted selection in an outbred population for differing marker bracket sizes and with two identified quantitative trait loci.

Effect of flanking quantitative trait loci (QTL)-marker bracket size on genetic response to marker assisted selection in an outbred population was studied by simulation of a nucleus breeding scheme. In addition, genetic response with marker assisted selection (MAS) from two quantitative trait loci on the same and different chromosome(s) was investigated. QTL that explained either 5% or 10% of phenotypic variance were simulated. A polygenic component was simulated in addition to the quantitative trait loci. In total, 35% of the phenotypic variance was due to genetic factors. The trait was measured on females only. Having smaller marker brackets flanking the QTL increased the genetic response from MAS selection. This was due to the greater ability to trace the QTL transmission from one generation to the next with the smaller flanking QTL-marker bracket, which increased the accuracy of estimation of the QTL allelic effects. Greater negative covariance between effects at both QTL was observed when two QTL were located on the same chromosome compared to different chromosomes. Genetic response with MAS was greater when the QTL were on the same chromosome in the early generations and greater when they were on different chromosomes in the later generations of MAS.     

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.     

Genomic selection for marker-assisted improvement in line crosses

Efficiency of genomic selection with low-cost genotyping in a composite line from a cross between inbred lines was evaluated for a trait with heritability 0.10 or 0.25 using a low-density marker map. With genomic selection, selection was on the sum of estimates of effects of all marker intervals across the genome, fitted either as fixed (fixed GS) or random (random GS) effects. Reponses to selection over 10 generations, starting from the F₂, were compared with standard BLUP selection. Estimates of variance for each interval were assumed independent and equal. Both GS strategies outperformed BLUP selection, especially in initial generations. Random GS outperformed fixed GS in early generations and performed slightly better than fixed GS in later generations. Random GS gave higher genetic gain when the number of marker intervals was greater (180 or 10 cM intervals), whereas fixed GS gave higher genetic gain when the number of marker intervals was low (90 or 20 cM). Including interactions between generation and marker scores in the model resulted in lower genetic gains than models without interactions. When phenotypes were available only in the F₂ for GS, treating marker scores as fixed effects led to considerably lower genetic gain than random GS. Benefits of GS over standard BLUP were lower with high heritability. Genomic selection resulted in greater response than MAS based on only significant marker intervals (standard MAS) by increasing the frequency of QTL with both large and small effects. The efficiency of genomic selection over standard MAS depends on stringency of the threshold used for QTL detection. In conclusion, genomic selection can be effective in composite lines using a sparse marker map.     

Usefulness of marker‐assisted selection to improve maize for increased resistance to Sesamia nonagrioides attack with no detrimental effect on yield

Two decades of investigations on maize resistance to Mediterranean corn borer (Sesamia nonagrioides Lefebvre; MCB) have shown that breeding for increased resistance to stem tunnelling by MCB often resulted in reduced yield because significant genetic correlation between both traits exists in some backgrounds. Unlike phenotypic selection, marker‐assisted selection (MAS) could differentiate markers linked only to one trait from those linked simultaneously to yield potential and susceptibility to the pest. In the current study, the suitability of MAS for improving resistance to stem tunnelling without adverse effects on yield has been tested. The unfavourable genetic relationship between yield potential and susceptibility could be overcome using MAS. Gains obtained using MAS were weak, because genetic variance explained by the quantitative trait loci (QTL) was low but results encourage us to persevere in using marker information for simultaneous improvement of resistance and yield especially if genome‐wide approaches are applied. Approaches to detect QTL are widely used, but studies on the suitability of markers linked to QTL for performing MAS have been mostly neglected.     

Towards marker assisted selection in livestock

In recent years, genomic tools have become available for most livestock species and are now being used routinely to map quantitative trait loci underlying the genetic variance for numerous economically important traits. Fine-mapping methods are being devised to refine the initially coarse map positions of the quantitative trait loci to the point required for marker assisted selection and, eventually, the positional cloning of the underlying genes. Mapping information on QTL is beginning to be used to increase genetic response by enhancing genetic variance, selection accuracy, selection intensity and by reducing the generation interval. Optimal use of MAS will require the development of more robust methods for the routine genotyping of preimplantation embryos for multiple markers.     

孙女设计中标记密度对QTL定位精确性的影响

采用蒙特卡罗方法分析了在孙女设计中不同的嫩体结构、性状遗传力、ＱＴＬ效应大小和ＱＴＬ在染色体上的位置中个因素不同水平组合下４种标记密度（标记间隔５ｃＭ,１０ｃＭ,２０ｃＭ、５０ｃＭ对ＱＴＬ定位精确性（以均方误ＭＳＥ为衡量指标）的影响,并从经济学角度探讨了应用于标记辅助选（ＭＡＳ）的ＱＴＬ定位的最佳标记密度。结果表明,一般说来,在各因素水平都较低时,ＭＳＥ随标记密度加大而下降的相对幅度也较 小,反之     

Marker assisted selection with optimised contributions of the candidates to selection

The benefits of marker assisted selection (MAS) are evaluated under realistic assumptions in schemes where the genetic contributions of the candidates to selection are optimised for maximising the rate of genetic progress while restricting the accumulation of inbreeding. MAS schemes were compared with schemes where selection is directly on the QTL (GAS or gene assisted selection) and with schemes where genotype information is not considered (PHE or phenotypic selection). A methodology for including prior information on the QTL effect in the genetic evaluation is presented and the benefits from MAS were investigated when prior information was used. The optimisation of the genetic contributions has a great impact on genetic response but the use of markers leads to only moderate extra short-term gains. Optimised PHE did as well as standard truncation GAS (i.e. with fixed contributions) in the short-term and better in the long-term. The maximum accumulated benefit from MAS over PHE was, at the most, half of the maximum benefit achieved from GAS, even with very low recombination rates between the markers and the QTL. However, the use of prior information about the QTL effects can substantially increase genetic gain, and, when the accuracy of the priors is high enough, the responses from MAS are practically as high as those obtained with direct selection on the QTL.