• 《中国科学引文数据库(CSCD)》来源期刊
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  • 《中文核心期刊要目总览》核心期刊
  • RCCSE中国核心学术期刊

引导编辑系统研究进展

林秋鹏, 朱秀丽, 马琳莎, 姚鹏程

林秋鹏, 朱秀丽, 马琳莎, 等. 引导编辑系统研究进展[J]. 华南农业大学学报, 2024, 45(2): 159-171. DOI: 10.7671/j.issn.1001-411X.202309002
引用本文: 林秋鹏, 朱秀丽, 马琳莎, 等. 引导编辑系统研究进展[J]. 华南农业大学学报, 2024, 45(2): 159-171. DOI: 10.7671/j.issn.1001-411X.202309002
LIN Qiupeng, ZHU Xiuli, MA Linsha, et al. Recent advances in prime editing system[J]. Journal of South China Agricultural University, 2024, 45(2): 159-171. DOI: 10.7671/j.issn.1001-411X.202309002
Citation: LIN Qiupeng, ZHU Xiuli, MA Linsha, et al. Recent advances in prime editing system[J]. Journal of South China Agricultural University, 2024, 45(2): 159-171. DOI: 10.7671/j.issn.1001-411X.202309002

引导编辑系统研究进展

基金项目: 华南农业大学高层次引进人才项目
详细信息
    作者简介:

    林秋鹏,教授,博士,主要从事植物基因组精准编辑技术开发研究,E-mail: qiupenglin@scau.edu.cn

    朱秀丽,博士研究生,主要从事植物引导编辑系统优化研究,E-mail: zhuxiuli@stu.scau.edu.cn;†表示同等贡献

  • 中图分类号: Q78

Recent advances in prime editing system

More Information
    Author Bio:

    LIN Qiupeng:   林秋鹏,华南农业大学首聘教授,博士生导师,研究方向为作物基因组编辑技术开发及应用,主要围绕开发高效、安全的精准编辑体系并将该技术应用于农业育种及医疗领域等开展工作。在国际上率先建立了适用于植物的引导编辑技术体系,并开发了多套提升该系统效率的全新方法,此外还开发了一系列基因组编辑新策略并应用于植物基因功能研究或分子育种;相关工作已申请4项PCT国际专利。获博士后创新人才支持计划(博新计划)及中国博士后科学基金一等资助,获中国科学院优秀博士毕业论文。近年来以第一作者(含共同)在《Cell》《Nature Biotechnology》《Nature Protocols》《Molecular Cell》等国际权威杂志发表SCI论文10篇,累计影响因子超过350

  • 摘要:

    引导编辑(Prime editing,PE)系统是一种全新的、革命性的基因组编辑策略。该系统由引导编辑器(Prime editor)组成,包括nCas9(H840A)与逆转录酶(Reverse transcriptase,RT)的融合蛋白;以及包含PBS(Primer binding site)序列和RT模板(RT template,RTT)序列的pegRNA(Prime editing guide RNA)两大部分。PE系统可以在双链不断裂的情况下实现所有12种类型的碱基替换及小片段DNA增删,是精准编辑的全新范式。自2019年开发至今不到4年时间,PE系统作为一种通用的技术平台,已广泛应用于医疗、农业等各个领域,产生了一大批新种质资源、基因治疗药物等优秀应用案例。PE作为目前最灵活、最具发展前景的基因组精准编辑新手段,仍旧存在效率偏低、大片段操纵能力不足、系统组分设计复杂(如pegRNA)、安全性未全面评估等问题,仍需要深入研究。本文详细介绍了PE系统的技术原理及限制因素,全面总结了PE系统自开发以来的优化策略及在动植物系统、医疗领域的应用现状,并对PE的发展前景进行了展望。

    Abstract:

    Prime editing (PE) system is a newly developed and greatly revolutionized genome editing technology. The system is based on prime editors, which are composed of two components: A fusion protein of nCas9 (H840A) and reverse transcriptase (RT), and a pegRNA which contains a PBS (Primer binding site) sequence and an RT template (RTT) sequence. The PE system can realize all 12 types of base substitutions and small fragment DNA additions and deletions without double-strand breaks, which is a new paradigm for precision editing. In less than 4 years since its development in 2019, the PE system, as a universal technology platform, has been widely used in various fields such as healthcare and agriculture, generating a large number of excellent application cases such as new germplasm resources and gene therapy drugs. PE, as the most flexible and promising new means of precision genome editing, still suffers from low efficiency, insufficient ability to manipulate large fragments, complex design of system components (such as pegRNAs), incomplete evaluation of safety, and still requires in-depth research. This paper described in detail the technical principles and constraints of PE systems, comprehensively summarized the optimization strategies of PE systems since their development, and the current status of PE applications on animal and plant systems and medical fields. It also gave an outlook on the development prospects of PE.

  • 林木遗传改良通常需在田间试验基础上,估算参试样本(种源、家系、单株或无性系)目标性状的遗传参数,并运用一定的评价方法筛选出具有优异目标性状的种源、家系或无性系[1]。目前,林木遗传评估基本采用混合线性模型,即根据观测表型值和设计的关联矩阵,利用最佳线性无偏估计(Best linear unbiased estimation, BLUE)方法获得固定效应估计值,利用最佳线性无偏预测(Best linear unbiased prediction, BLUP)方法获得随机效应预测值。而育种值(Breeding value)即属随机效应预测值,且BLUP值依赖于混合线性模型的关联设计矩阵。然而,对林木田间试验而言,个体间存在竞争,且基因型与环境互作较为显著,即空间效应明显。如竞争效应和空间效应没有被考虑到混合线性模型中,遗传评估则会产生偏差,其中空间正相关易导致加性遗传方差偏大,中等竞争效应则抑制偏差[2]。因而,在林木遗传评估模型中,应纳入竞争效应和空间效应。

    空间效应(或称环境异质性)可以是空间连续的,其表现为土壤和小气候效应的相似模式,也可以是不连续的,表现为育林措施和测量方法的不同效应,此外还可以是随机的,其表现为微环境的异质性[3]。针对空间效应的空间分析(Spatial analysis)已被广泛用于农作物品种田间试验研究。与传统试验设计相比,空间分析可减少试验误差并提高农作物品种评估的准确性。空间分析方法通常有趋势面分析法和空间自相关分析法等。其中,趋势面分析法是采用空间坐标的多项式函数来模拟环境的变异[4],而空间自相关分析法则通过行和列的空间自相关分析剔除地点内的空间相关误差来提高遗传试验分析的精确性[5]

    在林木研究上,Costa等[6]利用空间自相关分析法分析了辐射松Pinus radiata等3个树种的子代和无性系试验林,发现空间自相关分析可更有效拟合试验林的空间变异,显著降低了区组和小区的方差,且极大提高了遗传评估的准确度与遗传增益。另外,Dutkowski等[7-8]在更大数据量的树种与试验林上进一步采用空间分析,也发现空间分析可显著提高种源、家系、亲本和无性系遗传效应估算的准确性。但林木是多年生植物,除空间差异外,往往还存在个体竞争[9]。虽然Cappa等[10]采用遗传干预模型(Genotypic interference model, GIM)评估了林木遗传加性效应和竞争加性效应,但是没有考虑空间效应,因此模型比较受限。值得注意的是,Stringer等[11]提出空间效应与竞争效应的联合模型进行糖蔗品种试验的遗传分析,模型采用等根二阶自回归法(Equal-roots second-order autoregressive, EAR2)拟合环境的空间效应,而遗传的竞争效应则采用处理干预模型(Treatment interference model, TIM)。此外,Cappa等[12]提出拟合空间效应与竞争效应的林木单株混合模型,并采用贝叶斯法估计遗传参数。上述模型均在一定程度上提高了林木遗传评估的准确性,但模型涵盖的遗传竞争效应均为单一值,而实际上林木个体周边的遗传竞争效应可能不同,因此有必要提出新的单株混合模型,同时拟合不同的遗传竞争效应和异质的空间效应。基于此,本研究根据现代线性统计理论,构建了异质空间效应和不同竞争效应的联合模型,并通过数据模拟的方式,结合实际测定数据进行验证,同时运用REML 法估算遗传参数,对不同模型进行比较,进而探讨异质空间效应和不同竞争效应的分析模型在林木遗传测定中的有效性,以期为林木精确的遗传评估和后续可靠的良种选育提供理论参考和技术支撑。

    单株混合模型为:y=Xb+Zgug+e。式中: y为个体性状表型值构成的向量; b为固定效应构成的向量; u为随机遗传效应构成的向量, ${\boldsymbol u}_{\rm{g}}' =\left( {{u}_{{{\rm{g}}_{\rm{d}}}}',{u}_{{{\rm{g}}_{{\rm{en}}}}}',} \right. $ $\left. {u}_{{{\rm{g}}_{{\rm{wn}}}}}', {u}_{{{\rm{g}}_{{\rm{sn}}}}}',{u}_{{{\rm{g}}_{{\rm{nn}}}}}' \right)$ ${u}_{{{\rm{g}}_{\rm{d}}}}'\text{、} {u}_{{{\rm{g}}_{{\rm{en}}}}}'\text{、} {u}_{{{\rm{g}}_{{\rm{wn}}}}}'\text{、} {u}_{{{\rm{g}}_{{\rm{sn}}}}}'\text{和 } {u}_{{{\rm{g}}_{{\rm{nn}}}}}'$ 分别为直接遗传效应、东向近邻效应、西向近邻效应、南向近邻效应和北向近邻效应;e为剩余误差向量;X为固定效应的关联矩阵;Zg为随机效应的关联矩阵。

    遗传效应的方差–协方差矩阵为:

    $$ {\rm{var}}\left( {{\boldsymbol{u}_{\rm{g}}}} \right) = \left[ \begin{array}{l} \begin{array}{*{20}{c}} {\delta _{{{\rm{g}}_{\rm{d}}}}^2} & {{\delta _{{{\rm{g}}_{{\rm{den}}}}}}} & {{\delta _{{{\rm{g}}_{{\rm{dwn}}}}}}}\\ {{\delta _{{{\rm{g}}_{{\rm{den}}}}}}} & {\delta _{{{\rm{g}}_{{\rm{en}}}}}^2} & {{\delta _{{{\rm{g}}_{{\rm{ewn}}}}}}}\\ {{\delta _{{{\rm{g}}_{{\rm{dwn}}}}}}} & {{\delta _{{{\rm{g}}_{{\rm{ewn}}}}}}} & {\delta _{{{\rm{g}}_{{\rm{wn}}}}}^2} \end{array}\begin{array}{*{20}{c}} {{\delta _{{{\rm{g}}_{{\rm{dsn}}}}}}} & {{\delta _{{{\rm{g}}_{{\rm{dnn}}}}}}}\\ {{\delta _{{{\rm{g}}_{{\rm{esn}}}}}}} & {{\delta _{{{\rm{g}}_{{\rm{enn}}}}}}}\\ {{\delta _{{{\rm{g}}_{{\rm{wsn}}}}}}} & {{\delta _{{{\rm{g}}_{{\rm{wnn}}}}}}} \end{array}\\ \begin{array}{*{20}{c}} {{\delta _{{{\rm{g}}_{{\rm{dsn}}}}}}} & {{\delta _{{{\rm{g}}_{{\rm{esn}}}}}}} & {{\delta _{{{\rm{g}}_{{\rm{wsn}}}}}}}\\ {{\delta _{{{\rm{g}}_{{\rm{dnn}}}}}}} & {{\delta _{{{\rm{g}}_{{\rm{enn}}}}}}} & {{\delta _{{{\rm{g}}_{{\rm{wnn}}}}}}} \end{array}\begin{array}{*{20}{c}} {\delta _{{{\rm{g}}_{{\rm{sn}}}}}^2} & {{\delta _{{{\rm{g}}_{{\rm{snn}}}}}}}\\ {{\delta _{{{\rm{g}}_{{\rm{snn}}}}}}} & {\delta _{{{\rm{g}}_{{\rm{nn}}}}}^2} \end{array} \end{array} \right] \otimes {\boldsymbol{I}_m}, $$

    其中,对角线上依次为直接遗传效应、东向近邻效应、西向近邻效应、南向近邻效应和北向近邻效应的方差分量,非对角线为上述两两效应之间的协方差分量。var代表方差,δ2δ 分别代表方差和协方差,Im代表单位矩阵。

    为减少计算量和促进模型收敛,可通过因子分析法[1]将遗传效应的方差–协方差矩阵改写为如下矩阵:

    $$ {\rm{var}}\left( \boldsymbol{u} \right) = \left( {\boldsymbol \varGamma \times \boldsymbol\varGamma ' + \boldsymbol\varPsi } \right) \otimes \boldsymbol{A}, $$

    其中,Γ 是因子载荷矩阵,Ψ 是特殊方差矩阵,A是加性遗传相关矩阵。

    在常规的空间分析[7]中,剩余误差(R)可分解为空间自相关误差方差( $\delta _\xi ^2$ )和随机测量误差方差( $\delta _\eta ^2$ ),因此剩余误差的方差协方差矩阵为:

    $$ \boldsymbol{R} = \delta _\xi ^2\left( {\sum\nolimits_c {\rm{(}}{\rho _{_c}}{\rm{)}} \otimes \sum\nolimits_r {\rm{(}}{\rho _{_r}}{\rm{)}}} \right) + \delta _\eta ^2\boldsymbol{I}, $$

    其中, $\sum\nolimits_c{\rm{(}}{\rho _{_c}}{\rm{)}}$ $\sum\nolimits_r{\rm{(}}{\rho _{_r}}{\rm{)}}$ 分别是列、行自回归的相关矩阵,ρcρr分别是列、行自相关值,I是单位矩阵。

    $\mathop \sum \nolimits {\rm{(}}\rho {\rm{)}}$ 自回归的相关矩阵,具体如下:

    $$ \sum {(\rho )} = \left| {\begin{array}{*{20}{c}} 1 & \rho & {{\rho ^2}} & \cdots & {{\rho ^{n - 1}}}\\ \rho & 1 & \rho & \cdots & \vdots \\ {{\rho ^2}} & \rho & 1 & \cdots & \vdots \\ \vdots & \vdots & \vdots & \ddots & \vdots \\ {{\rho ^{n - 1}}} & \cdots & \cdots & \cdots & 1 \end{array}} \right|\text{。} $$

    采用R软件[1]和R程序包breedR[13]进行数据模拟。为简化数据模拟,参考下述松树数据,以该松树胸径均值(13)和估算的加性方差( $\delta _a^2$ 为2.46)、剩余残差( $\delta _e^2$ 为3.00)作为模拟初始值,4次重复,5株行式小区,10个父本,10个母本,共30行、40列。为简化不同模型的比较,设置加性遗传效应与竞争遗传效应相关值[简称竞争相关值(Cr)]为–0.6, 空间行、列自相关值分别为0.6、–0.6,比较模型包括随机完全区组设计模型(RCBM)、空间模型(SM)、空间与测量误差模型(SUM)和空间与竞争模型(SCM)。为探究遗传竞争效应和环境空间效应的影响,设置竞争相关值(Cr= –0.3, –0.6, –0.9)以及空间行相关值( ρr= 0.3, 0.6, 0.9)、列自相关值(ρc= –0.3, –0.6, –0.9),通过正交设计 [1],产生9组数据。

    数据来源于文献[1]中的例6.4.4.2,该数据有46个全同胞家系,采用完全随机区组设计,设7个区组,5株行式小区,试验林共35行40列,测定性状是10年生胸径。

    所有模型分析均采用ASReml软件V4.0[14],除总体均值作为固定因子外,所有其他试验因子均作为随机因子。空间相关的残差采用行列AR1自相关结构,遗传竞争效应的方差采用单因子XFA1结构。拟合模型的原始数据图和残差图采用R包AAfun[15]生成,空间变异图variogram由ASReml运行生成,所有估计的参数由ASReml运行获得。

    单株狭义遗传力( $h_i^2$ )的计算公式如下:

    $$ h_i^2 = \delta _a^2/\left( {\delta _a^2 + \delta _e^2} \right), $$

    式中, $\delta _a^2$ 为加性方差, ${\delta _e^2}$ 为随机误差方差(剩余残差)。

    AIC值的计算公式如下:

    $$ {\rm AIC} = - 2 \times ({\rm ln}L - P)\text{,} $$

    式中,AIC值是赤池信息量准则,lnL是模型迭代收敛似然值,P是模型中随机参数的数量。

    对模拟数据设置随机区组设计模型(RCBM)、空间模型(SM)、空间与测量误差模型(SUM)和空间与竞争模型(SCM)4种模型,模型的拟合结果(表1)显示:模型RCBM的参数最少(4个),收敛似然值(lnL)最小(–1 927),AIC值最大(3 863),剩余残差(7.56)也最大,所估算的单株狭义遗传力也最小(0.24±0.08);模型SM的参数为6个,收敛似然值(lnL)稍微增加,为–1 904,AIC值下降至3 820,空间自相关误差为7.04,加性方差为3.44,比RCBM提高了45%,但估计的行列相关值远低于模拟设置值,行自相关值为0.25,列自相关值为–0.07;模型SUM的参数中,随机测量误差方差为5.72,空间自相关误差方差为1.83,行列相关值与模拟设置值一致,加性方差为2.43,估计的单株狭义遗传力为0.30±0.10;模型SCM 参数最多(10个),似然值(lnL)最大(–1 853),但AIC值(3 734)和随机测量误差方差(3.13)均最小,估计的单株狭义遗传力最高,为0.43±0.13。此外,模型SCM估计的4个近邻效应方差在0.34~0.74,并且均显著。根据似然值lnL最大和AIC值最小原则,可知SCM为最佳模型。

    表  1  模拟数据不同模型估计参数的比较1)
    Table  1.  Comparison of estimated parameters from different models for simulated data
    模型 参数/
    lnL AIC $\delta _{{\rm{rep}}}^2$ $\delta _{{\rm{plot}}}^2$ $\delta _e^2$ $\delta _{\rm{\eta }}^2$ $\delta _{\rm{\xi }}^2$ ρr ρc $\delta _a^2$ $\delta _{{{\rm{g}}_{{\rm{en}}}}}^2$ $\delta _{{{\rm{g}}_{{\rm{wn}}}}}^2$ $\delta _{{{\rm{g}}_{{\rm{sn}}}}}^2$ $\delta _{{{\rm{g}}_{{\rm{nn}}}}}^2$ $h_i^2$
    RCBM 4 –1 927 3 863 0.060.85 0.101.03 7.5613.52 2.372.67 0.24±0.08
    SM 6 –1 904 3 820 0.080.81 0.070.80 7.048.92 0.258.92 –0.075.87 3.442.52
    SUM 7 –1 897 3 808 0.070.89 0.080.84 5.729.02 1.834.21 0.615.84 –0.708.00 2.432.70 0.30±0.10
    SCM 10 –1 853 3 734 3.134.74 2.204.75 0.535.24 –0.709.63 2.372.81 0.592.22 0.341.60 0.682.39 0.742.54 0.43±0.13
     1) lnL为迭代收敛似然值,AIC 为赤池信息量准则, $\delta _{{\rm{rep}}}^2$为区组方差, $\delta _{{\rm{plot}}}^2$为小区方差, $\delta _e^2$为剩余残差, $\delta _{\rm{\eta }}^2$、 $\delta _{\rm{\xi }}^2$分别为随机测量误差方差和空间自相关误差方差,ρrρc 分别为行、列自相关值, $\delta _a^2$为加性方差, $\delta _{{{\rm{g}}_{{\rm{en}}}}}^2$、 $\delta _{{{\rm{g}}_{{\rm{wn}}}}}^2$、 $\delta _{{{\rm{g}}_{{\rm{sn}}}}}^2$、 $\delta _{{{\rm{g}}_{{\rm{nn}}}}}^2$分别为东向、西向、南向和北向近邻遗传竞争方差, $h_i^2$为单株狭义遗传力;表中的上标值是 t 检验统计量,为估计值与标准误的比值;…表示单株遗传力因没有随机测量误差而不能计算。
    下载: 导出CSV 
    | 显示表格

    残差图显示,RCBM的残差图(图1C)与原始数据图(图1A)非常相似,表明RCBM模型未能很好地拟合空间异质性。虽然SM模型比RCBM有所改进,但也只拟合了残差小的空间部分(图1D)。SUM和SCM模型,图形比较相似,有明显的行列空间趋势,但SCM模型更能消除部分残差高的区域(图1E1F)。从空间变异图可以看出,SM模型的图形比较平坦(图2B),表明捕获的空间变异很小,而SUM和SCM模型的图形比较相似(图2C2D),在行方向上存在明显的空间变异趋势,在列方向的突起尖峰表示列相关为负值,存在环境的竞争效应,表明SUM和SCM模型捕获空间变异的效果不错。此外,残差值与近邻表型均值的散点图(图2A)显示两者存在明显的负相关(r= –0.30),表明存在遗传竞争效应。上述结果与 表1的结果相一致。

    图  1  不同模型的剩余残差图
    Figure  1.  Residual plot of different model
    图  2  残差与近邻均值的散点图(A)和不同模型的空间变异图(B、C、D)
    Figure  2.  Scatter plot of residuals and neighbor(A) means as well as spatial variogram of different models(B, C, D)

    为了解不同初始参数值条件下,SCM模型拟合的效果,根据竞争相关值、行列自相关值设计了9组数据,结果(表2)表明,所有数据拟合的加性方差为2.25~2.52,随机测量误差方差为2.90~3.96,空间自相关误差方差为1.31~2.43,除了列相关值(pc= –0.3)拟合得一般外,其他均与初始值接近。各近邻效应方差多数在0.45~0.80,基本上都显著;此外,估计的单株狭义遗传力差异不大,均在0.40左右。可见,SCM 模型对于空间效应和竞争效应的拟合效果稳定性较好。

    表  2  不同参数初始值的SCM模型估计结果1)
    Table  2.  Estimated results of SCM with different initial values for parameters
    参数初始值 ρr ρc $\delta _{\rm{\eta }}^2$ $\delta _{\rm{\xi }}^2$ $\delta _a^2$ $\delta _{{{\rm{g}}_{{\rm{en}}}}}^2$ $\delta _{{{\rm{g}}_{{\rm{wn}}}}}^2$ $\delta _{{{\rm{g}}_{{\rm{sn}}}}}^2$ $\delta _{{{\rm{g}}_{{\rm{nn}}}}}^2$ $h_i^2$
    Cr–0.3/ρr0.3/ρc–0.3 0.362.24 –0.675.45 3.965.20 1.312.77 2.472.72 0.742.43 0.241.32 0.562.01 0.652.43 0.38±0.12
    Cr–0.3/ρr0.6/ρc–0.9 0.586.00 –0.9133.47 3.285.61 2.395.05 2.452.75 0.652.36 0.331.71 0.492.32 0.762.58 0.43±0.13
    Cr–0.3/ρr0.9/ρc–0.6 0.9125.41 –0.738.56 3.245.37 1.803.95 2.522.77 0.912.60 0.472.03 0.522.35 0.832.83 0.44±0.13
    Cr–0.6/ρr0.3/ρc–0.9 0.403.35 –0.9131.91 3.356.09 2.375.08 2.252.89 0.532.28 0.411.89 0.602.63 0.622.44 0.40±0.12
    Cr–0.6/ρr0.6/ρc–0.6 0.535.24 –0.709.63 3.134.74 2.204.75 2.362.81 0.592.22 0.341.60 0.682.39 0.742.54 0.43±0.13
    Cr–0.6/ρr0.9/ρc–0.3 0.9440.34 0.010.04 3.214.77 2.213.69 2.273.29 0.452.12 0.451.88 0.802.61 0.512.18 0.41±0.11
    Cr–0.9/ρr0.3/ρc–0.6 0.383.04 –0.687.71 3.965.20 1.312.77 2.472.72 0.742.43 0.241.32 0.562.01 0.652.43 0.44±0.08
    Cr–0.9/ρr0.6/ρc–0.6 0.545.36 –0.668.38 2.904.56 2.414.51 2.353.24 0.452.10 0.431.87 0.802.58 0.592.33 0.43±0.13
    Cr–0.9/ρr0.9/ρc–0.9 0.9120.45 –0.9224.47 3.246.82 2.433.05 2.383.64 0.512.41 0.572.48 0.652.77 0.532.51 0.42±0.10
     1) Cr为竞争相关值,ρrρc分别为行、列自相关值, $\delta _{\rm{\eta }}^2$、 $\delta _{\rm{\xi }}^2$分别为随机测量误差方差和空间自相关误差方差, $\delta _a^2$为加性方差, $\delta _{{{\rm{g}}_{{\rm{en}}}}}^2$、 $\delta _{{{\rm{g}}_{{\rm{wn}}}}}^2$、 $\delta _{{{\rm{g}}_{{\rm{sn}}}}}^2$、 $\delta _{{{\rm{g}}_{{\rm{nn}}}}}^2$分别为东向、西向、南向和北向近邻遗传竞争方差, $h_i^2$为单株狭义遗传力;表中的上标值是t检验统计量,为估计值与标准误的比值。
    下载: 导出CSV 
    | 显示表格

    真实数据拟合的结果(表3)显示,常规的RCBM模型,估计的区组方差为0.19,加性方差为2.46,剩余残差为2.96;仅有空间行列自相关的模型SM,加性方差被高估为7.58;加上区组因子后的空间模型SM1,这种偏差被修正,加性方差为2.74,空间自相关误差方差为2.84,但行、列自相关值仅为0.11、0.13;“空间+测量”误差模型SUM,有较好的拟合结果,加性方差为2.21,随机测量误差方差为2.80,空间自相关误差方差为0.80;空间与竞争模型SCM为最优模型,加性方差为2.25,随机测量误差方差和空间自相关误差方差均比SUM有所降低,分别为2.41和0.72,所估算的近邻竞争方差在0.01~0.13,但没达到显著水平。从似然值lnL和AIC值可知,SCM模型仍是最优。

    表  3  真实数据不同模型估计参数的比较1)
    Table  3.  Comparison of estimated parameters from different models for real data
    模型 参数/
    lnL AIC $\delta _{{\rm{rep}}}^2$ $\delta _{{\rm{plot}}}^2$ $\delta _e^2$ $\delta _{\rm{\eta }}^2$ $\delta _{\rm{\xi }}^2$ ρr ρc $\delta _a^2$ $\delta _{{{\rm{g}}_{{\rm{en}}}}}^2$ $\delta _{{{\rm{g}}_{{\rm{wn}}}}}^2$ $\delta _{{{\rm{g}}_{{\rm{sn}}}}}^2$ $\delta _{{{\rm{g}}_{{\rm{nn}}}}}^2$ $h_i^2$
    RCBM 4 –1 605 3 219 0.191.54 0.00 2.968.04 2.463.82 0.45±0.09
    SM 4 –1 578 3 165 0.816.84 0.9839.29 0.9842.75 7.5824.29
    SM1 6 –1 600 3 212 0.191.49 0.00 2.846.84 0.118.92 0.132.69 2.743.69
    SUM 7 –1 569 3 153 0.010.14 0.00 2.808.37 0.801.26 0.9738.63 0.9839.76 2.213.81 0.44±0.10
    SCM 10 –1 559 3 138 2.416.43 0.722.96 0.9120.33 0.9326.38 2.253.75 0.130.75 0.091.13 0.010.32 0.051.00 0.48±0.10
     1) lnL为迭代收敛似然值,AIC为赤池信息量准则, $\delta _{{\rm{rep}}}^2$为区组方差, $\delta _{{\rm{plot}}}^2$为小区方差, $\delta _e^2$为剩余残差, $\delta _{\rm{\eta }}^2$、 $\delta _{\rm{\xi }}^2$分别为测量随机误差方差和空间自相关误差方差,ρrρc分别为行、列自相关值, $\delta _a^2$为加性方差, $\delta _{{{\rm{g}}_{{\rm{en}}}}}^2$、 $\delta _{{{\rm{g}}_{{\rm{wn}}}}}^2$、 $\delta _{{{\rm{g}}_{{\rm{sn}}}}}^2$、 $\delta _{{{\rm{g}}_{{\rm{nn}}}}}^2$分别为东向、西向、南向和北向近邻遗传竞争方差, $h_i^2$为单株狭义遗传力;表中的上标值是t检验统计量,为估计值与标准误的比值;…表示单株遗传力因没有随机测量误差而不能计算。
    下载: 导出CSV 
    | 显示表格

    本研究利用R软件及其程序包breedR模拟数据,结合实测数据,采用XFA1结构拟合加性效应、近邻竞争效应和AR1结构拟合空间效应,建立了RCBM、SM、SUM和SCM 4种模型并进行比较,发现SCM是最优模型,该模型可大大降低随机误差方差,并估算到四周近邻竞争方差,设置参数的不同初始值,SCM估计的参数结果均较为稳定。

    对林木研究而言,试验林面积一般较大,采用常规试验设计难以避免环境异质性,传统的分析模型因忽略环境的异质性而导致遗传参数和育种值估算的偏差,因此Dutkowski 等[8]提出林木遗传试验数据应尽可能采用空间分析。对于环境的空间变异,一般使用3次B样条函数(Cubic B spline)或行列自回归拟合。考虑到残差结构的灵活性,本文采用行列自回归法进行空间分析。从模拟数据的似然值lnL和AIC值结果可以看出,空间分析模型比常规分析模型拟合的结果更好,但是没有随机误差方差的空间模型,随机误差被纳入空间相关误差,从而导致空间相关误差方差被高估,因此,也造成空间变异趋势被抑制,使得行、列自相关值被低估,这从残差图和空间变异图也可得到佐证。然而,仅有行、列自相关的空间模型会导致加性方差被严重高估[3],本文也得到一致的结果。Magnussen[2] 指出,环境异质性一般存在于试验林早期,林木个体周围相似的微环境会导致个体间存在正相关,但随着林木生长,个体间的竞争日趋严重,将会导致个体间存在负相关。因此,对于多年生的林木试验林,个体间存在竞争效应是必然的,这是造成个体致死率的重要因素[9]。换言之,林木性状的表型中,会同时存在空间效应和竞争效应,忽略任何一个因素,都可能会造成遗传评估的偏差,并进一步影响后续的选择工作。对于遗传竞争效应,拟合的方法有GIM[10]、TIM[11]和CSM[12],但上述研究中遗传竞争效应均为单一值,忽略了林木个体周边的遗传竞争效应可能不同。本文提出的以加性效应及其四周近邻竞争效应的方差–协方差矩阵作为G结构,替代常规的加性G结构,从而达到拟合近邻竞争效应值不唯一的目的。考虑到降低运算量和模型收敛,采用因子分析法来拟合G结构。模拟数据的拟合结果表明,新的SCM模型,拟合方差虽然没有增加,但随机误差方差大大降低了,分别由7.56(RCBM)、5.72(SUM)降低到3.13(SCM),分别降低了58.6%、45.3%,这与同时考虑竞争效应和空间效应的研究模型结果[12]相一致。此外,拟合数据的SCM模型单株狭义遗传力保持在0.40左右,高于RCBM(0.24)和SUM模型(0.30)。而针对参数不同初始值的模拟数据,SCM模型的拟合结果都比较稳定,表明其可用于林木的遗传分析。最后,利用松树的实测数据验证SCM模型,估算结果与模拟结果比较一致,笔者认为,新单株混合模型即SCM模型可用于林木遗传分析,在提高遗传评估的准确性和可靠性方面具有优势。

  • 图  1   引导编辑器的构成及引导编辑的原理示意图

    左侧为引导编辑器的构成,右侧为flap的转换及DNA修复过程;其中,PE3和PE3b系统需要额外的nicking sgRNA产生缺口,而PE2则不需要

    Figure  1.   Schematic diagram of prime editor and desired prime edit installing

    Left panel: Diagram of prime editor, right panel: Flap transition and DNA repair process; The PE3 and PE3b systems use an additional nicking sgRNA to generate a nick on DNA compared to PE2

    表  1   PE的优化策略

    Table  1   Optimization strategies of PE

    种类
    Type
    PE版本
    PE version
    测试物种
    Test species
    优化策略
    Optimization strategy
    优化效率1)
    Optimization efficiency
    参考文献
    Renference
    植物
    Plant
    MS2PE 水稻 原位招募RT 1.2~10.1倍 [47]
    PE-P3-RT-M 水稻、玉米 Cas9的N端融合RT/RTT,引入同义突变 7.0~10.0倍 [51]
    Pol II-PE3/PE3b 玉米、水稻 增加pegRNA转录 1.2~2.9倍 [52]
    PPE3-evopreQ1 水稻 epegRNA策略/高温处理 20.0%~60.5% [53]
    ePPE 水稻 删除RT的RNaseH结构域/添加病毒核衣壳蛋白 平均5.8倍 [54]
    ePPEplus/CMPE 小麦 ePPE基础上融合RT增效突变/PEmax策略 平均33.0倍 [55]
    PE2 (v2) 水稻 引入T5核酸外切酶 1.7~2.9倍 [56]
    enpPE2 水稻 Pol II-PE策略/epegRNA策略/PEmax策略 平均43.5倍 [57]
    PBS Tm + dual-pegRNA 水稻 设计优化PBS Tm/双pegRNA策略 2.9~17.4倍 [58]
    ePE2 水稻 在enpPE2基础上融合ePPE策略 1.1~1.9倍 [59]
    ePE5max 玉米 Pol II-PE策略/epegRNA策略/PEmax策略 1.4%~21.5% [60]
    种类
    Type
    PE版本
    PE version
    测试物种
    Test species
    优化策略
    Optimization strategy
    优化效率1)
    Optimization efficiency
    参考文献
    Renference
    动物
    Animal
    epegRNA 人类 添加结构化RNA基序 3.0~4.0倍 [42]
    G-PE 人类 添加G四联体结构 1.7~1.9倍 [43]
    ePE 人类、小鼠 添加Csy4识别位点 1.9~4.9倍 [44]
    xr-PE 人类、小鼠 添加xrRNA结状三级结构 2.5~4.5倍 [45]
    sPEs/tPEs/SnPEs 人类 pegRNA结构改造 2.0~4.0倍 [49]
    spegRNA/apegRNA 人类 RTT引入同义突变 平均353.0倍 [50]
    p2PE3 人类 采用Pol II型启动子 1.6~13.3倍 [61]
    PE+CPC/HDACi 人类、猪 添加小分子药剂(CPC/HDACi) >4.0倍 [62]
    PE6 人类、小鼠 更换紧凑型RT酶/连续定向进化Cas9 24.0倍 [63]
    PE2* 人类 优化核定位信号序列 1.5~1.9倍 [64]
    PE5max 人类、小鼠 抑制DNA错配修复(MMR)/PE载体优化/引入nCas9增效突变 2.0~7.7倍 [65]
    hyPE2 人类 连接处添加结合蛋白Rad51结构域 1.0~2.6倍 [66]
    CMP-PE3 + dsgRNA 人类、小鼠 使用dead sgRNA/融合染色质调节肽 3.6~5.1倍 [67]
    IN-PE2 人类、小鼠 PE蛋白N端融合多肽序列 1.6倍 [68]
    HOPE 人类 使用双pegRNA 1.5~3.5倍 [69]
     1)除ePE2的优化效率是与enpPE2相比外,其余PE版本的优化效率均是与常规PE系统比较
     1) In addition to the optimization efficiency of ePE2 comparing with enpPE2, the optimization efficiency of other PE versions is compared with conventional PE system
    下载: 导出CSV
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  • 收稿日期:  2023-09-02
  • 网络出版日期:  2023-12-10
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