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基因组选择(GS,也称全基因组选择)是利用捕捉到一个乃至多个数量性状大多数基因位点的全基因组标记估计基因组育种值(即所有标记效应的总和),并以此对目标性状进行选择的方法。目前,这种新的选择方法正在给家畜育种实践带来一场革命性的变化。同样的方法和设想也适用于林木育种。事实上,漫长的世代时间以及大多数复杂性状晚期表达的特性历来都是林木育种所面临的巨大困难和挑战。不仅如此,林木植物还具备诸多其它的有利条件有助于GS的开展和应用,例如:易于收集并建立较大的参考群体且某些性状已作过准确的表型分析;一些改良群体的连锁不平衡(LD)程度较高,这其中包括林木高世代育种程序中所常用到的一些有效群体大小(Ne)较小的群体。本研究利用确定性方程就LD(通过Ne和标记间距离进行模拟),训练集的大小,性状遗传力,以及数量性状位点(QTL)的数目等因素对GS的预期准确性的影响进行了分析评估。结果显示,GS有可能使树木育种的有效性得到根本性的提高。当Ne≤30时,即便标记密度仅大约2个标记/厘摩(cM),采用GS就能达到传统BLUP(最佳线性无偏预测)选择的基准精度。不过,当Ne较大时,标记密度则需达到20个标记/cM。采用GS可使育种周期缩短50%,进而使育种效率增加100%或以上。随着技术的快速进步和基因分型成本的下降,我们谨慎而乐观地看好GS在加速树木育种进程和提高育种效率上的巨大潜力。不过,在将此项技术推广应用之前,尚需作进一步的模拟研究及概念验证试验。
Genome selection (GS, also known as genome-wide selection) is the use of genome-wide markers that capture most gene loci for one or more quantitative traits to estimate genomic breeding values (ie, sum of all marker effects) and to target traits The method of choice. At present, this new selection method is bringing a revolutionary change to livestock breeding practice. The same methodology and assumptions apply to tree breeding. In fact, the long generational times and the late-expressed traits of most complex traits have historically been a tremendous challenge and challenge for tree breeding. In addition, tree plants have many other favorable conditions for the development and application of GS. For example, it is easy to collect and establish large reference populations and some traits have been accurately phenotyped; some improved population linkage Higher levels of imbalance (LD) include those with smaller effective (Ne) populations commonly used in high-generation tree breeding programs. In this study, deterministic equations were used to determine the effect of GS on the expected accuracy of LD (through the distance between Ne and marker), the size of training set, heritability and the number of quantitative trait loci (QTLs) Analysis and evaluation. The results show that GS may make the effectiveness of tree breeding is fundamentally improved. When Ne ≤ 30, the accuracy of the traditional BLUP (Best Linear Unbiased Prediction) selection can be achieved with GS even at marker densities of only about 2 markers / centimeter (cM). However, when Ne is large, the mark density needs to reach 20 marks / cM. With GS, the breeding cycle can be shortened by 50%, resulting in an increase of 100% or more in breeding efficiency. With the rapid advances in technology and the decline in genotyping costs, we are cautiously optimistic about the tremendous potential of GS in accelerating tree breeding and improving breeding efficiency. However, before this technology is popularized and applied, further simulation studies and proof-of-concept tests are needed.