Mapping as you go: An effective approach for marker-assisted selection of complex traits
Mapping as you go: An effective approach for marker-assisted selection of complex traits
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DOI：
10.2135/cropsci2004.1560
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发表时间：
2004-09-01
期刊：
影响因子：
2.3
通讯作者：
Cooper, M
中科院分区：
文献类型：
--
作者：
Podlich, DW;Winkler, CR;Cooper, M
随着高通量分子技术的出现，人们期望育种计划将使用标记-性状关联来进行性状的标记辅助选择（MAS）。对于所谓的复杂性状，这种分子育种方法存在许多挑战。迄今为止，一个主要的限制是检测和量化标记-性状关系的能力有限，特别是对于受基因-基因和基因-环境相互作用影响的性状。另一个复杂的问题是，数量性状基因座（QTL）效应的估计是有偏差的，因为需要在有限的环境中使用有限的基因型，因此，当在育种计划中更广泛地使用时，这些估计的应用并不像预期的那样有效。在本文中考虑的方法，被称为绘图随你去（MAYG）的方法，不断修订估计QTL等位基因的影响，通过重新定位新的精英种质产生的选择周期，从而确保QTL估计保持相关的育种计划中的当前一组种质。Mapping As You Go是一种定位-MAS策略，它明确认识到复杂性状的QTL等位基因可以随着当前育种材料的变化而具有不同的值。模拟被用来调查MAYG方法适用于复杂性状的有效性。结果表明，与不经常修订或根本不修订估计数的情况相比，经常修订估计数的情况下，得到的答复水平更高，而且这些答复的可变性更小。
The advent of high throughput molecular technologies has led to an expectation that breeding programs will use marker-trait associations to conduct marker-assisted selection (MAS) for traits. Many challenges exist with this molecular breeding approach for so-called complex traits. A major restriction to date has been the limited ability to detect and quantify marker-trait relationships, especially for traits influenced by the effects of gene-by-gene and gene-by-environment interactions. A further complication has been that estimates of quantitative trait loci (QTL) effects are biased by the necessity of working with a limited set of genotypes in a limited set of environments, and hence the applications of these estimates are not as effective as expected when used more broadly within a breeding program. The approach considered in this paper, referred to as the Mapping As You Go (MAYG) approach, continually revises estimates of QTL allele effects by remapping new elite germplasm generated over cycles of selection, thus ensuring that QTL estimates remain relevant to the current set of germplasm in the breeding program. Mapping As You Go is a mapping-MAS strategy that explicitly recognizes that alleles of QTL for complex traits can have different values as the current breeding material changes with time. Simulation was used to investigate the effectiveness of the MAYG approach applied to complex traits. The results indicated that greater levels of response were achieved and these responses were less variable when estimates were revised frequently compared with situations where estimates were revised infrequently or not at all.