内布拉斯加大学林肯分校四川农大玉米和高粱的遗传驯化特征

很多谷类作物是我们的祖先历时近万年从野生的禾本科植物驯化选择而来，据估计有2500种植物曾经历过人类的驯化选择，但仅有10%被驯化为我们今天人类赖以生存的作物。在数百种作物中，水稻、小麦和玉米为全人类提供了近一半的卡里路。这些作物通常具有一些共性的表型性状，如落粒性和休眠性的丢失、种子数量或重量的增加，以及碳水化合物的增加、蛋白质含量的减少等等。目前，控制这些性状的基因也已明确。遗传学家将不同作物经驯化以后获得共有的平行性状（重叠性状）称为“驯化综合特征”。平行的表型变化可以通过对同源基因的选择获得，也可以通过选择具有平行表型效应的非同源基因而实现。近日，内布拉斯加大学林肯分校和四川农业大学玉米研究所的研究人员，利用玉米和高粱两种作物的野生种和驯化种的重测序信息，分析禾本科作物驯化性状的人工选择特征。研究发现，对于玉米和高粱而言，在野生种与驯化种的杂交群体中，通过QTL定位发现的许多“经典”的驯化基因的确呈现出平行选择的特征。然而，在玉米和高粱中，具有平行选择特征的基因总数与预期的并无显著差异。研究结果表明，虽然具有较强表型效应的少数基因在驯化过程中被人为地反复选择，但那些表型效应较低而且没有重叠性状的大量基因可能在精细调控作物的驯化进程以及降低平行性状潜在风险等方面发挥重要作用。

Plant J, 19 December 2017

Largely unlinked gene sets targeted by selection for domestication syndrome phenotypes in maize and sorghum

Authors

Xianjun Lai,Lang Yan,Yanli Lu,James C. Schnable

Abstract

The domestication of diverse grain crops from wild grasses resulted from artificial selection for a suite of overlapping traits producing changes referred to in aggregate as “domestication syndrome”. Parallel phenotypic change can be accomplished by either selection on orthologous genes, or selection on non-orthologous genes with parallel phenotypic effects. To determine how often artificial selection for domestication traits in the grasses targeted orthologous genes, we employed resequencing data from wild and domesticated accessions of Zea (maize) and Sorghum (sorghum). Many “classic” domestication genes identified through QTL mapping in populations resulting from wild/domesticated crosses indeed show signatures of parallel selection in both maize and sorghum. However, the overall number of genes showing signatures of parallel selection in both species is not significantly different from that expected by chance. This suggests that, while a small number of genes will extremely large phenotypic effects have been targeted repeatedly by artificial selection during domestication, the optimization portion of domestication targeted small and largely non-overlapping subsets of all possible genes which could produce equivalent phenotypic alterations.

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