Linkage and association mapping of Arabidopsis thaliana flowering time in nature.
Linkage and association mapping of Arabidopsis thaliana flowering time in nature.
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DOI：
10.1371/journal.pgen.1000940
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发表时间：
2010-05-06
期刊：
影响因子：
4.5
通讯作者：
Roux F
中科院分区：
文献类型：
--
作者：
Brachi B;Faure N;Horton M;Flahauw E;Vazquez A;Nordborg M;Bergelson J;Cuguen J;Roux F
开花时间是植物生活史中一个重要的生活史性状。大多数关于拟南芥开花时间遗传学的研究都是在温室条件下进行的。在这里，我们描述了一项关于开花时间遗传学的研究，该研究在两个重要方面与以前的研究不同：第一，我们在更复杂和生态现实的环境中测量开花时间;第二，我们联合收割机了全基因组关联（GWA）和传统连锁（QTL）作图的优势。我们的实验涉及在田间条件下超过2个冬天的近20，000株植物的表型分析，包括来自13个独立杂交的184份全球天然种质，其中216，509个SNP和4，366个RILs基因型分析，以最大限度地提高遗传和表型多样性。基于光热时间模型，在我们的田间试验中获得的开花时间变化与以前在温室条件下获得的开花时间变化相关性很差，加强了以前的证明基因型与环境相互作用在A. thaliana和需要研究自然条件下的适应性变化。4,366个RILs的使用，为分析A.在我们特定的野外条件下。我们描述了超过60个加性QTL，所有相对较小到中等的影响，并组织在5个主要集群。我们表明，QTL定位增加了我们的权力，以区分真正的假协会在GWA映射。QTL作图还允许鉴定假阴性，即当应用控制群体结构的GWA方法时丢失的致病SNP。在本研究中，温室中控制开花时间的主效基因与开花时间无关。相反，我们发现了一种普遍存在的基因参与调节植物生物钟。此外，我们确定了新的基因组区域缺乏明显的候选基因。剖析适应性特征的遗传基础在进化生物学中是至关重要的。在这项研究中，我们结合了全基因组关联（GWA）的研究与传统的连锁图谱，以检测植物拟南芥开花时间在生态现实条件下的自然变异的遗传基础。我们的研究涉及在温带气候的田间条件下，在2个冬天对近20，000株植物进行表型分析。我们表明，结合联动和关联映射显然优于单独的方法，当涉及到识别真正的关联。这突出了结合不同方法定位复杂性状自然变异基因的实用性。在这项研究中发现的大多数候选基因都参与了植物生物钟的调节，令人惊讶的是，它们与温室条件下的开花时间无关。虽然在高通量基因分型和测序方面取得了快速进展，但在自然条件下对复杂性状进行高通量表型分析将是解剖“实验室”模式生物中适应性变异的遗传基础的下一个挑战。
Flowering time is a key life-history trait in the plant life cycle. Most studies to unravel the genetics of flowering time in Arabidopsis thaliana have been performed under greenhouse conditions. Here, we describe a study about the genetics of flowering time that differs from previous studies in two important ways: first, we measure flowering time in a more complex and ecologically realistic environment; and, second, we combine the advantages of genome-wide association (GWA) and traditional linkage (QTL) mapping. Our experiments involved phenotyping nearly 20,000 plants over 2 winters under field conditions, including 184 worldwide natural accessions genotyped for 216,509 SNPs and 4,366 RILs derived from 13 independent crosses chosen to maximize genetic and phenotypic diversity. Based on a photothermal time model, the flowering time variation scored in our field experiment was poorly correlated with the flowering time variation previously obtained under greenhouse conditions, reinforcing previous demonstrations of the importance of genotype by environment interactions in A. thaliana and the need to study adaptive variation under natural conditions. The use of 4,366 RILs provides great power for dissecting the genetic architecture of flowering time in A. thaliana under our specific field conditions. We describe more than 60 additive QTLs, all with relatively small to medium effects and organized in 5 major clusters. We show that QTL mapping increases our power to distinguish true from false associations in GWA mapping. QTL mapping also permits the identification of false negatives, that is, causative SNPs that are lost when applying GWA methods that control for population structure. Major genes underpinning flowering time in the greenhouse were not associated with flowering time in this study. Instead, we found a prevalence of genes involved in the regulation of the plant circadian clock. Furthermore, we identified new genomic regions lacking obvious candidate genes. Dissecting the genetic bases of adaptive traits is of primary importance in evolutionary biology. In this study, we combined a genome-wide association (GWA) study with traditional linkage mapping in order to detect the genetic bases underlying natural variation in flowering time in ecologically realistic conditions in the plant Arabidopsis thaliana. Our study involved phenotyping nearly 20,000 plants over 2 winters under field conditions in a temperate climate. We show that combined linkage and association mapping clearly outperforms each method alone when it comes to identifying true associations. This highlights the utility of combining different methods to localize genes involved in complex trait natural variation. Most candidate genes found in this study are involved in the regulation of the plant circadian clock and, surprisingly, were not associated with flowering time scored under greenhouse conditions. While rapid advances have been made in high-throughput genotyping and sequencing, high-throughput phenotyping of complex traits under natural conditions will be the next challenge for dissecting the genetic bases of adaptive variation in “laboratory” model organisms.
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