Genome-wide association mapping in plants
Genome-wide association mapping in plants
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DOI：
10.1007/s00122-015-2497-x
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发表时间：
2015-06-01
影响因子：
5.4
通讯作者：
Cavanagh, Colin
中科院分区：
文献类型：
--
作者：
George, Andrew W.;Cavanagh, Colin
我们提出了新的关联作图方法，这些方法解决了分析来自多环境植物研究的全基因组数据的独特挑战。与人类研究不同，植物研究包含重复，其数据可能在不同环境中记录。植物研究也经常采用复杂的实验设计来控制外来的表型变异。因此，植物研究数据的全基因组分析可能具有挑战性。在本文中，我们提出了基于QK的关联映射的数据分析，从植物的关联性研究。在这样做的过程中，我们已经开发出：（a）一个通用的多变量QK框架的关联映射在植物研究的任意复杂性;（B）一个新的加权两阶段的分析方法QK为基础的关联映射;（c）一个启发式的程序，以确定何时两阶段的分析是适当的;和（d）蒙特卡洛抽样程序控制全基因组I型错误率。我们进行了模拟研究，以评估我们的全基因组定位技术的性能。我们还分析了小麦多环境关联研究的数据。
We present new association mapping methods which address the unique challenges of analyzing genome-wide data from multi-environment plant studies.Association studies on a genome-wide scale are being performed in plants. Unlike human studies, plant studies contain replicates whose data may be recorded across different environments. Plant studies also often employ elaborate experimental designs for controlling extraneous phenotypic variation. As a result, the genome-wide analysis of data from plant studies can be challenging. In this paper, we present QK-based association mapping for the analysis of data from plant association studies. In doing so, we have developed: (a) a general multivariate QK framework for association mapping in plant studies of arbitrary complexity; (b) a new weighted two-stage analysis approach for QK-based association mapping; (c) a heuristic procedure for determining when two-stage analysis is appropriate; and (d) a Monte Carlo sampling procedure for controlling the genome-wide type I error rate. We conduct a simulation study to evaluate the performance of our genome-wide mapping technique. We also analyze data from a multi-environment association study in wheat.