GENOME WIDE ASSOCIATION MAPPING IN ARABIDOPSIS THALIANA

描述（由申请人提供）：我们的提案将利用模式植物拟南芥研究复杂性状的遗传和分子基础及其与环境的相互作用。我们将实现一种多用途、高密度的寡核苷酸平铺阵列，用于全基因组重测序。该样本将包括384个野生拟南芥基因组的大部分未结构化核心集。这将用于开发一个非常高分辨率的单倍型图谱，揭示基因组广泛的变异模式，并提出自然选择下的位点。在两个季节和两个地理环境中，对拟南芥的开花时间和幼苗伸长光响应的生态学相关数量性状进行了测量。这些和未来的群落表型数据将用于开发和测试基于高密度单倍型图谱的小尺度定量性状位点（QTL）关联扫描方法。全基因组关联制图将使用聚结模型进行检测和精细制图。我们将利用拟南芥遗传学的全部力量确定两个新QTL的功能分子变化。重要的是，这一建议将开发新的技术进展，使用平铺阵列生成高密度单倍型图，作为全基因组关联研究的基础。这些方法一旦建立，就可以扩展到其他模型系统。发展精细尺度连杆不平衡映射方法具有广泛的应用前景。人们对绘制复杂疾病关联图谱有着极大的兴趣，但对不同的方法存在很多争论，迄今为止取得的成功很少。在拟南芥中提出的研究将为这项艰巨的任务提供成功的途径，因为可以快速确认关联以识别新的QTL。

DESCRIPTION (provided by applicant): Our proposal will investigate the genetic and molecular basis of complex traits and their interactions with the environment using the model plant Arabidopsis thaliana. We will implement a multi use, high density oligo- nucleotide tiling array for whole genome resequencing. The sample will include a largely unstructured core set of 384 wild A. thaliana genomes. This will be used to develop a very high resolution haplotype map, reveal genome wide patterns of variation, and suggest sites under natural selection. The ecologically relevant quantitative traits of flowering time and seedling elongation light response will be measured across two seasonal and two geographic environments which span the native range of A. thaliana. This and future community phenotypic data will be used to develop and test methods for fine scale quantitative trait locus (QTL) association scanning capitalizing on the high density haplotype map. Whole genome association mapping will be developed using coalescent models for detection and fine mapping. We will determine the functional molecular changes underlying two novel QTL utilizing the full power of Arabidopsis genetics. Importantly this proposal will develop new technological inroads for using tiling arrays to generate high density haplotype maps as the foundation for whole genome association studies. These methods, once established, can then be extended to other model systems. The development of fine scale linkage disequilibrium mapping methods will be broadly applicable. There is a tremendous interest in complex disease association mapping, but much debate over different approaches and little success to date. The studies proposed here in Arabidopsis will suggest successful paths for this daunting undertaking, as associations can be quickly confirmed to identify novel QTL.