大规模全基因组关联研究（GWAS）已成为人类基因组学中的一个重要工具，其关注焦点大多是疾病，但也关注肤色等适应性变异。现在，这种方法被发现在植物中也同样有用。Atwell等人报告了对自然出现的近交系拟南芥中的超过100种基因型所做的一项GWA研究。他们获得的结果从“有显著关联”（通常是对单个基因）到“比较难以解读的发现”都有，这说明复杂遗传因素和种群结构交互影响。
由Todesco等人发表的另一篇相伴的论文显示了这种方法检测“主要影响”基因位点的能力。利用正向遗传学和GWA分析，他们发现，拟南芥单一位点（ACD6）上的变异是造成植物生长及抗感染能力方面的表现型变异的原因。由这个位点上的等位基因之一所调控的抵抗力的显著增强，可以解释它为什么能在全世界的自然种群中持久存在，尽管这会大大减少新叶的生成。（生物谷Bioon.com）
高烧的GWAS——生物谷盘点2009
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Nucleic Acids Res.：基于通路的GWAS数据网络分析平台开发成功
生物谷推荐原文出处：
Nature doi:10.1038/nature08800
Genome-wide association study of 107 phenotypes in Arabidopsis thaliana inbred lines
Susanna Atwell,Yu S. Huang,Bjarni J. Vilhjálmsson,Glenda Willems,Matthew Horton,Yan Li,Dazhe Meng,Alexander Platt,Aaron M. Tarone,Tina T. Hu,Rong Jiang,N. Wayan Muliyati,Xu Zhang,Muhammad Ali Amer,Ivan Baxter,Benjamin Brachi,Joanne Chory,Caroline Dean,Marilyne Debieu,Juliette de Meaux,Joseph R. Ecker,Nathalie Faure,Joel M. Kniskern,Jonathan D. G. Jones,Todd Michael,et al
Although pioneered by human geneticists as a potential solution to the challenging problem of finding the genetic basis of common human diseases1, 2, genome-wide association (GWA) studies have, owing to advances in genotyping and sequencing technology, become an obvious general approach for studying the genetics of natural variation and traits of agricultural importance. They are particularly useful when inbred lines are available, because once these lines have been genotyped they can be phenotyped multiple times, making it possible (as well as extremely cost effective) to study many different traits in many different environments, while replicating the phenotypic measurements to reduce environmental noise. Here we demonstrate the power of this approach by carrying out a GWA study of 107 phenotypes in Arabidopsis thaliana, a widely distributed, predominantly self-fertilizing model plant known to harbour considerable genetic variation for many adaptively important traits3. Our results are dramatically different from those of human GWA studies, in that we identify many common alleles of major effect, but they are also, in many cases, harder to interpret because confounding by complex genetics and population structure make it difficult to distinguish true associations from false. However, a-priori candidates are significantly over-represented among these associations as well, making many of them excellent candidates for follow-up experiments. Our study demonstrates the feasibility of GWA studies in A.thaliana and suggests that the approach will be appropriate for many other organisms.
