An increasing number of field studies have shown that the phenotype of an individual plant depends not only on its genotype but also on those of neighboring plants; however, this fact is not taken into consideration in genome-wide association studies (GWAS). Based on the Ising model of ferromagnetism, we incorporated neighbor genotypic identity into a regression model, named "Neighbor GWAS". Our simulations showed that the effective range of neighbor effects could be estimated using an observed phenotype from when the proportion of phenotypic variation explained (PVE) by neighbor effects peaked. The spatial scale of the first nearest neighbors gave the maximum power to detect the causal variants responsible for neighbor effects, unless their effective range was too broad. However, if the effective range of the neighbor effects was broad and minor allele frequencies were low, there was collinearity between the self and neighbor effects. To suppress the false positive detection of neighbor effects, the fixed effect and variance components involved in the neighbor effects should be tested in comparison with a standard GWAS model. We applied neighbor GWAS to field herbivory data from 199 accessions of Arabidopsis thaliana and found that neighbor effects explained 8% more of the PVE of the observed damage than standard GWAS. The neighbor GWAS method provides a novel tool that could facilitate the analysis of complex traits in spatially structured environments and is available as an R package at CRAN (https://cran.rproject.org/package=rNeighborGWAS).

中文翻译：

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邻居GWAS：将邻居基因型同一性纳入野草食草动物的全基因组关联研究

越来越多的田间研究表明，单个植物的表型不仅取决于其基因型，而且还取决于邻近植物的基因型。但是，在全基因组关联研究（GWAS）中并未考虑到这一事实。基于铁磁性的伊辛模型，我们将邻居的基因型同一性纳入了回归模型，称为“邻居GWAS”。我们的模拟表明，当邻居效应解释的表型变异比例（PVE）达到峰值时，可以使用观察到的表型估算邻居效应的有效范围。除非它们的有效范围太广，否则最接近的邻居的空间尺度将提供最大的能力来检测造成邻居效应的因果变体。然而，如果邻居效应的有效范围宽而次要等位基因频率低，则自我效应和邻居效应之间存在共线性。为了抑制对邻居效应的误报检测，应该与标准GWAS模型进行比较，测试与邻居效应有关的固定效应和方差分量。我们将邻近的GWAS应用于来自199个保藏区的野草食草数据拟南芥（Arabidopsis thaliana），发现邻居效应比标准GWAS解释了所观察到的损害的PVE高8％。邻居GWAS方法提供了一种新颖的工具，可以促进空间结构化环境中复杂性状的分析，并且可以作为R包在CRAN（https://cran.rproject.org/package=rNeighborGWAS）上获得。