High-dimensional variable selection has emerged as one of the prevailing statistical challenges in the big data revolution. Many variable selection methods have been adapted for identifying single nucleotide polymorphisms (SNPs) linked to phenotypic variation in genome-wide association studies. We develop a Bayesian variable selection regression model for identifying SNPs linked to phenotypic variation. We modify our Bayesian variable selection regression models to control the false discovery rate of SNPs using a knockoff variable approach. We reduce spurious associations by regressing the phenotype of interest against a set of basis functions that account for the relatedness of individuals. Using a restricted regression approach, we simultaneously estimate the SNP-level effects while removing variation in the phenotype that can be explained by population structure. We also accommodate the spatial structure among causal SNPs by modeling their inclusion probabilities jointly with a reduced rank Gaussian process. In a simulation study, we demonstrate that our spatial Bayesian variable selection regression model controls the false discovery rate and increases power when the relevant SNPs are clustered. We conclude with an analysis of Arabidopsis thaliana flowering time, a polygenic trait that is confounded with population structure, and find the discoveries of our method cluster near described flowering time genes.

翻译：高维变量选择已成为大数据革命中普遍存在的统计挑战之一。许多变量选择方法已被用于在全基因组关联研究中识别与表型变异相关的单核苷酸多态性。我们开发了一种贝叶斯变量选择回归模型，用于识别与表型变异相关的SNPs。我们通过采用敲除变量方法，修改了贝叶斯变量选择回归模型以控制SNPs的错误发现率。我们通过将目标表型回归到一组能够解释个体亲缘关系的基函数上来减少虚假关联。利用限制性回归方法，我们在估计SNP水平效应的同时，移除了可由群体结构解释的表型变异。我们还通过使用降秩高斯过程联合建模因果SNPs的纳入概率，以适应因果SNPs之间的空间结构。在一项模拟研究中，我们证明了当相关SNPs成簇分布时，我们的空间贝叶斯变量选择回归模型能够控制错误发现率并提高统计功效。最后，我们分析了拟南芥开花时间这一受群体结构混淆的多基因性状，发现我们方法识别的结果聚集在已知的开花时间基因附近。