We present a comprehensive statistical framework to analyze data from genome-wide association studies of polygenic traits, producing interpretable findings while controlling the false discovery rate. In contrast with standard approaches, our method can leverage sophisticated multivariate algorithms but makes no parametric assumptions about the unknown relation between genotypes and phenotype. Instead, we recognize that genotypes can be considered as a random sample from an appropriate model, encapsulating our knowledge of genetic inheritance and human populations. This allows the generation of imperfect copies (knockoffs) of these variables that serve as ideal negative controls, correcting for linkage disequilibrium and accounting for unknown population structure, which may be due to diverse ancestries or familial relatedness. The validity and effectiveness of our method are demonstrated by extensive simulations and by applications to the UK Biobank data. These analyses confirm our method is powerful relative to state-of-the-art alternatives, while comparisons with other studies validate most of our discoveries. Finally, fast software is made available for researchers to analyze Biobank-scale datasets.

我们提出了一个全面的统计框架来分析来自多基因性状的全基因组关联研究的数据，在控制错误发现率的同时产生可解释的发现。与标准方法相比，我们的方法可以利用复杂的多变量算法，但不对基因型和表型之间的未知关系做出参数假设。相反，我们认识到基因型可以被视为来自适当模型的随机样本，封装了我们对遗传遗传和人类群体的了解。这允许生成这些变量的不完美拷贝（仿制品），作为理想的阴性对照，纠正连锁不平衡并解释未知的种群结构，这可能是由于不同的祖先或家族相关性。我们的方法的有效性和有效性通过广泛的模拟和对英国生物银行数据的应用得到证明。这些分析证实我们的方法相对于最先进的替代方法是强大的，而与其他研究的比较验证了我们的大部分发现。最后，研究人员可以使用快速软件来分析 Biobank 规模的数据集。