Department of Statistics, Stanford University, Stanford, CA, 94305, USA.
Department of Statistics, Carnegie Mellon University, Pittsburgh, PA, 15213, USA.
Nat Commun. 2020 Feb 27;11(1):1093. doi: 10.1038/s41467-020-14791-2.
In the statistical analysis of genome-wide association data, it is challenging to precisely localize the variants that affect complex traits, due to linkage disequilibrium, and to maximize power while limiting spurious findings. Here we report on KnockoffZoom: a flexible method that localizes causal variants at multiple resolutions by testing the conditional associations of genetic segments of decreasing width, while provably controlling the false discovery rate. Our method utilizes artificial genotypes as negative controls and is equally valid for quantitative and binary phenotypes, without requiring any assumptions about their genetic architectures. Instead, we rely on well-established genetic models of linkage disequilibrium. We demonstrate that our method can detect more associations than mixed effects models and achieve fine-mapping precision, at comparable computational cost. Lastly, we apply KnockoffZoom to data from 350k subjects in the UK Biobank and report many new findings.
在全基因组关联数据的统计分析中，由于连锁不平衡，精确定位影响复杂性状的变异是具有挑战性的，同时还需要在限制虚假发现的同时最大化功效。在这里，我们报告了 KnockoffZoom：一种灵活的方法，通过测试遗传片段宽度逐渐减小的条件关联，在多个分辨率下定位因果变异，同时可证明控制虚假发现率。我们的方法利用人工基因型作为阴性对照，并且对定量和二项表型同样有效，而不需要对其遗传结构做出任何假设。相反，我们依赖于已建立的连锁不平衡遗传模型。我们证明，与混合效应模型相比，我们的方法可以检测到更多的关联，并在可比的计算成本下实现精细映射精度。最后，我们将 KnockoffZoom 应用于 UK Biobank 中 35 万受试者的数据，并报告了许多新的发现。
