Multi-resolution localization of causal variants across the genome
Multi-resolution localization of causal variants across the genome
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DOI：
10.1038/s41467-020-14791-2
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发表时间：
2019-05
影响因子：
16.6
通讯作者：
Matteo Sesia;E. Katsevich;Stephen Bates;E. Candès;C. Sabatti
中科院分区：
文献类型：
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作者：
Matteo Sesia;E. Katsevich;Stephen Bates;E. Candès;C. Sabatti
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，并在限制虚假发现的同时最大化力量，是具有挑战性的。在这里，我们报告了Knock off Zoom：一种灵活的方法，通过测试宽度减小的基因片段的条件关联来定位多个分辨率的因果变量，同时可证明地控制错误发现率。我们的方法使用人工基因型作为阴性对照，对数量和二进制表型同样有效，不需要任何关于它们的遗传结构的假设。取而代之的是，我们依赖已建立的连锁不平衡遗传模型。我们证明，我们的方法可以检测到比混合效应模型更多的关联，并在相当的计算成本下实现精细映射精度。最后，我们应用了英国生物库中35万名受试者的Knoackoff Zoomto数据，并报告了许多新的发现。
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 onKnockoffZoom: 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 applyKnockoffZoomto data from 350k subjects in the UK Biobank and report many new findings.