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Rapid and Accurate Multiple Testing Correction and Power Estimation for Millions of Correlated Markers

机译：快速准确地对数百万个相关标记进行多次测试校正和功率估计

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With the development of high-throughput sequencing and genotyping technologies, the number of markers collected in genetic association studies is growing rapidly, increasing the importance of methods for correcting for multiple hypothesis testing. The permutation test is widely considered the gold standard for accurate multiple testing correction, but it is often computationally impractical for these large datasets. Recently, several studies proposed efficient alternative approaches to the permutation test based on the multivariate normal distribution (MVN). However, they cannot accurately correct for multiple testing in genome-wide association studies for two reasons. First, these methods require partitioning of the genome into many disjoint blocks and ignore all correlations between markers from different blocks. Second, the true null distribution of the test statistic often fails to follow the asymptotic distribution at the tails of the distribution. We propose an accurate and efficient method for multiple testing correction in genome-wide association studies—SLIDE. Our method accounts for all correlation within a sliding window and corrects for the departure of the true null distribution of the statistic from the asymptotic distribution. In simulations using the Wellcome Trust Case Control Consortium data, the error rate of SLIDE's corrected p-values is more than 20 times smaller than the error rate of the previous MVN-based methods' corrected p-values, while SLIDE is orders of magnitude faster than the permutation test and other competing methods. We also extend the MVN framework to the problem of estimating the statistical power of an association study with correlated markers and propose an efficient and accurate power estimation method SLIP. SLIP and SLIDE are available at http://slide.cs.ucla.edu.
机译：随着高通量测序和基因分型技术的发展，遗传关联研究中收集到的标记数量迅​​速增长，从而增加了校正多种假设检验的方法的重要性。置换测试被广泛认为是准确进行多重测试校正的黄金标准，但是对于这些大型数据集而言，在计算上通常是不切实际的。最近，一些研究提出了基于多元正态分布（MVN）的置换测试的有效替代方法。但是，由于两个原因，他们无法准确校正在全基因组关联研究中的多次测试。首先，这些方法需要将基因组划分为许多不相交的区块，并忽略来自不同区块的标记之间的所有相关性。其次，检验统计量的真实零分布通常无法遵循分布尾部的渐近分布。我们提出了一种准确有效的方法，可用于在全基因组关联研究中进行多重测试校正。我们的方法考虑了滑动窗口内的所有相关性，并校正了统计量的真实零分布与渐近分布的偏离。在使用Wellcome Trust Case Control Consortium数据进行的模拟中，SLIDE校正后的p值的错误率比以前的基于MVN的方法校正后的p值的错误率小20倍以上，而SLIDE则快了几个数量级而不是排列测试和其他竞争方法。我们还将MVN框架扩展到了使用相关标记估算关联研究的统计功效的问题，并提出了一种有效而准确的功效估算方法SLIP。可在http://slide.cs.ucla.edu获得SLIP和SLIDE。

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