A Bayesian Approach to the Overlap Analysis of Epidemiologically Linked Traits.
A Bayesian Approach to the Overlap Analysis of Epidemiologically Linked Traits.
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DOI：
10.1002/gepi.21919
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发表时间：
2015-12
影响因子：
2.1
通讯作者：
Barroso I
中科院分区：
文献类型：
--
作者：
Asimit JL;Panoutsopoulou K;Wheeler E;Berndt SI;GIANT consortium, the arcOGEN consortium;Cordell HJ;Morris AP;Zeggini E;Barroso I
疾病往往共同发生在个人往往比预期的机会，并可能被解释为共同的潜在遗传病因。遗传重叠分析的一种常见方法是使用全基因组关联研究数据来确定在选定的P值阈值下与多个性状相关的单核苷酸多态性（SNP）。然而，P值不考虑功效差异，而贝叶斯因子（BF）可以，并且可以使用汇总统计量近似。我们使用模拟研究，比较功率的频率论和贝叶斯方法与重叠分析，并决定适当的阈值比较两种方法之间。经验表明，随着单疾病相关性研究规模的增加，BF具有I类错误率降低的P值优势。因此，来自不同规模研究的特征的重叠分析遇到了公平P值阈值选择的问题，而BF自动调整。广泛的模拟表明，贝叶斯重叠分析往往比那些用P值评估关联强度的分析具有更高的功效，特别是在低功效场景中。提供了一系列样本量的BF和P值之间的校准表，以及校准表中未列出的样本量的近似方法。虽然P值有时被认为更直观，但这些表格有助于消除贝叶斯阈值的不透明性，也可用于选择BF阈值以满足特定的I类错误率。我们的方法的应用程序被用来识别与肥胖和骨关节炎相关的变异。
Diseases often cooccur in individuals more often than expected by chance, and may be explained by shared underlying genetic etiology. A common approach to genetic overlap analyses is to use summary genome‐wide association study data to identify single‐nucleotide polymorphisms (SNPs) that are associated with multiple traits at a selected P‐value threshold. However, P‐values do not account for differences in power, whereas Bayes’ factors (BFs) do, and may be approximated using summary statistics. We use simulation studies to compare the power of frequentist and Bayesian approaches with overlap analyses, and to decide on appropriate thresholds for comparison between the two methods. It is empirically illustrated that BFs have the advantage over P‐values of a decreasing type I error rate as study size increases for single‐disease associations. Consequently, the overlap analysis of traits from different‐sized studies encounters issues in fair P‐value threshold selection, whereas BFs are adjusted automatically. Extensive simulations show that Bayesian overlap analyses tend to have higher power than those that assess association strength with P‐values, particularly in low‐power scenarios. Calibration tables between BFs and P‐values are provided for a range of sample sizes, as well as an approximation approach for sample sizes that are not in the calibration table. Although P‐values are sometimes thought more intuitive, these tables assist in removing the opaqueness of Bayesian thresholds and may also be used in the selection of a BF threshold to meet a certain type I error rate. An application of our methods is used to identify variants associated with both obesity and osteoarthritis.