Comparison of multiple imputation and other methods for the analysis of imputed genotypes.
Comparison of multiple imputation and other methods for the analysis of imputed genotypes.
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DOI：
10.1186/s12864-023-09415-0
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发表时间：
2023-06-06
期刊：
影响因子：
4.4
通讯作者：
中科院分区：
文献类型：
--
作者：
插补基因型分析是全基因组关联研究的一个重要和常规组成部分，插补参考组的规模不断增加，促进了插补和检验低频变异关联的能力。在基因型插补的背景下，真实的基因型是未知的，并且使用统计模型以不确定性推断基因型。在这里，我们提出了一种新的方法，将填补不确定性统计关联检验使用完全条件多重填补（MI）的方法，这是使用实质性模型兼容的完全条件规范（SMCFCS）。我们比较了这种方法的性能，一个无条件MI和两个额外的方法，已被证明表现出优异的性能：回归剂量和回归模型（MRM）的混合物。我们的模拟考虑了一系列等位基因频率和插补质量的基础上，从英国生物银行的数据。我们发现，无条件MI在计算上是昂贵的，并且在广泛的设置中过于保守。与无条件MI相比，使用Dosage、MRM或MI SMCFCS分析数据可获得更大的功效，包括低频变异，同时有效控制I型错误率。MRM和MI SMCFCS都比使用Dosage计算更密集。关联检验的无条件MI方法过于保守，我们不建议将其用于插补基因型。鉴于其性能、速度和易于实施，我们建议对MAF 0.001和Rsq 0.3的插补基因型使用Dosage。在线版本包含补充材料，可通过10.1186/s12864-023-09415-0获得。
Analysis of imputed genotypes is an important and routine component of genome-wide association studies and the increasing size of imputation reference panels has facilitated the ability to impute and test low-frequency variants for associations. In the context of genotype imputation, the true genotype is unknown and genotypes are inferred with uncertainty using statistical models. Here, we present a novel method for integrating imputation uncertainty into statistical association tests using a fully conditional multiple imputation (MI) approach which is implemented using the Substantive Model Compatible Fully Conditional Specification (SMCFCS). We compared the performance of this method to an unconditional MI and two additional approaches that have been shown to demonstrate excellent performance: regression with dosages and a mixture of regression models (MRM). Our simulations considered a range of allele frequencies and imputation qualities based on data from the UK Biobank. We found that the unconditional MI was computationally costly and overly conservative across a wide range of settings. Analyzing data with Dosage, MRM, or MI SMCFCS resulted in greater power, including for low frequency variants, compared to unconditional MI while effectively controlling type I error rates. MRM andl MI SMCFCS are both more computationally intensive then using Dosage. The unconditional MI approach for association testing is overly conservative and we do not recommend its use in the context of imputed genotypes. Given its performance, speed, and ease of implementation, we recommend using Dosage for imputed genotypes with MAF 0.001 and Rsq 0.3. The online version contains supplementary material available at 10.1186/s12864-023-09415-0.
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影响因子：
30.8
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Alzheimer's Disease Neuroimaging Initiative*
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Marchini J
影响因子：
30.8
作者：
Das, Sayantan;Forer, Lukas;Schoenherr, Sebastian;Sidore, Carlo;Locke, Adam E.;Kwong, Alan;Vrieze, Scott I.;Chew, Emily Y.;Levy, Shawn;McGue, Matt;Schlessinger, David;Stambolian, Dwight;Loh, Po-Ru;Iacono, William G.;Swaroop, Anand;Scott, Laura J.;Cucca, Francesco;Kronenberg, Florian;Boehnke, Michael;Abecasis, Goncalo R.;Fuchsberger, Christian
通讯作者：
Fuchsberger, Christian