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Association Studies with Imputed Variants Using Expectation-Maximization Likelihood-Ratio Tests


Huang, Kuan-Chieh; Sun, Wei; Wu, Ying; Chen, Mengjie; Mohlke, Karen L.; Lange, Leslie A.; & Li, Yun (2014). Association Studies with Imputed Variants Using Expectation-Maximization Likelihood-Ratio Tests. PLOS ONE, 9(11), e110679.


Genotype imputation has become standard practice in modern genetic studies. As sequencing-based reference panels continue to grow, increasingly more markers are being well or better imputed but at the same time, even more markers with relatively low minor allele frequency are being imputed with low imputation quality. Here, we propose new methods that incorporate imputation uncertainty for downstream association analysis, with improved power and/or computational efficiency. We consider two scenarios: I) when posterior probabilities of all potential genotypes are estimated; and II) when only the one-dimensional summary statistic, imputed dosage, is available. For scenario I, we have developed an expectation-maximization likelihood-ratio test for association based on posterior probabilities. When only imputed dosages are available (scenario II), we first sample the genotype probabilities from its posterior distribution given the dosages, and then apply the EM-LRT on the sampled probabilities. Our simulations show that type I error of the proposed EM-LRT methods under both scenarios are protected. Compared with existing methods, EM-LRT-Prob (for scenario I) offers optimal statistical power across a wide spectrum of MAF and imputation quality. EM-LRT-Dose (for scenario II) achieves a similar level of statistical power as EM-LRT-Prob and, outperforms the standard Dosage method, especially for markers with relatively low MAF or imputation quality. Applications to two real data sets, the Cebu Longitudinal Health and Nutrition Survey study and the Women’s Health Initiative Study, provide further support to the validity and efficiency of our proposed methods.


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Huang, Kuan-Chieh
Sun, Wei
Wu, Ying
Chen, Mengjie
Mohlke, Karen L.
Lange, Leslie A.
Li, Yun