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Enhancing the power to detect low-frequency variants in genome-wide screens

Chang-Yun Lin; Guan Xing; Hung-Chih Ku; Robert C. Elston; Chao Xing

(Profiled Author: Chao Xing)

Genetics. 2014;196(4):1293-1302.


In genetic association studies a conventional test statistic is proportional to the correlation coefficient between the trait and the variant, with the result that it lacks power to detect association for low-frequency variants. Considering the link between the conventional association test statistics and the linkage disequilibrium measure r2, we propose a test statistic analogous to the standardized linkage disequilibrium D9 to increase the power of detecting association for low-frequency variants. By both simulation and real data analysis we show that the proposed D9 test is more powerful than the conventional methods for detecting association for low-frequency variants in a genome-wide setting. The optimal coding strategy for the D9 test and its asymptotic properties are also investigated. In summary, we advocate using the D9 test in a dominant model as a complementary approach to enhancing the power of detecting association for low-frequency variants with moderate to large effect sizes in case-control genome-wide association studies. © 2014 by the Genetics Society of America.

PMID: 24496013     PMCID: PMC3982702    

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