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

Research output: Contribution to journalArticle

3 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)1293-1302
Number of pages10
JournalGenetics
Volume196
Issue number4
DOIs
Publication statusPublished - 2014

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ASJC Scopus subject areas

  • Genetics
  • Medicine(all)

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