Enhancing the power to detect low-frequency variants in genome-wide screens.
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 r(2), we propose a test statistic analogous to the standardized linkage disequilibrium D' to increase the power of detecting association for low-frequency variants. By both simulation and real data analysis we show that the proposed D' 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 D' test and its asymptotic properties are also investigated. In summary, we advocate using the D' 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.
McDermott Center of Human Growth and Development, University of Texas Southwestern Medical Center, Dallas, Texas 75390.
Chang-Yun
CY
Lin
Guan
G
Xing
Hung-Chih
HC
Ku
Robert C
RC
Elston
Chao
C
Xing
D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D005787:Gene Frequency; D014644:Genetic Variation; D016678:Genome; D055106:Genome-Wide Association Study / Q000379:methods*; D015810:Linkage Disequilibrium; D008957:Models, Genetic; D015233:Models, Statistical; D020641:Polymorphism, Single Nucleotide
case-control study; genome-wide screen; linkage disequilibrium; low-frequency variants