Increasing power for tests of genetic association in the presence of phenotype and/or genotype error by use of double-sampling.
Phenotype and/or genotype misclassification can: significantly increase type II error probabilities for genetic case/control association, causing decrease in statistical power; and produce inaccurate estimates of population frequency parameters. We present a method, the likelihood ratio test allowing for errors (LRTae) that incorporates double-sample information for phenotypes and/or genotypes on a sub-sample of cases/controls. Population frequency parameters and misclassification probabilities are determined using a double-sample procedure as implemented in the Expectation-Maximization (EM) method. We perform null simulations assuming a SNP marker or a 4-allele (multi-allele) marker locus. To compare our method with the standard method that makes no adjustment for errors (LRTstd), we perform power simulations using a 2/k factorial design with high and low settings of: case/control samples, phenotype/genotype costs, double-sampled phenotypes/genotypes costs, phenotype/genotype error, and proportions of double-sampled individuals. All power simulations are performed fixing equal costs for the LRTstd and LRTae methods. We also consider case/control ApoE genotype data for an actual Alzheimer's study. The LRTae method maintains correct type I error proportions for all null simulations and all significance level thresholds (10%, 5%, 1%). LRTae average estimates of population frequencies and misclassification probabilities are equal to the true values, with variances of 10e-7 to 10e-8. For power simulations, the median power difference LRTae-LRTstd at the 5% significance level is 0.06 for multi-allele data and 0.01 for SNP data. For the ApoE data example, the LRTae and LRTstd p-values are 5.8 x 10e-5 and 1.6 x 10e-3, respectively. The increase in significance is due to adjustment in the LRTae for misclassification of the most commonly reported risk allele. We have developed freely available software that performs our LRTae statistic.
Gordon
Derek
D
Rockefeller University. gordon@biology.rutgers.edu
Yang
Yaning
Y
Haynes
Chad
C
Finch
Stephen J
SJ
Mendell
Nancy R
NR
Brown
Abraham M
AM
Haroutunian
Vahram
V