Linear trend tests for case-control genetic association that incorporate random phenotype and genotype misclassification error

The purpose of this work is the development of linear trend tests that allow for error (LTT_ae), specifically incorporating double-sampling information on phenotypes and/or genotypes. We use a likelihood framework. Misclassification errors are estimated via double sampling. Unbiased estimates of penetrances and genotype frequencies are determined through application of the Expectation-Maximization algorithm. We perform simulation studies to evaluate false-positive rates for various genotype classification weights (recessive, dominant, additive). We compare simulated power between the LTT _ae and its genotypic test equivalent, the LRT_ae, in the presence of phenotype and genotype misclassification, to evaluate power gains of the LTT_ae for multi-locus haplotype association with a dominant mode of inheritance. Finally, we apply LTT_ae and a method without double-sample information (LTT_std) to double-sampled phenotype data for an actual Alzheimer's disease (AD) case-control study with ApoE genotypes. Simulation results suggest that the LTT_ae maintains correct false-positive rates in the presence of misclassification. For power simulations, the LTT_ae method is at least as powerful as LRT _ae method, with a maximum power gain of 0.42 over the LRT _ae method for certain parameter settings. For AD data, LTT _ae provides more significant evidence for association (permutation p = 0.0522) than LTT_std (permutation p = 0.1684). This is due to observed phenotype misclassification. The LTT_ae statistic enables researchers to apply linear trend tests to case-control genetic data, increasing power to detect association in the presence of misclassification. If the disease MOI is known, LTT_ae methods are usually more powerful due to the fact that the statistic has fewer degrees of freedom.