A generalized, multi-stage adjusted, latent class linear mixed model for testing genetic association


COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, vol.48, no.8, pp.2301-2312, 2019 (SCI-Expanded) identifier identifier


In this study, a Generalized, Multi-Stage Adjusted, Latent Class Linear Mixed Model is proposed for modeling the heterogeneous distributed phenotype and genetic information across the whole genome in the presence of both serial and familial correlations. Genome data were analyzed by applying the proposed model to Genetic Analysis Workshop (GAW) data, and the model results were compared to the results of standard models. Moreover, the potential of the model is discussed compared to simulated data. As a result of model comparisons, the information criteria and the genomic control parameter were found to be smaller. The results of a power analysis show that the proposed model is more powerful.