Modelling single-nucleotide polymorphism to assess genetic contribution to disease progression

This paper investigates the association between the Apolipoprotein E4 single nucleotide polymorphism (SNP) and brain Tensor Based Morphometry (TBM) as a potential candidate gene for Alzheimer's disease. The Linear Mixed Model (LMM) is used to analyse and model the data. The imputed data was used to compare the results of the LMM to those obtained from the complete dataset, allowing for the selection of the optimal model and the assessment of missing values' impact on performance. Ultimately, the LMM with a random intercept and random slope was found to be the most representative model. These models and such analysis could have important implications for understanding the underlying mechanisms of Alzheimer's disease. This study provides valuable modeling insights into the relationship between genetic factors, brain structure, and the development of Alzheimer's disease.