## 1 Introduction  
Selecting the relevant variables for modeling health outcomes and estimating the effects of said outcomes both frequently cause problems, with many inconsistencies regarding the best choice of the type of analysis, which becomes more acute with genetics due to the enormous volume of potential predictor variables. This problem is a driving force of our research. A genetic variant is an alteration of a human DNA sequence that is shared by at least 1% of the population; this threshold emphasizes that the variant is not limited to a small number of people in which the alteration could have appeared via a de novo mutation, but is rather a genetic polymorphism among humans. Given that this variability is inherited and not caused by the environment, it is expected that genetic variants are responsible for all kinds of phenotypic variability within the species. Many human diseases, although not exclusively caused by genetic factors, can be influenced by the genetic backgrounds of patients, e.g., some genes might exhibit DNA sequence variants that cause subliminal effects on their function, adding susceptibility to the individuals' diseases; some DNA sequence variants might cause compensatory effects towards previous variants, thus leading to a lower probability of developing the condition; some DNA genetic variants might have better detoxification enzymes (again, due to genetic variants of those enzymes' genes) and thus might even influence the effectiveness of the drugs given to the individuals in order to treat that same condition. All these differences will stratify the population according to disease susceptibility and prognosis and therapy outcomes, making DNA sequence variants particularly relevant for achieving more effective (personalized) medicine. Additionally, increasing our knowledge of the complex network of variants-genes-environment interactions relating to a particular disease is bound to improve our knowledge regarding the pathophysiology of the disease itself, with obvious gains in the advancement of biomedicine. Alzheimer's disease (AD) is a neurodegenerative condition that is initially characterized by memory impairment and cognitive decline, followed by behavior, speech, visuospatial orientation, and motor system alterations. It is a complex disorder, and its cause is multifactorial--both environmental and genetic factors can influence its onset [1; 2]. One of the most challenging tasks of genetics research has been to uncover the genetic background of such diseases. Indeed, this is particularly challenging due to gene-gene and gene-environment interactions that can diminish and/or modulate the influence of individual genes or variants on the individual's phenotype. Since most of these diseases would benefit from early diagnosis, meaning disease progression can be decelerated, it is important to be able to predict AD prior to the initial symptoms, making genetic risk calculation an appealing hypothesis. Nevertheless, the genetic variants that were associated with AD until present have a very low predictability power, due to the above-mentioned limitations, and it is therefore of great importance to obtain better statistics for this purpose. Genome-wide association studies (GWAS) are populational studies in which individual DNA variants are tested for association with a particular trait. This is achieved by comparing, variant by variant, the allelic frequencies of the affected or unaffected subjects, specifically looking for discrepancies. Whenever an SNP exhibits a positive association with a trait, the alternative allele will be more frequent in people with that trait than in those without it. In recent years, GWAS have been conducted with genome-scale datasets of genetic variants (e.g., single nucleotide polymorphisms--SNPs). The statistics utilized within GWAS frequently use a model of regression; specifically, a logistic model for case/control designs or qualitative traits, and a linear model for quantitative ones.