During decades scientists tried to unveil the genetic architecture of Alzheimer's disease (AD), recurring to increasingly larger sample numbers for genome-wide association studies (GWAS) in hope for higher statistical gains. Here, a retrospective look on the most prominent GWAS was performed, focusing on the quality of the diagnosis associated with the used data and databases. Different methods for AD diagnosis (or absence) carry different levels of accuracy and certainty applied to both subsets of cases and controls. Furthermore, the different phenotypes included in these databases were explored, as several incorporate other ageing comorbidities and might be encompassing many confounding agents as well. Age of the samples' donors and origin populations were also investigated as these could be biasing factors in posterior analyses. A tendency for looser diagnostic methods in more recent GWAS was observed, where greater datasets of individuals are analyzed, which may have been hampering the discovery of associated genetic variants. Specifically for AD, a diagnostic method conveying a clinical outcome may be distinct from the disease neuropathological assessment, since the first has a practical perspective that not necessarily needs a confirmation. Due to its properties and complex diagnosis, this work highlights the importance of the neuropathological confirmation of AD (or its absence) in the subjects considered for research purposes to avoid reaching statistically weak and/or misleading conclusions that may trigger further studies with powerless groundwork.