 principal component analysis, PCA, and the linear mixed effects model, LMM, are two popular methods used to analyze genetic data. However, previous research has shown that these methods can produce different results when applied to real-world data. In this study, the authors evaluated the performance of PCA and LMM under various conditions, such as varying the number of principal components using real-world data sets, and varying the number of related individuals. The authors found that LMM performed better than PCA in all cases, especially when there were many distant relatives or when the data set included environmental factors. Additionally, they found that PCA failed to account for the effects of relatedness structure in multi-ethnic data sets, while LMM did so successfully. Overall, this study provides valuable insight into the strengths and weaknesses of each method, which will help researchers make informed decisions about which approach to use in their own analyses. This article was offered by Ichiyou and Alejandro Ochoa.