While techniques that aggregate information using dimension reduction are attractive, the results must be both interpretable and biologically meaningful. Our results here illustrate how SFA can yield more interpretable data summaries than other methods, such as PCA, facilitating the systematic aggregation of a large collection of phenotypic information without sacrificing interpretability. Regardless of the dimension reduction technique used to identify putative associations, it is important to examine the raw phenotypes to determine how a given SNP correlates with the underlying phenotypic data (Figure 2F and Figure 3F). In both of the potential associations presented in this study, the phenotypes that loaded onto the factor overlapped well with, but were not identical to, the phenotypes that were influenced by the identified SNPs (Figure 2A and Figure 3A).
