Classifying SNPs based on their association patterns also lends itself to several additional possible follow-up analyses. Examining the function of the clusters of SNPs associated with multiple outcomes may be informative as to shared underlying pathways that contribute to their etiologies. For example, gene set enrichment analysis (GSEA) (Zhang et al. 2010, 2014) could be conducted to test whether certain genetic pathways are overrepresented in the clusters of SNPs identified in the LPA. Clusters of SNPs identified may also be used to construct polygene scores for use in other analyses or datasets that aim to characterize developmental changes associated with genetic effects and/or gene-environment interaction (Salvatore et al. 2014a, b).
