Analytic approaches to heterogeneity in neurogenetic syndrome research

Neurogenetic syndromes are heterogeneous in both their phenotypic features and in the severity of phenotypic outcomes. Examining within-syndrome variability in outcomes can increase understanding of the nature and sources of this heterogeneity, and can facilitate the development of personalized supports and interventions. Mixture modeling is a broad class of statistical models used to discern unobserved classes or patterns of responses from data. Mixture models group sets of observations that maximize both within latent class homogeneity and between latent class heterogeneity, where latent classes are patterns of responses that are used to identify subpopulations within a data set. Mixture models are useful for bridging the gap between nomothetic and idiographic research and can provide insights into developmental disorders that may have otherwise been missed. This manuscript provides an overview of two types of mixture models (i.e., Latent Profile Analysis; Latent Growth Mixture Modeling). Equipped with these tools, researchers may be able to better identify risk and protective factors for a host of neurogenetic syndromes, identify early markers of comorbid conditions that may take time to develop, and more precisely target intervention efforts to those in greatest need.