Abstract
While genome-wide association (GWA) studies have yielded notable findings with regard to the identification of risk variants in diseases such as obesity and diabetes, similar studies of schizophrenia - and neuropsychiatric diseases in general - have failed to produce strong findings. One, plausible explanation for this relates to phenotypic heterogeneity and what may be inherent imprecision associated with diagnostic categories in neuropsychiatric disorders. In this review we discuss a general approach to addressing the problem of heterogeneity that draws on concepts in behavioral informatics and the use of multivariable behavioral profiles in genetic studies of neuropsychiatric disease. The use of behavioral profiles as phenotypes eliminates the need for categorizing individuals with different 'subtypes' of a disease into one group and provides a way to investigate genetic susceptibility to different neuropsychiatric disorders that share similar clinical characteristics, such as schizophrenia and bipolar disorder. Further, behavioral profiles are a direct, quantitative representation of the emotional, personality, and neurocognitive functioning of the individuals being studied, and as such, the use of these profiles may provide increased statistical power to detect genetic associations and linkages. We describe and discuss four general data analysis approaches that can be used to analyze and integrate multivariate behavioral profile data and high-dimensional genomic data. Ultimately, we propose that behavioral profile-based phenotypes provide a meaningful alternative to the use of single measures, such as diagnostic category, in genetic association studies of neuropsychiatric disease.