Improved association analyses of disease subtypes in case-parent triads

The sampling of case-parent triads is an appealing strategy for conducting association analyses of complex diseases. In certain situations, one may have interest in using the triads to identify genetic variants that are associated with a specific subtype of disease, perhaps related to a characteristic cluster of symptoms. A straightforward strategy for conducting such a subtype analysis would be to analyze only those triads with the subtype of interest. While such a strategy is valid, we show that triads without the subtype of interest can provide additional genetic information that increases power to detect association with the subtype of interest. We incorporate this additional information using a likelihood-based framework that permits flexible modeling and estimation of allelic effects on disease subtypes and also allows for missing parental data. Using simulated data under a variety of genetic models, we show that our proposed association test consistently outperforms association tests that only analyze triads with the subtype of interest. We also apply our method to a triad study of attention-deficit hyperactivity disorder and identify a genetic variant in the dopamine transporter gene that is associated with a subtype characterized by extreme levels of both inattentive and hyperactive-impulsive symptoms.