Dissecting trait heterogeneity: a comparison of three clustering methods applied to genotypic data

Background: Trait heterogeneity, which exists when a trait has been defined with insufficient specificity such that it is actually two or more distinct traits, has been implicated as a confounding factor in traditional statistical genetics of complex human disease. In the absence of detailed phenotypic data collected consistently in combination with genetic data, unsupervised computational methodologies offer the potential for discovering underlying trait heterogeneity. The performance of three such methods

• Type: Journal
• Year: 2006
• Where: BMCBI
• Authors: Tricia A. Thornton-Wells, Jason H. Moore, Jonathan L. Haines
• Keywords: Bayesian Classification, BMCBI 2006, Complex Human Disease, Trait Heterogeneity