Universal continuous severity traits underlying hundreds of Parkinson\'s disease clinical features
作者： Cynthia Sandor , Stephanie Millin , Andrew Dahl , Michael Lawton , Leon Hubbard
DOI: 10.1101/655217
关键词:
摘要: The generation of deeply phenotyped patient cohorts offers an enormous potential to identify disease subtypes with prognostic and therapeutic utility. Here, we quantify diverse Parkinson9s phenotypes on continuous scales by identifying the underlying axes phenotypic variation using a Bayesian multiple phenotype mixed model that incorporates genotypic relationships. This approach overcomes many limitations associated clustering methods better reflects more observed amongst patients. We three principal which are reproducibly found across independent, diversely UK US cohorts. These explain over 75% clinical remain robustly captured fraction clinically-recorded features. Using these as quantitative traits, significant overlaps in genetic risk each axis other human complex diseases, namely coronary artery schizophrenia, providing new avenues for disease-modifying therapies. Our study demonstrates how can be used latent heritable traits.
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