Background: There is large individual variation in both clinical presentation and progression between Parkinson's disease patients. Generation of deeply and longitudinally phenotyped patient cohorts has enormous potential to identify disease subtypes for prognosis and therapeutic targeting.
Methods: Replicating across three large Parkinson's cohorts (Oxford Discovery cohort (n = 842)/Tracking UK Parkinson's study (n = 1807) and Parkinson's Progression Markers Initiative (n = 472)) with clinical observational measures collected longitudinally over 5-10 years, we developed a Bayesian multiple phenotypes mixed model incorporating genetic relationships between individuals able to explain many diverse clinical measurements as a smaller number of continuous underlying factors ("phenotypic axes").
A major challenge in medical genomics is to understand why individuals with the same disorder have different clinical symptoms and why those who carry the same mutation may be affected by different disorders. In every complex disorder, identifying the contribution of different genetic and non-genetic risk factors is a key obstacle to understanding disease mechanisms. Genetic studies rely on precise phenotypes and are unable to uncover the genetic contributions to a disorder when phenotypes are imprecise.
View Article and Find Full Text PDFBacteria producing hydrolytic exoenzymes are of great importance considering their contribution to the host metabolism as well as for their various applications in industrial bioprocesses. In this work hydrolytic capacity of bacteria isolated from the gastrointestinal tract of Bombay duck () was analyzed and the enzyme-producing bacteria were genetically characterized. A total of twenty gut-associated bacteria, classified into seventeen different species, were isolated and screened for the production of protease, lipase, pectinase, cellulase and amylase enzymes.
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