Approved Research
A validation study using UK Biobank for discovery-driven analysis of temporal disease progression patterns using GCAT, a Catalan linked electronic health registry cohort.
Lay summary
Human disease, is a complex trait that occurs once in a life, based on a biological basis, however its expression is variable, on intensity, onset and duration, because its personal genetic basis (gene-centred and genome-wide) and their environment. In this point one disease itself or mediated by secondary effects of it treatment, modifies the presentation of other conditions, that also based on their genetics (shared or not) and their environment, modify the occurrence of further diseases or their evolution. This is the rule for most of common diseases, chronic conditions that occurs once and that are treated chronically.
Our research plan is based on the time and order in which diseases are diagnosed in the population: First, we will apply different technical approaches to identify trajectories based on single diagnostics or aggregation analysis using cluster comorbidities, based on age-gender strata. Second, using genetic profiles (genome wide), we will analyse shared genetics among members of trajectories, o lead disease among clusters, to identify polygenic profiles that could identify temporary progression. PRS will be used as Instrumental variables. Adjusted variables will be used to account for impact of ethnic and educational level on disease diagnostics. Third, UKBB, will be used as a validation steep, using the same approach used in the Catalan cohort, to validate the results. The increased power of the UKBB database, will allow the exploration of additional observed comorbidities or temporary patterns that due to lack of power in the discovery cohort could not be explored in the first steep.
The identification of the genetic profile, as biomarkers or as etiological determinants, will help us gain a better understanding of the diseases and the health of the population, moving to an informed practice of the health-care management. This will impact the health-care system, with a preventive and more personalized management of diseases using agnostic genetic information. In addition, promising disease relationships would be derived for further investigated using more in depth approached to identify pleiotropic loci, as a step for a better drug repositioning strategy.