Genome-Centric Multimodal Data Integration in Personalised Cardiovascular Medicine.

Variant prioritisation

Genomic sequencing identifies variations in the genetic code. To develop diagnostic and treatment processes, variants need to be linked to diseases, and the “clinical validity” of a suggested gene-disease relationship determined (variant annotation). This evidence-based process classifies relationships based on the level and quality of evidence. Genomic analysis produces variants lists from which gene-disease relationships are to be deduced by clinical scientists using established, but time-consuming, interpretation protocols. In NextGen we use machine learning to develop improved variant prioritisation algorithms so that genes that are more likely to be causally related to the disease are ranked higher to reduce the manual processing time by an order of magnitude with the downstream benefit of shortening the time between presentation and diagnosis and improving patient outcomes.

Tools Barriers addressed: Inefficiencies in the ranking of variants by significance.