Genomic data is vast and increasingly sparse. This problem grows as we sequence more and more patients. Herein, Professor Ennis will describe an algorithmic tool that reduces the sparsity of genomic data to provide a gene-level pathogenic score (GenePy) - for all individuals for all genes. She will describe how this tool has been effective in: 1) identifying missed rare disease diagnoses and 2) detecting critical genes impacting quantitative traits.

Data will further demonstrate how integration of GenePy scores with rich, standardised clinical data (digitally extracted using large language models), can be used to extract monogenic diagnoses in patients diagnosed with common diseases at scale.