Augusto Rendon

The paper establishes a global consortium to discover rare genetic associations by integrating data from multiple biobanks, identifying 514 gene-trait associations, many detectable only through cross-ancestry meta-analysis.

GenePy identifies potential missed biallelic diagnoses in the 100KGP by scoring variants and analyzing phenotypic overlaps, finding 14% of undiagnosed participants may have a missed diagnosis.

The paper introduces MuAt, a deep learning model that learns representations of somatic mutations to predict tumour types and subtypes with high accuracy.

This study identifies TMEM63B variants in patients with developmental and epileptic encephalopathy and neurodegeneration, showing altered cation currents and hematological abnormalities.

This study uses whole-genome sequencing to identify regulatory and novel genic variants contributing to childhood cardiomyopathy.

The paper identifies gene-specific mutational signatures caused by replication errors and endogenous DNA damage using CRISPR-Cas9 knockouts and whole-genome sequencing.

This study demonstrates that whole genome sequencing can effectively detect repeat expansions in neurological disorders with high sensitivity and specificity, enabling diagnosis in previously undiagnosed patients.

This study uses whole-genome sequencing in a national healthcare system to diagnose rare diseases and discover new aetiological variants, identifying 95 Mendelian associations and 4 novel non-coding disease-causing variants.

The paper develops a machine learning model to predict genes causing rare neurological disorders and shows it accurately identifies disease-associated genes.

This study compares the epigenomes of neutrophils and monocytes to identify regulatory elements and chromatin states, revealing differences in enhancer activity and DNA methylation.