Topic: polygenic risk scores

This workshop connects genomic discovery with prediction of epilepsy outcomes. It examines genome-based multi-omics in multigenerational cohorts, multimodal deep learning that combines clinical and genomic information to predict antiseizure medication response, and polygenic risk scores integrated with machine learning to estimate disease severity and seizure control. The presentations link these complementary approaches to the problem of clinical heterogeneity and assess their practical role in more individualized treatment. Sunday 6 December, 14:00–15:30 Mountain Standard Time (America/Denv

Whilst epilepsy may be a consequence of an acquired insult including trauma, stroke, and brain tumours, the genetic component to epilepsies has been greatly under-estimated. Considerable progress has recently occurred in the understanding of epilepsy genetics, both at a clinical genetic level and in the basic science of epilepsies. The clinical evidence for genetic components will be first briefly discussed including data from population studies, twin analyses and multiplex family studies. Initial molecular discoveries occurred via classical methods of linkage and gene identification. Recent l

Background: Recent discovery of hundreds of common gene variants associated with schizophrenia has enabled polygenic risk scores (PRS) to be measured in the population. It is hypothesized that normal variation in genetic risk of schizophrenia should be associated with MRI changes in brain morphometry and tissue composition. Methods: We used the largest extant genome-wide association dataset (N = 69,369 cases and N = 236,642 healthy controls) to measure PRS for schizophrenia in a large sample of adults from the UK Biobank (Nmax = 29,878) who had multiple micro- and macro-structural MRI metrics