Technological advances in brain neuroimaging, genotyping and sequencing have enabled rapid data collection, leading to large studies such as UK Biobank (N=500K participants) and international consortia, for example Psychiatric Genomic Consortium (PGC) and ENIGMA.
Complemented with rich phenotypic data, these studies provide new opportunities for studying complex human brain phenotypes, linking brain morphology and functioning to a wide range of psychiatric and neurological disorders.
Better statistical tools further enhance our ability of making scientifically and clinically relevant discoveries.
In this seminar we will discuss recent successes of the Genome-wide association studies (GWAS) and highlight some challenges that hinder an effective application of the standard machine learning techniques in human genetics.
Part of the seminar will be devoted to the new statistical methodology, including pleiotropy-informed false discovery rate, and multivariate omnibus statistical test.
We will also discuss the concept of polygenic risk prediction, and precision medicine, which are becoming effective in personalized risk prediction for certain types of cancer and risk stratification for Alzheimer’s disorder.