Use of deep learning strategies to account for biological complexity and the genetics of neuropsychiatric disease

Dr. Gabriel de Erausquin

University of Texas Health San Antonio

Friday, September 24, 2021

11 AM – 12 PM CST

https://utsa.zoom.us/j/98953233499

Abstract:

Complex phenotypes present several challenges for genetic studies including the presence of epistasis, pleiotropy, and heterogeneity. We approached these problems in a data-driven fashion to test the hypothesis that the heritability expected from twin studies but unexplained by genetic studies is distributed in heterogeneous partitions of a complex trait, each with distinct genotypic-phenotypic associations. We designed a machine learning algorithm termed PGMRA to identify naturally occurring partitions in the data in an unsupervised fashion. PGMRA first dissects genome-wide data and uncovers a genotypic architecture composed of sets of variations shared by subsets of individuals. Next, phenotypic data are independently organized into natural sets of features such as clinical manifestations, voxels of neuroimages, or personality traits in a phenomic-like approach. Cross-matching of the two types of sets reveals multiple associations restricted to subgroups of individuals, thereby uncovering the genotypic-phenotypic architecture of a trait and accounting for its distributed genetic risk or propensity.