Elucidating phenotype and etiology of substance use disorders via integrative analysis of multi-dimensional datasets

Project Summary Substance use disorders (SUDs) have heterogeneous clinical manifestations and environmental and genetic risk factors intertwined etiology, demanding phenotype refinement and etiology elucidation for precise prevention, diagnosis, and treatment. Many genome-wide association studies (GWASs) have been carried out in recent years,

aiming to discover the genetic risk factors of various forms of SUDs, such as cocaine and opioid use disorders. The high level of heterogeneity in both clinical presentations and etiology of SUDs compromises the effort for their genetic association discovery. As a result, the identified associations only explain a very small portion of

the estimated heritability in twin-based studies, implying that the majority is still in the wild. In existing association studies, a heterogeneous composite trait (e.g., cocaine dependence diagnosis and diagnostic criteria count) was often used as the outcome variable and the specific set of phenotypes associated genetic variants is

unclarified. Furthermore, the lack of mechanistic understanding of the identified associations hampers the translation of these discoveries into actionable targets to improve the disease management. In response to these challenges, novel machine learning methods will be developed enabling the integrative analysis of data from

multiple dimensions, including phenotype, environment, genotype, and functional genomics. The developed methods will be employed to mine a large dataset aggregated for genetic study of SUDs and data available from multiple repositories, such as dbGap, UKBiobank, Roadmap, ENCODE, and NCBI GEO, aiming at 1) deriving

severity indices of SUDs that have maximum heritability estimate, 2) identifying novel genetic risk factors for SUDs, 3) unraveling the association between heterogeneous clinical presentations and genetic variations in candidate genomic regions, and 4) elucidating the functional impact of genetic variants associated with SUDs and

producing actionable findings. In Aim #1, a machine learning method for deriving severity indices by heritable component analysis taking into account gene-environment interplay will be developed and used to derive severity indices of SUDs, followed by GWASs. In Aim #2, a multi-view clustering framework that accounts for gene-

environment interplay will be developed and used to elucidate SUD phenotypes associated with genetic variations in candidate genomic regions, followed by GWASs. In Aim #3, deep neural networks with novel architectures will be trained under a novel multi-task learning framework to predict functional genomic events in varying cell

types from a wide range of brain regions and used to elucidate the functional impact of the genetic variants discovered by GWASs.