Tianzhou (Charles) Ma

Associate Professor

Department of Epidemiology and Biostatistics

University of Maryland School of Public Health

Presentation Title

Post-GWAS methods using large-scale biobank data

Presentation Abstract

Large-scale biobanks (e.g. UK Biobank, All of Us) have generated unprecedented volumes of multimodal health data, including genetic, imaging, EHR and survey data, with deep phenotyping across diverse populations. These resources can potentially advance our understanding of the human health and disease, but also post new statistical challenges. In particular, biobank data involve multiple high-dimensional exposures (e.g., genetic and environmental risk factors), correlated and heterogeneous outcomes (e.g., comorbid disease phenotypes derived from EHR), and potentially related intermediate endophenotypes (e.g., imaging or molecular features), requiring novel principled methods for the integrative analysis and inference. In this talk, I will present a few statistical genetic methods recently developed in our lab for post-GWAS analysis (e.g. fine mapping, TWAS, Mendelian Randomization and causal mediation analysis) using large biobank data.

These methods combine modern statistical and machine learning tools to enable the transition from identifying simple genetic associations in GWAS to characterize more complex causal, functional, and multi-omics relationship at population scale. We further integrate external curated biological resources (e.g., eQTL, pathway databases) to improve interpretability and biological plausibility. These methods are expected to improve the translation of GWAS findings into mechanistic insights and actionable targets and discovery of modifiable pathways for disease prevention at the population level.

Biography

Tianzhou (Charles) Ma is an Associate Professor of Biostatistics in the Department of Epidemiology and Biostatistics at the University of Maryland School of Public Health. His current research focuses on the development of statistical and machine learning methods and software tools in statistical genetics, multi-omics and imaging data analysis and their application in aging, neuroscience and cancer fields. He has received NIH’s early career K01 award as well as several seed grants from the University of Maryland (e.g. Grand Challenge grant and MPower Brain Health and Human Performance Initiative grant) on statistical genetic and brain aging research. His recent works have been published on Proceedings of the National Academy of Sciences (PNAS), eBioMedicine (Lancet), JRSS Series C, Briefings in Bioinformatics and Bioinformatics.