Developing computational, statistical and machine learning methods to uncover biological mechanisms of complex phenotypes

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My research program develops computational methods to analyze large omic datasets to understand gene regulatory mechanisms of complex human traits. My long-term objective is to understand how the epigenetic landscape affects the genetic background in a complex, multifactorial fashion. Integration of approaches including genome-wide association studies and single-cell sequencing technology is critical in understanding complex traits in multicellular organisms. Obesity is a complex trait defined as an excessive or abnormal accumulation of fat, and it can increase the risk of various health problems. The prevalence rate of obesity is increasing in Canada, imposing significant health, economical, and social impacts. A lack of understanding of molecular mechanisms underlying obesity prevents developing preventative approaches. As my short-term objective, I will focus on studying cellular and molecular mechanisms of BMI, which is the most commonly used proxy for obesity. Several studies show that BMI-associated variants are enriched on regulatory sites of certain cell types, supporting a role in gene regulation in BMI. However, the specific gene regulatory mechanisms underlying BMI are still largely unknown. To gain mechanistic insight into gene regulation in BMI, I will develop effective computational methods to: Aim 1: Identify specific BMI-relevant regulatory factors. Transcription factors (TF) play a central role in gene regulation of human traits. However, the specific TFs relevant to BMI and the molecular mechanisms underlying their effects are largely unknown. A major thrust of my research will develop computational approaches to uncover BMI-relevant TFs, and understand functional mechanisms through which the associated variants affect regulatory activities of their target TFs. Aim 2: Uncover specific BMI-relevant cell types. Identifying specific cells in which regulation of obesity-relevant genes are affected is crucial for uncovering gene regulatory mechanisms. Multiple studies using bulk epigenomic data support the role of brain cells in BMI. However, brain cell types are highly heterogeneous and the specific subsets of brain cells that are most relevant cannot be identified using bulk sequencing data. I will develop a computational method that integrates single-cell epigenetic and genetics association data to identify BMI-relevant cells. This proposal outlines the beginnings of a long-term NSERC-funded research program uniquely aimed at generating a fundamental understanding of gene regulatory mechanisms in complex traits. While initially using BMI as the target complex trait, this work will develop a methodology that can be applied to the myriad of other complex traits that exist. The work will vastly enhance our knowledge of trait-relevant regulatory pathways and their sites of actions (ie. cell types). I predict our methods will become widely used resources by the genomics community, enabling discoveries in many complex traits.