Title: Bivariate Causal Discovery and its Applications to Gene Expression and Imaging Data Analysis,
Abstract: The mainstream of research in genetics, epigenetics and imaging data analysis
focuses on statistical association or exploring statistical dependence between
variables. Despite their significant progresses in genetic research,
understanding the etiology and mechanism of complex phenotypes remains elusive.
Using association analysis as a major analytical platform for the complex data
analysis is a key issue that hampers the theoretic development of genomic
science and its application in practice. Causal inference is an essential
component for the discovery of mechanical relationships among complex
phenotypes. Many researchers suggest making the transition from association to
causation. Despite its fundamental role in science, engineering and
biomedicine, the traditional methods for causal inference require at least
three variables. However, quantitative genetic analysis such as QTL, eQTL,
mQTL, and genomic-imaging data analysis requires exploring the causal
relationships between two variables. This paper will focus on bivariate causal
discovery. We will introduce independence of cause and mechanism (ICM) as a
basic principle for causal inference, algorithmic information theory and
additive noise model (ANM) as major tools for bivariate causal discovery.
Large-scale simulations will be performed to evaluate the feasibility of the
ANM for bivariate causal discovery. To further evaluate their performance for
causal inference, the ANM will be applied to the construction of gene
regulatory networks. Also, the ANM will be applied to trait-imaging data
analysis to illustrate three scenarios: presence of both causation and
association, presence of association while absence of causation, and presence
of causation, while lack of association between two variables.