New Frontiers in Measuring Association: Methods, Applications, and Challenges

This session focuses on the measures of association, with a particular emphasis on their applications in biomedical data analysis. We have invited leading statisticians who develop novel theoretical measures, alongside bioinformaticians who apply these methods in real-world data analysis to uncover meaningful biological insights. The aim of the session is to foster collaboration between these two groups, driving innovation and enhancing the future development of association measures. Additionally, we will feature a discussant who will address the issue of statistical rigor in the use of different measures. Session Format: The session will feature four distinguished speakers who will present their latest research on association measures and their genetic applications: 1. Mona Azadkia Title: Measuring Dependence and Conditional Dependence Dr. Azadkia will discuss a newly developed measure of dependence, which is both simple, akin to classical coefficients like Pearson's correlation, and robust. This measure consistently captures the strength of dependence between variables, equating to zero only when variables are independent, and to one when one variable is a measurable function of the other. 2. Lucy Xia Title: Conditional Semi-Distance Correlation Dr. Xia will introduce a novel measure of conditional dependence designed to assess the relationship between a categorical random variable and a potentially high-dimensional random vector, conditioned on another random vector. Her work also includes a conditional independence test, with asymptotic distributions derived under the null hypothesis. She proposes the use of this correlation as a screening tool for group variables, with particular insights into spatial transcriptomics. 3. Han Chen Title: Efficient Mixed Model Association Test for Nonlinear Effects in Large-Scale Human Genetic Studies Dr. Chen will present a generalized additive mixed model framework for testing nonlinear genetic effects using smoothing splines. This method addresses multi-allelic genetic variations, even in the presence of nonlinear effects. He will also introduce a variance component score test for nonlinear effects and a joint test for linear and nonlinear genetic effects, demonstrated through a real-world GWAS example. 4. Changhu Wang Title: A Powerful Framework for False Discovery Rate Control in High-Dimensional Variable Selection Dr. Wang will present a novel framework for controlling the FDR in high-dimensional variable selection which maintains the integrity of the original data. It is versatile, easy to implement, and consistently outperforms state-of-the-art methods, such as knockoffs and data-splitting, in terms of FDR control, statistical power, and computational efficiency across various statistical models. Following these presentations, a discussant will summarize the four presentations, offering a brief discussion on the statistical rigor in using different measures of association. Each talk and the subsequent discussion will last approximately 20 minutes, including a 5-minute Q&A session after each presentation. Significance: Measures of association are foundational to statistics and have broad applications across various fields, particularly in biomedical science. This session aims to refine statistical methodologies, particularly in genetics, promoting more rigorous statistical practices in biomedical research in the era of big data-an alignment with the theme.