Innovations in Integrative Association Testing for Large Genetic Data

This project will develop new statistical tools to enhance the power and precision of discovering disease-associated genes. By integrating diverse sources of genomic information and improving how prior knowledge and statistical evidence are combined, the project aims to uncover subtle genetic signals that might otherwise be missed. These innovations have the potential to transform the understanding and treatment of complex diseases, such as neurodegenerative disorders. The project supports national interests by promoting scientific advancement and improving health outcomes. It also fosters education in statistics, data science, and bioinformatics, supports workforce development, and promotes collaboration across disciplines and sectors to enhance the societal impact of statistical research. The research will advance statistical theory, methodology, and computation for integrative association testing in heterogeneous genomic data. It focuses on two core challenges: (1) designing more effective weighting strategies for incorporating prior information when combining statistical significances, and (2) developing new methods to integrate discrete statistics within a general hypothesis testing framework. The project will implement and apply these approaches to harmonized whole genome sequencing datasets, with a focus on amyotrophic lateral sclerosis and related neurodegenerative diseases. It also supports interdisciplinary education and research infrastructure by connecting expertise in statistics, computational biology, and medical genetics. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.