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Learn Model-X statistical inference methods for analyzing genome-wide association studies (GWAS) data in this 33-minute conference talk from the Computational Genomics Summer Institute. Explore advanced statistical techniques that enable robust inference in high-dimensional genomic datasets while controlling for false discovery rates. Discover how Model-X knockoffs and related methodologies can be applied to identify genuine genetic associations while maintaining statistical rigor in the presence of complex correlation structures typical in genomic data. Examine practical implementations of these methods for real-world GWAS applications, including strategies for handling population structure, linkage disequilibrium, and multiple testing challenges. Gain insights into the theoretical foundations underlying Model-X inference and understand how these approaches differ from traditional statistical methods in genomics research.
