A0433
Title: Data-adaptive omnibus tests by combining high-dimensional statistical inference for testing SNP-set effects
Authors: Haitao Yang - Hebei Medical University (China) [presenting]
Abstract: Genome-wide association studies have identified numerous genetic variants associated with complex disease. However, these variants can only explain a small portion of heritability in many diseases. SNP-set based association methods have been proved to be alternative strategies to capture some missing heritability, but the majority of these methods cannot be generalized to high-dimensional data. Recent advances in high-dimensional model development have been shifted from high-dimensional variable selection to high-dimensional statistical inference. While these models have limited power to detect weak genetic signals. Theoretically, the maximum statistic distribution method and the p-value combination method can respectively improve the power of two genetic effect hypotheses which are main loci determinism and loci micro effect accumulation theory. Motivated by this, we proposed to construct a SNP-set based high-dimensional statistical inference procedure that can be adaptive to the two genetic effect hypotheses by combing the Omnibus Test, to improve the power of detecting genetic variants, and to provide a novel statistical framework for the study of genetic mechanism underlying complex human diseases.
