57 Integrated Association Test in High-Dimensional Genomic Data

Oregon Convention Center 

With high throughput technologies, investigators can measure genetic variations in multiple forms. New methods are needed to interrogate the relationship between genomic variations and endpoints of interest. We formerly developed POST procedure to associate gene sets/pathways with a clinical variable. We used similar dimension reduction machinery on each form of omics data at a locus/gene to collectively test the association between multiform genomic data and an endpoint of interest. The probe level signals of each form of omics data at a locus/gene are first projected to an orthogonal subspace and the corresponding eigenvalues are rescaled to sum to 1 for each form of omics data. The projected data are then subjected to a parametric association test to obtain z-statistics. The test statistic is defined as weighted sum squares of individual z-statistics. The correlation structure of z-statistics is approximated by bootstrap resampling and a generalized χ2 distribution approximates the p-value. We investigated the performance in simulation studies and applied the proposed method to a gene profiling and methylation data set of 187 pediatric AML from NCI TARGET.

Genomics

Gene profiling

Orthogonal projection

Data integration 

Section on Statistics in Genomics and Genetics