Robust Summarization of Genomic Data to Perform Functional Variant Analysis

• Activity Number: 123
• Type: Topic Contributed
• Date/Time: Monday, August 2, 2010 : 8:30 AM to 10:20 AM
• Sponsor: Section on Nonparametric Statistics
• Author(s): Kelci Miclaus*+ and Russ Wolfinger and Mindy Zhang
• Companies: SAS Institute and SAS Institute and Genzyme Corporation

Abstract

Multiple testing correction concerns are extremely common in genetic data analysis and genomic applications due to the large number of tests that can be performed across millions of SNPs and thousands of genes. A current popular area of research is in SNP-combining tests and gene set enrichment analysis to find functional genes and pathways implicated in a disease association study. Whereas p-value combining methods and GSEA-like methods require results from individual tests for each SNP or gene as inputs; we present a simple, novel framework for the aggregation of genomic data to produce SNP-set and/or gene-set scores for each sample in a study. These scores, computed using robust summary statistics, can subsequently be used to perform a reduced number of tests for disease association aimed specifically at interrogating functional regions of the genome.