Identifying Combinations of Genetic Variants

• Activity Number: 455
• Type: Topic Contributed
• Date/Time: Wednesday, August 1, 2012 : 8:30 AM to 10:20 AM
• Sponsor: Biometrics Section
• Author(s): Benjamin Raphael*+
• Companies: Brown University
• Keywords: statistical genetics ; bioinformatics ; algorithms

Abstract

Genetic heterogeneity is a feature of many diseases, with different causal variants, or mutations, present in different individuals with the disease. Genetic heterogeneity complicates the identification of disease-causing variants as the association between a single variant and the disease might be relatively weak. An alternative approach is to test association for groups, or combinations, of variants, rather than individual variants. However, exhaustive testing of all combinations of variants is neither computationally nor statistically feasible. We describe two computational approaches to find combinations of variants and assess their statistical significance. In the first approach, we use prior information about interactions between genes as stored in an interaction network, and identify subnetworks of genes containing variants with significant disease association. In the second approach, we optimize a measure derived from the statistical properties of variants in biological pathways. I will describe applications these techniques to data from The Cancer Genome Atlas (TCGA) and discuss generalizations to other genetic association studies.