Incorporating Prior Information into Signal-Detection Analyzes Across Biologically Informed Gene-Sets

• Activity Number: 663 - Regression, Clustering and Gene Set Methods in Genomics
• Type: Contributed
• Date/Time: Thursday, August 1, 2019 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistics in Genomics and Genetics
• Author(s): Mengqi Zhang* and Sahar Gelfman and Janice McCarthy and David B Goldstein and Andrew S Allen
• Companies: Duke University and Institute of Genomic Medicine,Columbia University and Duke University and Institute of Genomic Medicine, Columbia University and Duke University
• Keywords: Signal detection analyses; gene sets; Higher Criticism; Prior information

Abstract

Signal detection analyses are used to assess whether there is any evidence of signal within a large collection of hypotheses. Such analyses typically treat all genes within the sets similarly, even though there is substantial information concerning the likely importance of each gene within each set. For example, deleterious variants within genes that show evidence of purifying selection are more likely to substantially affect the phenotype than genes that are not under purifying selection, at least for traits that are themselves subject to purifying selection. Here we improve such analyses by incorporating prior information into a higher-criticism-based signal detection analysis. We show that when this prior information is predictive of whether a gene is associated with disease, our approach can lead to a significant increase in power. We illustrate our approach with a gene-set analysis of amyotrophic lateral sclerosis (ALS), which implicates a number of gene-sets containing SOD1 and NEK1 as well as showing enrichment of small p-values for gene-sets containing known ALS genes.