title: Evaluating methods for combining rare variant data in pathway-based tests of genetic association.
abstract: Analyzing sets of genes in genome-wide association studies is a relatively new approach that aims to capitalize on biological knowledge about the interactions of genes in biological pathways. This approach, called pathway analysis or gene set analysis, has not yet been applied to the analysis of rare variants. Applying pathway analysis to rare variants offers two competing approaches. In the first approach rare variant statistics are used to generate p-values for each gene (e.g., combined multivariate collapsing [CMC] or weighted-sum [WS]) and the gene-level p-values are combined using standard pathway analysis methods (e.g., gene set enrichment analysis or Fisher's combined probability method). In the second approach, rare variant methods (e.g., CMC and WS) are applied directly to sets of single-nucleotide polymorphisms (SNPs) representing all SNPs within genes in a pathway. In this paper we use simulated phenotype and real next-generation sequencing data from Genetic Analysis Workshop 17 to analyze sets of rare variants using these two competing approaches. The initial results suggest substantial differences in the methods, with Fisher's combined probability method and the direct application of the WS method yielding the best power. Evidence suggests that the WS method works well in most situations, although Fisher's method was more likely to be optimal when the number of causal SNPs in the set was low but the risk of the causal SNPs was high.
authors.affiliation: Departments of Mathematics, Computer Science, and Statistics, St. Olaf College, 1520 St. Olaf Avenue, Northfield, MN 55057, USA.
authors.forename: Ashley
authors.initials: A
authors.lastname: Petersen
authors.affiliation: Department of Mathematics, Wittenberg University, 200 West Ward Street, Springfield, OH 45501, USA.
authors.forename: Alexandra
authors.initials: A
authors.lastname: Sitarik
authors.affiliation: Division of Applied Mathematics, Brown University, 151 Thayer Street, Providence, RI 02912, USA.
authors.forename: Alexander
authors.initials: A
authors.lastname: Luedtke
authors.affiliation: Department of Statistics and Operations Research, University of North Carolina, 318 Hanes Hall, CB 3260, Chapel Hill, NC 27599-3260, USA.
authors.forename: Scott
authors.initials: S
authors.lastname: Powers
authors.affiliation: Department of Mathematics, Statistics and Computer Science, Dordt College, 498 4th Ave. NE, Sioux Center, IA 51250, USA.
authors.forename: Airat
authors.initials: A
authors.lastname: Bekmetjev
authors.affiliation: Department of Mathematics, Statistics and Computer Science, Dordt College, 498 4th Ave. NE, Sioux Center, IA 51250, USA.
authors.forename: Nathan L
authors.initials: NL
authors.lastname: Tintle