A weighted accumulation test for associating rare genetic variation with quantitative phenotypes

• Type of Material: Article
• Authors: Chuanhua Xing, Duke University; Glen Satten, Emory University; Andrew S. Allen, Duke University
• Language: English
• Date: 2011-12-01
• Publisher: BMC (part of Springer Nature)
• Publication Version: Final Published Version
• Copyright Statement: © 2011 Xing et al; licensee BioMed Central Ltd.
• License: Creative Commons Attribution 2.0 Generic
• Final Published Version (URL): http://dx.doi.org/10.1186/1753-6561-5-S9-S6
• Title of Journal or Parent Work: BMC Proceedings
• ISSN: 1753-6561
• Volume: 5
• Issue: SUPPL. 9
• Start Page: S6
• End Page: S6
• Grant/Funding Information: CX and ASA acknowledge support from the National Institutes of Health (NIH) through National Institute of Mental Health (NIMH) grant R01 MH084680.; The Genetic Analysis Workshop is supported by NIH grant R01 GM031575.
• Keywords: signaling pathway; phenotype Q1; disease development; simulation modelling; weighted association; rare variant; Genetic Analysis Workshop
• Research Categories: Biology, Biostatistics; Biology, Genetics

Abstract

Currently there is a great deal of interest in developing methods for testing the role that rare variation plays in disease development. Here we propose a weighted association test that accumulates genetic variation across a signaling pathway. We evaluate our approach by analyzing simulated phenotype data from an exome sequencing study of 697 unrelated individuals from the Genetic Analysis Workshop 17 (GAW17) data set. Although our weighted approach identifies several interesting pathways associated with phenotype Q1, so does an alternative unweighted accumulation approach. Such a result is not unexpected because there is no systematic relationship between the allele frequency of a variant and its effect on phenotype in the GAW17 simulation model.