Genome-wide association analysis of rheumatoid arthritis data via haplotype sharing.

• Type of Material: Article
• Authors: Andrew S. Allen, Duke University; Glen Satten, Emory University
• Language: English
• Date: 2009-12-15
• Publisher: BMC (part of Springer Nature)
• Publication Version: Final Published Version
• Copyright Statement: ©2009 Allen and Satten; licensee BioMed Central Ltd.
• License: Creative Commons Attribution 2.0 Generic
• Final Published Version (URL): http://dx.doi.org/10.1186/1753-6561-3-s7-s30
• Title of Journal or Parent Work: BMC Proceedings
• ISSN: 1753-6561
• Volume: 3 Suppl 7
• Issue: Suppl 7
• Start Page: S30
• End Page: S30
• Grant/Funding Information: The Genetic Analysis Workshops are supported by NIH grant R01 GM031575 from the National Institute of General Medical Sciences.; ASA acknowledges support from grants R01 MH084680 and K25 HL077663 from the National Institutes of Health.
• Keywords: population stratification; Genetic Analysis Workshop; algorithms; association test; genome-wide association study; haplotype
• Research Categories: Biology, Biostatistics; Biology, Genetics

Abstract

We present computationally simple association tests based on haplotype sharing that can be easily applied to genome-wide association studies, while allowing use of fast (but not likelihood-based) haplotyping algorithms, and properly accounting for the uncertainty introduced by using inferred haplotypes. We also give haplotype sharing analyses that adjust for population stratification. We apply our methods to a genome-wide association study of rheumatoid arthritis available as Problem 1 of Genetic Analysis Workshop 16. In addition to the HLA region on chromosome 6, we find genome-wide significant signals at 7q33 and 13q31.3. These regions contain genes with interesting potential connections with rheumatoid arthritis and are not identified using single single-nucleotide polymorphism methods.