Abstract
The availability of very large number of markers by modern technology makes genome-wide association studies very popular. The usual approach is to test single-nucleotide polymorphisms (SNPs) one at a time for association with disease status. However, it may not be possible to detect marginally significant effects by single-SNP analysis. Simultaneous analysis of SNPs enables detection of even those SNPs with small effect by evaluating the collective impact of several neighboring SNPs. Also, false-positive signals may be weakened by the presence of other neighboring SNPs included in the analysis. We analyzed the North American Rheumatoid Arthritis Consortium data of Genetic Analysis Workshop 16 using HLasso, a new method for simultaneous analysis of SNPs. The simultaneous analysis approach has excellent control of type I error, and many of the previously reported results of single-SNP analyses were confirmed by this approach.
Department(s)
Mathematics
Document Type
Article
DOI
https://doi.org/10.1186/1753-6561-3-s7-s11
Rights Information
© 2009 The authors; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Publication Date
12-15-2009
Recommended Citation
Mathew, George, Hongyan Xu, and Varghese George. "Simultaneous analysis of all single-nucleotide polymorphisms in genome-wide association study of rheumatoid arthritis." In BMC proceedings, vol. 3, no. S7, p. S11. BioMed Central, 2009.
Journal Title
BMC proceedings
