This article is part of the supplement: Genetic Analysis Workshop 16
Simultaneous analysis of all single-nucleotide polymorphisms in genome-wide association study of rheumatoid arthritis
1 Department of Mathematics, Missouri State University, 901 South National Avenue, Springfield, MO 65897, USA
2 Department of Biostatistics, Medical College of Georgia, 1120 15th Street, Augusta, GA 30912, USA
BMC Proceedings 2009, 3(Suppl 7):S11 doi:Published: 15 December 2009
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.