Abstract
Most genetic association studies only genotype a small proportion of cataloged single-nucleotide polymorphisms (SNPs) in regions of interest. With the catalogs of high-density SNP data available (e.g., HapMap) to researchers today, it has become possible to impute genotypes at untyped SNPs. This in turn allows us to test those untyped SNPs, the motivation being to increase power in association studies. Several imputation methods and corresponding software packages have been developed for this purpose. The objective of our study is to apply three widely used imputation methods and corresponding software packages to a data from a genome-wide association study of rheumatoid arthritis from the North American Rheumatoid Arthritis Consortium in Genetic Analysis Workshop 16, to compare the performances of the three methods, to evaluate their strengths and weaknesses, and to identify additional susceptibility loci underlying rheumatoid arthritis. The software packages used in this paper included a program for Bayesian imputation-based association mapping (BIMBAM), a program for imputing unobserved genotypes in case-control association studies (IMPUTE), and a program for testing untyped alleles (TUNA). We found some untyped SNP that showed significant association with rheumatoid arthritis. Among them, a few of these were not located near any typed SNP that was found to be significant and thus may be worth further investigation.
Highlights
Advances in the understanding of a disease’s pathogenesis often lead to improvements in strategy for the prevention, diagnosis, and/or treatment of the disease
Note that all three software packages automatically removed some single-nucleotide polymorphisms (SNPs) from the analysis; the total number of typed SNPs tested was less than the total number of typed SNPs remaining after the preprocessing step
For Bayesian imputationbased association mapping (BIMBAM), a SNP was significant if (BF) was bigger 3.5, which was comparable to the log10(BF) of 3.2 used by Servin and Stephens [5]
Summary
Advances in the understanding of a disease’s pathogenesis often lead to improvements in strategy for the prevention, diagnosis, and/or treatment of the disease. Studies have shown that genetic factors play an important role in the pathogenesis of many complex human diseases. Improving public health and preventing disease provides sufficient motivation for dissecting the genetic etiology of complex human diseases. In a typical GWAS, a large number of population samples of cases and controls are genotype at hundreds of thousands of single-nucleotide polymorphisms (SNPs). Even at these numbers, the SNPs that are genotyped in GWAS will only account for a small proportion of cataloged SNPs. In particular, it is likely that disease susceptibility variants are not directly assayed. Testing untyped SNPs can facilitate the selection of SNPs to be genotyped in follow-up studies and can allow for comparison of findings or joint analysis of data from different studies that use different SNP panels and genotyping platforms
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