Dataset Information

Application of imputation methods to the analysis of rheumatoid arthritis data in genome-wide association studies.

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.

SUBMITTER: Childers DK

PROVIDER: S-EPMC2795921 | biostudies-literature | 2009 Dec

REPOSITORIES: biostudies-literature

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Application of imputation methods to the analysis of rheumatoid arthritis data in genome-wide association studies.

Childers Douglas K DK Kang Guolian G Liu Nianjun N Gao Guimin G Zhang Kui K

BMC proceedings 20091215

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 pu ...[more]

PMID: 20018014

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Project description:GWAS owe their popularity to the expectation that they will make a major impact on diagnosis, prognosis and management of disease by uncovering genetics underlying clinical phenotypes. The dominant paradigm in GWAS data analysis so far consists of extensive reliance on methods that emphasize contribution of individual SNPs to statistical association with phenotypes. Multivariate methods, however, can extract more information by considering associations of multiple SNPs simultaneously. Recent advances in other genomics domains pinpoint multivariate causal graph-based inference as a promising principled analysis framework for high-throughput data. Designed to discover biomarkers in the local causal pathway of the phenotype, these methods lead to accurate and highly parsimonious multivariate predictive models. In this paper, we investigate the applicability of causal graph-based method TIE* to analysis of GWAS data. To test the utility of TIE*, we focus on anti-CCP positive rheumatoid arthritis (RA) GWAS datasets, where there is a general consensus in the community about the major genetic determinants of the disease.Application of TIE* to the North American Rheumatoid Arthritis Cohort (NARAC) GWAS data results in six SNPs, mostly from the MHC locus. Using these SNPs we develop two predictive models that can classify cases and disease-free controls with an accuracy of 0.81 area under the ROC curve, as verified in independent testing data from the same cohort. The predictive performance of these models generalizes reasonably well to Swedish subjects from the closely related but not identical Epidemiological Investigation of Rheumatoid Arthritis (EIRA) cohort with 0.71-0.78 area under the ROC curve. Moreover, the SNPs identified by the TIE* method render many other previously known SNP associations conditionally independent of the phenotype.Our experiments demonstrate that application of TIE* captures maximum amount of genetic information about RA in the data and recapitulates the major consensus findings about the genetic factors of this disease. In addition, TIE* yields reproducible markers and signatures of RA. This suggests that principled multivariate causal and predictive framework for GWAS analysis empowers the community with a new tool for high-quality and more efficient discovery.This article was reviewed by Prof. Anthony Almudevar, Dr. Eugene V. Koonin, and Prof. Marianthi Markatou.

Dataset Information

Application of imputation methods to the analysis of rheumatoid arthritis data in genome-wide association studies.

Publications

Application of imputation methods to the analysis of rheumatoid arthritis data in genome-wide association studies.

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