Causal graph-based analysis of genome-wide association data in rheumatoid arthritis
| Autor: | Bo Ding, Jizhou Ai, Alexander Statnikov, Nikita Igorevych Lytkin, Alexander V. Alekseyenko, Leonid Padyukov, Constantin F. Aliferis |
|---|---|
| Rok vydání: | 2011 |
| Předmět: |
Canada
Multivariate statistics Immunology Inference Genomics Single-nucleotide polymorphism Genome-wide association study Disease Computational biology Biology Models Biological Polymorphism Single Nucleotide General Biochemistry Genetics and Molecular Biology Arthritis Rheumatoid Major Histocompatibility Complex 03 medical and health sciences 0302 clinical medicine Humans SNP lcsh:QH301-705.5 Ecology Evolution Behavior and Systematics 030304 developmental biology Sweden Genetics 0303 health sciences Agricultural and Biological Sciences(all) Biochemistry Genetics and Molecular Biology(all) Gene Expression Profiling Research Applied Mathematics Computational Biology United States 3. Good health lcsh:Biology (General) Conditional independence 030220 oncology & carcinogenesis Modeling and Simulation General Agricultural and Biological Sciences Algorithms Genome-Wide Association Study |
| Zdroj: | Biology Direct Biology Direct, Vol 6, Iss 1, p 25 (2011) |
| ISSN: | 1745-6150 |
| DOI: | 10.1186/1745-6150-6-25 |
| Popis: | Background 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. Results 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. Conclusions 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. Reviewers This article was reviewed by Prof. Anthony Almudevar, Dr. Eugene V. Koonin, and Prof. Marianthi Markatou. |
| Databáze: | OpenAIRE |
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