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Causal graph-based analysis of genome-wide association data in rheumatoid arthritis.

Alekseyenko AV, Lytkin NI, Ai J, Ding B, Padyukov L, Aliferis CF, Statnikov A - Biol. Direct (2011)

Bottom Line: Anthony Almudevar, Dr. Eugene V.Koonin, and Prof.Marianthi Markatou.

View Article: PubMed Central - HTML - PubMed

Affiliation: Center for Health Informatics and Bioinformatics, New York University School of Medicine, New York, NY 10016, USA. alexander.alekseyenko@nyumc.org

ABSTRACT

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.

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ROC curves for the two causal graph-based predictive models applied to NARAC testing set. Model denoted with "MB1" was fit using five SNPs from the first Markov boundary; model denoted with "MB2" was fit using five SNPs from the second Markov boundary.
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Figure 2: ROC curves for the two causal graph-based predictive models applied to NARAC testing set. Model denoted with "MB1" was fit using five SNPs from the first Markov boundary; model denoted with "MB2" was fit using five SNPs from the second Markov boundary.

Mentions: Application of the causal graph-based method TIE* to the training set of NARAC subjects results in two five-SNP information equivalent Markov boundaries of the rheumatoid arthritis phenotypic response variable (denoting case and control status of the subjects). The two Markov boundaries jointly contain a total of six SNPs and have four SNPs in common (Table 1). SNP rs9275374 that is included in the first Markov boundary is substituted by SNP rs9275390 in the second Markov boundary due to these two SNPs exhibiting complete linkage disequilibrium (LD) in our dataset (R2 = 1, see Table 2). We fit kernel ridge regression to both Markov boundaries to produce predictive models (molecular signatures) of anti-CCP positive rheumatoid arthritis. Each of these predictive models achieves 0.81 AUC (95% confidence interval: [0.78; 0.84] AUC) in the independent testing set of NARAC cohort (Figure 2).


Causal graph-based analysis of genome-wide association data in rheumatoid arthritis.

Alekseyenko AV, Lytkin NI, Ai J, Ding B, Padyukov L, Aliferis CF, Statnikov A - Biol. Direct (2011)

ROC curves for the two causal graph-based predictive models applied to NARAC testing set. Model denoted with "MB1" was fit using five SNPs from the first Markov boundary; model denoted with "MB2" was fit using five SNPs from the second Markov boundary.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3118953&req=5

Figure 2: ROC curves for the two causal graph-based predictive models applied to NARAC testing set. Model denoted with "MB1" was fit using five SNPs from the first Markov boundary; model denoted with "MB2" was fit using five SNPs from the second Markov boundary.
Mentions: Application of the causal graph-based method TIE* to the training set of NARAC subjects results in two five-SNP information equivalent Markov boundaries of the rheumatoid arthritis phenotypic response variable (denoting case and control status of the subjects). The two Markov boundaries jointly contain a total of six SNPs and have four SNPs in common (Table 1). SNP rs9275374 that is included in the first Markov boundary is substituted by SNP rs9275390 in the second Markov boundary due to these two SNPs exhibiting complete linkage disequilibrium (LD) in our dataset (R2 = 1, see Table 2). We fit kernel ridge regression to both Markov boundaries to produce predictive models (molecular signatures) of anti-CCP positive rheumatoid arthritis. Each of these predictive models achieves 0.81 AUC (95% confidence interval: [0.78; 0.84] AUC) in the independent testing set of NARAC cohort (Figure 2).

Bottom Line: Anthony Almudevar, Dr. Eugene V.Koonin, and Prof.Marianthi Markatou.

View Article: PubMed Central - HTML - PubMed

Affiliation: Center for Health Informatics and Bioinformatics, New York University School of Medicine, New York, NY 10016, USA. alexander.alekseyenko@nyumc.org

ABSTRACT

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.

Show MeSH
Related in: MedlinePlus