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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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Graphical representation of the local pathway concept. The local pathway of the phenotype (shown with the ash blue colour) contains all its direct causes (C1, C2, C3), direct effects (E1, E2, E3), and direct causes of the direct effects (CE1). This is exactly the Markov boundary of the phenotype. Other variables (X1, X2, X3, X4, X5) do not belong to the local pathway. This definition of a local pathway ties in a theoretically rigorous manner causality with predictivity, since the Markov boundary is the smallest set of variables that contains the maximum predictive information about the phenotype that is contained in the data. Alternative definitions of the local causal pathway that exclude direct causes of the direct effects (the so-called "spouse variables", such as CE1) are also useful and specialized algorithms exist to infer them from data. In GWAS data, the two definitions coincide because of lack of spouse variables in GWAS designs.
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Figure 1: Graphical representation of the local pathway concept. The local pathway of the phenotype (shown with the ash blue colour) contains all its direct causes (C1, C2, C3), direct effects (E1, E2, E3), and direct causes of the direct effects (CE1). This is exactly the Markov boundary of the phenotype. Other variables (X1, X2, X3, X4, X5) do not belong to the local pathway. This definition of a local pathway ties in a theoretically rigorous manner causality with predictivity, since the Markov boundary is the smallest set of variables that contains the maximum predictive information about the phenotype that is contained in the data. Alternative definitions of the local causal pathway that exclude direct causes of the direct effects (the so-called "spouse variables", such as CE1) are also useful and specialized algorithms exist to infer them from data. In GWAS data, the two definitions coincide because of lack of spouse variables in GWAS designs.

Mentions: Of particular relevance to the goals of GWAS are recent multivariate causal graph-based methods that are computationally efficient and can scale well to the dimensionality of GWAS data [12-14]. Unlike simple discovery of univariate associations, these methods can discover biomarkers (SNPs) in the local pathway of the phenotype (referred to as "local causal biomarkers", see Figure 1 for a graphical representation) under reasonably broad assumptions [12-14]. Local causal biomarkers constitute the Markov boundary and yield the highest accuracy predictions of the phenotype, while other biomarkers do not contribute additional predictive information beyond what is contained in the local causal ones [12-15]. In addition, the set of local causal biomarkers exhibits maximum parsimony, beyond which predictive accuracy is compromised [12-14,16].


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)

Graphical representation of the local pathway concept. The local pathway of the phenotype (shown with the ash blue colour) contains all its direct causes (C1, C2, C3), direct effects (E1, E2, E3), and direct causes of the direct effects (CE1). This is exactly the Markov boundary of the phenotype. Other variables (X1, X2, X3, X4, X5) do not belong to the local pathway. This definition of a local pathway ties in a theoretically rigorous manner causality with predictivity, since the Markov boundary is the smallest set of variables that contains the maximum predictive information about the phenotype that is contained in the data. Alternative definitions of the local causal pathway that exclude direct causes of the direct effects (the so-called "spouse variables", such as CE1) are also useful and specialized algorithms exist to infer them from data. In GWAS data, the two definitions coincide because of lack of spouse variables in GWAS designs.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 1: Graphical representation of the local pathway concept. The local pathway of the phenotype (shown with the ash blue colour) contains all its direct causes (C1, C2, C3), direct effects (E1, E2, E3), and direct causes of the direct effects (CE1). This is exactly the Markov boundary of the phenotype. Other variables (X1, X2, X3, X4, X5) do not belong to the local pathway. This definition of a local pathway ties in a theoretically rigorous manner causality with predictivity, since the Markov boundary is the smallest set of variables that contains the maximum predictive information about the phenotype that is contained in the data. Alternative definitions of the local causal pathway that exclude direct causes of the direct effects (the so-called "spouse variables", such as CE1) are also useful and specialized algorithms exist to infer them from data. In GWAS data, the two definitions coincide because of lack of spouse variables in GWAS designs.
Mentions: Of particular relevance to the goals of GWAS are recent multivariate causal graph-based methods that are computationally efficient and can scale well to the dimensionality of GWAS data [12-14]. Unlike simple discovery of univariate associations, these methods can discover biomarkers (SNPs) in the local pathway of the phenotype (referred to as "local causal biomarkers", see Figure 1 for a graphical representation) under reasonably broad assumptions [12-14]. Local causal biomarkers constitute the Markov boundary and yield the highest accuracy predictions of the phenotype, while other biomarkers do not contribute additional predictive information beyond what is contained in the local causal ones [12-15]. In addition, the set of local causal biomarkers exhibits maximum parsimony, beyond which predictive accuracy is compromised [12-14,16].

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