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GPA: a statistical approach to prioritizing GWAS results by integrating pleiotropy and annotation.

Chung D, Yang C, Li C, Gelernter J, Zhao H - PLoS Genet. (2014)

Bottom Line: Results from Genome-Wide Association Studies (GWAS) have shown that complex diseases are often affected by many genetic variants with small or moderate effects.Using our hypothesis testing framework, statistically significant pleiotropic effects were detected among these psychiatric disorders, and the markers annotated in the central nervous system genes and eQTLs from the Genotype-Tissue Expression (GTEx) database were significantly enriched.GPA was able to detect cell lines that are biologically more relevant to bladder cancer.

View Article: PubMed Central - PubMed

Affiliation: Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, United States of America; Department of Public Health Sciences, Medical University of South Carolina, Charleston, South Carolina, United States of America.

ABSTRACT
Results from Genome-Wide Association Studies (GWAS) have shown that complex diseases are often affected by many genetic variants with small or moderate effects. Identifications of these risk variants remain a very challenging problem. There is a need to develop more powerful statistical methods to leverage available information to improve upon traditional approaches that focus on a single GWAS dataset without incorporating additional data. In this paper, we propose a novel statistical approach, GPA (Genetic analysis incorporating Pleiotropy and Annotation), to increase statistical power to identify risk variants through joint analysis of multiple GWAS data sets and annotation information because: (1) accumulating evidence suggests that different complex diseases share common risk bases, i.e., pleiotropy; and (2) functionally annotated variants have been consistently demonstrated to be enriched among GWAS hits. GPA can integrate multiple GWAS datasets and functional annotations to seek association signals, and it can also perform hypothesis testing to test the presence of pleiotropy and enrichment of functional annotation. Statistical inference of the model parameters and SNP ranking is achieved through an EM algorithm that can handle genome-wide markers efficiently. When we applied GPA to jointly analyze five psychiatric disorders with annotation information, not only did GPA identify many weak signals missed by the traditional single phenotype analysis, but it also revealed relationships in the genetic architecture of these disorders. Using our hypothesis testing framework, statistically significant pleiotropic effects were detected among these psychiatric disorders, and the markers annotated in the central nervous system genes and eQTLs from the Genotype-Tissue Expression (GTEx) database were significantly enriched. We also applied GPA to a bladder cancer GWAS data set with the ENCODE DNase-seq data from 125 cell lines. GPA was able to detect cell lines that are biologically more relevant to bladder cancer. The R implementation of GPA is currently available at http://dongjunchung.github.io/GPA/.

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Related in: MedlinePlus

The type I error rate and power of the pleiotropy test. Here we varied  to evaluate the power for sample size  = 500 (Upper Left panel), 1000 (Upper Right panel), and 2000 (Lower Left panel), respectively.We used  to evaluate the type I errors of the pleiotropy test (Lower Right panel). In each setting, we also varied sample size  = 1000, 2000, and 10000. Note that type I error rate and power of the pleiotropy test remain almost the same in presence of annotation (see Figure S9 in Text S1).
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pgen-1004787-g005: The type I error rate and power of the pleiotropy test. Here we varied to evaluate the power for sample size  = 500 (Upper Left panel), 1000 (Upper Right panel), and 2000 (Lower Left panel), respectively.We used to evaluate the type I errors of the pleiotropy test (Lower Right panel). In each setting, we also varied sample size  = 1000, 2000, and 10000. Note that type I error rate and power of the pleiotropy test remain almost the same in presence of annotation (see Figure S9 in Text S1).

Mentions: We further evaluated the type I error rate and power of GPA for the test of pleiotropy in our simulations. The simulation parameters were the same as those in the previous simulations. Power was evaluated at , , , and . The type I error rate was evaluated at , corresponding to the expected shared proportion of risk SNPs in the absence of pleiotropy. As shown in Figure 5, power increased as decreased and as and increased, whereas the type I error rate was appropriately controlled in all cases. Please note that type I errors and power remained almost the same for hypothesis testing of pleiotropy in the presence of annotation (see Figures S2–S4 in Text S1).


GPA: a statistical approach to prioritizing GWAS results by integrating pleiotropy and annotation.

Chung D, Yang C, Li C, Gelernter J, Zhao H - PLoS Genet. (2014)

The type I error rate and power of the pleiotropy test. Here we varied  to evaluate the power for sample size  = 500 (Upper Left panel), 1000 (Upper Right panel), and 2000 (Lower Left panel), respectively.We used  to evaluate the type I errors of the pleiotropy test (Lower Right panel). In each setting, we also varied sample size  = 1000, 2000, and 10000. Note that type I error rate and power of the pleiotropy test remain almost the same in presence of annotation (see Figure S9 in Text S1).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4230845&req=5

pgen-1004787-g005: The type I error rate and power of the pleiotropy test. Here we varied to evaluate the power for sample size  = 500 (Upper Left panel), 1000 (Upper Right panel), and 2000 (Lower Left panel), respectively.We used to evaluate the type I errors of the pleiotropy test (Lower Right panel). In each setting, we also varied sample size  = 1000, 2000, and 10000. Note that type I error rate and power of the pleiotropy test remain almost the same in presence of annotation (see Figure S9 in Text S1).
Mentions: We further evaluated the type I error rate and power of GPA for the test of pleiotropy in our simulations. The simulation parameters were the same as those in the previous simulations. Power was evaluated at , , , and . The type I error rate was evaluated at , corresponding to the expected shared proportion of risk SNPs in the absence of pleiotropy. As shown in Figure 5, power increased as decreased and as and increased, whereas the type I error rate was appropriately controlled in all cases. Please note that type I errors and power remained almost the same for hypothesis testing of pleiotropy in the presence of annotation (see Figures S2–S4 in Text S1).

Bottom Line: Results from Genome-Wide Association Studies (GWAS) have shown that complex diseases are often affected by many genetic variants with small or moderate effects.Using our hypothesis testing framework, statistically significant pleiotropic effects were detected among these psychiatric disorders, and the markers annotated in the central nervous system genes and eQTLs from the Genotype-Tissue Expression (GTEx) database were significantly enriched.GPA was able to detect cell lines that are biologically more relevant to bladder cancer.

View Article: PubMed Central - PubMed

Affiliation: Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, United States of America; Department of Public Health Sciences, Medical University of South Carolina, Charleston, South Carolina, United States of America.

ABSTRACT
Results from Genome-Wide Association Studies (GWAS) have shown that complex diseases are often affected by many genetic variants with small or moderate effects. Identifications of these risk variants remain a very challenging problem. There is a need to develop more powerful statistical methods to leverage available information to improve upon traditional approaches that focus on a single GWAS dataset without incorporating additional data. In this paper, we propose a novel statistical approach, GPA (Genetic analysis incorporating Pleiotropy and Annotation), to increase statistical power to identify risk variants through joint analysis of multiple GWAS data sets and annotation information because: (1) accumulating evidence suggests that different complex diseases share common risk bases, i.e., pleiotropy; and (2) functionally annotated variants have been consistently demonstrated to be enriched among GWAS hits. GPA can integrate multiple GWAS datasets and functional annotations to seek association signals, and it can also perform hypothesis testing to test the presence of pleiotropy and enrichment of functional annotation. Statistical inference of the model parameters and SNP ranking is achieved through an EM algorithm that can handle genome-wide markers efficiently. When we applied GPA to jointly analyze five psychiatric disorders with annotation information, not only did GPA identify many weak signals missed by the traditional single phenotype analysis, but it also revealed relationships in the genetic architecture of these disorders. Using our hypothesis testing framework, statistically significant pleiotropic effects were detected among these psychiatric disorders, and the markers annotated in the central nervous system genes and eQTLs from the Genotype-Tissue Expression (GTEx) database were significantly enriched. We also applied GPA to a bladder cancer GWAS data set with the ENCODE DNase-seq data from 125 cell lines. GPA was able to detect cell lines that are biologically more relevant to bladder cancer. The R implementation of GPA is currently available at http://dongjunchung.github.io/GPA/.

Show MeSH
Related in: MedlinePlus