Limits...
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/.

Show MeSH

Related in: MedlinePlus

Comparisons of receiver operating characteristic curves measured by AUCs (Left) and partial AUCs (Right) between GPA and the conditional FDR approach at sample size  = 5000 and number of risk SNPs  = 1000.The results are based on 200 simulations.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4230845&req=5

pgen-1004787-g003: Comparisons of receiver operating characteristic curves measured by AUCs (Left) and partial AUCs (Right) between GPA and the conditional FDR approach at sample size  = 5000 and number of risk SNPs  = 1000.The results are based on 200 simulations.

Mentions: As a comparison, we also used the “conditional FDR” approach proposed by Andreassen et al. [17] to prioritize SNPs in our simulations. The comparison results between GPA and the conditional FDR at and are shown in Figure 3 (other results are provided in Figures S77–S87 in Text S1). GPA significantly outperformed the conditional FDR approach in SNP prioritization. More importantly, in the absence of pleiotropy, the conditional FDR approach had worse accuracy than single-GWAS analysis using the standard FDR approach, whereas GPA achieved comparable accuracy with single-GWAS analysis in this scenario. This suggests that GPA was able to take advantage of pleiotropy when it exists while, in clear contrast to the conditional FDR approach, it does not sacrifice much statistical power than the conditional FDR approach when it is absent.


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)

Comparisons of receiver operating characteristic curves measured by AUCs (Left) and partial AUCs (Right) between GPA and the conditional FDR approach at sample size  = 5000 and number of risk SNPs  = 1000.The results are based on 200 simulations.
© Copyright Policy
Related In: Results  -  Collection

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

pgen-1004787-g003: Comparisons of receiver operating characteristic curves measured by AUCs (Left) and partial AUCs (Right) between GPA and the conditional FDR approach at sample size  = 5000 and number of risk SNPs  = 1000.The results are based on 200 simulations.
Mentions: As a comparison, we also used the “conditional FDR” approach proposed by Andreassen et al. [17] to prioritize SNPs in our simulations. The comparison results between GPA and the conditional FDR at and are shown in Figure 3 (other results are provided in Figures S77–S87 in Text S1). GPA significantly outperformed the conditional FDR approach in SNP prioritization. More importantly, in the absence of pleiotropy, the conditional FDR approach had worse accuracy than single-GWAS analysis using the standard FDR approach, whereas GPA achieved comparable accuracy with single-GWAS analysis in this scenario. This suggests that GPA was able to take advantage of pleiotropy when it exists while, in clear contrast to the conditional FDR approach, it does not sacrifice much statistical power than the conditional FDR approach when it is absent.

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