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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

Enrichment of the DNase I hypersenstivity site annotation data from 125 cell lines for bladder cancer.Left panel:  of hypothesis testing (13) vs. fold enrichment . The vertical red line corresponds to the significance level ( = 0.05) after Bonferroni correction. The horizontal red line corresponds to ratio = 1. Right panel: The normalized variance component  (2) given by LMM v.s.  given by GPA.
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pgen-1004787-g008: Enrichment of the DNase I hypersenstivity site annotation data from 125 cell lines for bladder cancer.Left panel: of hypothesis testing (13) vs. fold enrichment . The vertical red line corresponds to the significance level ( = 0.05) after Bonferroni correction. The horizontal red line corresponds to ratio = 1. Right panel: The normalized variance component (2) given by LMM v.s. given by GPA.

Mentions: We applied GPA to analyze the bladder cancer GWAS -values with one annotation dataset at a time, and performed hypothesis testing to assess the significance of enrichment. The results are shown in the left panel of Figure 8. Under significance level after Bonferroni correction, annotations from 19 cell lines were statistically significantly enriched for bladder cancer risk associated SNPs. Most of these cell lines were derived from lymphocytes from normal blood (e.g., T cells CD4+ Th0 adult, Monocytes CD14+ RO01746), while some cell lines came from cancer patients (e.g., Gliobla and HeLa-S3). The above results suggest that involvement of the immune system and carcinoma pathways in bladder cancer. These results also demonstrate that GPA may be an effective way to explore functional roles of GWAS hits by testing enrichment on phenotype-related annotations or user-specified annotations.


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)

Enrichment of the DNase I hypersenstivity site annotation data from 125 cell lines for bladder cancer.Left panel:  of hypothesis testing (13) vs. fold enrichment . The vertical red line corresponds to the significance level ( = 0.05) after Bonferroni correction. The horizontal red line corresponds to ratio = 1. Right panel: The normalized variance component  (2) given by LMM v.s.  given by GPA.
© Copyright Policy
Related In: Results  -  Collection

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

pgen-1004787-g008: Enrichment of the DNase I hypersenstivity site annotation data from 125 cell lines for bladder cancer.Left panel: of hypothesis testing (13) vs. fold enrichment . The vertical red line corresponds to the significance level ( = 0.05) after Bonferroni correction. The horizontal red line corresponds to ratio = 1. Right panel: The normalized variance component (2) given by LMM v.s. given by GPA.
Mentions: We applied GPA to analyze the bladder cancer GWAS -values with one annotation dataset at a time, and performed hypothesis testing to assess the significance of enrichment. The results are shown in the left panel of Figure 8. Under significance level after Bonferroni correction, annotations from 19 cell lines were statistically significantly enriched for bladder cancer risk associated SNPs. Most of these cell lines were derived from lymphocytes from normal blood (e.g., T cells CD4+ Th0 adult, Monocytes CD14+ RO01746), while some cell lines came from cancer patients (e.g., Gliobla and HeLa-S3). The above results suggest that involvement of the immune system and carcinoma pathways in bladder cancer. These results also demonstrate that GPA may be an effective way to explore functional roles of GWAS hits by testing enrichment on phenotype-related annotations or user-specified annotations.

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