GPA: a statistical approach to prioritizing GWAS results by integrating pleiotropy and annotation.
paper
Cited
Public
- Authors
- Chung, Dongjun; Yang, Can; Li, Cong; Gelernter, Joel; Zhao, Hongyu
- Year
- 2014
- Journal
- PLoS genetics
- PMID
- 25393678
- DOI
- 10.1371/journal.pgen.1004787
- PMCID
- PMC4230845
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/.
Figure 1
AUC (left), partial AUC (Middle) and power (right) of GPA for SNP prioritization with sample size = 5000 and number of risk SNPs = 1000.The results are based on 200 simulations.
Global false discovery rates of GPA at sample size = 5000 and number of risk SNPs = 1000.Upper panel: Global false discovery rates of GPA with annotation. Lower panel: Global false discovery rates of GPA without annotation. From left to right: FDR of first GWAS (joint analysis), FDR of second GWAS (joint analysis), FDR of first GWAS (separate analysis), FDR of second GWAS (separate analysis) and FDR of risk variants shared by both GWAS. For all scenarios, the global false discovery rates of GPA are controlled at the nominal level.
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.
Figure 4
The comparison between GPA and GSEA at number of risk SNPs = 1000.Here we fixed and varied to evaluate the power for sample size = 2000 (Upper Left panel), 5000 (Upper Right panel), 10000 (Lower Left panel), respectively. We used to evaluate the type I errors (Lower Right panel). The results are based on 500 simulations.
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).
Manhattan plots of BPD and SCZ.Top left panel: separate analysis without annotation. Top right panel: separate analysis with CNS annotation. Bottom left panel: joint analysis without annotation. Bottom right panel: joint analysis with CNS annotation. The red and blue lines indicate local = 0.05 and 0.1, respectively.
Manhattan plots of local false discovery rates and (Equations (11) and (12)) for detecting BPD-SCZ-sharing SNPs.Left panel: joint analysis without annotation. Right panel: joint analysis with annotation. The red and blue lines indicate local = 0.05 and 0.1, respectively.
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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| The Evolving Field of Genetic Epidemiology: From Familial Aggregation to Genomic Sequencing. | Duggal P et al. | — | 2019 | → |
| A Bayesian framework for multiple trait colocalization from summary association statistics. | Giambartolomei C et al. | — | 2018 | → |
| A correction for sample overlap in genome-wide association studies in a polygenic pleiotropy-informed framework. | LeBlanc M et al. | — | 2018 | → |
| Additional common variants associated with type 2 diabetes and coronary artery disease detected using a pleiotropic cFDR method. | Zhang Q et al. | — | 2018 | → |
| Adjustment for covariates using summary statistics of genome-wide association studies. | Wang T et al. | — | 2018 | → |
| A Mixed-Effects Model for Powerful Association Tests in Integrative Functional Genomics. | Su YR et al. | — | 2018 | → |
| An efficient Bayesian meta-analysis approach for studying cross-phenotype genetic associations. | Majumdar A et al. | — | 2018 | → |
| A novel joint analysis framework improves identification of differentially expressed genes in cross disease transcriptomic analysis. | Qin W et al. | — | 2018 | → |
| Beyond heritability: improving discoverability in imaging genetics. | Fan CC et al. | — | 2018 | → |
| Identifying and exploiting trait-relevant tissues with multiple functional annotations in genome-wide association studies. | Hao X et al. | — | 2018 | → |
| Identifying potentially common genes between dyslipidemia and osteoporosis using novel analytical approaches. | Lin X et al. | — | 2018 | → |
| Improved detection of genetic loci in estimated glomerular filtration rate and type 2 diabetes using a pleiotropic cFDR method. | Liu HM et al. | — | 2018 | → |
| Improving SNP prioritization and pleiotropic architecture estimation by incorporating prior knowledge using graph-GPA. | Kim HJ et al. | — | 2018 | → |
| Leveraging multiple gene networks to prioritize GWAS candidate genes via network representation learning. | Wu M et al. | — | 2018 | → |
| LPG: A four-group probabilistic approach to leveraging pleiotropy in genome-wide association studies. | Yang Y et al. | — | 2018 | → |
| Pharmacogenomics study of thiazide diuretics and QT interval in multi-ethnic populations: the cohorts for heart and aging research in genomic epidemiology. | Seyerle AA et al. | — | 2018 | → |
| Pleiotropic mapping and annotation selection in genome-wide association studies with penalized Gaussian mixture models. | Zeng P et al. | — | 2018 | → |
| Principled multi-omic analysis reveals gene regulatory mechanisms of phenotype variation. | Hanson C et al. | — | 2018 | → |
| ShinyGPA: An interactive visualization toolkit for investigating pleiotropic architecture using GWAS datasets. | Kortemeier E et al. | — | 2018 | → |
| The Emerging Immunogenetic Architecture of Schizophrenia. | Pouget JG | — | 2018 | → |
| The ubiquity of pleiotropy in human disease. | Chesmore K et al. | — | 2018 | → |
| Accurate and reproducible functional maps in 127 human cell types via 2D genome segmentation. | Zhang Y et al. | — | 2017 | → |
| Annotation Regression for Genome-Wide Association Studies with an Application to Psychiatric Genomic Consortium Data. | Shin S et al. | — | 2017 | → |
| A systematic SNP selection approach to identify mechanisms underlying disease aetiology: linking height to post-menopausal breast and colorectal cancer risk. | Elands RJ et al. | — | 2017 | → |
| A two-stage inter-rater approach for enrichment testing of variants associated with multiple traits. | Asimit JL et al. | — | 2017 | → |
| cepip: context-dependent epigenomic weighting for prioritization of regulatory variants and disease-associated genes. | Li MJ et al. | — | 2017 | → |
| Comprehensive evaluation of disease- and trait-specific enrichment for eight functional elements among GWAS-identified variants. | Markunas CA et al. | — | 2017 | → |
| Constraints on eQTL Fine Mapping in the Presence of Multisite Local Regulation of Gene Expression. | Zeng B et al. | — | 2017 | → |
| Dissecting the genetics of complex traits using summary association statistics. | Pasaniuc B et al. | — | 2017 | → |
| Genetic factor common to schizophrenia and HIV infection is associated with risky sexual behavior: antagonistic vs. synergistic pleiotropic SNPs enriched for distinctly different biological functions. | Wang Q et al. | — | 2017 | → |
| graph-GPA: A graphical model for prioritizing GWAS results and investigating pleiotropic architecture. | Chung D et al. | — | 2017 | → |
| IGESS: a statistical approach to integrating individual-level genotype data and summary statistics in genome-wide association studies. | Dai M et al. | — | 2017 | → |
| Improved methods for multi-trait fine mapping of pleiotropic risk loci. | Kichaev G et al. | — | 2017 | → |
| Integration of expression quantitative trait loci and pleiotropy identifies a novel psoriasis susceptibility gene, PTPN1. | Yin X et al. | — | 2017 | → |
| Joint modeling of genetically correlated diseases and functional annotations increases accuracy of polygenic risk prediction. | Hu Y et al. | — | 2017 | → |
| Linking Alzheimer's disease and type 2 diabetes: Novel shared susceptibility genes detected by cFDR approach. | Wang XF et al. | — | 2017 | → |
| Novel common variants associated with body mass index and coronary artery disease detected using a pleiotropic cFDR method. | Lv WQ et al. | — | 2017 | → |
| Novel Common Variants Associated with Obesity and Type 2 Diabetes Detected Using a cFDR Method. | Zhang Q et al. | — | 2017 | → |
| Optimal detection of weak positive latent dependence between two sequences of multiple tests. | Zhao SD et al. | — | 2017 | → |
| Polygenic risk assessment reveals pleiotropy between sarcoidosis and inflammatory disorders in the context of genetic ancestry. | Lareau CA et al. | — | 2017 | → |
| Polygenic scores via penalized regression on summary statistics. | Mak TSH et al. | — | 2017 | → |
| Simultaneous inference of phenotype-associated genes and relevant tissues from GWAS data via Bayesian integration of multiple tissue-specific gene networks. | Wu M et al. | — | 2017 | → |
| Sparse simultaneous signal detection for identifying genetically controlled disease genes. | Zhao SD et al. | — | 2017 | → |
| Statistical methods to detect pleiotropy in human complex traits. | Hackinger S et al. | — | 2017 | → |
| Substantial contribution of genetic variation in the expression of transcription factors to phenotypic variation revealed by eRD-GWAS. | Lin HY et al. | — | 2017 | → |
| Testing Genetic Pleiotropy with GWAS Summary Statistics for Marginal and Conditional Analyses. | Deng Y et al. | — | 2017 | → |
| Using GWAS to identify novel therapeutic targets for osteoporosis. | Sabik OL et al. | — | 2017 | → |
| An Analytic Solution to the Computation of Power and Sample Size for Genetic Association Studies under a Pleiotropic Mode of Inheritance. | Gordon D et al. | — | 2016 | → |
| Application of computational methods in genetic study of inflammatory bowel disease. | Li J et al. | — | 2016 | → |
| Common variants at PVT1, ATG13-AMBRA1, AHI1 and CLEC16A are associated with selective IgA deficiency. | Bronson PG et al. | — | 2016 | → |
| Computational discovery of transcription factors associated with drug response. | Hanson C et al. | — | 2016 | → |
| DNA co-methylation modules in postmortem prefrontal cortex tissues of European Australians with alcohol use disorders. | Wang F et al. | — | 2016 | → |
| EPS: an empirical Bayes approach to integrating pleiotropy and tissue-specific information for prioritizing risk genes. | Liu J et al. | — | 2016 | → |
| Exploring the Genetic Patterns of Complex Diseases via the Integrative Genome-Wide Approach. | Teng B et al. | — | 2016 | → |
| Genetic Variant Selection: Learning Across Traits and Sites. | Stell L et al. | — | 2016 | → |
| Genome-wide Association Studies of Posttraumatic Stress Disorder in 2 Cohorts of US Army Soldiers. | Stein MB et al. | — | 2016 | → |
| Genome-wide Association Study of Cannabis Dependence Severity, Novel Risk Variants, and Shared Genetic Risks. | Sherva R et al. | — | 2016 | → |
| GenoWAP: GWAS signal prioritization through integrated analysis of genomic functional annotation. | Lu Q et al. | — | 2016 | → |
| GPA-MDS: A Visualization Approach to Investigate Genetic Architecture among Phenotypes Using GWAS Results. | Wei W et al. | — | 2016 | → |
| Identification of cell types, tissues and pathways affected by risk loci in psoriasis. | Lin Y et al. | — | 2016 | → |
| Integrative Tissue-Specific Functional Annotations in the Human Genome Provide Novel Insights on Many Complex Traits and Improve Signal Prioritization in Genome Wide Association Studies. | Lu Q et al. | — | 2016 | → |
| Joint Bayesian inference of risk variants and tissue-specific epigenomic enrichments across multiple complex human diseases. | Li Y et al. | — | 2016 | → |
| New statistical approaches exploit the polygenic architecture of schizophrenia--implications for the underlying neurobiology. | Schork AJ et al. | — | 2016 | → |
| Post-GWAS Prioritization Through Data Integration Provides Novel Insights on Chronic Obstructive Pulmonary Disease. | Lu Q et al. | — | 2016 | → |
| Several Critical Cell Types, Tissues, and Pathways Are Implicated in Genome-Wide Association Studies for Systemic Lupus Erythematosus. | Liu L et al. | — | 2016 | → |
| An Adaptive Association Test for Multiple Phenotypes with GWAS Summary Statistics. | Kim J et al. | — | 2015 | → |
| Genetics of complex traits in psychiatry. | Gelernter J | — | 2015 | → |
| Implications of pleiotropy: challenges and opportunities for mining Big Data in biomedicine. | Yang C et al. | — | 2015 | → |
| Leveraging Functional-Annotation Data in Trans-ethnic Fine-Mapping Studies. | Kichaev G et al. | — | 2015 | → |
| Meta-analysis of genome-wide association studies identifies common susceptibility polymorphisms for colorectal and endometrial cancer near SH2B3 and TSHZ1. | Cheng TH et al. | — | 2015 | → |
| Pervasive pleiotropy between psychiatric disorders and immune disorders revealed by integrative analysis of multiple GWAS. | Wang Q et al. | — | 2015 | → |
| The role of regulatory variation in complex traits and disease. | Albert FW et al. | — | 2015 | → |