ArticleBioinformatics (Oxford, England)2011
PRIMe: a method for characterization and evaluation of pleiotropic regions from multiple genome-wide association studies.
Article in Bioinformatics (Oxford, England), 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers, 8 of them syntheses that pooled it.
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Who cites it
46 citing papers in PubMed, 8 syntheses or guidelines pooled it.
- Meta-analysis of sample-level dbGaP data reveals novel shared genetic link between body height and Crohn's disease.Human genetics · 2021Pooled it
- Meta-analysis of correlated traits via summary statistics from GWASs with an application in hypertension.American journal of human genetics · 2015Pooled it
- Pleiotropic genes for metabolic syndrome and inflammation.Molecular genetics and metabolism · 2014Pooled it
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- Overlap between common genetic polymorphisms underpinning kidney traits and cardiovascular disease phenotypes: the CKDGen consortium.American journal of kidney diseases : the official journal of the National Kidney Foundation · 2013Pooled it
- A subset-based approach improves power and interpretation for the combined analysis of genetic association studies of heterogeneous traits.American journal of human genetics · 2012Pooled it
- Can genetic pleiotropy replicate common clinical constellations of cardiovascular disease and risk?PloS one · 2012Pooled it
- Abundant pleiotropy in human complex diseases and traits.American journal of human genetics · 2011Pooled it
- Dissecting pleiotropy to gain mechanistic insights into human disease.Nature reviews. Genetics · 2026Review
- Genome-wide meta-analysis in lichen sclerosus identifies 14 genomic risk loci.The British journal of dermatology · 2026Article
- A comprehensive study of genetic regulation and disease associations of plasma circulatory microRNAs using population-level data.Genome biology · 2024Article
- Large-scale genomic analyses reveal insights into pleiotropy across circulatory system diseases and nervous system disorders.Nature communications · 2022Article
- Progress in the genetics of uveitis.Genes and immunity · 2022Review
- AGNEP: An Agglomerative Nesting Clustering Algorithm for Phenotypic Dimension Reduction in Joint Analysis of Multiple Phenotypes.Frontiers in genetics · 2021Article
- Associating Multivariate Traits with Genetic Variants Using Collapsing and Kernel Methods with Pedigree- or Population-Based Studies.Computational and mathematical methods in medicine · 2021Article
- Meta-MultiSKAT: Multiple phenotype meta-analysis for region-based association test.Genetic epidemiology · 2019Article
- Multi-SKAT: General framework to test for rare-variant association with multiple phenotypes.Genetic epidemiology · 2019Article
- Adjustment for covariates using summary statistics of genome-wide association studies.Genetic epidemiology · 2018Article
- Anti-Tumor Necrosis Factor Therapy and Incidence of Parkinson Disease Among Patients With Inflammatory Bowel Disease.JAMA neurology · 2018Article
- Statistical methods to detect pleiotropy in human complex traits.Open biology · 2017Review
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3 authors.
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Abstract
motivationThe concept of pleiotropy was proposed a century ago, though up to now there have been insufficient efforts to design robust statistics and software aimed at visualizing and evaluating pleiotropy at a regional level. The Pleiotropic Region Identification Method (PRIMe) was developed to evaluate potentially pleiotropic loci based upon data from multiple genome-wide association studies (GWAS).
methodsWe first provide a software tool to systematically identify and characterize genomic regions where low association P-values are observed with multiple traits. We use the term Pleiotropy Index to denote the number of traits with low association P-values at a particular genomic region. For GWAS assumed to be uncorrelated, we adopted the binomial distribution to approximate the statistical significance of the Pleiotropy Index. For GWAS conducted on traits with known correlation coefficients, simulations are performed to derive the statistical distribution of the Pleiotropy Index under the null hypothesis of no genotype-phenotype association. For six hematologic and three blood pressure traits where full GWAS results were available from the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium, we estimated the trait correlations and applied the simulation approach to examine genomic regions with statistical evidence of pleiotropy. We then applied the approximation approach to explore GWAS summarized in the National Human Genome Research Institute (NHGRI) GWAS Catalog.
resultsBy simulation, we identified pleiotropic regions including SH2B3 and BRAP (12q24.12) for hematologic and blood pressure traits. By approximation, we confirmed the genome-wide significant pleiotropy of these two regions based on the GWAS Catalog data, together with an exploration on other regions which highlights the FTO, GCKR and ABO regions. AVAILABILITY AND IMPLEMENTATION: The Perl and R scripts are available at http://www.framinghamheartstudy.org/research/gwas_pleiotropictool.html.
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