ArticleBioinformatics (Oxford, England)2015
MGAS: a powerful tool for multivariate gene-based genome-wide association analysis.
Article in Bioinformatics (Oxford, England), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 3 of them syntheses that pooled it.
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Who cites it
24 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- A Bayesian hierarchically structured prior for gene-based association testing with multiple traits in genome-wide association studies.Genetic epidemiology · 2022Pooled it
- Subset testing and analysis of multiple phenotypes.Genetic epidemiology · 2019Pooled it
- Meta-analysis of quantitative pleiotropic traits for next-generation sequencing with multivariate functional linear models.European journal of human genetics : EJHG · 2017Pooled it
- The goldmine of GWAS summary statistics: a systematic review of methods and tools.BioData mining · 2024Article
- A copula-based set-variant association test for bivariate continuous, binary or mixed phenotypes.The international journal of biostatistics · 2023Article
- Summary statistics-based association test for identifying the pleiotropic effects with set of genetic variants.Bioinformatics (Oxford, England) · 2023Article
- Bivariate quantitative Bayesian LASSO for detecting association of rare haplotypes with two correlated continuous phenotypes.Frontiers in genetics · 2023Article
- Multivariate genome wide association and network analysis of subcortical imaging phenotypes in Alzheimer's disease.BMC genomics · 2020Article
- Pre-selected class-level testing of longitudinal biomarkers reduces required multiple testing corrections to yield novel insights in longitudinal small sample human studies.Statistical communications in infectious diseases · 2020Article
- Multifaceted genome-wide study identifies novel regulatory loci in SLC22A11 and ZNF45 for body mass index in Indians.Molecular genetics and genomics : MGG · 2020Article
- Multi-trait analysis of rare-variant association summary statistics using MTAR.Nature communications · 2020Article
- Genome-wide association study of blood lipids in Indians confirms universality of established variants.Journal of human genetics · 2019Article
- Powerful and Efficient Strategies for Genetic Association Testing of Symptom and Questionnaire Data in Psychiatric Genetic Studies.Scientific reports · 2019Article
- Comparison of methods for multivariate gene-based association tests for complex diseases using common variants.European journal of human genetics : EJHG · 2019Article
- Powerful and efficient SNP-set association tests across multiple phenotypes using GWAS summary data.Bioinformatics (Oxford, England) · 2019Article
- Pleiotropic mapping and annotation selection in genome-wide association studies with penalized Gaussian mixture models.Bioinformatics (Oxford, England) · 2018Article
- Statistical Analysis of Multiple Phenotypes in Genetic Epidemiologic Studies: From Cross-Phenotype Associations to Pleiotropy.American journal of epidemiology · 2018Review
- New Method Application for Marker-Trait Association Studies in Plants: Partial Least Square Regression Aids Detection of Simultaneous Correlations.Agricultural research & technology : open access journal · 2017Article
- Gene- and pathway-based association tests for multiple traits with GWAS summary statistics.Bioinformatics (Oxford, England) · 2017Article
- Genome-wide association test of multiple continuous traits using imputed SNPs.Statistics and its interface · 2017Article
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7 authors.
Funding
Abstract
motivationStandard genome-wide association studies, testing the association between one phenotype and a large number of single nucleotide polymorphisms (SNPs), are limited in two ways: (i) traits are often multivariate, and analysis of composite scores entails loss in statistical power and (ii) gene-based analyses may be preferred, e.g. to decrease the multiple testing problem.
resultsHere we present a new method, multivariate gene-based association test by extended Simes procedure (MGAS), that allows gene-based testing of multivariate phenotypes in unrelated individuals. Through extensive simulation, we show that under most trait-generating genotype-phenotype models MGAS has superior statistical power to detect associated genes compared with gene-based analyses of univariate phenotypic composite scores (i.e. GATES, multiple regression), and multivariate analysis of variance (MANOVA). Re-analysis of metabolic data revealed 32 False Discovery Rate controlled genome-wide significant genes, and 12 regions harboring multiple genes; of these 44 regions, 30 were not reported in the original analysis.
conclusionMGAS allows researchers to conduct their multivariate gene-based analyses efficiently, and without the loss of power that is often associated with an incorrectly specified genotype-phenotype models. AVAILABILITY AND IMPLEMENTATION: MGAS is freely available in KGG v3.0 (http://statgenpro.psychiatry.hku.hk/limx/kgg/download.php). Access to the metabolic dataset can be requested at dbGaP (https://dbgap.ncbi.nlm.nih.gov/). The R-simulation code is available from http://ctglab.nl/people/sophie_van_der_sluis. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.