ArticlePLoS genetics2013
TATES: efficient multivariate genotype-phenotype analysis for genome-wide association studies.
Article in PLoS genetics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 127 papers, 6 of them syntheses that pooled it.
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Who cites it
127 citing papers in PubMed, 6 syntheses or guidelines pooled it.
- The genetic architecture of the human cerebral cortex.Science (New York, N.Y.) · 2020Pooled it
- Multivariate genome-wide analyses of the well-being spectrum.Nature genetics · 2019Pooled it
- metaCCA: summary statistics-based multivariate meta-analysis of genome-wide association studies using canonical correlation analysis.Bioinformatics (Oxford, England) · 2016Pooled it
- Meta-analysis of correlated traits via summary statistics from GWASs with an application in hypertension.American journal of human genetics · 2015Pooled it
- Genome-wide analyses of borderline personality features.Molecular psychiatry · 2014Pooled it
- Pooled it
- The common variants implicated in microstructural abnormality of first episode and drug-naïve patients with schizophrenia.Scientific reports · 2017Trial
- Research on multi-trait genome association study method based on Shannon information entropy.BMC bioinformatics · 2026Article
- Divided-and-combined association test for pleiotropic effects with GWAS summary statistics.BMC genomics · 2026Article
- The genetic architecture of human cerebellar morphology supports a key role for the cerebellum in human evolution and psychopathology.Communications biology · 2026Article
- Network construction using sparse Gaussian graphical model based on GWAS summary statistics.Scientific reports · 2025Article
- Pleiotropic and sex-specific genetic mechanisms of circulating metabolic markers.Nature communications · 2025Article
- Exploring beyond diagnoses in electronic health records to improve discovery: a review of the phenome-wide association study.JAMIA open · 2025Review
- A novel phenotype imputation method with copula model.BMC bioinformatics · 2024Article
- Modern Plant Breeding Techniques in Crop Improvement and Genetic Diversity: From Molecular Markers and Gene Editing to Artificial Intelligence-A Critical Review.Plants (Basel, Switzerland) · 2024Review
- Using Genetics to Investigate Relationships between Phenotypes: Application to Endometrial Cancer.Genes · 2024Review
- An adaptive and robust method for multi-trait analysis of genome-wide association studies using summary statistics.European journal of human genetics : EJHG · 2024Article
- GbyE: an integrated tool for genome widely association study and genome selection based on genetic by environmental interaction.BMC genomics · 2024Article
- Multiple phenotype association tests based on sliced inverse regression.BMC bioinformatics · 2024Article
- Maize green leaf area index dynamics: genetic basis of a new secondary trait for grain yield in optimal and drought conditions.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2024Article
67 more citing papers are in PubMed but not listed here.
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3 authors.
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Abstract
To date, the genome-wide association study (GWAS) is the primary tool to identify genetic variants that cause phenotypic variation. As GWAS analyses are generally univariate in nature, multivariate phenotypic information is usually reduced to a single composite score. This practice often results in loss of statistical power to detect causal variants. Multivariate genotype-phenotype methods do exist but attain maximal power only in special circumstances. Here, we present a new multivariate method that we refer to as TATES (Trait-based Association Test that uses Extended Simes procedure), inspired by the GATES procedure proposed by Li et al (2011). For each component of a multivariate trait, TATES combines p-values obtained in standard univariate GWAS to acquire one trait-based p-value, while correcting for correlations between components. Extensive simulations, probing a wide variety of genotype-phenotype models, show that TATES's false positive rate is correct, and that TATES's statistical power to detect causal variants explaining 0.5% of the variance can be 2.5-9 times higher than the power of univariate tests based on composite scores and 1.5-2 times higher than the power of the standard MANOVA. Unlike other multivariate methods, TATES detects both genetic variants that are common to multiple phenotypes and genetic variants that are specific to a single phenotype, i.e. TATES provides a more complete view of the genetic architecture of complex traits. As the actual causal genotype-phenotype model is usually unknown and probably phenotypically and genetically complex, TATES, available as an open source program, constitutes a powerful new multivariate strategy that allows researchers to identify novel causal variants, while the complexity of traits is no longer a limiting factor.
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