ArticleGenetics2016
Efficient and Accurate Multiple-Phenotype Regression Method for High Dimensional Data Considering Population Structure.
Article in Genetics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Meta-Analysis of Polymyositis and Dermatomyositis Microarray Data Reveals Novel Genetic Biomarkers.Genes · 2019Pooled it
- GAMMA-RAY: A Fully Automated and Rapid System for High-Dimensional Multi-Phenotype Analysis Considering Population Structure.Biology · 2026Article
- Genome-wide association insights into the genomic regions controlling vegetative and oil production traits in Acrocomia aculeata.BMC plant biology · 2024Article
- Transcriptome-Wide Association Analysis of Flavonoid Biosynthesis Genes and Their Correlation With Leaf Phenotypes in Hawk Tea (Ecology and evolution · 2024Article
- JASPER: Fast, powerful, multitrait association testing in structured samples gives insight on pleiotropy in gene expression.American journal of human genetics · 2024Article
- Interaction models matter: an efficient, flexible computational framework for model-specific investigation of epistasis.BioData mining · 2024Article
- JASPER: fast, powerful, multitrait association testing in structured samples gives insight on pleiotropy in gene expression.bioRxiv : the preprint server for biology · 2023Article
- A fast non-parametric test of association for multiple traits.Genome biology · 2023Article
- Dissecting Complex Traits Using Omics Data: A Review on the Linear Mixed Models and Their Application in GWAS.Plants (Basel, Switzerland) · 2022Review
- Diversifying maize genomic selection models.Molecular breeding : new strategies in plant improvement · 2021Article
- Multivariate Analysis and Modelling of multiple Brain endOphenotypes: Let's MAMBO!Computational and structural biotechnology journal · 2021Review
- Article
- How Well Can Multivariate and Univariate GWAS Distinguish Between True and Spurious Pleiotropy?Frontiers in genetics · 2020Article
- Integrative genomic and transcriptomic analysis of genetic markers in Dupuytren's disease.BMC medical genomics · 2019Article
- Comparison ofFrontiers in genetics · 2019Article
- Finding associated variants in genome-wide association studies on multiple traits.Bioinformatics (Oxford, England) · 2018Article
- Statistical methods to detect pleiotropy in human complex traits.Open biology · 2017Review
- Powerful Genetic Association Analysis for Common or Rare Variants with High-Dimensional Structured Traits.Genetics · 2017Article
- Multiple testing correction in linear mixed models.Genome biology · 2016Article
- Many Phenotypes Without Many False Discoveries: Error Controlling Strategies for Multitrait Association Studies.Genetic epidemiology · 2016Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
A typical genome-wide association study tests correlation between a single phenotype and each genotype one at a time. However, single-phenotype analysis might miss unmeasured aspects of complex biological networks. Analyzing many phenotypes simultaneously may increase the power to capture these unmeasured aspects and detect more variants. Several multivariate approaches aim to detect variants related to more than one phenotype, but these current approaches do not consider the effects of population structure. As a result, these approaches may result in a significant amount of false positive identifications. Here, we introduce a new methodology, referred to as GAMMA for generalized analysis of molecular variance for mixed-model analysis, which is capable of simultaneously analyzing many phenotypes and correcting for population structure. In a simulated study using data implanted with true genetic effects, GAMMA accurately identifies these true effects without producing false positives induced by population structure. In simulations with this data, GAMMA is an improvement over other methods which either fail to detect true effects or produce many false positive identifications. We further apply our method to genetic studies of yeast and gut microbiome from mice and show that GAMMA identifies several variants that are likely to have true biological mechanisms.
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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.