ArticleScientific reports2016
An Adaptive Fisher's Combination Method for Joint Analysis of Multiple Phenotypes in Association Studies.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Network construction using sparse Gaussian graphical model based on GWAS summary statistics.Scientific reports · 2025Article
- A novel phenotype imputation method with copula model.BMC bioinformatics · 2024Article
- Leveraging error-prone algorithm-derived phenotypes: Enhancing association studies for risk factors in EHR data.Journal of biomedical informatics · 2024Article
- A novel method for multiple phenotype association studies based on genotype and phenotype network.PLoS genetics · 2024Article
- A clustering linear combination method for multiple phenotype association studies based on GWAS summary statistics.Scientific reports · 2023Article
- Gene-Based Association Tests Using New Polygenic Risk Scores and Incorporating Gene Expression Data.Genes · 2022Article
- Article
- A Novel Hierarchical Clustering Approach for Joint Analysis of Multiple Phenotypes Uncovers Obesity Variants Based on ARIC.Frontiers in genetics · 2022Article
- A computationally efficient clustering linear combination approach to jointly analyze multiple phenotypes for GWAS.PloS one · 2022Article
- SNP Alleles Associated With Low Bolting Tendency in Sugar Beet.Frontiers in plant science · 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
- A Novel Approach Integrating Hierarchical Clustering and Weighted Combination for Association Study of Multiple Phenotypes and a Genetic Variant.Frontiers in genetics · 2021Article
- Joint analysis of multiple phenotypes using a clustering linear combination method based on hierarchical clustering.Genetic epidemiology · 2020Article
- A clustering linear combination approach to jointly analyze multiple phenotypes for GWAS.Bioinformatics (Oxford, England) · 2019Article
- Joint Analysis of Multiple Phenotypes in Association Studies based on Cross-Validation Prediction Error.Scientific reports · 2019Article
- A gene based approach to test genetic association based on an optimally weighted combination of multiple traits.PloS one · 2019Article
- Joint analysis of multiple phenotypes in association studies using allele-based clustering approach for non-normal distributions.Annals of human genetics · 2018Article
- A hierarchical clustering method for dimension reduction in joint analysis of multiple phenotypes.Genetic epidemiology · 2018Article
- Testing an optimally weighted combination of common and/or rare variants with multiple traits.PloS one · 2018Article
- A novel method to test associations between a weighted combination of phenotypes and genetic variants.PloS one · 2018Article
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4 authors.
Funding
Abstract
Currently, the analyses of most genome-wide association studies (GWAS) have been performed on a single phenotype. There is increasing evidence showing that pleiotropy is a widespread phenomenon in complex diseases. Therefore, using only one single phenotype may lose statistical power to identify the underlying genetic mechanism. There is an increasing need to develop and apply powerful statistical tests to detect association between multiple phenotypes and a genetic variant. In this paper, we develop an Adaptive Fisher's Combination (AFC) method for joint analysis of multiple phenotypes in association studies. The AFC method combines p-values obtained in standard univariate GWAS by using the optimal number of p-values which is determined by the data. We perform extensive simulations to evaluate the performance of the AFC method and compare the power of our method with the powers of TATES, Tippett's method, Fisher's combination test, MANOVA, MultiPhen, and SUMSCORE. Our simulation studies show that the proposed method has correct type I error rates and is either the most powerful test or comparable with the most powerful test. Finally, we illustrate our proposed methodology by analyzing whole-genome genotyping data from a lung function study.
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