ArticleBMC bioinformatics2016
An efficient genome-wide association test for multivariate phenotypes based on the Fisher combination function.
Article in BMC bioinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed.
- DrFARM: identification of pleiotropic genetic variants in genome-wide association studies.Nature communications · 2025Article
- metacp: a versatile software package for combining dependent or independent p-values.BMC bioinformatics · 2025Article
- A novel method for multiple phenotype association studies based on genotype and phenotype network.PLoS genetics · 2024Article
- Multiple phenotype association tests based on sliced inverse regression.BMC bioinformatics · 2024Article
- A copula-based set-variant association test for bivariate continuous, binary or mixed phenotypes.The international journal of biostatistics · 2023Article
- Integrating multi-omics summary data using a Mendelian randomization framework.Briefings in bioinformatics · 2022Article
- A Novel Hierarchical Clustering Approach for Joint Analysis of Multiple Phenotypes Uncovers Obesity Variants Based on ARIC.Frontiers in genetics · 2022Article
- A Comparison of Methods for Gene-Based Testing That Account for Linkage Disequilibrium.Frontiers in genetics · 2022Article
- Gene-based mediation analysis in epigenetic studies.Briefings in bioinformatics · 2021Article
- A Novel Approach Integrating Hierarchical Clustering and Weighted Combination for Association Study of Multiple Phenotypes and a Genetic Variant.Frontiers in genetics · 2021Article
- SNP Alleles Associated With Low Bolting Tendency in Sugar Beet.Frontiers in plant science · 2021Article
- Statistical methods for mediation analysis in the era of high-throughput genomics: Current successes and future challenges.Computational and structural biotechnology journal · 2021Review
- Article
- Joint analysis of multiple phenotypes using a clustering linear combination method based on hierarchical clustering.Genetic epidemiology · 2020Article
- Genome-Wide Gene-Based Multi-Trait Analysis.Frontiers in genetics · 2020Article
- Family-Based Quantitative Trait Meta-Analysis Implicates Rare Noncoding Variants in DENND1A in Polycystic Ovary Syndrome.The Journal of clinical endocrinology and metabolism · 2019Article
- Extended methods for gene-environment-wide interaction scans in studies of admixed individuals with varying degrees of relationships.Genetic epidemiology · 2019Article
- 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
- Joint analysis of multiple phenotypes in association studies using allele-based clustering approach for non-normal distributions.Annals of human genetics · 2018Article
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Authors and funding
4 authors.
Funding
Abstract
backgroundIn genome-wide association studies (GWAS) for complex diseases, the association between a SNP and each phenotype is usually weak. Combining multiple related phenotypic traits can increase the power of gene search and thus is a practically important area that requires methodology work. This study provides a comprehensive review of existing methods for conducting GWAS on complex diseases with multiple phenotypes including the multivariate analysis of variance (MANOVA), the principal component analysis (PCA), the generalizing estimating equations (GEE), the trait-based association test involving the extended Simes procedure (TATES), and the classical Fisher combination test. We propose a new method that relaxes the unrealistic independence assumption of the classical Fisher combination test and is computationally efficient. To demonstrate applications of the proposed method, we also present the results of statistical analysis on the Study of Addiction: Genetics and Environment (SAGE) data.
resultsOur simulation study shows that the proposed method has higher power than existing methods while controlling for the type I error rate. The GEE and the classical Fisher combination test, on the other hand, do not control the type I error rate and thus are not recommended. In general, the power of the competing methods decreases as the correlation between phenotypes increases. All the methods tend to have lower power when the multivariate phenotypes come from long tailed distributions. The real data analysis also demonstrates that the proposed method allows us to compare the marginal results with the multivariate results and specify which SNPs are specific to a particular phenotype or contribute to the common construct.
conclusionsThe proposed method outperforms existing methods in most settings and also has great applications in GWAS on complex diseases with multiple phenotypes such as the substance abuse disorders.
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