ArticleGenetics2017
Powerful Genetic Association Analysis for Common or Rare Variants with High-Dimensional Structured Traits.
Article in Genetics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- The sequence kernel association test for the proportional odds model.Bioinformatics (Oxford, England) · 2025Article
- JASPER: Fast, powerful, multitrait association testing in structured samples gives insight on pleiotropy in gene expression.American journal of human genetics · 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 maximum kernel-based association test to detect the pleiotropic genetic effects on multiple phenotypes.Bioinformatics (Oxford, England) · 2023Article
- Kernel-based genetic association analysis for microbiome phenotypes identifies host genetic drivers of beta-diversity.Microbiome · 2023Article
- Accommodating multiple potential normalizations in microbiome associations studies.BMC bioinformatics · 2023Article
- Random field modeling of multi-trait multi-locus association for detecting methylation quantitative trait loci.Bioinformatics (Oxford, England) · 2022Article
- An optimal kernel-based multivariate U-statistic to test for associations with multiple phenotypes.Biostatistics (Oxford, England) · 2022Article
- Adaptive and powerful microbiome multivariate association analysis via feature selection.NAR genomics and bioinformatics · 2022Article
- A Kernel-based Test of Independence for Cluster-correlated Data.Advances in neural information processing systems · 2021Article
- VCSEL: PRIORITIZING SNP-SET BY PENALIZED VARIANCE COMPONENT SELECTION.The annals of applied statistics · 2021Article
- Understanding the metabolome and metagenome as extended phenotypes: The next frontier in macroalgae domestication and improvement.Journal of the World Aquaculture Society · 2021Article
- Identifying genetic risk variants associated with brain volumetric phenotypes via K-sample Ball Divergence method.Genetic epidemiology · 2021Article
- Comparison of adaptive multiple phenotype association tests using summary statistics in genome-wide association studies.Human molecular genetics · 2021Article
- Genome-wide association study and its applications in the non-model crop Sesamum indicum.BMC plant biology · 2021Review
- A Bayesian hierarchically structured prior for rare-variant association testing.Genetic epidemiology · 2021Article
- A method for subtype analysis with somatic mutations.Bioinformatics (Oxford, England) · 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
- Multi-trait multi-locus SEM model discriminates SNPs of different effects.BMC genomics · 2020Article
- Multi-trait analysis of rare-variant association summary statistics using MTAR.Nature communications · 2020Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Many genetic association studies collect a wide range of complex traits. As these traits may be correlated and share a common genetic mechanism, joint analysis can be statistically more powerful and biologically more meaningful. However, most existing tests for multiple traits cannot be used for high-dimensional and possibly structured traits, such as network-structured transcriptomic pathway expressions. To overcome potential limitations, in this article we propose the dual kernel-based association test (DKAT) for testing the association between multiple traits and multiple genetic variants, both common and rare. In DKAT, two individual kernels are used to describe the phenotypic and genotypic similarity, respectively, between pairwise subjects. Using kernels allows for capturing structure while accommodating dimensionality. Then, the association between traits and genetic variants is summarized by a coefficient which measures the association between two kernel matrices. Finally, DKAT evaluates the hypothesis of nonassociation with an analytical
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