A Unified Framework for Association Analysis with Multiple Related Phenotypes

We consider the problem of assessing associations between multiple related outcome variables, and a single explanatory variable of interest. This problem arises in many settings, including genetic association studies, where the explanatory variable is genotype at a genetic variant. We outline a fram...

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Veröffentlicht in:PloS one Jg. 8; H. 7; S. e65245
1. Verfasser: Stephens, Matthew
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Public Library of Science 05.07.2013
Public Library of Science (PLoS)
Schlagworte:
Algorithms
Association analysis
Bayes Theorem
Bayesian analysis
Biology
Cholesterol
Computer Simulation
Enzymes
Genetic aspects
Genetic Association Studies
Genetic diversity
Genetic variance
Genome-wide association studies
Genomes
Genomics
Genotype
Humans
Hypotheses
Lipid Metabolism
Lipids
Mathematics
Medicine
Models, Genetic
Models, Statistical
Multivariate Analysis
Phenotype
Phenotypes
Regression Analysis
Statistics
ISSN:1932-6203, 1932-6203
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Zusammenfassung:We consider the problem of assessing associations between multiple related outcome variables, and a single explanatory variable of interest. This problem arises in many settings, including genetic association studies, where the explanatory variable is genotype at a genetic variant. We outline a framework for conducting this type of analysis, based on Bayesian model comparison and model averaging for multivariate regressions. This framework unifies several common approaches to this problem, and includes both standard univariate and standard multivariate association tests as special cases. The framework also unifies the problems of testing for associations and explaining associations - that is, identifying which outcome variables are associated with genotype. This provides an alternative to the usual, but conceptually unsatisfying, approach of resorting to univariate tests when explaining and interpreting significant multivariate findings. The method is computationally tractable genome-wide for modest numbers of phenotypes (e.g. 5-10), and can be applied to summary data, without access to raw genotype and phenotype data. We illustrate the methods on both simulated examples, and to a genome-wide association study of blood lipid traits where we identify 18 potential novel genetic associations that were not identified by univariate analyses of the same data.
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Competing Interests: The author has declared that no competing interests exist.
Conceived and designed the experiments: MS. Performed the experiments: MS. Analyzed the data: MS. Contributed reagents/materials/analysis tools: MS. Wrote the paper: MS.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0065245