Format

Send to

Choose Destination
See comment in PubMed Commons below
PLoS One. 2013 Jul 5;8(7):e65245. doi: 10.1371/journal.pone.0065245. Print 2013.

A unified framework for association analysis with multiple related phenotypes.

Author information

1
Department of Statistics and Department of Human Genetics, University of Chicago, Chicago, Illinois, USA. mstephens@uchicago.edu

Abstract

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.

PMID:
23861737
PMCID:
PMC3702528
DOI:
10.1371/journal.pone.0065245
[Indexed for MEDLINE]
Free PMC Article
PubMed Commons home

PubMed Commons

0 comments

    Supplemental Content

    Full text links

    Icon for Public Library of Science Icon for PubMed Central
    Loading ...
    Support Center