title: Statistical Methods for Testing Genetic Pleiotropy.
abstract: Genetic pleiotropy is when a single gene influences more than one trait. Detecting pleiotropy and understanding its causes can improve the biological understanding of a gene in multiple ways, yet current multivariate methods to evaluate pleiotropy test the null hypothesis that none of the traits are associated with a variant; departures from the null could be driven by just one associated trait. A formal test of pleiotropy should assume a null hypothesis that one or no traits are associated with a genetic variant. For the special case of two traits, one can construct this null hypothesis based on the intersection-union (IU) test, which rejects the null hypothesis only if the null hypotheses of no association for both traits are rejected. To allow for more than two traits, we developed a new likelihood-ratio test for pleiotropy. We then extended the testing framework to a sequential approach to test the null hypothesis that [Formula: see text] traits are associated, given that the null of k traits are associated was rejected. This provides a formal testing framework to determine the number of traits associated with a genetic variant, while accounting for correlations among the traits. By simulations, we illustrate the type I error rate and power of our new methods; describe how they are influenced by sample size, the number of traits, and the trait correlations; and apply the new methods to multivariate immune phenotypes in response to smallpox vaccination. Our new approach provides a quantitative assessment of pleiotropy, enhancing current analytic practice.
authors.affiliation: Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota 55905 schaid@mayo.edu.
authors.forename: Daniel J
authors.initials: DJ
authors.lastname: Schaid
authors.affiliation: School of Statistics, Beijing Normal University, Beijing, China.
authors.forename: Xingwei
authors.initials: X
authors.lastname: Tong
authors.affiliation: Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota 55905.
authors.forename: Beth
authors.initials: B
authors.lastname: Larrabee
authors.affiliation: Mayo Clinic Vaccine Research Group, Mayo Clinic, Rochester, Minnesota 55905.
authors.forename: Richard B
authors.initials: RB
authors.lastname: Kennedy
authors.affiliation: Mayo Clinic Vaccine Research Group, Mayo Clinic, Rochester, Minnesota 55905.
authors.forename: Gregory A
authors.initials: GA
authors.lastname: Poland
authors.affiliation: Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota 55905.
authors.forename: Jason P
authors.initials: JP
authors.lastname: Sinnwell
mesh_terms: D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D008040:Genetic Linkage / Q000235:genetics; D058685:Genetic Pleiotropy; D014644:Genetic Variation; D006801:Humans; D008962:Models, Theoretical; D010641:Phenotype; D040641:Quantitative Trait Loci / Q000235:genetics*
keywords: constrained model; likelihood-ratio test; multivariate analysis; seemingly unrelated regression; sequential testing