Projection regression models for multivariate imaging phenotype.

Abstract:

:This paper presents a projection regression model (PRM) to assess the relationship between a multivariate phenotype and a set of covariates, such as a genetic marker, age, and gender. In the existing literature, a standard statistical approach to this problem is to fit a multivariate linear model to the multivariate phenotype and then use Hotelling's T(2) to test hypotheses of interest. An alternative approach is to fit a simple linear model and test hypotheses for each individual phenotype and then correct for multiplicity. However, even when the dimension of the multivariate phenotype is relatively small, say 5, such standard approaches can suffer from the issue of low statistical power in detecting the association between the multivariate phenotype and the covariates. The PRM generalizes a statistical method based on the principal component of heritability for association analysis in genetic studies of complex multivariate phenotypes. The key components of the PRM include an estimation procedure for extracting several principal directions of multivariate phenotypes relating to covariates and a test procedure based on wild-bootstrap method for testing the association between the weighted multivariate phenotype and explanatory variables. Simulation studies and an imaging genetic dataset are used to examine the finite sample performance of the PRM.

journal_name

Genet Epidemiol

journal_title

Genetic epidemiology

authors

Lin JA,Zhu H,Knickmeyer R,Styner M,Gilmore J,Ibrahim JG

doi

10.1002/gepi.21658

subject

Has Abstract

pub_date

2012-09-01 00:00:00

pages

631-41

issue

6

eissn

0741-0395

issn

1098-2272

journal_volume

36

pub_type

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