Projection regression models for multivariate imaging phenotype.
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.
Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA.
Ja-an
JA
Lin
Hongtu
H
Zhu
Rebecca
R
Knickmeyer
Martin
M
Styner
John
J
Gilmore
Joseph G
JG
Ibrahim
D001921:Brain / Q000254:growth & development* / Q000502:physiology; D003198:Computer Simulation; D005260:Female; D005787:Gene Frequency; D006801:Humans; D007231:Infant, Newborn; D016014:Linear Models; D008279:Magnetic Resonance Imaging; D008297:Male; D008957:Models, Genetic; D015233:Models, Statistical; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide; D011247:Pregnancy; D012044:Regression Analysis