Functional-Mixed Effects Models for Candidate Genetic Mapping in Imaging Genetic Studies

Abstract

The aim of this paper is to develop a functional mixed effects modeling (FMEM) framework for the joint analysis of high-dimensional imaging data in a large number of locations (called voxels) of a three-dimensional volume with a set of genetic markers and clinical covariates. Our FMEM is extremely useful for effciently carrying out the candidate gene approaches in imaging genetic studies. FMEM consists of two novel components including a mixed effects model for modeling nonlinear genetic effects on imaging phenotypes by introducing the genetic random effects at each voxel and a jumping surface model for modeling the variance components of the genetic random effects and fixed effects as piecewise smooth functions of the voxels. Moreover, FMEM naturally accommodates the correlation structure of genetic markers at each voxel, while the jumping surface model explicitly incorporates the intrinsically spatial smoothness of the imaging data. We propose a novel two-stage adaptive smoothing procedure to spatially estimate the piecewise smooth functions, particularly the irregular functional genetic variance components, while preserving their edges among different piecewise-smooth regions. We develop weighted likelihood ratio tests and derive their exact approximations to test the effect of the genetic markers across voxels. Simulation studies show that FMEM significantly outperforms voxel-wise approaches in terms of higher sensitivity and specificity to identify regions of interest for carrying out candidate genetic mapping in imaging genetic studies. Finally, FMEM is used to identify brain regions affected by three candidate genes including CR1, CD2AP, and PICALM, thereby hoping to shed light on the pathological interactions between these candidate genes and brain structure and function.

Recent publications from the Alzheimer's Disease Neuroimaging Initiative: Reviewing progress toward improved AD clinical trials

“…Univariate analysis comparing single SNPs with single traits suffers from problems of high dimensionality and type-I errors and may not reveal significant associations without meta-analytic approaches. ADNI studies have assessed the extent of these problems [237,238], improved the computational efficiency of mass univariate analyses [239–242], and developed methods for the selection of the most informative SNPs or quantitative features to improve power to detect associations [243–247]. Two studies have developed summary measures representing associations between selected SNPs and traits of interest [202,248].…”

FGWAS: Functional genome wide association analysis

“…We use a multivariate varying coefficient model (MVCM) as a special function-on-scalar regression model to fit the functional phenotypes with a large number of genetic variants (Zhu et al, 2011; Di et al, 2009; Zipunnikov et al, 2011; Zhu et al, 2014; Guo, 2002; Lin et al, 2014b), while explicitly accounting for their three key functional features as discussed above. The use of the MVCM can project N V imaging measures into the N V 0 –dimensional space, leading to computational and efficiency gains on the order of O ( N V /N V 0 ).…”