A Novel Bayesian Semi-parametric Model for Learning Heritable Imaging Traits

Abstract

Heritability analysis is an important research topic in brain imaging genetics. Its primary motivation is to identify highly heritable imaging quantitative traits (QTs) for subsequent in-depth imaging genetic analyses. Most existing studies perform heritability analyses on regional imaging QTs using predefined brain parcellation schemes such as the AAL atlas. However, the power to dissect genetic underpinnings under QTs defined in such an unsupervised fashion is largely deteriorate with inner partition noise and signal dilution. To bridge the gap, we propose a new semi-parametric Bayesian heritability estimation model to construct highly heritable imaging QTs. Our method leverages the aggregate of genetic signals to imaging QT construction by developing a new brain parcellation driven by voxel-level heritability. To ensure biological plausibility and clinical interpretability of the resulting brain heritability parcellations, hierarchical sparsity and smoothness, coupled with structural connectivity of the brain, are properly imposed on genetic effects to induce spatial contiguity of heritable imaging QTs. Using the ADNI imaging genetic data, we demonstrate the strength of our proposed method, in comparison with the standard GCTA method, in identifying highly heritable and biologically meaningful new imaging QTs.

Keywords: Bayesian semi-parametric modeling; Heritability estimation; Imaging genetics.

Fig. 1.

The illustration of our proposed method Brain Heritability Mapping (BHM).

Fig. 2.

Heritability maps estimated by (a) the conventional GCTA method and (b) the proposed BHM method. In the GCTA map, the entire ROI is painted with the estimated heritability. In the BHM map, only the identified voxels forming new heritable imaging QTs are painted with the estimated heritability.