# ACE of Space: Estimating Genetic Components of High-Dimensional Imaging Data

## TL;DR

This paper introduces a scalable, positive semidefinite estimator for genetic heritability and covariance patterns in high-dimensional brain imaging data, improving accuracy over existing methods and revealing detailed genetic influences on brain structure.

## Contribution

Develops a novel positive semidefinite estimator for genetic covariance in high-dimensional imaging data, addressing bias issues and enabling detailed heritability analysis.

## Key findings

- Improved heritability estimates over existing methods
- Identified localized and widespread genetic covariance patterns in brain data
- Demonstrated scalability to high-dimensional imaging with 60,000 vertices

## Abstract

It is of great interest to quantify the contributions of genetic variation to brain structure and function, which are usually measured by high-dimensional imaging data (e.g., magnetic resonance imaging). In addition to the variance, the covariance patterns in the genetic effects of a functional phenotype are of biological importance, and covariance patterns have been linked to psychiatric disorders. The aim of this paper is to develop a scalable method to estimate heritability and the non-stationary covariance components in high-dimensional imaging data from twin studies. Our motivating example is from the Human Connectome Project (HCP). Several major big-data challenges arise from estimating the genetic and environmental covariance functions of functional phenotypes extracted from imaging data, such as cortical thickness with 60,000 vertices. Notably, truncating to positive eigenvalues and their eigenfunctions from unconstrained estimators can result in large bias. This motivated our development of a novel estimator ensuring positive semidefiniteness. Simulation studies demonstrate large improvements over existing approaches, both with respect to heritability estimates and covariance estimation. We applied the proposed method to cortical thickness data from the HCP. Our analysis suggests fine-scale differences in covariance patterns, identifying locations in which genetic control is correlated with large areas of the brain and locations where it is highly localized.