Genetic Regression Analysis of Human Brain Connectivity Using an Efficient Estimator of Genetic Covariance

TL;DR

This paper introduces a fast, accurate genetic covariance estimation method for high-dimensional brain connectome data, revealing moderate genetic predictability of functional connectivity from structural data.

Contribution

It develops a novel, computationally efficient estimator for genetic covariance matrices and applies regularized regression techniques to analyze brain connectivity genetics.

Findings

Functional connectivity is moderately predictable from structure at the genetic level (max R^2 = 0.34)

Observed data shows low predictability (max R^2 = 0.03) due to environmental masking

Proposed methods outperform existing approaches in speed and accuracy

Abstract

Non-invasive measurements of the human brain using magnetic resonance imaging (MRI) have significantly improved our understanding the brain's network organization by enabling measurement of anatomical connections between brain regions (structural connectivity) and their coactivation (functional connectivity). Heritability analyses have established that genetics account for considerable intersubject variability in structural and functional connectivity. However, characterizing how genetics shape the relationship between structural and functional connectomes remains challenging, since this association is obscured by unique environmental exposures in observed data. To address this, we develop a regression analysis framework that enables characterization of the relationship between latent genetic contributions to structural and functional connectivity. Implementing the proposed framework requires estimating genetic covariance matrices in multivariate random effects models, which is computationally intractable for high-dimensional connectome data using existing methods. We introduce a constrained method-of-moments estimator that is several orders of magnitude faster than existing methods without sacrificing estimation accuracy. For the genetic regression analysis, we develop regularized estimation approaches, including ridge, lasso, and tensor regression. Applying our method to Human Connectome Project data, we find that functional connectivity is moderately predictable from structure at the genetic level (max R^2 = 0.34), though it is not directly predictable in the observed data (max R^2 = 0.03). This stark contrast suggests that unique environmental factors mask strong genetically-encoded structure-function relationships.