Joint Genetic Correlation Modeling of Human Cortical Structures: Network Insights into Brain Morphology

Abstract

Understanding the genetic underpinnings of human cortical structures has been a longstanding challenge in neuroscience and genetics. Traditional single-structure genetic studies have provided insights, but they often fail to capture the intricate network-like dependencies among cortical regions. In this research, we employ joint genetic correlation modeling to analyze the genetic architectures underlying multiple cortical structures simultaneously. By integrating genome-wide association studies (GWAS) with advanced network-based statistical models, we explore how shared genetic influences shape brain morphology. Our results reveal significant genetic correlations among various cortical regions, suggesting a coordinated genetic influence on structural development. We also demonstrate that these genetic correlations align with known functional and connectivity-based brain networks. The findings provide a deeper understanding of cortical organization, potentially impacting neurodevelopmental and neuropsychiatric research.