Multivariate analysis reveals shared genetic architecture of brain morphology and human behavior

Area of Science:

• Neurogenetics
• Quantitative Genetics
• Human Behavior Genetics

Background:

• Human brain morphology and behavior exhibit significant heritability and interconnectedness.
• The precise genetic relationships between specific brain features and behavioral traits remain largely uncharacterized.

Purpose of the Study:

• To develop a computationally efficient method, multivariate genomic-relatedness-based restricted maximum likelihood (MGREML), for estimating genetic correlations among numerous phenotypes.
• To investigate the heritability of gray-matter volume in 74 brain regions and map genetic correlations with health-relevant behaviors, including intelligence, using UK Biobank data.

Main Methods:

• Implementation of the multivariate genomic-relatedness-based restricted maximum likelihood (MGREML) approach.
• Analysis of individual-level data from the UK Biobank (N=20,190).
• Estimation of heritability for gray-matter volume in 74 brain regions of interest (ROIs).
• Mapping of genetic correlations between brain ROIs and behavioral outcomes.

Main Results:

• Identification of four genetically distinct clusters within the brain, corresponding to established neuroanatomical subdivisions.
• Demonstration of unique genetic correlations between specific behavioral traits and brain morphology.
• Evidence suggesting region-specific relevance of brain ROIs for particular traits.

Conclusions:

• The MGREML method provides an efficient way to estimate high-dimensional genetic correlation matrices from large datasets.
• Genetic correlations between brain morphology and behavior are complex and trait-specific.
• Findings highlight the utility of MGREML for dissecting the genetic architecture underlying human brain-behavior relationships.