Autism-associated promoter variant in MET impacts functional and structural brain networks

Abstract

As genes that confer increased risk for autism spectrum disorder (ASD) are identified, a crucial next step is to determine how these risk factors impact brain structure and function and contribute to disorder heterogeneity. With three converging lines of evidence, we show that a common, functional ASD risk variant in the Met Receptor Tyrosine Kinase (MET) gene is a potent modulator of key social brain circuitry in children and adolescents with and without ASD. MET risk genotype predicted atypical fMRI activation and deactivation patterns to social stimuli (i.e., emotional faces), as well as reduced functional and structural connectivity in temporo-parietal regions known to have high MET expression, particularly within the default mode network. Notably, these effects were more pronounced in individuals with ASD. These findings highlight how genetic stratification may reduce heterogeneity and help elucidate the biological basis of complex neuropsychiatric disorders such as ASD.

Figure 1

Functional MRI activation patterns to emotional faces in MET risk carriers. A) Within group whole-brain activation (orange) and deactivation (blue) maps for CC “risk” group, GG “non-risk” group, and between groups (risk>non-risk; purple). B) Averages and standard errors for functional activation parameter estimates from regions in risk>non-risk contrast for each genotype phenotype subgroup (Full-scale IQ and MRI scanner included as covariates in 2×3 ANOVA model). *p<0.05.

Figure 2

Reduced default mode network (DMN) functional connectivity in MET risk carriers. A) DMN connectivity within CC “risk” group, GG “non-risk” group, and between groups (risk>non-risk; purple). B) Averages and standard errors for functional connectivity between posterior cingulate seed and medial prefrontal and frontal orbital clusters from GG>CC contrast for each genotype phenotype subgroup (age and IQ included as covariates in 2×3 ANOVA). PCC = posterior cingulate cortex. *p<0.05, **p<0.01.

Figure 3

Reduced white matter integrity in MET risk carriers. A) Results of Tract-Based Spatial Statistics analysis comparing fractional anisotropy (FA) in GG “non-risk” group vs. CC “risk” group (p<0.05, corrected). B) Averages and standard errors for FA values in tracts from non-risk>risk contrast for each genotype phenotype subgroup (age and IQ included as covariates in 2×3 ANOVA). ***p<0.001.

Figure 4

Schematic depicting a strategy for addressing phenotypic heterogeneity (applicable across different disorders). The shading of the ovals indicates variability in a given phenotypic measure (e.g., brain connectivity). The green outline of the ovals indicates individuals with a clinical diagnosis (e.g., ASD) relative to typically developing controls (TD). Although group differences on a phenotypic measure may be detected between a clinical sample and matched controls, considerable overlap often exists (1). Stratifying individuals by neuroimaging endophenotypes independent of diagnosis reveals a continuum of phenotypes (2). Common risk variants (>5% of the population) for a disorder (e.g., MET rs1858830 C/G SNP) may impact brain circuitry and thus offer a means to stratify populations, particularly when these variants are functional in nature (3). Sample stratification by diagnosis and genotype allows for enhanced parsing of phenotypic heterogeneity (4), ultimately providing new insights on the neural mechanisms underlying psychiatric disorders.