Genetic and environmental influences on neuroimaging phenotypes: a meta-analytical perspective on twin imaging studies

Abstract

Because brain structure and function are affected in neurological and psychiatric disorders, it is important to disentangle the sources of variation in these phenotypes. Over the past 15 years, twin studies have found evidence for both genetic and environmental influences on neuroimaging phenotypes, but considerable variation across studies makes it difficult to draw clear conclusions about the relative magnitude of these influences. Here we performed the first meta-analysis of structural MRI data from 48 studies on >1,250 twin pairs, and diffusion tensor imaging data from 10 studies on 444 twin pairs. The proportion of total variance accounted for by genes (A), shared environment (C), and unshared environment (E), was calculated by averaging A, C, and E estimates across studies from independent twin cohorts and weighting by sample size. The results indicated that additive genetic estimates were significantly different from zero for all meta-analyzed phenotypes, with the exception of fractional anisotropy (FA) of the callosal splenium, and cortical thickness (CT) of the uncus, left parahippocampal gyrus, and insula. For many phenotypes there was also a significant influence of C. We now have good estimates of heritability for many regional and lobar CT measures, in addition to the global volumes. Confidence intervals are wide and number of individuals small for many of the other phenotypes. In conclusion, while our meta-analysis shows that imaging measures are strongly influenced by genes, and that novel phenotypes such as CT measures, FA measures, and brain activation measures look especially promising, replication across independent samples and demographic groups is necessary.

FIGURE 1

Relative influences of variance components A, C, and E on neuroimaging measures according to the meta-analysis.

FIGURE 2

Heritability estimates with corresponding 95% confidence intervals according to the meta-analysis.

FIGURE 3

Meta-estimates of heritability plotted against the respective average sizes of structures (volumetric measures) obtained from the avg152T1 MNI template. Volumes were automatically segmented using the IBASPM Toolbox (Individual Brain Atlases using Statistical Parametric Mapping), authored by Lester Melie Garcia and Yasser Aleman-Gomez. Volumes are measured in milliliters (cm³), and displayed on a base 10 logarithmic scale for the purposes of separating data points representing smaller volumes (<15 ml). This graph includes all global volumes, cerebellar volume, subcortical volumes, all gyral cortical thickness regions of interest, and total lateral ventricle volume. This graph demonstrates that smaller structures tend to have lower heritability values than global-based and lobar-based measures. Smaller structures also show considerable variability in their heritability estimates.