Heritability analysis with repeat measurements and its application to resting-state functional connectivity

Abstract

Heritability, defined as the proportion of phenotypic variation attributable to genetic variation, provides important information about the genetic basis of a trait. Existing heritability analysis methods do not discriminate between stable effects (e.g., due to the subject's unique environment) and transient effects, such as measurement error. This can lead to misleading assessments, particularly when comparing the heritability of traits that exhibit different levels of reliability. Here, we present a linear mixed effects model to conduct heritability analyses that explicitly accounts for intrasubject fluctuations (e.g., due to measurement noise or biological transients) using repeat measurements. We apply the proposed strategy to the analysis of resting-state fMRI measurements-a prototypic data modality that exhibits variable levels of test-retest reliability across space. Our results reveal that the stable components of functional connectivity within and across well-established large-scale brain networks can be considerably heritable. Furthermore, we demonstrate that dissociating intra- and intersubject variation can reveal genetic influence on a phenotype that is not fully captured by conventional heritability analyses.

Keywords: functional connectivity; heritability; repeat measurements; resting-state fMRI; test–retest reliability.

Fig. S1.

Results of simulation studies. Phenotypes were simulated using real HCP covariates, family structure, and number of repeat measurements. The heritability due to intersubject variation was 80%. The proposed heritability analysis method (black) and the classical ACE model (red) were used to estimate heritability when different levels of intrasubject measurement-to-measurement variation (ranged from 10 to 80% of the total phenotypic variance) was added to the phenotype. The simulation was repeated 1,000 times for each level of intrasubject variation. The average heritability estimates and empirical standard errors of point estimates are shown.

Fig. 1.

Functional connectivity measurements between pairs of 51 brain regions. (A) Surface representation of the seven-network parcellation (18), which can be split into 51 spatially contiguous regions across the two hemispheres. (B) Average functional connectivity measurements between pairs of the 51 brain regions across subjects in the HCP sample (Left) and GSP sample (Right).

Fig. 2.

Intra- and intersubject variation of the resting-state functional connectivity measurements. (A) Proportion of the total phenotypic variation attributable to intrasubject measurement-to-measurement variation (Left) and stable, nontransient intersubject variation (Right) estimated in the HCP sample. (B) Test–retest reliability of the functional connectivity measurements estimated using the HCP sample (Left) and GSP sample (Right). (C) Test–retest reliability of the functional connectivity measurements estimated in the HCP (Left) and GSP (Right) plotted against the proportion of phenotypic variance explained by the intersubject variation estimated in the HCP sample.

Fig. 3.

Heritability of functional connectivity measurements. (A) Conventional heritability estimates of functional connectivity measurements computed by averaging repeat measurements for each subject and applying the classical ACE model (Left) and the heritability estimates of the stable, nontransient component of functional connectivity computed using the proposed model (Right). (B) Average of the heritability estimates of within-network functional connectivity measurements for each of the seven functional networks. Heritability was estimated using the proposed method (black) and the classical ACE model (red). The standard errors shown were estimated by a block-jackknife procedure.

Fig. 4.

Analysis of the functional connectivity profile seeded at PCC in the HCP sample. (A) Average functional connectivity profile across subjects. (B) Test–retest reliability of the functional connectivity profile. (C) Heritability estimates of the connectivity profile computed using the classical ACE model. (D) Heritability estimates of the stable, nontransient component of the connectivity profile computed using the proposed method. (E) Difference of the heritability estimates computed by the proposed model and the classical ACE model, with the boundaries of the seven functional networks overlaid. (F) Proportion of the total phenotypic variation attributable to intrasubject variation.