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

• Published in: Proceedings of the National Academy of Sciences - PNAS Vol. 114; no. 21; pp. 5521 - 5526
• Main Authors: Ge, Tian, Holmes, Avram J., Buckner, Randy L., Smoller, Jordan W., Sabuncu, Mert R.
• Format: Journal Article
• Language: English
• Published: United States National Academy of Sciences 23.05.2017
• Subjects: Assessments; Biological Sciences; Biological variation; Brain; Brain mapping; Component reliability; Data processing; Functional anatomy; Functional magnetic resonance imaging; Genetic diversity; Genotype & phenotype; Heritability; Information dissemination; Networks; Neural networks; NMR; Noise measurement; Noise prediction; Nuclear magnetic resonance; Phenotypic variations; functional connectivity; repeat measurements; test–retest reliability; resting-state fMRI; heritability
• ISSN: 0027-8424; 1091-6490; 1091-6490
• DOI: 10.1073/pnas.1700765114

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.