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

Area of Science:

• Neuroscience
• Behavioral Genetics
• Biostatistics

Background:

• Heritability quantifies genetic influence on traits but often conflates stable and transient variations.
• Conventional methods may yield misleading heritability estimates, especially for traits with varying reliability, like functional connectivity.

Purpose of the Study:

• To develop a novel heritability analysis model that distinguishes stable genetic effects from transient fluctuations.
• To apply this model to resting-state functional magnetic resonance imaging (fMRI) data to assess the heritability of brain network connectivity.

Main Methods:

• Developed a linear mixed-effects model incorporating repeat measurements to account for intrasubject variability.
• Applied the model to resting-state fMRI data, analyzing functional connectivity within and across large-scale brain networks.

Main Results:

• The stable components of functional connectivity in brain networks demonstrate significant heritability.
• The proposed method successfully dissociates intra- and intersubject variation, revealing genetic influences missed by standard analyses.

Conclusions:

• Explicitly modeling intrasubject fluctuations enhances the accuracy of heritability estimation for complex traits.
• This approach provides a more nuanced understanding of the genetic architecture of brain functional connectivity.