Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02129;
Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114.
Proc Natl Acad Sci U S A. 2017 May 23;114(21):5521-5526. doi: 10.1073/pnas.1700765114. Epub 2017 May 8.
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.
遗传力定义为表型变异归因于遗传变异的比例，提供了关于特征遗传基础的重要信息。现有的遗传力分析方法不能区分稳定效应（例如，由于个体的独特环境）和瞬态效应，如测量误差。这可能导致误导性评估，特别是在比较表现出不同可靠性水平的特征的遗传力时。在这里，我们提出了一种线性混合效应模型，该模型使用重复测量来进行遗传力分析，明确考虑了个体内波动（例如，由于测量噪声或生物瞬变）。我们将提出的策略应用于静息态 fMRI 测量的分析——这是一种典型的数据模式，在空间上表现出不同的测试-重测可靠性水平。我们的结果表明，在既定的大脑大网络内和跨网络的功能连接的稳定成分具有相当大的遗传性。此外，我们证明，区分个体内和个体间的变异可以揭示遗传对表型的影响，而传统的遗传力分析并不能完全捕捉到这种影响。
