Estimating the heritability of psychological measures in the Human Connectome Project dataset

Abstract

The Human Connectome Project (HCP) is a large structural and functional MRI dataset with a rich array of behavioral measures and extensive family structure. This makes it a valuable resource for investigating questions about individual differences, including questions about heritability. While its MRI data have been analyzed extensively in this regard, to our knowledge a comprehensive estimation of the heritability of the behavioral dataset has never been conducted. Using a set of behavioral measures of personality, emotion and cognition, we show that it is possible to re-identify the same individual across two testing times, and identify identical twins. Using machine-learning (univariate linear model, Ridge classifier and Random Forest model) we estimated the heritability of 37 behavioral measures and compared the results to those derived from twin correlations. Correlations between the standard heritability metric and each set of model weights ranged from 0.42 to 0.67, and questionnaire-based and task-based measures did not differ significantly in their heritability. We further derived nine latent factors from the 37 measures and repeated the heritability estimation; in this case, the correlations between the standard heritability and each set of model weights were lower, ranging from 0.15 to 0.38. One specific discrepancy arose for the general intelligence factor, which all models assigned high importance, but the standard heritability calculation did not. We present an alternative method for qualitatively estimating the heritability of the behavioral measures in the HCP as a resource for other investigators, and recommend the use of machine-learning models for estimating heritability.