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

Han, Yanting and Adolphs, Ralph (2019) Estimating the heritability of psychological measures in the Human Connectome Project dataset. . (Unpublished) https://resolver.caltech.edu/CaltechAUTHORS:20190717-074315447

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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.


Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription
https://doi.org/10.1101/704023DOIDiscussion Paper
ORCID:
AuthorORCID
Adolphs, Ralph0000-0002-8053-9692
Additional Information:The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. bioRxiv preprint first posted online Jul. 16, 2019. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Author contributions: Y.H. and R.A. developed the overall general analysis framework. Y.H. conducted all final analyses and produced all figures. Y.H. and R.A. wrote the manuscript. Funding: Funded by NSF grant BCS-1840756 and BCS-1845958. Data were provided [in part] by the Human Connectome Project, WU-Minn Consortium. (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University.
Funders:
Funding AgencyGrant Number
NSFBCS-1840756
NSFBCS-1845958
NIH1U54MH091657
Washington UniversityUNSPECIFIED
Subject Keywords:Human Connectome Project, heritability, machine learning, twin studies, Ridge, Random Forest
Record Number:CaltechAUTHORS:20190717-074315447
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20190717-074315447
Official Citation:Estimating the heritability of psychological measures in the Human Connectome Project dataset. Yanting Han, Ralph Adolphs. bioRxiv 704023; doi: https://doi.org/10.1101/704023
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:97178
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:17 Jul 2019 17:27
Last Modified:03 Oct 2019 21:29

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