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

Han, Yanting and Adolphs, Ralph (2020) Estimating the heritability of psychological measures in the Human Connectome Project dataset. PLoS ONE, 15 (7). Art. No. e0235860. ISSN 1932-6203. PMCID PMC7347217. 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 and genotypic measures, as well as a biologically verified 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 (fingerprinting), and to identify identical twins significantly above chance. Standard heritability estimates of 37 behavioral measures were derived from twin correlations, and machine-learning models (univariate linear model, Ridge classifier and Random Forest model) were trained to classify monozygotic twins and dizygotic twins. Correlations between the standard heritability metric and each set of model weights ranged from 0.36 to 0.7, and questionnaire-based and task-based measures did not differ significantly in their heritability. We further explored the heritability of a smaller number of latent factors extracted 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 0.05 to 0.43. One specific discrepancy arose for the general intelligence factor, which all models assigned high importance, but the standard heritability calculation did not. We present a thorough investigation of the heritabilities of the behavioral measures in the HCP as a resource for other investigators, and illustrate the utility of machine-learning methods for qualitative characterization of the differential heritability across diverse measures.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1371/journal.pone.0235860DOIArticle
https://doi.org/10.1101/704023DOIDiscussion Paper
https://doi.org/10.1371/journal.pone.0235860.s001DOIS1 Fig.
https://doi.org/10.1371/journal.pone.0235860.s002DOIS2 Fig.
https://doi.org/10.1371/journal.pone.0235860.s003DOIS3 Fig.
https://doi.org/10.1371/journal.pone.0235860.s004DOIS4 Fig.
https://doi.org/10.1371/journal.pone.0235860.s005DOIS5 Fig.
https://doi.org/10.1371/journal.pone.0235860.s006DOIS6 Fig.
https://doi.org/10.1371/journal.pone.0235860.s007DOIS1 Table
https://doi.org/10.1371/journal.pone.0235860.s008DOIS2 Table
https://doi.org/10.1371/journal.pone.0235860.s009DOIS3 Table
http://www.ncbi.nlm.nih.gov/pmc/articles/pmc7347217/PubMed Centrala
ORCID:
AuthorORCID
Han, Yanting0000-0003-3381-2059
Adolphs, Ralph0000-0002-8053-9692
Additional Information:© 2020 Han, Adolphs. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Received: March 12, 2020; Accepted: June 24, 2020; Published: July 9, 2020. We thank Julien Dubois, Umit Keles for helpful comments on the manuscript. Data Availability Statement: All data files are available from the Human Connectome Project (https://www.humanconnectome.org/study/hcpyoung-adult/document/1200-subjects-datarelease). Funded by NSF grants BCS-1840756 and BCS-1845958. Data were provided in part by the Human Connectome Project, WU-Minn Consortium (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. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The authors have declared that no competing interests exist. Author Contributions: Conceptualization: Yanting Han, Ralph Adolphs. Data curation: Yanting Han. Formal analysis: Yanting Han. Funding acquisition: Ralph Adolphs. Investigation: Yanting Han, Ralph Adolphs. Methodology: Yanting Han, Ralph Adolphs. Project administration: Yanting Han, Ralph Adolphs. Software: Yanting Han. Supervision: Ralph Adolphs. Visualization: Yanting Han. Writing – original draft: Yanting Han, Ralph Adolphs. Writing – review & editing: Yanting Han, Ralph Adolphs.
Group:Tianqiao and Chrissy Chen Institute for Neuroscience
Funders:
Funding AgencyGrant Number
NSFBCS-1840756
NSFBCS-1845958
NIH1U54MH091657
Washington UniversityUNSPECIFIED
Subject Keywords:Human Connectome Project, heritability, machine learning, twin studies, Ridge, Random Forest
Issue or Number:7
PubMed Central ID:PMC7347217
Record Number:CaltechAUTHORS:20190717-074315447
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20190717-074315447
Official Citation:Han Y, Adolphs R (2020) Estimating the heritability of psychological measures in the Human Connectome Project dataset. PLoS ONE 15(7): e0235860. https://doi.org/10.1371/journal. pone.0235860
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:27 Aug 2021 18:30

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