Heritability of fractional anisotropy in human white matter: A comparison of Human Connectome Project and ENIGMA-DTI data
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The degree to which genetic factors influence brain connectivity is beginning to be understood. Large-scale efforts are underway to map the profile of genetic effects in various brain regions. The NIH-funded Human Connectome Project (HCP) is providing data valuable for analyzing the degree of genetic influence underlying brain connectivity revealed by state-of-the-art neuroimaging methods. We calculated the heritability of the fractional anisotropy (FA) measure derived from diffusion tensor imaging (DTI) reconstruction in 481 HCP subjects (194/287 M/F) consisting of 57/60 pairs of mono- and dizygotic twins, and 246 siblings. FA measurements were derived using (Enhancing NeuroImaging Genetics through Meta-Analysis) ENIGMA DTI protocols and heritability estimates were calculated using the SOLAR-Eclipse imaging genetic analysis package. We compared heritability estimates derived from HCP data to those publicly available through the ENIGMA-DTI consortium, which were pooled together from five-family based studies across the US, Europe, and Australia. FA measurements from the HCP cohort for eleven major white matter tracts were highly heritable (h(2)=0.53-0.90, p<10(-5)), and were significantly correlated with the joint-analytical estimates from the ENIGMA cohort on the tract and voxel-wise levels. The similarity in regional heritability suggests that the additive genetic contribution to white matter microstructure is consistent across populations and imaging acquisition parameters. It also suggests that the overarching genetic influence provides an opportunity to define a common genetic search space for future gene-discovery studies. Uniquely, the measurements of additive genetic contribution performed in this study can be repeated using online genetic analysis tools provided by the HCP ConnectomeDB web application.
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Cortical thickness and surface area have considerable genetic influences that vary by cortical region but share little genetic variance, indicating regulation by different sets of genetic influences.
“Cortical thickness and surface area also have considerable genetic influences that vary by cortical region, but interestingly share little shared genetic variance between them, indicating that these two aspects of cortical volume are regulated by different sets of genetic influences 144, 145.”
From Imaging structural and functional brain development in early childhood (2018) · cites this paper as [144]
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Convergent Evidence for Predispositional Effects of Brain Gray Matter Volume on Alcohol Consumption
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Self-citationEpigenetic Age Acceleration Assessed with Human White-Matter Images
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Whole mouse brain connectomics
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Self-citationThe Shared Genetic Basis of Educational Attainment and Cerebral Cortical Morphology
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Self-citationSystematic Bias of Machine Learning Regression Models and Correction
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Reproducibility of quantitative structural and physiological MRI measurements
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Self-citationGenetic imaging consortium for addiction medicine
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Self-citationWhite matter pathways mediate parental effects on children’s reading precursors
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Self-citationIntergenerational Neuroimaging of Human Brain Circuitry
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Evidence for genetic correlation between human cerebral white matter microstructure and inflammation
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Genetics of structural connectivity and information processing in the brain
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Hemispheric Asymmetry in the Genetic Overlap between Schizophrenia and White Matter Microstructure
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- Authors
- Kochunov, P., Jahanshad, N., Marcus, D., Winkler, A., Sprooten, E., Nichols, T. E., Wright, S. N., Hong, L. E., Patel, B., Behrens, T., Jbabdi, S., Andersson, J., Lenglet, C., Yacoub, E., Moeller, S., Auerbach, E., Ugurbil, K., Sotiropoulos, S. N., Brouwer, R. M., Landman, B., Lemaitre, H., den Braber, A., Zwiers, M. P., Ritchie, S., van Hulzen, K., Almasy, L., Curran, J., deZubicaray, G. I., Duggirala, R., Fox, P., Martin, N. G., McMahon, K. L., Mitchell, B., Olvera, R. L., Peterson, C., Starr, J., Sussmann, J., Wardlaw, J., Wright, M., Boomsma, D. I., Kahn, R., de Geus, E. J. C., Williamson, D. E., Hariri, A., van 't Ent, D., Bastin, M. E., McIntosh, A., Deary, I. J., Hulshoff pol, H. E., Blangero, J., Thompson, P. M., Glahn, D. C., & Van Essen, D. C.
- Affiliations
- University of Maryland, Baltimore (US)
- University of Southern California (US)
- Washington University in St. Louis (US)
- University of Oxford (GB)
- Hartford Hospital (US)
- University of Warwick (GB)
- University of Minnesota (US)
- Utrecht University (NL)
- Vanderbilt University (US)
- Radboud University Nijmegen (NL)
- University of Edinburgh (GB)
- Texas Biomedical Research Institute (US)
- The University of Texas at San Antonio Health Science Center (US)
- QIMR Berghofer Medical Research Institute (AU)
- The University of Queensland (AU)
- Vrije Universiteit Amsterdam (NL)
- Duke University (US)
- Corresponding author
- Kochunov, Peter
- ORCID
- Kochunov, Peter
- Jahanshad, Neda
- Winkler, Anderson
- Sprooten, Emma
- Nichols, Thomas E.
- Hong, L. Elliot
- Patel, Binish
- Behrens, Timothy
- Andersson, Jesper
- Lenglet, Christophe
- Yacoub, Essa
- Moeller, Steen
- Sotiropoulos, Stamatios N.
- Brouwer, Rachel M.
- Landman, Bennett
- Zwiers, Marcel P.
- Ritchie, Stuart
- Almasy, Laura
- Curran, Joanne
- Fox, Peter
- Martin, Nicholas G.
- McMahon, Katie L.
- Mitchell, Braxton
- Olvera, Rene L.
- Starr, John
- Wardlaw, Joanna
- Boomsma, Dorret I.
- Williamson, Douglas E.
- Hariri, Ahmad
- Bastin, Mark E.
- McIntosh, Andrew
- Deary, Ian J.
- Blangero, John
- Thompson, Paul M.
- Glahn, David C.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2015-05
- Volume
- 111
- Pages
- 300-311
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC4387079
- OpenAlex
- W1985450416
- Cited by
- 215
- References
- 63
- Citations by year
- 2026: 7 · 2025: 15 · 2024: 21 · 2023: 9 · 2022: 8 · 2021: 19 · 2020: 29 · 2019: 17 · 2018: 33 · 2017: 24 · 2016: 21 · 2015: 12
- FWCI
- 24.32
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- Amsterdam UMC (VU Amsterdam) - Institutional Repository (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Repository@Nottingham (University of Nottingham) (submittedVersion, cc-by)
- Radboud Repository (Radboud University) (submittedVersion)
- ScholarWorks @ UTRGV (The University of Texas Rio Grande Valley) (submittedVersion)
- Utrecht University Repository (Utrecht University) (submittedVersion, other-oa)
- ORBi (University of Liège) (submittedVersion, other-oa)
- Pure Amsterdam UMC (submittedVersion)
- VU Research Portal (submittedVersion)
- Utrecht University Repository (Utrecht University) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Funders
- NIH R01 (EB008432, EB008281, EB007813)
- NIH (087727/Z/08/Z, U54 EB020403)
- Biotechnology Research Center (P41 EB0 15894)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Keywords
- heritability, fractional anisotropy, white matter microstructure, diffusion tensor imaging, monozygotic and dizygotic twins, siblings, white matter tracts, Human Connectome Project, imaging genetics, gene discovery
- MeSH terms
- Adult, Female, Humans, Male, Nerve Net, Registries, Cohort Studies, Anisotropy, Genetic Phenomena, Young Adult, Diffusion Tensor Imaging, Connectome, White Matter
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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