Cortical thickness or grey matter volume? The importance of selecting the phenotype for imaging genetics studies
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Choosing the appropriate neuroimaging phenotype is critical to successfully identify genes that influence brain structure or function. While neuroimaging methods provide numerous potential phenotypes, their role for imaging genetics studies is unclear. Here we examine the relationship between brain volume, grey matter volume, cortical thickness and surface area, from a genetic standpoint. Four hundred and eighty-six individuals from randomly ascertained extended pedigrees with high-quality T1-weighted neuroanatomic MRI images participated in the study. Surface-based and voxel-based representations of brain structure were derived, using automated methods, and these measurements were analysed using a variance-components method to identify the heritability of these traits and their genetic correlations. All neuroanatomic traits were significantly influenced by genetic factors. Cortical thickness and surface area measurements were found to be genetically and phenotypically independent. While both thickness and area influenced volume measurements of cortical grey matter, volume was more closely related to surface area than cortical thickness. This trend was observed for both the volume-based and surface-based techniques. The results suggest that surface area and cortical thickness measurements should be considered separately and preferred over gray matter volumes for imaging genetic studies.
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Heritability analyses using twins data have demonstrated that there are different genetic drivers for GM volume and cortical thickness measures.
“Heritability analyses using twins data have demonstrated that there are different genetic drivers for GM volume and cortical thickness measures (Winkler et al., 2010).”
From Widespread reductions in gray matter volume in depression (2013) · cites this paper as Winkler et al., 2010
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- Authors
- Winkler, A. M., Kochunov, P., Blangero, J., Almasy, L., Zilles, K., Fox, P. T., Duggirala, R., & Glahn, D. C.
- Affiliations
- Yale University (US)
- The University of Texas at San Antonio Health Science Center (US)
- Texas Biomedical Research Institute (US)
- Forschungszentrum Jülich (DE)
- Corresponding author
- Winkler, Anderson M.
- ORCID
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2010-11
- Volume
- 53
- Issue
- 3
- Pages
- 1135-1146
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC2891595
- OpenAlex
- W2143742215
- Cited by
- 1,172
- References
- 84
- Citations by year
- 2026: 61 · 2025: 65 · 2024: 62 · 2023: 83 · 2022: 78 · 2021: 95 · 2020: 88 · 2019: 94 · 2018: 82 · 2017: 70 · 2016: 98 · 2015: 84 · 2014: 59 · 2013: 70 · 2012: 39
- FWCI
- 15.60
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- ORBi (University of Liège) (submittedVersion, other-oa)
- ScholarWorks @ UTRGV (The University of Texas Rio Grande Valley) (submittedVersion)
- JuSER (Forschungszentrum Jülich) (submittedVersion)
- Funders
- NIMH (MH0708143, MH078111, MH083824, MH59490)
- NIBIB (EB006395)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Cognitive Abilities and Testing
- Keywords
- cortical thickness, gray matter volume, cortical surface area, brain structure genetics, heritability, genetic correlation, extended pedigrees, surface-based morphometry, voxel-based morphometry, brain volume
- MeSH terms
- Adult, Aged, Aged, 80 and over, Brain, Brain Mapping, Female, Humans, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Male, Middle Aged, Pedigree, Phenotype, Quantitative Trait, Heritable
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