Multidimensional heritability analysis of neuroanatomical shape
Author(s)
Ge, Tian; Winkler, Anderson M.; Holmes, Avram J.; Lee, Phil H.; Tirrell, Lee S.; Roffman, Joshua L.; Buckner, Randy L.; Smoller, Jordan W.; Reuter, Klaus Martin; Sabuncu, Mert R; ... Show more Show less
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In the dawning era of large-scale biomedical data, multidimensional phenotype vectors will play an increasing role in examining the genetic underpinnings of brain features, behaviour and disease. For example, shape measurements derived from brain MRI scans are multidimensional geometric descriptions of brain structure and provide an alternate class of phenotypes that remains largely unexplored in genetic studies. Here we extend the concept of heritability to multidimensional traits, and present the first comprehensive analysis of the heritability of neuroanatomical shape measurements across an ensemble of brain structures based on genome-wide SNP and MRI data from 1,320 unrelated, young and healthy individuals. We replicate our findings in an extended twin sample from the Human Connectome Project (HCP). Our results demonstrate that neuroanatomical shape can be significantly heritable, above and beyond volume, and can serve as a complementary phenotype to study the genetic determinants and clinical relevance of brain structure.
Date issued
2016-09Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence LaboratoryJournal
Nature Communications
Publisher
Nature Publishing Group
Citation
Ge, Tian; Reuter, Martin; Winkler, Anderson M.; Holmes, Avram J.; Lee, Phil H.; Tirrell, Lee S.; Roffman, Joshua L.; Buckner, Randy L.; Smoller, Jordan W.; Sabuncu, Mert R. “Multidimensional Heritability Analysis of Neuroanatomical Shape.” Nature Communications 7 (November 15, 2016): 13291.
Version: Final published version
ISSN
2041-1723