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Hum Brain Mapp. 2018 Nov 29. doi: 10.1002/hbm.24479. [Epub ahead of print]

Genomic kinship construction to enhance genetic analyses in the human connectome project data.

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Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland.
Department of Medicine, University of Maryland School of Medicine, Baltimore, Maryland.
Geriatrics Research and Education Clinical Center, Baltimore Veterans Administration Medical Center, Baltimore, Maryland.
Department of Statistics, University of Oxford, Oxford, United Kingdom.
QIMR Berghofer Medical Research Institute, Herston, Australia.
Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging and Informatics, Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, California.
University of Texas Rio Grand Valley, Harlingen, Texas.
Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York.
Department of Radiology, Washington University School of Medicine, St. Louis, Missouri.
Department of Neuroscience, Washington University in St. Louis, St. Louis, Missouri.
Olin Neuropsychiatry Research Center, Institute of Living, Hartford Hospital, Hartford, Connecticut.
Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut.
Big Data Science Institute, Department of Statistics, University of Oxford, Oxford, United Kingdom.


Imaging genetic analyses quantify genetic control over quantitative measurements of brain structure and function using coefficients of relationship (CR) that code the degree of shared genetics between subjects. CR can be inferred through self-reported relatedness or calculated empirically using genome-wide SNP scans. We hypothesized that empirical CR provides a more accurate assessment of shared genetics than self-reported relatedness. We tested this in 1,046 participants of the Human Connectome Project (HCP) (480 M/566 F) recruited from the Missouri twin registry. We calculated the heritability for 17 quantitative traits drawn from four categories (brain diffusion and structure, cognition, and body physiology) documented by the HCP. We compared the heritability and genetic correlation estimates calculated using self-reported and empirical CR methods Kinship-based INference for GWAS (KING) and weighted allelic correlation (WAC). The polygenetic nature of traits was assessed by calculating the empirical CR from chromosomal SNP sets. The heritability estimates based on whole-genome empirical CR were higher but remained significantly correlated (r ∼0.9) with those obtained using self-reported values. Population stratification in the HCP sample has likely influenced the empirical CR calculations and biased heritability estimates. Heritability values calculated using empirical CR for chromosomal SNP sets were significantly correlated with the chromosomal length (r 0.7) suggesting a polygenic nature for these traits. The chromosomal heritability patterns were correlated among traits from the same knowledge domains; among traits with significant genetic correlations; and among traits sharing biological processes, without being genetically related. The pedigree structures generated in our analyses are available online as a web-based calculator (


DTI; DWI; diffusion; human connectome project; imaging genetics; pedigree


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