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

Peter Kochunov, Brian Donohue, Braxton D. Mitchell, Habib Ganjgahi, Bhim Adhikari, Meghann Ryan, Sarah E. Medland, Neda Jahanshad, Paul M. Thompson, John Blangero, Els Fieremans, Dmitry S. Novikov, Daniel Marcus, David C. Van Essen, David C. Glahn, L. Elliot Hong, Thomas E. Nichols

Research output: Contribution to journalArticle

2 Scopus citations

Abstract

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 (www.solar-eclipse-genetics.org/HCP).

Original languageEnglish
Pages (from-to)1677-1688
Number of pages12
JournalHuman Brain Mapping
Volume40
Issue number5
DOIs
StatePublished - Apr 1 2019

Keywords

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

Fingerprint Dive into the research topics of 'Genomic kinship construction to enhance genetic analyses in the human connectome project data'. Together they form a unique fingerprint.

  • Cite this

    Kochunov, P., Donohue, B., Mitchell, B. D., Ganjgahi, H., Adhikari, B., Ryan, M., Medland, S. E., Jahanshad, N., Thompson, P. M., Blangero, J., Fieremans, E., Novikov, D. S., Marcus, D., Van Essen, D. C., Glahn, D. C., Elliot Hong, L., & Nichols, T. E. (2019). Genomic kinship construction to enhance genetic analyses in the human connectome project data. Human Brain Mapping, 40(5), 1677-1688. https://doi.org/10.1002/hbm.24479