Open AccessGenomic kinship construction to enhance genetic analyses in the human connectome project data
Author(s) -
Kochunov Peter,
Donohue Brian,
Mitchell Braxton D.,
Ganjgahi Habib,
Adhikari Bhim,
Ryan Meghann,
Medland Sarah E.,
Jahanshad Neda,
Thompson Paul M.,
Blangero John,
Fieremans Els,
Novikov Dmitry S.,
Marcus Daniel,
Van Essen David C.,
Glahn David C,
Elliot Hong L.,
Nichols Thomas E.
Publication year - 2019
Publication title -
human brain mapping
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.005
H-Index - 191
eISSN - 1097-0193
pISSN - 1065-9471
DOI - 10.1002/hbm.24479
Subject(s) - heritability , genome wide association study , biology , genetics , human connectome project , genetic correlation , twin study , correlation , genetic association , quantitative trait locus , kinship , snp , evolutionary biology , single nucleotide polymorphism , genetic variation , genotype , mathematics , gene , functional connectivity , geometry , neuroscience , political science , law
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 ).