Comparing empirical kinship derived heritability for imaging genetics traits in the UK biobank and human connectome project.

Autor: Gao S; Department of Psychiatry, Maryland Psychiatric Research Center, School of Medicine, University of Maryland, Baltimore, MD, United States., Donohue B; Department of Psychiatry, Maryland Psychiatric Research Center, School of Medicine, University of Maryland, Baltimore, MD, United States., Hatch KS; Department of Psychiatry, Maryland Psychiatric Research Center, School of Medicine, University of Maryland, Baltimore, MD, United States., Chen S; Department of Psychiatry, Maryland Psychiatric Research Center, School of Medicine, University of Maryland, Baltimore, MD, United States., Ma T; Department of Epidemiology and Biostatistics, University of Maryland, College Park, MD, United States., Ma Y; Department of Psychiatry, Maryland Psychiatric Research Center, School of Medicine, University of Maryland, Baltimore, MD, United States., Kvarta MD; Department of Psychiatry, Maryland Psychiatric Research Center, School of Medicine, University of Maryland, Baltimore, MD, United States., Bruce H; Department of Psychiatry, Maryland Psychiatric Research Center, School of Medicine, University of Maryland, Baltimore, MD, United States., Adhikari BM; Department of Psychiatry, Maryland Psychiatric Research Center, School of Medicine, University of Maryland, Baltimore, MD, United States., Jahanshad N; Department of Neurology, Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States., Thompson PM; Department of Neurology, Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States., Blangero J; University of Texas Rio Grande Valley, Harlingen, TX, United States., Hong LE; Department of Psychiatry, Maryland Psychiatric Research Center, School of Medicine, University of Maryland, Baltimore, MD, United States., Medland SE; QIMR Berghofer Medical Research Institute, Queensland, Australia., Ganjgahi H; Department of Statistics, Big Data Science Institute, University of Oxford, Oxford, United Kingdom., Nichols TE; Department of Statistics, Big Data Science Institute, University of Oxford, Oxford, United Kingdom., Kochunov P; Department of Psychiatry, Maryland Psychiatric Research Center, School of Medicine, University of Maryland, Baltimore, MD, United States. Electronic address: pkochunov@som.umaryland.edu.
Jazyk: angličtina
Zdroj: NeuroImage [Neuroimage] 2021 Dec 15; Vol. 245, pp. 118700. Date of Electronic Publication: 2021 Nov 02.
DOI: 10.1016/j.neuroimage.2021.118700
Abstrakt: Imaging genetics analyses use neuroimaging traits as intermediate phenotypes to infer the degree of genetic contribution to brain structure and function in health and/or illness. Coefficients of relatedness (CR) summarize the degree of genetic similarity among subjects and are used to estimate the heritability - the proportion of phenotypic variance explained by genetic factors. The CR can be inferred directly from genome-wide genotype data to explain the degree of shared variation in common genetic polymorphisms (SNP-heritability) among related or unrelated subjects. We developed a central processing and graphics processing unit (CPU and GPU) accelerated Fast and Powerful Heritability Inference (FPHI) approach that linearizes likelihood calculations to overcome the ∼N 2-3 computational effort dependency on sample size of classical likelihood approaches. We calculated for 60 regional and 1.3 × 10 5 voxel-wise traits in N = 1,206 twin and sibling participants from the Human Connectome Project (HCP) (550 M/656 F, age = 28.8 ± 3.7 years) and N = 37,432 (17,531 M/19,901 F; age = 63.7 ± 7.5 years) participants from the UK Biobank (UKBB). The FPHI estimates were in excellent agreement with heritability values calculated using Genome-wide Complex Trait Analysis software (r = 0.96 and 0.98 in HCP and UKBB sample) while significantly reducing computational (10 2-4 times). The regional and voxel-wise traits heritability estimates for the HCP and UKBB were likewise in excellent agreement (r = 0.63-0.76, p < 10 -1 0 ). In summary, the hardware-accelerated FPHI made it practical to calculate heritability values for voxel-wise neuroimaging traits, even in very large samples such as the UKBB. The patterns of additive genetic variance in neuroimaging traits measured in a large sample of related and unrelated individuals showed excellent agreement regardless of the estimation method. The code and instruction to execute these analyses are available at www.solar-eclipse-genetics.org.
Competing Interests: Declaration of Competing Interest The authors declare no competing financial interests.
(Copyright © 2021. Published by Elsevier Inc.)
Databáze: MEDLINE
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