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

被引：3

作者：

Gao, Si ^{[1
]}

Donohue, Brian ^{[1
]}

Hatch, Kathryn S. ^{[1
]}

Chen, Shuo ^{[1
]}

Ma, Tianzhou ^{[2
]}

Ma, Yizhou ^{[1
]}

Kvarta, Mark D. ^{[1
]}

Bruce, Heather ^{[1
]}

Adhikari, Bhim M. ^{[1
]}

Jahanshad, Neda ^{[3
]}

Thompson, Paul M. ^{[3
]}

Blangero, John ^{[4
]}

Hong, L. Elliot ^{[1
]}

Medland, Sarah E. ^{[5
]}

Ganjgahi, Habib ^{[6
]}

Nichols, Thomas E. ^{[6
]}

Kochunov, Peter ^{[1
]}

机构：

[1] Univ Maryland, Sch Med, Dept Psychiat, Maryland Psychiat Res Ctr, Baltimore, MD 21201 USA

[2] Univ Maryland, Dept Epidemiol & Biostat, College Pk, MD 20742 USA

[3] Univ Southern Calif, Keck Sch Med, Mark & Mary Stevens Inst Neuroimaging & Informat, Dept Neurol,Imaging Genet Ctr, Marina Del Rey, CA USA

[4] Univ Texas Rio Grande Valley, Harlingen, TX USA

[5] QIMR Berghofer Med Res Inst, Brisbane, Qld, Australia

[6] Univ Oxford Oxford, Big Data Sci Inst, Dept Stat, Oxford, England

来源：

NEUROIMAGE | 2021年 / 245卷

基金：

澳大利亚国家健康与医学研究理事会;

关键词：

Heritability; Imaging genetics; Computational methods; Pedigree; FPHI; GCTA; CEREBRAL WHITE-MATTER; FRACTIONAL ANISOTROPY; QUALITY-CONTROL; MULTISITE; INFERENCE; LINKAGE; MODELS; SIZE; MRI;

D O I：

10.1016/j.neuroimage.2021.118700

中图分类号：

Q189 [神经科学];

学科分类号：

071006 ;

摘要：

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 similarly 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 similar to N2-3 computational effort dependency on sample size of classical likelihood approaches. We calculated for 60 regional and 1.3 x 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(-10) ). 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.

引用

页数：11

共 2 条

[1] Heritability of surface area and cortical thickness: a comparison between the Human Connectome Project and the UK Biobank dataset
Le Guen, Yann
Karkar, Slim
Grigis, Antoine
Philippe, Cathy
Mangin, Jean-Francois
Frouin, Vincent
2019 IEEE 16TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI 2019), 2019, : 1887 - 1890
[2] Heritability and genotypic correlation of CMR-derived LV phenotypes in the UK Biobank population imaging study
Aung, N.
Vargas, J. D.
Manichaikul, A. W.
Yang, C. P.
Cabrera, C. P.
Warren, H. R.
Fung, K.
Tzanis, E.
Barnes, M. R.
Piechnik, S. K.
Neubauer, S.
Bluemke, D. A.
Munroe, P. B.
Petersen, S. E.
EUROPEAN HEART JOURNAL-CARDIOVASCULAR IMAGING, 2019, 20 : 377 - 378

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