Accelerating Heritability, Genetic Correlation, and Genome‐Wide Association Imaging Genetic Analyses in Complex Pedigrees

National and international biobanking efforts led to the collection of large and inclusive imaging genetics datasets that enable examination of the contribution of genetic and environmental factors to human brains in illness and health. High-resolution neuroimaging (~10<sup>4-6</sup> voxels) and genetic (10<sup>6-8</sup> single nucleotide polymorphic [SNP] variants) data are available in statistically powerful (N = 10<sup>3-5</sup>) epidemiological and disorder-focused samples. Performing imaging genetics analyses at full resolution afforded in these datasets is a formidable computational task even under the assumption of unrelatedness among the subjects. The computational complexity rises as ~N<sup>2-3</sup> (where N is the sample size), when accounting for relatedness among subjects. We describe fast, non-iterative simplifications to accelerate classical variance component (VC) methods including heritability, genetic correlation, and genome-wide association in dense and complex empirical pedigrees. These approaches linearize (from N<sup>2-3</sup> to N<sup>~1</sup>) computational effort while maintaining fidelity (r ~ 0.95) with the VC results and take advantage of parallel computing provided by central and graphics processing units (CPU and GPU). We show that the new approaches lead to a 10<sup>4</sup>- to 10<sup>6</sup>-fold reduction in computational complexity-making voxel-wise heritability, genetic correlation, and genome-wide association studies (GWAS) analysis practical for large and complex samples such as those provided by the Amish and Human Connectome Projects (N = 406 and 1052 subjects, respectively) and UK Biobank (N = 31,681). These developments are shared in open-source, SOLAR-Eclipse software.

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Publication Details

Journal
Human Brain Mapping
Published
2024-11-26
DOI
https://doi.org/10.1002/hbm.70044
Primary Topic
Advanced Neuroimaging Techniques and Applications
Type
article
Field-Weighted Citation Impact
0.00

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article

Accelerating Heritability, Genetic Correlation, and Genome‐Wide Association Imaging Genetic Analyses in Complex Pedigrees

John Blangero, Sarah Elizabeth Medland, Si Gao, Neda Jahanshad et al.
Human Brain Mapping
Advanced Neuroimaging Techniques and Applications
article

Accelerating Heritability, Genetic Correlation, and Genome‐Wide Association Imaging Genetic Analyses in Complex Pedigrees

John Blangero, Sarah Elizabeth Medland, Si Gao, Neda Jahanshad, Peter Kochunov, Brian Donohue, Braxton D. Mitchell, Thomas E. Nichols, Tianzhou Ma, Elizabeth Humphries, Bhim M. Adhikari, Yizhou Ma, Seth A. Ament, Francis J. McMahon, Shuo Chen, Paul M. Thompson, L. Elliot Hong, William Burroughs
article en

Abstract

National and international biobanking efforts led to the collection of large and inclusive imaging genetics datasets that enable examination of the contribution of genetic and environmental factors to human brains in illness and health. High-resolution neuroimaging (~10<sup>4-6</sup> voxels) and genetic (10<sup>6-8</sup> single nucleotide polymorphic [SNP] variants) data are available in statistically powerful (N = 10<sup>3-5</sup>) epidemiological and disorder-focused samples. Performing imaging genetics analyses at full resolution afforded in these datasets is a formidable computational task even under the assumption of unrelatedness among the subjects. The computational complexity rises as ~N<sup>2-3</sup> (where N is the sample size), when accounting for relatedness among subjects. We describe fast, non-iterative simplifications to accelerate classical variance component (VC) methods including heritability, genetic correlation, and genome-wide association in dense and complex empirical pedigrees. These approaches linearize (from N<sup>2-3</sup> to N<sup>~1</sup>) computational effort while maintaining fidelity (r ~ 0.95) with the VC results and take advantage of parallel computing provided by central and graphics processing units (CPU and GPU). We show that the new approaches lead to a 10<sup>4</sup>- to 10<sup>6</sup>-fold reduction in computational complexity-making voxel-wise heritability, genetic correlation, and genome-wide association studies (GWAS) analysis practical for large and complex samples such as those provided by the Amish and Human Connectome Projects (N = 406 and 1052 subjects, respectively) and UK Biobank (N = 31,681). These developments are shared in open-source, SOLAR-Eclipse software.

Human Brain MappingVol. 45(17)
University of Southern California (US), University of Maryland, Baltimore (US), National Institutes of Health (US), QIMR Berghofer Medical Research Institute (AU), The University of Texas Rio Grande Valley (US), Open Data Institute (GB), University of Oxford (GB), National Institute of Mental Health (US), University of Maryland, College Park (US), The University of Texas Health Science Center at Houston (US)
National Institutes of Health, McDonnell Center for Systems Neuroscience, National Institute on Aging, National Institute of Mental Health, National Human Genome Research Institute, National Cancer Institute, National Institute of Biomedical Imaging and Bioengineering
Openalex Percentile: Top 62%
Advanced Neuroimaging Techniques and Applications
DOIOpen Access PDFOpenAlex
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