| Title: | Comparing empirical kinship derived heritability for imaging genetics traits in the UK biobank and human connectome project |
| Journal: | NeuroImage |
| Published: | 2 Nov 2021 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/34740793/ |
| DOI: | https://doi.org/10.1016/j.neuroimage.2021.118700 |
| Citations: | 2 (2 in last 2 years) as of 8 Aug 2024 |
Publication 4571
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Abstract
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 ∼N2-3 computational effort dependency on sample size of classical likelihood approaches. We calculated for 60 regional and 1.3 × 105 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 (102-4 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.</p>
17 Authors
- Si Gao
- Brian Donohue
- Kathryn S. Hatch
- Shuo Chen
- Tianzhou Ma
- Yizhou Ma
- Mark D. Kvarta
- Heather Bruce
- Bhim M. Adhikari
- Neda Jahanshad
- Paul M. Thompson
- John Blangero
- L. Elliot Hong
- Sarah E. Medland
- Habib Ganjgahi
- Thomas E. Nichols
- Peter Kochunov
Enabling scientific discoveries that improve human health