Comparing empirical kinship derived heritability for imaging genetics traits in the UK biobank and human connectome project
| العنوان: | Comparing empirical kinship derived heritability for imaging genetics traits in the UK biobank and human connectome project |
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| المؤلفون: | Si Gao, Heather Bruce, Neda Jahanshad, Paul M. Thompson, Shuo Chen, Kathryn S. Hatch, John Blangero, Peter Kochunov, Mark D. Kvarta, Habib Ganjgahi, Yizhou Ma, Bhim M. Adhikari, L. Elliot Hong, Tianzhou Ma, Thomas E. Nichols, Brian Donohue, Sarah E. Medland |
| المصدر: | NeuroImage NeuroImage, Vol 245, Iss, Pp 118700-(2021) |
| بيانات النشر: | Elsevier BV, 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | Adult, Male, Imaging genetics, Cognitive Neuroscience, Neurosciences. Biological psychiatry. Neuropsychiatry, Neuroimaging, Biology, Polymorphism, Single Nucleotide, Article, Heritability, Genetic variation, Statistics, Connectome, Computational methods, Humans, Sibling, Biological Specimen Banks, GCTA, Human Connectome Project, FPHI, Computational Biology, Middle Aged, Explained variation, Pedigree, Phenotype, Neurology, Sample size determination, Female, Genetic Phenomena, Algorithms, RC321-571, Genome-Wide Association Study |
| الوصف: | 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. |
| تدمد: | 1053-8119 |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6856d79d382846914ebda8a784757994Test https://doi.org/10.1016/j.neuroimage.2021.118700Test |
| حقوق: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....6856d79d382846914ebda8a784757994 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 10538119 |
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