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dc.contributor.authorGao, Si
dc.contributor.authorDonohue, Brian
dc.contributor.authorHatch, Kathryn S
dc.contributor.authorChen, Shuo
dc.contributor.authorMa, Tianzhou
dc.contributor.authorMa, Yizhou
dc.contributor.authorKvarta, Mark D
dc.contributor.authorBruce, Heather
dc.contributor.authorAdhikari, Bhim M
dc.contributor.authorJahanshad, Neda
dc.contributor.authorThompson, Paul M
dc.contributor.authorBlangero, John
dc.contributor.authorHong, L Elliot
dc.contributor.authorMedland, Sarah E
dc.contributor.authorGanjgahi, Habib
dc.contributor.authorNichols, Thomas E
dc.contributor.authorKochunov, Peter
dc.date.accessioned2021-11-18T18:19:10Z
dc.date.available2021-11-18T18:19:10Z
dc.date.issued2021-11-02
dc.identifier.urihttp://hdl.handle.net/10713/17149
dc.description.abstractImaging 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.en_US
dc.description.urihttps://doi.org/10.1016/j.neuroimage.2021.118700en_US
dc.language.isoenen_US
dc.publisherElsevier Inc.en_US
dc.relation.ispartofNeuroImageen_US
dc.rightsCopyright © 2021. Published by Elsevier Inc.en_US
dc.subjectComputational methodsen_US
dc.subjectFPHIen_US
dc.subjectGCTAen_US
dc.subjectHeritabilityen_US
dc.subjectImaging geneticsen_US
dc.subjectPedigreeen_US
dc.titleComparing empirical kinship derived heritability for imaging genetics traits in the UK biobank and human connectome projecten_US
dc.typeArticleen_US
dc.identifier.doi10.1016/j.neuroimage.2021.118700
dc.identifier.pmid34740793
dc.source.volume245
dc.source.beginpage118700
dc.source.endpage
dc.source.countryUnited States


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