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dc.contributor.authorKochunov, P.
dc.contributor.authorPatel, B.
dc.contributor.authorGanjgahi, H.
dc.date.accessioned2019-09-13T16:41:56Z
dc.date.available2019-09-13T16:41:56Z
dc.date.issued2019
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85064277755&doi=10.3389%2ffninf.2019.00016&partnerID=40&md5=7d0ec2ef9c2dc007497fffec95d044db
dc.identifier.urihttp://hdl.handle.net/10713/10656
dc.description.abstractImaging genetic analyses use heritability calculations to measure the fraction of phenotypic variance attributable to additive genetic factors. We tested the agreement between heritability estimates provided by four methods that are used for heritability estimates in neuroimaging traits. SOLAR-Eclipse and OpenMx use iterative maximum likelihood estimation (MLE) methods. Accelerated Permutation inference for ACE (APACE) and fast permutation heritability inference (FPHI), employ fast, non-iterative approximation-based methods. We performed this evaluation in a simulated twin-sibling pedigree and phenotypes and in diffusion tensor imaging (DTI) data from three twin-sibling cohorts, the human connectome project (HCP), netherlands twin register (NTR) and BrainSCALE projects provided as a part of the enhancing neuro imaging genetics analysis (ENIGMA) consortium. We observed that heritability estimate may differ depending on the underlying method and dataset. The heritability estimates from the two MLE approaches provided excellent agreement in both simulated and imaging data. The heritability estimates for two approximation approaches showed reduced heritability estimates in datasets with deviations from data normality. We propose a data homogenization approach (implemented in solar-eclipse; www.solar-eclipse-genetics.org) to improve the convergence of heritability estimates across different methods. The homogenization steps include consistent regression of any nuisance covariates and enforcing normality on the trait data using inverse Gaussian transformation. Under these conditions, the heritability estimates for simulated and DTI phenotypes produced converging heritability estimates regardless of the method. Thus, using these simple suggestions may help new heritability studies to provide outcomes that are comparable regardless of software package. Copyright 2019 The Authors.en_US
dc.description.urihttps://doi.org/10.3389/fninf.2019.00016en_US
dc.language.isoen-USen_US
dc.publisherFrontiers Media S.A.en_US
dc.relation.ispartofFrontiers in Neuroinformatics
dc.subjectComputational methodsen_US
dc.subjectDTIen_US
dc.subjectGeneticsen_US
dc.subjectHeritabilityen_US
dc.subjectImaging geneticsen_US
dc.subjectPopulationen_US
dc.subjectReproducabilityen_US
dc.titleHomogenizing estimates of heritability among SOLAR-eclipse, OpenMX, APACE, and FPHI software packages in neuroimaging dataen_US
dc.typeArticleen_US
dc.identifier.doi10.3389/fninf.2019.00016


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