Homogenizing estimates of heritability among SOLAR-eclipse, OpenMX, APACE, and FPHI software packages in neuroimaging data

Peter Kochunov*, Binish Patel, Habib Ganjgahi, Brian Donohue, Meghann Ryan, Elliot L. Hong, Xu Chen, Bhim Adhikari, Neda Jahanshad, Paul M. Thompson, Dennis Van’t Ent, Anouk den Braber, Eco J.C. de Geus, Rachel M. Brouwer, Dorret I. Boomsma, Hilleke E. Hulshoff Pol, Greig I. de Zubicaray, Katie L. McMahon, Nicholas G. Martin, Margaret J. WrightThomas E. Nichols

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

Imaging 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.

Original languageEnglish
Article number16
Pages (from-to)1-11
Number of pages11
JournalFrontiers in Neuroinformatics
Volume13
Issue numberMarch
Early online date12 Mar 2019
DOIs
Publication statusPublished - Mar 2019

Keywords

  • Computational methods
  • DTI
  • Genetics
  • Heritability
  • Imaging genetics
  • Population
  • Reproducability

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    Kochunov, P., Patel, B., Ganjgahi, H., Donohue, B., Ryan, M., Hong, E. L., Chen, X., Adhikari, B., Jahanshad, N., Thompson, P. M., Van’t Ent, D., den Braber, A., de Geus, E. J. C., Brouwer, R. M., Boomsma, D. I., Hulshoff Pol, H. E., de Zubicaray, G. I., McMahon, K. L., Martin, N. G., ... Nichols, T. E. (2019). Homogenizing estimates of heritability among SOLAR-eclipse, OpenMX, APACE, and FPHI software packages in neuroimaging data. Frontiers in Neuroinformatics, 13(March), 1-11. [16]. https://doi.org/10.3389/fninf.2019.00016