Homogenizing Estimates of Heritability Among SOLAR-Eclipse, OpenMx, APACE, and FPHI Software Packages in Neuroimaging Data

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.

Keywords: DTI; computational methods; genetics; heritability; imaging genetics; population; reproducability.

Figure 1

The scatter plot of heritability estimates for 10,000 simulated traits are shown for two ML-based approaches (left). Heritability estimates by two approximation approaches: accelerated permutation inference for ACE (APACE; center) and fast permutation heritability inference (FPHI; right) were plotted vs. the average maximum likelihood estimation (MLE) based values.

Figure 2

The scatter plot of heritability estimates for 49-regional fractional anisotropy (FA) values calculated by the enhancing neuro imaging genetics analysis (ENIGMA)-diffusion tensor imaging (DTI) pipeline. Heritability estimates for two approximation approaches were plotted vs. the average estimate obtained for two ML-based methods: SOLAR-Eclipse and OpenMX. The lines represent linear regression fit vs. ML-based estimates with slope (β), intercept (α) and Pearson correlation values (r).

Figure 3

The scatter plot of heritability estimates for 49-regional FA values calculated by ENIGMA-DTI pipeline and then normalized using the trait normalization function in SOLAR-Eclipse. Heritability estimates for two approximation approaches were plotted vs. the average estimate obtained for two ML-based methods: SOLAR-Eclipse and OpenMX. The lines represent linear regression fit vs. ML-based estimates with slope (β), intercept (α) and Pearson correlation values (r).

Figure 4

Histograms for the dataset that showed reduced heritability estimates for fast vs. MLE based heritability estimation approaches. APACE showed reduced heritability estimates in superior corona-radiata-right (SCR-L) and SLF-L tracts in the human connectome project (HCP) cohort due to deviations from normal distribution (top panel). FPHI showed reduced heritability estimates in the externalcapsule-right (EC-R) and inferior fronto-occipital tract-left (IFO-L) tracts in the HCP cohort due to the high kurtosis and non-Gaussian shape of the histograms for EC-R and IFO-L, respectively.