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

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

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

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

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

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

MRI for gray matter: statistical modelling for in-vivo application and histological validation of dMRI

Gray matter (GM) forms the ‘computational engine’ of our brain and plays the key role in brain function. Measures derived from MRI (e.g., structural MRI (sMRI) and diffusion MRI (dMRI)) provide a unique opportunity to non-invasively study GM structure in-vivo and thus can be used to probe GM pathology in development, aging and neuropsychiatric disorders. Investigation of the influence of various factors on MRI measures in GM is critical to facilitate their use for future non-invasive studies in healthy and diseased populations. In this dissertation, GM structure was studied with MRI to understand how it is influenced by genetic and environmental factors. Validation of dMRI- derived measures was conducted by comparing them with histological data from monkeys to better understand the cytoarchitectural features that influence GM measures. First, the influence of genetic and environmental factors was quantified on gray matter macrostructure and microstructure measures using phenotypic modelling of structural and diffusion MRI data obtained from a large twin and sibling population (N = 840). Results of this study showed that in GM, while sMRI measures like cortical thickness and GM volume are mainly affected by genetic factors, advanced dMRI measures of mean squared displacement (MSD) and return to origin probability (RTOP) derived from advanced biexponential model can tap into regionally specific patterns of both genetic and environmental influence in cortical and subcortical GM. Our results thus highlight the potential of these advanced dMRI measures for use in future studies that aim to investigate and follow in healthy and clinical population changes in GM microstructure linked with both genes and environment. Second, using data from a large healthy population (n=550), we investigated changes in sMRI tissue contrast at the gray-white matter boundary with biological development during adolescence to assess how this affects estimation of the developmental trajectory of cortical thickness. Results of this study suggest that increased myelination during brain development contributes to age-related changes in gray-white boundary contrast in sMRI scans causing an apparent shift of the estimated gray-white boundary towards the cortical surface, in turn reducing estimations of cortical thickness and its developmental trajectory. Based on these results, we emphasize the importance of accounting for the effects of myelination on T1 gray-white matter boundary contrast to enable more precise estimation of cortical thickness during neurodevelopment. Finally, we conducted histological validation of dMRI measures in gray matter by comparing dMRI measures derived from two models, conventional Diffusion Tensor Imaging (DTI) model and an advanced biexponential model with histology acquired from the same 4 rhesus monkeys. Results demonstrate differences in the ability of distinct dMRI measures including DTI-derived measures of fractional anisotropy (FA), Trace and advanced Biexponential model-derived measures of MSD and RTOP to capture the biological features of underlying cytoarchitecture and identify the dMRI measures that best reflect underlying gray matter cytoarchitectural properties. Investigation of the contribution of underlying cytoarchitecture (cellular organization) to dMRI measures in gray matter provides validation of dMRI measures of average and regional heterogeneity in MSD & Trace as markers of cytoarchitecture as measured by regional average and heterogeneity in cell area density. This postmortem validation of these dMRI measures makes their use possible for treatment monitoring of various GM pathologies. These studies and their results together demonstrate the utility of imaging measures to investigate the complex relationships between GM cellular organization, brain development, environment and genes

The bed nucleus of the Stria Terminalis:Connections, genetics, & trait associations

This thesis examines the functional and structural connections of the Bed Nucleus of the Stria Terminalis (BNST). The principal motivation in doing so stems from the documented gap in our knowledge between the prolific pre-clinical animal BNST research, and that of human BNST research (Lebow & Chen, 2016). Understanding the human BNST may prove to be clinically important, as animal models often implicate this structure as being key in processes underlying the stress-response, disorders of negative affect, and in substance misuse- particularly related to alcohol (Herman et al., 2020; Maita et al., 2021). Therefore I further set out to test BNST connectivity relationships with related psychological phenotypes and examine any genetic associations. Chapter 1 provides an overview of the relevant BNST literature and a brief summary of the methods used in this thesis. In Chapter 2 I use the Human Connectome Project young human adults sample (n = ~1000) to map the intrinsic connectivity network of the BNST. In addition, I compare this network to that of the central nucleus of the amygdala, an area anatomically and functionally associated with the BNST (Alheid, 2009). Next, I test for associations across this network with self-report traits relating to dispositional negativity and alcohol use. Finally, I examine the heritability of specific BNST- amygdala sub-region functional connectivity, and co-heritability with the selfreport traits. In Chapter 3 I use the large UK biobank sample (n = ~ 19,000) to run a genome-wide association analysis, aiming to uncover specific common genetic variants that may be linked with BNST – amygdala sub-region functional connectivity. In Chapter 4, I focus on structural connectivity and use a mixture of macaque tracttracing analysis, and human and macaque diffusion MRI probabilistic tractography to examine the evidence for a connection between the subiculum and the BNST. As well, I test for associations between measures of white-matter microstructure and self-report dispositional negativity and alcohol-use phenotypes. Finally, in the Discussion, I bring together the findings of the research, noting their implications within the wider BNST literature and making several suggestions for improving similar analysis in future