Heritability of fractional anisotropy in human white matter: A comparison of Human Connectome Project and ENIGMA-DTI data
Introduction
Imaging genetics/genomics is an active research direction aimed at improving our understanding of the genetic underpinnings of brain structure, function, and connectivity in health and disease. The availability of data from a growing number of large-scale imaging projects enables meta-analyses that provide increased analytic power by combining data across projects. The ENIGMA (Enhancing Neuroimaging Genetics through Meta-Analysis) consortium was organized to facilitate this by bringing together genetic imaging researchers and developing methods for multi-site data harmonization and analyses (Thompson et al., 2014). The ENIGMA-DTI workgroup is focused on the analyses of Diffusion Tensor Imaging (DTI) data. Here, we compare the estimates of additive genetic contribution (heritability) to fractional anisotropy (FA) measurements previously reported for the ENIGMA-DTI (Kochunov et al., 2014) with comparably analyzed DTI data from the Human Connectome Project (HCP) (Van Essen et al., 2013). The HCP is a large-scale international collaboration aimed at elucidating the genetic and environmental sources of normal variability within the structural and functional connections of the human brain. The HCP is collecting and sharing data from a large cohort of healthy young adult twins and siblings using state of the art, high resolution, neuroimaging acquisition and analysis methods (Glasser et al., 2013, Van Essen et al., 2013). The HCP diffusion imaging data differs from those used in previous ENIGMA-DTI studies in several important ways, including higher spatial resolution (1.25 mm isotropic voxels vs. 2–3 mm for ENIGMA-DTI studies) and higher number of diffusion directions (270 vs. 30–100 for ENIGMA-DTI studies) (Sotiropoulos et al., 2013). Here, we tested whether the estimates of heritability obtained from the HCP data are comparable to published ENIGMA-DTI joint-analytic estimates and whether new insights and information emerge by analyzing the higher-resolution HCP data. Toward this aim, we compare regional and voxelwise heritability estimates for FA values in the current HCP public data sample with heritability estimates pooled from multiple sites across the world and published by the ENIGMA-DTI workgroup (http://enigma.ini.usc.edu) (Kochunov et al., 2014).
FA is a widely used quantitative measure of white matter microstructure (Basser et al., 1994, Basser and Pierpaoli, 1996) calculated from the diffusion tensor (DTI) model of water diffusion (Thomason and Thompson, 2011). This is an important biomarker in clinical studies, as it can sensitively track the white matter changes in Alzheimer's disease (AD) (Clerx et al., 2012, Teipel et al., 2012), general cognitive function (Penke et al., 2010a, Penke et al., 2010b), and several neurological and psychiatric disorders (Barysheva et al., 2013, Carballedo et al., 2012, Kochunov et al., 2012, Mandl et al., 2013, Sprooten et al., 2011). The ENIGMA-DTI workgroup has developed a standardized protocol (http://enigma.ini.usc.edu/ongoing/dti-working-group/) for extraction and harmonization of phenotypes for genetic analyses of FA traits (Jahanshad et al., 2013, Kochunov et al., 2014). This protocol was previously evaluated in five family-based cohorts including 2248 children and adults (ages: 9–85). The findings were summarized in two ways. In the meta-analytic approach, heritability results across cohorts were normalized using a standard error (SE)-weighted model to yield meta-analytical estimates of heritability. In the mega-analytic approach, all the data were shared and synthesized pedigree was used to directly estimate heritability (Kochunov et al., 2014). Here, we applied the ENIGMA-DTI protocol to HCP DTI data to report the whole-brain and regional estimate heritability of FA values in the HCP sample in voxel-wise and region-of-interest based tests. Then, we compared the global and regional heritability estimates in HCP to the joint-analytic estimates previously reported by ENIGMA-DTI. Finally, we took advantage of the high spatial resolution of HCP acquisition to study the heritability pattern of the white matter periphery, where the common 2 mm or larger resolution of standard DTI scans leads to artificial lowering of FA magnitude in regions of diverging fibers due to partial voxel averaging effects (Basser et al., 1994, Basser and Pierpaoli, 1996).
This analysis is based on the previous studies of the ENIGMA-DTI workgroup that quantified heritability of the whole-brain and regional FA values in geographically and ethnically diverse cohorts (Jahanshad et al., 2013, Kochunov et al., 2014). It aimed to identify the “genetic search space” for FA measurements: a set of endophenotypes that are significantly heritable regardless of age, ethnicity and family structure to be used for follow-up genome-wide association (GWAS) analyses. To qualify as an endophenotype, a measurement must show a significant and reproducible heritability value across diverse cohorts. While significant heritability alone offers no guarantee that specific genetic variants associated to the trait will be discovered, measures that are not reliably heritable may be unstable and are unlikely to be influenced by genetic variants with effect sizes that are detectable in GWAS. In our prior work, the whole-brain average FA was found to be significantly heritable in all cohorts with tight confidence intervals. The regional FA measurements showed a variable additive genetic contribution (Kochunov et al., 2014) that suggested that there may be a consistent pattern of additive genetic contributions to variance in FA values across the brain regions assessed. Here, we extend this work by testing the reliability and generalizability of ENIGMA-DTI to the HCP cohort and attempt to take a deeper view on the spatial variability of heritability of FA values across brain regions. We demonstrate the consistency of heritability measurements across populations by showing that regional heritability estimates from an HCP cohort fall in line with the pooled estimates derived from independent populations.
Section snippets
Subjects
ENIGMA-DTI processing of FA images and heritability analyses were performed in 481 (194/287 M/F) participants of the Human Connectome Project (HCP) for whom the scans and data were released in June 2014 (humanconnectome.org) after passing the HCP quality control and assurance standards (Marcus et al., 2013). The details of this release are available in the HCP reference manual. The participants in the HCP study were recruited from the Missouri Family and Twin Registry that includes individuals
Results
Heritability estimates for whole-brain averaged and by-tract FA values are shown in Table 1. The whole-brain average and regional FA values in the HCP subjects were significantly heritable (p < 0.001) (Fig. 2). The covariates (age, sex, age2, age × sex, and age2 × sex) explained 10.9% of the phenotypic variance in the whole-brain averaged FA values (Table 1). Sex was the only significant covariate (p = 6.6 · 10− 8) and female subjects showed ~ 2% higher average FA values (FA = 0.40 ± 0.12 vs 0.39 ± 0.14 for
Discussion
In this study, we performed three analyses: (1) A comprehensive heritability analysis of whole-brain and regional FA values in the HCP cohort indicated that FA measurements extracted using the ENIGMA-DTI protocol were highly heritable, with ~ 70–80% of the total phenotypic variance explained by additive genetic factors. (2) When compared to meta-and-mega-genetic estimates of heritability, the heritability measurements in HCP cohort were generally higher. Nonetheless, the agreement between the
Conclusion
The ENIGMA-DTI FA homogenization protocol was tested in the state-of-the-art data collected by the HCP. This research helps to define the genetic search space for future localization of risk factors that affect white matter integrity in behavioral, neurological, and neuropsychiatric disorders. Limiting genetic searches to the traits that show significant and replicable heritability will improve confidence in outcomes of these analyses and reduce the number of degrees of freedom. In agreement,
Acknowledgments
This study was supported by R01 EB015611 to PK, R01 HD050735 to PT, MH0708143 and MH083824 grants to DCG and by MH078111 and MH59490 to JB. Additional support for algorithm development was provided by NIH R01 grants EB008432, EB008281, and EB007813 (to PT). JES is supported by a Clinical Research Training Fellowship from the Wellcome Trust (087727/Z/08/Z). AMM is supported by a NARSAD Independent Investigator Award and by a Scottish Funding Council Senior Clinical Fellowship.
This work was
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2022, NeuroImageCitation Excerpt :To the extent that DTI and qR1 metrics are genetically correlated and share genetic variance, it would be expected that these metrics would show strong within-subject correlations. Studies in humans and NHPs demonstrate significant heritability of DTI parameters (Geng et al., 2012; Jahanshad et al., 2013; Kochunov et al., 2010, 2014, 2015; Luo et al., 2021; Tromp et al., 2019), whereas the extent to which qR1 measures are heritable remains unknown. The reason why our study fails to detect between-subject relaxometry-DTI associations, while others report this association, is unclear.
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