Genetics of microstructure of cerebral white matter using diffusion tensor imaging

Abstract

We analyzed the degree of genetic control over intersubject variability in the microstructure of cerebral white matter (WM) using diffusion tensor imaging (DTI). We performed heritability, genetic correlation and quantitative trait loci (QTL) analyses for the whole-brain and 10 major cerebral WM tracts. Average measurements for fractional anisotropy (FA), radial (L_⊥) and axial (L_║) diffusivities served as quantitative traits. These analyses were done in 467 healthy individuals (182 males/285 females; average age 47.9 ± 13.5 years; age range: 19–85 years), recruited from randomly-ascertained pedigrees of extended families. Significant heritability was observed for FA (h² = 0.52 ± 0.11; p = 10^− 7) and L_⊥ (h² = 0.37 ± 0.14; p = 0.001), while L_║ measurements were not significantly heritable (h² = 0.09 ± 0.12; p = 0.20). Genetic correlation analysis indicated that the FA and L_⊥ shared 46% of the genetic variance. Tract-wise analysis revealed a regionally diverse pattern of genetic control, which was unrelated to ontogenic factors, such as tract-wise age-of-peak FA values and rates of age-related change in FA. QTL analysis indicated linkages for whole-brain average FA (LOD = 2.36) at the marker D15S816 on chromosome 15q25, and for L_⊥ (LOD = 2.24) near the marker D3S1754 on the chromosome 3q27. These sites have been reported to have significant co-inheritance with two psychiatric disorders (major depression and obsessive-compulsive disorder) in which patients show characteristic alterations in cerebral WM. Our findings suggest that the microstructure of cerebral white matter is under a strong genetic control and further studies in healthy as well as patients with brain-related illnesses are imperative to identify the genes that may influence cerebral white matter.

Introduction

Cerebral white matter (WM) consists of axonal bundles that connect proximal and distal brain regions and create large-scale neural networks facilitating complex behaviors (Le Bihan, 2003). Although an appreciation of the genetic mechanisms that control intersubject variability in these networks is critical for understanding normal and pathological function, the lack of noninvasive methodologies to study WM had held back discoveries in this field (Crick and Jones, 1993). Recent developments in magnetic resonance diffusion tensor imaging (DTI) have enabled in vivo measurement of WM microstructure (Basser, 1995, Basser et al., 1994). In parallel, methods of statistical genomics have been developed to measure the degree of genetic control over intersubject variability in a trait and to localize chromosomal regions that control this variability. These advances lead to several recent studies that demonstrated evidence for genetic control over variability in cerebral WM through analysis of fractional anisotropy (Chiang et al., 2009) and regional WM density (Hulshoff Pol et al., 2006) in twins. Additionally, genetically linked aberrations in the WM integrity were reported in the Williams syndrome (Hoeft et al., 2007, Marenco et al., 2007) and schizophrenia (Konrad et al., 2009, McIntosh et al., 2008, Winterer et al., 2008). In this manuscript, we synergistically combined these methodological advances in neuroimaging and statistical genomics to study the genetic causes of the intersubject variability in the microstructure of the cerebral WM.

The first objective of this study was to test the hypothesis that intersubject variability in the WM microstructure, measured using DTI, is under a high degree of genetic control. The DTI technique provides direct quantification of the tensor describing translational motion of water molecules within WM axons. In myelinated axons, this motion is enhanced along the axial direction and constrained in the direction perpendicular to it. Therefore, DTI-extracted parameters are highly sensitive to the degree of axonal myelination, average axonal diameter and axonal density (Song et al., 2003, Song et al., 2005). To this end, we used three DTI measurements: diffusivity in the axial (along the longitudinal axis L_║) and radial (across the axonal wall, L_⊥) directions and fractional anisotropy (FA) of water diffusion. The axial and radial diffusivities provide direct and quantitative measurements of regional water diffusion in mm²/s (Basser, 1995, Basser et al., 1994). Additionally, axial and radial diffusivities are commonly combined into a unitless parameter, FA, that measures the difference in the magnitudes of the water diffusion between axial and radial directions. FA values are high (maximum theoretical value is 1.0) in heavily myelinated WM tracts, low (0.05–0.2) in gray matter and near zero in cerebrospinal fluid (Basser, 1994, Conturo et al., 1996, Pierpaoli and Basser, 1996, Ulug et al., 1995).

The second objective was to analyze the regional pattern of genetic control over intersubject differences in the microstructure of the major cerebral WM tracts. Specifically, we hypothesized that the degree of the regional genetic control was modulated by several ontogenic and senescence factors. Prior studies of the genetics of intersubject variations in regional brain morphology indicated a possible connection between ontogenic factors such as variability in the prenatal neurohormonal environment and the degree of genetic contribution to variability of brain structures. For instance, a progressively lower degree of genetic contribution was found for cortical structures that appeared later in cerebral development (Brun et al., 2008, Cheverud et al., 1990, Lohmann et al., 1999).

These findings imply that intersubject variability in the later developing structures may have higher contributions from environmental factors. To test these regional-control hypotheses, we used findings from our recent study of heterogeneity and heterochronicity of cerebral myelination trends in a large group of healthy subjects (N = 820) aged 10 to 90 years(Kochunov et al., 2010b). There, we showed that cerebral myelination, measured by FA, followed a quadratic trajectory with age, with peak myelination levels observed between 2nd and 4th decades of life. We carefully mapped by-tract myelination rates, age-of-peak and senescence rates in ten major cerebral WM. Here, we used the results of this study to specifically test whether the earlier developing WM tracts, that carry sensory and motor information, were under higher genetic control than later-developing associative tracts.

The third objective was to localize specific chromosomal regions that influence intersubject variability in DTI parameters using quantitative trait linkage (QTL) analysis. QTL analysis identifies chromosomal regions that control phenotypic variability by calculating the degree of co-inheritance between a phenotype and a set of specific genetic markers distributed homogenously across all chromosomes. Additionally, QTL analysis was used to study whether the genetic influences in different DTI parameters would map to the same or different chromosomal regions thereby providing a direct estimate of the degree of shared genetic control among DTI parameters. Successful genetic linkage analyses were recently performed on a neuroimaging phenotype, volume of hyperintense WM lesions (DeStefano et al., 2006, Kochunov et al., 2009, Turner et al., 2005) providing chromosomal targets that are responsible for variability in this trait and explicating genetic relationships between hypertension and the volume of hyperintense WM. More recently, candidate gene analysis of DTI scans (Chiang et al., in 2009) revealed that polymorphism in the brain-derived neurotrophic factor (BDNF) gene influence fiber integrity in normal subjects. In a study of 258 twins, the BDNF polymorphism significantly contributed to the variation in FA in the posterior cingulate gyrus, where it accounted for around 90–95% of the total variance in FA, suggesting that common genetic variants may strongly determine white matter integrity.

Recently, an analytic method, tract-based spatial statistics (TBSS), was developed to quantify voxel-level variation in DTI parameters among subjects along the major WM tracts (Smith et al., 2006a, Smith et al., 2007). TBSS was developed to overcome the shortcomings of voxel-based analysis of DTI data. Hardware of MR scanners limits the spatial resolution of DTI data, therefore making intersubject voxel-based analysis susceptible to misalignment and partial-volume effects necessitating specialized processing (Chiang et al., 2008, Smith et al., 2006b). TBSS processing addresses this by extracting the spatial course of major WM tracts, and then analyzing diffusion parameters values that correspond to the middle of the tract. Applying this technique to DTI data collected from randomly selected individuals from large extended pedigrees allowed us to investigate the specific hypotheses proposed in aims 1 to 3 and to study the extent to which intersubject variation in DTI parameters is under genetic control.