Gene network effects on brain microstructure and intellectual performance identified in 472 twins

A major challenge in neuroscience is finding which genes affect brain integrity, connectivity, and intellectual function. Discovering influential genes holds vast promise for neuroscience, but typical genome-wide searches assess approximately one million genetic variants one-by-one, leading to intractable false positive rates, even with vast samples of subjects. Even more intractable is the question of which genes interact and how they work together to affect brain connectivity. Here, we report a novel approach that discovers which genes contribute to brain wiring and fiber integrity at all pairs of points in a brain scan. We studied genetic correlations between thousands of points in human brain images from 472 twins and their nontwin siblings (mean age: 23.7 ± 2.1 SD years; 193 male/279 female). We combined clustering with genome-wide scanning to find brain systems with common genetic determination. We then filtered the image in a new way to boost power to find causal genes. Using network analysis, we found a network of genes that affect brain wiring in healthy young adults. Our new strategy makes it computationally more tractable to discover genes that affect brain integrity. The gene network showed small-world and scale-free topologies, suggesting efficiency in genetic interactions and resilience to network disruption. Genetic variants at hubs of the network influence intellectual performance by modulating associations between performance intelligence quotient and the integrity of major white matter tracts, such as the callosal genu and splenium, cingulum, optic radiations, and the superior longitudinal fasciculus

Mapping Genetic Influence on Brain Structure

Neuroimaging is playing an increasingly crucial role in delineating pathological conditions that cannot be typically defined by non-specific clinical symptom. The goal of this thesis was to characterize the genetic influence on grey and white matter indices and evaluate their potential as a reliable “structural MRI signatures”. We first assessed the effects of spatial resolution and smoothing on heritability estimation (Chapter 3). We then investigated heritability patterns of MRI measures of grey and white matter (Chapters 4-5). We then performed a cross-sectional evaluation of how heritability changes over the lifespan for both grey and white matter (Chapter 6). Finally, multivariate structural equation modeling was used to investigate the genetic correlation between grey matter structure and white matter connectivity (Chapter 7), in the default mode network (DMN). Our results show that several key brain structures were moderate to highly heritable and that this heritability was both spatially and temporally heterogeneous. At a network level, the DMN was found to have distinct genetic factors that modulated the grey matter regions and white matter tracts separately. We conclude that the spatial and temporal heterogeneity are likely to reflect gene expression patterns that are related to the developmental of specific brain regions and circuits over time

White matter connections : developmental neuroimaging studies of the associations between genes, brain and behavior

Development of cognitive abilities across childhood and adulthood parallels brain maturation in typically developing samples. Cognitive abilities such as reading and working memory have been linked to neuroimaging measures in relevant brain regions. Though the correlations between inter-individual brain differences and their related cognitive abilities are well established, the cause of this inter-individual variability is still not fully known. This thesis aims to understand the neural bases of the inter-individual variability in reading ability by studying the associations between dyslexia susceptibility genes and white and gray matter brain structures, and determine whether the measures of associated regions correlate with variability in reading ability. Moreover, it aims to identify the brain measures that correlate with concurrent measures of working memory and those that are predictive of future working memory, using a longitudinal cohort of typically developing children and young adults. Studies I and II: Three genes, DYX1C1, DCDC2 and KIAA0319, have been previously associated with dyslexia, neuronal migration, and ciliary function. We investigated whether the polymorphisms within these genes would affect variability in white and gray matter brain structures. Rs3743204 (DYX1C1), rs793842 (DCDC2), and rs6935076 (KIAA0319) were associated with left temporo-parietal white matter volume connecting middle temporal cortex to angular and supramarginal gyri as well as lateral occipital cortex. Rs793842 was significantly associated with thickness of left parietal areas and the lateral occipital cortex. Both white and gray matter measures correlated with current reading ability, but only white matter predicted future reading. Study III: We aimed to investigate whether MRPL19/C2ORF3 dyslexia genes, found to be correlated with verbal and non-verbal IQ, have a significant influence on white matter brain structures. Rs917235 showed a significant association with white matter volume in bilateral posterior parts of the corpus callosum and the cingulum, with connections to parietal, occipital and temporal cortices that are involved in both language and general cognitive abilities. Study IV: ROBO1 is a dyslexia gene that has been associated with axonal guidance and midline crossing. We assessed whether the polymorphisms within this gene have an influence on structure of the corpus callosum. Rs7631357 was associated with probability of connections within the fibers extending through the body of corpus callosum to parietal brain regions. The results fit well with previous reports on the role of Robo1 in axonal path finding in mice. Study V: Working memory has been associated with greater brain activity, thinner cortex, and white matter maturation in cross-sectional studies of children and young adults. Here, we aimed to investigate the role of differences in brain structure and function in the development of working memory. We assessed the concurrent and predictive relationships between working memory performance and neuroimaging measures in the fronto-parietal and fronto-striatal networks important for working memory. Working memory performance correlated with brain activity in frontal and parietal regions, cortical thickness in parietal cortex, and white matter volume of fronto-parietal and fronto-striatal tracts. White matter microstructure and brain activity in the caudate predicted future working memory. This work highlights the impact of imaging genetics research, revealing important associations between genes, brain and behavior. The results identify the neural mechanism underlying two cognitive abilities, reading and working memory. Specifically, the findings identify the important role of white matter in driving the development of working memory and reading ability, connecting the related cortical areas, as well as bridging the gap between genes and behavior

INVESTIGATING THE GENETICS OF MICROSTRUCTURE OF THE CORPUS CALLOSUM IN THE AGEING BRAIN

Abstract: Age-related atrophy of the corpus callosum (CC) has been associated with cognitive impairment and neurodegenerative disease. To date, there is limited knowledge about the role genetics plays in age-related microstructural changes of CC. The current study sought to examine the role of genetic factors on the microstructure of CC in older individuals. Heritability, genetic correlation analyses and a genome-wide search for SNPs associated with the microstructural integrity of CC were undertaken. Brain imaging scans were collected from two studies of community-dwelling older adults the Older Australian Twins Study (OATS) and the Sydney Memory and Ageing Study (MAS). Diffusion tensor imaging (DTI) measures were estimated for the whole CC as well as for five subregions. Parcellation of the CC was performed using Analyze® software. Heritability analyses for CC DTI measures were undertaken in 284 healthy older twins (66% female; 79 MZ and 63 DZ pairs) from OATS (mean age = 69.82, SD=4.76 years). Heritability and genetic correlation analyses were undertaken using the SOLAR software package. Genome-wide association studies (GWAS) for CC DTI measures were undertaken in MAS and replication performed in OATS. Heritability (h2) analysis for the whole CC, indicated significant h2 for fractional anisotropy (FA) (h2=0.56), mean diffusivity (MD) (h2=0.52), radial diffusivity (RD) (h2=0.49) and axial diffusivity (AD) (h2=0.37). Bivariate genetic correlation analyses were also performed between whole CC DTI measures. Across the DTI measures for the whole CC, MD and RD shared 84% of the common genetic variance, followed by MD- AD (77%), FA - RD (52%), RD- AD (37%) and FA MD (11%). The GWAS did not identify any significant SNPs nor were any of the suggestive SNPs replicated in OATS. In addition, candidate CC DTI SNPs were not associated with CC DTI measures. These findings suggest that the CC white matter microstructure in older adults is generally under moderate genetic control. There was also evidence of shared genetic factors between all four CC DTI measures. This study did not identify any significant SNPs associated with CC DTI measures in the GWAS analysis. This work suggests larger genetic association studies may be required to find CC-DTI associated SNPs

Imaging genetics : Methodological approaches to overcoming high dimensional barriers

Imaging genetics is still a quite novel area of research which attempts to discover how genetic factors affect brain structures and functions. In this thesis, using a various methodological approaches I showed how it can contribute to our understanding of the complex genetic architecture of the human brain

Quantitative tract-based white matter heritability in twin neonates

Studies in adults indicate that white matter microstructure, assessed with diffusion tensor imaging (DTI), has high heritability. Little is known about genetic and environmental influences on DTI parameters, measured along fiber tracts particularly, in early childhood. In the present study, we report comprehensive heritability data of white matter microstructure fractional anisotropy (FA), radial diffusion (RD), and axial diffusion (AD) along 47 fiber tracts using the quantitative tractography in a large sample of neonatal twins (n=356). We found significant genetic influences in almost all tracts with similar heritabilities for FA, RD, and AD as well as positive relationships between these parameters and heritability. In a single tract analysis, genetic influences along the length of the tract were highly variable. These findings suggest that at birth, there is marked heterogeneity of genetic influences of white matter microstructure within white matter tracts. This study provides a basis for future studies of developmental changes in genetic and environmental influences during early childhood, a period of rapid development that likely plays a major role in individual differences in white matter structure and function