Genome-wide scan of healthy human connectome discoversSPON1gene variant influencing dementia severity

Abstract

Aberrant connectivity is implicated in many neurological and psychiatric disorders, including Alzheimer's disease and schizophrenia. However, other than a few disease-associated candidate genes, we know little about the degree to which genetics play a role in the brain networks; we know even less about specific genes that influence brain connections. Twin and family-based studies can generate estimates of overall genetic influences on a trait, but genome-wide association scans (GWASs) can screen the genome for specific variants influencing the brain or risk for disease. To identify the heritability of various brain connections, we scanned healthy young adult twins with high-field, highangular resolution diffusion MRI. We adapted GWASs to screen the brain's connectivity pattern, allowing us to discover genetic variants that affect the human brain's wiring. The association of connectivity with the SPON1 variant at rs2618516 on chromosome 11 (11p15.2) reached connectome-wide, genome-wide significance after stringent statistical corrections were enforced, and it was replicated in an independent subsample. rs2618516 was shown to affect brain structure in an elderly population with varying degrees of dementia. Older people who carried the connectivity variant had significantly milder clinical dementia scores and lower risk of Alzheimer's disease. As a posthoc analysis, we conducted GWASs on several organizational and topological network measures derived from the matrices to discover variants in and around genes associated with autism (MACROD2), development (NEDD4), and mental retardation (UBE2A) significantly associated with connectivity. Connectome-wide, genome-wide screening offers substantial promise to discover genes affecting brain connectivity and risk for brain diseases.diffusion tensor imaging | neuroimaging genetics | graph theory | HARDI tractography | multiple comparisons correction H uman brain anatomy involves a complex network of structural and functional pathways that connect anatomically distinct regions. These pathways can be visualized, on a gross anatomical scale, with diffusion imaging-based tractography (1). Neural pathways change as our brains develop (2, 3) and are altered in neurodegenerative conditions, including Alzheimer's disease (4). Our individual genetic makeup exerts a strong influence on the functional synchronization of brain regions (5) and the patterning of cortical structure (6, 7), but particular genes that impact the brain's neural connectivity are still largely unknown.To empower the search for genes that affect the brain's connectivity patterns, we first studied a large twin and family cohort consisting of 366 young adults (ages 20-29 y) from 223 families. We used anatomical brain MRI combined with high-angular resolution diffusion imaging (HARDI) at high magnetic field (4 T) to subdivide cortical regions into areas of known structure and function (8), while also mapping the white matter fiber pathways between them with high-resolution tractography. We defined connectivity maps...

Cerebral functional connectivity periodically (de)synchronizes with anatomical constraints

“…This measure of so-called functional connectivity (FC) [see Friston (2011) for a review] is organized in robust resting-state networks (Beckmann et al 2005;Damoiseaux et al 2006;Moussa et al 2012), and has been used to explore a range of properties such as cognition (Richiardi et al 2011;Heine et al 2012), emotions , and learning (Bassett et al 2011). From an anatomical point of view, structural connectivity (SC) and its multi-scale spatial organization have also been characterized (Sporns et al 2004(Sporns et al , 2005 and linked to brain diseases (Kaiser 2013;Griffa et al 2013;Engel et al 2013) and genetic influences (Jahanshad et al 2013;Ziegler et al 2013). The relationship between SC and FC, and more particularly the way cerebral anatomy shapes neuronal functions is a question that has been addressed ever since neuroimaging techniques allowed to collect both structural and functional information [e.g., McIntosh and Gonzalez-Lima (1994)].…”

Micro-, Meso- and Macro-Connectomics of the Brain

“…Pilot studies showed that the data could be analyzed in a consistent way (Jahanshad et al 2013a;Kochunov et al 2014). As the ENIGMA3 project involves a cortical volumetric analysis, the current plan is for ENIGMA to use those cortical regions as the basis for a structural connectivity analysis, using the same voxel-wise analysis of the connections as advocated in Stein et al (2010) and Jahanshad et al (2013b). It will be interesting to see if similar sample sizes, tens of thousands, are needed to find and replicate genetic associations with measures of structural brain connectivity.…”

Genetics of the Connectome and the ENIGMA Project

“…Structural connectivity and patterns of organization are also influenced by genetic factors. Jahanshad et al (2013b) showed that a fraction of the total number of detected connections are indeed highly heritable, while Bohlken et al (2014) studied the network's topology to establish heritability for other global measures of fiber connections.…”