Common genetic variants influence human subcortical brain structures

Abstract

The highly complex structure of the human brain is strongly shaped by genetic influences1. Subcortical brain regions form circuits with cortical areas to coordinate movement2, learning, memory3 and motivation4, and altered circuits can lead to abnormal behaviour and disease2. To investigate how common genetic variants affect the structure of these brain regions, here we conduct genome-wide association studies of the volumes of seven subcortical regions and the intracranial volume derived from magnetic resonance images of 30,717 individuals from 50 cohorts. We identify five novel genetic variants influencing the volumes of the putamen and caudate nucleus. We also find stronger evidence for three loci with previously established influences on hippocampal volume5 and intracranial volume6. These variants show specific volumetric effects on brain structures rather than global effects across structures. The strongest effects were found for the putamen, where a novel intergenic locus with replicable influence on volume (rs945270; P = 1.08 × 10−33; 0.52% variance explained) showed evidence of altering the expression of the KTN1 gene in both brain and blood tissue. Variants influencing putamen volume clustered near developmental genes that regulate apoptosis, axon guidance and vesicle transport. Identification of these genetic variants provides insight into the causes of variability inhuman brain development, and may help to determine mechanisms of neuropsychiatric dysfunction.

Structural and functional brain network correlates of depressive symptoms in premanifest Huntington's disease

“…However, we were unable to include these structures in our analysis as automatic segmentation of these regions are not sufficiently reliable [Hibar et al, 2015]. …”

“…The globus pallidus, nucleus accumbens and amygdala were excluded, as automatic segmentation of these regions is not sufficiently reliable [Hibar et al, 2015]. The cerebellum was excluded due partial coverage necessary to maintain acceptable imaging times.…”

The genome‐wide expression effects of escitalopram and its relationship to neurogenesis, hippocampal volume, and antidepressant response

“…We achieved this using the pathway analysis tool, MAGMA (Leeuw, Mooij, Heskes, & Posthuma, 2015), with a 35 kb 5′ and 10 kb 3′ window around genes and the results from two large genome‐wide association studies (GWAS). There were genome‐wide summary statistic data from the ENIGMA Consortium, which performed a meta‐analysis identifying the genetic predictors of hippocampal volume in a total of 30,717 subjects (http://enigma.ini.usc.edu) (Hibar et al, 2015) and results from the NEWMEDS project, which has investigated genetic predictors of antidepressant response (Tansey et al, 2012). Within NEWMEDS we utilize the GWAS results specifically relating to SSRIs, which is derived from a total of 1,222 major depressive disorder patients (Tansey et al, 2012).…”

Gene expression and DNA methylation are extensively coordinated with MRI-based brain microstructural characteristics

“…Progress towards a systematic molecular basis for neuroimaging findings indicates that regional volumes are shaped by genetic factors (Hibar et al 2015), and that spatial patterns of gene expression correspond to cell type distributions (Krienen et al 2016), structural (Fulcher and Fornito 2016) and functional connectivity patterns (Hawrylycz et al 2015;Richiardi et al 2015;Vértes et al 2016;Wang et al 2015). While these studies suggest that gene expression and aspects of brain microstructure have similar spatial patterns, it is unclear to what extent they covary within a particular brain region or across regions.…”

“…However, studies which span these two approaches to test the covariation of gene expression and brain structure are limited. Efforts to unite molecular biology with neuroimaging in the context of disease through Bimaging genetics^have identified a small number of polymorphisms tied to variation in brain structures (Hibar et al 2015;Munafò et al 2008;Stein et al 2012) including a subset of AD GWAS variants (Braskie et al 2011;Erk et al 2011;Kohannim et al 2013;P. Zhang et al 2015).…”