Publications

Abstract

Cortical asymmetry is a ubiquitous feature of brain organization that is altered in neurodevelopmental disorders and aging. Achieving consensus on cortical asymmetries in humans is necessary to uncover the genetic-developmental mechanisms that shape them and factors moderating cortical lateralization. Here, we delineate population-level asymmetry in cortical thickness and surface area vertex-wise in 7 datasets and chart asymmetry trajectories across life (4-89 years; observations = 3937; 70% longitudinal). We reveal asymmetry interrelationships, heritability, and test associations in UK Biobank (N=∼37,500). Cortical asymmetry was robust across datasets. Whereas areal asymmetry is predominantly stable across life, thickness asymmetry grows in development and declines in aging. Areal asymmetry correlates in specific regions, whereas thickness asymmetry is globally interrelated across cortex and suggests high directional variability in global thickness lateralization. Areal asymmetry is moderately heritable (max h2SNP ∼19%), and phenotypic correlations are reflected by high genetic correlations, whereas heritability of thickness asymmetry is low. Finally, we detected an asymmetry association with cognition and confirm recently-reported handedness links. Results suggest areal asymmetry is developmentally stable and arises in early life, whereas developmental changes in thickness asymmetry may lead to directional variability of global thickness lateralization. Our results bear enough reproducibility to serve as a standard for future brain asymmetry studies.

Abstract

Left–right asymmetry is an important organizing feature of the healthy brain that may be altered in schizophrenia, but most studies have used relatively small samples and heterogeneous approaches, resulting in equivocal findings. We carried out the largest case–control study of structural brain asymmetries in schizophrenia, with MRI data from 5,080 affected individuals and 6,015 controls across 46 datasets, using a single image analysis protocol. Asymmetry indexes were calculated for global and regional cortical thickness, surface area, and subcortical volume measures. Differences of asymmetry were calculated between affected individuals and controls per dataset, and effect sizes were meta-analyzed across datasets. Small average case–control differences were observed for thickness asymmetries of the rostral anterior cingulate and the middle temporal gyrus, both driven by thinner left-hemispheric cortices in schizophrenia. Analyses of these asymmetries with respect to the use of antipsychotic medication and other clinical variables did not show any significant associations. Assessment of age- and sex-specific effects revealed a stronger average leftward asymmetry of pallidum volume between older cases and controls. Case–control differences in a multivariate context were assessed in a subset of the data (N = 2,029), which revealed that 7% of the variance across all structural asymmetries was explained by case–control status. Subtle case–control differences of brain macrostructural asymmetry may reflect differences at the molecular, cytoarchitectonic, or circuit levels that have functional relevance for the disorder. Reduced left middle temporal cortical thickness is consistent with altered left-hemisphere language network organization in schizophrenia.

Abstract

White matter tracts form the structural basis of large-scale brain networks. We applied brain-wide tractography to diffusion images from 30,810 adults (U.K. Biobank) and found significant heritability for 90 node-level and 851 edge-level network connectivity measures. Multivariate genome-wide association analyses identified 325 genetic loci, of which 80% had not been previously associated with brain metrics. Enrichment analyses implicated neurodevelopmental processes including neurogenesis, neural differentiation, neural migration, neural projection guidance, and axon development, as well as prenatal brain expression especially in stem cells, astrocytes, microglia, and neurons. The multivariate association profiles implicated 31 loci in connectivity between core regions of the left-hemisphere language network. Polygenic scores for psychiatric, neurological, and behavioral traits also showed significant multivariate associations with structural connectivity, each implicating distinct sets of brain regions with trait-relevant functional profiles. This large-scale mapping study revealed common genetic contributions to variation in the structural connectome of the human brain.

Abstract

Laterality indices (LIs) quantify the left-right asymmetry of brain and behavioural variables and provide a measure that is statistically convenient and seemingly easy to interpret. Substantial variability in how structural and functional asymmetries are recorded, calculated, and reported, however, suggest little agreement on the conditions required for its valid assessment. The present study aimed for consensus on general aspects in this context of laterality research, and more specifically within a particular method or technique (i.e., dichotic listening, visual half-field technique, performance asymmetries, preference bias reports, electrophysiological recording, functional MRI, structural MRI, and functional transcranial Doppler sonography). Experts in laterality research were invited to participate in an online Delphi survey to evaluate consensus and stimulate discussion. In Round 0, 106 experts generated 453 statements on what they considered good practice in their field of expertise. Statements were organised into a 295-statement survey that the experts then were asked, in Round 1, to independently assess for importance and support, which further reduced the survey to 241 statements that were presented again to the experts in Round 2. Based on the Round 2 input, we present a set of critically reviewed key recommendations to record, assess, and report laterality research for various methods.

Abstract

Intracranial volume reflects the maximally attained brain size during development, and remains stable with loss of tissue in late
life. It is highly heritable, but the underlying genes remain largely undetermined. In a genome-wide association study of 32,438
adults, we discovered five previously unknown loci for intracranial volume and confirmed two known signals. Four of the loci were
also associated with adult human stature, but these remained associated with intracranial volume after adjusting for height.
We found a high genetic correlation with child head circumference (genetic = 0.748), which indicates a similar genetic
background and allowed us to identify four additional loci through meta-analysis (Ncombined = 37,345). Variants for intracranial
volume were also related to childhood and adult cognitive function, and Parkinson’s disease, and were enriched near genes
involved in growth pathways, including PI3K-AKT signaling. These findings identify the biological underpinnings of intracranial
volume and provide genetic support for theories on brain reserve and brain overgrowth.

Abstract

Genome-wide association screens aim to identify common genetic variants contributing to the phenotypic variability of complex traits, such as human height or brain morphology. The identified genetic variants are mostly within noncoding genomic regions and the biology of the genotype–phenotype association typically remains unclear. In this article, we propose a complementary targeted strategy to reveal the genetic underpinnings of variability in subcortical brain volumes, by specifically selecting genomic loci that are experimentally validated forebrain enhancers, active in early embryonic development. We hypothesized that genetic variation within these enhancers may affect the development and ultimately the structure of subcortical brain regions in adults. We tested whether variants in forebrain enhancer regions showed an overall enrichment of association with volumetric variation in subcortical structures of >13,000 healthy adults. We observed significant enrichment of genomic loci that affect the volume of the hippocampus within forebrain enhancers (empirical P = 0.0015), a finding which robustly passed the adjusted threshold for testing of multiple brain phenotypes (cutoff of P < 0.0083 at an alpha of 0.05). In analyses of individual single nucleotide polymorphisms (SNPs), we identified an association upstream of the ID2 gene with rs7588305 and variation in hippocampal volume. This SNP-based association survived multiple-testing correction for the number of SNPs analyzed but not for the number of subcortical structures. Targeting known regulatory regions offers a way to understand the underlying biology that connects genotypes to phenotypes, particularly in the context of neuroimaging genetics. This biology-driven approach generates testable hypotheses regarding the functional biology of identified associations.

Abstract

Recent genome wide association scans (GWAS) for reading and language abilities have pin-pointed promising new candidate loci. However, the potential contributions of these loci remain to be validated. In the present study, we tested 17 of the most significantly associated single nucleotide polymorphisms (SNPs) from these GWAS studies (p < 10−6 in the original studies) in a new independent population dataset from the Netherlands: known as FIOLA (Familial Influences On Literacy Abilities). This dataset comprised 483 children from 307 nuclear families, plus 505 adults (including parents of participating children), and provided adequate statistical power to detect the effects that were previously reported. The following measures of reading and language performance were collected: word reading fluency, nonword reading fluency, phonological awareness, and rapid automatized naming. Two SNPs (rs12636438, rs7187223) were associated with performance in multivariate and univariate testing, but these did not remain significant after correction for multiple testing. Another SNP (rs482700) was only nominally associated in the multivariate test. For the rest of the SNPs we did not find supportive evidence of association. The findings may reflect differences between our study and the previous investigations in respects such as the language of testing, the exact tests used, and the recruitment criteria. Alternatively, most of the prior reported associations may have been false positives. A larger scale GWAS meta-analysis than those previously performed will likely be required to obtain robust insights into the genomic architecture underlying reading and language.

Abstract

Schizophrenia is a devastating psychiatric illness with high heritability. Brain structure and function differ, on average, between people with schizophrenia and healthy individuals. As common genetic associations are emerging for both schizophrenia and brain imaging phenotypes, we can now use genome-wide data to investigate genetic overlap. Here we integrated results from common variant studies of schizophrenia (33,636 cases, 43,008 controls) and volumes of several (mainly subcortical) brain structures (11,840 subjects). We did not find evidence of genetic overlap between schizophrenia risk and subcortical volume measures either at the level of common variant genetic architecture or for single genetic markers. These results provide a proof of concept (albeit based on a limited set of structural brain measures) and define a roadmap for future studies investigating the genetic covariance between structural or functional brain phenotypes and risk for psychiatric disorders

Abstract

Background

Reading and language skills have overlapping genetic bases, most of which are still unknown. Part of the missing heritability may be caused by copy number variants (CNVs).
Methods

In a dataset of children recruited for a history of reading disability (RD, also known as dyslexia) or attention deficit hyperactivity disorder (ADHD) and their siblings, we investigated the effects of CNVs on reading and language performance. First, we called CNVs with PennCNV using signal intensity data from Illumina OmniExpress arrays (~723,000 probes). Then, we computed the correlation between measures of CNV genomic burden and the first principal component (PC) score derived from several continuous reading and language traits, both before and after adjustment for performance IQ. Finally, we screened the genome, probe-by-probe, for association with the PC scores, through two complementary analyses: we tested a binary CNV state assigned for the location of each probe (i.e., CNV+ or CNV−), and we analyzed continuous probe intensity data using FamCNV.
Results

No significant correlation was found between measures of CNV burden and PC scores, and no genome-wide significant associations were detected in probe-by-probe screening. Nominally significant associations were detected (p~10−2–10−3) within CNTN4 (contactin 4) and CTNNA3 (catenin alpha 3). These genes encode cell adhesion molecules with a likely role in neuronal development, and they have been previously implicated in autism and other neurodevelopmental disorders. A further, targeted assessment of candidate CNV regions revealed associations with the PC score (p~0.026–0.045) within CHRNA7 (cholinergic nicotinic receptor alpha 7), which encodes a ligand-gated ion channel and has also been implicated in neurodevelopmental conditions and language impairment. FamCNV analysis detected a region of association (p~10−2–10−4) within a frequent deletion ~6 kb downstream of ZNF737 (zinc finger protein 737, uncharacterized protein), which was also observed in the association analysis using CNV calls.
Conclusions

These data suggest that CNVs do not underlie a substantial proportion of variance in reading and language skills. Analysis of additional, larger datasets is warranted to further assess the potential effects that we found and to increase the power to detect CNV effects on reading and language.

Abstract

Pinpointing genes involved in non-right-handedness has the potential to clarify developmental contributions to human brain lateralization. Major-gene models have been considered for human handedness which allow for phenocopy and reduced penetrance, i.e. an imperfect correspondence between genotype and phenotype. However, a recent genome-wide association scan did not detect any common polymorphisms with substantial genetic effects. Previous linkage studies in families have also not yielded significant findings. Genetic heterogeneity and/or polygenicity are therefore indicated, but it remains possible that relatively rare, or even unique, major-genetic effects may be detectable in certain extended families with many non-right-handed members. Here we applied whole exome sequencing to 17 members from a single, large consanguineous family from Pakistan. Multipoint linkage analysis across all autosomes did not yield clear candidate genomic regions for involvement in the trait and single-point analysis of exomic variation did not yield clear candidate mutations/genes. Any genetic contribution to handedness in this unusual family is therefore likely to have a complex etiology, as at the population level.