Gennady V. Roshchupkin

Heritability of the shape of subcortical brain structures in the general population

The volumes of subcortical brain structures are highly heritable, but genetic underpinnings of their shape remain relatively obscure. Here we determine the relative contribution of genetic factors to individual variation in the shape of seven bilateral subcortical structures: the nucleus accumbens, amygdala, caudate, hippocampus, pallidum, putamen and thalamus. In 3,686 unrelated individuals aged between 45 and 98 years, brain magnetic resonance imaging and genotyping was performed. The maximal heritability of shape varies from 32.7 to 53.3% across the subcortical structures. Genetic contributions to shape extend beyond influences on intracranial volume and the gross volume of the respective structure. The regional variance in heritability was related to the reliability of the measurements, but could not be accounted for by technical factors only. These findings could be replicated in an independent sample of 1,040 twins. Differences in genetic contributions within a single region reveal the value of refined brain maps to appreciate the genetic complexity of brain structures.

HASE : Framework for efficient high-dimensional association analyses

High-throughput technology can now provide rich information on a person’s biological makeup and environmental surroundings. Important discoveries have been made by relating these data to various health outcomes in fields such as genomics, proteomics, and medical imaging. However, cross-investigations between several high-throughput technologies remain impractical due to demanding computational requirements (hundreds of years of computing resources) and unsuitability for collaborative settings (terabytes of data to share). Here we introduce the HASE framework that overcomes both of these issues. Our approach dramatically reduces computational time from years to only hours and also requires several gigabytes to be exchanged between collaborators. We implemented a novel meta-analytical method that yields identical power as pooled analyses without the need of sharing individual participant data. The efficiency of the framework is illustrated by associating 9 million genetic variants with 1.5 million brain imaging voxels in three cohorts (total N = 4,034) followed by meta-analysis, on a standard computational infrastructure. These experiments indicate that HASE facilitates high-dimensional association studies enabling large multicenter association studies for future discoveries.

Partial derivatives meta-analysis: pooled analyses when individual participant data cannot be shared

Joint analysis of data from multiple studies in collaborative efforts strengthens scientific evidence, with the gold standard approach being the pooling of individual participant data (IPD). However, sharing IPD often has legal, ethical, and logistic constraints for sensitive or high-dimensional data, such as in clinical trials, observational studies, and large-scale omics studies. Therefore, meta-analysis of study-level effect estimates is routinely done, but this compromises on statistical power, accuracy, and flexibility. Here we propose a novel meta-analytical approach, named partial derivatives meta-analysis, that is mathematically equivalent to using IPD, yet only requires the sharing of aggregate data. It not only yields identical results as pooled IPD analyses, but also allows post-hoc adjustments for covariates and stratification without the need for site-specific re-analysis. Thus, in case that IPD cannot be shared, partial derivatives meta-analysis still produces gold standard results, which can be used to better inform guidelines and policies on clinical practice.

A spatio-temporal reference model of the aging brain

ABSTRACT Both normal aging and neurodegenerative disorders such as Alzheimers disease (AD) cause morphological changes of the brain. It is generally difficult to distinguish these two causes of morphological change by visual inspection of magnetic resonance (MR) images. To facilitate making this distinction and thus aid the diagnosis of neurodegenerative disorders, we propose a method for developing a spatio‐temporal model of morphological differences in the brain due to normal aging. The method utilizes groupwise image registration to characterize morphological variation across brain scans of people with different ages. To extract the deformations that are due to normal aging we use partial least squares regression, which yields modes of deformations highly correlated with age, and corresponding scores for each input subject. Subsequently, we determine a distribution of morphologies as a function of age by fitting smooth percentile curves to these scores. This distribution is used as a reference to which a persons morphology score can be compared. We validate our method on two different datasets, using images from both cognitively normal subjects and patients with Alzheimer disease (AD). Results show that the proposed framework extracts the expected atrophy patterns. Moreover, the morphology scores of cognitively normal subjects are on average lower than the scores of AD subjects, indicating that morphology differences between AD subjects and healthy subjects can be partly explained by accelerated aging. With our methods we are able to assess accelerated brain aging on both population and individual level. A spatio‐temporal aging brain model derived from 988 T1‐weighted MR brain scans from a large population imaging study (age range 45.9–91.7y, mean age 68.3y) is made publicly available at www.agingbrain.nl. HighlightsA model to assess morphological differences in the brain due to aging is developed.The method can assess accelerated brain aging on population and individual level.The model derived from 988 MR brain is made publicly available at www.agingbrain.nl.

Gray matter heritability in family-based and population-based studies using voxel-based morphometry

The combination of genetics and imaging has improved their understanding of the brain through studies of aggregate measures obtained from high‐resolution structural imaging. Voxel‐wise analyses have the potential to provide more detailed information of genetic influences on the brain. Here they report a large‐scale study of the heritability of gray matter at voxel resolution (1 × 1 × 1 mm).

Modeling the brain morphology distribution in the general aging population

Both normal aging and neurodegenerative diseases such as Alzheimer’s disease cause morphological changes of the brain. To better distinguish between normal and abnormal cases, it is necessary to model changes in brain morphology owing to normal aging. To this end, we developed a method for analyzing and visualizing these changes for the entire brain morphology distribution in the general aging population. The method is applied to 1000 subjects from a large population imaging study in the elderly, from which 900 were used to train the model and 100 were used for testing. The results of the 100 test subjects show that the model generalizes to subjects outside the model population. Smooth percentile curves showing the brain morphology changes as a function of age and spatiotemporal atlases derived from the model population are publicly available via an interactive web application at agingbrain.bigr.nl.

Genetic Determinants of Cortical Structure (Thickness, Surface Area and Volumes) among Disease Free Adults in the CHARGE Consortium

Cortical thickness, surface area and volumes (MRI cortical measures) vary with age and cognitive function, and in neurological and psychiatric diseases. We examined heritability, genetic correlations and genome-wide associations of cortical measures across the whole cortex, and in 34 anatomically predefined regions. Our discovery sample comprised 22,822 individuals from 20 cohorts within the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium and the United Kingdom Biobank. Significant associations were replicated in the Enhancing Neuroimaging Genetics through Meta-analysis (ENIGMA) consortium, and their biological implications explored using bioinformatic annotation and pathway analyses. We identified genetic heterogeneity between cortical measures and brain regions, and 161 genome-wide significant associations pointing to wnt/β-catenin, TGF-β and sonic hedgehog pathways. There was enrichment for genes involved in anthropometric traits, hindbrain development, vascular and neurodegenerative disease and psychiatric conditions. These data are a rich resource for studies of the biological mechanisms behind cortical development and aging.

HIGH-DIMENSIONAL ANALYSIS OF RNA EXPRESSION WITH CORTICAL THICKNESS

Background: The majority of Late Onset Alzheimer’s Disease (LOAD) GWAS associated SNPs are in noncoding regions of the genome, suggesting regulatory function. In addition, changes in gene expression in LOAD vs. healthy control brains have been described, and several groups reported expression quantitative trait loci (eQTLs) within LOAD associated regions. However, these disease and expression associations represent indirect links that may be attributed to other variants in high linkage disequilibrium with the associated tagging variants. Our goal was to define regulatory elements in the vicinity of the LOAD associated regions that are likely to influence the expression of genes important in LOAD etiology. Towards this goal we applied a bioinformatics approach using public databases for functional annotations. These analyses represent the first step in a global strategy to identify causal variants involved in LOAD. Methods:Genomic regions 60.5Mb surrounding LOAD GWAS-associated SNPs from the were integrated with data from The Roadmap Epigenomics Mapping Consortium for chromatin state segmentation (25-state model) to identify potential active enhancers for specific brain tissue vulnerable in LOAD. This data was aligned with CTCF transcription factor (TF) binding sites determined by ChIP-seq data from ENCODE to identify 3D chromatin structure, looping, between distal enhancer elements and promoter regions. Results:A total of 494 genes map within the defined LOAD GWAS regions. As an example, across the 1Mb region tagged by rs3865444 (CD33) we found 4 enhancer segments (brain hippocampus middle) that include CTCF ChIP-seq peaks. The CTCF signals mapped to the promoters of 4 genes (KLK6, KLK10, IGLON5 and SPACA6), suggesting that these enhancers are likely to regulate these target genes. Conclusions:We describe a valuable resource for testing the hypothesis that causal variants are positioned in regulatory elements of critical LOAD genes, and contribute directly to LOAD susceptibility by affecting gene regulation. These results will inform experimental work for direct validation of regulatory function using model systems such as isogenic iPSC-derived models generated by CRISPR/Cas9 genome editing. Our study is a foundational step in a larger strategy for progressing from GWAS association signals to target genes and specific causal variants for LOAD.

Heritability of connectivity and disconnectivity of the brain in a population-based study

We present the largest population-based heritability study of the human brain structural connectome, including a pathology-sensitive extension, the disconnectome. The disconnectome maps the effect of white matter lesions throughout the brain. The connectome and disconnectome were generated from diffusion-weighted images of 3255 unrelated subjects from the Rotterdam Study aged between 45 and 99 years. Graph theory measures were derived for both the connectome and disconnectome. Genotypes were used to derive genetic relationship matrices between individuals for heritability analyses. High measures of heritability, from 33% to 51%, were found across all connectivity measures. The disconnectome showed more significantly heritable connectivity measures than the connectome, suggesting that the new proposed measure may reveal additional or complementary information about the genetic architecture of the human brain.

Heritability and genome-wide associations studies of cerebral blood flow in the general population:

Cerebral blood flow is an important process for brain functioning and its dysregulation is implicated in multiple neurological disorders. While environmental risk factors have been identified, it remains unclear to what extent the flow is regulated by genetics. Here we performed heritability and genome-wide association analyses of cerebral blood flow in a population-based cohort study. We included 4472 persons free of cortical infarcts who underwent genotyping and phase-contrast magnetic resonance flow imaging (mean age 64.8 ± 10.8 years). The flow rate, cross-sectional area of the vessel, and flow velocity through the vessel were measured in the basilar artery and bilateral carotids. We found that the flow rate of the basilar artery is most heritable (h2 (SE) = 24.1 (9.8), p-value = 0.0056), and this increased over age. The association studies revealed two significant loci for the right carotid artery area (rs12546630, p-value = 2.0 × 10−8) and velocity (rs2971609, p-value = 1.4 × 10−8), with the latter showing a concordant effect in an independent sample (N = 1350, p-value = 0.057, meta-analyzed p-value = 2.5 × 10−9). These loci were also associated with other cerebral blood flow parameters below genome-wide significance, and rs2971609 lies in a known migraine locus. These findings establish that cerebral blood flow is under genetic control with potential relevance for neurological diseases.