Polygenic scores for autism are associated with neurite density in adults and children from the general population

Genetic variants linked to autism are thought to change cognition and behaviour by altering the structure and function of the brain. Although a substantial body of literature has identified structural brain differences in autism, it is unknown whether autism-associated common genetic variants are linked to changes in cortical macro- and micro-structure. We investigated this using neuroimaging and genetic data from adults (UK Biobank, N = 31,748) and children (ABCD, N = 4,928). Using polygenic scores and genetic correlations we observe a robust negative association between common variants for autism and a magnetic resonance imaging derived phenotype for neurite density (intracellular volume fraction) in the general population. This result is consistent across both children and adults, in both the cortex and in white matter tracts, and confirmed using polygenic scores and genetic correlations. There were no sex differences in this association. Mendelian randomisation analyses provide no evidence for a causal relationship between autism and intracellular volume fraction, although this should be revisited using better powered instruments. Overall, this study provides evidence for shared common variant genetics between autism and cortical neurite density.

Principal and independent genomic components of brain structure and function

The highly polygenic and pleiotropic nature of behavioural traits, psychiatric disorders and structural and functional brain phenotypes complicate mechanistic interpretation of related genome-wide association study (GWAS) signals, thereby obscuring underlying causal biological processes. We propose genomic principal and independent component analysis (PCA, ICA) to decompose a large set of univariate GWAS statistics of multimodal brain traits into more interpretable latent genomic components. Here we introduce and evaluate this novel methods various analytic parameters and reproducibility across independent samples. Two UK Biobank GWAS summary statistic releases of 2240 imaging-derived phenotypes (IDPs) were retrieved. Genome-wide beta-values and their corresponding standard-error scaled z-values were decomposed using genomic PCA/ICA. We evaluated variance explained at multiple dimensions up to 200. We tested the inter-sample reproducibility of output of dimensions 5, 10, 25 and 50. Reproducibility statistics of the respective univariate GWAS served as benchmarks. Reproducibility of 10-dimensional PCs and ICs showed the best trade-off between model complexity and robustness and variance explained (PCs: |rz  - max| = 0.33, |rraw  - max| = 0.30; ICs: |rz  - max| = 0.23, |rraw  - max| = 0.19). Genomic PC and IC reproducibility improved substantially relative to mean univariate GWAS reproducibility up to dimension 10. Genomic components clustered along neuroimaging modalities. Our results indicate that genomic PCA and ICA decompose genetic effects on IDPs from GWAS statistics with high reproducibility by taking advantage of the inherent pleiotropic patterns. These findings encourage further applications of genomic PCA and ICA as fully data-driven methods to effectively reduce the dimensionality, enhance the signal to noise ratio and improve interpretability of high-dimensional multitrait genome-wide analyses.

A profile of brain reserve in adults at genetic risk of Alzheimer's disease

INTRODUCTION

The apolipoprotein E (APOE) ε4 allele is the greatest genetic risk factor for Alzheimer's disease (AD). Our aim was to identify the structural brain measures that mitigate the negative effect of APOE ε4 on cognition, which would have implications for AD diagnosis and treatment trial selection.

METHODS

A total of 742 older adults (mean age: 70.1 ± 8.7 years) were stratified by APOE status and classified as cognitively normal (CDR 0) or with very mild dementia (CDR 0.5). Regional brain volume and cognitive performance were measured.

RESULTS

There were significant interactions between APOE and CDR on the left precuneus and on bilateral superior frontal volumes. These regions were preserved in CDR-0 ε3/ε4 and ε4/ε4 carriers but were reduced in CDR-0.5 ε3/ε4 and ε4/ε4 carriers, compared to their respective ε3/ε3 counterparts. Educational attainment predicted greater brain reserve.

DISCUSSION

This pattern of preserved brain structure in cognitively normal ε4 carriers with comprised medial temporal volume is consistent with the theory of brain reserve.

Toward Robust Functional Neuroimaging Genetics of Cognition

A commonly held assumption in cognitive neuroscience is that, because measures of human brain function are closer to underlying biology than distal indices of behavior/cognition, they hold more promise for uncovering genetic pathways. Supporting this view is an influential fMRI-based study of sentence reading/listening by Pinel et al. (2012), who reported that common DNA variants in specific candidate genes were associated with altered neural activation in language-related regions of healthy individuals that carried them. In particular, different single-nucleotide polymorphisms (SNPs) of FOXP2 correlated with variation in task-based activation in left inferior frontal and precentral gyri, whereas a SNP at the KIAA0319/TTRAP/THEM2 locus was associated with variable functional asymmetry of the superior temporal sulcus. Here, we directly test each claim using a closely matched neuroimaging genetics approach in independent cohorts comprising 427 participants, four times larger than the original study of 94 participants. Despite demonstrating power to detect associations with substantially smaller effect sizes than those of the original report, we do not replicate any of the reported associations. Moreover, formal Bayesian analyses reveal substantial to strong evidence in support of the null hypothesis (no effect). We highlight key aspects of the original investigation, common to functional neuroimaging genetics studies, which could have yielded elevated false-positive rates. Genetic accounts of individual differences in cognitive functional neuroimaging are likely to be as complex as behavioral/cognitive tests, involving many common genetic variants, each of tiny effect. Reliable identification of true biological signals requires large sample sizes, power calculations, and validation in independent cohorts with equivalent paradigms.SIGNIFICANCE STATEMENT A pervasive idea in neuroscience is that neuroimaging-based measures of brain function, being closer to underlying neurobiology, are more amenable for uncovering links to genetics. This is a core assumption of prominent studies that associate common DNA variants with altered activations in task-based fMRI, despite using samples (10-100 people) that lack power for detecting the tiny effect sizes typical of genetically complex traits. Here, we test central findings from one of the most influential prior studies. Using matching paradigms and substantially larger samples, coupled to power calculations and formal Bayesian statistics, our data strongly refute the original findings. We demonstrate that neuroimaging genetics with task-based fMRI should be subject to the same rigorous standards as studies of other complex traits.

Revolution of Resting-State Functional Neuroimaging Genetics in Alzheimer's Disease

The quest to comprehend genetic, biological, and symptomatic heterogeneity underlying Alzheimer's disease (AD) requires a deep understanding of mechanisms affecting complex brain systems. Neuroimaging genetics is an emerging field that provides a powerful way to analyze and characterize intermediate biological phenotypes of AD. Here, we describe recent studies showing the differential effect of genetic risk factors for AD on brain functional connectivity in cognitively normal, preclinical, prodromal, and AD dementia individuals. Functional neuroimaging genetics holds particular promise for the characterization of preclinical populations; target populations for disease prevention and modification trials. To this end, we emphasize the need for a paradigm shift towards integrative disease modeling and neuroimaging biomarker-guided precision medicine for AD and other neurodegenerative diseases.

Mild traumatic brain injury is associated with reduced cortical thickness in those at risk for Alzheimer's disease

Moderate-to-severe traumatic brain injury is one of the strongest environmental risk factors for the development of neurodegenerative diseases such as late-onset Alzheimer's disease, although it is unclear whether mild traumatic brain injury, or concussion, also confers risk. This study examined mild traumatic brain injury and genetic risk as predictors of reduced cortical thickness in brain regions previously associated with early Alzheimer's disease, and their relationship with episodic memory. Participants were 160 Iraq and Afghanistan War veterans between the ages of 19 and 58, many of whom carried mild traumatic brain injury and post-traumatic stress disorder diagnoses. Whole-genome polygenic risk scores for the development of Alzheimer's disease were calculated using summary statistics from the largest Alzheimer's disease genome-wide association study to date. Results showed that mild traumatic brain injury moderated the relationship between genetic risk for Alzheimer's disease and cortical thickness, such that individuals with mild traumatic brain injury and high genetic risk showed reduced cortical thickness in Alzheimer's disease-vulnerable regions. Among males with mild traumatic brain injury, high genetic risk for Alzheimer's disease was associated with cortical thinning as a function of time since injury. A moderated mediation analysis showed that mild traumatic brain injury and high genetic risk indirectly influenced episodic memory performance through cortical thickness, suggesting that cortical thinning in Alzheimer's disease-vulnerable brain regions is a mechanism for reduced memory performance. Finally, analyses that examined the apolipoprotein E4 allele, post-traumatic stress disorder, and genetic risk for schizophrenia and depression confirmed the specificity of the Alzheimer's disease polygenic risk finding. These results provide evidence that mild traumatic brain injury is associated with greater neurodegeneration and reduced memory performance in individuals at genetic risk for Alzheimer's disease, with the caveat that the order of causal effects cannot be inferred from cross-sectional studies. These results underscore the importance of documenting head injuries even within the mild range as they may interact with genetic risk to produce negative long-term health consequences such as neurodegenerative disease.

Effects of norepinephrine transporter gene variants on NET binding in ADHD and healthy controls investigated by PET

Attention deficit hyperactivity disorder (ADHD) is a heterogeneous disorder with a strong genetic component. The norepinephrine transporter (NET) is a key target for ADHD treatment and the NET gene has been of high interest as a possible modulator of ADHD pathophysiology. Therefore, we conducted an imaging genetics study to examine possible effects of single nucleotide polymorphisms (SNPs) within the NET gene on NET nondisplaceable binding potential (BPND ) in patients with ADHD and healthy controls (HCs). Twenty adult patients with ADHD and 20 HCs underwent (S,S)-[18F]FMeNER-D2 positron emission tomography (PET) and were genotyped on a MassARRAY MALDI-TOF platform using the Sequenom iPLEX assay. Linear mixed models analyses revealed a genotype-dependent difference in NET BPND between groups in the thalamus and cerebellum. In the thalamus, a functional promoter SNP (-3081 A/T) and a 5'-untranslated region (5'UTR) SNP (-182 T/C), showed higher binding in ADHD patients compared to HCs depending on the major allele. Furthermore, we detected an effect of genotype in HCs, with major allele carriers having lower binding. In contrast, for two 3'UTR SNPs (*269 T/C, *417 A/T), ADHD subjects had lower binding in the cerebellum compared to HCs depending on the major allele. Additionally, symptoms of hyperactivity and impulsivity correlated with NET BPND in the cerebellum depending on genotype. Symptoms correlated positively with cerebellar NET BPND for the major allele, while symptoms correlated negatively to NET BPND in minor allele carriers. Our findings support the role of genetic influence of the NE system on NET binding to be pertubated in ADHD.

A single nucleotide polymorphism associated with reduced alcohol intake in the RASGRF2 gene predicts larger cortical volumes but faster longitudinal ventricular expansion in the elderly

A recent genome-wide association meta-analysis showed a suggestive association between alcohol intake in humans and a common single nucleotide polymorphism in the ras-specific guanine nucleotide releasing factor 2 gene. Here, we tested whether this variant – associated with lower alcohol consumption – showed associations with brain structure and longitudinal ventricular expansion over time, across two independent elderly cohorts, totaling 1,032 subjects. We first examined a large sample of 738 elderly participants with neuroimaging and genetic data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI1). Then, we assessed the generalizability of the findings by testing this polymorphism in a replication sample of 294 elderly subjects from a continuation of the first ADNI project (ADNI2) to minimize the risk of reporting false positive results. The minor allele – previously linked with lower alcohol intake – was associated with larger volumes in various cortical regions, notably the medial prefrontal cortex and cingulate gyrus in both cohorts. Intriguingly, the same allele also predicted faster ventricular expansion rates in the ADNI1 cohort at 1- and 2-year follow up. Despite a lack of alcohol consumption data in this study cohort, these findings, combined with earlier functional imaging investigations of the same gene, suggest the existence of reciprocal interactions between genes, brain, and drinking behavior.

Personalized medicine in psychiatry: problems and promises

The central theme of personalized medicine is the premise that an individual's unique physiologic characteristics play a significant role in both disease vulnerability and in response to specific therapies. The major goals of personalized medicine are therefore to predict an individual's susceptibility to developing an illness, achieve accurate diagnosis, and optimize the most efficient and favorable response to treatment. The goal of achieving personalized medicine in psychiatry is a laudable one, because its attainment should be associated with a marked reduction in morbidity and mortality. In this review, we summarize an illustrative selection of studies that are laying the foundation towards personalizing medicine in major depressive disorder, bipolar disorder, and schizophrenia. In addition, we present emerging applications that are likely to advance personalized medicine in psychiatry, with an emphasis on novel biomarkers and neuroimaging.

Perspective: Identification of genetic variants associated with dopaminergic compensatory mechanisms in early Parkinson's disease

Parkinson's disease (PD) is slowly progressive, and heterogeneity of its severity among individuals may be due to endogenous mechanisms that counterbalance the striatal dopamine loss. In this perspective paper, we introduce a neuroimaging-genetic approach to identify genetic variants, which may contribute to this compensation. First, we briefly review current known potential compensatory mechanisms for premotor and early disease PD, located in the striatum and other brain regions. Then, we claim that a mismatch between mild symptomatic disease, manifested by low motor score on the Unified PD Rating Scale (UPDRS), and extensive Nigro-Striatal (NS) degeneration, manifested by reduced uptake of [¹²³I]FP-CIT, is indicative of compensatory processes. If genetic variants are associated with the severity of motor symptoms, while the level of striatal terminals degeneration measured by ligand uptake is taken into account and controlled in the analysis, then these variants may be involved in functional compensatory mechanisms for striatal dopamine deficit. To demonstrate feasibility of this approach, we performed a small “proof of concept” study (candidate gene design) in a sample of 28 Jewish PD patients, and preliminary results are presented.

Genetic and environmental influences on neuroimaging phenotypes: a meta-analytical perspective on twin imaging studies

Because brain structure and function are affected in neurological and psychiatric disorders, it is important to disentangle the sources of variation in these phenotypes. Over the past 15 years, twin studies have found evidence for both genetic and environmental influences on neuroimaging phenotypes, but considerable variation across studies makes it difficult to draw clear conclusions about the relative magnitude of these influences. Here we performed the first meta-analysis of structural MRI data from 48 studies on >1,250 twin pairs, and diffusion tensor imaging data from 10 studies on 444 twin pairs. The proportion of total variance accounted for by genes (A), shared environment (C), and unshared environment (E), was calculated by averaging A, C, and E estimates across studies from independent twin cohorts and weighting by sample size. The results indicated that additive genetic estimates were significantly different from zero for all meta-analyzed phenotypes, with the exception of fractional anisotropy (FA) of the callosal splenium, and cortical thickness (CT) of the uncus, left parahippocampal gyrus, and insula. For many phenotypes there was also a significant influence of C. We now have good estimates of heritability for many regional and lobar CT measures, in addition to the global volumes. Confidence intervals are wide and number of individuals small for many of the other phenotypes. In conclusion, while our meta-analysis shows that imaging measures are strongly influenced by genes, and that novel phenotypes such as CT measures, FA measures, and brain activation measures look especially promising, replication across independent samples and demographic groups is necessary.

Piccolo genotype modulates neural correlates of emotion processing but not executive functioning

Major depressive disorder (MDD) is characterized by affective symptoms and cognitive impairments, which have been associated with changes in limbic and prefrontal activity as well as with monoaminergic neurotransmission. A genome-wide association study implicated the polymorphism rs2522833 in the piccolo (PCLO) gene--involved in monoaminergic neurotransmission--as a risk factor for MDD. However, the role of the PCLO risk allele in emotion processing and executive function or its effect on their neural substrate has never been studied. We used functional magnetic resonance imaging (fMRI) to investigate PCLO risk allele carriers vs noncarriers during an emotional face processing task and a visuospatial planning task in 159 current MDD patients and healthy controls. In PCLO risk allele carriers, we found increased activity in the left amygdala during processing of angry and sad faces compared with noncarriers, independent of psychopathological status. During processing of fearful faces, the PCLO risk allele was associated with increased amygdala activation in MDD patients only. During the visuospatial planning task, we found no genotype effect on performance or on BOLD signal in our predefined areas as a function of increasing task load. The PCLO risk allele was found to be specifically associated with altered emotion processing, but not with executive dysfunction. Moreover, the PCLO risk allele appears to modulate amygdala function during fearful facial processing in MDD and may constitute a possible link between genotype and susceptibility for depression via altered processing of fearful stimuli. The current results may therefore aid in better understanding underlying neurobiological mechanisms in MDD.

Genetics of Path Lengths in Brain Connectivity Networks: HARDI-Based Maps in 457 Adults

Brain connectivity analyses are increasingly popular for investigating organization. Many connectivity measures including path lengths are generally defined as the number of nodes traversed to connect a node in a graph to the others. Despite its name, path length is purely topological, and does not take into account the physical length of the connections. The distance of the trajectory may also be highly relevant, but is typically overlooked in connectivity analyses. Here we combined genotyping, anatomical MRI and HARDI to understand how our genes influence the cortical connections, using whole-brain tractography. We defined a new measure, based on Dijkstra's algorithm, to compute path lengths for tracts connecting pairs of cortical regions. We compiled these measures into matrices where elements represent the physical distance traveled along tracts. We then analyzed a large cohort of healthy twins and show that our path length measure is reliable, heritable, and influenced even in young adults by the Alzheimer's risk gene, CLU.