Novel genetic loci associated with hippocampal volume

Ultraprocessed Foods and Neuropsychiatric Outcomes: Putative Mechanisms

A body of evidence indicates an association between ultraprocessed foods (UPFs) and health outcomes. Most of it has been obtained through preclinical studies, although a number of observational studies substantiate how a high intake of these products increases the risk of neuropsychiatric disorders, and an increasing amount of dietary intervention studies confirm these findings. The aim of this narrative review is to describe some of the putative mechanisms involved in the deleterious effects of a high intake of UPFs on neuropsychiatric outcomes. A myriad of unhealthy actions may be associated with the consumption of UPFs, and some mechanisms are being discussed. They include UPFs' high caloric density; their high sugar, sodium, and additives content and low amounts of fiber; and a high palatability that induces overconsumption, acting as obesogens. Moreover, thermal treatment of these foods generates oxidative products such as glycotoxins, lipotoxins, and acrolein, all of which affect the brain. The chemical products act, directly or indirectly, on the gut microbiome and affect the gut-brain axis, causing neuroinflammation, oxidative stress, and neurodegeneration. UPFs also exert various epigenetic effects that affect mental health and might explain the intergenerational inheritance of neuropsychiatric disorders. A diet containing a high proportion of these foods has a low nutritional density, including bioactive protective agents such as antioxidant and anti-inflammatory compounds that promote eubiosis. The evidence shows that UPFs intake affects neuropsychiatric outcomes such as neurodegeneration, cognitive decline, dementia, and mood disorders and reinforces the need to promote a healthy dietary pattern throughout all life stages, thus interfering with the current commercial determinants of health.

Vision Transformer Autoencoders for Unsupervised Representation Learning: Capturing Local and Non-Local Features in Brain Imaging to Reveal Genetic Associations

The discovery of genetic loci associated with brain architecture can provide deeper insights into neuroscience and improved personalized medicine outcomes. Previously, we designed the Unsupervised Deep learning-derived Imaging Phenotypes (UDIPs) approach to extract endophenotypes from brain imaging using a convolutional (CNN) autoencoder, and conducted brain imaging GWAS on UK Biobank (UKBB). In this work, we leverage a vision transformer (ViT) model due to a different inductive bias and its ability to potentially capture unique patterns through its pairwise attention mechanism. Our approach based on 128 endophenotypes derived from average pooling discovered 10 loci previously unreported by CNN-based UDIP model, 3 of which were not found in the GWAS catalog to have had any associations with brain structure. Our interpretation results demonstrate the ViT's capability in capturing non-local patterns such as left-right hemisphere symmetry within brain MRI data, by leveraging its attention mechanism and positional embeddings. Our results highlight the advantages of transformer-based architectures in feature extraction and representation for genetic discovery.

Genetics impact risk of Alzheimer's disease through mechanisms modulating structural brain morphology in late life

BACKGROUND

Alzheimer's disease (AD)-related neuropathological changes can occur decades before clinical symptoms. We aimed to investigate whether neurodevelopment and/or neurodegeneration affects the risk of AD, through reducing structural brain reserve and/or increasing brain atrophy, respectively.

METHODS

We used bidirectional two-sample Mendelian randomisation to estimate the effects between genetic liability to AD and global and regional cortical thickness, estimated total intracranial volume, volume of subcortical structures and total white matter in 37 680 participants aged 8-81 years across 5 independent cohorts (Adolescent Brain Cognitive Development, Generation R, IMAGEN, Avon Longitudinal Study of Parents and Children and UK Biobank). We also examined the effects of global and regional cortical thickness and subcortical volumes from the Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) Consortium on AD risk in up to 37 741 participants.

RESULTS

Our findings show that AD risk alleles have an age-dependent effect on a range of cortical and subcortical brain measures that starts in mid-life, in non-clinical populations. Evidence for such effects across childhood and young adulthood is weak. Some of the identified structures are not typically implicated in AD, such as those in the striatum (eg, thalamus), with consistent effects from childhood to late adulthood. There was little evidence to suggest brain morphology alters AD risk.

CONCLUSIONS

Genetic liability to AD is likely to affect risk of AD primarily through mechanisms affecting indicators of brain morphology in later life, rather than structural brain reserve. Future studies with repeated measures are required for a better understanding and certainty of the mechanisms at play.

Genome-wide association study of idiopathic epilepsy in the Italian Spinone dog breed

Idiopathic epilepsy (IE) has a high prevalence and a severe clinical course in the Italian Spinone breed of dog. A genome-wide association study meta-analysis of 52 cases and 51 controls was conducted to identify genomic regions that may be involved with the development of IE. Subsequent to the meta-analysis, a set of 175 controls and an independent validation set of 23 cases and 23 controls were genotyped for SNPs showing suggestive association with IE to find variants exhibiting evidence of replicable association and to test the predictiveness of SNPs for IE status when combined in a weighted risk score. Although two regions showed statistically significant association with IE in the GWAS meta-analysis, and additional regions with suggestive association were identified, the findings were not emulated in the validation set. This is the first GWAS of IE in the Italian Spinone, and the findings suggest that IE in the breed is not monogenic and demonstrates the challenges when investigating a multigenic or complex inherited disease in a numerically small domesticated animal population.

Mendelian randomization identifies proteins involved in neurodegenerative diseases

Proteins are involved in multiple biological functions. High-throughput technologies have allowed the measurement of thousands of proteins in population biobanks. In this study, we aimed to identify proteins related to Alzheimer's disease, Parkinson's disease, multiple sclerosis and amyotrophic lateral sclerosis by leveraging large-scale genetic and proteomic data. We performed a two-sample cis Mendelian randomization study by selecting instrumental variables for the abundance of >2700 proteins measured by either Olink or SomaScan platforms in plasma from the UK Biobank and the deCODE Health Study. We also used the latest publicly available genome-wide association studies for the neurodegenerative diseases of interest. The potentially causal effect of proteins on neurodegenerative diseases was estimated based on the Wald ratio. We tested 13 377 protein-disease associations, identifying 169 associations that were statistically significant (5% false discovery rate). Evidence of co-localization between plasma protein abundance and disease risk (posterior probability > 0.80) was identified for 61 protein-disease pairs, leading to 50 unique protein-disease associations. Notably, 23 of 50 protein-disease associations corresponded to genetic loci not previously reported by genome-wide association studies. The two-sample Mendelian randomization and co-localization analysis also showed that APOE abundance in plasma was associated with three subcortical volumes (hippocampus, amygdala and nucleus accumbens) and white matter hyper-intensities, whereas PILRA and PILRB abundance in plasma was associated with caudate nucleus volume. Our study provided a comprehensive assessment of the effect of the human proteome that is currently measurable through two different platforms on neurodegenerative diseases. The newly associated proteins indicated the involvement of complement (C1S and C1R), microglia (SIRPA, SIGLEC9 and PRSS8) and lysosomes (CLN5) in Alzheimer's disease; the interleukin-6 pathway (CTF1) in Parkinson's disease; lysosomes (TPP1), blood-brain barrier integrity (MFAP2) and astrocytes (TNFSF13) in amyotrophic lateral sclerosis; and blood-brain barrier integrity (VEGFB), oligodendrocytes (PARP1), node of Ranvier and dorsal root ganglion (NCS1, FLRT3 and CDH15) and the innate immune system (CR1, AHSG and WARS) in multiple sclerosis. Our study demonstrates how harnessing large-scale genomic and proteomic data can yield new insights into the role of the plasma proteome in the pathogenesis of neurodegenerative diseases.

NeuroDISK: An AI Approach to Automate Continuous Inquiry-Driven Discoveries in Neuroimaging Genetics

Collaborative and multi-site neuroimaging studies have greatly accelerated the rate at which new and existing data can be aggregated to answer a neuroscientific question. New research initiatives are continuously collecting more data, allowing opportunities to refine previous published findings through continuous and dynamic updates. Yet, we lack a practical framework for researchers to systematically, automatically, and continuously update published findings. We developed NeuroDISK, an automated artificial intelligence based framework that: 1) performs automated and inquiry-driven analyses, and 2) continuously updates these analyses as new data becomes available. NeuroDISK was evaluated using published results from the ENIGMA consortium's work on the genetic architecture of the cerebral cortex. We incorporate both meta-analysis and meta-regression options to showcase our framework on the effect of specific genotypes and moderators on select brain regions. Initial NeuroDISK meta-analysis results replicate the original publication, and we show result updates after adding new data. The NeuroDISK framework can be generalized for users to define question(s), run corresponding workflow(s) and access results interactively and continuously.

Hippocampal gray matter volume alterations in patients with first-episode and recurrent major depressive disorder and their associations with gene profiles

BACKGROUND

Recent studies indicate that patients with first-episode drug-naïve (FEDN) and recurrent major depressive disorder (R-MDD) exhibit distinct atrophy patterns in the hippocampal subregions along the proximal-distal axis. However, it remains unclear whether such differences occur along the long axis and how they may relate to specific genes.

METHODS

In the present study, we analyzed T1-weighted images from 421 patients (FEDN: n = 232; R-MDD: n = 189) and 544 normal controls (NC) as part of the REST-meta-MDD consortium. Additionally, transcriptome maps and structural Magnetic Resonance Imaging (MRI) data of six donated brains were obtained from the Allen Human Brain Atlas (AHBA). We first identified changes in gray matter volume (GMV) within the hippocampus of both FEDN and R-MDD patients and then integrated these findings with AHBA transcriptome data to investigate the genes associated with hippocampal GMV changes.

RESULTS

Compared to NC, FEDN patients displayed reduced GMV in the left hippocampal tail, whereas R-MDD patients exhibited decreased GMV in the bilateral hippocampal body and increased GMV in the bilateral hippocampal tail. Further analysis revealed that expression levels of SYTL2 positively correlated with GMV changes in the hippocampus of FEDN patients, while SORCS3 and SLIT2 positively correlated with those in R-MDD.

CONCLUSIONS

Our results suggest that GMV alterations in hippocampal subfields along the long axis differ between FEDN and R-MDD, reflecting progressive hippocampal deterioration with prolonged depression, potentially supported by the expression of specific genes. These findings offer valuable insights into the distinct neural and genetic mechanisms underlying FEDN and R-MDD, which may aid in the development of more targeted and effective treatment strategies for MDD subtypes.

Childhood maltreatment and the structural development of hippocampus across childhood and adolescence

BACKGROUND

Prior studies suggest that childhood maltreatment is associated with altered hippocampal volume. However, longitudinal studies are currently scarce, making it difficult to determine how alterations in hippocampal volume evolve over time. The current study examined the relationship between childhood maltreatment and hippocampal volumetric development across childhood and adolescence in a community sample.

METHODS

In this longitudinal study, a community sample of 795 participants underwent brain magnetic resonance imaging (MRI) in three waves spanning ages 6-21 years. Childhood maltreatment was assessed using parent-report and children´s self-report at baseline (6-12 years old). Mixed models were used to examine the relationship between childhood maltreatment and hippocampal volume across time.

RESULTS

The quadratic term of age was significantly associated with both right and left hippocampal volume development. High exposure to childhood maltreatment was associated with reduced offset of right hippocampal volume and persistent reduced volume throughout adolescence.Critically, the relationship between childhood maltreatment and reduced right hippocampal volume remained significant after adjusting for the presence of any depressive disorder during late childhood and adolescence and hippocampal volume polygenic risk scores. Time-by-CM and Sex-by-CM interactions were not statistically significant.

CONCLUSIONS

The present study showed that childhood maltreatment is associated with persistent reduction of hippocampal volume in children and adolescents, even after adjusting for the presence of major depressive disorder and genetic determinants of hippocampal structure.

Adaptation in the Alleyways: Candidate Genes Under Potential Selection in Urban Coyotes

In the context of evolutionary time, cities are an extremely recent development. Although our understanding of how urbanization alters ecosystems is well developed, empirical work examining the consequences of urbanization on adaptive evolution remains limited. To facilitate future work, we offer candidate genes for one of the most prominent urban carnivores across North America. The coyote (Canis latrans) is a highly adaptable carnivore distributed throughout urban and nonurban regions in North America. As such, the coyote can serve as a blueprint for understanding the various pathways by which urbanization can influence the genomes of wildlife via comparisons along urban-rural gradients, as well as between metropolitan areas. Given the close evolutionary relationship between coyotes and domestic dogs, we leverage the well-annotated dog genome and highly conserved mammalian genes from model species to outline how urbanization may alter coyote genotypes and shape coyote phenotypes. We identify variables that may alter selection pressure for urban coyotes and offer suggestions of candidate genes to explore. Specifically, we focus on pathways related to diet, health, behavior, cognition, and reproduction. In a rapidly urbanizing world, understanding how species cope and adapt to anthropogenic change can facilitate the persistence of, and coexistence with, these species.

A multi-omics Mendelian randomization study identifies new therapeutic targets for alcohol use disorder and problem drinking

Integrating proteomic and transcriptomic data with genetic architectures of problematic alcohol use and alcohol consumption behaviours can advance our understanding and help identify therapeutic targets. We conducted systematic screens using genome-wise association study data from ~3,500 cortical proteins (N = 722) and ~6,100 genes in 8 canonical brain cell types (N = 192) with 4 alcohol-related outcomes (N ≤ 537,349), identifying 217 cortical proteins and 255 cell-type genes associated with these behaviours, with 36 proteins and 37 cell-type genes being new. Although there was limited overlap between proteome and transcriptome targets, downstream neuroimaging revealed shared neurophysiological pathways. Colocalization with independent genome-wise association study data further prioritized 16 proteins, including CAB39L and NRBP1, and 12 cell-type genes, implicating mechanisms such as mTOR signalling. In addition, genes such as SAMHD1, VIPAS39, NUP160 and INO80E were identified as having favourable neuropsychiatric profiles. These findings provide insights into the genetic landscapes governing problematic alcohol use and alcohol consumption behaviours, highlighting promising therapeutic targets for future research.

Genomic analysis of intracranial and subcortical brain volumes yields polygenic scores accounting for variation across ancestries

Subcortical brain structures are involved in developmental, psychiatric and neurological disorders. Here we performed genome-wide association studies meta-analyses of intracranial and nine subcortical brain volumes (brainstem, caudate nucleus, putamen, hippocampus, globus pallidus, thalamus, nucleus accumbens, amygdala and the ventral diencephalon) in 74,898 participants of European ancestry. We identified 254 independent loci associated with these brain volumes, explaining up to 35% of phenotypic variance. We observed gene expression in specific neural cell types across differentiation time points, including genes involved in intracellular signaling and brain aging-related processes. Polygenic scores for brain volumes showed predictive ability when applied to individuals of diverse ancestries. We observed causal genetic effects of brain volumes with Parkinson's disease and attention-deficit/hyperactivity disorder. Findings implicate specific gene expression patterns in brain development and genetic variants in comorbid neuropsychiatric disorders, which could point to a brain substrate and region of action for risk genes implicated in brain diseases.

Genetic architectures of the human hippocampus and those involved in neuropsychiatric traits

BACKGROUND

The hippocampus, with its complex subfields, is linked to numerous neuropsychiatric traits. While most research has focused on its global structure or a few specific subfields, a comprehensive analysis of hippocampal substructures and their genetic correlations across a wide range of neuropsychiatric traits remains underexplored. Given the hippocampus's high heritability, considering hippocampal and subfield volumes (HASV) as endophenotypes for neuropsychiatric conditions is essential.

METHODS

We analyzed MRI-derived volumetric data of hippocampal and subfield structures from 41,525 UK Biobank participants. Genome-wide association studies (GWAS) on 24 HASV traits were conducted, followed by genetic correlation, overlap, and Mendelian randomization (MR) analyses with 10 common neuropsychiatric traits. Polygenic risk scores (PRS) based on HASV traits were also evaluated for predicting these traits.

RESULTS

Our analysis identified 352 independent genetic variants surpassing a significance threshold of 2.1 × 10-9 within the 24 HASV traits, located across 93 chromosomal regions. Notably, the regions 12q14.3, 17q21.31, 12q24.22, 6q21, 9q33.1, 6q25.1, and 2q24.2 were found to influence multiple HASVs. Gene set analysis revealed enrichment of neural differentiation and signaling pathways, as well as protein binding and degradation. Of 240 HASV-neuropsychiatric trait pairs, 75 demonstrated significant genetic correlations (P < 0.05/240), revealing 433 pleiotropic loci. Particularly, genes like ACBD4, ARHGAP27, KANSL1, MAPT, ARL17A, and ARL17B were involved in over 50 HASV-neuropsychiatric pairs. Leveraging Mendelian randomization analysis, we further confirmed that atrophy in the left hippocampus, right hippocampus, right hippocampal body, and right CA1-3 region were associated with an increased risk of developing Parkinson's disease (PD). Furthermore, PRS for all four HASVs were significantly linked to a higher risk of Parkinson's disease (PD), with the highest hazard ratio (HR) of 1.30 (95% CI 1.18-1.43, P = 6.15 × 10⁻⁸) for right hippocampal volume.

CONCLUSIONS

These findings highlight the extensive distribution of pleiotropic genetic determinants between HASVs and neuropsychiatric traits. Moreover, they suggest a significant potential for effectively managing and intervening in these diseases during their early stages.

Associations between antagonistic SNPs for neuropsychiatric disorders and human brain structure

A previously published genome-wide association study (GWAS) meta-analysis across eight neuropsychiatric disorders identified antagonistic single-nucleotide polymorphisms (SNPs) at eleven genomic loci where the same allele was protective against one neuropsychiatric disorder and increased the risk for another. Until now, these antagonistic SNPs have not been further investigated regarding their link to brain structural phenotypes. Here, we explored their associations with cortical surface area and cortical thickness (in 34 brain regions and one global measure each) as well as the volumes of eight subcortical structures using summary statistics of large-scale GWAS of brain structural phenotypes. We assessed if significantly associated brain structural phenotypes were previously reported to be associated with major neuropsychiatric disorders in large-scale case-control imaging studies by the ENIGMA consortium. We further characterized the effects of the antagonistic SNPs on gene expression in brain tissue and their association with additional cognitive and behavioral phenotypes, and performed an exploratory voxel-based whole-brain analysis in the FOR2107 study (n = 754 patients with major depressive disorder and n = 847 controls). We found that eight antagonistic SNPs were significantly associated with brain structural phenotypes in regions such as anterior parts of the cingulate cortex, the insula, and the superior temporal gyrus. Case-control differences in implicated brain structural phenotypes have previously been reported for bipolar disorder, major depressive disorder, and schizophrenia. In addition, antagonistic SNPs were associated with gene expression changes in brain tissue and linked to several cognitive-behavioral traits. In our exploratory whole-brain analysis, we observed significant associations of gray matter volume in the left superior temporal pole and left superior parietal region with the variants rs301805 and rs1933802, respectively. Our results suggest that multiple antagonistic SNPs for neuropsychiatric disorders are linked to brain structural phenotypes. However, to further elucidate these findings, future case-control genomic imaging studies are required.

Unveiling Promising Neuroimaging Biomarkers for Schizophrenia Through Clinical and Genetic Perspectives

Schizophrenia is a complex and serious brain disorder. Neuroscientists have become increasingly interested in using magnetic resonance-based brain imaging-derived phenotypes (IDPs) to investigate the etiology of psychiatric disorders. IDPs capture valuable clinical advantages and hold biological significance in identifying brain abnormalities. In this review, we aim to discuss current and prospective approaches to identify potential biomarkers for schizophrenia using clinical multimodal neuroimaging and imaging genetics. We first described IDPs through their phenotypic classification and neuroimaging genomics. Secondly, we discussed the applications of multimodal neuroimaging by clinical evidence in observational studies and randomized controlled trials. Thirdly, considering the genetic evidence of IDPs, we discussed how can utilize neuroimaging data as an intermediate phenotype to make association inferences by polygenic risk scores and Mendelian randomization. Finally, we discussed machine learning as an optimum approach for validating biomarkers. Together, future research efforts focused on neuroimaging biomarkers aim to enhance our understanding of schizophrenia.

Type 2 diabetes, glycaemic traits, structural brain capacity and cognitive function: A Mendelian randomization analysis

AIM

To estimate the causal associations of type 2 diabetes and glycaemic traits with cognitive function, and to determine the potential mediating role of various brain imaging-derived phenotypes (IDPs) using Mendelian randomization (MR) analysis.

METHODS

Using publicly available summary data, we performed a series of univariable and multivariable MR analysis to infer causality. Two-step MR analysis was then conducted in turn to evaluate the potential mediating role of each brain IDP.

RESULTS

There was no evidence of causal associations between type 2 diabetes and cognitive function outcomes. Each 1-SD unit higher genetically predicted fasting proinsulin was associated with worse cognitive performance, as evidenced by both univariable (beta: -0.022; 95% confidence interval [CI] -0.038, -0.007) and multivariable MR analysis (beta: -0.027; 95% CI -0.048, -0.005). In addition, the univariable MR analysis identified several causal associations between fasting proinsulin and brain IDPs, and between brain IDPs and cognitive performance. The inverse association of genetically predicted fasting proinsulin with cognitive performance did not attenuate after adjusting for each of the brain IDPs in multivariable MR analysis.

CONCLUSIONS

The present MR study provided credible evidence for the causal association between genetically predicted fasting proinsulin and cognitive function, informing a potential diagnosis and intervention target for patients with cognitive impairment. No significant brain IDP included in this study was identified as lying on the causal pathway from fasting proinsulin to cognitive performance. Future research involving more specific and granular brain IDP or other brain process is warranted to explore the potential biological mechanism underlying the association.

Proteomic analyses reveal plasma EFEMP1 and CXCL12 as biomarkers and determinants of neurodegeneration

INTRODUCTION

Plasma proteomic analyses of unique brain atrophy patterns may illuminate peripheral drivers of neurodegeneration and identify novel biomarkers for predicting clinically relevant outcomes.

METHODS

We identified proteomic signatures associated with machine learning-derived aging- and Alzheimer's disease (AD) -related brain atrophy patterns in the Baltimore Longitudinal Study of Aging (n = 815). Using data from five cohorts, we examined whether candidate proteins were associated with AD endophenotypes and long-term dementia risk.

RESULTS

Plasma proteins associated with distinct patterns of age- and AD-related atrophy were also associated with plasma/cerebrospinal fluid (CSF) AD biomarkers, cognition, AD risk, as well as mid-life (20-year) and late-life (8-year) dementia risk. EFEMP1 and CXCL12 showed the most consistent associations across cohorts and were mechanistically implicated as determinants of brain structure using genetic methods, including Mendelian randomization.

DISCUSSION

Our findings reveal plasma proteomic signatures of unique aging- and AD-related brain atrophy patterns and implicate EFEMP1 and CXCL12 as important molecular drivers of neurodegeneration.

HIGHLIGHTS

Plasma proteomic signatures are associated with unique patterns of brain atrophy. Brain atrophy-related proteins predict clinically relevant outcomes across cohorts. Genetic variation underlying plasma EFEMP1 and CXCL12 influences brain structure. EFEMP1 and CXCL12 may be important molecular drivers of neurodegeneration.

Genomic analysis of intracranial and subcortical brain volumes yields polygenic scores accounting for variation across ancestries

Subcortical brain structures are involved in developmental, psychiatric and neurological disorders. We performed GWAS meta-analyses of intracranial and nine subcortical brain volumes (brainstem, caudate nucleus, putamen, hippocampus, globus pallidus, thalamus, nucleus accumbens, amygdala and, for the first time, the ventral diencephalon) in 74,898 participants of European ancestry. We identified 254 independent loci associated with these brain volumes, explaining up to 35% of phenotypic variance. We observed gene expression in specific neural cell types across differentiation time points, including genes involved in intracellular signalling and brain ageing-related processes. Polygenic scores for brain volumes showed predictive ability when applied to individuals of diverse ancestries. We observed causal genetic effects of brain volumes with Parkinson's disease and ADHD. Findings implicate specific gene expression patterns in brain development and genetic variants in comorbid neuropsychiatric disorders, which could point to a brain substrate and region of action for risk genes implicated in brain diseases.

Towards cascading genetic risk in Alzheimer's disease

Alzheimer's disease typically progresses in stages, which have been defined by the presence of disease-specific biomarkers: amyloid (A), tau (T) and neurodegeneration (N). This progression of biomarkers has been condensed into the ATN framework, in which each of the biomarkers can be either positive (+) or negative (-). Over the past decades, genome-wide association studies have implicated ∼90 different loci involved with the development of late-onset Alzheimer's disease. Here, we investigate whether genetic risk for Alzheimer's disease contributes equally to the progression in different disease stages or whether it exhibits a stage-dependent effect. Amyloid (A) and tau (T) status was defined using a combination of available PET and CSF biomarkers in the Alzheimer's Disease Neuroimaging Initiative cohort. In 312 participants with biomarker-confirmed A-T- status, we used Cox proportional hazards models to estimate the contribution of APOE and polygenic risk scores (beyond APOE) to convert to A+T- status (65 conversions). Furthermore, we repeated the analysis in 290 participants with A+T- status and investigated the genetic contribution to conversion to A+T+ (45 conversions). Both survival analyses were adjusted for age, sex and years of education. For progression from A-T- to A+T-, APOE-e4 burden showed a significant effect [hazard ratio (HR) = 2.88; 95% confidence interval (CI): 1.70-4.89; P < 0.001], whereas polygenic risk did not (HR = 1.09; 95% CI: 0.84-1.42; P = 0.53). Conversely, for the transition from A+T- to A+T+, the contribution of APOE-e4 burden was reduced (HR = 1.62; 95% CI: 1.05-2.51; P = 0.031), whereas the polygenic risk showed an increased contribution (HR = 1.73; 95% CI: 1.27-2.36; P < 0.001). The marginal APOE effect was driven by e4 homozygotes (HR = 2.58; 95% CI: 1.05-6.35; P = 0.039) as opposed to e4 heterozygotes (HR = 1.74; 95% CI: 0.87-3.49; P = 0.12). The genetic risk for late-onset Alzheimer's disease unfolds in a disease stage-dependent fashion. A better understanding of the interplay between disease stage and genetic risk can lead to a more mechanistic understanding of the transition between ATN stages and a better understanding of the molecular processes leading to Alzheimer's disease, in addition to opening therapeutic windows for targeted interventions.

Genetic architecture of brain morphology and overlap with neuropsychiatric traits

Uncovering the genetic architectures of brain morphology offers valuable insights into brain development and disease. Genetic association studies of brain morphological phenotypes have discovered thousands of loci. However, interpretation of these loci presents a significant challenge. One potential solution is exploring the genetic overlap between brain morphology and disorders, which can improve our understanding of their complex relationships, ultimately aiding in clinical applications. In this review, we examine current evidence on the genetic associations between brain morphology and neuropsychiatric traits. We discuss the impact of these associations on the diagnosis, prediction, and treatment of neuropsychiatric diseases, along with suggestions for future research directions.

The relationship between coffee-related factors and cortical and hippocampal structure: a triangulation of evidence approach and Mendelian randomization research

Objective

Existing studies have reported sustained changes in the cortical structure of rats due to coffee-related factors, which are speculated to occur in the human body. However, there is a lack of research on this topic. Additionally, previous observational studies have found the impact of diseases on cortical structure and the potential therapeutic effects of coffee on these diseases. Our aim was to study the causal effects of coffee-related factors on the human brain using SNPs (single nucleotide polymorphisms). We will connect these discovered causal effects to the impact of diseases on the brain. Through triangulating evidence, we will reveal the potential active areas of coffee in preventing diseases.

Methods

We utilized GWAS data from multiple cohorts and their databases, selecting instrumental variables for genetic prediction of coffee intake and plasma levels of caffeine and its direct metabolites. We applied these instrumental variables to individual data on cortical thickness and surface area, as well as hippocampal volume, from the ENIGMA and CHARGE consortium for Mendelian randomization analysis (MR). Triangular evidence was obtained by integrating existing evidence through a specified retrieval strategy, calculating the overlap between coffee's effects on brain regions and disease-related brain regions to identify potential regions of action.

Results

The MR analysis yielded 93 positive results for 9 exposures, among which theobromine, a metabolite in the caffeine pathway, was found to be associated with increased hippocampal volume. For cortical structure, theobromine in the caffeine pathway was associated with a decrease in total surface area, while theobromine and caffeine in the pathway were associated with an increase in total thickness. The overlap rate of triangular evidence showed no difference in both overall and subgroup analyses, indicating a high overlap between the effects of coffee on brain regions and disease.

Conclusions

From predicted outcomes from causal effects, coffee intake-related factors may have lasting effects on cortical structure. Additionally, theobromine and theophylline have the greatest impact on certain brain gyri, rather than caffeine. Triangulation evidence indicates that disease and coffee intake-related factors act on the same cortical regions, suggesting the presence of potential shared or antagonistic pathways.

Genetic variants for head size share genes and pathways with cancer

The size of the human head is highly heritable, but genetic drivers of its variation within the general population remain unmapped. We perform a genome-wide association study on head size (N = 80,890) and identify 67 genetic loci, of which 50 are novel. Neuroimaging studies show that 17 variants affect specific brain areas, but most have widespread effects. Gene set enrichment is observed for various cancers and the p53, Wnt, and ErbB signaling pathways. Genes harboring lead variants are enriched for macrocephaly syndrome genes (37-fold) and high-fidelity cancer genes (9-fold), which is not seen for human height variants. Head size variants are also near genes preferentially expressed in intermediate progenitor cells, neural cells linked to evolutionary brain expansion. Our results indicate that genes regulating early brain and cranial growth incline to neoplasia later in life, irrespective of height. This warrants investigation of clinical implications of the link between head size and cancer.

The circulating proteome and brain health: Mendelian randomisation and cross-sectional analyses

Decline in cognitive function is the most feared aspect of ageing. Poorer midlife cognitive function is associated with increased dementia and stroke risk. The mechanisms underlying variation in cognitive function are uncertain. Here, we assessed associations between 1160 proteins' plasma levels and two measures of cognitive function, the digit symbol substitution test (DSST) and the Montreal Cognitive Assessment in 1198 PURE-MIND participants. We identified five DSST performance-associated proteins (NCAN, BCAN, CA14, MOG, CDCP1), with NCAN and CDCP1 showing replicated association in an independent cohort, GS (N = 1053). MRI-assessed structural brain phenotypes partially mediated (8-19%) associations between NCAN, BCAN, and MOG, and DSST performance. Mendelian randomisation analyses suggested higher CA14 levels might cause larger hippocampal volume and increased stroke risk, whilst higher CDCP1 levels might increase intracranial aneurysm risk. Our findings highlight candidates for further study and the potential for drug repurposing to reduce the risk of stroke and cognitive decline.

Multimodal joint deconvolution and integrative signature selection in proteomics

Deconvolution is an efficient approach for detecting cell-type-specific (cs) transcriptomic signals without cellular segmentation. However, this type of methods may require a reference profile from the same molecular source and tissue type. Here, we present a method to dissect bulk proteome by leveraging tissue-matched transcriptome and proteome without using a proteomics reference panel. Our method also selects the proteins contributing to the cellular heterogeneity shared between bulk transcriptome and proteome. The deconvoluted result enables downstream analyses such as cs-protein Quantitative Trait Loci (cspQTL) mapping. We benchmarked the performance of this multimodal deconvolution approach through CITE-seq pseudo bulk data, a simulation study, and the bulk multi-omics data from human brain normal tissues and breast cancer tumors, individually, showing robust and accurate cell abundance quantification across different datasets. This algorithm is implemented in a tool MICSQTL that also provides cspQTL and multi-omics integrative visualization, available at https://bioconductor.org/packages/MICSQTL .

Unsupervised deep representation learning enables phenotype discovery for genetic association studies of brain imaging

Understanding the genetic architecture of brain structure is challenging, partly due to difficulties in designing robust, non-biased descriptors of brain morphology. Until recently, brain measures for genome-wide association studies (GWAS) consisted of traditionally expert-defined or software-derived image-derived phenotypes (IDPs) that are often based on theoretical preconceptions or computed from limited amounts of data. Here, we present an approach to derive brain imaging phenotypes using unsupervised deep representation learning. We train a 3-D convolutional autoencoder model with reconstruction loss on 6130 UK Biobank (UKBB) participants' T1 or T2-FLAIR (T2) brain MRIs to create a 128-dimensional representation known as Unsupervised Deep learning derived Imaging Phenotypes (UDIPs). GWAS of these UDIPs in held-out UKBB subjects (n = 22,880 discovery and n = 12,359/11,265 replication cohorts for T1/T2) identified 9457 significant SNPs organized into 97 independent genetic loci of which 60 loci were replicated. Twenty-six loci were not reported in earlier T1 and T2 IDP-based UK Biobank GWAS. We developed a perturbation-based decoder interpretation approach to show that these loci are associated with UDIPs mapped to multiple relevant brain regions. Our results established unsupervised deep learning can derive robust, unbiased, heritable, and interpretable brain imaging phenotypes.

The genetic architecture of the human hypothalamus and its involvement in neuropsychiatric behaviours and disorders

Despite its crucial role in the regulation of vital metabolic and neurological functions, the genetic architecture of the hypothalamus remains unknown. Here we conducted multivariate genome-wide association studies (GWAS) using hypothalamic imaging data from 32,956 individuals to uncover the genetic underpinnings of the hypothalamus and its involvement in neuropsychiatric traits. There were 23 significant loci associated with the whole hypothalamus and its subunits, with functional enrichment for genes involved in intracellular trafficking systems and metabolic processes of steroid-related compounds. The hypothalamus exhibited substantial genetic associations with limbic system structures and neuropsychiatric traits including chronotype, risky behaviour, cognition, satiety and sympathetic-parasympathetic activity. The strongest signal in the primary GWAS, the ADAMTS8 locus, was replicated in three independent datasets (N = 1,685-4,321) and was strengthened after meta-analysis. Exome-wide association analyses added evidence to the association for ADAMTS8, and Mendelian randomization showed lower ADAMTS8 expression with larger hypothalamic volumes. The current study advances our understanding of complex structure-function relationships of the hypothalamus and provides insights into the molecular mechanisms that underlie hypothalamic formation.

Cell subtype-specific effects of genetic variation in the Alzheimer's disease brain

The relationship between genetic variation and gene expression in brain cell types and subtypes remains understudied. Here, we generated single-nucleus RNA sequencing data from the neocortex of 424 individuals of advanced age; we assessed the effect of genetic variants on RNA expression in cis (cis-expression quantitative trait loci) for seven cell types and 64 cell subtypes using 1.5 million transcriptomes. This effort identified 10,004 eGenes at the cell type level and 8,099 eGenes at the cell subtype level. Many eGenes are only detected within cell subtypes. A new variant influences APOE expression only in microglia and is associated with greater cerebral amyloid angiopathy but not Alzheimer's disease pathology, after adjusting for APOEε4, providing mechanistic insights into both pathologies. Furthermore, only a TMEM106B variant affects the proportion of cell subtypes. Integration of these results with genome-wide association studies highlighted the targeted cell type and probable causal gene within Alzheimer's disease, schizophrenia, educational attainment and Parkinson's disease loci.

Genetic architecture of the structural connectome

Myelinated axons form long-range connections that enable rapid communication between distant brain regions, but how genetics governs the strength and organization of these connections remains unclear. We perform genome-wide association studies of 206 structural connectivity measures derived from diffusion magnetic resonance imaging tractography of 26,333 UK Biobank participants, each representing the density of myelinated connections within or between a pair of cortical networks, subcortical structures or cortical hemispheres. We identify 30 independent genome-wide significant variants after Bonferroni correction for the number of measures studied (126 variants at nominal genome-wide significance) implicating genes involved in myelination (SEMA3A), neurite elongation and guidance (NUAK1, STRN, DPYSL2, EPHA3, SEMA3A, HGF, SHTN1), neural cell proliferation and differentiation (GMNC, CELF4, HGF), neuronal migration (CCDC88C), cytoskeletal organization (CTTNBP2, MAPT, DAAM1, MYO16, PLEC), and brain metal transport (SLC39A8). These variants have four broad patterns of spatial association with structural connectivity: some have disproportionately strong associations with corticothalamic connectivity, interhemispheric connectivity, or both, while others are more spatially diffuse. Structural connectivity measures are highly polygenic, with a median of 9.1 percent of common variants estimated to have non-zero effects on each measure, and exhibited signatures of negative selection. Structural connectivity measures have significant genetic correlations with a variety of neuropsychiatric and cognitive traits, indicating that connectivity-altering variants tend to influence brain health and cognitive function. Heritability is enriched in regions with increased chromatin accessibility in adult oligodendrocytes (as well as microglia, inhibitory neurons and astrocytes) and multiple fetal cell types, suggesting that genetic control of structural connectivity is partially mediated by effects on myelination and early brain development. Our results indicate pervasive, pleiotropic, and spatially structured genetic control of white-matter structural connectivity via diverse neurodevelopmental pathways, and support the relevance of this genetic control to healthy brain function.

Sex-Specific Association of Body Mass Index with Hippocampal Subfield Volume and Cognitive Function in Non-Demented Chinese Older Adults

Recent research suggests a possible association between midlife obesity and an increased risk of dementia in later life. However, the underlying mechanisms remain unclear. Little is known about the relationship between body mass index (BMI) and hippocampal subfield atrophy. In this study, we aimed to explore the associations between BMI and hippocampal subfield volumes and cognitive function in non-demented Chinese older adults. Hippocampal volumes were assessed using structural magnetic resonance imaging. Cognitive function was evaluated using the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS). A total of 66 participants were included in the final analysis, with 35 females and 31 males. We observed a significant correlation between BMI and the hippocampal fissure volume in older females. In addition, there was a negative association between BMI and the RBANS total scale score, the coding score, and the story recall score, whereas no significant correlations were observed in older males. In conclusion, our findings revealed sex-specific associations between BMI and hippocampal subfield volumes and cognitive performance, providing valuable insights into the development of effective interventions for the early prevention of cognitive decline.

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.

Dentate Gyrus Microstructure Is Associated With Resilience After Exposure to Maternal Stress Across Two Human Cohorts

BACKGROUND

Maternal stress (MS) is a well-documented risk factor for impaired emotional development in offspring. Rodent models implicate the dentate gyrus (DG) of the hippocampus in the effects of MS on offspring depressive-like behaviors, but mechanisms in humans remain unclear. Here, we tested whether MS was associated with depressive symptoms and DG micro- and macrostructural alterations in offspring across 2 independent cohorts.

METHODS

We analyzed DG diffusion tensor imaging-derived mean diffusivity (DG-MD) and volume in a three-generation family risk for depression study (TGS; n = 69, mean age = 35.0 years) and in the Adolescent Brain Cognitive Development (ABCD) Study (n = 5196, mean age = 9.9 years) using generalized estimating equation models and mediation analysis. MS was assessed by the Parenting Stress Index (TGS) and a measure compiled from the Adult Response Survey from the ABCD Study. The Patient Health Questionnaire-9 and rumination scales (TGS) and the Child Behavior Checklist (ABCD Study) measured offspring depressive symptoms at follow-up. The Schedule for Affective Disorders and Schizophrenia-Lifetime interview was used to assign depression diagnoses.

RESULTS

Across cohorts, MS was associated with future symptoms and higher DG-MD (indicating disrupted microstructure) in offspring. Higher DG-MD was associated with higher symptom scores measured 5 years (in the TGS) and 1 year (in the ABCD Study) after magnetic resonance imaging. In the ABCD Study, DG-MD was increased in high-MS offspring who had depressive symptoms at follow-up, but not in offspring who remained resilient or whose mother had low MS.

CONCLUSIONS

Converging results across 2 independent samples extend previous rodent studies and suggest a role for the DG in exposure to MS and offspring depression.

Large-Scale Neuroimaging of Mental Illness

Neuroimaging has provided important insights into the brain variations related to mental illness. Inconsistencies in prior studies, however, call for methods that lead to more replicable and generalizable brain markers that can reliably predict illness severity, treatment course, and prognosis. A paradigm shift is underway with large-scale international research teams actively pooling data and resources to drive consensus findings and test emerging methods aimed at achieving the goals of precision psychiatry. In parallel with large-scale psychiatric genomics studies, international consortia combining neuroimaging data are mapping the transdiagnostic brain signatures of mental illness on an unprecedented scale. This chapter discusses the major challenges, recent findings, and a roadmap for developing better neuroimaging-based tools and markers for mental illness.

Amyloid β-Oligomers Inhibit the Nuclear Ca2+ Signals and the Neuroprotective Gene Expression Induced by Gabazine in Hippocampal Neurons

Hippocampal neuronal activity generates dendritic and somatic Ca2+ signals, which, depending on stimulus intensity, rapidly propagate to the nucleus and induce the expression of transcription factors and genes with crucial roles in cognitive functions. Soluble amyloid-beta oligomers (AβOs), the main synaptotoxins engaged in the pathogenesis of Alzheimer's disease, generate aberrant Ca2+ signals in primary hippocampal neurons, increase their oxidative tone and disrupt structural plasticity. Here, we explored the effects of sub-lethal AβOs concentrations on activity-generated nuclear Ca2+ signals and on the Ca2+-dependent expression of neuroprotective genes. To induce neuronal activity, neuron-enriched primary hippocampal cultures were treated with the GABAA receptor blocker gabazine (GBZ), and nuclear Ca2+ signals were measured in AβOs-treated or control neurons transfected with a genetically encoded nuclear Ca2+ sensor. Incubation (6 h) with AβOs significantly reduced the nuclear Ca2+ signals and the enhanced phosphorylation of cyclic AMP response element-binding protein (CREB) induced by GBZ. Likewise, incubation (6 h) with AβOs significantly reduced the GBZ-induced increases in the mRNA levels of neuronal Per-Arnt-Sim domain protein 4 (Npas4), brain-derived neurotrophic factor (BDNF), ryanodine receptor type-2 (RyR2), and the antioxidant enzyme NADPH-quinone oxidoreductase (Nqo1). Based on these findings we propose that AβOs, by inhibiting the generation of activity-induced nuclear Ca2+ signals, disrupt key neuroprotective gene expression pathways required for hippocampal-dependent learning and memory processes.

Association between NTRK2 Polymorphisms, Hippocampal Volumes and Treatment Resistance in Major Depressive Disorder

Despite the increasing availability of antidepressant drugs, a high rate of patients with major depression (MDD) does not respond to pharmacological treatments. Brain-derived neurotrophic factor (BDNF)-tyrosine receptor kinase B (TrkB) signaling is thought to influence antidepressant efficacy and hippocampal volumes, robust predictors of treatment resistance. We therefore hypothesized the possible role of BDNF and neurotrophic receptor tyrosine kinase 2 (NTRK2)-related polymorphisms in affecting both hippocampal volumes and treatment resistance in MDD. A total of 121 MDD inpatients underwent 3T structural MRI scanning and blood sampling to obtain genotype information. General linear models and binary logistic regressions were employed to test the effect of genetic variations related to BDNF and NTRK2 on bilateral hippocampal volumes and treatment resistance, respectively. Finally, the possible mediating role of hippocampal volumes on the relationship between genetic markers and treatment response was investigated. A significant association between one NTRK2 polymorphism with hippocampal volumes and antidepressant response was found, with significant indirect effects. Our results highlight a possible mechanistic explanation of antidepressant action, possibly contributing to the understanding of MDD pathophysiology.

Genome-wide association study of hippocampal blood-oxygen-level-dependent-cerebral blood flow correlation in Chinese Han population

Correlation between blood-oxygen-level-dependent (BOLD) and cerebral blood flow (CBF) has been used as an index of neurovascular coupling. Hippocampal BOLD-CBF correlation is associated with neurocognition, and the reduced correlation is associated with neuropsychiatric disorders. We conducted the first genome-wide association study of the hippocampal BOLD-CBF correlation in 4,832 Chinese Han subjects. The hippocampal BOLD-CBF correlation had an estimated heritability of 16.2-23.9% and showed reliable genome-wide significant association with a locus at 3q28, in which many variants have been linked to neuroimaging and cerebrospinal fluid markers of Alzheimer's disease. Gene-based association analyses showed four significant genes (GMNC, CRTC2, DENND4B, and GATAD2B) and revealed enrichment for mast cell calcium mobilization, microglial cell proliferation, and ubiquitin-related proteolysis pathways that regulate different cellular components of the neurovascular unit. This is the first unbiased identification of the association of hippocampal BOLD-CBF correlation, providing fresh insights into the genetic architecture of hippocampal neurovascular coupling.

A fast non-parametric test of association for multiple traits

The increasing availability of multidimensional phenotypic data in large cohorts of genotyped individuals requires efficient methods to identify genetic effects on multiple traits. Permutational multivariate analysis of variance (PERMANOVA) offers a powerful non-parametric approach. However, it relies on permutations to assess significance, which hinders the analysis of large datasets. Here, we derive the limiting null distribution of the PERMANOVA test statistic, providing a framework for the fast computation of asymptotic p values. Our asymptotic test presents controlled type I error and high power, often outperforming parametric approaches. We illustrate its applicability in the context of QTL mapping and GWAS.

Genetics of Alzheimer's Disease in the African American Population

Black/African American (AA) individuals have a higher risk of Alzheimer's disease (AD) than White non-Hispanic persons of European ancestry (EUR) for reasons that may include economic disparities, cardiovascular health, quality of education, and biases in the methods used to diagnose AD. AD is also heritable, and some of the differences in risk may be due to genetics. Many AD-associated variants have been identified by candidate gene studies, genome-wide association studies (GWAS), and genome-sequencing studies. However, most of these studies have been performed using EUR cohorts. In this paper, we review the genetics of AD and AD-related traits in AA individuals. Importantly, studies of genetic risk factors in AA cohorts can elucidate the molecular mechanisms underlying AD risk in AA and other populations. In fact, such studies are essential to enable reliable precision medicine approaches in persons with considerable African ancestry. Furthermore, genetic studies of AA cohorts allow exploration of the ways the impact of genes can vary by ancestry, culture, and economic and environmental disparities. They have yielded important gains in our knowledge of AD genetics, and increasing AA individual representation within genetic studies should remain a priority for inclusive genetic study design.

The evolution of Big Data in neuroscience and neurology

Neurological diseases are on the rise worldwide, leading to increased healthcare costs and diminished quality of life in patients. In recent years, Big Data has started to transform the fields of Neuroscience and Neurology. Scientists and clinicians are collaborating in global alliances, combining diverse datasets on a massive scale, and solving complex computational problems that demand the utilization of increasingly powerful computational resources. This Big Data revolution is opening new avenues for developing innovative treatments for neurological diseases. Our paper surveys Big Data's impact on neurological patient care, as exemplified through work done in a comprehensive selection of areas, including Connectomics, Alzheimer's Disease, Stroke, Depression, Parkinson's Disease, Pain, and Addiction (e.g., Opioid Use Disorder). We present an overview of research and the methodologies utilizing Big Data in each area, as well as their current limitations and technical challenges. Despite the potential benefits, the full potential of Big Data in these fields currently remains unrealized. We close with recommendations for future research aimed at optimizing the use of Big Data in Neuroscience and Neurology for improved patient outcomes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40537-023-00751-2.

Tracing the development and lifespan change of population-level structural asymmetry in the cerebral cortex

Cortical asymmetry is a ubiquitous feature of brain organization that is subtly altered in some neurodevelopmental disorders, yet we lack knowledge of how its development proceeds across life in health. Achieving consensus on the precise cortical asymmetries in humans is necessary to uncover the developmental timing of asymmetry and the extent to which it arises through genetic and later influences in childhood. Here, we delineate population-level asymmetry in cortical thickness and surface area vertex-wise in seven datasets and chart asymmetry trajectories longitudinally across life (4-89 years; observations = 3937; 70% longitudinal). We find replicable asymmetry interrelationships, heritability maps, and test asymmetry associations in large-scale data. Cortical asymmetry was robust across datasets. Whereas areal asymmetry is predominantly stable across life, thickness asymmetry grows in childhood and peaks in early adulthood. Areal asymmetry is low-moderately heritable (max h2SNP ~19%) and correlates phenotypically and genetically in specific regions, indicating coordinated development of asymmetries partly through genes. In contrast, thickness asymmetry is globally interrelated across the cortex in a pattern suggesting highly left-lateralized individuals tend towards left-lateralization also in population-level right-asymmetric regions (and vice versa), and exhibits low or absent heritability. We find less areal asymmetry in the most consistently lateralized region in humans associates with subtly lower cognitive ability, and confirm small handedness and sex effects. Results suggest areal asymmetry is developmentally stable and arises early in life through genetic but mainly subject-specific stochastic effects, whereas childhood developmental growth shapes thickness asymmetry and may lead to directional variability of global thickness lateralization in the population.

Cross-ancestry genome-wide association meta-analyses of hippocampal and subfield volumes

The hippocampus is critical for memory and cognition and neuropsychiatric disorders, and its subfields differ in architecture and function. Genome-wide association studies on hippocampal and subfield volumes are mainly conducted in European populations; however, other ancestral populations are under-represented. Here we conduct cross-ancestry genome-wide association meta-analyses in 65,791 individuals for hippocampal volume and 38,977 for subfield volumes, including 7,009 individuals of East Asian ancestry. We identify 339 variant-trait associations at P < 1.13 × 10^-9 for 44 hippocampal traits, including 23 new associations. Common genetic variants have similar effects on hippocampal traits across ancestries, although ancestry-specific associations exist. Cross-ancestry analysis improves the fine-mapping precision and the prediction performance of polygenic scores in under-represented populations. These genetic variants are enriched for Wnt signaling and neuron differentiation and affect cognition, emotion and neuropsychiatric disorders. These findings may provide insight into the genetic architectures of hippocampal and subfield volumes.

Association of Mitochondrial DNA Copy Number With Brain MRI Markers and Cognitive Function: A Meta-analysis of Community-Based Cohorts

BACKGROUND AND OBJECTIVES

Previous studies suggest that lower mitochondrial DNA (mtDNA) copy number (CN) is associated with neurodegenerative diseases. However, whether mtDNA CN in whole blood is related to endophenotypes of Alzheimer disease (AD) and AD-related dementia (AD/ADRD) needs further investigation. We assessed the association of mtDNA CN with cognitive function and MRI measures in community-based samples of middle-aged to older adults.

METHODS

We included dementia-free participants from 9 diverse community-based cohorts with whole-genome sequencing in the Trans-Omics for Precision Medicine (TOPMed) program. Circulating mtDNA CN was estimated as twice the ratio of the average coverage of mtDNA to nuclear DNA. Brain MRI markers included total brain, hippocampal, and white matter hyperintensity volumes. General cognitive function was derived from distinct cognitive domains. We performed cohort-specific association analyses of mtDNA CN with AD/ADRD endophenotypes assessed within ±5 years (i.e., cross-sectional analyses) or 5-20 years after blood draw (i.e., prospective analyses) adjusting for potential confounders. We further explored associations stratified by sex and age (<60 vs ≥60 years). Fixed-effects or sample size-weighted meta-analyses were performed to combine results. Finally, we performed mendelian randomization (MR) analyses to assess causality.

RESULTS

We included up to 19,152 participants (mean age 59 years, 57% women). Higher mtDNA CN was cross-sectionally associated with better general cognitive function (β = 0.04; 95% CI 0.02-0.06) independent of age, sex, batch effects, race/ethnicity, time between blood draw and cognitive evaluation, cohort-specific variables, and education. Additional adjustment for blood cell counts or cardiometabolic traits led to slightly attenuated results. We observed similar significant associations with cognition in prospective analyses, although of reduced magnitude. We found no significant associations between mtDNA CN and brain MRI measures in meta-analyses. MR analyses did not reveal a causal relation between mtDNA CN in blood and cognition.

DISCUSSION

Higher mtDNA CN in blood is associated with better current and future general cognitive function in large and diverse communities across the United States. Although MR analyses did not support a causal role, additional research is needed to assess causality. Circulating mtDNA CN could serve nevertheless as a biomarker of current and future cognitive function in the community.

Genotype determining aerobic exercise capacity associates with behavioral plasticity in middle-aged rats

Good aerobic fitness associates positively with cognitive performance and brain health and conversely, low aerobic fitness predisposes to neurodegenerative diseases. To study how genotype together with exercise, started at older age, affects brain and behavior, we utilized rats that differ in inherited aerobic fitness. Rats bred for Low Capacity for Running (LCR) are shown to display less synaptic plasticity and more inflammation in the hippocampus and perform worse than rats bred for a High Capacity for Running (HCR) in tasks requiring flexible cognition. Here we used middle-aged (∼ 16 months) HCR and LCR rats to study how genotype and sex associate with anxiety and neural information filtering, termed sensory gating. Further, we assessed how inherited aerobic capacity associates with hippocampus-dependent learning, measured with contextual fear conditioning task. In females, we also investigated the effects of voluntary wheel running (5 weeks) on these characteristics. Our results indicate that independent of sex or voluntary running, HCR rats were more anxious in open-field tasks, exhibited lower sensory gating and learned more efficiently in contextual fear conditioning task than LCR rats. Voluntary running did not markedly affect innate behavior but slightly decreased the differences between female LCR and HCR rats in fear learning. In conclusion, inherited fitness seems to determine cognitive and behavioral traits independent of sex. Although the traits proved to be rather resistant to change at adult age, learning was slightly improved following exercise in LCR females, prone to obesity and poor fitness.

Brain structure and allelic associations in Alzheimer's disease

BACKGROUND

Alzheimer's disease (AD), the most prevalent form of dementia, affects 6.5 million Americans and over 50 million people globally. Clinical, genetic, and phenotypic studies of dementia provide some insights of the observed progressive neurodegenerative processes, however, the mechanisms underlying AD onset remain enigmatic.

AIMS

This paper examines late-onset dementia-related cognitive impairment utilizing neuroimaging-genetics biomarker associations.

MATERIALS AND METHODS

The participants, ages 65-85, included 266 healthy controls (HC), 572 volunteers with mild cognitive impairment (MCI), and 188 Alzheimer's disease (AD) patients. Genotype dosage data for AD-associated single nucleotide polymorphisms (SNPs) were extracted from the imputed ADNI genetics archive using sample-major additive coding. Such 29 SNPs were selected, representing a subset of independent SNPs reported to be highly associated with AD in a recent AD meta-GWAS study by Jansen and colleagues.

RESULTS

We identified the significant correlations between the 29 genomic markers (GMs) and the 200 neuroimaging markers (NIMs). The odds ratios and relative risks for AD and MCI (relative to HC) were predicted using multinomial linear models.

DISCUSSION

In the HC and MCI cohorts, mainly cortical thickness measures were associated with GMs, whereas the AD cohort exhibited different GM-NIM relations. Network patterns within the HC and AD groups were distinct in cortical thickness, volume, and proportion of White to Gray Matter (pct), but not in the MCI cohort. Multinomial linear models of clinical diagnosis showed precisely the specific NIMs and GMs that were most impactful in discriminating between AD and HC, and between MCI and HC.

CONCLUSION

This study suggests that advanced analytics provide mechanisms for exploring the interrelations between morphometric indicators and GMs. The findings may facilitate further clinical investigations of phenotypic associations that support deep systematic understanding of AD pathogenesis.

inMTSCCA: An Integrated Multi-task Sparse Canonical Correlation Analysis for Multi-omic Brain Imaging Genetics

Identifying genetic risk factors for Alzheimer's disease (AD) is an important research topic. To date, different endophenotypes, such as imaging-derived endophenotypes and proteomic expression-derived endophenotypes, have shown the great value in uncovering risk genes compared to case-control studies. Biologically, a co-varying pattern of different omics-derived endophenotypes could result from the shared genetic basis. However, existing methods mainly focus on the effect of endophenotypes alone; the effect of cross-endophenotype (CEP) associations remains largely unexploited. In this study, we used both endophenotypes and their CEP associations of multi-omic data to identify genetic risk factors, and proposed two integrated multi-task sparse canonical correlation analysis (inMTSCCA) methods, i.e., pairwise endophenotype correlation-guided MTSCCA (pcMTSCCA) and high-order endophenotype correlation-guided MTSCCA (hocMTSCCA). pcMTSCCA employed pairwise correlations between magnetic resonance imaging (MRI)-derived, plasma-derived, and cerebrospinal fluid (CSF)-derived endophenotypes as an additional penalty. hocMTSCCA used high-order correlations among these multi-omic data for regularization. To figure out genetic risk factors at individual and group levels, as well as altered endophenotypic markers, we introduced sparsity-inducing penalties for both models. We compared pcMTSCCA and hocMTSCCA with three related methods on both simulation and real (consisting of neuroimaging data, proteomic analytes, and genetic data) datasets. The results showed that our methods obtained better or comparable canonical correlation coefficients (CCCs) and better feature subsets than benchmarks. Most importantly, the identified genetic loci and heterogeneous endophenotypic markers showed high relevance. Therefore, jointly using multi-omic endophenotypes and their CEP associations is promising to reveal genetic risk factors. The source code and manual of inMTSCCA are available at https://ngdc.cncb.ac.cn/biocode/tools/BT007330.

De novo variants in MAST4 related to neurodevelopmental disorders with developmental delay and infantile spasms: Genotype-phenotype association

OBJECTIVE

This study aims to prove that the de novo variants in MAST4 gene are associated with neurodevelopmental disorders (NDD) with developmental delay (DD) and infantile spasm (IS) and to determine the genotype-phenotype correlations.

METHODS

Trio-based exome sequencing (ES) was performed on the four families enrolled in this study. We collected and systematically reviewed the four probands' clinical data, magnetic resonance images (MRI), and electroencephalography (EEG). We also carried out bioinformatics analysis by integrating published exome/genome sequencing data and human brain transcriptomic data.

RESULTS

We described four patients whose median age of seizure onset was 5 months. The primary manifestation was infantile spasms with typical hypsarrhythmia on EEG. Developmental delays or intellectual disabilities varied among the four individuals. Three de novo missense variants in MAST4 gene were identified from four families, including chr5:66438324 (c.2693T > C: p.Ile898Thr) z, chr5:66459419 (c.4412C > T: p.Thr1471Ile), and chr5:66462662 (c.7655C > G:p.Ser2552Trp). The missense variant p.Ile898Thr is mapped to the AGC-kinase C-terminal with phosphatase activity. The other variant p.Ser2552Trp is located in a phosphoserine-modified residue which may affect cell membrane stability and signal transduction. Besides, the variant p.Thr1471Ile is a recurrent site screened out in two unrelated patients. Compared to private mutations (found only in a single family or a small population) of MAST4 in the gnomAD non-neuro subset, all de novo variants were predicted to be damaging or probably damaging through different bioinformatic analyses. Significantly higher CADD scores of the variant p.Thr1471Ile indicate more deleteriousness of the recurrent site. And the affected amino acids are highly conserved across multiple species. According to the Brainspan Atlas database, MAST4 is expressed primarily in the mediodorsal nucleus of the thalamus and medial prefrontal cortex during the prenatal period, potentially contributing to embryonic brain development.

CONCLUSION

Our results revealed that the variants of MAST4 gene might lead to neurodevelopmental disorders with developmental delay and infantile spasm. Thus, MAST4 variants should be considered the potential candidate gene in patients with neurodevelopmental disorders clinically marked by infantile spasms.

The emergence of genotypic divergence and future precision medicine applications

Genotypic divergence is a term adapted from population genetics and intimately linked to evolution. We use divergence here to emphasize the differences that set individuals apart in any cohort. The history of genetics is filled with descriptions of genotypic differences, but causal inference of interindividual biological variation has been scarce. We suggest that the practice of precision medicine requires a divergent approach, an approach dependent on the causal interpretation of previous convergent (and preliminary) knowledge in the field. This knowledge has relied on convergent descriptive syndromology (lumping), which has overemphasized a reductionistic gene determinism on the quest of seeking associations without causal understanding. Regulatory variants with small effect and somatic mutations are some of the modifying factors that lead to incomplete penetrance and intrafamilial variable expressivity often observed in apparently monogenic clinical disorders. A truly divergent approach to precision medicine requires splitting, that is, the consideration of different layers of genetic phenomena that interact causally in a nonlinear fashion. This chapter reviews convergences and divergences in genetics and genomics, aiming to discuss what can be causally understood to approximate the as-yet utopian lands of Precision Medicine for patients with neurodegenerative disorders.

Genetic associations between bipolar disorder and brain structural phenotypes

Patients with bipolar disorder (BD) and their first-degree relatives exhibit alterations in brain volume and cortical structure, whereas the underlying genetic mechanisms remain unclear. In this study, based on the published genome-wide association studies (GWAS), the extent of polygenic overlap between BD and 15 brain structural phenotypes was investigated using linkage disequilibrium score regression and MiXeR tool, and the shared genomic loci were discovered by conjunctional false discovery rate (conjFDR) and expression quantitative trait loci (eQTL) analyses. MiXeR estimated the overall measure of polygenic overlap between BD and brain structural phenotypes as 4-53% on a 0-100% scale (as quantified by the Dice coefficient). Subsequent conjFDR analyses identified 54 independent loci (71 risk single-nucleotide polymorphisms) jointly associated with BD and brain structural phenotypes with a conjFDR < 0.05, among which 33 were novel that had not been reported in the previous BD GWAS. Follow-up eQTL analyses in respective brain regions both confirmed well-known risk genes (e.g. CACNA1C, NEK4, GNL3, MAPK3) and discovered novel risk genes (e.g. LIMK2 and CAMK2N2). This study indicates a substantial shared genetic basis between BD and brain structural phenotypes, and provides novel insights into the developmental origin of BD and related biological mechanisms.

Evaluating the Genetic Effects of Gut Microbiota on the Development of Neuroticism and General Happiness: A Polygenic Score Analysis and Interaction Study Using UK Biobank Data

Limited efforts have been invested in exploring the interaction effects between genetic factors and gut microbiota on neuroticism and general happiness. The polygenic risk scores (PRS) of gut microbiota were calculated from individual-level genotype data of the UK Biobank cohort. Linear regression models were then used to assess the associations between individual PRS of gut microbiota and mental traits and interaction analysis was performed by PLINK2.0. KOBAS-i was used to conduct gene ontology (GO) enrichment analysis of the identified genes. We observed suggestive significant associations between neuroticism and PRS for the genus Bifidobacterium (rank-normal transformation, RNT) (beta = -1.10, P = 4.16 × 10-3) and the genus Desulfovibrio (RNT) (beta = 0.54, P = 7.46 × 10-3). PRS for the genus Bifidobacterium (hurdle binary, HB) (beta = 1.99, P = 5.24 × 10-3) and the genus Clostridium (RNT) (beta = 1.26, P = 9.27 × 10-3) were found to be suggestive positively associated with general happiness. Interaction analysis identified several significant genes that interacted with gut microbiota, such as RORA (rs575949009, beta = -45.00, P = 1.82 × 10-9) for neuroticism and ASTN2 (rs36005728, beta = 19.15, P = 3.37 × 10-8) for general happiness. Our study results support the genetic effects of gut microbiota on the development of neuroticism and general happiness.

Deciphering the Effect of Different Genetic Variants on Hippocampal Subfield Volumes in the General Population

The aim of this study was to disentangle the effects of various genetic factors on hippocampal subfield volumes using three different approaches: a biologically driven candidate gene approach, a hypothesis-free GWAS approach, and a polygenic approach, where AD risk alleles are combined with a polygenic risk score (PRS). The impact of these genetic factors was investigated in a large dementia-free general population cohort from the Study of Health in Pomerania (SHIP, n = 1806). Analyses were performed using linear regression models adjusted for biological and environmental risk factors. Hippocampus subfield volume alterations were found for APOE ε4, BDNF Val, and 5-HTTLPR L allele carriers. In addition, we were able to replicate GWAS findings, especially for rs17178139 (MSRB3), rs1861979 (DPP4), rs7873551 (ASTN2), and rs572246240 (MAST4). Interaction analyses between the significant SNPs as well as the PRS for AD revealed no significant results. Our results confirm that hippocampal volume reductions are influenced by genetic variation, and that different variants reveal different association patterns that can be linked to biological processes in neurodegeneration. Thus, this study underlines the importance of specific genetic analyses in the quest for acquiring deeper insights into the biology of hippocampal volume loss, memory impairment, depression, and neurodegenerative diseases.

Association between brain structures and migraine: A bidirectional Mendelian randomization study

Background

Accumulating evidence of clinical and neuroimaging studies indicated that migraine is related to brain structural alterations. However, it is still not clear whether the associations of brain structural alterations with migraine are likely to be causal, or could be explained by reverse causality confounding.

Methods

We carried on a bidirectional Mendelian randomization analysis in order to identify the causal relationship between brain structures and migraine risk. Summary-level data and independent variants used as instruments came from large genome-wide association studies of total surface area and average thickness of cortex (33,992 participants), gray matter volume (8,428 participants), white matter hyperintensities (50,970 participants), hippocampal volume (33,536 participants), and migraine (102,084 cases and 771,257 controls).

Results

We identified suggestive associations of the decreased surface area (OR = 0.85; 95% CI, 0.75-0.96; P = 0.007), and decreased hippocampal volume (OR = 0.74; 95% CI, 0.55-1.00; P = 0.047) with higher migraine risk. We did not find any significant association of gray matter volume, cortical thickness, or white matter hyperintensities with migraine. No evidence supporting the significant association was found in the reverse MR analysis.

Conclusion

We provided suggestive evidence that surface area and hippocampal volume are causally associated with migraine risk.

Neuroimaging Genetics and Network Analysis in Alzheimer's Disease

The issue of the genetics in brain imaging phenotypes serves as a crucial link between two distinct scientific fields: neuroimaging genetics (NG). The articles included here provide solid proof that this NG link has considerable synergy. There is a suitable collection of articles that offer a wide range of viewpoints on how genetic variations affect brain structure and function. They serve as illustrations of several study approaches used in contemporary genetics and neuroscience. Genome-wide association studies and candidate-gene association are two examples of genetic techniques. Cortical gray matter structural/volumetric measures from magnetic resonance imaging (MRI) are sources of information on brain phenotypes. Together, they show how various scientific disciplines have benefited from significant technological advances, such as the single-nucleotide polymorphism array in genetics and the development of increasingly higher-resolution MRI imaging. Moreover, we discuss NG's contribution to expanding our knowledge about the heterogeneity within Alzheimer's disease as well as the benefits of different network analyses.