Larger cerebral cortex is genetically correlated with greater frontal area and dorsal thickness.

Human cortical expansion has occurred non-uniformly across the brain. We assessed the genetic architecture of cortical global expansion and regionalization by comparing two sets of genome-wide association studies of 24 cortical regions with and without adjustment for global measures (i.e., total surface area, mean cortical thickness) using a genetically informed parcellation in 32,488 adults. We found 393 and 756 significant loci with and without adjusting for globals, respectively, where 8% and 45% loci were associated with more than one region. Results from analyses without adjustment for globals recovered loci associated with global measures. Genetic factors that contribute to total surface area of the cortex particularly expand anterior/frontal regions, whereas those contributing to thicker cortex predominantly increase dorsal/frontal-parietal thickness. Interactome-based analyses revealed significant genetic overlap of global and dorsolateral prefrontal modules, enriched for neurodevelopmental and immune system pathways. Consideration of global measures is important in understanding the genetic variants underlying cortical morphology.

TMEM161B modulates radial glial scaffolding in neocortical development.

TMEM161B encodes an evolutionarily conserved widely expressed novel 8-pass transmembrane protein of unknown function in human. Here we identify TMEM161B homozygous hypomorphic missense variants in our recessive polymicrogyria (PMG) cohort. Patients carrying TMEM161B mutations exhibit striking neocortical PMG and intellectual disability. Tmem161b knockout mice fail to develop midline hemispheric cleavage, whereas knock-in of patient mutations and patient-derived brain organoids show defects in apical cell polarity and radial glial scaffolding. We found that TMEM161B modulates actin filopodia, functioning upstream of the Rho-GTPase CDC42. Our data link TMEM161B with human PMG, likely regulating radial glia apical polarity during neocortical development.

Dissecting unique and common variance across body and brain health indicators using age prediction.

Ageing is a heterogeneous multisystem process involving different rates of decline in physiological integrity across biological systems. The current study dissects the unique and common variance across body and brain health indicators and parses inter-individual heterogeneity in the multisystem ageing process. Using machine-learning regression models on the UK Biobank data set (N = 32,593, age range 44.6-82.3, mean age 64.1 years), we first estimated tissue-specific brain age for white and gray matter based on diffusion and T1-weighted magnetic resonance imaging (MRI) data, respectively. Next, bodily health traits, including cardiometabolic, anthropometric, and body composition measures of adipose and muscle tissue from bioimpedance and body MRI, were combined to predict 'body age'. The results showed that the body age model demonstrated comparable age prediction accuracy to models trained solely on brain MRI data. The correlation between body age and brain age predictions was 0.62 for the T1 and 0.64 for the diffusion-based model, indicating a degree of unique variance in brain and bodily ageing processes. Bayesian multilevel modelling carried out to quantify the associations between health traits and predicted age discrepancies showed that higher systolic blood pressure and higher muscle-fat infiltration were related to older-appearing body age compared to brain age. Conversely, higher hand-grip strength and muscle volume were related to a younger-appearing body age. Our findings corroborate the common notion of a close connection between somatic and brain health. However, they also suggest that health traits may differentially influence age predictions beyond what is captured by the brain imaging data, potentially contributing to heterogeneous ageing rates across biological systems and individuals.

Brain-wide associations between white matter and age highlight the role of fornix microstructure in brain ageing.

Unveiling the details of white matter (WM) maturation throughout ageing is a fundamental question for understanding the ageing brain. In an extensive comparison of brain age predictions and age-associations of WM features from different diffusion approaches, we analyzed UK Biobank diffusion magnetic resonance imaging (dMRI) data across midlife and older age (N = 35,749, 44.6-82.8 years of age). Conventional and advanced dMRI approaches were consistent in predicting brain age. WM-age associations indicate a steady microstructure degeneration with increasing age from midlife to older ages. Brain age was estimated best when combining diffusion approaches, showing different aspects of WM contributing to brain age. Fornix was found as the central region for brain age predictions across diffusion approaches in complement to forceps minor as another important region. These regions exhibited a general pattern of positive associations with age for intra axonal water fractions, axial, radial diffusivities, and negative relationships with age for mean diffusivities, fractional anisotropy, kurtosis. We encourage the application of multiple dMRI approaches for detailed insights into WM, and the further investigation of fornix and forceps as potential biomarkers of brain age and ageing.

Shared genetic architecture between schizophrenia and subcortical brain volumes implicates early neurodevelopmental processes and brain development in childhood.

Patients with schizophrenia have consistently shown brain volumetric abnormalities, implicating both etiological and pathological processes. However, the genetic relationship between schizophrenia and brain volumetric abnormalities remains poorly understood. Here, we applied novel statistical genetic approaches (MiXeR and conjunctional false discovery rate analysis) to investigate genetic overlap with mixed effect directions using independent genome-wide association studies of schizophrenia (n = 130,644) and brain volumetric phenotypes, including subcortical brain and intracranial volumes (n = 33,735). We found brain volumetric phenotypes share substantial genetic variants (74-96%) with schizophrenia, and observed 107 distinct shared loci with sign consistency in independent samples. Genes mapped by shared loci revealed (1) significant enrichment in neurodevelopmental biological processes, (2) three co-expression clusters with peak expression at the prenatal stage, and (3) genetically imputed thalamic expression of CRHR1 and ARL17A was associated with the thalamic volume as early as in childhood. Together, our findings provide evidence of shared genetic architecture between schizophrenia and brain volumetric phenotypes and suggest that altered early neurodevelopmental processes and brain development in childhood may be involved in schizophrenia development.

Shared genetic architecture between mental health and the brain functional connectome in the UK Biobank.

Psychiatric disorders are complex clinical conditions with large heterogeneity and overlap in symptoms, genetic liability and brain imaging abnormalities. Building on a dimensional conceptualization of mental health, previous studies have reported genetic overlap between psychiatric disorders and population-level mental health, and between psychiatric disorders and brain functional connectivity. Here, in 30,701 participants aged 45-82 from the UK Biobank we map the genetic associations between self-reported mental health and resting-state fMRI-based measures of brain network function. Multivariate Omnibus Statistical Test revealed 10 genetic loci associated with population-level mental symptoms. Next, conjunctional FDR identified 23 shared genetic variants between these symptom profiles and fMRI-based brain network measures. Functional annotation implicated genes involved in brain structure and function, in particular related to synaptic processes such as axonal growth (e.g. NGFR and RHOA). These findings provide further genetic evidence of an association between brain function and mental health traits in the population.

Improved multimodal prediction of progression from MCI to Alzheimer's disease combining genetics with quantitative brain MRI and cognitive measures.

INTRODUCTION: There is a pressing need for non-invasive, cost-effective tools for early detection of Alzheimer's disease (AD). METHODS: Using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), Cox proportional models were conducted to develop a multimodal hazard score (MHS) combining age, a polygenic hazard score (PHS), brain atrophy, and memory to predict conversion from mild cognitive impairment (MCI) to dementia. Power calculations estimated required clinical trial sample sizes after hypothetical enrichment using the MHS. Cox regression determined predicted age of onset for AD pathology from the PHS. RESULTS: The MHS predicted conversion from MCI to dementia (hazard ratio for 80th versus 20th percentile: 27.03). Models suggest that application of the MHS could reduce clinical trial sample sizes by 67%. The PHS alone predicted age of onset of amyloid and tau. DISCUSSION: The MHS may improve early detection of AD for use in memory clinics or for clinical trial enrichment. HIGHLIGHTS: A multimodal hazard score (MHS) combined age, genetics, brain atrophy, and memory. The MHS predicted time to conversion from mild cognitive impairment to dementia. MHS reduced hypothetical Alzheimer's disease (AD) clinical trial sample sizes by 67%. A polygenic hazard score predicted age of onset of AD neuropathology.

Distributed genetic effects of the corpus callosum subregions suggest links to neuropsychiatric disorders and related traits.

BACKGROUND: The corpus callosum (CC) is a brain structure with a high heritability and potential role in psychiatric disorders. However, the genetic architecture of the CC and the genetic link with psychiatric disorders remain largely unclear. We investigated the genetic architectures of the volume of the CC and its subregions and the genetic overlap with psychiatric disorders. METHODS: We applied multivariate genome-wide association study (GWAS) to genetic and T1-weighted magnetic resonance imaging (MRI) data of 40,894 individuals from the UK Biobank, aiming to boost genetic discovery and to assess the pleiotropic effects across volumes of the five subregions of the CC (posterior, mid-posterior, central, mid-anterior and anterior) obtained by FreeSurfer 7.1. Multivariate GWAS was run combining all subregions, co-varying for relevant variables. Gene-set enrichment analyses were performed using MAGMA. Linkage disequilibrium score regression (LDSC) was used to determine Single nucleotide polymorphism (SNP)-based heritability of total CC volume and volumes of its subregions as well as their genetic correlations with relevant psychiatric traits. RESULTS: We identified 70 independent loci with distributed effects across the five subregions of the CC (p < 5 × 10-8). Additionally, we identified 33 significant loci in the anterior subregion, 23 in the mid-anterior, 29 in the central, 7 in the mid-posterior and 56 in the posterior subregion. Gene-set analysis revealed 156 significant genes contributing to volume of the CC subregions (p < 2.6 × 10-6). LDSC estimated the heritability of CC to (h2SNP = 0.38, SE = 0.03) and subregions ranging from 0.22 (SE = 0.02) to 0.37 (SE = 0.03). We found significant genetic correlations of total CC volume with bipolar disorder (BD, rg = -0.09, SE = 0.03; p = 5.9 × 10-3) and drinks consumed per week (rg = -0.09, SE = 0.02; p = 4.8 × 10-4), and volume of the mid-anterior subregion with BD (rg = -0.12, SE = 0.02; p = 2.5 × 10-4), major depressive disorder (MDD) (rg = -0.12, SE = 0.04; p = 3.6 × 10-3), drinks consumed per week (rg = -0.13, SE = 0.04; p = 1.8 × 10-3) and cannabis use (rg = -0.09, SE = 0.03; p = 8.4 × 10-3). CONCLUSIONS: Our results demonstrate that the CC has a polygenic architecture implicating multiple genes and show that CC subregion volumes are heritable. We found that distinct genetic factors are involved in the development of anterior and posterior subregions, consistent with their divergent functional specialisation. Significant genetic correlation between volumes of the CC and BD, drinks per week, MDD and cannabis consumption subregion volumes with psychiatric traits is noteworthy and deserving of further investigation.

Generalization of cortical MOSTest genome-wide associations within and across samples.

Genome-Wide Association studies have typically been limited to univariate analysis in which a single outcome measure is tested against millions of variants. Recent work demonstrates that a Multivariate Omnibus Statistic Test (MOSTest) is well powered to discover genomic effects distributed across multiple phenotypes. Applied to cortical brain MRI morphology measures, MOSTest has resulted in a drastic improvement in power to discover loci when compared to established approaches (min-P). One question that arises is how well these discovered loci replicate in independent data. Here we perform 10 times cross validation within 34,973 individuals from UK Biobank for imaging measures of cortical area, thickness and sulcal depth (>1,000 dimensionality for each). By deploying a replication method that aggregates discovered effects distributed across multiple phenotypes, termed PolyVertex Score (MOSTest-PVS), we demonstrate a higher replication yield and comparable replication rate of discovered loci for MOSTest (# replicated loci: 242-496, replication rate: 96-97%) in independent data when compared with the established min-P approach (# replicated loci: 26-55, replication rate: 91-93%). An out-of-sample replication of discovered loci was conducted with a sample of 4,069 individuals from the Adolescent Brain Cognitive Development® (ABCD) study, who are on average 50 years younger than UK Biobank individuals. We observe a higher replication yield and comparable replication rate of MOSTest-PVS compared to min-P. This finding underscores the importance of using well-powered multivariate techniques for both discovery and replication of high dimensional phenotypes in Genome-Wide Association studies.

Associations between brain imaging and polygenic scores of mental health and educational attainment in children aged 9-11.

Psychiatric disorders are highly heritable and polygenic, and many have their peak onset in late childhood and adolescence, a period of tremendous changes. Although the neurodevelopmental antecedents of mental illness are widely acknowledged, research in youth population cohorts is still scarce, preventing our progress towards the early characterization of these disorders. We included 7,124 children (9-11 years old) from the Adolescent Brain and Cognitive Development Study to map the associations of structural and diffusion brain imaging with common genetic variants and polygenic scores for psychiatric disorders and educational attainment. We used principal component analysis to derive imaging components, and calculated their heritability. We then assessed the relationship of imaging components with genetic and clinical psychiatric risk with univariate models and Canonical correlation analysis (CCA). Most imaging components had moderate heritability. Univariate models showed limited evidence and small associations of polygenic scores with brain structure at this age. CCA revealed two significant modes of covariation. The first mode linked higher polygenic scores for educational attainment with less externalizing problems and larger surface area. The second mode related higher polygenic scores for schizophrenia, bipolar disorder, and autism spectrum disorder to higher global cortical thickness, smaller white matter volumes of the fornix and cingulum, larger medial occipital surface area and smaller surface area of lateral and medial temporal regions. While cross-validation suggested limited generalizability, our results highlight the potential of multivariate models to better understand the transdiagnostic and distributed relationships between mental health and brain structure in late childhood.

A comparison of intracranial volume estimation methods and their cross-sectional and longitudinal associations with age.

Intracranial volume (ICV) is frequently used in volumetric magnetic resonance imaging (MRI) studies, both as a covariate and as a variable of interest. Findings of associations between ICV and age have varied, potentially due to differences in ICV estimation methods. Here, we compared five commonly used ICV estimation methods and their associations with age. T1-weighted cross-sectional MRI data was included for 651 healthy individuals recruited through the NORMENT Centre (mean age = 46.1 years, range = 12.0-85.8 years) and 2410 healthy individuals recruited through the UK Biobank study (UKB, mean age = 63.2 years, range = 47.0-80.3 years), where longitudinal data was also available. ICV was estimated with FreeSurfer (eTIV and sbTIV), SPM12, CAT12, and FSL. We found overall high correlations across ICV estimation method, with the lowest observed correlations between FSL and eTIV (r = .87) and between FSL and CAT12 (r = .89). Widespread proportional bias was found, indicating that the agreement between methods varied as a function of head size. Body weight, age, sex, and mean ICV across methods explained the most variance in the differences between ICV estimation methods, indicating possible confounding for some estimation methods. We found both positive and negative cross-sectional associations with age, depending on dataset and ICV estimation method. Longitudinal ICV reductions were found for all ICV estimation methods, with annual percentage change ranging from -0.293% to -0.416%. This convergence of longitudinal results across ICV estimation methods offers strong evidence for age-related ICV reductions in mid- to late adulthood.

Sex- and age-specific associations between cardiometabolic risk and white matter brain age in the UK Biobank cohort.

Cardiometabolic risk (CMR) factors are associated with accelerated brain aging and increased risk for sex-dimorphic illnesses such as Alzheimer's disease (AD). Yet, it is unknown how CMRs interact with sex and apolipoprotein E-ϵ4 (APOE4), a known genetic risk factor for AD, to influence brain age across different life stages. Using age prediction based on multi-shell diffusion-weighted imaging data in 21,308 UK Biobank participants, we investigated whether associations between white matter Brain Age Gap (BAG) and body mass index (BMI), waist-to-hip ratio (WHR), body fat percentage (BF%), and APOE4 status varied (i) between males and females, (ii) according to age at menopause in females, and (iii) across different age groups in males and females. We report sex differences in associations between BAG and all three CMRs, with stronger positive associations among males compared to females. Independent of APOE4 status, higher BAG (older brain age relative to chronological age) was associated with greater BMI, WHR, and BF% in males, whereas in females, higher BAG was associated with greater WHR, but not BMI and BF%. These divergent associations were most prominent within the oldest group of females (66-81 years), where greater BF% was linked to lower BAG. Earlier menopause transition was associated with higher BAG, but no interactions were found with CMRs. In conclusion, the findings point to sex- and age-specific associations between CMRs and brain age. Incorporating sex as a factor of interest in studies addressing CMR may promote sex-specific precision medicine, consequently improving health care for both males and females.

Effects of copy number variations on brain structure and risk for psychiatric illness: Large-scale studies from the ENIGMA working groups on CNVs.

The Enhancing NeuroImaging Genetics through Meta-Analysis copy number variant (ENIGMA-CNV) and 22q11.2 Deletion Syndrome Working Groups (22q-ENIGMA WGs) were created to gain insight into the involvement of genetic factors in human brain development and related cognitive, psychiatric and behavioral manifestations. To that end, the ENIGMA-CNV WG has collated CNV and magnetic resonance imaging (MRI) data from ~49,000 individuals across 38 global research sites, yielding one of the largest studies to date on the effects of CNVs on brain structures in the general population. The 22q-ENIGMA WG includes 12 international research centers that assessed over 533 individuals with a confirmed 22q11.2 deletion syndrome, 40 with 22q11.2 duplications, and 333 typically developing controls, creating the largest-ever 22q11.2 CNV neuroimaging data set. In this review, we outline the ENIGMA infrastructure and procedures for multi-site analysis of CNVs and MRI data. So far, ENIGMA has identified effects of the 22q11.2, 16p11.2 distal, 15q11.2, and 1q21.1 distal CNVs on subcortical and cortical brain structures. Each CNV is associated with differences in cognitive, neurodevelopmental and neuropsychiatric traits, with characteristic patterns of brain structural abnormalities. Evidence of gene-dosage effects on distinct brain regions also emerged, providing further insight into genotype-phenotype relationships. Taken together, these results offer a more comprehensive picture of molecular mechanisms involved in typical and atypical brain development. This "genotype-first" approach also contributes to our understanding of the etiopathogenesis of brain disorders. Finally, we outline future directions to better understand effects of CNVs on brain structure and behavior.

Genetic control of variability in subcortical and intracranial volumes.

Sensitivity to external demands is essential for adaptation to dynamic environments, but comes at the cost of increased risk of adverse outcomes when facing poor environmental conditions. Here, we apply a novel methodology to perform genome-wide association analysis of mean and variance in ten key brain features (accumbens, amygdala, caudate, hippocampus, pallidum, putamen, thalamus, intracranial volume, cortical surface area, and cortical thickness), integrating genetic and neuroanatomical data from a large lifespan sample (n = 25,575 individuals; 8-89 years, mean age 51.9 years). We identify genetic loci associated with phenotypic variability in thalamus volume and cortical thickness. The variance-controlling loci involved genes with a documented role in brain and mental health and were not associated with the mean anatomical volumes. This proof-of-principle of the hypothesis of a genetic regulation of brain volume variability contributes to establishing the genetic basis of phenotypic variance (i.e., heritability), allows identifying different degrees of brain robustness across individuals, and opens new research avenues in the search for mechanisms controlling brain and mental health.

Population-based neuroimaging reveals traces of childbirth in the maternal brain.

Maternal brain adaptations have been found across pregnancy and postpartum, but little is known about the long-term effects of parity on the maternal brain. Using neuroimaging and machine learning, we investigated structural brain characteristics in 12,021 middle-aged women from the UK Biobank, demonstrating that parous women showed less evidence of brain aging compared to their nulliparous peers. The relationship between childbirths and a "younger-looking" brain could not be explained by common genetic variation or relevant confounders. Although prospective longitudinal studies are needed, the results suggest that parity may involve neural changes that could influence women's brain aging later in life.

Vertex-wise multivariate genome-wide association study identifies 780 unique genetic loci associated with cortical morphology.

Brain morphology has been shown to be highly heritable, yet only a small portion of the heritability is explained by the genetic variants discovered so far. Here we extended the Multivariate Omnibus Statistical Test (MOSTest) and applied it to genome-wide association studies (GWAS) of vertex-wise structural magnetic resonance imaging (MRI) cortical measures from N=35,657 participants in the UK Biobank. We identified 695 loci for cortical surface area and 539 for cortical thickness, in total 780 unique genetic loci associated with cortical morphology robustly replicated in 8,060 children of mixed ethnicity from the Adolescent Brain Cognitive Development (ABCD) Study®. This reflects more than 8-fold increase in genetic discovery at no cost to generalizability compared to the commonly used univariate GWAS methods applied to region of interest (ROI) data. Functional follow up including gene-based analyses implicated 10% of all protein-coding genes and pointed towards pathways involved in neurogenesis and cell differentiation. Power analysis indicated that applying the MOSTest to vertex-wise structural MRI data triples the effective sample size compared to conventional univariate GWAS approaches. The large boost in power obtained with the vertex-wise MOSTest together with pronounced replication rates and highlighted biologically meaningful pathways underscores the advantage of multivariate approaches in the context of highly distributed polygenic architecture of the human brain.

Fast qualitY conTrol meThod foR derIved diffUsion Metrics (YTTRIUM) in big data analysis: U.K. Biobank 18,608 example.

Deriving reliable information about the structural and functional architecture of the brain in vivo is critical for the clinical and basic neurosciences. In the new era of large population-based datasets, when multiple brain imaging modalities and contrasts are combined in order to reveal latent brain structural patterns and associations with genetic, demographic and clinical information, automated and stringent quality control (QC) procedures are important. Diffusion magnetic resonance imaging (dMRI) is a fertile imaging technique for probing and visualising brain tissue microstructure in vivo, and has been included in most standard imaging protocols in large-scale studies. Due to its sensitivity to subject motion and technical artefacts, automated QC procedures prior to scalar diffusion metrics estimation are required in order to minimise the influence of noise and artefacts. However, the QC procedures performed on raw diffusion data cannot guarantee an absence of distorted maps among the derived diffusion metrics. Thus, robust and efficient QC methods for diffusion scalar metrics are needed. Here, we introduce Fast qualitY conTrol meThod foR derIved diffUsion Metrics (YTTRIUM), a computationally efficient QC method utilising structural similarity to evaluate diffusion map quality and mean diffusion metrics. As an example, we applied YTTRIUM in the context of tract-based spatial statistics to assess associations between age and kurtosis imaging and white matter tract integrity maps in U.K. Biobank data (n = 18,608). To assess the influence of outliers on results obtained using machine learning (ML) approaches, we tested the effects of applying YTTRIUM on brain age prediction. We demonstrated that the proposed QC pipeline represents an efficient approach for identifying poor quality datasets and artefacts and increases the accuracy of ML based brain age prediction.

Genetic variants associated with cardiometabolic abnormalities during treatment with selective serotonin reuptake inhibitors: a genome-wide association study.

Selective serotonin reuptake inhibitors (SSRIs) are prescribed both to patients with schizophrenia and bipolar disorder. Previous studies have shown associations between SSRI treatment and cardiometabolic alterations. The aim of the present study was to investigate genetic variants associated with cardiometabolic adverse effects in patients treated with SSRIs in a naturalistic setting, using a genome-wide cross-sectional approach in a genetically homogeneous sample. We included and genotyped 1981 individuals with schizophrenia or bipolar disorder, of whom 1180 had information available on the outcomes low-density lipoprotein cholesterol (LDL-cholesterol), high-density lipoprotein cholesterol (HDL-cholesterol), triglycerides, and body mass index (BMI) and investigated interactions between SNPs and SSRI use (N = 246) by conducting a genome-wide GxE analysis. We report 13 genome-wide significant interaction effects of SNPs and SSRI serum concentrations on LDL-cholesterol, HDL-cholesterol, and BMI, located in four distinct genomic loci. This study provides new insight into the pharmacogenetics of SSRI but warrants replication in independent populations.

Association between complement component 4A expression, cognitive performance and brain imaging measures in UK Biobank.

Abstract. BACKGROUND: Altered expression of the complement component C4A gene is a known risk factor for schizophrenia. Further, predicted brain C4A expression has also been associated with memory function highlighting that altered C4A expression in the brain may be relevant for cognitive and behavioral traits. METHODS: We obtained genetic information and performance measures on seven cognitive tasks for up to 329 773 individuals from the UK Biobank, as well as brain imaging data for a subset of 33 003 participants. Direct genotypes for variants (n = 3213) within the major histocompatibility complex region were used to impute C4 structural variation, from which predicted expression of the C4A and C4B genes in human brain tissue were predicted. We investigated if predicted brain C4A or C4B expression were associated with cognitive performance and brain imaging measures using linear regression analyses. RESULTS: We identified significant negative associations between predicted C4A expression and performance on select cognitive tests, and significant associations with MRI-based cortical thickness and surface area in select regions. Finally, we observed significant inconsistent partial mediation of the effects of predicted C4A expression on cognitive performance, by specific brain structure measures. CONCLUSIONS: These results demonstrate that the C4 risk locus is associated with the central endophenotypes of cognitive performance and brain morphology, even when considered independently of other genetic risk factors and in individuals without mental or neurological disorders.

Brain scans from 21,297 individuals reveal the genetic architecture of hippocampal subfield volumes.

The hippocampus is a heterogeneous structure, comprising histologically distinguishable subfields. These subfields are differentially involved in memory consolidation, spatial navigation and pattern separation, complex functions often impaired in individuals with brain disorders characterized by reduced hippocampal volume, including Alzheimer's disease (AD) and schizophrenia. Given the structural and functional heterogeneity of the hippocampal formation, we sought to characterize the subfields' genetic architecture. T1-weighted brain scans (n = 21,297, 16 cohorts) were processed with the hippocampal subfields algorithm in FreeSurfer v6.0. We ran a genome-wide association analysis on each subfield, co-varying for whole hippocampal volume. We further calculated the single-nucleotide polymorphism (SNP)-based heritability of 12 subfields, as well as their genetic correlation with each other, with other structural brain features and with AD and schizophrenia. All outcome measures were corrected for age, sex and intracranial volume. We found 15 unique genome-wide significant loci across six subfields, of which eight had not been previously linked to the hippocampus. Top SNPs were mapped to genes associated with neuronal differentiation, locomotor behaviour, schizophrenia and AD. The volumes of all the subfields were estimated to be heritable (h2 from 0.14 to 0.27, all p < 1 × 10-16) and clustered together based on their genetic correlations compared with other structural brain features. There was also evidence of genetic overlap of subicular subfield volumes with schizophrenia. We conclude that hippocampal subfields have partly distinct genetic determinants associated with specific biological processes and traits. Taking into account this specificity may increase our understanding of hippocampal neurobiology and associated pathologies.