The neuronal chromatin landscape in adult schizophrenia brains is linked to early fetal development.

Non-coding variants increase risk of neuropsychiatric disease. However, our understanding of the cell-type specific role of the non-coding genome in disease is incomplete. We performed population scale (N=1,393) chromatin accessibility profiling of neurons and non-neurons from two neocortical brain regions: the anterior cingulate cortex and dorsolateral prefrontal cortex. Across both regions, we observed notable differences in neuronal chromatin accessibility between schizophrenia cases and controls. A per-sample disease pseudotime was positively associated with genetic liability for schizophrenia. Organizing chromatin into cis- and trans-regulatory domains, identified a prominent neuronal trans-regulatory domain (TRD1) active in immature glutamatergic neurons during fetal development. Polygenic risk score analysis using genetic variants within chromatin accessibility of TRD1 successfully predicted susceptibility to schizophrenia in the Million Veteran Program cohort. Overall, we present the most extensive resource to date of chromatin accessibility in the human cortex, yielding insights into the cell-type specific etiology of schizophrenia.

Development and cadherin-mediated control of prefrontal corticostriatal projections in mice.

Action-outcome associations depend on prefrontal cortex (PFC) projections to the dorsal striatum. To assess how these projections form, we measured PFC axon patterning, synapse formation, and functional maturation in the postnatally developing mouse striatum. Using Hotspot analysis, we show that PFC axons form an adult-like pattern of clustered terminations in the first postnatal week that remains largely stable thereafter. PFC-striatal synaptic strength is adult-like by P21, while excitatory synapse density increases until adulthood. We then tested how the targeted deletion of a candidate adhesion/guidance protein, Cadherin-8 (Cdh8), from corticostriatal neurons regulates pathway development. Mutant mice showed diminished PFC axon targeting and reduced spontaneous glutamatergic synaptic activity in the dorsal striatum. They also exhibited impaired behavioral performance in action-outcome learning. The data show that PFC-striatal axons form striatal territories through an early, directed growth model and they highlight essential contributions of Cdh8 to the anatomical and functional features critical for the formation of action-outcome associations.

The neuronal chromatin landscape in adult schizophrenia brains is linked to early fetal development.

Non-coding variants increase risk of neuropsychiatric disease. However, our understanding of the cell-type specific role of the non-coding genome in disease is incomplete. We performed population scale (N=1,393) chromatin accessibility profiling of neurons and non-neurons from two neocortical brain regions: the anterior cingulate cortex and dorsolateral prefrontal cortex. Across both regions, we observed notable differences in neuronal chromatin accessibility between schizophrenia cases and controls. A per-sample disease pseudotime was positively associated with genetic liability for schizophrenia. Organizing chromatin into cis- and trans-regulatory domains, identified a prominent neuronal trans-regulatory domain (TRD1) active in immature glutamatergic neurons during fetal development. Polygenic risk score analysis using genetic variants within chromatin accessibility of TRD1 successfully predicted susceptibility to schizophrenia in the Million Veteran Program cohort. Overall, we present the most extensive resource to date of chromatin accessibility in the human cortex, yielding insights into the cell-type specific etiology of schizophrenia.

Genome-wide analysis of a model-derived binge eating disorder phenotype identifies risk loci and implicates iron metabolism.

Binge eating disorder (BED) is the most common eating disorder, yet its genetic architecture remains largely unknown. Studying BED is challenging because it is often comorbid with obesity, a common and highly polygenic trait, and it is underdiagnosed in biobank data sets. To address this limitation, we apply a supervised machine-learning approach (using 822 cases of individuals diagnosed with BED) to estimate the probability of each individual having BED based on electronic medical records from the Million Veteran Program. We perform a genome-wide association study of individuals of African (n = 77,574) and European (n = 285,138) ancestry while controlling for body mass index to identify three independent loci near the HFE, MCHR2 and LRP11 genes and suggest APOE as a risk gene for BED. We identify shared heritability between BED and several neuropsychiatric traits, and implicate iron metabolism in the pathophysiology of BED. Overall, our findings provide insights into the genetics underlying BED and suggest directions for future translational research.

Depression pathophysiology, risk prediction of recurrence and comorbid psychiatric disorders using genome-wide analyses.

Depression is a common psychiatric disorder and a leading cause of disability worldwide. Here we conducted a genome-wide association study meta-analysis of six datasets, including >1.3 million individuals (371,184 with depression) and identified 243 risk loci. Overall, 64 loci were new, including genes encoding glutamate and GABA receptors, which are targets for antidepressant drugs. Intersection with functional genomics data prioritized likely causal genes and revealed new enrichment of prenatal GABAergic neurons, astrocytes and oligodendrocyte lineages. We found depression to be highly polygenic, with ~11,700 variants explaining 90% of the single-nucleotide polymorphism heritability, estimating that >95% of risk variants for other psychiatric disorders (anxiety, schizophrenia, bipolar disorder and attention deficit hyperactivity disorder) were influencing depression risk when both concordant and discordant variants were considered, and nearly all depression risk variants influenced educational attainment. Additionally, depression genetic risk was associated with impaired complex cognition domains. We dissected the genetic and clinical heterogeneity, revealing distinct polygenic architectures across subgroups of depression and demonstrating significantly increased absolute risks for recurrence and psychiatric comorbidity among cases of depression with the highest polygenic burden, with considerable sex differences. The risks were up to 5- and 32-fold higher than cases with the lowest polygenic burden and the background population, respectively. These results deepen the understanding of the biology underlying depression, its disease progression and inform precision medicine approaches to treatment.

Efficient differential expression analysis of large-scale single cell transcriptomics data using dreamlet.

Advances in single-cell and -nucleus transcriptomics have enabled generation of increasingly large-scale datasets from hundreds of subjects and millions of cells. These studies promise to give unprecedented insight into the cell type specific biology of human disease. Yet performing differential expression analyses across subjects remains difficult due to challenges in statistical modeling of these complex studies and scaling analyses to large datasets. Our open-source R package dreamlet (DiseaseNeurogenomics.github.io/dreamlet) uses a pseudobulk approach based on precision-weighted linear mixed models to identify genes differentially expressed with traits across subjects for each cell cluster. Designed for data from large cohorts, dreamlet is substantially faster and uses less memory than existing workflows, while supporting complex statistical models and controlling the false positive rate. We demonstrate computational and statistical performance on published datasets, and a novel dataset of 1.4M single nuclei from postmortem brains of 150 Alzheimer's disease cases and 149 controls.

Development of prefrontal corticostriatal connectivity in mice.

Rational decision making is grounded in learning to associate actions with outcomes, a process that depends on projections from prefrontal cortex to dorsomedial striatum. Symptoms associated with a variety of human pathological conditions ranging from schizophrenia and autism to Huntington's and Parkinson's disease point toward functional deficits in this projection, but its development is not well understood, making it difficult to investigate how perturbations in development of this circuitry could contribute to pathophysiology. We applied a novel strategy based on Hotspot Analysis to assess the developmental progression of anatomical positioning of prefrontal cortex to striatal projections. Corticostriatal axonal territories established at P7 expand in concert with striatal growth but remain largely unchanged in positioning through adulthood, indicating they are generated by directed, targeted growth and not modified extensively by postnatal experience. Consistent with these findings, corticostriatal synaptogenesis increased steadily from P7 to P56, with no evidence for widescale pruning. As corticostriatal synapse density increased over late postnatal ages, the strength of evoked PFC input onto dorsomedial striatal projection neurons also increased, but spontaneous glutamatergic synaptic activity was stable. Based on its pattern of expression, we asked whether the adhesion protein, Cdh8, influenced this progression. In mice lacking Cdh8 in PFC corticostriatal projection neurons, axon terminal fields in dorsal striatum shifted ventrally. Corticostriatal synaptogenesis was unimpeded, but spontaneous EPSC frequency declined and mice failed to learn to associate an action with an outcome. Collectively these findings show that corticostriatal axons grow to their target zone and are restrained from an early age, do not undergo postnatal synapse pruning as the most dominant models predict, and that a relatively modest shift in terminal arbor positioning and synapse function has an outsized, negative impact on corticostriatal-dependent behavior.

Efficient differential expression analysis of large-scale single cell transcriptomics data using dreamlet.

Advances in single-cell and -nucleus transcriptomics have enabled generation of increasingly large-scale datasets from hundreds of subjects and millions of cells. These studies promise to give unprecedented insight into the cell type specific biology of human disease. Yet performing differential expression analyses across subjects remains difficult due to challenges in statistical modeling of these complex studies and scaling analyses to large datasets. Our open-source R package dreamlet (DiseaseNeurogenomics.github.io/dreamlet) uses a pseudobulk approach based on precision-weighted linear mixed models to identify genes differentially expressed with traits across subjects for each cell cluster. Designed for data from large cohorts, dreamlet is substantially faster and uses less memory than existing workflows, while supporting complex statistical models and controlling the false positive rate. We demonstrate computational and statistical performance on published datasets, and a novel dataset of 1.4M single nuclei from postmortem brains of 150 Alzheimer's disease cases and 149 controls.

Detecting and Adjusting for Hidden Biases due to Phenotype Misclassification in Genome-Wide Association Studies.

With the advent of healthcare-based genotyped biobanks, genome-wide association studies (GWAS) leverage larger sample sizes, incorporate patients with diverse ancestries and introduce noisier phenotypic definitions. Yet the extent and impact of phenotypic misclassification on large-scale datasets is not currently well understood due to a lack of statistical methods to estimate relevant parameters from empirical data. Here, we develop a statistical method and scalable software, PheMED, Phenotypic Measurement of Effective Dilution, to quantify phenotypic misclassification across GWAS using only summary statistics. We illustrate how the parameters estimated by PheMED relate to the negative and positive predictive value of the labeled phenotype, compared to ground truth, and how misclassification of the phenotype yields diluted effect-sizes of variant-phenotype associations. Furthermore, we apply our methodology to detect multiple instances of statistically significant dilution in real-world data. We demonstrate how effective dilution biases downstream GWAS replication and heritability analyses despite utilizing current best practices, and provide a dilution-aware meta-analysis approach that outperforms existing methods. Consequently, we anticipate that PheMED will be a valuable tool for researchers to address phenotypic data quality issues both within and across cohorts.

Genome-wide analyses of ADHD identify 27 risk loci, refine the genetic architecture and implicate several cognitive domains.

Attention-deficit hyperactivity disorder (ADHD) is a prevalent neurodevelopmental disorder with a major genetic component. Here, we present a genome-wide association study meta-analysis of ADHD comprising 38,691 individuals with ADHD and 186,843 controls. We identified 27 genome-wide significant loci, highlighting 76 potential risk genes enriched among genes expressed particularly in early brain development. Overall, ADHD genetic risk was associated with several brain-specific neuronal subtypes and midbrain dopaminergic neurons. In exome-sequencing data from 17,896 individuals, we identified an increased load of rare protein-truncating variants in ADHD for a set of risk genes enriched with probable causal common variants, potentially implicating SORCS3 in ADHD by both common and rare variants. Bivariate Gaussian mixture modeling estimated that 84-98% of ADHD-influencing variants are shared with other psychiatric disorders. In addition, common-variant ADHD risk was associated with impaired complex cognition such as verbal reasoning and a range of executive functions, including attention.

Long-Term Depression Learning in Spinal Cord Networks.

Investigating learning in networks of spinal cord neurons can provide insight into the dynamics of connectivity in human spinal cords. It may also hold implications for developing neural prosthetics and neurocomputers. Culturing neural networks on microelectrode arrays (MEAs) allows for the repeated observation and stimulation of electrophysiological activity in vitro. Here we used MEAs to demonstrate learning in networks of spinal cord neurons. This was done by exposing E17 mouse spinal cord cultures to high frequency artificial spike trains, or tetanization. Unexpectedly, when comparing the networks' responses to low-frequency probing stimulations before and after tetanization, the cultures were found to demonstrate long-term depression (LTD). LTD was most significantly observed between 500-1000 ms after low-frequency probing. These results indicate that periodic high-frequency excitation of spinal cord networks can result in decreased synaptic efficacy.