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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.

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,PhenotypicMeasurement ofEffectiveDilution, 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.

To effectively reduce vision loss due to age-related macular generation (AMD) on a global scale, knowledge of its genetic architecture in diverse populations is necessary. A critical element, AMD risk profiles in African and Hispanic/Latino ancestries, remains largely unknown due to lower lifetime prevalence. We combined genetic and clinical data in the Million Veteran Program with five other cohorts to conduct the first multi-ancestry genome-wide association study of AMD and discovered 63 loci (30 novel). We observe marked cross-ancestry heterogeneity at major risk loci, especially in African-ancestry populations which demonstrate a primary signal in a Major Histocompatibility Complex Class II haplotype and reduced risk at the established CFH and ARMS2/HTRA1 loci. Broadening efforts to include ancestrally-distinct populations helped uncover genes and pathways which boost risk in an ancestry-dependent manner, and are potential targets for corrective therapies.One Sentence Summaryrobing electronic health record data with genomics unearths novel paths to age-related macular degeneration.

Treatment resistant depression (TRD), often defined by absence of symptomatic remission following at least two adequate treatment trials, occurs in roughly a third of all individuals with major depressive disorder (MDD). Prior work has suggested a significant common variant genetic component of liability to TRD, with heritability estimates of 8% when comparing to non-treatment resistant MDD. Despite this evidence of heritability, no replicated genetic loci have been identified and the genetic architecture of TRD remains unclear. A key barrier to this work has been the paucity of adequately powered cohorts for investigation, largely because of the challenge in prospectively investigating this phenotype. Using electroconvulsive therapy (ECT) as a surrogate for TRD, we applied standard machine learning methods to electronic health record (EHR) data to derive predicted probabilities of receiving ECT. We applied these probabilities as a quantitative trait in a genome-wide association study (GWAS) over 154,433 genotyped patients across four large biobanks. With this approach, we demonstrate heritability ranging from 2% to 4.2% and significant genetic overlap with cognition, attention deficit hyperactivity disorder, schizophrenia, alcohol and smoking traits and body mass index. We identify two genome-wide significant loci, both previously implicated in metabolic traits, suggesting shared biology and potential pharmacological implications. This work provides support for the utility of estimation of disease probability for genomic investigation and provides insights into the genetic architecture and biology of TRD.

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 datasets. To address this limitation, we apply a supervised machine learning approach 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 on 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, which are reproducible across three independent cohorts. We identify genetic association 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.

Undergraduate students participating in the UCLA Undergraduate Research Consortium for Functional Genomics (URCFG) have conducted a two-phased screen using RNA interference (RNAi) in combination with fluorescent reporter proteins to identify genes important for hematopoiesis in Drosophila. This screen disrupted the function of approximately 3500 genes and identified 137 candidate genes for which loss of function leads to observable changes in the hematopoietic development. Targeting RNAi to maturing, progenitor, and regulatory cell types identified key subsets that either limit or promote blood cell maturation. Bioinformatic analysis reveals gene enrichment in several previously uncharacterized areas, including RNA processing and export and vesicular trafficking. Lastly, the participation of students in this course-based undergraduate research experience (CURE) correlated with increased learning gains across several areas, as well as increased STEM retention, indicating that authentic, student-driven research in the form of a CURE represents an impactful and enriching pedagogical approach.

Significance Here, we show that expression of key circadian clock genes in the skin is altered by acute inflammation in mice treated topically with the immune activator imiquimod and by chronic inflammation in human psoriatic lesions. We show time-of-day–dependent activation of the interferon pathway, a key pathway involved in the host defense response. Mice lacking circadian rhythms have greater epidermal hyperplasia and more robust activation of the interferon pathway. Furthermore, we show that daytime-restricted feeding shifts the phase of interferon-sensitive gene expression in mouse skin. These findings demonstrate a role for the circadian clock in a major defense pathway in the skin’s response to microbes and cancer and suggest that timing of feeding can affect this response in the skin., The circadian clock coordinates a variety of immune responses with signals from the external environment to promote survival. We investigated the potential reciprocal relationship between the circadian clock and skin inflammation. We treated mice topically with the Toll-like receptor 7 (TLR7) agonist imiquimod (IMQ) to activate IFN-sensitive gene (ISG) pathways and induce psoriasiform inflammation. IMQ transiently altered core clock gene expression, an effect mirrored in human patient psoriatic lesions. In mouse skin 1 d after IMQ treatment, ISGs, including the key ISG transcription factor IFN regulatory factor 7 (Irf7), were more highly induced after treatment during the day than the night. Nuclear localization of phosphorylated-IRF7 was most prominently time-of-day dependent in epidermal leukocytes, suggesting that these cell types play an important role in the diurnal ISG response to IMQ. Mice lacking Bmal1 systemically had exacerbated and arrhythmic ISG/Irf7 expression after IMQ. Furthermore, daytime-restricted feeding, which affects the phase of the skin circadian clock, reverses the diurnal rhythm of IMQ-induced ISG expression in the skin. These results suggest a role for the circadian clock, driven by BMAL1, as a negative regulator of the ISG response, and highlight the finding that feeding time can modulate the skin immune response. Since the IFN response is essential for the antiviral and antitumor effects of TLR activation, these findings are consistent with the time-of-day–dependent variability in the ability to fight microbial pathogens and tumor initiation and offer support for the use of chronotherapy for their treatment.

Amyotrophic lateral sclerosis (ALS) is a progressively fatal neurodegenerative disease affecting motor neurons in the brain and spinal cord. We used 380 post-mortem tissue RNA-seq transcriptomes from 154 ALS cases and 49 control individuals from cervical, thoracic, and lumbar spinal cord segments to investigate the gene expression response to ALS. We observed an increase in microglia and astrocyte expression, accompanied by a decrease in oligodendrocytes. By creating a gene co-expression network in the ALS samples, we identify several activated microglia modules that negatively correlate with retrospective disease duration.We map molecular quantitative trait loci and find several potential ALS risk loci that may act through gene expression or splicing in the spinal cord and assign putative cell-types for FNBP1, ACSL5, SH3RF1 and NFASC. Finally, we outline how repeat expansions that alter splicing of C9orf72 are tagged by common variants, and use this to suggest ATXN3 as a putative risk gene.

The migrating keratinocyte wound front is required for skin wound closure. Despite significant advances in wound healing research, we do not fully understand the molecular mechanisms that orchestrate collective keratinocyte migration. Here, we show that, in the wound front, the epidermal transcription factor Grainyhead like-3 (GRHL3) mediates decreased expression of the adherens junction protein E-cadherin; this results in relaxed adhesions between suprabasal keratinocytes, thus promoting collective cell migration and wound closure. Wound fronts from mice lacking GRHL3 in epithelial cells (Grhl3-cKO) have lower expression of Fascin-1 (FSCN1), a known negative regulator of E-cadherin. Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) on wounded keratinocytes shows decreased wound-induced chromatin accessibility near the Fscn1 gene in Grhl3-cKO mice, a region enriched for GRHL3 motifs. These data reveal a wound-induced GRHL3/FSCN1/E-cadherin pathway that regulates keratinocyte-keratinocyte adhesion during wound-front migration; this pathway is activated in acute human wounds and is altered in diabetic wounds in mice, suggesting translational relevance.

Amyotrophic lateral sclerosis (ALS) is a progressively fatal neurodegenerative disease affecting motor neurons in the brain and spinal cord. In this study, we investigated gene expression changes in ALS via RNA sequencing in 380 postmortem samples from cervical, thoracic and lumbar spinal cord segments from 154 individuals with ALS and 49 control individuals. We observed an increase in microglia and astrocyte gene expression, accompanied by a decrease in oligodendrocyte gene expression. By creating a gene co-expression network in the ALS samples, we identified several activated microglia modules that negatively correlate with retrospective disease duration. We mapped molecular quantitative trait loci and found several potential ALS risk loci that may act through gene expression or splicing in the spinal cord and assign putative cell types for FNBP1, ACSL5, SH3RF1 and NFASC. Finally, we outline how common genetic variants associated with splicing of C9orf72 act as proxies for the well-known repeat expansion, and we use the same mechanism to suggest ATXN3 as a putative risk gene.

Previous genome-wide association studies (GWAS) of hematological traits have identified over 10,000 distinct trait-specific risk loci, but the underlying causal mechanisms at these loci remain incompletely characterized. We performed a transcriptome-wide association study (TWAS) of 29 hematological traits in 399,835 UK Biobank (UKB) participants of European ancestry using gene expression prediction models trained from whole blood RNA-seq data in 922 individuals. We discovered 557 TWAS signals associated with hematological traits distinct from previously discovered GWAS variants, including 10 completely novel gene-trait pairs corresponding to 9 unique genes. Among the 557 associations, 301 were available for replication in a cohort of 141,286 participants of European ancestry from the Million Veteran Program (MVP). Of these 301 associations, 199 replicated at a nominal threshold (α = 0.05) and 108 replicated at a strict Bonferroni adjusted threshold (α = 0.05/301). Using our TWAS results, we systematically assigned 4,261 out of 16,900 previously identified hematological trait GWAS variants to putative target genes. Compared to coloc, our TWAS results show reduced specificity and increased sensitivity to assign variants to target genes.

BackgroundRecent efforts have identified genetic loci that are associated with coronavirus disease 2019 (COVID-19) infection rates and disease outcome severity. Translating these genetic findings into druggable genes and readily available compounds that reduce COVID-19 host susceptibility is a critical next step.MethodsWe integrate COVID-19 genetic susceptibility variants, multi-tissue genetically regulated gene expression (GReX) and perturbargen signatures to identify candidate genes and compounds that reverse the predicted gene expression dysregulation associated with COVID-19 susceptibility. The top candidate gene is validated by testing both its GReX and observed blood transcriptome association with COVID-19 severity, as well as by in vitro perturbation to quantify effects on viral load and molecular pathway dysregulation. We validate the in silico drug repositioning analysis by examining whether the top candidate compounds decrease COVID-19 incidence based on epidemiological evidence.ResultsWe identify IL10RB as the top key regulator of COVID-19 host susceptibility. Predicted GReX up-regulation of IL10RB and higher IL10RB expression in COVID-19 patient blood is associated with worse COVID-19 outcomes. In vitro IL10RB overexpression is associated with increased viral load and activation of immune-related molecular pathways. Azathioprine and retinol are prioritized as candidate compounds to reduce the likelihood of testing positive for COVID-19.ConclusionsWe establish an integrative data-driven approach for gene target prioritization. We identify and validate IL10RB as a suitable molecular target for modulation of COVID-19 host susceptibility. Finally, we provide evidence for a few readily available medications that would warrant further investigation as drug repositioning candidates.

Although depression is a common disorder, its underlying biological basis remains poorly understood. The results of clinical lab tests available for research in electronic health records can be used to identify biomarkers that may reveal biological processes involved in the development of depression or may be markers of physiological changes due to depression. Here, we leveraged clinical laboratory tests and integrated biobank data to evaluate the relationship between genetic risk for depression and 315 routinely collected quantitative lab measures. Analyses across four health care systems (N = 382,452) robustly implicate increased white blood cell count as both a risk factor and consequence of depression diagnosis and revealed neutrophils, lymphocytes, and monocytes as the primary cell types responsible for this association. Our results highlight the importance of the immune system in the etiology of depression and motivate future development of clinical biomarkers and targeted treatment options for depression and its systemic effects.

Key Points Question Are genes that increase predisposition to depression associated with increased inflammatory biomarkers, specifically white blood cell count? Findings In this genetic association study of 382 485 participants, an association was noted between depression polygenic scores and white blood cell count across 4 independent biobanks. Mediation analyses suggest a bidirectional association between white blood cell count and depression diagnosis and implicate neutrophils as the main driver of the association. Meaning These findings suggest that genes associated with depression (rather than only the clinical presentation of depressive symptoms) may be implicated in the proinflammatory state observed in clinical depression; this outcome may motivate future development of targeted biomarker panels and treatments., This genetic association study uses data from Vanderbilt University Medical Center and the PsycheMERGE network to investigate the association between inflammatory biomarkers, such as white blood cell count, and the genes that predispose an individual to depression., Importance Although depression is a common psychiatric disorder, its underlying biological basis remains poorly understood. Pairing depression polygenic scores with the results of clinical laboratory tests can reveal biological processes involved in depression etiology and in the physiological changes resulting from depression. Objective To characterize the association between depression polygenic scores and an inflammatory biomarker, ie, white blood cell count. Design, Setting, and Participants This genetic association study was conducted from May 19, 2019, to June 5, 2021, using electronic health record data from 382 452 patients across 4 health care systems. Analyses were conducted separately in each health care system and meta-analyzed across all systems. Primary analyses were conducted in Vanderbilt University Medical Center’s biobank. Replication analyses were conducted across 3 other PsycheMERGE sites: Icahn School of Medicine at Mount Sinai, Mass General Brigham, and the Million Veteran Program. All patients with available genetic data and recorded white blood cell count measurements were included in the analyses. Primary analyses were conducted in individuals of European descent and then repeated in a population of individuals of African descent. Exposures Depression polygenic scores. Main Outcomes and Measures White blood cell count. Results Across the 4 PsycheMERGE sites, there were 382 452 total participants of European ancestry (18.7% female; median age, 57.9 years) and 12 383 participants of African ancestry (61.1% female; median age, 39.0 [range, birth-90.0 years]). A laboratory-wide association scan revealed a robust association between depression polygenic scores and white blood cell count (β, 0.03; SE, 0.004; P = 1.07 × 10−17), which was replicated in a meta-analysis across the 4 health care systems (β, 0.03; SE, 0.002; P = 1.03 × 10−136). Mediation analyses suggested a bidirectional association, with white blood cell count accounting for 2.5% of the association of depression polygenic score with depression diagnosis (95% CI, 2.2%-20.8%; P = 2.84 × 10−70) and depression diagnosis accounting for 9.8% of the association of depression polygenic score with white blood cell count (95% CI, 8.4%-11.1%; P = 1.78 × 10−44). Mendelian randomization provided additional support for an association between increased white blood count and depression risk, but depression modeled as the exposure showed no evidence of an influence on white blood cell counts. Conclusions and Relevance This genetic association study found that increased depression polygenic scores were associated with increased white blood cell count, and suggests that this association may be bidirectional. These findings highlight the potential importance of the immune system in the etiology of depression and may motivate future development of clinical biomarkers and targeted treatment options for depression.

Neocortical circuits consist of stereotypical motifs that must self-assemble during development. Recent evidence suggests that the subtype identity of both excitatory projection neurons (PNs) and inhibitory interneurons (INs) is important for this process. We knocked out the transcription factor Satb2 in PNs to induce those of the intratelencephalic (IT) type to adopt a pyramidal tract (PT)-type identity. Loss of IT-type PNs selectively disrupted the lamination and circuit integration of INs derived from the caudal ganglionic eminence (CGE). Strikingly, reprogrammed PNs demonstrated reduced synaptic targeting of CGE-derived INs relative to controls. In control mice, IT-type PNs targeted neighboring CGE INs, while PT-type PNs did not in deep layers, confirming this lineage-dependent motif. Finally, single-cell RNA sequencing revealed that major CGE IN subtypes were conserved after loss of IT PNs, but with differential transcription of synaptic proteins and signaling molecules. Thus, IT-type PNs influence CGE-derived INs in a non-cell-autonomous manner during cortical development.