Landscape of Conditional eQTL in Dorsolateral Prefrontal Cortex and Co-localization with Schizophrenia GWAS

Unravelling the genetic basis of Schizophrenia

Neuronal development is a highly regulated mechanism that is central to organismal function in animals. In humans, disruptions to this process can lead to a range of neurodevelopmental phenotypes, including Schizophrenia (SCZ). SCZ has a significant genetic component, whereby an individual with an SCZ affected family member is eight times more likely to develop the disease than someone with no family history of SCZ. By examining a combination of genomic, transcriptomic and epigenomic datasets, large-scale 'omics' studies aim to delineate the relationship between genetic variation and abnormal cellular activity in the SCZ brain. Herein, we provide a brief overview of some of the key omics methods currently being used in SCZ research, including RNA-seq, the assay for transposase-accessible chromatin with high-throughput sequencing (ATAC-seq) and high-throughput chromosome conformation capture (3C) approaches (e.g., Hi-C), as well as single-cell/nuclei iterations of these methods. We also discuss how these techniques are being employed to further our understanding of the genetic basis of SCZ, and to identify associated molecular pathways, biomarkers, and candidate drug targets.

Exploring COVID-19 causal genes through disease-specific Cis-eQTLs

Genome-wide association study (GWAS) analysis has exposed that genetic factors play important roles in COVID-19. Whereas a deeper understanding of the underlying mechanism of COVID-19 was hindered by the lack of expression of quantitative trait loci (eQTL) data specific for disease. To this end, we identified COVID-19-specific cis-eQTLs by integrating nucleotide sequence variations and RNA-Seq data from COVID-19 samples. These identified eQTLs have different regulatory effect on genes between patients and controls, indicating that SARS-CoV-2 infection may cause alterations in the human body's internal environment. Individuals with the TT genotype in the rs1128320 region seemed more susceptible to SARS-CoV-2 infection and developed into severe COVID-19 due to the abnormal expression of IFITM1. We subsequently discovered potential causal genes, of the result, a total of 48 genes from six tissues were identified. siRNA-mediated depletion assays in SARS-CoV-2 infection proved that 14 causal genes were directly associated with SARS-CoV-2 infection. These results enriched existing research on COVID-19 causal genes and provided a new sight in the mechanism exploration for COVID-19.

Combined GWAS and eGWAS reveals the genetic basis underlying drought tolerance in emmer wheat (Triticum turgidum L.)

Drought is one of the major environmental constraints for wheat production world-wide. As the progenitor and genetic reservoir of common wheat, emmer wheat is considered as an invaluable gene pool for breeding drought-tolerant wheat. Combining GWAS and eGWAS analysis of 107 accessions, we identified 86 QTLs, 105 462 eQTLs as well as 68 eQTL hotspots associating with drought tolerance (DT) in emmer wheat. A complex regulatory network composed of 185 upstream regulator and 2432 downstream drought-responsive candidates was developed, of which TtOTS1 was found to play a negative effect in determining DT through affecting root development. This study sheds light on revealing the genetic basis underlying DT, which will provide the indispensable genes and germplasm resources for elite drought tolerance wheat improvement and breeding.

Genetics of sleep medication purchases suggests causality from sleep problems to psychiatric traits

STUDY OBJECTIVES

Over 10% of the population in Europe and in the United States use sleep medication to manage sleep problems. Our objective was to elucidate genetic risk factors and clinical correlates that contribute to sleep medication purchase and estimate the comorbid impact of sleep problems.

METHODS

We performed epidemiological analysis for psychiatric diagnoses, and genetic association studies of sleep medication purchase in 797 714 individuals from FinnGen Release 7 (N = 311 892) and from the UK Biobank (N = 485 822). Post-association analyses included genetic correlation, co-localization, Mendelian randomization (MR), and polygenic risk estimation.

RESULTS

In a GWAS we identified 27 genetic loci significantly associated with sleep medication, located in genes associated with sleep; AUTS2, CACNA1C, MEIS1, KIRREL3, PAX8, GABRA2, psychiatric traits; CACNA1C, HIST1H2BD, NUDT12. TOPAZ1 and TSNARE1. Co-localization and expression analysis emphasized effects on the KPNA2, GABRA2, and CACNA1C expression in the brain. Sleep medications use was epidemiologically related to psychiatric traits in FinnGen (OR [95% (CI)] = 3.86 [3.78 to 3.94], p < 2 × 10-16), and the association was accentuated by genetic correlation and MR; depression (rg = 0.55 (0.027), p = 2.86 × 10-89, p MR = 4.5 × 10-5), schizophrenia (rg = 0.25 (0.026), p = 2.52 × 10-21, p MR = 2 × 10-4), and anxiety (rg = 0.44 (0.047), p = 2.88 × 10-27, p MR = 8.6 × 10-12).

CONCLUSIONS

These results demonstrate the genetics behind sleep problems and the association between sleep problems and psychiatric traits. Our results highlight the scientific basis for sleep management in treating the impact of psychiatric diseases.

Deciphering the link: ferroptosis and its role in glioma

Glioma, as the most frequently occurring primary malignancy in the central nervous system, significantly impacts patients' quality of life and cognitive abilities. Ferroptosis, a newly discovered form of cell death, is characterized by significant iron accumulation and lipid peroxidation. This process is fundamentally dependent on iron. Various factors inducing ferroptosis can either directly or indirectly influence glutathione peroxidase, leading to reduced antioxidant capabilities and an increase in lipid reactive oxygen species (ROS) within cells, culminating in oxidative cell death. Recent research indicates a strong connection between ferroptosis and a range of pathophysiological conditions, including tumors, neurological disorders, ischemia-reperfusion injuries, kidney damage, and hematological diseases. The regulation of ferroptosis to intervene in the progression of these diseases has emerged as a major area of interest in etiological research and therapy. However, the exact functional alterations and molecular mechanisms underlying ferroptosis remain to be extensively studied. The review firstly explores the intricate relationship between ferroptosis and glioma, highlighting how ferroptosis contributes to glioma pathogenesis and how glioma cells may resist this form of cell death. Then, we discuss recent studies that have identified potential ferroptosis inducers and inhibitors, which could serve as novel therapeutic strategies for glioma. We also examine the current challenges in targeting ferroptosis in glioma treatment, including the complexity of its regulation and the need for precise delivery methods. This review aims to provide a comprehensive overview of the current state of research on ferroptosis in glioma, offering insights into future therapeutic strategies and the broader implications of this novel cell death pathway in cancer biology.

Polygenic risk for schizophrenia converges on alternative polyadenylation as molecular mechanism underlying synaptic impairment

Schizophrenia (SCZ) is a genetically heterogenous psychiatric disorder of highly polygenic nature. Correlative evidence from genetic studies indicate that the aggregated effects of distinct genetic risk factor combinations found in each patient converge onto common molecular mechanisms. To prove this on a functional level, we employed a reductionistic cellular model system for polygenic risk by differentiating induced pluripotent stem cells (iPSCs) from 104 individuals with high polygenic risk load and controls into cortical glutamatergic neurons (iNs). Multi-omics profiling identified widespread differences in alternative polyadenylation (APA) in the 3' untranslated region of many synaptic transcripts between iNs from SCZ patients and healthy donors. On the cellular level, 3'APA was associated with a reduction in synaptic density of iNs. Importantly, differential APA was largely conserved between postmortem human prefrontal cortex from SCZ patients and healthy donors, and strongly enriched for transcripts related to synapse biology. 3'APA was highly correlated with SCZ polygenic risk and affected genes were significantly enriched for SCZ associated common genetic variation. Integrative functional genomic analysis identified the RNA binding protein and SCZ GWAS risk gene PTBP2 as a critical trans-acting factor mediating 3'APA of synaptic genes in SCZ subjects. Functional characterization of PTBP2 in iNs confirmed its key role in 3'APA of synaptic transcripts and regulation of synapse density. Jointly, our findings show that the aggregated effects of polygenic risk converge on 3'APA as one common molecular mechanism that underlies synaptic impairments in SCZ.

Common genetic variation impacts stress response in the brain

To explain why individuals exposed to identical stressors experience divergent clinical outcomes, we determine how molecular encoding of stress modifies genetic risk for brain disorders. Analysis of post-mortem brain (n=304) revealed 8557 stress-interactive expression quantitative trait loci (eQTLs) that dysregulate expression of 915 eGenes in response to stress, and lie in stress-related transcription factor binding sites. Response to stress is robust across experimental paradigms: up to 50% of stress-interactive eGenes validate in glucocorticoid treated hiPSC-derived neurons (n=39 donors). Stress-interactive eGenes show brain region- and cell type-specificity, and, in post-mortem brain, implicate glial and endothelial mechanisms. Stress dysregulates long-term expression of disorder risk genes in a genotype-dependent manner; stress-interactive transcriptomic imputation uncovered 139 novel genes conferring brain disorder risk only in the context of traumatic stress. Molecular stress-encoding explains individualized responses to traumatic stress; incorporating trauma into genomic studies of brain disorders is likely to improve diagnosis, prognosis, and drug discovery.

Massively parallel functional dissection of schizophrenia-associated noncoding genetic variants

Schizophrenia (SCZ) is a highly heritable mental disorder with thousands of associated genetic variants located mostly in the noncoding space of the genome. Translating these associations into insights regarding the underlying pathomechanisms has been challenging because the causal variants, their mechanisms of action, and their target genes remain largely unknown. We implemented a massively parallel variant annotation pipeline (MVAP) to perform SCZ variant-to-function mapping at scale in disease-relevant neural cell types. This approach identified 620 functional variants (1.7%) that operate in a highly developmental context and neuronal-activity-dependent manner. Multimodal integration of epigenomic and CRISPRi screening data enabled us to link these functional variants to target genes, biological processes, and ultimately alterations of neuronal physiology. These results provide a multistage prioritization strategy to map functional single-nucleotide polymorphism (SNP)-to-gene-to-endophenotype relations and offer biological insights into the context-dependent molecular processes modulated by SCZ-associated genetic variation.

Adipose tissue eQTL meta-analysis reveals the contribution of allelic heterogeneity to gene expression regulation and cardiometabolic traits

Complete characterization of the genetic effects on gene expression is needed to elucidate tissue biology and the etiology of complex traits. Here, we analyzed 2,344 subcutaneous adipose tissue samples and identified 34K conditionally distinct expression quantitative trait locus (eQTL) signals in 18K genes. Over half of eQTL genes exhibited at least two eQTL signals. Compared to primary signals, non-primary signals had lower effect sizes, lower minor allele frequencies, and less promoter enrichment; they corresponded to genes with higher heritability and higher tolerance for loss of function. Colocalization of eQTL with conditionally distinct genome-wide association study signals for 28 cardiometabolic traits identified 3,605 eQTL signals for 1,861 genes. Inclusion of non-primary eQTL signals increased colocalized signals by 46%. Among 30 genes with ≥2 pairs of colocalized signals, 21 showed a mediating gene dosage effect on the trait. Thus, expanded eQTL identification reveals more mechanisms underlying complex traits and improves understanding of the complexity of gene expression regulation.

Endoplasmic reticulum stress related genome-wide Mendelian randomization identifies therapeutic genes for ulcerative colitis and Crohn's disease

Background: Endoplasmic reticulum stress (ERS) is an important pathophysiological mechanism in ulcerative colitis (UC) and Crohn's disease (CD). ERS-related genes may be influenced by genetic factors and intestinal inflammation. However, the role of ERS as a trigger or potential etiological factor for UC and CD is unclear, as the expression of ERS-related genes in UC and CD may be the cause or subsequent changes in intestinal inflammation. Here, we used a three-step summary data-based Mendelian randomization (SMR) approach integrating multi-omics data to identify putative causal effects of ERS-related genes in UC and CD. Methods: Genome-wide association study (GWAS) summary data for UC (6,968 cases and 20,464 controls) and CD (5,956 cases and 14,927 controls) were extracted as outcome, and DNA methylation quantitative trait loci (mQTL, 1,980 participants) data and expression QTL data (eQTL, 31,684 participants) from the blood were obtained as exposure. The ERS-related genes were extracted from the GeneCards database, and then the GWAS summary data were integrated with the mQTL and eQTL data associated with ERS genes by SMR. Sensitivity analysis included two-sample MR analysis, power calculations, Bayesian co-localization analysis, and phenotype scanning were performed to evaluate the robustness of the results. Results: A total of 1,193 ERS-related genes were obtained. The three-step SMR analysis showed that cg24011261 CpG site regulating GPX1 expression was associated with a low risk of UC, whereas GPX1 expression regulated by a combination of cg05055782, cg24011261, and cg05551922 CpG sites was associated with a low risk of CD. Sensitivity analysis further supports these findings. Conclusion: This multi-omics integration study identifies a causal relationship between the role of ERS in UC and CD and suggests potential new therapeutic targets for clinical practice.

Leveraging osteoclast genetic regulatory data to identify genes with a role in osteoarthritis

There has been a growing interest in the role of the subchondral bone and its resident osteoclasts in the progression of osteoarthritis (OA). A recent genome-wide association study (GWAS) identified 100 independent association signals for OA traits. Most of these signals are led by noncoding variants, suggesting that genetic regulatory effects may drive many of the associations. We have generated a unique human osteoclast-like cell-specific expression quantitative trait locus (eQTL) resource for studying the genetics of bone disease. Considering the potential role of osteoclasts in the pathogenesis of OA, we performed an integrative analysis of this dataset with the recently published OA GWAS results. Summary data-based Mendelian randomization (SMR) and colocalization analyses identified 38 genes with a potential role in OA, including some that have been implicated in Mendelian diseases with joint/skeletal abnormalities, such as BICRA, EIF6, CHST3, and FBN2. Several OA GWAS signals demonstrated colocalization with more than one eQTL peak, including at 19q13.32 (hip OA with BCAM, PRKD2, and BICRA eQTL). We also identified a number of eQTL signals colocalizing with more than one OA trait, including FAM53A, GCAT, HMGN1, MGAT4A, RRP7BP, and TRIOBP. An SMR analysis identified 3 loci with evidence of pleiotropic effects on OA-risk and gene expression: LINC01481, CPNE1, and EIF6. Both CPNE1 and EIF6 are located at 20q11.22, a locus harboring 2 other strong OA candidate genes, GDF5 and UQCC1, suggesting the presence of an OA-risk gene cluster. In summary, we have used our osteoclast-specific eQTL dataset to identify genes potentially involved with the pathogenesis of OA.

SLCO5A1 and synaptic assembly genes contribute to impulsivity in juvenile myoclonic epilepsy

Elevated impulsivity is a key component of attention-deficit hyperactivity disorder (ADHD), bipolar disorder and juvenile myoclonic epilepsy (JME). We performed a genome-wide association, colocalization, polygenic risk score, and pathway analysis of impulsivity in JME (n = 381). Results were followed up with functional characterisation using a drosophila model. We identified genome-wide associated SNPs at 8q13.3 (P = 7.5 × 10-9) and 10p11.21 (P = 3.6 × 10-8). The 8q13.3 locus colocalizes with SLCO5A1 expression quantitative trait loci in cerebral cortex (P = 9.5 × 10-3). SLCO5A1 codes for an organic anion transporter and upregulates synapse assembly/organisation genes. Pathway analysis demonstrates 12.7-fold enrichment for presynaptic membrane assembly genes (P = 0.0005) and 14.3-fold enrichment for presynaptic organisation genes (P = 0.0005) including NLGN1 and PTPRD. RNAi knockdown of Oatp30B, the Drosophila polypeptide with the highest homology to SLCO5A1, causes over-reactive startling behaviour (P = 8.7 × 10-3) and increased seizure-like events (P = 6.8 × 10-7). Polygenic risk score for ADHD genetically correlates with impulsivity scores in JME (P = 1.60 × 10-3). SLCO5A1 loss-of-function represents an impulsivity and seizure mechanism. Synaptic assembly genes may inform the aetiology of impulsivity in health and disease.

Integration of eQTL and GWAS analysis uncovers a genetic regulation of natural ionomic variation in Arabidopsis

KEY MESSAGE

This study provided important insights into the genetic architecture of variations in A. thaliana leaf ionome in a cell-type-specific manner. The functional interpretation of traits associated variants by expression quantitative trait loci (eQTL) analysis is usually performed in bulk tissue samples. While the regulation of gene expression is context-dependent, such as cell-type-specific manner. In this study, we estimated cell-type abundances from 728 bulk tissue samples using single-cell RNA-sequencing dataset, and performed cis-eQTL mapping to identify cell-type-interaction eQTL (cis-eQTLs(ci)) in A. thaliana. Also, we performed Genome-wide association studies (GWAS) analyses for 999 accessions to identify the genetic basis of variations in A. thaliana leaf ionome. As a result, a total of 5,664 unique eQTL genes and 15,038 unique cis-eQTLs(ci) were significant. The majority (62.83%) of cis-eQTLs(ci) were cell-type-specific eQTLs. Using colocalization, we uncovered one interested gene AT2G25590 in Phloem cell, encoding a kind of plant Tudor-like protein with possible chromatin-associated functions, which colocalized with the most significant cis-eQTL(ci) of a Mo-related locus (Chr2:10,908,806:A:C; P = 3.27 × 10-27). Furthermore, we prioritized eight target genes associated with AT2G25590, which were previously reported in regulating the concentration of Mo element in A. thaliana. This study revealed the genetic regulation of ionomic variations and provided a foundation for further studies on molecular mechanisms of genetic variants controlling the A. thaliana ionome.

The influence of tissue pH and RNA integrity number on gene expression of human postmortem brain

Background

Evaluating and controlling confounders are necessary when investigating molecular pathogenesis using human postmortem brain tissue. Particularly, tissue pH and RNA integrity number (RIN) are valuable indicators for controlling confounders. However, the influences of these indicators on the expression of each gene in postmortem brain have not been fully investigated. Therefore, we aimed to assess these effects on gene expressions of human brain samples.

Methods

We isolated total RNA from occipital lobes of 13 patients with schizophrenia and measured the RIN and tissue pH. Gene expression was analyzed and gene sets affected by tissue pH and RIN were identified. Moreover, we examined the functions of these genes by enrichment analysis and upstream regulator analysis.

Results

We identified 2,043 genes (24.7%) whose expressions were highly correlated with pH; 3,004 genes (36.3%) whose expressions were highly correlated with RIN; and 1,293 genes (15.6%) whose expressions were highly correlated with both pH and RIN. Genes commonly affected by tissue pH and RIN were highly associated with energy production and the immune system. In addition, genes uniquely affected by tissue pH were highly associated with the cell cycle, whereas those uniquely affected by RIN were highly associated with RNA processing.

Conclusion

The current study elucidated the influence of pH and RIN on gene expression profiling and identified gene sets whose expressions were affected by tissue pH or RIN. These findings would be helpful in the control of confounders for future postmortem brain studies.

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.

Cell type and condition specific functional annotation of schizophrenia associated non-coding genetic variants

Schizophrenia (SCZ) is a highly polygenic disease and genome wide association studies have identified thousands of genetic variants that are statistically associated with this psychiatric disorder. However, our ability to translate these associations into insights on the disease mechanisms has been challenging since the causal genetic variants, their molecular function and their target genes remain largely unknown. In order to address these questions, we established a functional genomics pipeline in combination with induced pluripotent stem cell technology to functionally characterize ~35,000 non-coding genetic variants associated with schizophrenia along with their target genes. This analysis identified a set of 620 (1.7%) single nucleotide polymorphisms as functional on a molecular level in a highly cell type and condition specific fashion. These results provide a high-resolution map of functional variant-gene combinations and offer comprehensive biological insights into the developmental context and stimulation dependent molecular processes modulated by SCZ associated genetic variation.

Perturbed iron biology in the prefrontal cortex of people with schizophrenia

Despite loss of grey matter volume and emergence of distinct cognitive deficits in young adults diagnosed with schizophrenia, current treatments for schizophrenia do not target disruptions in late maturational reshaping of the prefrontal cortex. Iron, the most abundant transition metal in the brain, is essential to brain development and function, but in excess, it can impair major neurotransmission systems and lead to lipid peroxidation, neuroinflammation and accelerated aging. However, analysis of cortical iron biology in schizophrenia has not been reported in modern literature. Using a combination of inductively coupled plasma-mass spectrometry and western blots, we quantified iron and its major-storage protein, ferritin, in post-mortem prefrontal cortex specimens obtained from three independent, well-characterised brain tissue resources. Compared to matched controls (n = 85), among schizophrenia cases (n = 86) we found elevated tissue iron, unlikely to be confounded by demographic and lifestyle variables, by duration, dose and type of antipsychotic medications used or by copper and zinc levels. We further observed a loss of physiologic age-dependent iron accumulation among people with schizophrenia, in that the iron level among cases was already high in young adulthood. Ferritin, which stores iron in a redox-inactive form, was paradoxically decreased in individuals with the disorder. Such iron-ferritin uncoupling could alter free, chemically reactive, tissue iron in key reasoning and planning areas of the young-adult schizophrenia cortex. Using a prediction model based on iron and ferritin, our data provide a pathophysiologic link between perturbed cortical iron biology and schizophrenia and indicate that achievement of optimal cortical iron homeostasis could offer a new therapeutic target.

Integrating genetics and transcriptomics to study major depressive disorder: a conceptual framework, bioinformatic approaches, and recent findings

Major depressive disorder (MDD) is a complex and heterogeneous psychiatric syndrome with genetic and environmental influences. In addition to neuroanatomical and circuit-level disturbances, dysregulation of the brain transcriptome is a key phenotypic signature of MDD. Postmortem brain gene expression data are uniquely valuable resources for identifying this signature and key genomic drivers in human depression; however, the scarcity of brain tissue limits our capacity to observe the dynamic transcriptional landscape of MDD. It is therefore crucial to explore and integrate depression and stress transcriptomic data from numerous, complementary perspectives to construct a richer understanding of the pathophysiology of depression. In this review, we discuss multiple approaches for exploring the brain transcriptome reflecting dynamic stages of MDD: predisposition, onset, and illness. We next highlight bioinformatic approaches for hypothesis-free, genome-wide analyses of genomic and transcriptomic data and their integration. Last, we summarize the findings of recent genetic and transcriptomic studies within this conceptual framework.

Non-additive effects of schizophrenia risk genes reflect convergent downstream function

Genetic studies of schizophrenia (SCZ) reveal a complex polygenic risk architecture comprised of hundreds of risk variants, the majority of which are common in the population at-large and confer only modest increases in disorder risk. Precisely how genetic variants with individually small predicted effects on gene expression combine to yield substantial clinical impacts in aggregate is unclear. Towards this, we previously reported that the combinatorial perturbation of four SCZ risk genes ("eGenes", whose expression is regulated by common variants) resulted in gene expression changes that were not predicted by individual perturbations, being most non-additive among genes associated with synaptic function and SCZ risk. Now, across fifteen SCZ eGenes, we demonstrate that non-additive effects are greatest within groups of functionally similar eGenes. Individual eGene perturbations reveal common downstream transcriptomic effects ("convergence"), while combinatorial eGene perturbations result in changes that are smaller than predicted by summing individual eGene effects ("sub-additive effects"). Unexpectedly, these convergent and sub-additive downstream transcriptomic effects overlap and constitute a large proportion of the genome-wide polygenic risk score, suggesting that functional redundancy of eGenes may be a major mechanism underlying non-additivity. Single eGene perturbations likewise fail to predict the magnitude or directionality of cellular phenotypes resulting from combinatorial perturbations. Overall, our results indicate that polygenic risk cannot be extrapolated from experiments testing one risk gene at a time and must instead be empirically measured. By unravelling the interactions between complex risk variants, it may be possible to improve the clinical utility of polygenic risk scores through more powerful prediction of symptom onset, clinical trajectory, and treatment response, or to identify novel targets for therapeutic intervention.

Brain expression quantitative trait locus and network analyses reveal downstream effects and putative drivers for brain-related diseases

Identification of therapeutic targets from genome-wide association studies (GWAS) requires insights into downstream functional consequences. We harmonized 8,613 RNA-sequencing samples from 14 brain datasets to create the MetaBrain resource and performed cis- and trans-expression quantitative trait locus (eQTL) meta-analyses in multiple brain region- and ancestry-specific datasets (n ≤ 2,759). Many of the 16,169 cortex cis-eQTLs were tissue-dependent when compared with blood cis-eQTLs. We inferred brain cell types for 3,549 cis-eQTLs by interaction analysis. We prioritized 186 cis-eQTLs for 31 brain-related traits using Mendelian randomization and co-localization including 40 cis-eQTLs with an inferred cell type, such as a neuron-specific cis-eQTL (CYP24A1) for multiple sclerosis. We further describe 737 trans-eQTLs for 526 unique variants and 108 unique genes. We used brain-specific gene-co-regulation networks to link GWAS loci and prioritize additional genes for five central nervous system diseases. This study represents a valuable resource for post-GWAS research on central nervous system diseases.

Effect of all-but-one conditional analysis for eQTL isolation in peripheral blood

Expression quantitative trait locus detection has become increasingly important for understanding how noncoding variants contribute to disease susceptibility and complex traits. The major challenges in expression quantitative trait locus fine-mapping and causal variant discovery relate to the impact of linkage disequilibrium on signals due to one or multiple functional variants that lie within a credible set. We perform expression quantitative trait locus fine-mapping using the all-but-one approach, conditioning each signal on all others detected in an interval, on the Consortium for the Architecture of Gene Expression cohorts of microarray-based peripheral blood gene expression in 2,138 European-ancestry human adults. We contrast these results with traditional forward stepwise conditional analysis and a Bayesian localization method. All-but-one conditioning significantly modifies effect-size estimates for 51% of 2,351 expression quantitative trait locus peaks, but only modestly affects credible set size and location. On the other hand, both conditioning approaches result in unexpectedly low overlap with Bayesian credible sets, with just 57% peak concordance and between 50% and 70% SNP sharing, leading us to caution against the assumption that any one localization method is superior to another. We also cross reference our results with ATAC-seq data, cell-type-specific expression quantitative trait locus, and activity-by-contact-enhancers, leading to the proposal of a 5-tier approach to further reduce credible set sizes and prioritize likely causal variants for all known inflammatory bowel disease risk loci active in immune cells.

MicroRNA-eQTLs in the developing human neocortex link miR-4707-3p expression to brain size

Expression quantitative trait loci (eQTL) data have proven important for linking non-coding loci to protein-coding genes. But eQTL studies rarely measure microRNAs (miRNAs), small non-coding RNAs known to play a role in human brain development and neurogenesis. Here, we performed small-RNA sequencing across 212 mid-gestation human neocortical tissue samples, measured 907 expressed miRNAs, discovering 111 of which were novel, and identified 85 local-miRNA-eQTLs. Colocalization of miRNA-eQTLs with GWAS summary statistics yielded one robust colocalization of miR-4707-3p expression with educational attainment and brain size phenotypes, where the miRNA expression increasing allele was associated with decreased brain size. Exogenous expression of miR-4707-3p in primary human neural progenitor cells decreased expression of predicted targets and increased cell proliferation, indicating miR-4707-3p modulates progenitor gene regulation and cell fate decisions. Integrating miRNA-eQTLs with existing GWAS yielded evidence of a miRNA that may influence human brain size and function via modulation of neocortical brain development.

The missing link between genetic association and regulatory function

The genetic basis of most traits is highly polygenic and dominated by non-coding alleles. It is widely assumed that such alleles exert small regulatory effects on the expression of cis-linked genes. However, despite the availability of gene expression and epigenomic datasets, few variant-to-gene links have emerged. It is unclear whether these sparse results are due to limitations in available data and methods, or to deficiencies in the underlying assumed model. To better distinguish between these possibilities, we identified 220 gene-trait pairs in which protein-coding variants influence a complex trait or its Mendelian cognate. Despite the presence of expression quantitative trait loci near most GWAS associations, by applying a gene-based approach we found limited evidence that the baseline expression of trait-related genes explains GWAS associations, whether using colocalization methods (8% of genes implicated), transcription-wide association (2% of genes implicated), or a combination of regulatory annotations and distance (4% of genes implicated). These results contradict the hypothesis that most complex trait-associated variants coincide with homeostatic expression QTLs, suggesting that better models are needed. The field must confront this deficit and pursue this 'missing regulation.'

Chromatin-associated microprocessor assembly is regulated by the U1 snRNP auxiliary protein PRP40

In plants, microRNA (miRNA) biogenesis involves cotranscriptional processing of RNA polymerase II (RNAPII)-generated primary transcripts by a multi-protein complex termed the microprocessor. Here, we report that Arabidopsis (Arabidopsis thaliana) PRE-MRNA PROCESSING PROTEIN 40 (PRP40), the U1 snRNP auxiliary protein, positively regulates the recruitment of SERRATE, a core component of the plant microprocessor, to miRNA genes. The association of DICER-LIKE1 (DCL1), the microprocessor endoribonuclease, with chromatin was altered in prp40ab mutant plants. Impaired cotranscriptional microprocessor assembly was accompanied by RNAPII accumulation at miRNA genes and retention of miRNA precursors at their transcription sites in the prp40ab mutant plants. We show that cotranscriptional microprocessor assembly, regulated by AtPRP40, positively affects RNAPII transcription of miRNA genes and is important to reach the correct levels of produced miRNAs.

Spatiotemporal and genetic regulation of A-to-I editing throughout human brain development

Posttranscriptional RNA modifications by adenosine-to-inosine (A-to-I) editing are abundant in the brain, yet elucidating functional sites remains challenging. To bridge this gap, we investigate spatiotemporal and genetically regulated A-to-I editing sites across prenatal and postnatal stages of human brain development. More than 10,000 spatiotemporally regulated A-to-I sites were identified that occur predominately in 3' UTRs and introns, as well as 37 sites that recode amino acids in protein coding regions with precise changes in editing levels across development. Hyper-edited transcripts are also enriched in the aging brain and stabilize RNA secondary structures. These features are conserved in murine and non-human primate models of neurodevelopment. Finally, thousands of cis-editing quantitative trait loci (edQTLs) were identified with unique regulatory effects during prenatal and postnatal development. Collectively, this work offers a resolved atlas linking spatiotemporal variation in editing levels to genetic regulatory effects throughout distinct stages of brain maturation.

Ferroptosis and Its Potential Role in Glioma: From Molecular Mechanisms to Therapeutic Opportunities

Glioma is the most common intracranial malignant tumor, and the current main standard treatment option is a combination of tumor surgical resection, chemotherapy and radiotherapy. Due to the terribly poor five-year survival rate of patients with gliomas and the high recurrence rate of gliomas, some new and efficient therapeutic strategies are expected. Recently, ferroptosis, as a new form of cell death, has played a significant role in the treatment of gliomas. Specifically, studies have revealed key processes of ferroptosis, including iron overload in cells, occurrence of lipid peroxidation, inactivation of cysteine/glutathione antiporter system Xc- (xCT) and glutathione peroxidase 4 (GPX4). In the present review, we summarized the molecular mechanisms of ferroptosis and introduced the application and challenges of ferroptosis in the development and treatment of gliomas. Moreover, we highlighted the therapeutic opportunities of manipulating ferroptosis to improve glioma treatments, which may improve the clinical outcome.

A tissue-level phenome-wide network map of colocalized genes and phenotypes in the UK Biobank

Phenome-wide association studies identified numerous loci associated with traits and diseases. To help interpret these associations, we constructed a phenome-wide network map of colocalized genes and phenotypes. We generated colocalized signals using the Genotype-Tissue Expression data and genome-wide association results in UK Biobank. We identified 9151 colocalized genes for 1411 phenotypes across 48 tissues. Then, we constructed bipartite networks using the colocalized signals in each tissue, and showed that the majority of links were observed in a single tissue. We applied the biLouvain clustering algorithm in each tissue-specific network to identify co-clusters of genes and phenotypes. We observed significant enrichments of these co-clusters with known biological and functional gene classes. Overall, the phenome-wide map provides links between genes, phenotypes and tissues, and can yield biological and clinical discoveries.

Natural Killer cells demonstrate distinct eQTL and transcriptome-wide disease associations, highlighting their role in autoimmunity

Natural Killer cells are innate lymphocytes with central roles in immunosurveillance and are implicated in autoimmune pathogenesis. The degree to which regulatory variants affect Natural Killer cell gene expression is poorly understood. Here we perform expression quantitative trait locus mapping of negatively selected Natural Killer cells from a population of healthy Europeans (n = 245). We find a significant subset of genes demonstrate expression quantitative trait loci specific to Natural Killer cells and these are highly informative of human disease, in particular autoimmunity. A Natural Killer cell transcriptome-wide association study across five common autoimmune diseases identifies further novel associations at 27 genes. In addition to these cis observations, we find novel master-regulatory regions impacting expression of trans gene networks at regions including 19q13.4, the Killer cell Immunoglobulin-like Receptor region, GNLY, MC1R and UVSSA. Our findings provide new insights into the unique biology of Natural Killer cells, demonstrating markedly different expression quantitative trait loci from other immune cells, with implications for disease mechanisms.

Natural Killer cells are key mediators of anti-tumour immunosurveillance and anti-viral immunity. Here, the authors map regulatory genetic variation in primary Natural Killer cells, providing new insights into their role in human health and disease.

Population-scale analysis of common and rare genetic variation associated with hearing loss in adults

To better understand the genetics of hearing loss, we performed a genome-wide association meta-analysis with 125,749 cases and 469,497 controls across five cohorts. We identified 53/c loci affecting hearing loss risk, including common coding variants in COL9A3 and TMPRSS3. Through exome sequencing of 108,415 cases and 329,581 controls, we observed rare coding associations with 11 Mendelian hearing loss genes, including additive effects in known hearing loss genes GJB2 (Gly12fs; odds ratio [OR] = 1.21, P = 4.2 × 10^-11) and SLC26A5 (gene burden; OR = 1.96, P = 2.8 × 10^-17). We also identified hearing loss associations with rare coding variants in FSCN2 (OR = 1.14, P = 1.9 × 10^-15) and KLHDC7B (OR = 2.14, P = 5.2 × 10^-30). Our results suggest a shared etiology between Mendelian and common hearing loss in adults. This work illustrates the potential of large-scale exome sequencing to elucidate the genetic architecture of common disorders where both common and rare variation contribute to risk.

Single-cell eQTL models reveal dynamic T cell state dependence of disease loci

Non-coding genetic variants may cause disease by modulating gene expression. However, identifying these expression quantitative trait loci (eQTLs) is complicated by differences in gene regulation across fluid functional cell states within cell types. These states-for example, neurotransmitter-driven programs in astrocytes or perivascular fibroblast differentiation-are obscured in eQTL studies that aggregate cells1,2. Here we modelled eQTLs at single-cell resolution in one complex cell type: memory T cells. Using more than 500,000 unstimulated memory T cells from 259 Peruvian individuals, we show that around one-third of 6,511 cis-eQTLs had effects that were mediated by continuous multimodally defined cell states, such as cytotoxicity and regulatory capacity. In some loci, independent eQTL variants had opposing cell-state relationships. Autoimmune variants were enriched in cell-state-dependent eQTLs, including risk variants for rheumatoid arthritis near ORMDL3 and CTLA4; this indicates that cell-state context is crucial to understanding potential eQTL pathogenicity. Moreover, continuous cell states explained more variation in eQTLs than did conventional discrete categories, such as CD4+ versus CD8+, suggesting that modelling eQTLs and cell states at single-cell resolution can expand insight into gene regulation in functionally heterogeneous cell types.

Molecular phenotypes associated with antipsychotic drugs in the human caudate nucleus

Antipsychotic drugs are the current first-line of treatment for schizophrenia and other psychotic conditions. However, their molecular effects on the human brain are poorly studied, due to difficulty of tissue access and confounders associated with disease status. Here we examine differences in gene expression and DNA methylation associated with positive antipsychotic drug toxicology status in the human caudate nucleus. We find no genome-wide significant differences in DNA methylation, but abundant differences in gene expression. These gene expression differences are overall quite similar to gene expression differences between schizophrenia cases and controls. Interestingly, gene expression differences based on antipsychotic toxicology are different between brain regions, potentially due to affected cell type differences. We finally assess similarities with effects in a mouse model, which finds some overlapping effects but many differences as well. As a first look at the molecular effects of antipsychotics in the human brain, the lack of epigenetic effects is unexpected, possibly because long term treatment effects may be relatively stable for extended periods.

Analyzing Association between Expression Quantitative Trait and CNV for Breast Cancer Based on Gene Interaction Network Clustering and Group Sparse Learning

Aims:

The occurrence and development of tumor is accompanied by the change of pathogenic gene expression. Tumor cells avoid the damage of immune cells by regulating the expression of immune related genes.

Background:

Tracing the causes of gene expression variation is helpful to understand tumor evolution and metastasis.

Objective:

Current gene expression variation explanation methods are confronted with several main challenges: low explanation power, insufficient prediction accuracy, and lack of biological meaning.

Method:

In this study, we propose a novel method to analyze the mRNA expression variations of breast cancers risk genes. Firstly, we collected some high-confidence risk genes related to breast cancer and then designed a rank-based method to preprocess the breast cancers copy number variation (CNV) and mRNA data. Secondly, to elevate the biological meaning and narrow down the combinatorial space, we introduced a prior gene interaction network and applied a network clustering algorithm to generate high density subnetworks. Lastly, to describe the interlinked structure within and between subnetworks and target genes mRNA expression, we proposed a group sparse learning model to identify CNVs for pathogenic genes expression variations.

Result:

The performance of the proposed method is evaluated by both significantly improved predication accuracy and biological meaning of pathway enrichment analysis.

Conclusion:

The experimental results show that our method has practical significance

A flexible summary statistics-based colocalization method with application to the mucin cystic fibrosis lung disease modifier locus

Mucus obstruction is a central feature in the cystic fibrosis (CF) airways. A genome-wide association study (GWAS) of lung disease by the CF Gene Modifier Consortium (CFGMC) identified a significant locus containing two mucin genes, MUC20 and MUC4. Expression quantitative trait locus (eQTL) analysis using human nasal epithelia (HNE) from 94 CF-affected Canadians in the CFGMC demonstrated MUC4 eQTLs that mirrored the lung association pattern in the region, suggesting that MUC4 expression may mediate CF lung disease. Complications arose, however, with colocalization testing using existing methods: the locus is complex and the associated SNPs span a 0.2 Mb region with high linkage disequilibrium (LD) and evidence of allelic heterogeneity. We previously developed the Simple Sum (SS), a powerful colocalization test in regions with allelic heterogeneity, but SS assumed eQTLs to be present to achieve type I error control. Here we propose a two-stage SS (SS2) colocalization test that avoids a priori eQTL assumptions, accounts for multiple hypothesis testing and the composite null hypothesis, and enables meta-analysis. We compare SS2 to published approaches through simulation and demonstrate type I error control for all settings with the greatest power in the presence of high LD and allelic heterogeneity. Applying SS2 to the MUC20/MUC4 CF lung disease locus with eQTLs from CF HNE revealed significant colocalization with MUC4 (p = 1.31 × 10⁻⁵) rather than with MUC20. The SS2 is a powerful method to inform the responsible gene(s) at a locus and guide future functional studies. SS2 has been implemented in the application LocusFocus.

Multi-ancestry eQTL meta-analysis of human brain identifies candidate causal variants for brain-related traits

While large-scale, genome-wide association studies (GWAS) have identified hundreds of loci associated with brain-related traits, identification of the variants, genes and molecular mechanisms underlying these traits remains challenging. Integration of GWAS with expression quantitative trait loci (eQTLs) and identification of shared genetic architecture have been widely adopted to nominate genes and candidate causal variants. However, this approach is limited by sample size, statistical power and linkage disequilibrium. We developed the multivariate multiple QTL approach and performed a large-scale, multi-ancestry eQTL meta-analysis to increase power and fine-mapping resolution. Analysis of 3,983 RNA-sequenced samples from 2,119 donors, including 474 non-European individuals, yielded an effective sample size of 3,154. Joint statistical fine-mapping of eQTL and GWAS identified 329 variant-trait pairs for 24 brain-related traits driven by 204 unique candidate causal variants for 189 unique genes. This integrative analysis identifies candidate causal variants and elucidates potential regulatory mechanisms for genes underlying schizophrenia, bipolar disorder and Alzheimer's disease.

Leveraging interindividual variability of regulatory activity for refining genetic regulation of gene expression in schizophrenia

Schizophrenia is a polygenic psychiatric disorder with limited understanding about the mechanistic changes in gene expression regulation. To elucidate on this, we integrate interindividual variability of regulatory activity (ChIP-sequencing for H3K27ac histone mark) with gene expression and genotype data captured from the prefrontal cortex of 272 cases and controls. By measuring interindividual correlation among proximal chromatin peaks, we show that regulatory element activity is structured into 10,936 and 10,376 cis-regulatory domains in cases and controls, respectively. The schizophrenia-specific cis-regulatory domains are enriched for fetal-specific (p = 0.0014, OR = 1.52) and depleted of adult-specific regulatory activity (p = 3.04 × 10^-50, OR = 0.57) and are enriched for SCZ heritability (p = 0.001). By studying the interplay among genetic variants, gene expression, and cis-regulatory domains, we ascertain that changes in coordinated regulatory activity tag alterations in gene expression levels (p = 3.43 × 10^-5, OR = 1.65), unveil case-specific QTL effects, and identify regulatory machinery changes for genes affecting synaptic function and dendritic spine morphology in schizophrenia. Altogether, we show that accounting for coordinated regulatory activity provides a novel mechanistic approach to reduce the search space for unveiling genetically perturbed regulation of gene expression in schizophrenia.

Integration of functional genomics data to uncover cell type-specific pathways affected in Parkinson's disease

Parkinson's disease (PD) is the second most prevalent late-onset neurodegenerative disorder worldwide after Alzheimer's disease for which available drugs only deliver temporary symptomatic relief. Loss of dopaminergic neurons (DaNs) in the substantia nigra and intracellular alpha-synuclein inclusions are the main hallmarks of the disease but the events that cause this degeneration remain uncertain. Despite cell types other than DaNs such as astrocytes, microglia and oligodendrocytes have been recently associated with the pathogenesis of PD, we still lack an in-depth characterisation of PD-affected brain regions at cell-type resolution that could help our understanding of the disease mechanisms. Nevertheless, publicly available large-scale brain-specific genomic, transcriptomic and epigenomic datasets can be further exploited to extract different layers of cell type-specific biological information for the reconstruction of cell type-specific transcriptional regulatory networks. By intersecting disease risk variants within the networks, it may be possible to study the functional role of these risk variants and their combined effects at cell type- and pathway levels, that, in turn, can facilitate the identification of key regulators involved in disease progression, which are often potential therapeutic targets.

Combining multivariate genomic approaches to elucidate the comorbidity between autism spectrum disorder and attention deficit hyperactivity disorder

BACKGROUND

Attention deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) are two highly heritable neurodevelopmental disorders. Several lines of evidence point towards the presence of shared genetic factors underlying ASD and ADHD. We conducted genomic analyses of common risk variants (i.e. single nucleotide polymorphisms, SNPs) shared by ASD and ADHD, and those specific to each disorder.

METHODS

With the summary data from two GWAS, one on ASD (N = 46,350) and another on ADHD (N = 55,374) individuals, we used genomic structural equation modelling and colocalization analysis to identify SNPs shared by ASD and ADHD and SNPs specific to each disorder. Functional genomic analyses were then conducted on shared and specific common genetic variants. Finally, we performed a bidirectional Mendelian randomization analysis to test whether the shared genetic risk between ASD and ADHD was interpretable in terms of reciprocal relationships between ASD and ADHD.

RESULTS

We found that 37.5% of the SNPs associated with ASD (at p < 1e-6) colocalized with ADHD SNPs and that 19.6% of the SNPs associated with ADHD colocalized with ASD SNPs. We identified genes mapped to SNPs that are specific to ASD or ADHD and that are shared by ASD and ADHD, including two novel genes INSM1 and PAX1. Our bidirectional Mendelian randomization analyses indicated that the risk of ASD was associated with an increased risk of ADHD and vice versa.

CONCLUSIONS

Using multivariate genomic analyses, the present study uncovers shared and specific genetic variants associated with ASD and ADHD. Further functional investigation of genes mapped to those shared variants may help identify pathophysiological pathways and new targets for treatment.

Integration of genetic, transcriptomic, and clinical data provides insight into 16p11.2 and 22q11.2 CNV genes

BACKGROUND

Deletions and duplications of the multigenic 16p11.2 and 22q11.2 copy number variant (CNV) regions are associated with brain-related disorders including schizophrenia, intellectual disability, obesity, bipolar disorder, and autism spectrum disorder (ASD). The contribution of individual CNV genes to each of these identified phenotypes is unknown, as well as the contribution of these CNV genes to other potentially subtler health implications for carriers. Hypothesizing that DNA copy number exerts most effects via impacts on RNA expression, we attempted a novel in silico fine-mapping approach in non-CNV carriers using both GWAS and biobank data.

METHODS

We first asked whether gene expression level in any individual gene in the CNV region alters risk for a known CNV-associated behavioral phenotype(s). Using transcriptomic imputation, we performed association testing for CNV genes within large genotyped cohorts for schizophrenia, IQ, BMI, bipolar disorder, and ASD. Second, we used a biobank containing electronic health data to compare the medical phenome of CNV carriers to controls within 700,000 individuals in order to investigate the full spectrum of health effects of the CNVs. Third, we used genotypes for over 48,000 individuals within the biobank to perform phenome-wide association studies between imputed expressions of individual 16p11.2 and 22q11.2 genes and over 1500 health traits.

RESULTS

Using large genotyped cohorts, we found individual genes within 16p11.2 associated with schizophrenia (TMEM219, INO80E, YPEL3), BMI (TMEM219, SPN, TAOK2, INO80E), and IQ (SPN), using conditional analysis to identify upregulation of INO80E as the driver of schizophrenia, and downregulation of SPN and INO80E as increasing BMI. We identified both novel and previously observed over-represented traits within the electronic health records of 16p11.2 and 22q11.2 CNV carriers. In the phenome-wide association study, we found seventeen significant gene-trait pairs, including psychosis (NPIPB11, SLX1B) and mood disorders (SCARF2), and overall enrichment of mental traits.

CONCLUSIONS

Our results demonstrate how integration of genetic and clinical data aids in understanding CNV gene function and implicates pleiotropy and multigenicity in CNV biology.

Brain-trait-associated variants impact cell-type-specific gene regulation during neurogenesis

Interpretation of the function of non-coding risk loci for neuropsychiatric disorders and brain-relevant traits via gene expression and alternative splicing quantitative trait locus (e/sQTL) analyses is generally performed in bulk post-mortem adult tissue. However, genetic risk loci are enriched in regulatory elements active during neocortical differentiation, and regulatory effects of risk variants may be masked by heterogeneity in bulk tissue. Here, we map e/sQTLs, and allele-specific expression in cultured cells representing two major developmental stages, primary human neural progenitors (n = 85) and their sorted neuronal progeny (n = 74), identifying numerous loci not detected in either bulk developing cortical wall or adult cortex. Using colocalization and genetic imputation via transcriptome-wide association, we uncover cell-type-specific regulatory mechanisms underlying risk for brain-relevant traits that are active during neocortical differentiation. Specifically, we identified a progenitor-specific eQTL for CENPW co-localized with common variant associations for cortical surface area and educational attainment.

Targeting synaptic plasticity in schizophrenia: insights from genomic studies

Patients with schizophrenia experience cognitive dysfunction and negative symptoms that do not respond to current drug treatments. Historical evidence is consistent with the hypothesis that these deficits are due, at least in part, to altered cortical synaptic plasticity (the ability of synapses to strengthen or weaken their activity), making this an attractive pathway for therapeutic intervention. However, while synaptic transmission and plasticity is well understood in model systems, it has been challenging to identify specific therapeutic targets for schizophrenia. New information is emerging from genomic findings, which converge on synaptic plasticity and provide a new window on the neurobiology of schizophrenia. Translating this information into therapeutic advances will require a multidisciplinary and collaborative approach.

Transcriptional-regulatory convergence across functional MDD risk variants identified by massively parallel reporter assays

Family and population studies indicate clear heritability of major depressive disorder (MDD), though its underlying biology remains unclear. The majority of single-nucleotide polymorphism (SNP) linkage blocks associated with MDD by genome-wide association studies (GWASes) are believed to alter transcriptional regulators (e.g., enhancers, promoters) based on enrichment of marks correlated with these functions. A key to understanding MDD pathophysiology will be elucidation of which SNPs are functional and how such functional variants biologically converge to elicit the disease. Furthermore, retinoids can elicit MDD in patients and promote depressive-like behaviors in rodent models, acting via a regulatory system of retinoid receptor transcription factors (TFs). We therefore sought to simultaneously identify functional genetic variants and assess retinoid pathway regulation of MDD risk loci. Using Massively Parallel Reporter Assays (MPRAs), we functionally screened over 1000 SNPs prioritized from 39 neuropsychiatric trait/disease GWAS loci, selecting SNPs based on overlap with predicted regulatory features-including expression quantitative trait loci (eQTL) and histone marks-from human brains and cell cultures. We identified >100 SNPs with allelic effects on expression in a retinoid-responsive model system. Functional SNPs were enriched for binding sequences of retinoic acid-receptive transcription factors (TFs), with additional allelic differences unmasked by treatment with all-trans retinoic acid (ATRA). Finally, motifs overrepresented across functional SNPs corresponded to TFs highly specific to serotonergic neurons, suggesting an in vivo site of action. Our application of MPRAs to screen MDD-associated SNPs suggests a shared transcriptional-regulatory program across loci, a component of which is unmasked by retinoids.

Genomic dissection of 43 serum urate-associated loci provides multiple insights into molecular mechanisms of urate control

High serum urate is a prerequisite for gout and associated with metabolic disease. Genome-wide association studies (GWAS) have reported dozens of loci associated with serum urate control; however, there has been little progress in understanding the molecular basis of the associated loci. Here, we employed trans-ancestral meta-analysis using data from European and East Asian populations to identify 10 new loci for serum urate levels. Genome-wide colocalization with cis-expression quantitative trait loci (eQTL) identified a further five new candidate loci. By cis- and trans-eQTL colocalization analysis, we identified 34 and 20 genes, respectively, where the causal eQTL variant has a high likelihood that it is shared with the serum urate-associated locus. One new locus identified was SLC22A9 that encodes organic anion transporter 7 (OAT7). We demonstrate that OAT7 is a very weak urate-butyrate exchanger. Newly implicated genes identified in the eQTL analysis include those encoding proteins that make up the dystrophin complex, a scaffold for signaling proteins and transporters at the cell membrane; MLXIP that, with the previously identified MLXIPL, is a transcription factor that may regulate serum urate via the pentose-phosphate pathway and MRPS7 and IDH2 that encode proteins necessary for mitochondrial function. Functional fine mapping identified six loci (RREB1, INHBC, HLF, UBE2Q2, SFMBT1 and HNF4G) with colocalized eQTL containing putative causal SNPs. This systematic analysis of serum urate GWAS loci identified candidate causal genes at 24 loci and a network of previously unidentified genes likely involved in control of serum urate levels, further illuminating the molecular mechanisms of urate control.

Cell-type-specific effects of genetic variation on chromatin accessibility during human neuronal differentiation

Common genetic risk for neuropsychiatric disorders is enriched in regulatory elements active during cortical neurogenesis. However, it remains poorly understood as to how these variants influence gene regulation. To model the functional impact of common genetic variation on the noncoding genome during human cortical development, we performed the assay for transposase accessible chromatin using sequencing (ATAC-seq) and analyzed chromatin accessibility quantitative trait loci (QTL) in cultured human neural progenitor cells and their differentiated neuronal progeny from 87 donors. We identified significant genetic effects on 988/1,839 neuron/progenitor regulatory elements, with highly cell-type and temporally specific effects. A subset (roughly 30%) of chromatin accessibility-QTL were also associated with changes in gene expression. Motif-disrupting alleles of transcriptional activators generally led to decreases in chromatin accessibility, whereas motif-disrupting alleles of repressors led to increases in chromatin accessibility. By integrating cell-type-specific chromatin accessibility-QTL and brain-relevant genome-wide association data, we were able to fine-map and identify regulatory mechanisms underlying noncoding neuropsychiatric disorder risk loci.

Genetic effects on liver chromatin accessibility identify disease regulatory variants

Identifying the molecular mechanisms by which genome-wide association study (GWAS) loci influence traits remains challenging. Chromatin accessibility quantitative trait loci (caQTLs) help identify GWAS loci that may alter GWAS traits by modulating chromatin structure, but caQTLs have been identified in a limited set of human tissues. Here we mapped caQTLs in human liver tissue in 20 liver samples and identified 3,123 caQTLs. The caQTL variants are enriched in liver tissue promoter and enhancer states and frequently disrupt binding motifs of transcription factors expressed in liver. We predicted target genes for 861 caQTL peaks using proximity, chromatin interactions, correlation with promoter accessibility or gene expression, and colocalization with expression QTLs. Using GWAS signals for 19 liver function and/or cardiometabolic traits, we identified 110 colocalized caQTLs and GWAS signals, 56 of which contained a predicted caPeak target gene. At the LITAF LDL-cholesterol GWAS locus, we validated that a caQTL variant showed allelic differences in protein binding and transcriptional activity. These caQTLs contribute to the epigenomic characterization of human liver and help identify molecular mechanisms and genes at GWAS loci.

Advances in protein-protein interaction network analysis for Parkinson's disease

Protein-protein interactions (PPIs) are a key component of the subcellular molecular networks which enable cells to function. Due to their importance in homeostasis, alterations to the networks can be detrimental, leading to cellular dysfunction and ultimately disease states. Parkinson's disease (PD) is a progressive neurodegenerative condition with multifactorial aetiology, spanning genetic variation and environmental modifiers. At a molecular and systems level, the characterisation of PD is the focus of extensive research, largely due to an unmet need for disease modifying therapies. PPI network analysis approaches are a valuable strategy to accelerate our understanding of the molecular crosstalk and biological processes underlying PD pathogenesis, especially due to the complex nature of this disease. In this review, we describe the utility of PPI network approaches in modelling complex systems, focusing on previous work in PD research. We discuss four principal strategies for using PPI network approaches: to infer PD related cellular functions, pathways and novel genes; to support genomics studies; to study the interactome of single PD related genes; and to compare the molecular basis of PD to other neurodegenerative disorders. This is an evolving area of research which is likely to further expand as omics data generation and availability increase. These approaches complement and bridge-the-gap between genetics and functional research to inform future investigations. In this review we outline several limitations that require consideration, acknowledging that ongoing challenges in this field continue to be addressed and the refinement of these approaches will facilitate further advances using PPI network analysis for understanding complex diseases.

Applying stem cells and CRISPR engineering to uncover the etiology of schizophrenia

Schizophrenia is a highly heritable, polygenic disorder. A growing list of common genetic variants have been associated with schizophrenia; there is a clear need to understand the role of these risk factors in the etiology of disease. The majority of these variants occur in noncoding regions of the genome and are thought to regulate the expression of one or more genes in a cell type-specific fashion. Recent advances in stem cell biology and molecular genetics have resulted in two invaluable advances: hiPSC technology makes possible the generation of donor-specific disease-relevant neural cell types, whereas CRISPR-based techniques can be applied to manipulate individual variants and/or their gene targets. New multiplexed gene manipulation and CRISPR screening techniques show great promise toward dissecting the complex interactions between the myriad disease-associated variants. This review outlines key advances in hiPSC and CRISPR technology, describing their applications and future potential in the field of schizophrenia research.

Whole-Genome and RNA Sequencing Reveal Variation and Transcriptomic Coordination in the Developing Human Prefrontal Cortex

Gene expression levels vary across developmental stage, cell type, and region in the brain. Genomic variants also contribute to the variation in expression, and some neuropsychiatric disorder loci may exert their effects through this mechanism. To investigate these relationships, we present BrainVar, a unique resource of paired whole-genome and bulk tissue RNA sequencing from the dorsolateral prefrontal cortex of 176 individuals across prenatal and postnatal development. Here we identify common variants that alter gene expression (expression quantitative trait loci [eQTLs]) constantly across development or predominantly during prenatal or postnatal stages. Both "constant" and "temporal-predominant" eQTLs are enriched for loci associated with neuropsychiatric traits and disorders and colocalize with specific variants. Expression levels of more than 12,000 genes rise or fall in a concerted late-fetal transition, with the transitional genes enriched for cell-type-specific genes and neuropsychiatric risk loci, underscoring the importance of cataloging developmental trajectories in understanding cortical physiology and pathology.

Cell-type-specific expression quantitative trait loci associated with Alzheimer disease in blood and brain tissue

Because regulation of gene expression is heritable and context-dependent, we investigated AD-related gene expression patterns in cell types in blood and brain. Cis-expression quantitative trait locus (eQTL) mapping was performed genome-wide in blood from 5257 Framingham Heart Study (FHS) participants and in brain donated by 475 Religious Orders Study/Memory & Aging Project (ROSMAP) participants. The association of gene expression with genotypes for all cis SNPs within 1 Mb of genes was evaluated using linear regression models for unrelated subjects and linear-mixed models for related subjects. Cell-type-specific eQTL (ct-eQTL) models included an interaction term for the expression of "proxy" genes that discriminate particular cell type. Ct-eQTL analysis identified 11,649 and 2533 additional significant gene-SNP eQTL pairs in brain and blood, respectively, that were not detected in generic eQTL analysis. Of note, 386 unique target eGenes of significant eQTLs shared between blood and brain were enriched in apoptosis and Wnt signaling pathways. Five of these shared genes are established AD loci. The potential importance and relevance to AD of significant results in myeloid cell types is supported by the observation that a large portion of GWS ct-eQTLs map within 1 Mb of established AD loci and 58% (23/40) of the most significant eGenes in these eQTLs have previously been implicated in AD. This study identified cell-type-specific expression patterns for established and potentially novel AD genes, found additional evidence for the role of myeloid cells in AD risk, and discovered potential novel blood and brain AD biomarkers that highlight the importance of cell-type-specific analysis.

Identification of Candidate Parkinson Disease Genes by Integrating Genome-Wide Association Study, Expression, and Epigenetic Data Sets

Importance

Substantial genome-wide association study (GWAS) work in Parkinson disease (PD) has led to the discovery of an increasing number of loci shown reliably to be associated with increased risk of disease. Improved understanding of the underlying genes and mechanisms at these loci will be key to understanding the pathogenesis of PD.

Objective

To investigate what genes and genomic processes underlie the risk of sporadic PD.

Design and Setting

This genetic association study used the bioinformatic tools Coloc and transcriptome-wide association study (TWAS) to integrate PD case-control GWAS data published in 2017 with expression data (from Braineac, the Genotype-Tissue Expression [GTEx], and CommonMind) and methylation data (derived from UK Parkinson brain samples) to uncover putative gene expression and splicing mechanisms associated with PD GWAS signals. Candidate genes were further characterized using cell-type specificity, weighted gene coexpression networks, and weighted protein-protein interaction networks.

Main Outcomes and Measures

It was hypothesized a priori that some genes underlying PD loci would alter PD risk through changes to expression, splicing, or methylation. Candidate genes are presented whose change in expression, splicing, or methylation are associated with risk of PD as well as the functional pathways and cell types in which these genes have an important role.

Results

Gene-level analysis of expression revealed 5 genes (WDR6 [OMIM 606031], CD38 [OMIM 107270], GPNMB [OMIM 604368], RAB29 [OMIM 603949], and TMEM163 [OMIM 618978]) that replicated using both Coloc and TWAS analyses in both the GTEx and Braineac expression data sets. A further 6 genes (ZRANB3 [OMIM 615655], PCGF3 [OMIM 617543], NEK1 [OMIM 604588], NUPL2 [NCBI 11097], GALC [OMIM 606890], and CTSB [OMIM 116810]) showed evidence of disease-associated splicing effects. Cell-type specificity analysis revealed that gene expression was overall more prevalent in glial cell types compared with neurons. The weighted gene coexpression performed on the GTEx data set showed that NUPL2 is a key gene in 3 modules implicated in catabolic processes associated with protein ubiquitination and in the ubiquitin-dependent protein catabolic process in the nucleus accumbens, caudate, and putamen. TMEM163 and ZRANB3 were both important in modules in the frontal cortex and caudate, respectively, indicating regulation of signaling and cell communication. Protein interactor analysis and simulations using random networks demonstrated that the candidate genes interact significantly more with known mendelian PD and parkinsonism proteins than would be expected by chance.

Conclusions and Relevance

Together, these results suggest that several candidate genes and pathways are associated with the findings observed in PD GWAS studies.