Large-Scale Identification of Common Trait and Disease Variants Affecting Gene Expression

Single-cell multi-cohort dissection of the schizophrenia transcriptome

The complexity and heterogeneity of schizophrenia have hindered mechanistic elucidation and the development of more effective therapies. Here, we performed single-cell dissection of schizophrenia-associated transcriptomic changes in the human prefrontal cortex across 140 individuals in two independent cohorts. Excitatory neurons were the most affected cell group, with transcriptional changes converging on neurodevelopment and synapse-related molecular pathways. Transcriptional alterations included known genetic risk factors, suggesting convergence of rare and common genomic variants on neuronal population-specific alterations in schizophrenia. Based on the magnitude of schizophrenia-associated transcriptional change, we identified two populations of individuals with schizophrenia marked by expression of specific excitatory and inhibitory neuronal cell states. This single-cell atlas links transcriptomic changes to etiological genetic risk factors, contextualizing established knowledge within the human cortical cytoarchitecture and facilitating mechanistic understanding of schizophrenia pathophysiology and heterogeneity.

Joint-tissue integrative analysis identifies high-risk genes for Parkinson's disease

The loss of dopaminergic neurons in the substantia nigra and the abnormal accumulation of synuclein proteins and neurotransmitters in Lewy bodies constitute the primary symptoms of Parkinson's disease (PD). Besides environmental factors, scholars are in the early stages of comprehending the genetic factors involved in the pathogenic mechanism of PD. Although genome-wide association studies (GWAS) have unveiled numerous genetic variants associated with PD, precisely pinpointing the causal variants remains challenging due to strong linkage disequilibrium (LD) among them. Addressing this issue, expression quantitative trait locus (eQTL) cohorts were employed in a transcriptome-wide association study (TWAS) to infer the genetic correlation between gene expression and a particular trait. Utilizing the TWAS theory alongside the enhanced Joint-Tissue Imputation (JTI) technique and Mendelian Randomization (MR) framework (MR-JTI), we identified a total of 159 PD-associated genes by amalgamating LD score, GTEx eQTL data, and GWAS summary statistic data from a substantial cohort. Subsequently, Fisher's exact test was conducted on these PD-associated genes using 5,152 differentially expressed genes sourced from 12 PD-related datasets. Ultimately, 29 highly credible PD-associated genes, including CTX1B, SCNA, and ARSA, were uncovered. Furthermore, GO and KEGG enrichment analyses indicated that these genes primarily function in tissue synthesis, regulation of neuron projection development, vesicle organization and transportation, and lysosomal impact. The potential PD-associated genes identified in this study not only offer fresh insights into the disease's pathophysiology but also suggest potential biomarkers for early disease detection.

De novo variants in GATAD2A in individuals with a neurodevelopmental disorder: GATAD2A-related neurodevelopmental disorder

GATA zinc finger domain containing 2A (GATAD2A) is a subunit of the nucleosome remodeling and deacetylase (NuRD) complex. NuRD is known to regulate gene expression during neural development and other processes. The NuRD complex modulates chromatin status through histone deacetylation and ATP-dependent chromatin remodeling activities. Several neurodevelopmental disorders (NDDs) have been previously linked to variants in other components of NuRD's chromatin remodeling subcomplex (NuRDopathies). We identified five individuals with features of an NDD that possessed de novo autosomal dominant variants in GATAD2A. Core features in affected individuals include global developmental delay, structural brain defects, and craniofacial dysmorphology. These GATAD2A variants are predicted to affect protein dosage and/or interactions with other NuRD chromatin remodeling subunits. We provide evidence that a GATAD2A missense variant disrupts interactions of GATAD2A with CHD3, CHD4, and CHD5. Our findings expand the list of NuRDopathies and provide evidence that GATAD2A variants are the genetic basis of a previously uncharacterized developmental disorder.

Germline modifiers of the tumor immune microenvironment implicate drivers of cancer risk and immunotherapy response

With the continued promise of immunotherapy for treating cancer, understanding how host genetics contributes to the tumor immune microenvironment (TIME) is essential to tailoring cancer screening and treatment strategies. Here, we study 1084 eQTLs affecting the TIME found through analysis of The Cancer Genome Atlas and literature curation. These TIME eQTLs are enriched in areas of active transcription, and associate with gene expression in specific immune cell subsets, such as macrophages and dendritic cells. Polygenic score models built with TIME eQTLs reproducibly stratify cancer risk, survival and immune checkpoint blockade (ICB) response across independent cohorts. To assess whether an eQTL-informed approach could reveal potential cancer immunotherapy targets, we inhibit CTSS, a gene implicated by cancer risk and ICB response-associated polygenic models; CTSS inhibition results in slowed tumor growth and extended survival in vivo. These results validate the potential of integrating germline variation and TIME characteristics for uncovering potential targets for immunotherapy.

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.

The shared genetic landscape of blood cell traits and risk of neurological and psychiatric disorders

Phenotypic associations have been reported between blood cell traits (BCTs) and a range of neurological and psychiatric disorders (NPDs), but in most cases, it remains unclear whether these associations have a genetic basis and, if so, to what extent genetic correlations reflect causality. Here, we report genetic correlations and Mendelian randomization analyses between 11 NPDs and 29 BCTs, using genome-wide association study summary statistics. We found significant genetic correlations for four BCT-NPD pairs, all of which have prior evidence for a phenotypic correlation. We identified a previously unreported causal effect of increased platelet distribution width on susceptibility to Parkinson's disease. We identified multiple functional genes and regulatory elements for specific BCT-NPD pairs, some of which are targets of known drugs. These results enrich our understanding of the shared genetic landscape underlying BCTs and NPDs and provide a robust foundation for future work to improve prognosis and treatment of common NPDs.

Best practices for multi-ancestry, meta-analytic transcriptome-wide association studies: Lessons from the Global Biobank Meta-analysis Initiative

The Global Biobank Meta-analysis Initiative (GBMI), through its diversity, provides a valuable opportunity to study population-wide and ancestry-specific genetic associations. However, with multiple ascertainment strategies and multi-ancestry study populations across biobanks, GBMI presents unique challenges in implementing statistical genetics methods. Transcriptome-wide association studies (TWASs) boost detection power for and provide biological context to genetic associations by integrating genetic variant-to-trait associations from genome-wide association studies (GWASs) with predictive models of gene expression. TWASs present unique challenges beyond GWASs, especially in a multi-biobank, meta-analytic setting. Here, we present the GBMI TWAS pipeline, outlining practical considerations for ancestry and tissue specificity, meta-analytic strategies, and open challenges at every step of the framework. We advise conducting ancestry-stratified TWASs using ancestry-specific expression models and meta-analyzing results using inverse-variance weighting, showing the least test statistic inflation. Our work provides a foundation for adding transcriptomic context to biobank-linked GWASs, allowing for ancestry-aware discovery to accelerate genomic medicine.

The HDAC9-associated risk locus promotes coronary artery disease by governing TWIST1

Genome wide association studies (GWAS) have identified thousands of single nucleotide polymorphisms (SNPs) associated with the risk of common disorders. However, since the large majority of these risk SNPs reside outside gene-coding regions, GWAS generally provide no information about causal mechanisms regarding the specific gene(s) that are affected or the tissue(s) in which these candidate gene(s) exert their effect. The ‘gold standard’ method for understanding causal genes and their mechanisms of action are laborious basic science studies often involving sophisticated knockin or knockout mouse lines, however, these types of studies are impractical as a high-throughput means to understand the many risk variants that cause complex diseases like coronary artery disease (CAD). As a solution, we developed a streamlined, data-driven informatics pipeline to gain mechanistic insights on complex genetic loci. The pipeline begins by understanding the SNPs in a given locus in terms of their relative location and linkage disequilibrium relationships, and then identifies nearby expression quantitative trait loci (eQTLs) to determine their relative independence and the likely tissues that mediate their disease-causal effects. The pipeline then seeks to understand associations with other disease-relevant genes, disease sub-phenotypes, potential causality (Mendelian randomization), and the regulatory and functional involvement of these genes in gene regulatory co-expression networks (GRNs). Here, we applied this pipeline to understand a cluster of SNPs associated with CAD within and immediately adjacent to the gene encoding HDAC9. Our pipeline demonstrated, and validated, that this locus is causal for CAD by modulation of TWIST1 expression levels in the arterial wall, and by also governing a GRN related to metabolic function in skeletal muscle. Our results reconciled numerous prior studies, and also provided clear evidence that this locus does not govern HDAC9 expression, structure or function. This pipeline should be considered as a powerful and efficient way to understand GWAS risk loci in a manner that better reflects the highly complex nature of genetic risk associated with common disorders.

Single-Cell Epigenomics and Functional Fine-Mapping of Atherosclerosis GWAS Loci

Supplemental Digital Content is available in the text.

Genome-wide association studies have identified hundreds of loci associated with coronary artery disease (CAD). Many of these loci are enriched in cisregulatory elements but not linked to cardiometabolic risk factors nor to candidate causal genes, complicating their functional interpretation.

Sorting Out Sorting Nexins Functions in the Nervous System in Health and Disease

Endocytosis is a fundamental process that controls protein/lipid composition of the plasma membrane, thereby shaping cellular metabolism, sensing, adhesion, signaling, and nutrient uptake. Endocytosis is essential for the cell to adapt to its surrounding environment, and a tight regulation of the endocytic mechanisms is required to maintain cell function and survival. This is particularly significant in the central nervous system (CNS), where composition of neuronal cell surface is crucial for synaptic functioning. In fact, distinct pathologies of the CNS are tightly linked to abnormal endolysosomal function, and several genome wide association analysis (GWAS) and biochemical studies have identified intracellular trafficking regulators as genetic risk factors for such pathologies. The sorting nexins (SNXs) are a family of proteins involved in protein trafficking regulation and signaling. SNXs dysregulation occurs in patients with Alzheimer’s disease (AD), Down’s syndrome (DS), schizophrenia, ataxia and epilepsy, among others, establishing clear roles for this protein family in pathology. Interestingly, restoration of SNXs levels has been shown to trigger synaptic plasticity recovery in a DS mouse model. This review encompasses an historical and evolutionary overview of SNXs protein family, focusing on its organization, phyla conservation, and evolution throughout the development of the nervous system during speciation. We will also survey SNXs molecular interactions and highlight how defects on SNXs underlie distinct pathologies of the CNS. Ultimately, we discuss possible strategies of intervention, surveying how our knowledge about the fundamental processes regulated by SNXs can be applied to the identification of novel therapeutic avenues for SNXs-related disorders.

Power analysis of transcriptome-wide association study: Implications for practical protocol choice

The transcriptome-wide association study (TWAS) has emerged as one of several promising techniques for integrating multi-scale 'omics' data into traditional genome-wide association studies (GWAS). Unlike GWAS, which associates phenotypic variance directly with genetic variants, TWAS uses a reference dataset to train a predictive model for gene expressions, which allows it to associate phenotype with variants through the mediating effect of expressions. Although effective, this core innovation of TWAS is poorly understood, since the predictive accuracy of the genotype-expression model is generally low and further bounded by expression heritability. This raises the question: to what degree does the accuracy of the expression model affect the power of TWAS? Furthermore, would replacing predictions with actual, experimentally determined expressions improve power? To answer these questions, we compared the power of GWAS, TWAS, and a hypothetical protocol utilizing real expression data. We derived non-centrality parameters (NCPs) for linear mixed models (LMMs) to enable closed-form calculations of statistical power that do not rely on specific protocol implementations. We examined two representative scenarios: causality (genotype contributes to phenotype through expression) and pleiotropy (genotype contributes directly to both phenotype and expression), and also tested the effects of various properties including expression heritability. Our analysis reveals two main outcomes: (1) Under pleiotropy, the use of predicted expressions in TWAS is superior to actual expressions. This explains why TWAS can function with weak expression models, and shows that TWAS remains relevant even when real expressions are available. (2) GWAS outperforms TWAS when expression heritability is below a threshold of 0.04 under causality, or 0.06 under pleiotropy. Analysis of existing publications suggests that TWAS has been misapplied in place of GWAS, in situations where expression heritability is low.

Chromatin Remodeling Complex NuRD in Neurodevelopment and Neurodevelopmental Disorders

The nucleosome remodeling and deacetylase (NuRD) complex presents one of the major chromatin remodeling complexes in mammalian cells. Here, we discuss current evidence for NuRD's role as an important epigenetic regulator of gene expression in neural stem cell (NSC) and neural progenitor cell (NPC) fate decisions in brain development. With the formation of the cerebellar and cerebral cortex, NuRD facilitates experience-dependent cerebellar plasticity and regulates additionally cerebral subtype specification and connectivity in postmitotic neurons. Consistent with these properties, genetic variation in NuRD's subunits emerges as important risk factor in common polygenic forms of neurodevelopmental disorders (NDDs) and neurodevelopment-related psychiatric disorders such as schizophrenia (SCZ) and bipolar disorder (BD). Overall, these findings highlight the critical role of NuRD in chromatin regulation in brain development and in mental health and disease.

Multi-tissue transcriptome analyses identify genetic mechanisms underlying neuropsychiatric traits

The genetic architecture of psychiatric disorders is characterized by a large number of small-effect variants1 located primarily in non-coding regions, suggesting that the underlying causal effects may influence disease risk by modulating gene expression2-4. We provide comprehensive analyses using transcriptome data from an unprecedented collection of tissues to gain pathophysiological insights into the role of the brain, neuroendocrine factors (adrenal gland) and gastrointestinal systems (colon) in psychiatric disorders. In each tissue, we perform PrediXcan analysis and identify trait-associated genes for schizophrenia (n associations = 499; n unique genes = 275), bipolar disorder (n associations = 17; n unique genes = 13), attention deficit hyperactivity disorder (n associations = 19; n unique genes = 12) and broad depression (n associations = 41; n unique genes = 31). Importantly, both PrediXcan and summary-data-based Mendelian randomization/heterogeneity in dependent instruments analyses suggest potentially causal genes in non-brain tissues, showing the utility of these tissues for mapping psychiatric disease genetic predisposition. Our analyses further highlight the importance of joint tissue approaches as 76% of the genes were detected only in difficult-to-acquire tissues.

Novel disease syndromes unveiled by integrative multiscale network analysis of diseases sharing molecular effectors and comorbidities

Background

Forty-two percent of patients experience disease comorbidity, contributing substantially to mortality rates and increased healthcare costs. Yet, the possibility of underlying shared mechanisms for diseases remains not well established, and few studies have confirmed their molecular predictions with clinical datasets.

Methods

In this work, we integrated genome-wide association study (GWAS) associating diseases and single nucleotide polymorphisms (SNPs) with transcript regulatory activity from expression quantitative trait loci (eQTL). This allowed novel mechanistic insights for noncoding and intergenic regions. We then analyzed pairs of SNPs across diseases to identify shared molecular effectors robust to multiple test correction (False Discovery Rate FDR_eRNA < 0.05). We hypothesized that disease pairs found to be molecularly convergent would also be significantly overrepresented among comorbidities in clinical datasets. To assess our hypothesis, we used clinical claims datasets from the Healthcare Cost and Utilization Project (HCUP) and calculated significant disease comorbidities (FDR_comorbidity < 0.05). We finally verified if disease pairs resulting molecularly convergent were also statistically comorbid more than by chance using the Fisher’s Exact Test.

Results

Our approach integrates: (i) 6175 SNPs associated with 238 diseases from ~ 1000 GWAS, (ii) eQTL associations from 19 tissues, and (iii) claims data for 35 million patients from HCUP. Logistic regression (controlled for age, gender, and race) identified comorbidities in HCUP, while enrichment analyses identified cis- and trans-eQTL downstream effectors of GWAS-identified variants. Among ~ 16,000 combinations of diseases, 398 disease-pairs were prioritized by both convergent eQTL-genetics (RNA overlap enrichment, FDR_eRNA < 0.05) and clinical comorbidities (OR > 1.5, FDR_comorbidity < 0.05). Case studies of comorbidities illustrate specific convergent noncoding regulatory elements. An intergenic architecture of disease comorbidity was unveiled due to GWAS and eQTL-derived convergent mechanisms between distinct diseases being overrepresented among observed comorbidities in clinical datasets (OR = 8.6, p-value = 6.4 × 10^− 5 FET).

Conclusions

These comorbid diseases with convergent eQTL genetic mechanisms suggest clinical syndromes. While it took over a decade to confirm the genetic underpinning of the metabolic syndrome, this study is likely highlighting hundreds of new ones. Further, this knowledge may improve the clinical management of comorbidities with precision and shed light on novel approaches of drug repositioning or SNP-guided precision molecular therapy inclusive of intergenic risks.

Electronic supplementary material

The online version of this article (10.1186/s12920-018-0428-9) contains supplementary material, which is available to authorized users.

Leveraging DNA-Methylation Quantitative-Trait Loci to Characterize the Relationship between Methylomic Variation, Gene Expression, and Complex Traits

Characterizing the complex relationship between genetic, epigenetic, and transcriptomic variation has the potential to increase understanding about the mechanisms underpinning health and disease phenotypes. We undertook a comprehensive analysis of common genetic variation on DNA methylation (DNAm) by using the Illumina EPIC array to profile samples from the UK Household Longitudinal study. We identified 12,689,548 significant DNA methylation quantitative trait loci (mQTL) associations (p < 6.52 × 10-14) occurring between 2,907,234 genetic variants and 93,268 DNAm sites, including a large number not identified by previous DNAm-profiling methods. We demonstrate the utility of these data for interpreting the functional consequences of common genetic variation associated with > 60 human traits by using summary-data-based Mendelian randomization (SMR) to identify 1,662 pleiotropic associations between 36 complex traits and 1,246 DNAm sites. We also use SMR to characterize the relationship between DNAm and gene expression and thereby identify 6,798 pleiotropic associations between 5,420 DNAm sites and the transcription of 1,702 genes. Our mQTL database and SMR results are available via a searchable online database as a resource to the research community.

Sorting nexin 3 mutation impairs development and neuronal function in Caenorhabditis elegans

The sorting nexins family of proteins (SNXs) plays pleiotropic functions in protein trafficking and intracellular signaling and has been associated with several disorders, namely Alzheimer's disease and Down's syndrome. Despite the growing association of SNXs with neurodegeneration, not much is known about their function in the nervous system. The aim of this work was to use the nematode Caenorhabditis elegans that encodes in its genome eight SNXs orthologs, to dissect the role of distinct SNXs, particularly in the nervous system. By screening the C. elegans SNXs deletion mutants for morphological, developmental and behavioral alterations, we show here that snx-3 gene mutation leads to an array of developmental defects, such as delayed hatching, decreased brood size and life span and reduced body length. Additionally, ∆snx-3 worms present increased susceptibility to osmotic, thermo and oxidative stress and distinct behavioral deficits, namely, a chemotaxis defect which is independent of the described snx-3 role in Wnt secretion. ∆snx-3 animals also display abnormal GABAergic neuronal architecture and wiring and altered AIY interneuron structure. Pan-neuronal expression of C. elegans snx-3 cDNA in the ∆snx-3 mutant is able to rescue its locomotion defects, as well as its chemotaxis toward isoamyl alcohol. Altogether, the present work provides the first in vivo evidence of the SNX-3 role in the nervous system.