Genetic regulatory effects modified by immune activation contribute to autoimmune disease associations

A multi-tissue atlas of allelic-specific expression reveals the characteristics, mechanisms, and relationship with dominant effects in cattle

BACKGROUND

Allele-specific expression (ASE) analysis is a crucial tool for validating expression quantitative trait loci (eQTLs), identifying causal variants associated with complex traits, and investigating the genetic mechanisms underlying heterosis. In this study, we characterized ASE variants across 35 tissues using 7532 publicly available RNA-seq datasets. Additionally, we explored the mechanisms driving ASE through integration with epigenomic data and examined the relationship between ASE and dominance effects on gene expression and milk-related traits in Holstein cattle.

RESULTS

ASE variants exhibited stronger tissue specificity and lower reproducibility compared to eQTLs. Interestingly, variants with opposite directional effects demonstrated greater resilience across diverse environments. Functional annotation revealed that ASE variants were enriched in both enhancer and promoter regions during transcription and implicated in post-transcriptional and translational processes, including mutations that affect mRNA splicing and trigger nonsense-mediated decay. Analysis of eQTLs, splicing QTLs (sQTLs), and validated QTLs associated with milk-related traits in Holstein cattle, coupled with enrichment analysis in QTL databases and effect size evaluation, indicated that ASE variants were more closely aligned with dominant effects than additive effects, particularly in reproductive and immune-related tissues/traits, which exhibited higher levels of heterosis.

CONCLUSIONS

Our findings not only enhance our understanding of the genetic mechanisms underlying heterosis and ASE formation but also provide a valuable resource of regulatory variants that can be leveraged to improve economic traits through molecular breeding or the strategic exploitation of heterosis.

Addressing missing context in regulatory variation across primate evolution

In primates, loci associated with adaptive trait variation often fall in non-coding regions. Understanding the mechanisms linking these regulatory variants to fitness-relevant phenotypes remains challenging, but can be addressed using functional genomic data. However, such data are rarely generated at scale in non-human primates. When they are, only select tissues, cell types, developmental stages, and cellular environments are typically considered, despite appreciation that adaptive variants often exhibit context-dependent effects. In this review, we 1) discuss why context-dependent regulatory loci might be especially evolutionarily relevant in primates, 2) explore challenges and emerging solutions for mapping such context-dependent variation, and 3) discuss the scientific questions these data could address. We argue that filling this gap will provide critical insights into evolutionary processes, human disease, and regulatory adaptation.

The identification of blood-derived response eQTLs reveals complex effects of regulatory variants on inflammatory and infectious disease risk

Hundreds of risk loci for immune mediated inflammatory and infectious diseases have been identified by genome-wide association studies (GWAS). Yet, what causal variants and genes in risk loci underpin the observed associations remains poorly understood for most. The identification of colocalized cis-expression Quantitative Trait Loci (cis-eQTLs) is a promising way to identify candidate causative genes. The catalogue of cis-eQTLs of the immune system is likely incomplete as many cis-eQTLs may be context-specific. We built a large cohort of 406 healthy individuals and expanded the immune cis-regulome through their whole blood transcriptome obtained after stimulation with specific toll-like receptor (TLR) agonists and T-cell receptor (TCR) antagonist. We report three mechanisms that may explain why an eQTL could only be revealed after immune stimulation. More than half of the cis-eQTLs detected in this study would have been overlooked without specific immune stimulations. We then mined this new catalogue of response (r)eQTLs, with public GWAS summary statistics of three diseases through a colocalization approach: inflammatory bowel diseases, rheumatoid arthritis and COVID-19 disease. We identified reQTL-specific colocalizations for risk loci for which no matching eQTL were reported before, revealing interesting new candidate causal genes.

Tracing the Evolution of Human Immunity Through Ancient DNA

Infections have imposed strong selection pressures throughout human evolution, making the study of natural selection's effects on immunity genes highly complementary to disease-focused research. This review discusses how ancient DNA studies, which have revolutionized evolutionary genetics, increase our understanding of the evolution of human immunity. These studies have shown that interbreeding between modern humans and Neanderthals or Denisovans has influenced present-day immune responses, particularly to viruses. Additionally, ancient genomics enables the tracking of how human immunity has evolved across cultural transitions, highlighting strong selection since the Bronze Age in Europe (<4,500 years) and potential genetic adaptations to epidemics raging during the Middle Ages and the European colonization of the Americas. Furthermore, ancient genomic studies suggest that the genetic risk for noninfectious immune disorders has gradually increased over millennia because alleles associated with increased risk for autoimmunity and inflammation once conferred resistance to infections. The challenge now is to extend these findings to diverse, non-European populations and to provide a more global understanding of the evolution of human immunity.

Single-cell multiomics of neuronal activation reveals context-dependent genetic control of brain disorders

Despite hundreds of genetic risk loci identified for neuropsychiatric disorders (NPD), most causal variants/genes remain unknown. A major hurdle is that disease risk variants may act in specific biological contexts, e.g., during neuronal activation, which is difficult to study in vivo at the population level. Here, we conducted a single-cell multiomics study of neuronal activation (stimulation) in human iPSC-induced excitatory and inhibitory neurons from 100 donors, and uncovered abundant neuronal stimulation-specific causal variants/genes for NPD. We surveyed NPD-relevant transcriptomic and epigenomic landscape of neuronal activation and identified thousands of genetic variants associated with activity-dependent gene expression (i.e., eQTL) and chromatin accessibility (i.e., caQTL). These caQTL explained considerably larger proportions of NPD heritability than the eQTL. Integrating the multiomic data with GWAS further revealed NPD risk variants/genes whose effects were only detected upon stimulation. Interestingly, multiple lines of evidence support a role of activity-dependent cholesterol metabolism in NPD. Our work highlights the power of cell stimulation to reveal context-dependent "hidden" genetic effects.

Response eQTLs, chromatin accessibility, and 3D chromatin structure in chondrocytes provide mechanistic insight into osteoarthritis risk

Osteoarthritis (OA) poses a significant healthcare burden with limited treatment options. While genome-wide association studies (GWASs) have identified over 100 OA-associated loci, translating these findings into therapeutic targets remains challenging. To address this gap, we mapped gene expression, chromatin accessibility, and 3D chromatin structure in primary human articular chondrocytes in both resting and OA-mimicking conditions. We identified thousands of differentially expressed genes, including those associated with differences in sex and age. RNA sequencing in chondrocytes from 101 donors across two conditions uncovered 3,782 unique eGenes, including 420 that exhibited strong and significant condition-specific effects. Colocalization with OA GWAS signals revealed 13 putative OA risk genes, 6 of which have not been previously identified. Chromatin accessibility and 3D chromatin structure provided insights into the mechanisms and conditional specificity of these variants. Our findings shed light on OA pathogenesis and highlight potential targets for therapeutic development.

Expression quantitative trait loci influence DNA damage-induced apoptosis in cancer

BACKGROUND

Genomic instability and evading apoptosis are two fundamental hallmarks of cancer and closely linked to DNA damage response (DDR). By analyzing expression quantitative trait loci (eQTL) upon cell stimulation (called exposure eQTL (e2QTL)) it is possible to identify context specific gene regulatory variants and connect them to oncological diseases based on genome-wide association studies (GWAS).

RESULTS

We isolate CD8+ T cells from 461 healthy donors and stimulate them with high doses of 5 different carcinogens to identify regulatory mechanisms of DNA damage-induced apoptosis. Across all stimuli, we find 5,373 genes to be differentially expressed, with 85% to 99% of these genes being suppressed. While upregulated genes are specific to distinct stimuli, downregulated genes are shared across conditions but exhibit enrichment in biological processes depending on the DNA damage type. Analysis of eQTL reveals 654 regulated genes across conditions. Among them, 47 genes are significant e2QTL, representing a fraction of 4% to 5% per stimulus. To unveil disease relevant genetic variants, we compare eQTL and e2QTL with GWAS risk variants. We identify gene regulatory variants for KLF2, PIP4K2A, GPR160, RPS18, ARL17B and XBP1 that represent risk variants for oncological diseases.

CONCLUSION

Our study highlights the relevance of gene regulatory variants influencing DNA damage-induced apoptosis in cancer. The results provide new insights in cellular mechanisms and corresponding genes contributing to inter-individual effects in cancer development.

Genotype-by-environment interactions shape ubiquitin-proteasome system activity

In genotype-by-environment interactions (GxE), the effect of a genetic variant on a trait depends on the environment. GxE influences numerous organismal traits across eukaryotic life. However, we have a limited understanding of how GxE shapes the molecular processes that give rise to organismal traits. Here, we characterized how GxE shapes protein degradation, an essential molecular process that influences numerous aspects of cellular and organismal physiology. Using the yeast Saccharomyces cerevisiae, we characterized GxE in the activity of the ubiquitin-proteasome system (UPS), the primary protein degradation system in eukaryotes. By mapping genetic influences on the degradation of six substrates that engage multiple distinct UPS pathways across eight diverse environments, we discovered extensive GxE in the genetics of UPS activity. Hundreds of locus effects on UPS activity varied depending on the substrate, the environment, or both. Most of these cases corresponded to loci that were present in one environment but not another ("presence / absence" GxE), while a smaller number of loci had opposing effects in different environments ("sign change" GxE). The number of loci exhibiting GxE, their genomic location, and the type of GxE (presence / absence or sign change) varied across UPS substrates. Loci exhibiting GxE were clustered at genomic regions that contain core UPS genes and especially at regions containing variation that affects the expression of thousands of genes, suggesting indirect contributions to UPS activity. Our results reveal highly complex interactions at the level of substrates and environments in the genetics of protein degradation.

Fine-mapping and molecular characterisation of primary sclerosing cholangitis genetic risk loci

Genome-wide association studies of primary sclerosing cholangitis have identified 23 susceptibility loci. The majority of these loci reside in non-coding regions of the genome and are thought to exert their effect by perturbing the regulation of nearby genes. Here, we aim to identify these genes to improve the biological understanding of primary sclerosing cholangitis, and nominate potential drug targets. We first build an eQTL map for six primary sclerosing cholangitis-relevant T-cell subsets obtained from the peripheral blood of primary sclerosing cholangitis and ulcerative colitis patients. These maps identify 10,459 unique eGenes, 87% of which are shared across all six primary sclerosing cholangitis T-cell types. We then search for colocalisations between primary sclerosing cholangitis loci and eQTLs and undertake Bayesian fine-mapping to identify disease-causing variants. In this work, colocalisation analyses nominate likely primary sclerosing cholangitis effector genes and biological mechanisms at five non-coding (UBASH3A, PRKD2, ETS2 and AP003774.1/CCDC88B) and one coding (SH2B3) primary sclerosing cholangitis loci. Through fine-mapping we identify likely causal variants for a third of all primary sclerosing cholangitis-associated loci, including two to single variant resolution.

Modeling common and rare genetic risk factors of neuropsychiatric disorders in human induced pluripotent stem cells

Recent genome-wide association studies (GWAS) and whole-exome sequencing of neuropsychiatric disorders, especially schizophrenia, have identified a plethora of common and rare disease risk variants/genes. Translating the mounting human genetic discoveries into novel disease biology and more tailored clinical treatments is tied to our ability to causally connect genetic risk variants to molecular and cellular phenotypes. When combined with the Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)/CRISPR-associated (Cas) nuclease-mediated genome editing system, human induced pluripotent stem cell (hiPSC)-derived neural cultures (both 2D and 3D organoids) provide a promising tractable cellular model for bridging the gap between genetic findings and disease biology. In this review, we first conceptualize the advances in understanding the disease polygenicity and convergence from the past decade of iPSC modeling of different types of genetic risk factors of neuropsychiatric disorders. We then discuss the major cell types and cellular phenotypes that are most relevant to neuropsychiatric disorders in iPSC modeling. Finally, we critically review the limitations of iPSC modeling of neuropsychiatric disorders and outline the need for implementing and developing novel methods to scale up the number of iPSC lines and disease risk variants in a systematic manner. Sufficiently scaled-up iPSC modeling and a better functional interpretation of genetic risk variants, in combination with cutting-edge CRISPR/Cas9 gene editing and single-cell multi-omics methods, will enable the field to identify the specific and convergent molecular and cellular phenotypes in precision for neuropsychiatric disorders.

Response eQTLs, chromatin accessibility, and 3D chromatin structure in chondrocytes provide mechanistic insight into osteoarthritis risk

Osteoarthritis (OA) poses a significant healthcare burden with limited treatment options. While genome-wide association studies (GWAS) have identified over 100 OA-associated loci, translating these findings into therapeutic targets remains challenging. Integrating expression quantitative trait loci (eQTL), 3D chromatin structure, and other genomic approaches with OA GWAS data offers a promising approach to elucidate disease mechanisms; however, comprehensive eQTL maps in OA-relevant tissues and conditions remain scarce. We mapped gene expression, chromatin accessibility, and 3D chromatin structure in primary human articular chondrocytes in both resting and OA-mimicking conditions. We identified thousands of differentially expressed genes, including those associated with differences in sex and age. RNA-seq in chondrocytes from 101 donors across two conditions uncovered 3782 unique eGenes, including 420 that exhibited strong and significant condition-specific effects. Colocalization with OA GWAS signals revealed 13 putative OA risk genes, 10 of which have not been previously identified. Chromatin accessibility and 3D chromatin structure provided insights into the mechanisms and conditional specificity of these variants. Our findings shed light on OA pathogenesis and highlight potential targets for therapeutic development.

Comparison of B-Cell Lupus and Lymphoma Using a Novel Immune Imbalance Transcriptomics Algorithm Reveals Potential Therapeutic Targets

BACKGROUND/OBJECTIVES

Systemic lupus erythematosus (lupus) and B-cell lymphoma (lymphoma) co-occur at higher-than-expected rates and primarily depend on B cells for their pathology. These observations implicate shared inflammation-related B cell molecular mechanisms as a potential cause of co-occurrence.

METHODS

We consequently implemented a novel Immune Imbalance Transcriptomics (IIT) algorithm and applied IIT to lupus, lymphoma, and healthy B cell RNA-sequencing (RNA-seq) data to find shared and contrasting mechanisms that are potential therapeutic targets.

RESULTS

We observed 7143 significantly dysregulated genes in both lupus and lymphoma. Of those genes, we found 5137 to have a significant immune imbalance, defined as a significant dysregulation by both diseases, as analyzed by IIT. Gene Ontology (GO) term and pathway enrichment of the IIT genes yielded immune-related "Neutrophil Degranulation" and "Adaptive Immune System", which validates that the IIT algorithm isolates biologically relevant genes in immunity and inflammation. We found that 344 IIT gene products are known targets for established and/or repurposed drugs. Among our results, we found 48 known and 296 novel lupus targets, along with 151 known and 193 novel lymphoma targets. Known disease drug targets in our IIT results further validate that IIT isolates genes with disease-relevant mechanisms.

CONCLUSIONS

We anticipate the IIT algorithm, together with the shared and contrasting gene mechanisms uncovered here, will contribute to the development of immune-related therapeutic options for lupus and lymphoma patients.

Oxygen-induced stress reveals context-specific gene regulatory effects in human brain organoids

The interaction between genetic variants and environmental stressors is key to understanding the mechanisms underlying neurological diseases. In this study, we used human brain organoids to explore how varying oxygen levels expose context-dependent gene regulatory effects. By subjecting a genetically diverse panel of 21 brain organoids to hypoxic and hyperoxic conditions, we identified thousands of gene regulatory changes that are undetectable under baseline conditions, with 1,745 trait-associated genes showing regulatory effects only in response to oxygen stress. To capture more nuanced transcriptional patterns, we employed topic modeling, which revealed context-specific gene regulation linked to dynamic cellular processes and environmental responses, offering a deeper understanding of how gene regulation is modulated in the brain. These findings underscore the importance of genotype-environment interactions in genetic studies of neurological disorders and provide new insights into the hidden regulatory mechanisms influenced by environmental factors in the brain.

Multi-omic characterization of allele-specific regulatory variation in hybrid pigs

Hybrid mapping is a powerful approach to efficiently identify and characterize genes regulated through mechanisms in cis. In this study, using reciprocal crosses of the phenotypically divergent Duroc and Lulai pig breeds, we perform a comprehensive multi-omic characterization of regulatory variation across the brain, liver, muscle, and placenta through four developmental stages. We produce one of the largest multi-omic datasets in pigs to date, including 16 whole genome sequenced individuals, as well as 48 whole genome bisulfite sequencing, 168 ATAC-Seq and 168 RNA-Seq samples. We develop a read count-based method to reliably assess allele-specific methylation, chromatin accessibility, and RNA expression. We show that tissue specificity was much stronger than developmental stage specificity in all of DNA methylation, chromatin accessibility, and gene expression. We identify 573 genes showing allele specific expression, including those influenced by parent-of-origin as well as allele genotype effects. We integrate methylation, chromatin accessibility, and gene expression data to show that allele specific expression can be explained in great part by allele specific methylation and/or chromatin accessibility. This study provides a comprehensive characterization of regulatory variation across multiple tissues and developmental stages in pigs.

Genotype × environment interactions in gene regulation and complex traits

Genotype × environment interactions (GxE) have long been recognized as a key mechanism underlying human phenotypic variation. Technological developments over the past 15 years have dramatically expanded our appreciation of the role of GxE in both gene regulation and complex traits. The richness and complexity of these datasets also required parallel efforts to develop robust and sensitive statistical and computational approaches. Although our understanding of the genetic architecture of molecular and complex traits has been maturing, a large proportion of complex trait heritability remains unexplained. Furthermore, there are increasing efforts to characterize the effect of environmental exposure on human health. We therefore review GxE in human gene regulation and complex traits, advocating for a comprehensive approach that jointly considers genetic and environmental factors in human health and disease.

Characterization of the genetic determinants of context-specific DNA methylation in primary monocytes

To better understand inter-individual variation in sensitivity of DNA methylation (DNAm) to immune activity, we characterized effects of inflammatory stimuli on primary monocyte DNAm (n = 190). We find that monocyte DNAm is site-dependently sensitive to lipopolysaccharide (LPS), with LPS-induced demethylation occurring following hydroxymethylation. We identify 7,359 high-confidence immune-modulated CpGs (imCpGs) that differ in genomic localization and transcription factor usage according to whether they represent a gain or loss in DNAm. Demethylated imCpGs are profoundly enriched for enhancers and colocalize to genes enriched for disease associations, especially cancer. DNAm is age associated, and we find that 24-h LPS exposure triggers approximately 6 months of gain in epigenetic age, directly linking epigenetic aging with innate immune activity. By integrating LPS-induced changes in DNAm with genetic variation, we identify 234 imCpGs under local genetic control. Exploring shared causal loci between LPS-induced DNAm responses and human disease traits highlights examples of disease-associated loci that modulate imCpG formation.

Shaping immunity: The influence of natural selection on population immune diversity

Humans exhibit considerable variability in their immune responses to the same immune challenges. Such variation is widespread and affects individual and population-level susceptibility to infectious diseases and immune disorders. Although the factors influencing immune response diversity are partially understood, what mechanisms lead to the wide range of immune traits in healthy individuals remain largely unexplained. Here, we discuss the role that natural selection has played in driving phenotypic differences in immune responses across populations and present-day susceptibility to immune-related disorders. Further, we touch on future directions in the field of immunogenomics, highlighting the value of expanding this work to human populations globally, the utility of modeling the immune response as a dynamic process, and the importance of considering the potential polygenic nature of natural selection. Identifying loci acted upon by evolution may further pinpoint variants critically involved in disease etiology, and designing studies to capture these effects will enrich our understanding of the genetic contributions to immunity and immune dysregulation.

The relationship between extreme inter-individual variation in macrophage gene expression and genetic susceptibility to inflammatory bowel disease

The differentiation of resident intestinal macrophages from blood monocytes depends upon signals from the macrophage colony-stimulating factor receptor (CSF1R). Analysis of genome-wide association studies (GWAS) indicates that dysregulation of macrophage differentiation and response to microorganisms contributes to susceptibility to chronic inflammatory bowel disease (IBD). Here, we analyzed transcriptomic variation in monocyte-derived macrophages (MDM) from affected and unaffected sib pairs/trios from 22 IBD families and 6 healthy controls. Transcriptional network analysis of the data revealed no overall or inter-sib distinction between affected and unaffected individuals in basal gene expression or the temporal response to lipopolysaccharide (LPS). However, the basal or LPS-inducible expression of individual genes varied independently by as much as 100-fold between subjects. Extreme independent variation in the expression of pairs of HLA-associated transcripts (HLA-B/C, HLA-A/F and HLA-DRB1/DRB5) in macrophages was associated with HLA genotype. Correlation analysis indicated the downstream impacts of variation in the immediate early response to LPS. For example, variation in early expression of IL1B was significantly associated with local SNV genotype and with subsequent peak expression of target genes including IL23A, CXCL1, CXCL3, CXCL8 and NLRP3. Similarly, variation in early IFNB1 expression was correlated with subsequent expression of IFN target genes. Our results support the view that gene-specific dysregulation in macrophage adaptation to the intestinal milieu is associated with genetic susceptibility to IBD.

Interaction molecular QTL mapping discovers cellular and environmental modifiers of genetic regulatory effects

Bulk-tissue molecular quantitative trait loci (QTLs) have been the starting point for interpreting disease-associated variants, and context-specific QTLs show particular relevance for disease. Here, we present the results of mapping interaction QTLs (iQTLs) for cell type, age, and other phenotypic variables in multi-omic, longitudinal data from the blood of individuals of diverse ancestries. By modeling the interaction between genotype and estimated cell-type proportions, we demonstrate that cell-type iQTLs could be considered as proxies for cell-type-specific QTL effects, particularly for the most abundant cell type in the tissue. The interpretation of age iQTLs, however, warrants caution because the moderation effect of age on the genotype and molecular phenotype association could be mediated by changes in cell-type composition. Finally, we show that cell-type iQTLs contribute to cell-type-specific enrichment of diseases that, in combination with additional functional data, could guide future functional studies. Overall, this study highlights the use of iQTLs to gain insights into the context specificity of regulatory effects.

Immune-response 3'UTR alternative polyadenylation quantitative trait loci contribute to variation in human complex traits and diseases

Genome-wide association studies (GWASs) have identified thousands of non-coding variants that are associated with human complex traits and diseases. The analysis of such GWAS variants in different contexts and physiological states is essential for deciphering the regulatory mechanisms underlying human disease. Alternative polyadenylation (APA) is a key post-transcriptional modification for most human genes that substantially impacts upon cell behavior. Here, we mapped 9,493 3'-untranslated region APA quantitative trait loci in 18 human immune baseline cell types and 8 stimulation conditions (immune 3'aQTLs). Through the comparison between baseline and stimulation data, we observed the high responsiveness of 3'aQTLs to immune stimulation (response 3'aQTLs). Co-localization and mendelian randomization analyses of immune 3'aQTLs identified 678 genes where 3'aQTL are associated with variation in complex traits, 27.3% of which were derived from response 3'aQTLs. Overall, these analyses reveal the role of immune 3'aQTLs in the determination of complex traits, providing new insights into the regulatory mechanisms underlying disease etiologies.

RET enhancer haplotype-dependent remodeling of the human fetal gut development program

Hirschsprung disease (HSCR) is associated with deficiency of the receptor tyrosine kinase RET, resulting in loss of cells of the enteric nervous system (ENS) during fetal gut development. The major contribution to HSCR risk is from common sequence variants in RET enhancers with additional risk from rare coding variants in many genes. Here, we demonstrate that these RET enhancer variants specifically alter the human fetal gut development program through significant decreases in gene expression of RET, members of the RET-EDNRB gene regulatory network (GRN), other HSCR genes, with an altered transcriptome of 2,382 differentially expressed genes across diverse neuronal and mesenchymal functions. A parsimonious hypothesis for these results is that beyond RET's direct effect on its GRN, it also has a major role in enteric neural crest-derived cell (ENCDC) precursor proliferation, its deficiency reducing ENCDCs with relative expansion of non-ENCDC cells. Thus, genes reducing RET proliferative activity can potentially cause HSCR. One such class is the 23 RET-dependent transcription factors enriched in early gut development. We show that their knockdown in human neuroblastoma SK-N-SH cells reduces RET and/or EDNRB gene expression, expanding the RET-EDNRB GRN. The human embryos we studied had major remodeling of the gut transcriptome but were unlikely to have had HSCR: thus, genetic or epigenetic changes in addition to those in RET are required for aganglionosis.

Leveraging molecular quantitative trait loci to comprehend complex diseases/traits from the omics perspective

Comprehending the molecular basis of quantitative genetic variation is a principal goal for complex diseases or traits. Molecular quantitative trait loci (molQTLs) have made it possible to investigate the effects of genetic variants hiding behind large-scale omics data. A deeper understanding of molQTL is urgently required in light of the multi-dimensionalization of omics data to more fully elucidate the pertinent biological mechanisms. Herein, we reviewed molQTLs with the corresponding resource from the omics perspective and further discussed the integrative strategy of GWAS-molQTL to infer their causal effects. Subsequently, we described the opportunities and challenges encountered by molQTL. The case studies showed that molQTL is essential for complex diseases and traits, whether single- or multi-omics QTLs. Overall, we highlighted the functional significance of genetic variants to employ the discovery of molQTL in complex diseases and traits.

Coexistence of Multiple Functional Variants and Genes Underlies Genetic Risk Locus 11p11.2 of Alzheimer's Disease

BACKGROUND

Genome-wide association studies have identified dozens of genetic risk loci for Alzheimer's disease (AD), yet the underlying causal variants and biological mechanisms remain elusive, especially for loci with complex linkage disequilibrium and regulation.

METHODS

To fully untangle the causal signal at a single locus, we performed a functional genomic study of 11p11.2 (the CELF1/SPI1 locus). Genome-wide association study signals at 11p11.2 were integrated with datasets of histone modification, open chromatin, and transcription factor binding to distill potentially functional variants (fVars). Their allelic regulatory activities were confirmed by allele imbalance, reporter assays, and base editing. Expressional quantitative trait loci and chromatin interaction data were incorporated to assign target genes to fVars. The relevance of these genes to AD was assessed by convergent functional genomics using bulk brain and single-cell transcriptomic, epigenomic, and proteomic datasets of patients with AD and control individuals, followed by cellular assays.

RESULTS

We found that 24 potential fVars, rather than a single variant, were responsible for the risk of 11p11.2. These fVars modulated transcription factor binding and regulated multiple genes by long-range chromatin interactions. Besides SPI1, convergent evidence indicated that 6 target genes (MTCH2, ACP2, NDUFS3, PSMC3, C1QTNF4, and MADD) of fVars were likely to be involved in AD development. Disruption of each gene led to cellular amyloid-β and phosphorylated tau changes, supporting the existence of multiple likely causal genes at 11p11.2.

CONCLUSIONS

Multiple variants and genes at 11p11.2 may contribute to AD risk. This finding provides new insights into the mechanistic and therapeutic challenges of AD.

eQTL Catalogue 2023: New datasets, X chromosome QTLs, and improved detection and visualisation of transcript-level QTLs

The eQTL Catalogue is an open database of uniformly processed human molecular quantitative trait loci (QTLs). We are continuously updating the resource to further increase its utility for interpreting genetic associations with complex traits. Over the past two years, we have increased the number of uniformly processed studies from 21 to 31 and added X chromosome QTLs for 19 compatible studies. We have also implemented Leafcutter to directly identify splice-junction usage QTLs in all RNA sequencing datasets. Finally, to improve the interpretability of transcript-level QTLs, we have developed static QTL coverage plots that visualise the association between the genotype and average RNA sequencing read coverage in the region for all 1.7 million fine mapped associations. To illustrate the utility of these updates to the eQTL Catalogue, we performed colocalisation analysis between vitamin D levels in the UK Biobank and all molecular QTLs in the eQTL Catalogue. Although most GWAS loci colocalised both with eQTLs and transcript-level QTLs, we found that visual inspection could sometimes be used to distinguish primary splicing QTLs from those that appear to be secondary consequences of large-effect gene expression QTLs. While these visually confirmed primary splicing QTLs explain just 6/53 of the colocalising signals, they are significantly less pleiotropic than eQTLs and identify a prioritised causal gene in 4/6 cases.

Applying an evolutionary mismatch framework to understand disease susceptibility

Noncommunicable diseases (NCDs) are on the rise worldwide. Obesity, cardiovascular disease, and type 2 diabetes are among a long list of "lifestyle" diseases that were rare throughout human history but are now common. The evolutionary mismatch hypothesis posits that humans evolved in environments that radically differ from those we currently experience; consequently, traits that were once advantageous may now be "mismatched" and disease causing. At the genetic level, this hypothesis predicts that loci with a history of selection will exhibit "genotype by environment" (GxE) interactions, with different health effects in "ancestral" versus "modern" environments. To identify such loci, we advocate for combining genomic tools in partnership with subsistence-level groups experiencing rapid lifestyle change. In these populations, comparisons of individuals falling on opposite extremes of the "matched" to "mismatched" spectrum are uniquely possible. More broadly, the work we propose will inform our understanding of environmental and genetic risk factors for NCDs across diverse ancestries and cultures.

Methods and Insights from Single-Cell Expression Quantitative Trait Loci

Recent advancements in single-cell technologies have enabled expression quantitative trait locus (eQTL) analysis across many individuals at single-cell resolution. Compared with bulk RNA sequencing, which averages gene expression across cell types and cell states, single-cell assays capture the transcriptional states of individual cells, including fine-grained, transient, and difficult-to-isolate populations at unprecedented scale and resolution. Single-cell eQTL (sc-eQTL) mapping can identify context-dependent eQTLs that vary with cell states, including some that colocalize with disease variants identified in genome-wide association studies. By uncovering the precise contexts in which these eQTLs act, single-cell approaches can unveil previously hidden regulatory effects and pinpoint important cell states underlying molecular mechanisms of disease. Here, we present an overview of recently deployed experimental designs in sc-eQTL studies. In the process, we consider the influence of study design choices such as cohort, cell states, and ex vivo perturbations. We then discuss current methodologies, modeling approaches, and technical challenges as well as future opportunities and applications.

Interaction molecular QTL mapping discovers cellular and environmental modifiers of genetic regulatory effects

Bulk tissue molecular quantitative trait loci (QTLs) have been the starting point for interpreting disease-associated variants, while context-specific QTLs show particular relevance for disease. Here, we present the results of mapping interaction QTLs (iQTLs) for cell type, age, and other phenotypic variables in multi-omic, longitudinal data from blood of individuals of diverse ancestries. By modeling the interaction between genotype and estimated cell type proportions, we demonstrate that cell type iQTLs could be considered as proxies for cell type-specific QTL effects. The interpretation of age iQTLs, however, warrants caution as the moderation effect of age on the genotype and molecular phenotype association may be mediated by changes in cell type composition. Finally, we show that cell type iQTLs contribute to cell type-specific enrichment of diseases that, in combination with additional functional data, may guide future functional studies. Overall, this study highlights iQTLs to gain insights into the context-specificity of regulatory effects.

Genetic insights into immune mechanisms of Alzheimer's and Parkinson's disease

Microglia, the macrophages of the brain, are vital for brain homeostasis and have been implicated in a broad range of brain disorders. Neuroinflammation has gained traction as a possible therapeutic target for neurodegeneration, however, the precise function of microglia in specific neurodegenerative disorders is an ongoing area of research. Genetic studies offer valuable insights into understanding causality, rather than merely observing a correlation. Genome-wide association studies (GWAS) have identified many genetic loci that are linked to susceptibility to neurodegenerative disorders. (Post)-GWAS studies have determined that microglia likely play an important role in the development of Alzheimer's disease (AD) and Parkinson's disease (PD). The process of understanding how individual GWAS risk loci affect microglia function and mediate susceptibility is complex. A rapidly growing number of publications with genomic datasets and computational tools have formulated new hypotheses that guide the biological interpretation of AD and PD genetic risk. In this review, we discuss the key concepts and challenges in the post-GWAS interpretation of AD and PD GWAS risk alleles. Post-GWAS challenges include the identification of target cell (sub)type(s), causal variants, and target genes. Crucially, the prediction of GWAS-identified disease-risk cell types, variants and genes require validation and functional testing to understand the biological consequences within the pathology of the disorders. Many AD and PD risk genes are highly pleiotropic and perform multiple important functions that might not be equally relevant for the mechanisms by which GWAS risk alleles exert their effect(s). Ultimately, many GWAS risk alleles exert their effect by changing microglia function, thereby altering the pathophysiology of these disorders, and hence, we believe that modelling this context is crucial for a deepened understanding of these disorders.

Pathogen-specific innate immune response patterns are distinctly affected by genetic diversity

Innate immune responses vary by pathogen and host genetics. We analyze quantitative trait loci (eQTLs) and transcriptomes of monocytes from 215 individuals stimulated by fungal, Gram-negative or Gram-positive bacterial pathogens. We identify conserved monocyte responses to bacterial pathogens and a distinct antifungal response. These include 745 response eQTLs (reQTLs) and corresponding genes with pathogen-specific effects, which we find first in samples of male donors and subsequently confirm for selected reQTLs in females. reQTLs affect predominantly upregulated genes that regulate immune response via e.g., NOD-like, C-type lectin, Toll-like and complement receptor-signaling pathways. Hence, reQTLs provide a functional explanation for individual differences in innate response patterns. Our identified reQTLs are also associated with cancer, autoimmunity, inflammatory and infectious diseases as shown by external genome-wide association studies. Thus, reQTLs help to explain interindividual variation in immune response to infection and provide candidate genes for variants associated with a range of diseases.

Genetic control of the dynamic transcriptional response to immune stimuli and glucocorticoids at single-cell resolution

Synthetic glucocorticoids, such as dexamethasone, have been used as a treatment for many immune conditions, such as asthma and, more recently, severe COVID-19. Single-cell data can capture more fine-grained details on transcriptional variability and dynamics to gain a better understanding of the molecular underpinnings of inter-individual variation in drug response. Here, we used single-cell RNA-seq to study the dynamics of the transcriptional response to glucocorticoids in activated peripheral blood mononuclear cells from 96 African American children. We used novel statistical approaches to calculate a mean-independent measure of gene expression variability and a measure of transcriptional response pseudotime. Using these approaches, we showed that glucocorticoids reverse the effects of immune stimulation on both gene expression mean and variability. Our novel measure of gene expression response dynamics, based on the diagonal linear discriminant analysis, separated individual cells by response status on the basis of their transcriptional profiles and allowed us to identify different dynamic patterns of gene expression along the response pseudotime. We identified genetic variants regulating gene expression mean and variability, including treatment-specific effects, and showed widespread genetic regulation of the transcriptional dynamics of the gene expression response.

The eQTL colocalization and transcriptome-wide association study identify potentially causal genes responsible for economic traits in Simmental beef cattle

BACKGROUND

A detailed understanding of genetic variants that affect beef merit helps maximize the efficiency of breeding for improved production merit in beef cattle. To prioritize the putative variants and genes, we ran a comprehensive genome-wide association studies (GWAS) analysis for 21 agronomic traits using imputed whole-genome variants in Simmental beef cattle. Then, we applied expression quantitative trait loci (eQTL) mapping between the genotype variants and transcriptome of three tissues (longissimus dorsi muscle, backfat, and liver) in 120 cattle.

RESULTS

We identified 1,580 association signals for 21 beef agronomic traits using GWAS. We then illuminated 854,498 cis-eQTLs for 6,017 genes and 46,970 trans-eQTLs for 1,903 genes in three tissues and built a synergistic network by integrating transcriptomics with agronomic traits. These cis-eQTLs were preferentially close to the transcription start site and enriched in functional regulatory regions. We observed an average of 43.5% improvement in cis-eQTL discovery using multi-tissue eQTL mapping. Fine-mapping analysis revealed that 111, 192, and 194 variants were most likely to be causative to regulate gene expression in backfat, liver, and muscle, respectively. The transcriptome-wide association studies identified 722 genes significantly associated with 11 agronomic traits. Via the colocalization and Mendelian randomization analyses, we found that eQTLs of several genes were associated with the GWAS signals of agronomic traits in three tissues, which included genes, such as NADSYN1, NDUFS3, LTF and KIFC2 in liver, GRAMD1C, TMTC2 and ZNF613 in backfat, as well as TIGAR, NDUFS3 and L3HYPDH in muscle that could serve as the candidate genes for economic traits.

CONCLUSIONS

The extensive atlas of GWAS, eQTL, fine-mapping, and transcriptome-wide association studies aid in the suggestion of potentially functional variants and genes in cattle agronomic traits and will be an invaluable source for genomics and breeding in beef cattle.

Systematic visualisation of molecular QTLs reveals variant mechanisms at GWAS loci

Splicing quantitative trait loci (QTLs) have been implicated as a common mechanism underlying complex trait associations. However, utilising splicing QTLs in target discovery and prioritisation has been challenging due to extensive data normalisation which often renders the direction of the genetic effect as well as its magnitude difficult to interpret. This is further complicated by the fact that strong expression QTLs often manifest as weak splicing QTLs and vice versa, making it difficult to uniquely identify the underlying molecular mechanism at each locus. We find that these ambiguities can be mitigated by visualising the association between the genotype and average RNA sequencing read coverage in the region. Here, we generate these QTL coverage plots for 1.7 million molecular QTL associations in the eQTL Catalogue identified with five quantification methods. We illustrate the utility of these QTL coverage plots by performing colocalisation between vitamin D levels in the UK Biobank and all molecular QTLs in the eQTL Catalogue. We find that while visually confirmed splicing QTLs explain just 6/53 of the colocalising signals, they are significantly less pleiotropic than eQTLs and identify a prioritised causal gene in 4/6 cases. All our association summary statistics and QTL coverage plots are freely available at https://www.ebi.ac.uk/eqtl/.

Stem Cell Models for Context-Specific Modeling in Psychiatric Disorders

Genome-wide association studies reveal the complex polygenic architecture underlying psychiatric disorder risk, but there is an unmet need to validate causal variants, resolve their target genes(s), and explore their functional impacts on disorder-related mechanisms. Disorder-associated loci regulate transcription of target genes in a cell type- and context-specific manner, which can be measured through expression quantitative trait loci. In this review, we discuss methods and insights from context-specific modeling of genetically and environmentally regulated expression. Human induced pluripotent stem cell-derived cell type and organoid models have uncovered context-specific psychiatric disorder associations by investigating tissue-, cell type-, sex-, age-, and stressor-specific genetic regulation of expression. Techniques such as massively parallel reporter assays and pooled CRISPR (clustered regularly interspaced short palindromic repeats) screens make it possible to functionally fine-map genome-wide association study loci and validate their target genes at scale. Integration of disorder-associated contexts with these patient-specific human induced pluripotent stem cell models makes it possible to uncover gene by environment interactions that mediate disorder risk, which will ultimately improve our ability to diagnose and treat psychiatric disorders.

IL16 and factor V gene variations are associated with asparaginase-related thrombosis in childhood acute lymphoblastic leukemia patients

Aim: We previously conducted exome-wide association study in acute lymphoblastic leukemia patients and identified association of five SNPs with asparaginase-related thrombosis. Here we aimed to replicate these findings in an independent patient cohort and through analyses in vitro. Patients & methods: SNPs located in IL16, MYBBP1A, PKD2L1, RIN3 and MPEG1 genes were analyzed in patients receiving Dana-Farber Cancer Institute acute lymphoblastic leukemia treatment protocols 05-001 and 11-001. Thrombophilia-related variations were also analysed. Results: IL16 rs11556218 conferred higher risk of thrombosis and higher in vitro sensitivity to asparaginase. The association was modulated by the treatment protocol, risk group and immunophenotype. A crosstalk between factor V Leiden, non-O blood groups and higher risk of thrombosis was also seen. Conclusion: IL16 and factor V Leiden variations are implicated in asparaginase-related thrombosis.

Translating non-coding genetic associations into a better understanding of immune-mediated disease

Genome-wide association studies have identified hundreds of genetic loci that are associated with immune-mediated diseases. Most disease-associated variants are non-coding, and a large proportion of these variants lie within enhancers. As a result, there is a pressing need to understand how common genetic variation might affect enhancer function and thereby contribute to immune-mediated (and other) diseases. In this Review, we first describe statistical and experimental methods to identify causal genetic variants that modulate gene expression, including statistical fine-mapping and massively parallel reporter assays. We then discuss approaches to characterise the mechanisms by which these variants modulate immune function, such as clustered regularly interspaced short palindromic repeats (CRISPR)-based screens. We highlight examples of studies that, by elucidating the effects of disease variants within enhancers, have provided important insights into immune function and uncovered key pathways of disease.

Evolutionary mismatch and the role of GxE interactions in human disease

Globally, we are witnessing the rise of complex, non-communicable diseases (NCDs) related to changes in our daily environments. Obesity, asthma, cardiovascular disease, and type 2 diabetes are part of a long list of "lifestyle" diseases that were rare throughout human history but are now common. A key idea from anthropology and evolutionary biology-the evolutionary mismatch hypothesis-seeks to explain this phenomenon. It posits that humans evolved in environments that radically differ from the ones experienced by most people today, and thus traits that were advantageous in past environments may now be "mismatched" and disease-causing. This hypothesis is, at its core, a genetic one: it predicts that loci with a history of selection will exhibit "genotype by environment" (GxE) interactions and have differential health effects in ancestral versus modern environments. Here, we discuss how this concept could be leveraged to uncover the genetic architecture of NCDs in a principled way. Specifically, we advocate for partnering with small-scale, subsistence-level groups that are currently transitioning from environments that are arguably more "matched" with their recent evolutionary history to those that are more "mismatched". These populations provide diverse genetic backgrounds as well as the needed levels and types of environmental variation necessary for mapping GxE interactions in an explicit mismatch framework. Such work would make important contributions to our understanding of environmental and genetic risk factors for NCDs across diverse ancestries and sociocultural contexts.

Genetic Regulation of SMC Gene Expression and Splicing Predict Causal CAD Genes

BACKGROUND

Coronary artery disease (CAD) is the leading cause of death worldwide. Recent meta-analyses of genome-wide association studies have identified over 175 loci associated with CAD. The majority of these loci are in noncoding regions and are predicted to regulate gene expression. Given that vascular smooth muscle cells (SMCs) play critical roles in the development and progression of CAD, we aimed to identify the subset of the CAD loci associated with the regulation of transcription in distinct SMC phenotypes.

METHODS

We measured gene expression in SMCs isolated from the ascending aortas of 151 heart transplant donors of various genetic ancestries in quiescent or proliferative conditions and calculated the association of their expression and splicing with ~6.3 million imputed single-nucleotide polymorphism markers across the genome.

RESULTS

We identified 4910 expression and 4412 splicing quantitative trait loci (sQTLs) representing regions of the genome associated with transcript abundance and splicing. A total of 3660 expression quantitative trait loci (eQTLs) had not been observed in the publicly available Genotype-Tissue Expression dataset. Further, 29 and 880 eQTLs were SMC-specific and sex-biased, respectively. We made these results available for public query on a user-friendly website. To identify the effector transcript(s) regulated by CAD loci, we used 4 distinct colocalization approaches. We identified 84 eQTL and 164 sQTL that colocalized with CAD loci, highlighting the importance of genetic regulation of mRNA splicing as a molecular mechanism for CAD genetic risk. Notably, 20% and 35% of the eQTLs were unique to quiescent or proliferative SMCs, respectively. One CAD locus colocalized with a sex-specific eQTL (TERF2IP), and another locus colocalized with SMC-specific eQTL (ALKBH8). The most significantly associated CAD locus, 9p21, was an sQTL for the long noncoding RNA CDKN2B-AS1, also known as ANRIL, in proliferative SMCs.

CONCLUSIONS

Collectively, our results provide evidence for the molecular mechanisms of genetic susceptibility to CAD in distinct SMC phenotypes.

Genetic regulation of circular RNA expression in human aortic smooth muscle cells and vascular traits

Circular RNAs (circRNAs) are a class of non-coding RNAs that have cell-type-specific expression and are relevant in cardiovascular disease. Aortic smooth muscle cells (SMCs) play a crucial role in cardiovascular disease. In this study, we employed a systems genetics approach to identify SMC circRNA transcripts and their relevance in cardiovascular traits across the genome. We quantified circRNA expression across 151 quiescent and proliferative human aortic SMCs from donors of various genetic ancestries. We identified 1,589 expressed circRNAs. Between quiescent and proliferative SMCs, we identified 173 differentially expressed circRNAs. To characterize the genetic regulation of circRNA expression, we associated the genotypes of 6.3 million single nucleotide polymorphisms (SNPs) with circRNA abundance and found 96 circRNAs that were associated with genetic loci. Three SNPs were associated with circRNA expression in proliferative SMCs but not quiescent SMCs. We identified six SNPs that had distinct association directions with circRNA isoforms from the same gene. Lastly, to identify the relevance of circRNAs in cardiovascular disease, we overlapped genetic loci associated with circRNA expression with vascular disease-related genome-wide association studies loci. We identified 14 blood pressure, one myocardial infarction, and three coronary artery disease loci, which were associated with a circRNA transcript but not an mRNA transcript. Overall, our results provide insight into the genetic basis of vascular disease traits mediated by circRNA expression.

Leveraging drug perturbation to reveal genetic regulators of hepatic gene expression in African Americans

Expression quantitative locus (eQTL) studies have paved the way in identifying genetic variation impacting gene expression levels. African Americans (AAs) are disproportionately underrepresented in eQTL studies, resulting in a lack of power to identify population-specific regulatory variants especially related to drug response. Specific drugs are known to affect the biosynthesis of drug metabolism enzymes as well as other genes. We used drug perturbation in cultured primary hepatocytes derived from AAs to determine the effect of drug treatment on eQTL mapping and to identify the drug response eQTLs (reQTLs) that show altered effect size following drug treatment. Whole-genome genotyping (Illumina MEGA array) and RNA sequencing were performed on 60 primary hepatocyte cultures after treatment with six drugs (Rifampin, Phenytoin, Carbamazepine, Dexamethasone, Phenobarbital, and Omeprazole) and at baseline (no treatment). eQTLs were mapped by treatment and jointly with Meta-Tissue. We found varying transcriptional changes across different drug treatments and identified Nrf2 as a potential general transcriptional regulator. We jointly mapped eQTLs with gene expression data across all drug treatments and baseline, which increased our power to detect eQTLs by 2.7-fold. We also identified 2,988 reQTLs (eQTLs with altered effect size after drug treatment). reQTLs were more likely to overlap transcription factor binding sites, and we uncovered reQTLs for drug metabolizing genes such as CYP3A5. Our results provide insights into the genetic regulation of gene expression in hepatocytes through drug perturbation and provide insight into SNPs that effect the liver's ability to respond to transcription upregulation.

Combined population transcriptomic and genomic analysis reveals cis-regulatory differentiation of non-coding RNAs in maize

Long intergenic non-coding RNA (lincRNA), cis-acting expression quantitative trait locus (cis-eQTL), maize, regulatory evolution. The law of genetic variation during domestication explains the evolutionary mechanism and provides a theoretical basis for improving existing varieties of maize. Previous studies focused on exploiting regulatory variations controlling the expression of protein-coding genes rather than of non-protein-coding genes. Here, we examined the genetic and evolutionary features of long non-coding RNAs from intergenic regions (long intergenic non-coding RNAs, lincRNAs) using population-scale transcriptome data and identified 1168 lincRNAs with cis-acting expression quantitative trait loci (cis-eQTLs). We found that lincRNAs are more likely to be regulated by cis-eQTLs, which exert stronger effects than the protein-coding genes. During maize domestication and improvement, upregulated alleles of lincRNAs, which originated from both standing variation and new mutation, accumulate more frequently and show larger effect sizes than the coding genes. A stronger signature of genetic differentiation was observed in their regulatory regions compared to those of randomly sampled lincRNAs. In addition, we found that cis-regulatory differentiation of lincRNAs is related to the sequence conservation of lincRNA transcripts. Non-conserved lincRNAs more tend to gain upregulated alleles and show a stronger relationship with selected traits than conserved lincRNAs between maize and its wild relatives. Our findings in maize improve the understanding of cis-regulatory variation in lincRNA genes during domestication and improvement and provide an effective approach for prioritizing candidates for further investigation.

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.'

Prioritizing autoimmunity risk variants for functional analyses by fine-mapping mutations under natural selection

Pathogen-driven selection shaped adaptive mutations in immunity genes, including those contributing to inflammatory disorders. Functional characterization of such adaptive variants can shed light on disease biology and past adaptations. This popular idea, however, was difficult to test due to challenges in pinpointing adaptive mutations in selection footprints. In this study, using a local-tree-based approach, we show that 28% of risk loci (153/535) in 21 inflammatory disorders bear footprints of moderate and weak selection, and part of them are population specific. Weak selection footprints allow partial fine-mapping, and we show that in 19% (29/153) of the risk loci under selection, candidate disease variants are hitchhikers, and only in 39% of cases they are likely selection targets. We predict function for a subset of these selected SNPs and highlight examples of antagonistic pleiotropy. We conclude by offering disease variants under selection that can be tested functionally using infectious agents and other stressors to decipher the poorly understood link between environmental stressors and genetic risk in inflammatory conditions.

Diverse environmental perturbations reveal the evolution and context-dependency of genetic effects on gene expression levels

There is increasing appreciation that, in addition to being shaped by an individual's genotype and environment, most complex traits are also determined by poorly understood interactions between these two factors. So-called "genotype × environment" (G×E) interactions remain difficult to map at the organismal level but can be uncovered using molecular phenotypes. To do so at large scale, we used TM3'seq to profile transcriptomes across 12 cellular environments in 544 immortalized B cell lines from the 1000 Genomes Project. We mapped the genetic basis of gene expression levels across environments and revealed a context-dependent genetic architecture: The average heritability of gene expression levels increased in treatment relative to control conditions, and on average, each treatment revealed new expression quantitative trait loci (eQTLs) at 11% of genes. Across our experiments, 22% of all identified eQTLs were context-dependent, and this group was enriched for trait- and disease-associated loci. Further, evolutionary analyses suggested that positive selection has shaped G×E loci involved in responding to immune challenges and hormones but not to man-made chemicals. We hypothesize that this reflects a reduced opportunity for selection to act on responses to molecules recently introduced into human environments. Together, our work highlights the importance of considering an exposure's evolutionary history when studying and interpreting G×E interactions, and provides new insight into the evolutionary mechanisms that maintain G×E loci in human populations.

Genetic determinants of chromatin reveal prostate cancer risk mediated by context-dependent gene regulation

Many genetic variants affect disease risk by altering context-dependent gene regulation. Such variants are difficult to study mechanistically using current methods that link genetic variation to steady-state gene expression levels, such as expression quantitative trait loci (eQTLs). To address this challenge, we developed the cistrome-wide association study (CWAS), a framework for identifying genotypic and allele-specific effects on chromatin that are also associated with disease. In prostate cancer, CWAS identified regulatory elements and androgen receptor-binding sites that explained the association at 52 of 98 known prostate cancer risk loci and discovered 17 additional risk loci. CWAS implicated key developmental transcription factors in prostate cancer risk that are overlooked by eQTL-based approaches due to context-dependent gene regulation. We experimentally validated associations and demonstrated the extensibility of CWAS to additional epigenomic datasets and phenotypes, including response to prostate cancer treatment. CWAS is a powerful and biologically interpretable paradigm for studying variants that influence traits by affecting transcriptional regulation.

Epigenomic analysis reveals a dynamic and context-specific macrophage enhancer landscape associated with innate immune activation and tolerance

BACKGROUND

Chromatin states and enhancers associate gene expression, cell identity and disease. Here, we systematically delineate the acute innate immune response to endotoxin in terms of human macrophage enhancer activity and contrast with endotoxin tolerance, profiling the coding and non-coding transcriptome, chromatin accessibility and epigenetic modifications.

RESULTS

We describe the spectrum of enhancers under acute and tolerance conditions and the regulatory networks between these enhancers and biological processes including gene expression, splicing regulation, transcription factor binding and enhancer RNA signatures. We demonstrate that the vast majority of differentially regulated enhancers on acute stimulation are subject to tolerance and that expression quantitative trait loci, disease-risk variants and eRNAs are enriched in these regulatory regions and related to context-specific gene expression. We find enrichment for context-specific eQTL involving endotoxin response and specific infections and delineate specific differential regions informative for GWAS variants in inflammatory bowel disease and multiple sclerosis, together with a context-specific enhancer involving a bacterial infection eQTL for KLF4. We show enrichment in differential enhancers for tolerance involving transcription factors NFκB-p65, STATs and IRFs and prioritize putative causal genes directly linking genetic variants and disease risk enhancers. We further delineate similarities and differences in epigenetic landscape between stem cell-derived macrophages and primary cells and characterize the context-specific enhancer activities for key innate immune response genes KLF4, SLAMF1 and IL2RA.

CONCLUSIONS

Our study demonstrates the importance of context-specific macrophage enhancers in gene regulation and utility for interpreting disease associations, providing a roadmap to link genetic variants with molecular and cellular functions.

Functional Characterization of Genetic Variant Effects on Expression

Thousands of common genetic variants in the human population have been associated with disease risk and phenotypic variation by genome-wide association studies (GWAS). However, the majority of GWAS variants fall into noncoding regions of the genome, complicating our understanding of their regulatory functions, and few molecular mechanisms of GWAS variant effects have been clearly elucidated. Here, we set out to review genetic variant effects, focusing on expression quantitative trait loci (eQTLs), including their utility in interpreting GWAS variant mechanisms. We discuss the interrelated challenges and opportunities for eQTL analysis, covering determining causal variants, elucidating molecular mechanisms of action, and understanding context variability. Addressing these questions can enable better functional characterization of disease-associated loci and provide insights into fundamental biological questions of the noncoding genetic regulatory code and its control of gene expression. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 5 is August 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Genetic associations at regulatory phenotypes improve fine-mapping of causal variants for 12 immune-mediated diseases

The resolution of causal genetic variants informs understanding of disease biology. We used regulatory quantitative trait loci (QTLs) from the BLUEPRINT, GTEx and eQTLGen projects to fine-map putative causal variants for 12 immune-mediated diseases. We identify 340 unique loci that colocalize with high posterior probability (≥98%) with regulatory QTLs and apply Bayesian frameworks to fine-map associations at each locus. We show that fine-mapping credible sets derived from regulatory QTLs are smaller compared to disease summary statistics. Further, they are enriched for more functionally interpretable candidate causal variants and for putatively causal insertion/deletion (INDEL) polymorphisms. Finally, we use massively parallel reporter assays to evaluate candidate causal variants at the ITGA4 locus associated with inflammatory bowel disease. Overall, our findings suggest that fine-mapping applied to disease-colocalizing regulatory QTLs can enhance the discovery of putative causal disease variants and enhance insights into the underlying causal genes and molecular mechanisms.

Some Good and Some Bad: Sand Fly Salivary Proteins in the Control of Leishmaniasis and in Autoimmunity

Sand flies are hematophagous insects responsible for the transmission of vector-borne diseases to humans. Prominent among these diseases is Leishmaniasis that affects the skin and mucous surfaces and organs such as liver and spleen. Importantly, the function of blood-sucking arthropods goes beyond merely transporting pathogens. The saliva of vectors of disease contains pharmacologically active components that facilitate blood feeding and often pathogen establishment. Transcriptomic and proteomic studies have enumerated the repertoire of sand fly salivary proteins and their potential use for the control of Leishmaniasis, either as biomarkers of vector exposure or as anti-Leishmania vaccines. However, a group of specific sand fly salivary proteins triggers formation of cross-reactive antibodies that bind the ectodomain of human desmoglein 1, a member of the epidermal desmosomal cadherins. These cross-reactive antibodies are associated with skin autoimmune blistering diseases, such as pemphigus, in certain immunogenetically predisposed individuals. In this review, we focus on two different aspects of sand fly salivary proteins in the context of human disease: The good, which refers to salivary proteins functioning as biomarkers of exposure or as anti-Leishmania vaccines, and the bad, which refers to salivary proteins as environmental triggers of autoimmune skin diseases.

Potential role of regulatory DNA variants in modifying the risk of severe cutaneous reactions induced by aromatic anti-seizure medications

Objective

Stevens–Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN) are severe cutaneous adverse drug reactions. Antiseizure medications (ASMs) with aromatic ring structure, including carbamazepine, are among the most common culprits. Screening for human leukocyte antigen (HLA) allele HLA‐B*15:02 is recommended prior to initiating treatment with carbamazepine in Asians, but this allele has low positive predictive value.

Methods

We performed whole genome sequencing and analyzed 6 199 696 common variants among 113 aromatic ASM‐induced SJS/TEN cases and 84 tolerant controls of Han Chinese ethnicity.

Results

In the primary analysis, nine variants reached genome‐wide significance (p < 5e‐08), one in the carbamazepine subanalysis (85 cases vs. 77 controls) and a further eight identified in HLA‐B*15:02‐negative subanalysis (35 cases and 53 controls). Interaction analysis between each novel variant from the primary analysis found that five increased risk irrespective of HLA‐B*15:02 status or zygosity. HLA‐B*15:02‐positive individuals were found to have reduced risk if they also carried a chromosome 12 variant, chr12.9426934 (heterozygotes: relative risk = .71, p = .001; homozygotes: relative risk = .23, p < .001). All significant variants lie within intronic or intergenic regions with poorly understood functional consequence. In silico functional analysis of suggestive variants (p < 5e‐6) identified through the primary and subanalyses (stratified by HLA‐B*15:02 status and drug exposure) suggests that genetic variation within regulatory DNA may contribute to risk indirectly by disrupting the regulation of pathology‐related genes. The genes implicated were specific either to the primary analysis (CD9), HLA‐B*15:02 carriers (DOCK10), noncarriers (ABCA1), carbamazepine exposure (HLA‐E), or phenytoin exposure (CD24).

Significance

We identified variants that could explain why some carriers of HLA‐B*15:02 tolerate treatment, and why some noncarriers develop ASM‐induced SJS/TEN. Additionally, this analysis suggests that the mixing of HLA‐B*15:02 carrier status in previous studies might have masked variants contributing to susceptibility, and that inheritance of risk for ASM‐induced SJS/TEN is complex, likely involving multiple risk variants.

Cytokine responses in nonlesional psoriatic skin as clinical predictor to anti-TNF agents

BACKGROUND

A major issue with the current management of psoriasis is our inability to predict treatment response.

OBJECTIVE

Our aim was to evaluate the ability to use baseline molecular expression profiling to assess treatment outcome for patients with psoriasis.

METHODS

We conducted a longitudinal study of 46 patients with chronic plaque psoriasis treated with anti-TNF agent etanercept, and molecular profiles were assessed in more than 200 RNA-seq samples.

RESULTS

We demonstrated correlation between clinical response and molecular changes during the course of the treatment, particularly for genes responding to IL-17A/TNF in keratinocytes. Intriguingly, baseline gene expressions in nonlesional, but not lesional, skin were the best marker of treatment response at week 12. We identified USP18, a known regulator of IFN responses, as positively correlated with Psoriasis Area and Severity Index (PASI) improvement (P = 9.8 × 10^-4) and demonstrate its role in regulating IFN/TNF responses in keratinocytes. Consistently, cytokine gene signatures enriched in baseline nonlesional skin expression profiles had strong correlations with PASI improvement. Using this information, we developed a statistical model for predicting PASI75 (ie, 75% of PASI improvement) at week 12, achieving area under the receiver-operating characteristic curve value of 0.75 and up to 80% accurate PASI75 prediction among the top predicted responders.

CONCLUSIONS

Our results illustrate feasibility of assessing drug response in psoriasis using nonlesional skin and implicate involvement of IFN regulators in anti-TNF responses.