Prioritizing disease and trait causal variants at the TNFAIP3 locus using functional and genomic features

Training deep learning models on personalized genomic sequences improves variant effect prediction

Sequence-to-function models have broad applications in interpreting the molecular impact of genetic variation, yet have been criticized for poor performance in this task. Here we show that training models on functional genomic data with matched personal genomes improves their performance at variant effect prediction. Variant effect representations are retained even when transfer learning models to unseen cellular contexts and experimental readouts. Our results have implications for interpreting trait-associated genetic variation.

Linking candidate causal autoimmune variants to T cell networks using genetic and epigenetic screens in primary human T cells

Genetic variants associated with autoimmune diseases are highly enriched within putative cis -regulatory regions of CD4 + T cells, suggesting that they alter disease risk via changes in gene regulation. However, very few genetic variants have been shown to affect T cell gene expression or function. We tested >18,000 autoimmune disease-associated variants for allele-specific expression using massively parallel reporter assays in primary human CD4 + T cells. The 545 expression-modulating variants (emVars) identified greatly enrich for likely causal variants. We provide evidence that many emVars are mediated by common upstream regulatory conduits, and that putative target genes of primary T cell emVars are highly enriched within a lymphocyte activation network. Using bulk and single-cell CRISPR-interference screens, we confirm that emVar-containing T cell cis -regulatory elements modulate both known and novel target genes that regulate T cell proliferation, providing plausible mechanisms by which these variants alter autoimmune disease risk.

Systemic lupus erythematosus genetics: insights into pathogenesis and implications for therapy

Systemic lupus erythematosus (SLE) is a prime example of how the interplay between genetic and environmental factors can trigger systemic autoimmunity, particularly in young women. Although clinical disease can take years to manifest, risk is established by the unique genetic makeup of an individual. Genome-wide association studies have identified almost 200 SLE-associated risk loci, yet unravelling the functional effect of these loci remains a challenge. New analytic tools have enabled researchers to delve deeper, leveraging DNA sequencing and cell-specific and immune pathway analysis to elucidate the immunopathogenic mechanisms. Both common genetic variants and rare non-synonymous mutations can interact to increase SLE risk. Notably, variants strongly associated with SLE are often located in genome super-enhancers that regulate MHC class II gene expression. Additionally, the 3D conformations of DNA and RNA contribute to genome regulation and innate immune system activation. Improved therapies for SLE are urgently needed and current and future knowledge from genetic and genomic research should provide new tools to facilitate patient diagnosis, enhance the identification of therapeutic targets and optimize testing of agents.

Massively parallel approaches for characterizing noncoding functional variation in human evolution

The genetic differences underlying unique phenotypes in humans compared to our closest primate relatives have long remained a mystery. Similarly, the genetic basis of adaptations between human groups during our expansion across the globe is poorly characterized. Uncovering the downstream phenotypic consequences of these genetic variants has been difficult, as a substantial portion lies in noncoding regions, such as cis-regulatory elements (CREs). Here, we review recent high-throughput approaches to measure the functions of CREs and the impact of variation within them. CRISPR screens can directly perturb CREs in the genome to understand downstream impacts on gene expression and phenotypes, while massively parallel reporter assays can decipher the regulatory impact of sequence variants. Machine learning has begun to be able to predict regulatory function from sequence alone, further scaling our ability to characterize genome function. Applying these tools across diverse phenotypes, model systems, and ancestries is beginning to revolutionize our understanding of noncoding variation underlying human evolution.

A systematic strategy for identifying causal single nucleotide polymorphisms and their target genes on Juvenile arthritis risk haplotypes

BACKGROUND

Although genome-wide association studies (GWAS) have identified multiple regions conferring genetic risk for juvenile idiopathic arthritis (JIA), we are still faced with the task of identifying the single nucleotide polymorphisms (SNPs) on the disease haplotypes that exert the biological effects that confer risk. Until we identify the risk-driving variants, identifying the genes influenced by these variants, and therefore translating genetic information to improved clinical care, will remain an insurmountable task. We used a function-based approach for identifying causal variant candidates and the target genes on JIA risk haplotypes.

METHODS

We used a massively parallel reporter assay (MPRA) in myeloid K562 cells to query the effects of 5,226 SNPs in non-coding regions on JIA risk haplotypes for their ability to alter gene expression when compared to the common allele. The assay relies on 180 bp oligonucleotide reporters ("oligos") in which the allele of interest is flanked by its cognate genomic sequence. Barcodes were added randomly by PCR to each oligo to achieve > 20 barcodes per oligo to provide a quantitative read-out of gene expression for each allele. Assays were performed in both unstimulated K562 cells and cells stimulated overnight with interferon gamma (IFNg). As proof of concept, we then used CRISPRi to demonstrate the feasibility of identifying the genes regulated by enhancers harboring expression-altering SNPs.

RESULTS

We identified 553 expression-altering SNPs in unstimulated K562 cells and an additional 490 in cells stimulated with IFNg. We further filtered the SNPs to identify those plausibly situated within functional chromatin, using open chromatin and H3K27ac ChIPseq peaks in unstimulated cells and open chromatin plus H3K4me1 in stimulated cells. These procedures yielded 42 unique SNPs (total = 84) for each set. Using CRISPRi, we demonstrated that enhancers harboring MPRA-screened variants in the TRAF1 and LNPEP/ERAP2 loci regulated multiple genes, suggesting complex influences of disease-driving variants.

CONCLUSION

Using MPRA and CRISPRi, JIA risk haplotypes can be queried to identify plausible candidates for disease-driving variants. Once these candidate variants are identified, target genes can be identified using CRISPRi informed by the 3D chromatin structures that encompass the risk haplotypes.

Functional dissection of complex and molecular trait variants at single nucleotide resolution

Identifying the causal variants and mechanisms that drive complex traits and diseases remains a core problem in human genetics. The majority of these variants have individually weak effects and lie in non-coding gene-regulatory elements where we lack a complete understanding of how single nucleotide alterations modulate transcriptional processes to affect human phenotypes. To address this, we measured the activity of 221,412 trait-associated variants that had been statistically fine-mapped using a Massively Parallel Reporter Assay (MPRA) in 5 diverse cell-types. We show that MPRA is able to discriminate between likely causal variants and controls, identifying 12,025 regulatory variants with high precision. Although the effects of these variants largely agree with orthogonal measures of function, only 69% can plausibly be explained by the disruption of a known transcription factor (TF) binding motif. We dissect the mechanisms of 136 variants using saturation mutagenesis and assign impacted TFs for 91% of variants without a clear canonical mechanism. Finally, we provide evidence that epistasis is prevalent for variants in close proximity and identify multiple functional variants on the same haplotype at a small, but important, subset of trait-associated loci. Overall, our study provides a systematic functional characterization of likely causal common variants underlying complex and molecular human traits, enabling new insights into the regulatory grammar underlying disease risk.

Non-coding DNA variants for risk in lupus

Systemic Lupus Erythematosus (SLE) is a multifactorial autoimmune disease that arises from a dynamic interplay between genetics and environmental triggers. The advent of sophisticated genomics technology has catalyzed a shift in our understanding of disease etiology, spotlighting the pivotal role of non-coding DNA variants in SLE pathogenesis. In this review, we present a comprehensive examination of the non-coding variants associated with SLE, shedding light on their role in influencing disease risk and progression. We discuss the latest methodological advancements that have been instrumental in the identification and functional characterization of these genomic elements, with a special focus on the transformative power of CRISPR-based gene-editing technologies. Additionally, the review probes into the therapeutic opportunities that arise from modulating non-coding regions associated with SLE. Through an exploration of the complex network of non-coding DNA, this review aspires to decode the genetic puzzle of SLE and set the stage for groundbreaking gene-based therapeutic interventions and the advancement of precision medicine strategies tailored to SLE management.

Comparison of the three-dimensional chromatin structures of adolescent and adult peripheral blood B cells: implications for the study of pediatric autoimmune diseases

Background/Purpose

Knowledge of the 3D genome is essential to elucidate genetic mechanisms driving autoimmune diseases. The 3D genome is distinct for each cell type, and it is uncertain whether cell lines faithfully recapitulate the 3D architecture of primary human cells or whether developmental aspects of the pediatric immune system require use of pediatric samples. We undertook a systematic analysis of B cells and B cell lines to compare 3D genomic features encompassing risk loci for juvenile idiopathic arthritis (JIA), systemic lupus (SLE), and type 1 diabetes (T1D).

Methods

We isolated B cells from healthy individuals, ages 9-17. HiChIP was performed using CTCF antibody, and CTCF peaks were identified. CTCF loops within the pediatric were compared to three datasets: 1) self-called CTCF consensus peaks called within the pediatric samples, 2) ENCODE's publicly available GM12878 CTCF ChIP-seq peaks, and 3) ENCODE's primary B cell CTCF ChIPseq peaks from two adult females. Differential looping was assessed within the pediatric samples and each of the three peak datasets.

Results

The number of consensus peaks called in the pediatric samples was similar to that identified in ENCODE's GM12878 and primary B cell datasets. We observed <1% of loops that demonstrated significantly differential looping between peaks called within the pediatric samples themselves and when called using ENCODE GM12878 peaks . Significant looping differences were even less when comparing loops of the pediatric called peaks to those of the ENCODE primary B cell peaks. When querying loops found in juvenile idiopathic arthritis, type 1 diabetes, or systemic lupus erythematosus risk haplotypes, we observed significant differences in only 2.2%, 1.0%, and 1.3% loops, respectively, when comparing peaks called within the pediatric samples and ENCODE GM12878 dataset. The differences were even less apparent when comparing loops called with the pediatric vs ENCODE adult primary B cell peak datasets.The 3D chromatin architecture in B cells is similar across pediatric, adult, and EBVtransformed cell lines. This conservation of 3D structure includes regions encompassing autoimmune risk haplotypes.

Conclusion

Thus, even for pediatric autoimmune diseases, publicly available adult B cell and cell line datasets may be sufficient for assessing effects exerted in the 3D genomic space.

Functional genomics in stroke: current and future applications of iPSCs and gene editing to dissect the function of risk variants

Stroke is an important disease with unmet clinical need. To uncover novel paths for treatment, it is of critical importance to develop relevant laboratory models that may help to shed light on the pathophysiological mechanisms of stroke. Induced pluripotent stem cells (iPSCs) technology has enormous potential to advance our knowledge into stroke by creating novel human models for research and therapeutic testing. iPSCs models generated from patients with specific stroke types and specific genetic predisposition in combination with other state of art technologies including genome editing, multi-omics, 3D system, libraries screening, offer the opportunity to investigate disease-related pathways and identify potential novel therapeutic targets that can then be tested in these models. Thus, iPSCs offer an unprecedented opportunity to make rapid progress in the field of stroke and vascular dementia research leading to clinical translation. This review paper summarizes some of the key areas in which patient-derived iPSCs technology has been applied to disease modelling and discusses the ongoing challenges and the future directions for the application of this technology in the field of stroke research.

Deletion mapping of regulatory elements for GATA3 in T cells reveals a distal enhancer involved in allergic diseases

GATA3 is essential for T cell differentiation and is surrounded by genome-wide association study (GWAS) hits for immune traits. Interpretation of these GWAS hits is challenging because gene expression quantitative trait locus (eQTL) studies lack power to detect variants with small effects on gene expression in specific cell types and the genome region containing GATA3 contains dozens of potential regulatory sequences. To map regulatory sequences for GATA3, we performed a high-throughput tiling deletion screen of a 2 Mb genome region in Jurkat T cells. This revealed 23 candidate regulatory sequences, all but one of which is within the same topological-associating domain (TAD) as GATA3. We then performed a lower-throughput deletion screen to precisely map regulatory sequences in primary T helper 2 (Th2) cells. We tested 25 sequences with ∼100 bp deletions and validated five of the strongest hits with independent deletion experiments. Additionally, we fine-mapped GWAS hits for allergic diseases in a distal regulatory element, 1 Mb downstream of GATA3, and identified 14 candidate causal variants. Small deletions spanning the candidate variant rs725861 decreased GATA3 levels in Th2 cells, and luciferase reporter assays showed regulatory differences between its two alleles, suggesting a causal mechanism for this variant in allergic diseases. Our study demonstrates the power of integrating GWAS signals with deletion mapping and identifies critical regulatory sequences for GATA3.

Whole-genome functional characterization of RE1 silencers using a modified massively parallel reporter assay

Both upregulation and downregulation by cis-regulatory elements help modulate precise gene expression. However, our understanding of repressive elements is far more limited than activating elements. To address this gap, we characterized RE1, a group of transcriptional silencers bound by REST, at genome-wide scale using a modified massively parallel reporter assay (MPRAduo). MPRAduo empirically defined a minimal binding strength of REST (REST motif-intrinsic value [m-value]), above which cofactors colocalize and silence transcription. We identified 1,500 human variants that alter RE1 silencing and found that their effect sizes are predictable when they overlap with REST-binding sites above the m-value. Additionally, we demonstrate that non-canonical REST-binding motifs exhibit silencer function only if they precisely align half sites with specific spacer lengths. Our results show mechanistic insights into RE1, which allow us to predict its activity and effect of variants on RE1, providing a paradigm for performing genome-wide functional characterization of transcription-factor-binding sites.

Functional studies of lung cancer GWAS beyond association

Fourteen years after the first genome-wide association study (GWAS) of lung cancer was published, approximately 45 genomic loci have now been significantly associated with lung cancer risk. While functional characterization was performed for several of these loci, a comprehensive summary of the current molecular understanding of lung cancer risk has been lacking. Further, many novel computational and experimental tools now became available to accelerate the functional assessment of disease-associated variants, moving beyond locus-by-locus approaches. In this review, we first highlight the heterogeneity of lung cancer GWAS findings across histological subtypes, ancestries and smoking status, which poses unique challenges to follow-up studies. We then summarize the published lung cancer post-GWAS studies for each risk-associated locus to assess the current understanding of biological mechanisms beyond the initial statistical association. We further summarize strategies for GWAS functional follow-up studies considering cutting-edge functional genomics tools and providing a catalog of available resources relevant to lung cancer. Overall, we aim to highlight the importance of integrating computational and experimental approaches to draw biological insights from the lung cancer GWAS results beyond association.

Multiplexed functional genomic assays to decipher the noncoding genome

Linkage disequilibrium and the incomplete regulatory annotation of the noncoding genome complicates the identification of functional noncoding genetic variants and their causal association with disease. Current computational methods for variant prioritization have limited predictive value, necessitating the application of highly parallelized experimental assays to efficiently identify functional noncoding variation. Here, we summarize two distinct approaches, massively parallel reporter assays and CRISPR-based pooled screens and describe their flexible implementation to characterize human noncoding genetic variation at unprecedented scale. Each approach provides unique advantages and limitations, highlighting the importance of multimodal methodological integration. These multiplexed assays of variant effects are undoubtedly poised to play a key role in the experimental characterization of noncoding genetic risk, informing our understanding of the underlying mechanisms of disease-associated loci and the development of more robust predictive classification algorithms.

Systemic lupus erythematosus variants modulate the function of an enhancer upstream of TNFAIP3

TNFAIP3/A20 is a prominent autoimmune disease risk locus that is correlated with hypomorphic TNFAIP3 expression and exhibits complex chromatin architecture with over 30 predicted enhancers. This study aimed to functionally characterize an enhancer ∼55 kb upstream of the TNFAIP3 promoter marked by the systemic lupus erythematosus (SLE) risk haplotype index SNP, rs10499197. Allele effects of rs10499197, rs58905141, and rs9494868 were tested by EMSA and/or luciferase reporter assays in immune cell types. Co-immunoprecipitation, ChIP-qPCR, and 3C-qPCR were performed on patient-derived EBV B cells homozygous for the non-risk or SLE risk TNFAIP3 haplotype to assess haplotype-specific effects on transcription factor binding and chromatin regulation at the TNFAIP3 locus. This study found that the TNFAIP3 locus has a complex chromatin regulatory network that spans ∼1M bp from the promoter region of IL20RA to the 3' untranslated region of TNFAIP3. Functional dissection of the enhancer demonstrated co-dependency of the RelA/p65 and CEBPB binding motifs that, together, increase IL20RA and IFNGR1 expression and decreased TNFAIP3 expression in the context of the TNFAIP3 SLE risk haplotype through dynamic long-range interactions up- and downstream. Examination of SNPs in linkage disequilibrium (D' = 1.0) with rs10499197 identified rs9494868 as a functional SNP with risk allele-specific increase in nuclear factor binding and enhancer activation in vitro. In summary, this study demonstrates that SNPs carried on the ∼109 kb SLE risk haplotype facilitate hypermorphic IL20RA and IFNGR1 expression, while suppressing TNFAIP3 expression, adding to the mechanistic potency of this critically important locus in autoimmune disease pathology.

Functional inference of gene regulation using single-cell multi-omics

Cells require coordinated control over gene expression when responding to environmental stimuli. Here we apply scATAC-seq and single-cell RNA sequencing (scRNA-seq) in resting and stimulated human blood cells. Collectively, we generate ~91,000 single-cell profiles, allowing us to probe the cis-regulatory landscape of the immunological response across cell types, stimuli, and time. Advancing tools to integrate multi-omics data, we develop functional inference of gene regulation (FigR), a framework to computationally pair scA-TAC-seq with scRNA-seq cells, connect distal cis-regulatory elements to genes, and infer gene-regulatory networks (GRNs) to identify candidate transcription factor (TF) regulators. Utilizing these paired multi-omics data, we define domains of regulatory chromatin (DORCs) of immune stimulation and find that cells alter chromatin accessibility and gene expression at timescales of minutes. Construction of the stimulation GRN elucidates TF activity at disease-associated DORCs. Overall, FigR enables elucidation of regulatory interactions across single-cell data, providing new opportunities to understand the function of cells within tissues.

SNP-to-gene linking strategies reveal contributions of enhancer-related and candidate master-regulator genes to autoimmune disease

We assess contributions to autoimmune disease of genes whose regulation is driven by enhancer regions (enhancer-related) and genes that regulate other genes in trans (candidate master-regulator). We link these genes to SNPs using several SNP-to-gene (S2G) strategies and apply heritability analyses to draw three conclusions about 11 autoimmune/blood-related diseases/traits. First, several characterizations of enhancer-related genes using functional genomics data are informative for autoimmune disease heritability after conditioning on a broad set of regulatory annotations. Second, candidate master-regulator genes defined using trans-eQTL in blood are also conditionally informative for autoimmune disease heritability. Third, integrating enhancer-related and master-regulator gene sets with protein-protein interaction (PPI) network information magnified their disease signal. The resulting PPI-enhancer gene score produced >2-fold stronger heritability signal and >2-fold stronger enrichment for drug targets, compared with the recently proposed enhancer domain score. In each case, functionally informed S2G strategies produced 4.1- to 13-fold stronger disease signals than conventional window-based strategies.

Prioritization of autoimmune disease-associated genetic variants that perturb regulatory element activity in T cells

Genome-wide association studies (GWASs) have uncovered hundreds of autoimmune disease-associated loci; however, the causal genetic variants within each locus are mostly unknown. Here, we perform high-throughput allele-specific reporter assays to prioritize disease-associated variants for five autoimmune diseases. By examining variants that both promote allele-specific reporter expression and are located in accessible chromatin, we identify 60 putatively causal variants that enrich for statistically fine-mapped variants by up to 57.8-fold. We introduced the risk allele of a prioritized variant (rs72928038) into a human T cell line and deleted the orthologous sequence in mice, both resulting in reduced BACH2 expression. Naive CD8 T cells from mice containing the deletion had reduced expression of genes that suppress activation and maintain stemness and, upon acute viral infection, displayed greater propensity to become effector T cells. Our results represent an example of an effective approach for prioritizing variants and studying their physiologically relevant effects.

High-speed fluorescence image-enabled cell sorting

Fast and selective isolation of single cells with unique spatial and morphological traits remains a technical challenge. Here, we address this by establishing high-speed image-enabled cell sorting (ICS), which records multicolor fluorescence images and sorts cells based on measurements from image data at speeds up to 15,000 events per second. We show that ICS quantifies cell morphology and localization of labeled proteins and increases the resolution of cell cycle analyses by separating mitotic stages. We combine ICS with CRISPR-pooled screens to identify regulators of the nuclear factor κB (NF-κB) pathway, enabling the completion of genome-wide image-based screens in about 9 hours of run time. By assessing complex cellular phenotypes, ICS substantially expands the phenotypic space accessible to cell-sorting applications and pooled genetic screening.

Functional interrogation of autoimmune disease genetics using CRISPR/Cas9 technologies and massively parallel reporter assays

Genetic studies, including genome-wide association studies, have identified many common variants that are associated with autoimmune diseases. Strikingly, in addition to being frequently observed in healthy individuals, a number of these variants are shared across diseases with diverse clinical presentations. This highlights the potential for improved autoimmune disease understanding which could be achieved by characterising the mechanism by which variants lead to increased risk of disease. Of particular interest is the potential for identifying novel drug targets or of repositioning drugs currently used in other diseases. The majority of autoimmune disease variants do not alter coding regions and it is often difficult to generate a plausible hypothetical mechanism by which variants affect disease-relevant genes and pathways. Given the interest in this area, considerable effort has been invested in developing and applying appropriate methodologies. Two of the most important technologies in this space include both low- and high-throughput genomic perturbation using the CRISPR/Cas9 system and massively parallel reporter assays. In this review, we introduce the field of autoimmune disease functional genomics and use numerous examples to demonstrate the recent and potential future impact of these technologies.

Systematic identification of genomic elements that regulate FCGR2A expression and harbor variants linked with autoimmune disease

BACKGROUND

FCGR2A binds antibody-antigen complexes to regulate the abundance of circulating and deposited complexes along with downstream immune and autoimmune responses. Although the abundance of FCRG2A may be critical in immune-mediated diseases, little is known about whether its surface expression is regulated through cis genomic elements and non-coding variants. In the current study, we aimed to characterize the regulation of FCGR2A expression, the impact of genetic variation and its association with autoimmune disease.

METHODS

We applied CRISPR-based interference and editing to scrutinize 1.7 Mb of open chromatin surrounding the FCGR2A gene to identify regulatory elements. Relevant transcription factors (TFs) binding to these regions were defined through public databases. Genetic variants affecting regulation were identified using luciferase reporter assays and were verified in a cohort of 1996 genotyped healthy individuals using flow cytometry.

RESULTS

We identified a complex proximal region and five distal enhancers regulating FCGR2A. The proximal region split into subregions upstream and downstream of the transcription start site, was enriched in binding of inflammation-regulated TFs, and harbored a variant associated with FCGR2A expression in primary myeloid cells. One distal enhancer region was occupied by CCCTC-binding factor (CTCF) whose binding site was disrupted by a rare genetic variant, altering gene expression.

CONCLUSIONS

The FCGR2A gene is regulated by multiple proximal and distal genomic regions, with links to autoimmune disease. These findings may open up novel therapeutic avenues where fine-tuning of FCGR2A levels may constitute a part of treatment strategies for immune-mediated diseases.

Gaining insight into metabolic diseases from human genetic discoveries

Human large-scale genetic association studies have identified sequence variations at thousands of genetic risk loci that are more common in patients with diverse metabolic disease compared with healthy controls. While these genetic associations have been replicated in multiple large cohorts and sometimes can explain up to 50% of heritability, the molecular and cellular mechanisms affected by common genetic variation associated with metabolic disease remains mostly unknown. A variety of new genome-wide data types, in conjunction with novel biostatistical and computational analytical methodologies and foundational experimental technologies, are paving the way for a principled approach to systematic variant-to-function (V2F) studies for metabolic diseases, turning associated regions into causal variants, cell types and states of action, effector genes, and cellular and physiological mechanisms. Identification of new target genes and cellular programs for metabolic risk loci will improve mechanistic understanding of disease biology and identification of novel therapeutic strategies.

Non-Coding Variants in Cancer: Mechanistic Insights and Clinical Potential for Personalized Medicine

The cancer genome is characterized by extensive variability, in the form of Single Nucleotide Polymorphisms (SNPs) or structural variations such as Copy Number Alterations (CNAs) across wider genomic areas. At the molecular level, most SNPs and/or CNAs reside in non-coding sequences, ultimately affecting the regulation of oncogenes and/or tumor-suppressors in a cancer-specific manner. Notably, inherited non-coding variants can predispose for cancer decades prior to disease onset. Furthermore, accumulation of additional non-coding driver mutations during progression of the disease, gives rise to genomic instability, acting as the driving force of neoplastic development and malignant evolution. Therefore, detection and characterization of such mutations can improve risk assessment for healthy carriers and expand the diagnostic and therapeutic toolbox for the patient. This review focuses on functional variants that reside in transcribed or not transcribed non-coding regions of the cancer genome and presents a collection of appropriate state-of-the-art methodologies to study them.

Direct characterization of cis-regulatory elements and functional dissection of complex genetic associations using HCR-FlowFISH

Effective interpretation of genome function and genetic variation requires a shift from epigenetic mapping of cis-regulatory elements (CREs) to characterization of endogenous function. We developed hybridization chain reaction fluorescence in situ hybridization coupled with flow cytometry (HCR-FlowFISH), a broadly applicable approach to characterize CRISPR-perturbed CREs via accurate quantification of native transcripts, alongside CRISPR activity screen analysis (CASA), a hierarchical Bayesian model to quantify CRE activity. Across >325,000 perturbations, we provide evidence that CREs can regulate multiple genes, skip over the nearest gene and display activating and/or silencing effects. At the cholesterol-level-associated FADS locus, we combine endogenous screens with reporter assays to exhaustively characterize multiple genome-wide association signals, functionally nominate causal variants and, importantly, identify their target genes.

Genome editing to define the function of risk loci and variants in rheumatic disease

Discoveries in human genetic studies have revolutionized our understanding of complex rheumatic and autoimmune diseases, including the identification of hundreds of genetic loci and single nucleotide polymorphisms that potentially predispose individuals to disease. However, in most cases, the exact disease-causing variants and their mechanisms of action remain unresolved. Functional follow-up of these findings is most challenging for genomic variants that are in non-coding genomic regions, where the large majority of common disease-associated variants are located, and/or that probably affect disease progression via cell type-specific gene regulation. To deliver on the therapeutic promise of human genetic studies, defining the mechanisms of action of these alleles is essential. Genome editing technology, such as CRISPR-Cas, has created a vast toolbox for targeted genetic and epigenetic modifications that presents unprecedented opportunities to decipher disease-causing loci, genes and variants in autoimmunity. In this Review, we discuss the past 5-10 years of progress in resolving the mechanisms underlying rheumatic disease-associated alleles, with an emphasis on how genomic editing techniques can enable targeted dissection and mechanistic studies of causal autoimmune risk variants.

Regulatory SNPs: Altered Transcription Factor Binding Sites Implicated in Complex Traits and Diseases

The vast majority of the genetic variants (mainly SNPs) associated with various human traits and diseases map to a noncoding part of the genome and are enriched in its regulatory compartment, suggesting that many causal variants may affect gene expression. The leading mechanism of action of these SNPs consists in the alterations in the transcription factor binding via creation or disruption of transcription factor binding sites (TFBSs) or some change in the affinity of these regulatory proteins to their cognate sites. In this review, we first focus on the history of the discovery of regulatory SNPs (rSNPs) and systematized description of the existing methodical approaches to their study. Then, we brief the recent comprehensive examples of rSNPs studied from the discovery of the changes in the TFBS sequence as a result of a nucleotide substitution to identification of its effect on the target gene expression and, eventually, to phenotype. We also describe state-of-the-art genome-wide approaches to identification of regulatory variants, including both making molecular sense of genome-wide association studies (GWAS) and the alternative approaches the primary goal of which is to determine the functionality of genetic variants. Among these approaches, special attention is paid to expression quantitative trait loci (eQTLs) analysis and the search for allele-specific events in RNA-seq (ASE events) as well as in ChIP-seq, DNase-seq, and ATAC-seq (ASB events) data.

Systemic sclerosis pathogenesis: contribution of recent advances in genetics

PURPOSE OF REVIEW

To review susceptibility genes and how they could integrate in systemic sclerosis (SSc) pathophysiology providing insight and perspectives for innovative therapies.

RECENT FINDINGS

SSc is a rare disease characterized by vasculopathy, dysregulated immunity and fibrosis. Genome-Wide association studies and ImmunoChip studies performed in recent years revealed associated genetic variants mainly localized in noncoding regions and mostly affecting the immune system of SSc patients. Gene variants were described in innate immunity (IRF5, IRF7 and TLR2), T and B cells activation (CD247, TNFAIP3, STAT4 and BLK) and NF-κB pathway (TNFAIP3 and TNIP1) confirming previous biological data. In addition to impacting immune response, CSK, DDX6, DNASE1L3 and GSDMA/B could also act in the vascular and fibrotic components of SSc.

SUMMARY

Although genetic studies highlighted the dysregulated immune response in SSc, future research must focus on a deeper characterization of these variants with determination of their functional effects. Moreover, the role of these genes or others on specific vasculopathy and fibrosis would provide insight. Establishment of polygenic score or integrated genome approaches could identify new targets specific of SSc clinical features. This will allow physicians to propose new therapies to SSc patients.

Global discovery of lupus genetic risk variant allelic enhancer activity

Genome-wide association studies of Systemic Lupus Erythematosus (SLE) nominate 3073 genetic variants at 91 risk loci. To systematically screen these variants for allelic transcriptional enhancer activity, we construct a massively parallel reporter assay (MPRA) library comprising 12,396 DNA oligonucleotides containing the genomic context around every allele of each SLE variant. Transfection into the Epstein-Barr virus-transformed B cell line GM12878 reveals 482 variants with enhancer activity, with 51 variants showing genotype-dependent (allelic) enhancer activity at 27 risk loci. Comparison of MPRA results in GM12878 and Jurkat T cell lines highlights shared and unique allelic transcriptional regulatory mechanisms at SLE risk loci. In-depth analysis of allelic transcription factor (TF) binding at and around allelic variants identifies one class of TFs whose DNA-binding motif tends to be directly altered by the risk variant and a second class of TFs that bind allelically without direct alteration of their motif by the variant. Collectively, our approach provides a blueprint for the discovery of allelic gene regulation at risk loci for any disease and offers insight into the transcriptional regulatory mechanisms underlying SLE.

Thousands of genetic variants have been associated with lupus, but causal variants and mechanisms are unknown. Here, the authors combine a massively parallel reporter assay with genome-wide ChIP experiments to identify risk variants with allelic enhancer activity mediated through transcription factor binding.

Editing GWAS: experimental approaches to dissect and exploit disease-associated genetic variation

Genome-wide association studies (GWAS) have uncovered thousands of genetic variants that influence risk for human diseases and traits. Yet understanding the mechanisms by which these genetic variants, mainly noncoding, have an impact on associated diseases and traits remains a significant hurdle. In this review, we discuss emerging experimental approaches that are being applied for functional studies of causal variants and translational advances from GWAS findings to disease prevention and treatment. We highlight the use of genome editing technologies in GWAS functional studies to modify genomic sequences, with proof-of-principle examples. We discuss the challenges in interrogating causal variants, points for consideration in experimental design and interpretation of GWAS locus mechanisms, and the potential for novel therapeutic opportunities. With the accumulation of knowledge of functional genetics, therapeutic genome editing based on GWAS discoveries will become increasingly feasible.

Preserving immune homeostasis with A20

A20/TNFAIP3 is a TNF induced gene that plays a profound role in preserving cellular and organismal homeostasis (Lee, et al., 2000; Opipari etal., 1990). This protein has been linked to multiple human diseases via genetic, epigenetic, and an emerging series of patients with mono-allelic coding mutations. Diverse cellular functions of this pleiotropically expressed protein include immune-suppressive, anti-inflammatory, and cell protective functions. The A20 protein regulates ubiquitin dependent cell signals; however, the biochemical mechanisms by which it performs these functions is surprisingly complex. Deciphering these cellular and biochemical facets of A20 dependent biology should greatly improve our understanding of murine and human disease pathophysiology as well as unveil new mechanisms of cell and tissue biology.

Functional genomics in autoimmune diseases

Associations between genetic loci and increased susceptibility to autoimmune disease have been well characterized, however, translating this knowledge into mechanistic insight and patient benefit remains a challenge. While improvements in the precision, completeness and accuracy of our genetic understanding of autoimmune diseases will undoubtedly be helpful, meeting this challenge will require two interlinked problems to be addressed: first which of the highly correlated variants at an individual locus is responsible for increased disease risk, and second what are the downstream effects of this variant. Given that the majority of loci are thought to affect non-coding regulatory elements, the second question is often reframed as what are the target gene(s) and pathways affected by causal variants. Currently, these questions are being addressed using a wide variety of novel techniques and datasets. In many cases, these approaches are complementary and it is likely that the most accurate picture will be generated by consolidating information relating to transcription, regulatory activity, chromatin accessibility, chromatin conformation and readouts from functional experiments, such as genome editing and reporter assays. It is clear that it will be necessary to gather this information from disease relevant cell types and conditions and that by doing so our understanding of disease etiology will be improved. This review is focused on the field of autoimmune disease functional genomics with a particular focus on the most exciting and significant research to be published within the last couple of years.

In The Blood: Connecting Variant to Function In Human Hematopoiesis

Genome-wide association studies (GWAS) have identified thousands of genetic variants associated with a range of human diseases and traits. However, understanding the mechanisms by which these genetic variants have an impact on associated diseases and traits, often referred to as the variant-to-function (V2F) problem, remains a significant hurdle. Solving the V2F challenge requires us to identify causative genetic variants, relevant cell types/states, target genes, and mechanisms by which variants can cause diseases or alter phenotypic traits. We discuss emerging functional approaches that are being applied to tackle the V2F problem for blood cell traits, illuminating how human genetic variation can impact on key mechanisms in hematopoiesis, as well as highlighting future prospects for this nascent field.

Host Epigenetics in Intracellular Pathogen Infections

Some intracellular pathogens are able to avoid the defense mechanisms contributing to host epigenetic modifications. These changes trigger alterations tothe chromatin structure and on the transcriptional level of genes involved in the pathogenesis of many bacterial diseases. In this way, pathogens manipulate the host cell for their own survival. The better understanding of epigenetic consequences in bacterial infection may open the door for designing new vaccine approaches and therapeutic implications. This article characterizes selected intracellular bacterial pathogens, including Mycobacterium spp., Listeria spp., Chlamydia spp., Mycoplasma spp., Rickettsia spp., Legionella spp. and Yersinia spp., which can modulate and reprogram of defense genes in host innate immune cells.

MAUDE: inferring expression changes in sorting-based CRISPR screens

Improved methods are needed to model CRISPR screen data for interrogation of genetic elements that alter reporter gene expression readout. We create MAUDE (Mean Alterations Using Discrete Expression) for quantifying the impact of guide RNAs on a target gene's expression in a pooled, sorting-based expression screen. MAUDE quantifies guide-level effects by modeling the distribution of cells across sorting expression bins. It then combines guides to estimate the statistical significance and effect size of targeted genetic elements. We demonstrate that MAUDE outperforms previous approaches and provide experimental design guidelines to best leverage MAUDE, which is available on https://github.com/Carldeboer/MAUDE.

Genomic resources for dissecting the role of non-protein coding variation in gene-environment interactions

The majority of single nucleotide variants (SNVs) identified in Genome Wide Association Studies (GWAS) fall within non-protein coding DNA and have the potential to alter gene expression. Non-protein coding DNA can control gene expression by acting as transcription factor (TF) binding sites or by regulating the organization of DNA into chromatin. SNVs in non-coding DNA sequences can disrupt TF binding and chromatin structure and this can result in pathology. Further, environmental health studies have shown that exposure to xenobiotics can disrupt the ability of TFs to regulate entire gene networks and result in pathology. However, there is a large amount of interindividual variability in exposure-linked health outcomes. One explanation for this heterogeneity is that genetic variation and exposure combine to disrupt gene regulation, and this eventually manifests in disease. Many resources exist that annotate common variants from GWAS and combine them with conservation, functional genomics, and TF binding data. These annotation tools provide clues regarding the biological implications of an SNV, as well as lead to the generation of hypotheses regarding potentially disrupted target genes, epigenetic markers, pathways, and cell types. Collectively this information can be used to predict how SNVs can alter an individual's response to exposure and disease risk. A basic understanding of the regulatory information contained within non-protein coding DNA is needed to predict the biological consequences of SNVs, and to determine how these SNVs impact exposure-related disease. We hope that this review will aid in the characterization of disease-associated genetic variation in the non-protein coding genome.