Direct Identification of Hundreds of Expression-Modulating Variants using a Multiplexed Reporter Assay

Systematic characterization of regulatory variants of blood pressure genes

High blood pressure (BP) is the major risk factor for cardiovascular disease. Genome-wide association studies have identified genetic variants for BP, but functional insights into causality and related molecular mechanisms lag behind. We functionally characterize 4,608 genetic variants in linkage with 135 BP loci in vascular smooth muscle cells and cardiomyocytes by massively parallel reporter assays. High densities of regulatory variants at BP loci (i.e., ULK4, MAP4, CFDP1, PDE5A) indicate that multiple variants drive genetic association. Regulatory variants are enriched in repeats, alter cardiovascular-related transcription factor motifs, and spatially converge with genes controlling specific cardiovascular pathways. Using heuristic scoring, we define likely causal variants, and CRISPR prime editing finally determines causal variants for KCNK9, SFXN2, and PCGF6, which are candidates for developing high BP. Our systems-level approach provides a catalog of functionally relevant variants and their genomic architecture in two trait-relevant cell lines for a better understanding of BP gene regulation.

Hypergraph factorization for multi-tissue gene expression imputation

Integrating gene expression across tissues and cell types is crucial for understanding the coordinated biological mechanisms that drive disease and characterise homeostasis. However, traditional multitissue integration methods cannot handle uncollected tissues or rely on genotype information, which is often unavailable and subject to privacy concerns. Here we present HYFA (Hypergraph Factorisation), a parameter-efficient graph representation learning approach for joint imputation of multi-tissue and cell-type gene expression. HYFA is genotype-agnostic, supports a variable number of collected tissues per individual, and imposes strong inductive biases to leverage the shared regulatory architecture of tissues and genes. In performance comparison on Genotype-Tissue Expression project data, HYFA achieves superior performance over existing methods, especially when multiple reference tissues are available. The HYFA-imputed dataset can be used to identify replicable regulatory genetic variations (eQTLs), with substantial gains over the original incomplete dataset. HYFA can accelerate the effective and scalable integration of tissue and cell-type transcriptome biorepositories.

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

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

Comprehensive epigenomic profiling reveals the extent of disease-specific chromatin states and informs target discovery in ankylosing spondylitis

Ankylosing spondylitis (AS) is a common, highly heritable inflammatory arthritis characterized by enthesitis of the spine and sacroiliac joints. Genome-wide association studies (GWASs) have revealed more than 100 genetic associations whose functional effects remain largely unresolved. Here, we present a comprehensive transcriptomic and epigenomic map of disease-relevant blood immune cell subsets from AS patients and healthy controls. We find that, while CD14⁺ monocytes and CD4⁺ and CD8⁺ T cells show disease-specific differences at the RNA level, epigenomic differences are only apparent upon multi-omics integration. The latter reveals enrichment at disease-associated loci in monocytes. We link putative functional SNPs to genes using high-resolution Capture-C at 10 loci, including PTGER4 and ETS1, and show how disease-specific functional genomic data can be integrated with GWASs to enhance therapeutic target discovery. This study combines epigenetic and transcriptional analysis with GWASs to identify disease-relevant cell types and gene regulation of likely pathogenic relevance and prioritize drug targets.

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.

Functional characterization of human genomic variation linked to polygenic diseases

The burden of human disease lies predominantly in polygenic diseases. Since the early 2000s, genome-wide association studies (GWAS) have identified genetic variants and loci associated with complex traits. These have ranged from variants in coding sequences to mutations in regulatory regions, such as promoters and enhancers, as well as mutations affecting mediators of mRNA stability and other downstream regulators, such as 5' and 3'-untranslated regions (UTRs), long noncoding RNA (lncRNA), and miRNA. Recent research advances in genetics have utilized a combination of computational techniques, high-throughput in vitro and in vivo screening modalities, and precise genome editing to impute the function of diverse classes of genetic variants identified through GWAS. In this review, we highlight the vastness of genomic variants associated with polygenic disease risk and address recent advances in how genetic tools can be used to functionally characterize them.

Computational Assessment of the Expression-modulating Potential for Non-coding Variants

Large-scale genome-wide association studies (GWAS) and expression quantitative trait locus (eQTL) studies have identified multiple non-coding variants associated with genetic diseases by affecting gene expression. However, pinpointing causal variants effectively and efficiently remains a serious challenge. Here, we developed CARMEN, a novel algorithm to identify functional non-coding expression-modulating variants. Multiple evaluations demonstrated CARMEN's superior performance over state-of-the-art tools. Applying CARMEN to GWAS and eQTL datasets further pinpointed several causal variants other than the reported lead single-nucleotide polymorphisms (SNPs). CARMEN scales well with the massive datasets, and is available online as a web server at http://carmen.gao-lab.org.

Large-scale functional screen identifies genetic variants with splicing effects in modern and archaic humans

Humans coexisted and interbred with other hominins which later became extinct. These archaic hominins are known to us only through fossil records and for two cases, genome sequences. Here, we engineer Neanderthal and Denisovan sequences into thousands of artificial genes to reconstruct the pre-mRNA processing patterns of these extinct populations. Of the 5,169 alleles tested in this massively parallel splicing reporter assay (MaPSy), we report 962 exonic splicing mutations that correspond to differences in exon recognition between extant and extinct hominins. Using MaPSy splicing variants, predicted splicing variants, and splicing quantitative trait loci, we show that splice-disrupting variants experienced greater purifying selection in anatomically modern humans than that in Neanderthals. Adaptively introgressed variants were enriched for moderate-effect splicing variants, consistent with positive selection for alternative spliced alleles following introgression. As particularly compelling examples, we characterized a unique tissue-specific alternative splicing variant at the adaptively introgressed innate immunity gene TLR1, as well as a unique Neanderthal introgressed alternative splicing variant in the gene HSPG2 that encodes perlecan. We further identified potentially pathogenic splicing variants found only in Neanderthals and Denisovans in genes related to sperm maturation and immunity. Finally, we found splicing variants that may contribute to variation among modern humans in total bilirubin, balding, hemoglobin levels, and lung capacity. Our findings provide unique insights into natural selection acting on splicing in human evolution and demonstrate how functional assays can be used to identify candidate causal variants underlying differences in gene regulation and phenotype.

Challenges and considerations for reproducibility of STARR-seq assays

High-throughput methods such as RNA-seq, ChIP-seq, and ATAC-seq have well-established guidelines, commercial kits, and analysis pipelines that enable consistency and wider adoption for understanding genome function and regulation. STARR-seq, a popular assay for directly quantifying the activities of thousands of enhancer sequences simultaneously, has seen limited standardization across studies. The assay is long, with more than 250 steps, and frequent customization of the protocol and variations in bioinformatics methods raise concerns for reproducibility of STARR-seq studies. Here, we assess each step of the protocol and analysis pipelines from published sources and in-house assays, and identify critical steps and quality control (QC) checkpoints necessary for reproducibility of the assay. We also provide guidelines for experimental design, protocol scaling, customization, and analysis pipelines for better adoption of the assay. These resources will allow better optimization of STARR-seq for specific research needs, enable comparisons and integration across studies, and improve the reproducibility of results.

RegVar: Tissue-specific Prioritization of Non-coding Regulatory Variants

Non-coding genomic variants constitute the majority of trait-associated genome variations; however, the identification of functional non-coding variants is still a challenge in human genetics, and a method for systematically assessing the impact of regulatory variants on gene expression and linking these regulatory variants to potential target genes is still lacking. Here, we introduce a deep neural network (DNN)-based computational framework, RegVar, which can accurately predict the tissue-specific impact of non-coding regulatory variants on target genes. We show that by robustly learning the genomic characteristics of massive variant-gene expression associations in a variety of human tissues, RegVar vastly surpasses all current non-coding variant prioritization methods in predicting regulatory variants under different circumstances. The unique features of RegVar make it an excellent framework for assessing the regulatory impact of any variant on its putative target genes in a variety of tissues. RegVar is available as a web server at https://regvar.omic.tech/.

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.

Colocalization of blood cell traits GWAS associations and variation in PU.1 genomic occupancy prioritizes causal noncoding regulatory variants

Genome-wide association studies (GWAS) have uncovered numerous trait-associated loci across the human genome, most of which are located in noncoding regions, making interpretations difficult. Moreover, causal variants are hard to statistically fine-map at many loci because of widespread linkage disequilibrium. To address this challenge, we present a strategy utilizing transcription factor (TF) binding quantitative trait loci (bQTLs) for colocalization analysis to identify trait associations likely mediated by TF occupancy variation and to pinpoint likely causal variants using motif scores. We applied this approach to PU.1 bQTLs in lymphoblastoid cell lines and blood cell traits GWAS data. Colocalization analysis revealed 69 blood cell trait GWAS loci putatively driven by PU.1 occupancy variation. We nominate PU.1 motif-altering variants as the likely shared causal variants at 51 loci. Such integration of TF bQTL data with other GWAS data may reveal transcriptional regulatory mechanisms and causal noncoding variants underlying additional complex traits.

Using QTL to Identify Genes and Pathways Underlying the Regulation and Production of Milk Components in Cattle

Milk is a complex liquid, and the concentrations of many of its components are under genetic control. Many genes and pathways are known to regulate milk composition, and the purpose of this review is to highlight how the discoveries of quantitative trait loci (QTL) for milk phenotypes can elucidate these pathways. The main body of this review focuses primarily on QTL discovered in cattle (Bos taurus) as a model species for the biology of lactation, and there are occasional references to sheep genetics. The following section describes a range of techniques that can be used to help identify the causative genes underlying QTL when the underlying mechanism involves the regulation of gene expression. As genotype and phenotype databases continue to grow and diversify, new QTL will continue to be discovered, and although proving the causality of underlying genes and variants remains difficult, these new data sets will further enhance our understanding of the biology of lactation.

Hacking hematopoiesis - emerging tools for examining variant effects

Hematopoiesis is a continuous process of blood and immune cell production. It is orchestrated by thousands of gene products that respond to extracellular signals by guiding cell fate decisions to meet the needs of the organism. Although much of our knowledge of this process comes from work in model systems, we have learned a great deal from studies on human genetic variation. Considerable insight has emerged from studies on presumed monogenic blood disorders, which continue to provide key insights into the mechanisms critical for hematopoiesis. Furthermore, the emergence of large-scale biobanks and cohorts has uncovered thousands of genomic loci associated with blood cell traits and diseases. Some of these blood cell trait-associated loci act as modifiers of what were once thought to be monogenic blood diseases. However, most of these loci await functional validation. Here, we discuss the validation bottleneck and emerging methods to more effectively connect variant to function. In particular, we highlight recent innovations in genome editing, which have paved the path forward for high-throughput functional assessment of loci. Finally, we discuss existing barriers to progress, including challenges in manipulating the genomes of primary hematopoietic cells.

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.

Widespread perturbation of ETS factor binding sites in cancer

Although >90% of somatic mutations reside in non-coding regions, few have been reported as cancer drivers. To predict driver non-coding variants (NCVs), we present a transcription factor (TF)-aware burden test based on a model of coherent TF function in promoters. We apply this test to NCVs from the Pan-Cancer Analysis of Whole Genomes cohort and predict 2555 driver NCVs in the promoters of 813 genes across 20 cancer types. These genes are enriched in cancer-related gene ontologies, essential genes, and genes associated with cancer prognosis. We find that 765 candidate driver NCVs alter transcriptional activity, 510 lead to differential binding of TF-cofactor regulatory complexes, and that they primarily impact the binding of ETS factors. Finally, we show that different NCVs within a promoter often affect transcriptional activity through shared mechanisms. Our integrated computational and experimental approach shows that cancer NCVs are widespread and that ETS factors are commonly disrupted.

Massively parallel characterization of psychiatric disorder-associated and cell-type-specific regulatory elements in the developing human cortex

Nucleotide changes in gene regulatory elements are important determinants of neuronal development and disease. Using massively parallel reporter assays in primary human cells from mid-gestation cortex and cerebral organoids, we interrogated the cis-regulatory activity of 102,767 sequences, including differentially accessible cell-type specific regions in the developing cortex and single-nucleotide variants associated with psychiatric disorders. In primary cells, we identified 46,802 active enhancer sequences and 164 disorder-associated variants that significantly alter enhancer activity. Activity was comparable in organoids and primary cells, suggesting that organoids provide an adequate model for the developing cortex. Using deep learning, we decoded the sequence basis and upstream regulators of enhancer activity. This work establishes a comprehensive catalog of functional gene regulatory elements and variants in human neuronal development.

Leveraging massively parallel reporter assays for evolutionary questions

A long-standing goal of evolutionary biology is to decode how gene regulation contributes to organismal diversity. Doing so is challenging because it is hard to predict function from non-coding sequence and to perform molecular research with non-model taxa. Massively parallel reporter assays (MPRAs) enable the testing of thousands to millions of sequences for regulatory activity simultaneously. Here, we discuss the execution, advantages, and limitations of MPRAs, with a focus on evolutionary questions. We propose solutions for extending MPRAs to rare taxa and those with limited genomic resources, and we underscore MPRA's broad potential for driving genome-scale, functional studies across organisms.

Utilizing Large Functional and Population Genomics Resources for CRISPR/Cas Perturbation Experiment Design

Genome sequencing technologies have rapidly evolved in the past decades, enabling us to interpret the human genome through multiple perspectives, ranging from cross-species comparisons, naturally occurring variation in health and disease state to regulatory mechanisms.Although such perspectives are all informative to narrow down the list of genes or variants for perturbation experiments based on specific biological aims, utilizing multiple sources of information is often challenging in practice.In this chapter, we provide an overview of major large-scale functional and population genomics resources, followed by a practical example of selecting target variants for genetic perturbation experiments involving genome engineering techniques such as CRISPR/Cas.

Regulatory dissection of the severe COVID-19 risk locus introgressed by Neanderthals

Individuals infected with the SARS-CoV-2 virus present with a wide variety of symptoms ranging from asymptomatic to severe and even lethal outcomes. Past research has revealed a genetic haplotype on chromosome 3 that entered the human population via introgression from Neanderthals as the strongest genetic risk factor for the severe response to COVID-19. However, the specific variants along this introgressed haplotype that contribute to this risk and the biological mechanisms that are involved remain unclear. Here, we assess the variants present on the risk haplotype for their likelihood of driving the genetic predisposition to severe COVID-19 outcomes. We do this by first exploring their impact on the regulation of genes involved in COVID-19 infection using a variety of population genetics and functional genomics tools. We then perform a locus-specific massively parallel reporter assay to individually assess the regulatory potential of each allele on the haplotype in a multipotent immune-related cell line. We ultimately reduce the set of over 600 linked genetic variants to identify four introgressed alleles that are strong functional candidates for driving the association between this locus and severe COVID-19. Using reporter assays in the presence/absence of SARS-CoV-2, we find evidence that these variants respond to viral infection. These variants likely drive the locus' impact on severity by modulating the regulation of two critical chemokine receptor genes: CCR1 and CCR5. These alleles are ideal targets for future functional investigations into the interaction between host genomics and COVID-19 outcomes.

Decoding the genetic and epigenetic basis of asthma

Asthma is a complex and heterogeneous chronic inflammatory disease of the airways. Alongside environmental factors, asthma susceptibility is strongly influenced by genetics. Given its high prevalence and our incomplete understanding of the mechanisms underlying disease susceptibility, asthma is frequently studied in genome-wide association studies (GWAS), which have identified thousands of genetic variants associated with asthma development. Virtually all these genetic variants reside in non-coding genomic regions, which has obscured the functional impact of asthma-associated variants and their translation into disease-relevant mechanisms. Recent advances in genomics technology and epigenetics now offer methods to link genetic variants to gene regulatory elements embedded within non-coding regions, which have started to unravel the molecular mechanisms underlying the complex (epi)genetics of asthma. Here, we provide an integrated overview of (epi)genetic variants associated with asthma, focusing on efforts to link these disease associations to biological insight into asthma pathophysiology using state-of-the-art genomics methodology. Finally, we provide a perspective as to how decoding the genetic and epigenetic basis of asthma has the potential to transform clinical management of asthma and to predict the risk of asthma development.

A single-cell massively parallel reporter assay detects cell-type-specific gene regulation

Massively parallel reporter gene assays are key tools in regulatory genomics but cannot be used to identify cell-type-specific regulatory elements without performing assays serially across different cell types. To address this problem, we developed a single-cell massively parallel reporter assay (scMPRA) to measure the activity of libraries of cis-regulatory sequences (CRSs) across multiple cell types simultaneously. We assayed a library of core promoters in a mixture of HEK293 and K562 cells and showed that scMPRA is a reproducible, highly parallel, single-cell reporter gene assay that detects cell-type-specific cis-regulatory activity. We then measured a library of promoter variants across multiple cell types in live mouse retinas and showed that subtle genetic variants can produce cell-type-specific effects on cis-regulatory activity. We anticipate that scMPRA will be widely applicable for studying the role of CRSs across diverse cell types.

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.

Systematic assays and resources for the functional annotation of non-coding variants

Identification of genetic variation in individual genomes is now a routine procedure in human genetic research and diagnostics. For many variants, however, insufficient evidence is available to establish a pathogenic effect, particularly for variants in non-coding regions. Furthermore, the sheer number of candidate variants renders testing in individual assays virtually impossible. While scalable approaches are being developed, the selection of methods and resources and the application of a given framework to a particular disease or trait remain major challenges. This limits the translation of results from both genome-wide association studies and genome sequencing. Here, we discuss computational and experimental approaches available for functional annotation of non-coding variation.

Optimized high-throughput screening of non-coding variants identified from genome-wide association studies

The vast majority of disease-associated single nucleotide polymorphisms (SNP) identified from genome-wide association studies (GWAS) are localized in non-coding regions. A significant fraction of these variants impact transcription factors binding to enhancer elements and alter gene expression. To functionally interrogate the activity of such variants we developed snpSTARRseq, a high-throughput experimental method that can interrogate the functional impact of hundreds to thousands of non-coding variants on enhancer activity. snpSTARRseq dramatically improves signal-to-noise by utilizing a novel sequencing and bioinformatic approach that increases both insert size and the number of variants tested per loci. Using this strategy, we interrogated known prostate cancer (PCa) risk-associated loci and demonstrated that 35% of them harbor SNPs that significantly altered enhancer activity. Combining these results with chromosomal looping data we could identify interacting genes and provide a mechanism of action for 20 PCa GWAS risk regions. When benchmarked to orthogonal methods, snpSTARRseq showed a strong correlation with in vivo experimental allelic-imbalance studies whereas there was no correlation with predictive in silico approaches. Overall, snpSTARRseq provides an integrated experimental and computational framework to functionally test non-coding genetic variants.

Quality assessment and refinement of chromatin accessibility data using a sequence-based predictive model

Chromatin accessibility assays are central to the genome-wide identification of gene regulatory elements associated with transcriptional regulation. However, the data have highly variable quality arising from several biological and technical factors. To surmount this problem, we developed a sequence-based machine learning method to evaluate and refine chromatin accessibility data. Our framework, gapped k-mer SVM quality check (gkmQC), provides the quality metrics for a sample based on the prediction accuracy of the trained models. We tested 886 DNase-seq samples from the ENCODE/Roadmap projects to demonstrate that gkmQC can effectively identify "high-quality" (HQ) samples with low conventional quality scores owing to marginal read depths. Peaks identified in HQ samples are more accurately aligned at functional regulatory elements, show greater enrichment of regulatory elements harboring functional variants, and explain greater heritability of phenotypes from their relevant tissues. Moreover, gkmQC can optimize the peak-calling threshold to identify additional peaks, especially for rare cell types in single-cell chromatin accessibility data.

Denisovan introgression has shaped the immune system of present-day Papuans

Modern humans have admixed with multiple archaic hominins. Papuans, in particular, owe up to 5% of their genome to Denisovans, a sister group to Neanderthals whose remains have only been identified in Siberia and Tibet. Unfortunately, the biological and evolutionary significance of these introgression events remain poorly understood. Here we investigate the function of both Denisovan and Neanderthal alleles characterised within a set of 56 genomes from Papuan individuals. By comparing the distribution of archaic and non-archaic variants we assess the consequences of archaic admixture across a multitude of different cell types and functional elements. We observe an enrichment of archaic alleles within cis-regulatory elements and transcribed regions of the genome, with Denisovan variants strongly affecting elements active within immune-related cells. We identify 16,048 and 10,032 high-confidence Denisovan and Neanderthal variants that fall within annotated cis-regulatory elements and with the potential to alter the affinity of multiple transcription factors to their cognate DNA motifs, highlighting a likely mechanism by which introgressed DNA can impact phenotypes. Lastly, we experimentally validate these predictions by testing the regulatory potential of five Denisovan variants segregating within Papuan individuals, and find that two are associated with a significant reduction of transcriptional activity in plasmid reporter assays. Together, these data provide support for a widespread contribution of archaic DNA in shaping the present levels of modern human genetic diversity, with different archaic ancestries potentially affecting multiple phenotypic traits within non-Africans.

Slight Variations in the Sequence Downstream of the Polyadenylation Signal Significantly Increase Transgene Expression in HEK293T and CHO Cells

Compared to transcription initiation, much less is known about transcription termination. In particular, large-scale mutagenesis studies have, so far, primarily concentrated on promoter and enhancer, but not terminator sequences. Here, we used a massively parallel reporter assay (MPRA) to systematically analyze the influence of short (8 bp) sequence variants (mutations) located downstream of the polyadenylation signal (PAS) on the steady-state mRNA level of the upstream gene, employing an eGFP reporter and human HEK293T cells as a model system. In total, we evaluated 227,755 mutations located at different overlapping positions within +17..+56 bp downstream of the PAS for their ability to regulate the reporter gene expression. We found that the positions +17..+44 bp downstream of the PAS are more essential for gene upregulation than those located more distal to the PAS, and that the mutation sequences ensuring high levels of eGFP mRNA expression are extremely T-rich. Next, we validated the positive effect of a couple of mutations identified in the MPRA screening on the eGFP and luciferase protein expression. The most promising mutation increased the expression of the reporter proteins 13-fold and sevenfold on average in HEK293T and CHO cells, respectively. Overall, these findings might be useful for further improving the efficiency of production of therapeutic products, e.g., recombinant antibodies.

Current challenges in understanding the role of enhancers in disease

Enhancers play a central role in the spatiotemporal control of gene expression and tend to work in a cell-type-specific manner. In addition, they are suggested to be major contributors to phenotypic variation, evolution and disease. There is growing evidence that enhancer dysfunction due to genetic, structural or epigenetic mechanisms contributes to a broad range of human diseases referred to as enhanceropathies. Such mechanisms often underlie the susceptibility to common diseases, but can also play a direct causal role in cancer or Mendelian diseases. Despite the recent gain of insights into enhancer biology and function, we still have a limited ability to predict how enhancer dysfunction impacts gene expression. Here we discuss the major challenges that need to be overcome when studying the role of enhancers in disease etiology and highlight opportunities and directions for future studies, aiming to disentangle the molecular basis of enhanceropathies.

Prediction of the cell-type-specific transcription of non-coding RNAs from genome sequences via machine learning

Gene transcription is regulated through complex mechanisms involving non-coding RNAs (ncRNAs). As the transcription of ncRNAs, especially of enhancer RNAs, is often low and cell type specific, how the levels of RNA transcription depend on genotype remains largely unexplored. Here we report the development and utility of a machine-learning model (MENTR) that reliably links genome sequence and ncRNA expression at the cell type level. Effects on ncRNA transcription predicted by the model were concordant with estimates from published studies in a cell-type-dependent manner, regardless of allele frequency and genetic linkage. Among 41,223 variants from genome-wide association studies, the model identified 7,775 enhancer RNAs and 3,548 long ncRNAs causally associated with complex traits across 348 major human primary cells and tissues, such as rare variants plausibly altering the transcription of enhancer RNAs to influence the risks of Crohn's disease and asthma. The model may aid the discovery of causal variants and the generation of testable hypotheses for biological mechanisms driving complex traits.

Deep learning-assisted genome-wide characterization of massively parallel reporter assays

Massively parallel reporter assay (MPRA) is a high-throughput method that enables the study of the regulatory activities of tens of thousands of DNA oligonucleotides in a single experiment. While MPRA experiments have grown in popularity, their small sample sizes compared to the scale of the human genome limits our understanding of the regulatory effects they detect. To address this, we develop a deep learning model, MpraNet, to distinguish potential MPRA targets from the background genome. This model achieves high discriminative performance (AUROC = 0.85) at differentiating MPRA positives from a set of control variants that mimic the background genome when applied to the lymphoblastoid cell line. We observe that existing functional scores represent very distinct functional effects, and most of them fail to characterize the regulatory effect that MPRA detects. Using MpraNet, we predict potential MPRA functional variants across the genome and identify the distributions of MPRA effect relative to other characteristics of genetic variation, including allele frequency, alternative functional annotations specified by FAVOR, and phenome-wide associations. We also observed that the predicted MPRA positives are not uniformly distributed across the genome; instead, they are clumped together in active regions comprising 9.95% of the genome and inactive regions comprising 89.07% of the genome. Furthermore, we propose our model as a screen to filter MPRA experiment candidates at genome-wide scale, enabling future experiments to be more cost-efficient by increasing precision relative to that observed from previous MPRAs.

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.

TVAR: assessing tissue-specific functional effects of non-coding variants with deep learning

MOTIVATION

Analysis of whole-genome sequencing (WGS) for genetics is still a challenge due to the lack of accurate functional annotation of non-coding variants, especially the rare ones. As eQTLs have been extensively implicated in the genetics of human diseases, we hypothesize that rare non-coding variants discovered in WGS play a regulatory role in predisposing disease risk.

RESULTS

With thousands of tissue- and cell-type-specific epigenomic features, we propose TVAR. This multi-label learning-based deep neural network predicts the functionality of non-coding variants in the genome based on eQTLs across 49 human tissues in the GTEx project. TVAR learns the relationships between high-dimensional epigenomics and eQTLs across tissues, taking the correlation among tissues into account to understand shared and tissue-specific eQTL effects. As a result, TVAR outputs tissue-specific annotations, with an average AUROC of 0.77 across these tissues. We evaluate TVAR's performance on four complex diseases (coronary artery disease, breast cancer, Type 2 diabetes and Schizophrenia), using TVAR's tissue-specific annotations, and observe its superior performance in predicting functional variants for both common and rare variants, compared with five existing state-of-the-art tools. We further evaluate TVAR's G-score, a scoring scheme across all tissues, on ClinVar, fine-mapped GWAS loci, Massive Parallel Reporter Assay (MPRA) validated variants and observe the consistently better performance of TVAR compared with other competing tools.

AVAILABILITY AND IMPLEMENTATION

The TVAR source code and its scores on the ClinVar catalog, fine mapped GWAS Loci, high confidence eQTLs from GTEx dataset, and MPRA validated functional variants are available at https://github.com/haiyang1986/TVAR.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Identifying differential regulatory control of APOE ɛ4 on African versus European haplotypes as potential therapeutic targets

We previously demonstrated that in Alzheimer's disease (AD) patients, European apolipoprotein E (APOE) ε4 carriers express significantly more APOE ε4 in their brains than African AD carriers. We examined single nucleotide polymorphisms near APOE with significant frequency differences between African and European/Japanese APOE ε4 haplotypes that could contribute to this difference in expression through regulation. Two enhancer massively parallel reporter assay (MPRA) approaches were performed, supplemented with single fragment reporter assays. We used Capture C analyses to support interactions with the APOE promoter. Introns within TOMM40 showed increased enhancer activity in the European/Japanese versus African haplotypes in astrocytes and microglia. This region overlaps with APOE promoter interactions as assessed by Capture C analysis. Single variant analyses pinpoints rs2075650/rs157581, and rs59007384 as functionally different on these haplotypes. Identification of the mechanisms for differential regulatory function for APOE expression between African and European/Japanese haplotypes could lead to therapeutic targets for APOE ε4 carriers.

Analysis of chromatin data supports a role for CD14+ monocytes/macrophages in mediating genetic risk for juvenile idiopathic arthritis

Introduction

Genome wide association studies (GWAS) have identified multiple regions that confer genetic risk for the polyarticular/oligoarticular forms of juvenile idiopathic arthritis (JIA). However, genome-wide scans do not identify the cells impacted by genetic polymorphisms on the risk haplotypes or the genes impacted by those variants. We have shown that genetic variants driving JIA risk are likely to affect both innate and adaptive immune functions. We provide additional evidence that JIA risk variants impact innate immunity.

Materials and methods

We queried publicly available H3K4me1/H3K27ac ChIP-seq data in CD14+ monocytes to determine whether the linkage disequilibrium (LD) blocks incorporating the SNPs that tag JIA risk loci showed enrichment for these epigenetic marks. We also queried monocyte/macrophage GROseq data, a functional readout of active enhancers. We defined the topologically associated domains (TADs) encompassing enhancers on the risk haplotypes and identified genes within those TADs expressed in monocytes. We performed ontology analyses of these genes to identify cellular processes that may be impacted by these variants. We also used whole blood RNAseq data from the Genotype-Tissue Expression (GTEx) data base to determine whether SNPs lying within monocyte GROseq peaks influence plausible target genes within the TADs encompassing the JIA risk haplotypes.

Results

The LD blocks encompassing the JIA genetic risk regions were enriched for H3K4me1/H3K27ac ChIPseq peaks (p=0.00021 and p=0.022) when compared to genome background. Eleven and sixteen JIA were enriched for resting and activated macrophage GROseq peaks, respectively risk regions (p=0.04385 and p=0.00004). We identified 321 expressed genes within the TADs encompassing the JIA haplotypes in human monocytes. Ontological analysis of these genes showed enrichment for multiple immune functions. Finally, we found that SNPs lying within the GROseq peaks are strongly associated with expression levels of plausible target genes in human whole blood.

Conclusions

These findings support the idea that both innate and adaptive immunity are impacted by JIA genetic risk variants.

Functional regulatory variants implicate distinct transcriptional networks in dementia

Predicting the function of noncoding variation is a major challenge in modern genetics. In this study, we used massively parallel reporter assays to screen 5706 variants identified from genome-wide association studies for both Alzheimer's disease (AD) and progressive supranuclear palsy (PSP), identifying 320 functional regulatory variants (frVars) across 27 loci, including the complex 17q21.31 region. We identified and validated multiple risk loci using CRISPR interference or excision, including complement 4 (C4A) and APOC1 in AD and PLEKHM1 and KANSL1 in PSP. Functional variants disrupt transcription factor binding sites converging on enhancers with cell type-specific activity in PSP and AD, implicating a neuronal SP1-driven regulatory network in PSP pathogenesis. These analyses suggest that noncoding genetic risk is driven by common genetic variants through their aggregate activity on specific transcriptional programs.

"Demystifying non-coding GWAS variants: an overview of computational tools and methods."

Genome-wide association studies (GWAS) have found the majority of disease-associated variants to be non-coding. Major efforts into the charting of the non-coding regulatory landscapes have allowed for the development of tools and methods which aim to aid in the identification of causal variants and their mechanism of action. In this review, we give an overview of current tools and methods for the analysis of non-coding GWAS variants in disease. We provide a workflow that allows for the accumulation of in silico evidence to generate novel hypotheses on mechanisms underlying disease and prioritize targets for follow-up study using non-coding GWAS variants. Lastly, we discuss the need for comprehensive benchmarks and novel tools for the analysis of non-coding variants.

PROBER identifies proteins associated with programmable sequence-specific DNA in living cells

DNA-protein interactions mediate physiologic gene regulation and may be altered by DNA variants linked to polygenic disease. To enhance the speed and signal-to-noise ratio (SNR) in the identification and quantification of proteins associated with specific DNA sequences in living cells, we developed proximal biotinylation by episomal recruitment (PROBER). PROBER uses high-copy episomes to amplify SNR, and proximity proteomics (BioID) to identify the transcription factors and additional gene regulators associated with short DNA sequences of interest. PROBER quantified both constitutive and inducible association of transcription factors and corresponding chromatin regulators to target DNA sequences and binding quantitative trait loci due to single-nucleotide variants. PROBER identified alterations in regulator associations due to cancer hotspot mutations in the hTERT promoter, indicating that these mutations increase promoter association with specific gene activators. PROBER provides an approach to rapidly identify proteins associated with specific DNA sequences and their variants in living cells.

Massively parallel identification of functionally consequential noncoding genetic variants in undiagnosed rare disease patients

Clinical whole genome sequencing has enabled the discovery of potentially pathogenic noncoding variants in the genomes of rare disease patients with a prior history of negative genetic testing. However, interpreting the functional consequences of noncoding variants and distinguishing those that contribute to disease etiology remains a challenge. Here we address this challenge by experimentally profiling the functional consequences of rare noncoding variants detected in a cohort of undiagnosed rare disease patients at scale using a massively parallel reporter assay. We demonstrate that this approach successfully identifies rare noncoding variants that alter the regulatory capacity of genomic sequences. In addition, we describe an integrative analysis that utilizes genomic features alongside patient clinical data to further prioritize candidate variants with an increased likelihood of pathogenicity. This work represents an important step towards establishing a framework for the functional interpretation of clinically detected noncoding variants.

Association of serum C1Q/TNF-related protein 4 levels with carotid atherosclerosis in subjects with type 2 diabetes mellitus: a cross-sectional study

BACKGROUND

Carotid atherosclerosis (CAS) is a common manifestation of macroangiopathy in type 2 diabetes mellitus (T2DM). C1Q/TNF-related protein 4 (CTRP4) was found to be involved in regulation of food intake behaviors and glucolipid metabolism, which were also key factors in the development of CAS. However, the relationship between serum CTRP4 and CAS in T2DM remains unclear.

METHODS

A total of 111 participants with T2DM were enrolled in the study and were divided into 2 groups (T2DM group and T2DM + CAS group) according to the result of carotid ultrasound examinations. Serum CTRP4 levels were measured by enzyme linked immunosorbent assay (ELISA). Trend χ² test and binary stepwise logistic regression were conducted to assess the association between serum CTRP4 and the risk of CAS in T2DM.

RESULTS

Serum CTRP4 concentrations in T2DM + CAS group were significantly lower compared with those in T2DM group [7.98 (5.53) vs. 11.29 (7.36) ng/ml, P < 0.01]. The risk of CAS in T2DM decreased with the increasing of CTRP4 quartiles (P for trend < 0.01). Binary stepwise logistic regression suggested that serum CTRP4 might be an independent influence factor for CAS in patients with T2DM (P < 0.01) and high concentrations of serum CTRP4 were related to low risk of CAS in T2DM.

CONCLUSIONS

The concentrations of serum CTRP4 are lower in T2DM patients with CAS compared to those without CAS. Serum CTRP4 levels are negatively related to the risk of CAS in T2DM.

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.

An Improved EMSA-based Method to Prioritize Candidate cis-REs for Further Functional Validation

Cells are the complex product of gene expression programs that involve the coordinated transcription of thousands of genes controlled by cis-regulatory elements (cis-REs). Therefore, identification of cis-REs is the key to decipher the mechanisms underlying the regulation of gene expression. Here, we describe a simple and time-effective protocol of fine-mapping cis-REs by using an electrophoresis mobility shift assay (EMSA)-based, in vitro, high-throughput (HTP) technique called regulatory element-sequencing (Reel-seq). Reel-seq can be applied to identify cis-REs at a high resolution and sensitivity over large genome regions, in a systematic and continuous manner. It can be used to prioritize candidate cis-REs as a complement to the existing approaches, such as massive parallel reporter assay (MPRA), chromatin immunoprecipitation DNA-sequencing (ChIP-seq), and the assay for transposase-accessible chromatin sequencing (ATAC-seq). Graphical abstract: Generation of the Reel-seq Library 1 and 2 (A) and identification of cis-REs by an electrophoresis mobility shift assay (EMSA)-based Reel-seq screen (B). NE: nuclear extract; NGS: next generation sequencing.

Redefining the hypotheses driving Parkinson's diseases research

Parkinson’s disease (PD) research has largely focused on the disease as a single entity centred on the development of neuronal pathology within the central nervous system. However, there is growing recognition that PD is not a single entity but instead reflects multiple diseases, in which different combinations of environmental, genetic and potential comorbid factors interact to direct individual disease trajectories. Moreover, an increasing body of recent research implicates peripheral tissues and non-neuronal cell types in the development of PD. These observations are consistent with the hypothesis that the initial causative changes for PD development need not occur in the central nervous system. Here, we discuss how the use of neuronal pathology as a shared, qualitative phenotype minimises insights into the possibility of multiple origins and aetiologies of PD. Furthermore, we discuss how considering PD as a single entity potentially impairs our understanding of the causative molecular mechanisms, approaches for patient stratification, identification of biomarkers, and the development of therapeutic approaches to PD. The clear consequence of there being distinct diseases that collectively form PD, is that there is no single biomarker or treatment for PD development or progression. We propose that diagnosis should shift away from the clinical definitions, towards biologically defined diseases that collectively form PD, to enable informative patient stratification. N-of-one type, clinical designs offer an unbiased, and agnostic approach to re-defining PD in terms of a group of many individual diseases.

A general framework for predicting the transcriptomic consequences of non-coding variation and small molecules

Genome wide association studies (GWASs) for complex traits have implicated thousands of genetic loci. Most GWAS-nominated variants lie in noncoding regions, complicating the systematic translation of these findings into functional understanding. Here, we leverage convolutional neural networks to assist in this challenge. Our computational framework, peaBrain, models the transcriptional machinery of a tissue as a two-stage process: first, predicting the mean tissue specific abundance of all genes and second, incorporating the transcriptomic consequences of genotype variation to predict individual abundance on a subject-by-subject basis. We demonstrate that peaBrain accounts for the majority (>50%) of variance observed in mean transcript abundance across most tissues and outperforms regularized linear models in predicting the consequences of individual genotype variation. We highlight the validity of the peaBrain model by calculating non-coding impact scores that correlate with nucleotide evolutionary constraint that are also predictive of disease-associated variation and allele-specific transcription factor binding. We further show how these tissue-specific peaBrain scores can be leveraged to pinpoint functional tissues underlying complex traits, outperforming methods that depend on colocalization of eQTL and GWAS signals. We subsequently: (a) derive continuous dense embeddings of genes for downstream applications; (b) highlight the utility of the model in predicting transcriptomic impact of small molecules and shRNA (on par with in vitro experimental replication of external test sets); (c) explore how peaBrain can be used to model difficult-to-study processes (such as neural induction); and (d) identify putatively functional eQTLs that are missed by high-throughput experimental approaches.

Exploiting deep transfer learning for the prediction of functional non-coding variants using genomic sequence

MOTIVATION

Though genome-wide association studies have identified tens of thousands of variants associated with complex traits and most of them fall within the non-coding regions, they may not be the causal ones. The development of high-throughput functional assays leads to the discovery of experimental validated non-coding functional variants. However, these validated variants are rare due to technical difficulty and financial cost. The small sample size of validated variants makes it less reliable to develop a supervised machine learning model for achieving a whole genome-wide prediction of non-coding causal variants.

RESULTS

We will exploit a deep transfer learning model, which is based on convolutional neural network, to improve the prediction for functional non-coding variants (NCVs). To address the challenge of small sample size, the transfer learning model leverages both large-scale generic functional NCVs to improve the learning of low-level features and context-specific functional NCVs to learn high-level features toward the context-specific prediction task. By evaluating the deep transfer learning model on three MPRA datasets and 16 GWAS datasets, we demonstrate that the proposed model outperforms deep learning models without pretraining or retraining. In addition, the deep transfer learning model outperforms 18 existing computational methods in both MPRA and GWAS datasets.

AVAILABILITY AND IMPLEMENTATION

https://github.com/lichen-lab/TLVar.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Computational and experimental methods for classifying variants of unknown clinical significance

The increase in sequencing capacity, reduction in costs, and national and international coordinated efforts have led to the widespread introduction of next-generation sequencing (NGS) technologies in patient care. More generally, human genetics and genomic medicine are gaining importance for more and more patients. Some communities are already discussing the prospect of sequencing each individual's genome at time of birth. Together with digital health records, this shall enable individualized treatments and preventive measures, so-called precision medicine. A central step in this process is the identification of disease causal mutations or variant combinations that make us more susceptible for diseases. Although various technological advances have improved the identification of genetic alterations, the interpretation and ranking of the identified variants remains a major challenge. Based on our knowledge of molecular processes or previously identified disease variants, we can identify potentially functional genetic variants and, using different lines of evidence, we are sometimes able to demonstrate their pathogenicity directly. However, the vast majority of variants are classified as variants of uncertain clinical significance (VUSs) with not enough experimental evidence to determine their pathogenicity. In these cases, computational methods may be used to improve the prioritization and an increasing toolbox of experimental methods is emerging that can be used to assay the molecular effects of VUSs. Here, we discuss how computational and experimental methods can be used to create catalogs of variant effects for a variety of molecular and cellular phenotypes. We discuss the prospects of integrating large-scale functional data with machine learning and clinical knowledge for the development of accurate pathogenicity predictions for clinical applications.

Multiple causal variants underlie genetic associations in humans

Associations between genetic variation and traits are often in noncoding regions with strong linkage disequilibrium (LD), where a single causal variant is assumed to underlie the association. We applied a massively parallel reporter assay (MPRA) to functionally evaluate genetic variants in high, local LD for independent cis-expression quantitative trait loci (eQTL). We found that 17.7% of eQTLs exhibit more than one major allelic effect in tight LD. The detected regulatory variants were highly and specifically enriched for activating chromatin structures and allelic transcription factor binding. Integration of MPRA profiles with eQTL/complex trait colocalizations across 114 human traits and diseases identified causal variant sets demonstrating how genetic association signals can manifest through multiple, tightly linked causal variants.

A multi-dimensional integrative scoring framework for predicting functional variants in the human genome

Attempts to identify and prioritize functional DNA elements in coding and non-coding regions, particularly through use of in silico functional annotation data, continue to increase in popularity. However, specific functional roles can vary widely from one variant to another, making it challenging to summarize different aspects of variant function with a one-dimensional rating. Here we propose multi-dimensional annotation-class integrative estimation (MACIE), an unsupervised multivariate mixed-model framework capable of integrating annotations of diverse origin to assess multi-dimensional functional roles for both coding and non-coding variants. Unlike existing one-dimensional scoring methods, MACIE views variant functionality as a composite attribute encompassing multiple characteristics and estimates the joint posterior functional probabilities of each genomic position. This estimate offers more comprehensive and interpretable information in the presence of multiple aspects of functionality. Applied to a variety of independent coding and non-coding datasets, MACIE demonstrates powerful and robust performance in discriminating between functional and non-functional variants. We also show an application of MACIE to fine-mapping and heritability enrichment analysis by using the lipids GWAS summary statistics data from the European Network for Genetic and Genomic Epidemiology Consortium.

Joint actions of diverse transcription factor families establish neuron-type identities and promote enhancer selectivity

To systematically investigate the complexity of neuron specification regulatory networks, we performed an RNA interference (RNAi) screen against all 875 transcription factors (TFs) encoded in Caenorhabditis elegans genome and searched for defects in nine different neuron types of the monoaminergic (MA) superclass and two cholinergic motoneurons. We identified 91 TF candidates to be required for correct generation of these neuron types, of which 28 were confirmed by mutant analysis. We found that correct reporter expression in each individual neuron type requires at least nine different TFs. Individual neuron types do not usually share TFs involved in their specification but share a common pattern of TFs belonging to the five most common TF families: homeodomain (HD), basic helix loop helix (bHLH), zinc finger (ZF), basic leucine zipper domain (bZIP), and nuclear hormone receptors (NHR). HD TF members are overrepresented, supporting a key role for this family in the establishment of neuronal identities. These five TF families are also prevalent when considering mutant alleles with previously reported neuronal phenotypes in C. elegans, Drosophila, and mouse. In addition, we studied terminal differentiation complexity focusing on the dopaminergic terminal regulatory program. We found two HD TFs (UNC-62 and VAB-3) that work together with known dopaminergic terminal selectors (AST-1, CEH-43, CEH-20). Combined TF binding sites for these five TFs constitute a cis-regulatory signature enriched in the regulatory regions of dopaminergic effector genes. Our results provide new insights on neuron-type regulatory programs in C. elegans that could help better understand neuron specification and evolution of neuron types.