Discovery of target genes and pathways at GWAS loci by pooled single-cell CRISPR screens

CRISPR-BEasy: a free web-based service for designing sgRNA tiling libraries for CRISPR-dependent base editing screens

CRISPR-dependent base editing (BE) enables the modeling and correction of genetic mutations at single-base resolution. Base editing screens, where point mutations are queried en masse, are powerful tools to systematically draw genotype-phenotype associations and characterise the function of genes and other genomic elements. However, the lack of user-friendly web-based tools for designing base editing screens can hinder broad technology adoption. Here, we introduce CRISPR-BEasy (https://crispr-beasy.cerc-genomic-medicine.ca), a free, automated web-based server that streamlines the creation of single guide (sg)RNA tiling libraries for base editing screens. Researchers can provide their genes or genomic features of interest, their base editors of choice, and target sequences to act as positive and negative controls. The server designs and annotates sgRNA libraries by integrating custom code with publicly available tools such as crisprVerse and Ensembl's Variant Effect Predictor. CRISPR-BEasy provides downloadable results, including sgRNA on/off-target scores, predicted mutational outcomes per base editor, and intuitive interactive visualizations for data quality assessment. CRISPR-BEasy also provides a separate tool that assembles sgRNA libraries into oligonucleotides for cloning following the detailed protocol documented in the searchable web server manual. Together, CRISPR-BEasy ensures the seamless design of cloning-ready sgRNA libraries, seeking to democratise access to base editing screening technologies.

Engineering mtDNA deletions by reconstituting end joining in human mitochondria

Recent breakthroughs in the genetic manipulation of mitochondrial DNA (mtDNA) have enabled precise base substitutions and the efficient elimination of genomes carrying pathogenic mutations. However, reconstituting mtDNA deletions linked to mitochondrial myopathies remains challenging. Here, we engineered mtDNA deletions in human cells by co-expressing end-joining (EJ) machinery and targeted endonucleases. Using mitochondrial EJ (mito-EJ) and mito-ScaI, we generated a panel of clonal cell lines harboring a ∼3.5 kb mtDNA deletion across the full spectrum of heteroplasmy. Investigating these cells revealed a critical threshold of ∼75% deleted genomes, beyond which oxidative phosphorylation (OXPHOS) protein depletion, metabolic disruption, and impaired growth in galactose-containing media were observed. Single-cell multiomic profiling identified two distinct nuclear gene deregulation responses: one triggered at the deletion threshold and another progressively responding to heteroplasmy. Ultimately, we show that our method enables the modeling of disease-associated mtDNA deletions across cell types and could inform the development of targeted therapies.

Variant-to-function approaches for adipose tissue: Insights into cardiometabolic disorders

Genome-wide association studies (GWASs) have identified thousands of genetic loci associated with cardiometabolic disorders. However, the functional interpretation of these loci remains a daunting challenge. This is particularly true for adipose tissue, a critical organ in systemic metabolism and the pathogenesis of various cardiometabolic diseases. We discuss how variant-to-function (V2F) approaches are used to elucidate the mechanisms by which GWAS loci increase the risk of cardiometabolic disorders by directly influencing adipose tissue. We outline GWAS traits most likely to harbor adipose-related variants and summarize tools to pinpoint the putative causal variants, genes, and cell types for the associated loci. We explain how large-scale perturbation experiments, coupled with imaging and multi-omics, can be used to screen variants' effects on cellular phenotypes and how these phenotypes can be tied to physiological mechanisms. Lastly, we discuss the challenges and opportunities that lie ahead for V2F research and propose a roadmap for future studies.

Trans-eQTL hotspots shape complex traits by modulating cellular states

Regulatory genetic variation shapes gene expression, providing an important mechanism connecting DNA variation and complex traits. The causal relationships between gene expression and complex traits remain poorly understood. Here, we integrated transcriptomes and 46 genetically complex growth traits in a large cross between two strains of the yeast Saccharomyces cerevisiae. We discovered thousands of genetic correlations between gene expression and growth, suggesting potential functional connections. Local regulatory variation was a minor source of these genetic correlations. Instead, genetic correlations tended to arise from multiple independent trans-acting regulatory loci. Trans-acting hotspots that affect the expression of numerous genes accounted for particularly large fractions of genetic growth variation and of genetic correlations between gene expression and growth. Genes with genetic correlations were enriched for similar biological processes across traits but with heterogeneous direction of effect. Our results reveal how trans-acting regulatory hotspots shape complex traits by altering cellular states.

Gene regulation technologies for gene and cell therapy

Gene therapy stands at the forefront of medical innovation, offering unique potential to treat the underlying causes of genetic disorders and broadly enable regenerative medicine. However, unregulated production of therapeutic genes can lead to decreased clinical utility due to various complications. Thus, many technologies for controlled gene expression are under development, including regulated transgenes, modulation of endogenous genes to leverage native biological regulation, mapping and repurposing of transcriptional regulatory networks, and engineered systems that dynamically react to cell state changes. Transformative therapies enabled by advances in tissue-specific promoters, inducible systems, and targeted delivery have already entered clinical testing and demonstrated significantly improved specificity and efficacy. This review highlights next-generation technologies under development to expand the reach of gene therapies by enabling precise modulation of gene expression. These technologies, including epigenome editing, antisense oligonucleotides, RNA editing, transcription factor-mediated reprogramming, and synthetic genetic circuits, have the potential to provide powerful control over cellular functions. Despite these remarkable achievements, challenges remain in optimizing delivery, minimizing off-target effects, and addressing regulatory hurdles. However, the ongoing integration of biological insights with engineering innovations promises to expand the potential for gene therapy, offering hope for treating not only rare genetic disorders but also complex multifactorial diseases.

Fine-mapping in admixed populations using CARMA-X, with applications to Latin American studies

Genome-wide association studies (GWASs) in ancestrally diverse populations are rapidly expanding, opening up unique opportunities for novel gene discoveries and increased utility of genetic findings in non-European individuals. A popular technique to identify putative causal variants at GWAS loci is via statistical fine-mapping. Despite tremendous efforts, fine-mapping remains a very challenging task, even in the relatively simple scenario of studies with a single, homogeneous population. For studies with admixed individuals, such as within Latin America and the Caribbean, methods for gene discovery are still limited. Here, we propose a Bayesian model for fine-mapping in admixed populations, CARMA-X, that addresses some of the unique challenges of admixed individuals. The proposed method includes an estimation method for the linkage disequilibrium (LD) matrix that accounts for small reference panels for admixed individuals, heterogeneity across populations and cross-ancestry LD, and a Bayesian hypothesis test that leads to robust fine-mapping when relying on external reference panels of modest size for LD estimation. Using simulations, we compare performance with recently proposed fine-mapping methods for multi-ancestry studies and show that the proposed model provides higher power while controlling false discoveries, especially when using an out-of-sample LD matrix. We further illustrate our approach through applications to two Latin American genetic studies, the Estudio Familiar de Influencia Genética en Alzheimer (EFIGA) study in the Dominican Republic and the Mexican Biobank, where we show the benefit of modeling ancestry-specific effects by prioritizing putative causal variants and genes, including several findings driven by ancestry-specific effects in the African and Native American ancestries.

Genetic regulation of gene expression across multiple tissues in chickens

The chicken is a valuable model for understanding fundamental biology and vertebrate evolution and is a major global source of nutrient-dense and lean protein. Despite being the first non-mammalian amniote to have its genome sequenced, a systematic characterization of functional variation on the chicken genome remains lacking. Here, we integrated bulk RNA sequencing (RNA-seq) data from 7,015 samples, single-cell RNA-seq data from 127,598 cells and 2,869 whole-genome sequences to present a pilot atlas of regulatory variants across 28 chicken tissues. This atlas reveals millions of regulatory effects on primary expression (protein-coding genes, long non-coding RNA and exons) and post-transcriptional modifications (alternative splicing and 3'-untranslated region alternative polyadenylation). We highlighted distinct molecular mechanisms underlying these regulatory variants, their context-dependent behavior and their utility in interpreting genome-wide associations for 39 chicken complex traits. Finally, our comparative analyses of gene regulation between chickens and mammals demonstrate how this resource can facilitate cross-species gene mapping of complex traits.

Rewriting regulatory DNA to dissect and reprogram gene expression

Regulatory DNA provides a platform for transcription factor binding to encode cell-type-specific patterns of gene expression. However, the effects and programmability of regulatory DNA sequences remain difficult to map or predict. Here, we develop variant effects from flow-sorting experiments with CRISPR targeting screens (Variant-EFFECTS) to introduce hundreds of designed edits to endogenous regulatory DNA and quantify their effects on gene expression. We systematically dissect and reprogram 3 regulatory elements for 2 genes in 2 cell types. These data reveal endogenous binding sites with effects specific to genomic context, transcription factor motifs with cell-type-specific activities, and limitations of computational models for predicting the effect sizes of variants. We identify small edits that can tune gene expression over a large dynamic range, suggesting new possibilities for prime-editing-based therapeutics targeting regulatory DNA. Variant-EFFECTS provides a generalizable tool to dissect regulatory DNA and to identify genome editing reagents that tune gene expression in an endogenous context.

Transcription factor networks disproportionately enrich for heritability of blood cell phenotypes

Most phenotype-associated genetic variants map to noncoding regulatory regions of the human genome, but their mechanisms remain elusive in most cases. We developed a highly efficient strategy, Perturb-multiome, to simultaneously profile chromatin accessibility and gene expression in single cells with CRISPR-mediated perturbation of master transcription factors (TFs). We examined the connection between TFs, accessible regions, and gene expression across the genome throughout hematopoietic differentiation. We discovered that variants within TF-sensitive accessible chromatin regions in erythroid differentiation, although representing <0.3% of the genome, show a ~100-fold enrichment for blood cell phenotype heritability, which is substantially higher than that for other accessible chromatin regions. Our approach facilitates large-scale mechanistic understanding of phenotype-associated genetic variants by connecting key cis-regulatory elements and their target genes within gene regulatory networks.

Transforming cancer treatment: integrating patient-derived organoids and CRISPR screening for precision medicine

The persistently high mortality rates associated with cancer underscore the imperative need for innovative, efficacious, and safer therapeutic agents, as well as a more nuanced understanding of tumor biology. Patient-derived organoids (PDOs) have emerged as innovative preclinical models with significant translational potential, capable of accurately recapitulating the structural, functional, and heterogeneous characteristics of primary tumors. When integrated with cutting-edge genomic tools such as CRISPR, PDOs provide a powerful platform for identifying cancer driver genes and novel therapeutic targets. This comprehensive review delves into recent advancements in CRISPR-mediated functional screens leveraging PDOs across diverse cancer types, highlighting their pivotal role in high-throughput functional genomics and tumor microenvironment (TME) modeling. Furthermore, this review highlights the synergistic potential of integrating PDOs with CRISPR screens in cancer immunotherapy, focusing on uncovering immune evasion mechanisms and improving the efficacy of immunotherapeutic approaches. Together, these cutting-edge technologies offer significant promise for advancing precision oncology.

Empowering genome-wide association studies via a visualizable test based on the regional association score

The genome-wide association studies identified genes associated with many diseases, but the identification and verification of disease variants are still challenging due to small effects and large number of individual variants. In this paper, we propose a powerful method that first quantifies the strength of regional associations at each single nucleotide polymorphism and converts these measures into time series data before using a change point detection algorithm to identify significant regions. In our extensive simulation study, the proposed method consistently demonstrates greater power than existing alternatives, achieving a relative increase of over 20% in challenging scenarios where true causal variants are sparse and multiple association regions exist at the same time, while maintaining a lower false positive rate.

Systematic reconstruction of molecular pathway signatures using scalable single-cell perturbation screens

Recent advancements in functional genomics have provided an unprecedented ability to measure diverse molecular modalities, but predicting causal regulatory relationships from observational data remains challenging. Here, we leverage pooled genetic screens and single-cell sequencing (Perturb-seq) to systematically identify the targets of signalling regulators in diverse biological contexts. We demonstrate how Perturb-seq is compatible with recent and commercially available advances in combinatorial indexing and next-generation sequencing, and perform more than 1,500 perturbations split across six cell lines and five biological signalling contexts. We introduce an improved computational framework (Mixscale) to address cellular variation in perturbation efficiency, alongside optimized statistical methods to learn differentially expressed gene lists and conserved molecular signatures. Finally, we demonstrate how our Perturb-seq derived gene lists can be used to precisely infer changes in signalling pathway activation for in vivo and in situ samples. Our work enhances our understanding of signalling regulators and their targets, and lays a computational framework towards the data-driven inference of an 'atlas' of perturbation signatures.

Specifying cellular context of transcription factor regulons for exploring context-specific gene regulation programs

Understanding the role of transcription and transcription factors (TFs) in cellular identity and disease, such as cancer, is essential. However, comprehensive data resources for cell line-specific TF-to-target gene annotations are currently limited. To address this, we employed a straightforward method to define regulons that capture the cell-specific aspects of TF binding and transcript expression levels. By integrating cellular transcriptome and TF binding data, we generated regulons for 40 common cell lines comprising both proximal and distal cell line-specific regulatory events. Through systematic benchmarking involving TF knockout experiments, we demonstrated performance on par with state-of-the-art methods, with our method being easily applicable to other cell types of interest. We present case studies using three cancer single-cell datasets to showcase the utility of these cell-type-specific regulons in exploring transcriptional dysregulation. In summary, this study provides a valuable pipeline and a resource for systematically exploring cell line-specific transcriptional regulations, emphasizing the utility of network analysis in deciphering disease mechanisms.

EPIraction - an atlas of candidate enhancer-gene interactions in human tissues and cell lines

Enhancer-gene maps are essential to understand gene regulation. The identification of enhancers contributing to regulating a given gene, however, is challenging as they may lie far from the gene and act in a tissue-specific manner. Here we present EPIraction, an enhancer-gene atlas in the human genome. As H3K27ac is a typical mark of enhancer function, EPIraction relies on an exhaustive collection of 1,538 H3K27ac ChIP-Seq datasets. We used these data together with other epigenomic data to measure enhancer activity. To predict enhancer-gene interactions we first applied the conventional Activity By Contact algorithm to score enhancer-gene pairs in individual tissues. Then, we re-scored the interaction rewarding those present in biologically similar tissues. We used our predictions to annotate the candidate target genes for 1,664,956 SNPs from 3,693 UK Biobank and 760 FinnGen GWAS traits. The EPIraction predictions can be accessed through the portal at https://epiraction.crg.es . The portal can be employed to produce annotations for user-provided lists of SNPs or genomic intervals.

The mutational landscape and functional effects of noncoding ultraconserved elements in human cancers

The mutational landscape of phylogenetically ultraconserved elements (UCEs), especially those in noncoding DNAs (ncUCEs), and their functional relevance in cancers remain poorly characterized. Here, we perform a systematic analysis of whole-genome and in-house targeted UCE sequencing datasets from more than 3000 patients with cancer of 13,736 UCEs and demonstrate that ncUCE somatic alterations are common. Using a multiplexed CRISPR knockout screen in colorectal cancer cells, we show that the loss of several altered ncUCEs significantly affects cell proliferation. In-depth functional studies in vitro and in vivo further reveal that specific ncUCEs can be enhancers of tumor suppressors (such as ARID1B) and silencers of oncogenic proteins (such as RPS13). Moreover, several miRNAs located in ncUCEs are recurrently mutated. Mutations in miR-142 locus can affect the Drosha-mediated processing of precursor miRNAs, resulting in the down-regulation of the mature transcript. These results provide systematic evidence that specific ncUCEs play diverse regulatory roles in cancer.

Prioritization of Lipid Metabolism Targets for the Diagnosis and Treatment of Cardiovascular Diseases

Background: Cardiovascular diseases (CVD) are a major global health issue strongly associated with altered lipid metabolism. However, lipid metabolism-related pharmacological targets remain limited, leaving the therapeutic challenge of residual lipid-associated cardiovascular risk. The purpose of this study is to identify potentially novel lipid metabolism-related genes by systematic genomic and phenomics analysis, with an aim to discovering potentially new therapeutic targets and diagnosis biomarkers for CVD. Methods: In this study, we conducted a comprehensive and multidimensional evaluation of 881 lipid metabolism-related genes. Using genome-wide association study (GWAS)-based mendelian randomization (MR) causal inference methods, we screened for genes causally linked to the occurrence and development of CVD. Further validation was performed through colocalization analysis in 2 independent cohorts. Then, we employed reverse screening using phenonome-wide association studies (PheWAS) and a drug target-drug association analysis. Finally, we integrated serum proteomic data to develop a machine learning model comprising 5 proteins for disease prediction. Results: Our initial screening yielded 54 genes causally linked to CVD. Colocalization analysis in validation cohorts prioritized this to 29 genes marked correlated with CVD. Comparison and interaction analysis identified 13 therapeutic targets with potential for treating CVD and its complications. A machine learning model incorporating 5 proteins for CVD prediction achieved a high accuracy of 96.1%, suggesting its potential as a diagnostic tool in clinical practice. Conclusion: This study comprehensively reveals the complex relationship between lipid metabolism regulatory targets and CVD. Our findings provide new insights into the pathogenesis of CVD and identify potential therapeutic targets and drugs for its treatment. Additionally, the machine learning model developed in this study offers a promising tool for the diagnosis and prediction of CVD, paving the way for future research and clinical applications.

Massively parallel in vivo Perturb-seq screening

Advances in genomics have identified thousands of risk genes impacting human health and diseases, but the functions of these genes and their mechanistic contribution to disease are often unclear. Moving beyond identification to actionable biological pathways requires dissecting risk gene function and cell type-specific action in intact tissues. This gap can in part be addressed by in vivo Perturb-seq, a method that combines state-of-the-art gene editing tools for programmable perturbation of genes with high-content, high-resolution single-cell genomic assays as phenotypic readouts. Here we describe a detailed protocol to perform massively parallel in vivo Perturb-seq using several versatile adeno-associated virus (AAV) vectors and provide guidance for conducting successful downstream analyses. Expertise in mouse work, AAV production and single-cell genomics is required. We discuss key parameters for designing in vivo Perturb-seq experiments across diverse biological questions and contexts. We further detail the step-by-step procedure, from designing a perturbation library to producing and administering AAV, highlighting where quality control checks can offer critical go-no-go points for this time- and cost-expensive method. Finally, we discuss data analysis options and available software. In vivo Perturb-seq has the potential to greatly accelerate functional genomics studies in mammalian systems, and this protocol will help others adopt it to answer a broad array of biological questions. From guide RNA design to tissue collection and data collection, this protocol is expected to take 9-15 weeks to complete, followed by data analysis.

Multi-locus CRISPRi targeting with a single truncated guide RNA

A critical goal in functional genomics is evaluating which non-coding elements contribute to gene expression, cellular function, and disease. Functional characterization remains a challenge due to the abundance and complexity of candidate elements. Here, we develop a CRISPRi-based approach for multi-locus screening of putative transcription factor binding sites with a single truncated guide. A truncated guide with hundreds of sequence match sites can reliably disrupt enhancer activity, which expands the targeting scope of CRISPRi while maintaining repressive efficacy. We screen over 13,000 possible CTCF binding sites with 24 guides at 10 nucleotides in spacer length. These truncated guides direct CRISPRi-mediated deposition of repressive H3K9me3 marks and disrupt transcription factor binding at most sequence match target sites. This approach can be a valuable screening step for testing transcription factor binding motifs or other repeated genomic sequences and is easily implemented with existing tools.

Just a SNP away: The future of in vivo massively parallel reporter assay

The human genome is largely noncoding, yet the field is still grasping to understand how noncoding variants impact transcription and contribute to disease etiology. The massively parallel reporter assay (MPRA) has been employed to characterize the function of noncoding variants at unprecedented scales, but its application has been largely limited by the in vitro context. The field will benefit from establishing a systemic platform to study noncoding variant function across multiple tissue types under physiologically relevant conditions. However, to date, MPRA has been applied to only a handful of in vivo conditions. Given the complexity of the central nervous system and its widespread interactions with all other organ systems, our understanding of neuropsychiatric disorder-associated noncoding variants would be greatly advanced by studying their functional impact in the intact brain. In this review, we discuss the importance, technical considerations, and future applications of implementing MPRA in the in vivo space with the focus on neuropsychiatric disorders.

Haplotype rather than single causal variants effects contribute to regulatory gene expression associations in human myeloid cells

Genome-wide association studies typically identify hundreds to thousands of loci, many of which harbor multiple independent peaks, each parsimoniously assumed to be due to the activity of a single causal variant. Fine-mapping of such variants has become a priority and since most associations are located within regulatory regions, it is also assumed that they colocalize with regulatory variants that influence the expression of nearby genes. Here we examine these assumptions by using a moderate throughput expression CROPseq protocol in which Cas9 nuclease is used to induce small insertions and deletions across the credible set of SNPs that may account for expression quantitative trait loci (eQTL) for genes associated with inflammatory bowel disease (IBD). Of the 4,384 SNPs targeted in 88 loci (an average of 50 per locus), 439 were significant and further examined for validation. From these, 98 significantly altered target gene expression in HL-60 myeloid cell line, 74 in induced macrophages from these HL-60 cells, and 78 in induced neutrophils for a total of 201 validated effects (46%), 43 of which were observed in at least two of the cell types. Considering the observed sensitivity and specificity of the controls, we estimate that there are at least 150 true positives per cell type, an average of almost 2.4 for each of the 64 eQTL for which putative causal variants have been fine-mapped. This implies that haplotype effects are likely to explain many of the associations. We also demonstrate that the same approach can be used to investigate the activity of very rare variants in regulatory regions for 89 genes, providing a rapid strategy for establishing clinical relevance of non-coding mutations.

Enhancer regulatory networks globally connect non-coding breast cancer loci to cancer genes

BACKGROUND

Genetic studies have associated thousands of enhancers with breast cancer (BC). However, the vast majority have not been functionally characterized. Thus, it remains unclear how BC-associated enhancers contribute to cancer.

RESULTS

Here, we perform single-cell CRISPRi screens of 3513 regulatory elements associated with breast cancer to measure the impact of these regions on transcriptional phenotypes. Analysis of > 500,000 single-cell transcriptomes in two breast cancer cell lines shows that perturbation of BC-associated enhancers disrupts breast cancer gene programs. We observe BC-associated enhancers that directly or indirectly regulate the expression of cancer genes. We also find one-to-multiple and multiple-to-one network motifs where enhancers indirectly regulate cancer genes. Notably, multiple BC-associated enhancers indirectly regulate TP53. Comparative studies illustrate subtype specific functions between enhancers in ER + and ER - cells. Finally, we develop the pySpade package to facilitate analysis of single-cell enhancer screens.

CONCLUSIONS

Overall, we demonstrate that enhancers form regulatory networks that link cancer genes in the genome, providing a more comprehensive understanding of the contribution of enhancers to breast cancer development.

Eleven Grand Challenges for Inflammatory Bowel Disease Genetics and Genomics

The past 2 decades have witnessed extraordinary advances in our understanding of the genetic factors influencing inflammatory bowel disease (IBD), providing a foundation for the approaching era of genomic medicine. On behalf of the NIDDK IBD Genetics Consortium, we herein survey 11 grand challenges for the field as it embarks on the next 2 decades of research utilizing integrative genomic and systems biology approaches. These involve elucidation of the genetic architecture of IBD (how it compares across populations, the role of rare variants, and prospects of polygenic risk scores), in-depth cellular and molecular characterization (fine-mapping causal variants, cellular contributions to pathology, molecular pathways, interactions with environmental exposures, and advanced organoid models), and applications in personalized medicine (unmet medical needs, working toward molecular nosology, and precision therapeutics). We review recent advances in each of the 11 areas and pose challenges for the genetics and genomics communities of IBD researchers.

Unraveling genetic mysteries: A comprehensive review of GWAS and DNA insights in animal and plant pathosystems

DNA serves as the carrier of genetic information, with sequence variations playing a pivotal role in defining hereditary traits. Genome-Wide Association Studies (GWAS) facilitate the investigation of the links between genetic variations and phenotypes, significantly influencing biological research, particularly in animal and plant pathology. By identifying genetic markers associated with specific traits or diseases, GWAS enhances our understanding of host-pathogen interactions and improves disease-resistant breeding strategies. It has been vital in revealing the genetic basis of disease resistance, pinpointing key genes and DNA loci, which enrich genetic resources for breeding programs and deepen our knowledge of disease resistance mechanisms at the DNA level. Additionally, GWAS contributes to pathogen population genetics, facilitating a thorough exploration of pathogen virulence. Integrating GWAS with marker-assisted selection enhances breeding efficiency and precision in selecting for disease-resistant traits. While previous research has largely focused on host genetics, the genetic variation of pathogens is equally significant. Notably, reports integrating animal and plant pathosystems are still lacking. Given the importance of these systems, this review summarizes key advancements in this field, addresses current challenges, and proposes future directions, thereby offering a vital reference for ongoing research.

GWAS-Informed data integration and non-coding CRISPRi screen illuminate genetic etiology of bone mineral density

Over 1,100 independent signals have been identified with genome-wide association studies (GWAS) for bone mineral density (BMD), a key risk factor for mortality-increasing fragility fractures; however, the effector gene(s) for most remain unknown. Informed by a variant-to-gene mapping strategy implicating 89 non-coding elements predicted to regulate osteoblast gene expression at BMD GWAS loci, we executed a single-cell CRISPRi screen in human fetal osteoblasts (hFOBs). The BMD relevance of hFOBs was supported by heritability enrichment from stratified LD-score regression involving 98 cell types grouped into 15 tissues. 23 genes showed perturbation in the screen, with four (ARID5B, CC2D1B, EIF4G2, and NCOA3) exhibiting consistent effects upon siRNA knockdown on three measures of osteoblast maturation and mineralization. Lastly, additional heritability enrichments, genetic correlations, and multi-trait fine-mapping revealed unexpectedly that many BMD GWAS signals are pleiotropic and likely mediate their effects via non-bone tissues. Extending our CRISPRi screening approach to these tissues could play a key role in fully elucidating the etiology of BMD.

Transcriptome-scale RNA-targeting CRISPR screens reveal essential lncRNAs in human cells

Mammalian genomes host a diverse array of RNA that includes protein-coding and noncoding transcripts. However, the functional roles of most long noncoding RNAs (lncRNAs) remain elusive. Using RNA-targeting CRISPR-Cas13 screens, we probed how the loss of ∼6,200 lncRNAs impacts cell fitness across five human cell lines and identified 778 lncRNAs with context-specific or broad essentiality. We confirm their essentiality with individual perturbations and find that the majority of essential lncRNAs operate independently of their nearest protein-coding genes. Using transcriptome profiling in single cells, we discover that the loss of essential lncRNAs impairs cell-cycle progression and drives apoptosis. Many essential lncRNAs demonstrate dynamic expression across tissues during development. Using ∼9,000 primary tumors, we pinpoint those lncRNAs whose expression in tumors correlates with survival, yielding new biomarkers and potential therapeutic targets. This transcriptome-wide survey of functional lncRNAs advances our understanding of noncoding transcripts and demonstrates the potential of transcriptome-scale noncoding screens with Cas13.

Advances and applications in single-cell and spatial genomics

The applications of single-cell and spatial technologies in recent times have revolutionized the present understanding of cellular states and the cellular heterogeneity inherent in complex biological systems. These advancements offer unprecedented resolution in the examination of the functional genomics of individual cells and their spatial context within tissues. In this review, we have comprehensively discussed the historical development and recent progress in the field of single-cell and spatial genomics. We have reviewed the breakthroughs in single-cell multi-omics technologies, spatial genomics methods, and the computational strategies employed toward the analyses of single-cell atlas data. Furthermore, we have highlighted the advances made in constructing cellular atlases and their clinical applications, particularly in the context of disease. Finally, we have discussed the emerging trends, challenges, and opportunities in this rapidly evolving field.

DNA regulatory element cooperation and competition in transcription

Regulation of eukaryotic transcription is a complex process that enables precise temporal and spatial control of gene expression. Promoters, which are cis-regulatory elements (CREs) located proximal to the transcription start site (TSS), selectively integrate regulatory cues from distal CREs, or enhancers, and their associated transcriptional machinery. In this review, we discuss current knowledge regarding CRE cooperation and competition impacting gene expression, including features of enhancer-promoter, enhancer-enhancer, and promoter-promoter interplay. We also provide an overview of recent insights into the underlying molecular mechanisms that facilitate physical and functional interaction of regulatory elements, such as the involvement of enhancer RNAs and biomolecular condensates. [BMB Reports 2024; 57(12): 509-520].

Analysis of single-cell CRISPR perturbations indicates that enhancers predominantly act multiplicatively

A single gene may have multiple enhancers, but how they work in concert to regulate transcription is poorly understood. To analyze enhancer interactions throughout the genome, we developed a generalized linear modeling framework, GLiMMIRS, for interrogating enhancer effects from single-cell CRISPR experiments. We applied GLiMMIRS to a published dataset and tested for interactions between 46,166 enhancer pairs and corresponding genes, including 264 "high-confidence" enhancer pairs. We found that enhancer effects combine multiplicatively but with limited evidence for further interactions. Only 31 enhancer pairs exhibited significant interactions (false discovery rate <0.1), none of which came from the high-confidence set, and 20 were driven by outlier expression values. Additional analyses of a second CRISPR dataset and in silico enhancer perturbations with Enformer both support a multiplicative model of enhancer effects without interactions. Altogether, our results indicate that enhancer interactions are uncommon or have small effects that are difficult to detect.

Engineering mtDNA Deletions by Reconstituting End-Joining in Human Mitochondria

Recent breakthroughs in the genetic manipulation of mitochondrial DNA (mtDNA) have enabled the precise introduction of base substitutions and the effective removal of genomes carrying harmful mutations. However, the reconstitution of mtDNA deletions responsible for severe mitochondrial myopathies and age-related diseases has not yet been achieved in human cells. Here, we developed a method to engineer specific mtDNA deletions in human cells by co-expressing end-joining (EJ) machinery and targeted endonucleases. As a proof-of-concept, we used mito-EJ and mito-ScaI to generate a panel of clonal cell lines harboring a ∼3.5 kb mtDNA deletion with the full spectrum of heteroplasmy. Investigating these isogenic cells revealed a critical threshold of ∼75% deleted genomes, beyond which cells exhibited depletion of OXPHOS proteins, severe metabolic disruption, and impaired growth in galactose-containing media. Single-cell multiomic analysis revealed two distinct patterns of nuclear gene deregulation in response to mtDNA deletion accumulation; one triggered at the deletion threshold and another progressively responding to increasing heteroplasmy. In summary, the co-expression of mito-EJ and programable nucleases provides a powerful tool to model disease-associated mtDNA deletions in different cell types. Establishing a panel of cell lines with a large-scale deletion at varying levels of heteroplasmy is a valuable resource for understanding the impact of mtDNA deletions on diseases and guiding the development of potential therapeutic strategies.

Highlights

Combining prokaryotic end-joining with targeted endonucleases generates specific mtDNA deletions in human cellsEngineering a panel of cell lines with a large-scale deletion that spans the full spectrum of heteroplasmy75% heteroplasmy is the threshold that triggers mitochondrial and cellular dysfunctionTwo distinct nuclear transcriptional programs in response to mtDNA deletions: threshold-triggered and heteroplasmy-sensing.

The Genetics of Chiari 1 Malformation

Chiari malformation type 1 (CM1) is a structural defect that involves the herniation of the cerebellar tonsils through the foramen magnum, causing mild to severe neurological symptoms. Little is known about the molecular and developmental mechanisms leading to its pathogenesis, prompting current efforts to elucidate genetic drivers. Inherited genetic disorders are reported in 2-3% of CM1 patients; however, CM1, including familial forms, is predominantly non-syndromic. Recent work has focused on identifying CM1-asscoiated variants through the study of both familial cases and de novo mutations using exome sequencing. This article aims to review the current understanding of the genetics of CM1. We discuss three broad classes of CM1 based on anatomy and link them with genetic lesions, including posterior fossa-linked, macrocephaly-linked, and connective tissue disorder-linked CM1. Although the genetics of CM1 are only beginning to be understood, we anticipate that additional studies with diverse patient populations, tissue types, and profiling technologies will reveal new insights in the coming years.

Helicase-assisted continuous editing for programmable mutagenesis of endogenous genomes

Deciphering the context-specific relationship between sequence and function is a major challenge in genomics. Existing tools for inducing locus-specific hypermutation and evolution in the native genome context are limited. Here we present a programmable platform for long-range, locus-specific hypermutation called helicase-assisted continuous editing (HACE). HACE leverages CRISPR-Cas9 to target a processive helicase-deaminase fusion that incurs mutations across large (>1000-base pair) genomic intervals. We applied HACE to identify mutations in mitogen-activated protein kinase kinase 1 (MEK1) that confer kinase inhibitor resistance, to dissect the impact of individual variants in splicing factor 3B subunit 1 (SF3B1)-dependent missplicing, and to evaluate noncoding variants in a stimulation-dependent immune enhancer of CD69. HACE provides a powerful tool for investigating coding and noncoding variants, uncovering combinatorial sequence-to-function relationships, and evolving new biological functions.

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.

A Mutation Losing an RBP-Binding Site in the LncRNA NORSF Transcript Influences Granulosa Cell Apoptosis and Sow Fertility

Sow fertility is an economically important quantitative trait. Hundreds of quantitative trait loci (QTLs) containing tens of thousands of potential candidate genes are excavated. However, among these genes, non-coding RNAs including long non-coding RNAs (lncRNAs) are often overlooked. Here, it is reported that NORSF is a novel causal lncRNA for sow fertility traits in QTLs. QTLs are characterized for sow fertility traits at the genome-wide level and identified 4,630 potential candidate lncRNAs, with 13 differentially expressed during sow follicular atresia. NORSF, a lncRNA that involved in sow granulosa cell (sGC) function, is identified as a candidate gene for sow fertility traits as a G to A transversion at 128 nt in its transcript is shown to be markedly associated with sow fertility traits. Mechanistically, after forming the RNA:dsDNA triplexes with the promoter of Caspase8, NORSF transcript with allele G binds to an RNA-binding protein (RBP) NR2C1 and recruits it to the promoter of Caspase8, to induce Caspase8 transcription in sGCs. Functionally, this leads to a loss of inducing effect of NORSF on sGC apoptosis by inactivating the death receptor-mediated apoptotic pathway. This study identified a novel causal lncRNA that can be used for the genetic improvement of sow fertility traits.

Exponential family measurement error models for single-cell CRISPR screens

CRISPR genome engineering and single-cell RNA sequencing have accelerated biological discovery. Single-cell CRISPR screens unite these two technologies, linking genetic perturbations in individual cells to changes in gene expression and illuminating regulatory networks underlying diseases. Despite their promise, single-cell CRISPR screens present considerable statistical challenges. We demonstrate through theoretical and real data analyses that a standard method for estimation and inference in single-cell CRISPR screens-"thresholded regression"-exhibits attenuation bias and a bias-variance tradeoff as a function of an intrinsic, challenging-to-select tuning parameter. To overcome these difficulties, we introduce GLM-EIV ("GLM-based errors-in-variables"), a new method for single-cell CRISPR screen analysis. GLM-EIV extends the classical errors-in-variables model to responses and noisy predictors that are exponential family-distributed and potentially impacted by the same set of confounding variables. We develop a computational infrastructure to deploy GLM-EIV across hundreds of processors on clouds (e.g. Microsoft Azure) and high-performance clusters. Leveraging this infrastructure, we apply GLM-EIV to analyze two recent, large-scale, single-cell CRISPR screen datasets, yielding several new insights.

Erythropoiesis: insights from a genomic perspective

Erythropoiesis, the process underlying the production of red blood cells, which are essential for oxygen transport, involves the development of hematopoietic stem cells into mature red blood cells. This review focuses on the critical roles of transcription factors and epigenetic mechanisms in modulating gene expression critical for erythroid differentiation. It emphasizes the significance of chromatin remodeling in ensuring gene accessibility, a key factor for the orderly progression of erythropoiesis. This review also discusses how dysregulation of these processes can lead to erythroid disorders and examines the promise of genome editing and gene therapy as innovative therapeutic approaches. By shedding light on the genomic regulation of erythropoiesis, this review suggests avenues for novel treatments for hematological conditions, underscoring the need for continued molecular studies to improve human health.

A genome-wide association study reveals candidate genes and regulatory regions associated with birth weight in pigs

Piglet birth weight is associated with preweaning survival, and its related traits have been included in the breeding program. Thus, understanding its genetic basis is essential. This study identified four birth weight-associated genomic regions on chromosomes 2, 4, 5, and 7 through genome-wide association study analysis in 7286 pigs from three different pure breeds using the FarmCPU model. The genetic and phenotypic variance explained by the four candidate regions is 8.42% and 1.85%, respectively. Twenty-eight candidate genes were detected, of which APPL2, TGFBI, MACROH2A1, and SEC22B have been reported to affect body growth or development. In addition, 21 H3K4me3-enriched peaks overlapped with the birth weight-associated genomic regions were identified by integrating the genome-wide association study results with our previous ChIP-seq and RNA-seq data generated in the pig placenta, a fetal organ relevant to birth weight, and three of the regulatory regions influence TGFBI, MACROH2A1, and SEC22B expression. This study provides new insights into understanding the mechanisms for birth weight. Further investigating the variants in the regulatory regions would help identify the functional variants for birth weight in pigs.

Neuronal enhancers fine-tune adaptive circuit plasticity

Neuronal activity-regulated gene expression plays a crucial role in sculpting neural circuits that underpin adaptive brain function. Transcriptional enhancers are now recognized as key components of gene regulation that orchestrate spatiotemporally precise patterns of gene transcription. We propose that the dynamics of enhancer activation uniquely position these genomic elements to finely tune activity-dependent cellular plasticity. Enhancer specificity and modularity can be exploited to gain selective genetic access to specific cell states, and the precise modulation of target gene expression within restricted cellular contexts enabled by targeted enhancer manipulation allows for fine-grained evaluation of gene function. Mounting evidence also suggests that enduring stimulus-induced changes in enhancer states can modify target gene activation upon restimulation, thereby contributing to a form of cell-wide metaplasticity. We advocate for focused exploration of activity-dependent enhancer function to gain new insight into the mechanisms underlying brain plasticity and cognitive dysfunction.

Tailoring a CRISPR/Cas-based Epigenome Editor for Programmable Chromatin Acylation and Decreased Cytotoxicity

Engineering histone acylation states can inform mechanistic epigenetics and catalyze therapeutic epigenome editing opportunities. Here, we developed engineered lysine acyltransferases that enable the programmable deposition of acetylation and longer-chain acylations. We show that targeting an engineered lysine crotonyltransferase results in weak levels of endogenous enhancer activation yet retains potency when targeted to promoters. We further identify a single mutation within the catalytic core of human p300 that preserves enzymatic activity while substantially reducing cytotoxicity, enabling improved viral delivery. We leveraged these capabilities to perform single-cell CRISPR activation screening and map enhancers to the genes they regulate in situ. We also discover acylation-specific interactions and find that recruitment of p300, regardless of catalytic activity, to prime editing sites can improve editing efficiency. These new programmable epigenome editing tools and insights expand our ability to understand the mechanistic role of lysine acylation in epigenetic and cellular processes and perform functional genomic screens.

Multiplex, single-cell CRISPRa screening for cell type specific regulatory elements

CRISPR-based gene activation (CRISPRa) is a strategy for upregulating gene expression by targeting promoters or enhancers in a tissue/cell-type specific manner. Here, we describe an experimental framework that combines highly multiplexed perturbations with single-cell RNA sequencing (sc-RNA-seq) to identify cell-type-specific, CRISPRa-responsive cis-regulatory elements and the gene(s) they regulate. Random combinations of many gRNAs are introduced to each of many cells, which are then profiled and partitioned into test and control groups to test for effect(s) of CRISPRa perturbations of both enhancers and promoters on the expression of neighboring genes. Applying this method to a library of 493 gRNAs targeting candidate cis-regulatory elements in both K562 cells and iPSC-derived excitatory neurons, we identify gRNAs capable of specifically upregulating intended target genes and no other neighboring genes within 1 Mb, including gRNAs yielding upregulation of six autism spectrum disorder (ASD) and neurodevelopmental disorder (NDD) risk genes in neurons. A consistent pattern is that the responsiveness of individual enhancers to CRISPRa is restricted by cell type, implying a dependency on either chromatin landscape and/or additional trans-acting factors for successful gene activation. The approach outlined here may facilitate large-scale screens for gRNAs that activate genes in a cell type-specific manner.

Transcription factor networks disproportionately enrich for heritability of blood cell phenotypes

Most phenotype-associated genetic variants map to non-coding regulatory regions of the human genome. Moreover, variants associated with blood cell phenotypes are enriched in regulatory regions active during hematopoiesis. To systematically explore the nature of these regions, we developed a highly efficient strategy, Perturb-multiome, that makes it possible to simultaneously profile both chromatin accessibility and gene expression in single cells with CRISPR-mediated perturbation of a range of master transcription factors (TFs). This approach allowed us to examine the connection between TFs, accessible regions, and gene expression across the genome throughout hematopoietic differentiation. We discovered that variants within the TF-sensitive accessible chromatin regions, while representing less than 0.3% of the genome, show a ~100-fold enrichment in heritability across certain blood cell phenotypes; this enrichment is strikingly higher than for other accessible chromatin regions. Our approach facilitates large-scale mechanistic understanding of phenotype-associated genetic variants by connecting key cis-regulatory elements and their target genes within gene regulatory networks.

Deciphering the impact of genomic variation on function

Our genomes influence nearly every aspect of human biology-from molecular and cellular functions to phenotypes in health and disease. Studying the differences in DNA sequence between individuals (genomic variation) could reveal previously unknown mechanisms of human biology, uncover the basis of genetic predispositions to diseases, and guide the development of new diagnostic tools and therapeutic agents. Yet, understanding how genomic variation alters genome function to influence phenotype has proved challenging. To unlock these insights, we need a systematic and comprehensive catalogue of genome function and the molecular and cellular effects of genomic variants. Towards this goal, the Impact of Genomic Variation on Function (IGVF) Consortium will combine approaches in single-cell mapping, genomic perturbations and predictive modelling to investigate the relationships among genomic variation, genome function and phenotypes. IGVF will create maps across hundreds of cell types and states describing how coding variants alter protein activity, how noncoding variants change the regulation of gene expression, and how such effects connect through gene-regulatory and protein-interaction networks. These experimental data, computational predictions and accompanying standards and pipelines will be integrated into an open resource that will catalyse community efforts to explore how our genomes influence biology and disease across populations.

Pleiotropy, epistasis and the genetic architecture of quantitative traits

Pleiotropy (whereby one genetic polymorphism affects multiple traits) and epistasis (whereby non-linear interactions between genetic polymorphisms affect the same trait) are fundamental aspects of the genetic architecture of quantitative traits. Recent advances in the ability to characterize the effects of polymorphic variants on molecular and organismal phenotypes in human and model organism populations have revealed the prevalence of pleiotropy and unexpected shared molecular genetic bases among quantitative traits, including diseases. By contrast, epistasis is common between polymorphic loci associated with quantitative traits in model organisms, such that alleles at one locus have different effects in different genetic backgrounds, but is rarely observed for human quantitative traits and common diseases. Here, we review the concepts and recent inferences about pleiotropy and epistasis, and discuss factors that contribute to similarities and differences between the genetic architecture of quantitative traits in model organisms and humans.

Human genetics and epigenetics of alcohol use disorder

Alcohol use disorder (AUD) is a prominent contributor to global morbidity and mortality. Its complex etiology involves genetics, epigenetics, and environmental factors. We review progress in understanding the genetics and epigenetics of AUD, summarizing the key findings. Advancements in technology over the decades have elevated research from early candidate gene studies to present-day genome-wide scans, unveiling numerous genetic and epigenetic risk factors for AUD. The latest GWAS on more than one million participants identified more than 100 genetic variants, and the largest epigenome-wide association studies (EWAS) in blood and brain samples have revealed tissue-specific epigenetic changes. Downstream analyses revealed enriched pathways, genetic correlations with other traits, transcriptome-wide association in brain tissues, and drug-gene interactions for AUD. We also discuss limitations and future directions, including increasing the power of GWAS and EWAS studies as well as expanding the diversity of populations included in these analyses. Larger samples, novel technologies, and analytic approaches are essential; these include whole-genome sequencing, multiomics, single-cell sequencing, spatial transcriptomics, deep-learning prediction of variant function, and integrated methods for disease risk prediction.

Multiplexed single-cell characterization of alternative polyadenylation regulators

Most mammalian genes have multiple polyA sites, representing a substantial source of transcript diversity regulated by the cleavage and polyadenylation (CPA) machinery. To better understand how these proteins govern polyA site choice, we introduce CPA-Perturb-seq, a multiplexed perturbation screen dataset of 42 CPA regulators with a 3' scRNA-seq readout that enables transcriptome-wide inference of polyA site usage. We develop a framework to detect perturbation-dependent changes in polyadenylation and characterize modules of co-regulated polyA sites. We find groups of intronic polyA sites regulated by distinct components of the nuclear RNA life cycle, including elongation, splicing, termination, and surveillance. We train and validate a deep neural network (APARENT-Perturb) for tandem polyA site usage, delineating a cis-regulatory code that predicts perturbation response and reveals interactions between regulatory complexes. Our work highlights the potential for multiplexed single-cell perturbation screens to further our understanding of post-transcriptional regulation.

Non-linear transcriptional responses to gradual modulation of transcription factor dosage

Genomic loci associated with common traits and diseases are typically non-coding and likely impact gene expression, sometimes coinciding with rare loss-of-function variants in the target gene. However, our understanding of how gradual changes in gene dosage affect molecular, cellular, and organismal traits is currently limited. To address this gap, we induced gradual changes in gene expression of four genes using CRISPR activation and inactivation. Downstream transcriptional consequences of dosage modulation of three master trans-regulators associated with blood cell traits (GFI1B, NFE2, and MYB) were examined using targeted single-cell multimodal sequencing. We showed that guide tiling around the TSS is the most effective way to modulate cis gene expression across a wide range of fold-changes, with further effects from chromatin accessibility and histone marks that differ between the inhibition and activation systems. Our single-cell data allowed us to precisely detect subtle to large gene expression changes in dozens of trans genes, revealing that many responses to dosage changes of these three TFs are non-linear, including non-monotonic behaviours, even when constraining the fold-changes of the master regulators to a copy number gain or loss. We found that the dosage properties are linked to gene constraint and that some of these non-linear responses are enriched for disease and GWAS genes. Overall, our study provides a straightforward and scalable method to precisely modulate gene expression and gain insights into its downstream consequences at high resolution.

Comprehensive transcription factor perturbations recapitulate fibroblast transcriptional states

Cell atlas projects have nominated recurrent transcriptional states as drivers of biological processes and disease, but their origins, regulation, and properties remain unclear. To enable complementary functional studies, we developed a scalable approach for recapitulating cell states in vitro using CRISPR activation (CRISPRa) Perturb-seq. Aided by a novel multiplexing method, we activated 1,836 transcription factors in two cell types. Measuring 21,958 perturbations showed that CRISPRa activated targets within physiological ranges, that epigenetic features predicted activatable genes, and that the protospacer seed region drove an off-target effect. Perturbations recapitulated in vivo fibroblast states, including universal and inflammatory states, and identified KLF4 and KLF5 as key regulators of the universal state. Inducing the universal state suppressed disease-associated states, highlighting its therapeutic potential. Our findings cement CRISPRa as a tool for perturbing differentiated cells and indicate that in vivo states can be elicited via perturbation, enabling studies of clinically relevant states ex vivo.

Scalable genetic screening for regulatory circuits using compressed Perturb-seq

Pooled CRISPR screens with single-cell RNA sequencing readout (Perturb-seq) have emerged as a key technique in functional genomics, but they are limited in scale by cost and combinatorial complexity. In this study, we modified the design of Perturb-seq by incorporating algorithms applied to random, low-dimensional observations. Compressed Perturb-seq measures multiple random perturbations per cell or multiple cells per droplet and computationally decompresses these measurements by leveraging the sparse structure of regulatory circuits. Applied to 598 genes in the immune response to bacterial lipopolysaccharide, compressed Perturb-seq achieves the same accuracy as conventional Perturb-seq with an order of magnitude cost reduction and greater power to learn genetic interactions. We identified known and novel regulators of immune responses and uncovered evolutionarily constrained genes with downstream targets enriched for immune disease heritability, including many missed by existing genome-wide association studies. Our framework enables new scales of interrogation for a foundational method in functional genomics.

Advancements in Precision Prevention: Top Bioinformatics and Translational Informatics Papers of 2023

OBJECTIVE

To identify and summarize the top bioinformatics and translational informatics (BTI) papers published in 2023, focusing on the area of precision prevention.

METHODS

We conducted a literature search to identify the top papers published in 2023 in the field of BTI. Candidate papers from the search were reviewed by the section co-editors and a panel of external reviewers to select the top three papers for this year.

RESULTS

Our literature search returned a total of 550 candidate papers, from which we identified our top 10 papers for external review. The papers were evaluated based on their novelty, significance, and quality. After rigorous review, three papers were selected as the top BTI papers for 2023. These papers showcased innovative approaches in leveraging machine learning models, integrating multi-omics data, and developing new experimental techniques. Highlights include advancements in single-cell genomics, dynamic surveillance systems, and multimodal data integration.

CONCLUSIONS

We found several trends in the ten candidate BTI papers, including the refinement of machine learning models, the expansion of diverse biological datasets, and the development of scalable experimental techniques. These trends reflect the growing importance of bioinformatics and translational informatics as a cornerstone for improving predictive and preventative healthcare measures.

From genetic associations to genes: methods, applications, and challenges

Genome-wide association studies (GWASs) have identified numerous genetic loci associated with human traits and diseases. However, pinpointing the causal genes remains a challenge, which impedes the translation of GWAS findings into biological insights and medical applications. In this review, we provide an in-depth overview of the methods and technologies used for prioritizing genes from GWAS loci, including gene-based association tests, integrative analysis of GWAS and molecular quantitative trait loci (xQTL) data, linking GWAS variants to target genes through enhancer-gene connection maps, and network-based prioritization. We also outline strategies for generating context-dependent xQTL data and their applications in gene prioritization. We further highlight the potential of gene prioritization in drug repurposing. Lastly, we discuss future challenges and opportunities in this field.

SpRY-mediated screens facilitate functional dissection of non-coding sequences at single-base resolution

CRISPR mutagenesis screens conducted with SpCas9 and other nucleases have identified certain cis-regulatory elements and genetic variants but at a limited resolution due to the absence of protospacer adjacent motif (PAM) sequences. Here, leveraging the broad targeting scope of the near-PAMless SpRY variant, we have demonstrated that saturated SpRY mutagenesis and base editing screens can faithfully identify functional regulatory elements and essential genetic variants for target gene expression at single-base resolution. We further extended this methodology to investigate a genome-wide association study (GWAS) locus at 10q22.1 associated with a red blood cell trait, where we identified potential enhancers regulating HK1 gene expression, despite not all of these enhancers exhibiting typical chromatin signatures. More importantly, our saturated base editing screens pinpoint multiple causal variants within this locus that would otherwise be missed by Bayesian statistical fine-mapping. Our approach is generally applicable to functional interrogation of all non-coding genomic elements while complementing other high-coverage CRISPR screens.