Multimodal CRISPR perturbations of GWAS loci associated with coronary artery disease in vascular endothelial cells

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.

Investigating mobile element variations by statistical genetics

The integration of structural variations (SVs) in statistical genetics provides an opportunity to understand the genetic factors influencing complex human traits and disease. Recent advances in long-read technology and variant calling methods for short reads have improved the accurate discovery and genotyping of SVs, enabling their use in expression quantitative trait loci (eQTL) analysis and genome-wide association studies (GWAS). Mobile elements are DNA sequences that insert themselves into various genome locations. Insertional polymorphisms of mobile elements between humans, called mobile element variations (MEVs), contribute to approximately 25% of human SVs. We recently developed a variant caller that can accurately identify and genotype MEVs from biobank-scale short-read whole-genome sequencing (WGS) datasets and integrate them into statistical genetics. The use of MEVs in eQTL analysis and GWAS has a minimal impact on the discovery of genome loci associated with gene expression and disease; most disease-associated haplotypes can be identified by single nucleotide variations (SNVs). On the other hand, it helps make hypotheses about causal variants or effector variants. Focusing on MEVs, we identified multiple MEVs that contribute to differential gene expression and one of them is a potential cause of skin disease, emphasizing the importance of the integration of MEVs in medical genetics. Here, I will provide an overview of MEVs, MEV calling from WGS, and the integration of MEVs in statistical genetics. Finally, I will discuss the unanswered questions about MEVs, such as rare variants.

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 osteoblast 1.19 cells (hFOBs). The BMD relevance of hFOBs was supported by heritability enrichment from cross-cell type stratified LD-score regression involving 98 cell types grouped into 15 tissues. 24 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 that many BMD GWAS signals are pleiotropic and likely mediate their effects via non-bone tissues that warrant attention in future screens.

Sparks of function by de novo protein design

Information in proteins flows from sequence to structure to function, with each step causally driven by the preceding one. Protein design is founded on inverting this process: specify a desired function, design a structure executing this function, and find a sequence that folds into this structure. This 'central dogma' underlies nearly all de novo protein-design efforts. Our ability to accomplish these tasks depends on our understanding of protein folding and function and our ability to capture this understanding in computational methods. In recent years, deep learning-derived approaches for efficient and accurate structure modeling and enrichment of successful designs have enabled progression beyond the design of protein structures and towards the design of functional proteins. We examine these advances in the broader context of classical de novo protein design and consider implications for future challenges to come, including fundamental capabilities such as sequence and structure co-design and conformational control considering flexibility, and functional objectives such as antibody and enzyme design.

Convergence of coronary artery disease genes onto endothelial cell programs

Linking variants from genome-wide association studies (GWAS) to underlying mechanisms of disease remains a challenge1-3. For some diseases, a successful strategy has been to look for cases in which multiple GWAS loci contain genes that act in the same biological pathway1-6. However, our knowledge of which genes act in which pathways is incomplete, particularly for cell-type-specific pathways or understudied genes. Here we introduce a method to connect GWAS variants to functions. This method links variants to genes using epigenomics data, links genes to pathways de novo using Perturb-seq and integrates these data to identify convergence of GWAS loci onto pathways. We apply this approach to study the role of endothelial cells in genetic risk for coronary artery disease (CAD), and discover 43 CAD GWAS signals that converge on the cerebral cavernous malformation (CCM) signalling pathway. Two regulators of this pathway, CCM2 and TLNRD1, are each linked to a CAD risk variant, regulate other CAD risk genes and affect atheroprotective processes in endothelial cells. These results suggest a model whereby CAD risk is driven in part by the convergence of causal genes onto a particular transcriptional pathway in endothelial cells. They highlight shared genes between common and rare vascular diseases (CAD and CCM), and identify TLNRD1 as a new, previously uncharacterized member of the CCM signalling pathway. This approach will be widely useful for linking variants to functions for other common polygenic diseases.

Protein interaction networks in the vasculature prioritize genes and pathways underlying coronary artery disease

Population-based association studies have identified many genetic risk loci for coronary artery disease (CAD), but it is often unclear how genes within these loci are linked to CAD. Here, we perform interaction proteomics for 11 CAD-risk genes to map their protein-protein interactions (PPIs) in human vascular cells and elucidate their roles in CAD. The resulting PPI networks contain interactions that are outside of known biology in the vasculature and are enriched for genes involved in immunity-related and arterial-wall-specific mechanisms. Several PPI networks derived from smooth muscle cells are significantly enriched for genetic variants associated with CAD and related vascular phenotypes. Furthermore, the networks identify 61 genes that are found in genetic loci associated with risk of CAD, prioritizing them as the causal candidates within these loci. These findings indicate that the PPI networks we have generated are a rich resource for guiding future research into the molecular pathogenesis of CAD.

High-throughput PRIME-editing screens identify functional DNA variants in the human genome

Despite tremendous progress in detecting DNA variants associated with human disease, interpreting their functional impact in a high-throughput and single-base resolution manner remains challenging. Here, we develop a pooled prime-editing screen method, PRIME, that can be applied to characterize thousands of coding and non-coding variants in a single experiment with high reproducibility. To showcase its applications, we first identified essential nucleotides for a 716 bp MYC enhancer via PRIME-mediated single-base resolution analysis. Next, we applied PRIME to functionally characterize 1,304 genome-wide association study (GWAS)-identified non-coding variants associated with breast cancer and 3,699 variants from ClinVar. We discovered that 103 non-coding variants and 156 variants of uncertain significance are functional via affecting cell fitness. Collectively, we demonstrate that PRIME is capable of characterizing genetic variants at single-base resolution and scale, advancing accurate genome annotation for disease risk prediction, diagnosis, and therapeutic target identification.

Disentangling the complexity of psoriasis in the post-genome-wide association era

In recent years, genome-wide association studies (GWAS) have been instrumental in unraveling the genetic architecture of complex diseases, including psoriasis. The application of large-scale GWA studies in psoriasis has illustrated several associated loci that participate in the cutaneous inflammation, however explaining a fraction of the disease heritability. With the advent of high-throughput sequencing technologies and functional genomics approaches, the post-GWAS era aims to unravel the functional mechanisms underlying the inter-individual variability in psoriasis patients. In this review, we present the key advances of psoriasis GWAS in under-represented populations, rare, non-coding and structural variants and epistatic phenomena that orchestrate the interplay between different cell types. We further review the gene-gene and gene-environment interactions contributing to the disease predisposition and development of comorbidities through Mendelian randomization studies and pleiotropic effects of psoriasis-associated loci. We finally examine the holistic approaches conducted in psoriasis through system genetics and state-of-the-art transcriptomic analyses, discussing their potential implication in the expanding field of precision medicine and characterization of comorbidities.

The Genetics of Coronary Artery Disease: A Vascular Perspective

Genome-wide association studies (GWAS) have identified a large number of genetic loci for coronary artery disease (CAD), with many located close to genes associated with traditional CAD risk pathways, such as lipid metabolism and inflammation. It is becoming evident with recent CAD GWAS meta-analyses that vascular pathways are also highly enriched and present an opportunity for novel therapeutics. This review examines GWAS-enriched vascular gene loci, the pathways involved and their potential role in CAD pathogenesis. The functionality of variants is explored from expression quantitative trait loci, massively parallel reporter assays and CRISPR-based gene-editing tools. We discuss how this research may lead to novel therapeutic tools to treat cardiovascular disorders.

A genome-wide association study for loin depth and muscle pH in pigs from intensely selected purebred lines

BACKGROUND

Genome-wide association studies (GWAS) aim at identifying genomic regions involved in phenotype expression, but identifying causative variants is difficult. Pig Combined Annotation Dependent Depletion (pCADD) scores provide a measure of the predicted consequences of genetic variants. Incorporating pCADD into the GWAS pipeline may help their identification. Our objective was to identify genomic regions associated with loin depth and muscle pH, and identify regions of interest for fine-mapping and further experimental work. Genotypes for ~ 40,000 single nucleotide morphisms (SNPs) were used to perform GWAS for these two traits, using de-regressed breeding values (dEBV) for 329,964 pigs from four commercial lines. Imputed sequence data was used to identify SNPs in strong ([Formula: see text] 0.80) linkage disequilibrium with lead GWAS SNPs with the highest pCADD scores.

RESULTS

Fifteen distinct regions were associated with loin depth and one with loin pH at genome-wide significance. Regions on chromosomes 1, 2, 5, 7, and 16, explained between 0.06 and 3.55% of the additive genetic variance and were strongly associated with loin depth. Only a small part of the additive genetic variance in muscle pH was attributed to SNPs. The results of our pCADD analysis suggests that high-scoring pCADD variants are enriched for missense mutations. Two close but distinct regions on SSC1 were associated with loin depth, and pCADD identified the previously identified missense variant within the MC4R gene for one of the lines. For loin pH, pCADD identified a synonymous variant in the RNF25 gene (SSC15) as the most likely candidate for the muscle pH association. The missense mutation in the PRKAG3 gene known to affect glycogen content was not prioritised by pCADD for loin pH.

CONCLUSIONS

For loin depth, we identified several strong candidate regions for further statistical fine-mapping that are supported in the literature, and two novel regions. For loin muscle pH, we identified one previously identified associated region. We found mixed evidence for the utility of pCADD as an extension of heuristic fine-mapping. The next step is to perform more sophisticated fine-mapping and expression quantitative trait loci (eQTL) analysis, and then interrogate candidate variants in vitro by perturbation-CRISPR assays.

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

Most variants associated with complex traits and diseases identified by genome-wide association studies (GWAS) map to noncoding regions of the genome with unknown effects. Using ancestrally diverse, biobank-scale GWAS data, massively parallel CRISPR screens, and single-cell transcriptomic and proteomic sequencing, we discovered 124 cis-target genes of 91 noncoding blood trait GWAS loci. Using precise variant insertion through base editing, we connected specific variants with gene expression changes. We also identified trans-effect networks of noncoding loci when cis target genes encoded transcription factors or microRNAs. Networks were themselves enriched for GWAS variants and demonstrated polygenic contributions to complex traits. This platform enables massively parallel characterization of the target genes and mechanisms of human noncoding variants in both cis and trans.

Multiomic Analysis and CRISPR Perturbation Screens Identify Endothelial Cell Programs and Novel Therapeutic Targets for Coronary Artery Disease

Endothelial cells (EC) are an important mediator of atherosclerosis and vascular disease. Their exposure to atherogenic risk factors such as hypertension and serum cholesterol leads to endothelial dysfunction and many disease-associated processes. Identifying which of these multiple EC functions is causally related to disease risk has been challenging. There is evidence from in vivo models and human sequencing studies that dysregulation of nitric oxide production directly affects risk of coronary artery disease. Human genetics can help prioritize the other EC functions with causal relationships because germline mutations are acquired at birth and serve as a randomized test of which pathways affect disease risk. Though several coronary artery disease risk variants have been linked to EC function, this process has been slow and laborious. Unbiased analyses of EC dysfunction using multiomic approaches promise to identify the causal genetic mechanisms responsible for vascular disease. Here, we review the data from genomic, epigenomic, and transcriptomic studies that prioritize EC-specific causal pathways. New methods that CRISPR (clustered regularly interspaced short palindromic repeats) perturbation technology with genomic, epigenomic, and transcriptomic analysis promise to speed up the characterization of disease-associated genetic variation. We summarize several recent studies in ECs which use high-throughput genetic perturbation to identify disease-relevant pathways and novel mechanisms of disease. These genetically validated pathways can accelerate the identification of drug targets for the prevention and treatment of atherosclerosis.