Joint genotypic and phenotypic outcome modeling improves base editing variant effect quantification.

CRISPR base editing screens enable analysis of disease-associated variants at scale; however, variable efficiency and precision confounds the assessment of variant-induced phenotypes. Here, we provide an integrated experimental and computational pipeline that improves estimation of variant effects in base editing screens. We use a reporter construct to measure guide RNA (gRNA) editing outcomes alongside their phenotypic consequences and introduce base editor screen analysis with activity normalization (BEAN), a Bayesian network that uses per-guide editing outcomes provided by the reporter and target site chromatin accessibility to estimate variant impacts. BEAN outperforms existing tools in variant effect quantification. We use BEAN to pinpoint common regulatory variants that alter low-density lipoprotein (LDL) uptake, implicating previously unreported genes. Additionally, through saturation base editing of LDLR, we accurately quantify missense variant pathogenicity that is consistent with measurements in UK Biobank patients and identify underlying structural mechanisms. This work provides a widely applicable approach to improve the power of base editing screens for disease-associated variant characterization.

Gene-metabolite annotation with shortest reactional distance enhances metabolite genome-wide association studies results.

Metabolite genome-wide association studies (mGWAS) have advanced our understanding of the genetic control of metabolite levels. However, interpreting these associations remains challenging due to a lack of tools to annotate gene-metabolite pairs beyond the use of conservative statistical significance threshold. Here, we introduce the shortest reactional distance (SRD) metric, drawing from the comprehensive KEGG database, to enhance the biological interpretation of mGWAS results. We applied this approach to three independent mGWAS, including a case study on sickle cell disease patients. Our analysis reveals an enrichment of small SRD values in reported mGWAS pairs, with SRD values significantly correlating with mGWAS p values, even beyond the standard conservative thresholds. We demonstrate the utility of SRD annotation in identifying potential false negatives and inaccuracies within current metabolic pathway databases. Our findings highlight the SRD metric as an objective, quantitative and easy-to-compute annotation for gene-metabolite pairs, suitable to integrate statistical evidence to biological networks.

Joint genotypic and phenotypic outcome modeling improves base editing variant effect quantification.

CRISPR base editing screens are powerful tools for studying disease-associated variants at scale. However, the efficiency and precision of base editing perturbations vary, confounding the assessment of variant-induced phenotypic effects. Here, we provide an integrated pipeline that improves the estimation of variant impact in base editing screens. We perform high-throughput ABE8e-SpRY base editing screens with an integrated reporter construct to measure the editing efficiency and outcomes of each gRNA alongside their phenotypic consequences. We introduce BEAN, a Bayesian network that accounts for per-guide editing outcomes and target site chromatin accessibility to estimate variant impacts. We show this pipeline attains superior performance compared to existing tools in variant classification and effect size quantification. We use BEAN to pinpoint common variants that alter LDL uptake, implicating novel genes. Additionally, through saturation base editing of LDLR, we enable accurate quantitative prediction of the effects of missense variants on LDL-C levels, which aligns with measurements in UK Biobank individuals, and identify structural mechanisms underlying variant pathogenicity. This work provides a widely applicable approach to improve the power of base editor screens for disease-associated variant characterization.

Association Analyses of Predicted Loss-of-Function Variants Prioritized 15 Genes as Blood Pressure Regulators.

BACKGROUND: Hypertension, clinically defined by elevated blood pressure (BP), is an important cause of mortality and morbidity worldwide. Many risk factors for hypertension are known, including a positive family history, which suggests that genetics contribute to interindividual BP variation. Genome-wide association studies (GWAS) have identified > 1000 loci associated with BP, yet the identity of the genes responsible for these associations remains largely unknown. METHODS: To pinpoint genes that causally affect variation of BP in humans, we analyzed predicted loss-of-function (pLoF) variants in the UK Biobank whole-exome sequencing dataset (n = 454,709 participants, 6% non-European ancestry). We analyzed genetic associations between systolic or diastolic BP (SBP/DBP) and single pLoF variants (additive and recessive genetic models) as well as with the burden of very rare pLoF variants (minor allele frequency [MAF] < 0.01%). RESULTS: Single pLoF variants in 10 genes were associated with BP (ANKDD1B, ENPEP, PNCK, BTN3A2, C1orf145 [OBSCN-AS1], CASP9, DBH, KIAA1161 [MYORG], OR4X1, and TMC3). We also found a burden of rare pLoF variants in 5 additional genes associated with BP (TTN, NOS3, FES, SMAD6, COL21A1). Except for PNCK, which is located on the X-chromosome, these genes map near variants previously associated with BP by GWAS, validating the study of pLoF variants to prioritize causal genes at GWAS loci. CONCLUSIONS: Our study highlights 15 genes that likely modulate BP in humans, including 5 genes that harbour pLoF variants associated with lower BP.

Systematic elucidation of genetic mechanisms underlying cholesterol uptake.

Genetic variation contributes greatly to LDL cholesterol (LDL-C) levels and coronary artery disease risk. By combining analysis of rare coding variants from the UK Biobank and genome-scale CRISPR-Cas9 knockout and activation screening, we substantially improve the identification of genes whose disruption alters serum LDL-C levels. We identify 21 genes in which rare coding variants significantly alter LDL-C levels at least partially through altered LDL-C uptake. We use co-essentiality-based gene module analysis to show that dysfunction of the RAB10 vesicle transport pathway leads to hypercholesterolemia in humans and mice by impairing surface LDL receptor levels. Further, we demonstrate that loss of function of OTX2 leads to robust reduction in serum LDL-C levels in mice and humans by increasing cellular LDL-C uptake. Altogether, we present an integrated approach that improves our understanding of the genetic regulators of LDL-C levels and provides a roadmap for further efforts to dissect complex human disease genetics.

Major cell-types in multiomic single-nucleus datasets impact statistical modeling of links between regulatory sequences and target genes.

Epigenomic profiling, including ATACseq, is one of the main tools used to define enhancers. Because enhancers are overwhelmingly cell-type specific, inference of their activity is greatly limited in complex tissues. Multiomic assays that probe in the same nucleus both the open chromatin landscape and gene expression levels enable the study of correlations (links) between these two modalities. Current best practices to infer the regulatory effect of candidate cis-regulatory elements (cCREs) in multiomic data involve removing biases associated with GC content by generating null distributions of matched ATACseq peaks drawn from different chromosomes. This strategy has been broadly adopted by popular single-nucleus multiomic workflows such as Signac. Here, we uncovered limitations and confounders of this approach. We found a strong loss of power to detect a regulatory effect for cCREs with high read counts in the dominant cell-type. We showed that this is largely due to cell-type-specific trans-ATACseq peak correlations creating bimodal null distributions. We tested alternative models and concluded that physical distance and/or the raw Pearson correlation coefficients are the best predictors for peak-gene links when compared to predictions from Epimap (e.g. CD14 area under the curve [AUC] = 0.51 with the method implemented in Signac vs. 0.71 with the Pearson correlation coefficients) or validation by CRISPR perturbations (AUC = 0.63 vs. 0.73).

Gene-metabolite annotation with shortest reactional distance enhances metabolite genome-wide association studies results.

Studies combining metabolomics and genetics, known as metabolite genome-wide association studies (mGWAS), have provided valuable insights into our understanding of the genetic control of metabolite levels. However, the biological interpretation of these associations remains challenging due to a lack of existing tools to annotate mGWAS gene-metabolite pairs beyond the use of conservative statistical significance threshold. Here, we computed the shortest reactional distance (SRD) based on the curated knowledge of the KEGG database to explore its utility in enhancing the biological interpretation of results from three independent mGWAS, including a case study on sickle cell disease patients. Results show that, in reported mGWAS pairs, there is an excess of small SRD values and that SRD values and p-values significantly correlate, even beyond the standard conservative thresholds. The added-value of SRD annotation is shown for identification of potential false negative hits, exemplified by the finding of gene-metabolite associations with SRD ≤1 that did not reach standard genome-wide significance cut-off. The wider use of this statistic as an mGWAS annotation would prevent the exclusion of biologically relevant associations and can also identify errors or gaps in current metabolic pathway databases. Our findings highlight the SRD metric as an objective, quantitative and easy-to-compute annotation for gene-metabolite pairs that can be used to integrate statistical evidence to biological networks.

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

Genome-wide association studies have identified >250 genetic variants associated with coronary artery disease (CAD), but the causal variants, genes and molecular mechanisms remain unknown at most loci. We performed pooled CRISPR screens to test the impact of sequences at or near CAD-associated genetic variants on vascular endothelial cell functions. Using CRISPR knockout, inhibition and activation, we targeted 1998 variants at 83 CAD loci to assess their effect on three adhesion proteins (E-selectin, ICAM1, VCAM1) and three key endothelial functions (nitric oxide and reactive oxygen species production, calcium signalling). At a false discovery rate ≤10%, we identified significant CRISPR perturbations near 42 variants located within 26 CAD loci. We used base editing to validate a putative causal variant in the promoter of the FES gene. Although a few of the loci include genes previously characterized in endothelial cells (e.g. AIDA, ARHGEF26, ADAMTS7), most are implicated in endothelial dysfunction for the first time. Detailed characterization of one of these new loci implicated the RNA helicase DHX38 in vascular endothelial cell senescence. While promising, our results also highlighted several limitations in using CRISPR perturbations to functionally dissect GWAS loci, including an unknown false negative rate and potential off-target effects.

Systematic elucidation of genetic mechanisms underlying cholesterol uptake.

Genetic variation contributes greatly to LDL cholesterol (LDL-C) levels and coronary artery disease risk. By combining analysis of rare coding variants from the UK Biobank and genome-scale CRISPR-Cas9 knockout and activation screening, we have substantially improved the identification of genes whose disruption alters serum LDL-C levels. We identify 21 genes in which rare coding variants significantly alter LDL-C levels at least partially through altered LDL-C uptake. We use co-essentiality-based gene module analysis to show that dysfunction of the RAB10 vesicle transport pathway leads to hypercholesterolemia in humans and mice by impairing surface LDL receptor levels. Further, we demonstrate that loss of function of OTX2 leads to robust reduction in serum LDL-C levels in mice and humans by increasing cellular LDL-C uptake. Altogether, we present an integrated approach that improves our understanding of genetic regulators of LDL-C levels and provides a roadmap for further efforts to dissect complex human disease genetics.

Variation and impact of polygenic hematologic traits in monogenic sickle cell disease.

Several of the complications observed in sickle cell disease (SCD) are influenced by variation in hematologic traits (HT), such as fetal hemoglobin (HbF) level and neutrophil count. Previous large-scale genome-wide association studies carried out in largely healthy individuals have identified thousands of variants associated with HT, which have then been used to develop multi-ancestry polygenic trait scores (PTS). Here, we tested whether these PTS associate with HT in SCD patients and if they can improve statistical models associated with SCD-related complications. In 2,056 SCD patients, we found that the PTS predicted less HT variance than in non-SCD individuals of African ancestry. This was particularly striking at the Duffy/DARC locus, where we observed an epistatic interaction between the SCD genotype and the Duffy null variant (rs2814778) that led to a two-fold weaker effect on neutrophil count. PTS for these HT which are measured as part of routine practice were not associated with complications in SCD. In contrast, we found that a simple PTS for HbF that includes only six variants explained a large fraction of the phenotypic variation (20.5-27.1%), associated with acute chest syndrome and stroke risk, and improved the statistical modeling of the vaso-occlusive crisis rate. Using Mendelian randomization, we found that increasing HbF by 4.8% reduces stroke risk by 39% (P=0.0006). Taken together, our results highlight the importance of validating PTS in large diseased populations before proposing their implementation in the context of precision medicine initiatives.

Genetic Modifiers of Sickle Cell Disease.

Sickle cell disease (SCD) is characterized by tremendous phenotypic heterogeneity across patients, but this clinical variability is poorly understood, thus motivating the search for genetic modifiers. The early identification of genetic variants that control fetal hemoglobin levels-a strong modifier of severity in SCD-served as a powerful example in support of these genetic experiments. Although there have been successful discoveries (eg, UGT1A, APOL1), many of the reported genetic associations remain controversial. The emergence of large-scale SCD cohorts and their integration into genetic and other omic-type research programs should bring SCD patients closer to the promises of precision medicine.

Importance of genetic testing in unexplained cardiac arrest.

AIMS: Genetic testing is recommended in specific inherited heart diseases but its role remains unclear and it is not currently recommended in unexplained cardiac arrest (UCA). We sought to assess the yield and clinical utility of genetic testing in UCA using whole-exome sequencing (WES). METHODS AND RESULTS: Survivors of UCA requiring external defibrillation were included from the Cardiac Arrest Survivor with Preserved Ejection fraction Registry. Whole-exome sequencing was performed, followed by assessment of rare variants in previously reported cardiovascular disease genes. A total of 228 UCA survivors (mean age at arrest 39 ± 13 years) were included. The majority were males (66%) and of European ancestry (81%). Following advanced clinical testing at baseline, the likely aetiology of cardiac arrest was determined in 21/228 (9%) cases. Whole-exome sequencing identified a pathogenic or likely pathogenic (P/LP) variant in 23/228 (10%) of UCA survivors overall, increasing the proportion of 'explained' cases from 9% only following phenotyping to 18% when combining phenotyping with WES. Notably, 13 (57%) of the 23 P/LP variants identified were located in genes associated with cardiomyopathy, in the absence of a diagnosis of cardiomyopathy at the time of arrest. CONCLUSIONS: Genetic testing identifies a disease-causing variant in 10% of apparent UCA survivors. The majority of disease-causing variants was located in cardiomyopathy-associated genes, highlighting the arrhythmogenic potential of such variants in the absence of an overt cardiomyopathy diagnosis. The present study supports the use of genetic testing including assessment of arrhythmia and cardiomyopathy genes in survivors of UCA.

Cohort profile: genomic data for 26 622 individuals from the Canadian Longitudinal Study on Aging (CLSA).

PURPOSE: The Canadian Longitudinal Study on Aging (CLSA) Comprehensive cohort was established to provide unique opportunities to study the genetic and environmental contributions to human disease as well as ageing process. The aim of this report was to describe the genomic data included in CLSA. PARTICIPANTS: A total of 26 622 individuals from the CLSA Comprehensive cohort of men and women aged 45-85 recruited between 2010 and 2015 underwent genome-wide genotyping of DNA samples collected from blood. Comprehensive quality control metrics were measured for genetic markers and samples, respectively. The genotypes were imputed to the TOPMed reference panel. Sex chromosome abnormalities were identified by copy number profiling. Classical human leukocyte antigen gene haplotypes were imputed at two-field (four-digit). FINDINGS TO DATE: Of the 26 622 genotyped participants, 24 655 (92.6%) were identified as having European ancestry. These genomic data were linked to physical, lifestyle, medical, economic, environmental and psychosocial factors collected longitudinally in CLSA. The combined analysis, including CLSA genomic data, uncovered over 100 novel loci associated with key parameters to define glaucoma. The CLSA genomic dataset validated the contribution of a polygenic risk score to screen individuals with high fracture risk. It is also a valuable resource to directly identify common genetic variations associated with conditions related to complex traits. Taking advantage of the comprehensive interview and physical information collected in CLSA, this genomic dataset has been linked to psychosocial factors to investigate both the independent and interactive effects on cardiovascular disease. FUTURE PLANS: The CLSA overall is ongoing. Follow-up data will continue to be collected from participants in the current genomic subcohort, including the DNA methylation and metabolomic data. Ongoing studies focus on elucidating the role of genetic factors in cognitive decline and cardiovascular diseases. This genomic data resource is available on request through the CLSA data access application process.

Transcriptome-wide association study in UK Biobank Europeans identifies associations with blood cell traits.

Previous genome-wide association studies (GWAS) of hematological traits have identified over 10 000 distinct trait-specific risk loci. However, at these loci, the underlying causal mechanisms remain incompletely characterized. To elucidate novel biology and better understand causal mechanisms at known loci, we performed a transcriptome-wide association study (TWAS) of 29 hematological traits in 399 835 UK Biobank (UKB) participants of European ancestry using gene expression prediction models trained from whole blood RNA-seq data in 922 individuals. We discovered 557 gene-trait associations for hematological traits distinct from previously reported GWAS variants in European populations. Among the 557 associations, 301 were available for replication in a cohort of 141 286 participants of European ancestry from the Million Veteran Program. Of these 301 associations, 108 replicated at a strict Bonferroni adjusted threshold ($\alpha$= 0.05/301). Using our TWAS results, we systematically assigned 4261 out of 16 900 previously identified hematological trait GWAS variants to putative target genes. Compared to coloc, our TWAS results show reduced specificity and increased sensitivity in external datasets to assign variants to target genes.

From GWAS variant to function: A study of ∼148,000 variants for blood cell traits.

Genome-wide association studies (GWASs) have identified hundreds of thousands of genetic variants associated with complex diseases and traits. However, most variants are noncoding and not clearly linked to genes, making it challenging to interpret these GWAS signals. We present a systematic variant-to-function study, prioritizing the most likely functional elements of the genome for experimental follow-up, for >148,000 variants identified for hematological traits. Specifically, we developed VAMPIRE: Variant Annotation Method Pointing to Interesting Regulatory Effects, an interactive web application implemented in R Shiny. This tool efficiently integrates and displays information from multiple complementary sources, including epigenomic signatures from blood-cell-relevant tissues or cells, functional and conservation summary scores, variant impact on protein and gene expression, chromatin conformation information, as well as publicly available GWAS and phenome-wide association study (PheWAS) results. Leveraging data generated from independently performed functional validation experiments, we demonstrate that our prioritized variants, genes, or variant-gene links are significantly more likely to be experimentally validated. This study not only has important implications for systematic and efficient revelation of functional mechanisms underlying GWAS variants for hematological traits but also provides a prototype that can be adapted to many other complex traits, paving the path for efficient variant-to-function (V2F) analyses.

Design of a Randomized Placebo-Controlled Trial to Evaluate the Anti-inflammatory and Senolytic Effects of Quercetin in Patients Undergoing Coronary Artery Bypass Graft Surgery.

Background: Following an acute coronary syndrome, patients display an elevated inflammatory profile, promoted in part by cellular senescence. For patients requiring a coronary artery bypass (CABG) surgery, exposure to the surgical intervention and cardiopulmonary bypass further exacerbate their residual inflammation. Experimental evidence identified quercetin, a natural senolytic drug, as a cardioprotective agent against inflammatory injuries. The Q-CABG study aims to explore the efficacy of quercetin to reduce inflammation, myocardial injury and senescence in patients undergoing CABG following an acute coronary syndrome. Methods: Q-CABG is a phase II, prospectively registered, randomized, double-blind and placebo-controlled clinical trial. Recruited patients awaiting CABG surgery at the Montreal Heart Institute (n = 100) will be randomly assigned in a 1:1 ratio to receive either quercetin supplementation (500 mg twice daily) or placebo, starting 2 days before surgery and until the seventh postoperative day. The primary endpoint examines the effects of quercetin on blood inflammatory cytokines and markers of myocardial injury and senescence in this patient population. Blood samples will be taken at four time points: baseline, postoperative day 1, postoperative day 4 and at hospital discharge, or after a maximum of seven postoperative days. The secondary endpoint is the assessment of endothelial (dys) function by looking at ex vivo vascular reactivity and mRNA expression of endothelial cells from the wall of discarded segments of internal mammary artery. Discussion: The preventive intake of quercetin supplementation may help limit the vigorous inflammatory response triggered by CABG and subsequent postoperative complications in patients suffering from an acute coronary syndrome. In an exploratory way, quercetin supplementation could also improve endothelial function by eliminating senescent vascular endothelial cells. The results of this trial should provide valuable information regarding a novel approach to improve biological, and potentially clinical, outcomes post CABG. Clinical Trial Registration: ClinicalTrials.gov, Identifier NCT04907253.