Integrative functional genomics analysis identifies pleiotropic genes for vascular diseases

Domain: neuroscience
Published: 2026-02-05
Editorial outcome: major_revision

Abstract
Several vascular diseases including coronary artery disease, hypertension, stroke, and abdominal aortic aneurysm, have significant genetic underpinnings. Genome-wide association studies have unveiled many genetic loci associated with one or more of these diseases. However, the causative genes at most of these loci are yet to be determined, which hampers the translation of the genetic findings into a better understanding of the disease mechanisms and the identification of new therapeutic targets. Here, in an integrative functional genomics analysis of these loci, we identify a panel of likely causal genes, some of which are pleiotropic for more than one of these vascular diseases. Pooled CRISPR knockout screen analyses of these likely causal genes indicate that many of them influence vascular smooth muscle cell behavior, and validation experiments of selected genes confirm that FES, BCAR1, CARF and SMARCA4 exert such effects. Further functional experiments focusing on FES, a pleiotropic gene for both coronary artery disease and hypertension, show that it modulates the expression of genes involved in vascular remodeling and that Fes knockout in mice promotes atherosclerosis as well as raises blood pressure. These findings provide an insight into the genetic basis of vascular diseases and inform targets for therapeutic development.

Introduction
In recent years, genome-wide association studies (GWAS) have unveiled genomic loci where genetic variants are associated with vascular diseases such as coronary artery disease (CAD)1, 2, 3, 4, hypertension5, stroke6, 7, and abdominal aortic aneurysm (AAA)5, 8. To facilitate the medical translation of these genetic discoveries, it is important to identify and investigate the causal genes at these loci.

Vascular smooth muscle cells (VSMC) are a major player in vascular diseases. VSMC phenotypic switching, migration, proliferation and apoptosis play important roles in the pathogenesis of atherosclerosis which promotes CAD, hypertension, stroke, and AAA9. Further, VSMC hypercontractility and vascular remodeling involving VSMC migration, proliferation and apoptosis contribute to elevated blood pressure10, VSMC loss and impaired contractility are hallmarks of AAA11, while VSMC degeneration causes cerebral small-vessel disease that can lead to ischemic and hemorrhagic strokes12.

As most disease-associated genetic variants revealed by GWASs are located in non-coding regions of the genome13, 14, a prevailing hypothesis is that these variants can alter the individual's disease risk through their effect on gene expression13, 14. Studies have shown that there is considerable inter-individual variation in VSMC behavior, largely due to genetic variation among individuals15, 16. Interestingly, some vascular disease-associated genetic variants have been reported to modulate gene expression in VSMCs and affect VSMC behavior15, 16, 17, 18. Therefore, it is likely that the genetic influence on vascular disease acts partly through VSMCs.

Here, we report results from an integrative functional genomics analysis on VSMCs (outlined in Figure 1), with the aim to identify causal genes at the genomic loci that have been found to be associated with CAD, hypertension, stroke and/or AAA in GWAS.

Results
Influences of vascular disease-associated genetic variants on gene expression
We recently established a large collection of VSMCs from different individuals (n = 1,486), and on this cell bank we performed genome-wide genotyping and whole-genome RNA-sequencing (RNA-seq) to generate a comprehensive catalogue of expression quantitative trait loci (eQTL) with stringent statistical significance16. We compiled a list of tagging single nucleotide polymorphisms (SNPs) that marked the genetic variants/haplotypes reported to be associated with CAD in GWAS1, 2, 3, 4 and then interrogated these tagging SNPs in the VSMC eQTL catalogue. As reported previously16, we observed that ~60% of the CAD-associated SNPs had an eQTL effect on gene expression, with two-thirds of them affecting more than one gene.

Here we compiled lists of tagging SNPs that marked the genetic variants/haplotypes reported to be associated with blood pressure/hypertension5, 19, stroke5, 6, 7 and/or AAA5, 8 in GWAS, and interrogated these tagging SNPs in the abovementioned VSMC eQTL catalogue. We found that ~59% of the blood pressure/hypertension-associated SNPs, ~55% of the stroke-associated SNPs, and ~59% of the AAA-associated SNPs, had eQTL effects in VSMCs (Supplementary Data 1-3 and Supplementary Figure 1). Of these SNPs, ~66% showed eQTL effects on more than one gene. Of the associated genes, ~77% were protein-coding and ~15% were long non-coding RNAs (lncRNAs) (highlighted in green and yellow, respectively, in Supplementary Data 1-3). When male samples (n = 780) and female samples (n = 706) were analyzed separately, the eQTL effects of approximately 50% of CAD-, blood pressure/hypertension-, stroke-, or AAA-associated SNPs were still detected (Supplementary Data 1-4), despite the reduction in the sample sizes per group, indicating that they are common to both sexes.

Likely causative variants/genes for CAD, hypertension, stroke, and AAA
To explore causal variants and related causal genes for each of the vascular diseases mentioned above, we performed colocalization tests, including eCAVIAR (eQTL and GWAS CAusal Variant Identification in Associated Regions)20 and SMR/HEIDI (summary-data-based Mendelian Randomization/Heterogeneity in Dependent Instruments)21, utilizing reported disease GWAS summary statistics in conjunction with the above-described VSMC eQTL dataset. These analyses detected panels of likely causal variants and likely causal genes for each of these vascular diseases. Supplementary Data 5-8 show the results of these colocalization analyses, including the identified likely causal variants (columns A-F), the identified likely causal genes together with eQTL data in relation to the likely causal variants (columns G-M), summary statistics of reported associations of the likely causal variants with the relevant vascular diseases (columns N-R), and the statistics of the eCAVIAR and SMR/HEIDI analyses (columns S-Y).

To investigate if any of the likely causal variants identified in the above colocalization analyses were located in transcriptionally active regions of the genome, we performed VSMC ATAC-seq (assay for transposase-accessible chromatin with sequencing) to delineate open chromatin regions, as well as DNA methylation assay and H3K27ac HiChIP-seq (histone H3 acetylation at lysine 27 high throughput chromosome conformation capture with chromatin immunoprecipitation with sequencing) since DNA demethylation and H3K27 acetylation upregulate gene expression. We interrogated the likely causal variants in regions of ATAC-seq peaks, low DNA methylation and H3K27ac, respectively. The analyses showed that a large proportion of the likely causal variants were located in regions of ATAC-seq open chromatin (Supplementary Data 9), low DNA-methylation (variants highlighted in yellow in Supplementary Data 10), and/or H3K27ac (Supplementary Data 11). The likely causal variants in relation to peaks of ATAC-seq, low DNA methylation and H3K27ac, as well as their associated eQTL genes and vascular diseases, are summarized in Supplementary Data 12. As an example, we found that the CAD associated variant rs1894401 in the FES gene resided in an ATAC-seq open chromatin region with lessened DNA-methylation (Supplementary Figures 2&3) and with H3K27ac (Supplementary Figure 4).

Because a genetic variant could possibly exert a long-range effect on the transcription of a gene located at a distance due to DNA looping, we utilized the VSMC H3K27ac HiChIP-seq data to investigate if any of the likely causal variants resided at a DNA interaction anchor, with a likely causal gene located at a linked DNA interaction anchor. This analysis detected multiple variant-gene pairs where a likely causal variant was detected within an anchor and a likely causal gene was found within a linked anchor (Supplementary Data 11 and Supplementary Figure 4), suggesting that the likely causal gene was brought physically closer to the likely causal variant through DNA looping. For instance, the CAD-associated variant rs1894401 was detected in an anchor and the FES gene 5'-region was observed in a linked anchor (Supplementary Figure 4A).

The aforementioned colocalization analyses identified 134 likely causal genes for CAD, whose eQTL signals in VSMCs showed significant colocalization with CAD GWAS signals, including 84 genes that we reported previously16 and a further 50 genes now identified using more recently available CAD GWAS data1 (Figure 2, Supplementary Figure 5A, and Supplementary Data 5). Further, the analyses identified 74 likely causal genes for hypertension (Figure 2, Supplementary Figure 5B, and Supplementary Data 6), 13 likely causal genes for stroke (Figure 2, Supplementary Figure 5C, and Supplementary Data 7), and 43 likely causal genes for AAA (Figure 2, Supplementary Figure 5D, and Supplementary Data 8).

In addition to the abovementioned colocalization analyses using eQTL data from the VSMC bank, we performed additional colocalization analyses using eQTL data from coronary artery and aortic artery tissues (from the GTEx Portal) together with the disease GWAS statistics. Noteworthily, most of the likely causal genes identified in the colocalization analyses using eQTL data from the VSMC bank were also significant in the colocalization analyses using eQTL data from coronary artery and aortic artery tissues (Supplementary Data 13), further indicating that they are likely to be causal genes.

Gene ontology enrichment analyses of the likely causal genes identified showed enrichment in several major biological processes and pathways, including transforming growth factor-beta (TGF) signaling in CAD (Supplementary Figure 6), vascular endothelial growth factor (VEGF) signaling in hypertension (Supplementary Figure 7), and signaling pathways of vascular endothelial growth factor receptor (VEGFR), platelet-derived growth factor receptor (PDGFR), integrin and cadherin in AAA (Supplementary Figure 8), suggesting that the genetic influence on these vascular diseases is, at least partly, mediated by these genes acting through these biological pathways.

Pleiotropic genes implicated in more than one vascular disease
A cross-trait linkage disequilibrium score regression analysis of GWAS summary statistics revealed significant genetic correlations between the various vascular diseases investigated in this study, indicating shared genetic influences (Figure 3A and Supplementary Data 14).

Further, comparing the likely causal genes identified for CAD, hypertension, stroke and AAA, respectively, we noticed that 18 genes were common for two or more of these diseases, suggesting that they have pleiotropic effects. Specifically, ARNTL, CSNK2B, FES, SNF8, TARID, and TCF21 were detected for both CAD and hypertension; CARF, NBEAL1, and PROCR were common in CAD and stroke; BCAR1, ILF3-DT, LPAL2, and SMARCA4 were shared by CAD and AAA; while PLCE1-AS1 was found for CAD and hypertension as well as AAA (Figure 3B). Additionally, ADO was identified for both hypertension and AAA, whilst SURF1 and 2 lncRNA genes were detected for both hypertension and stroke (Figure 3B).

Deleterious protein-coding variants in likely causal genes
The above colocalization analyses focused on regulatory SNPs that influenced gene expression. Next, we further explored if there also existed deleterious variants (such as frameshift mutations and deleterious missense substitutions) in the protein-coding regions of the likely causal genes that we had identified and if these variants were associated with the diseases under this study, because such evidence would further indicate that these genes were causal. Thus, we interrogated the likely causal genes in databases from the UK Biobank22, 23 and Finnish Biobank24 (through the AstraZeneca PheWAS Portal25 and the PheWeb browser, respectively) for deleterious coding variants identified by exome sequencing. This interrogation identified such variants in several of the likely causal genes, including the ADAMTS7, FES, ICA1L, MIA3, and MTAP genes in relation to CAD and the ADO, FES, and MAP4 genes in association with hypertension (Supplementary Data 15). However, no coding variants in any gene were associated with stroke or AAA at the genome-wide significance level (P < 110-8.7 in PheWAS and P <510-8 in PheWeb) in the UK Biobank and Finnish Biobank databases.

Druggability of likely causal genes
An interrogation of the likely causal genes in the Drug Gene Interaction Database (DGIdb)26 identified 63 potentially druggable targets for CAD, 39 for hypertension, 5 for stroke, and 25 for AAA (Figure 4, and Supplementary Data 16-23). Notably, 5 of these targets were applicable for both CAD and hypertension, 2 for both CAD and stroke, 2 for both CAD and AAA, and 1 for both hypertension and AAA (Figure 4). Among these potential targets, 38 encode proteins for which there are existing drugs26 (Figure 4, and Supplementary Data 17, 19, 21 & 23).

Influence of likely causal genes on VSMC behavior
To assess possible effects of the likely causal genes on VSMC behavior, we performed high-throughput pooled CRISPR-Cas9-nuclease knockout screens targeting the protein-coding genes among these likely causal genes (this technique is unable to test non-coding RNA genes). The results of these screens suggested that some of these genes might influence VSMC proliferation or migratory ability (Supplementary Data 24&25, and Figure 5A&B). A selection of these genes was subjected to validation experiments, with the following selection criteria: 1) among the top 10 genes ranked by score in the pooled CRISPR-Cas9-nuclease knockout screens; 2) considered to be a pleiotropic gene for more than one vascular disease in the aforementioned cross-trait analysis; and 3) likely to be druggable in the abovementioned druggability analysis. In the validation experiments, VSMCs were transfected with either siRNA for the selected gene or negative control siRNA, followed by VSMC proliferation or migration assay. The following genes met the selection criteria and were tested in the validation experiments: BCAR1, CARF, and SMARCA4 for VSMC proliferation, and CSNK2B and FES for VSMC migration. The validation experiments confirmed the CRISPR-Cas9-nuclease knockout screen findings for BCAR1, CARF, FES, and SMARCA4, with the same direction of effect in both the screen and validation experiments (Figure 5C&D, and Supplementary Figure 9A&9B), but not for CSNK2B (Supplementary Figure 9C). For instance, the CRISPR-Cas9-nuclease screen showed that FES knockout increased VSMC migration, and this finding was supported in the validation experiment where we observed that siRNA-induced knockdown of FES increased the migratory ability of VSMCs (Figure 5D).

Effect of FES on VSMC migration and production of matrix metalloproteinases
Following the validation experiments, we carried out further functional experiments with a particular focus on FES. As the FES protein has been reported to participate in intracellular signalling27, we investigated if FES knockdown might influence certain signaling pathways and result in changes in the production of certain proteins. Thus, we performed whole-genome RNA-seq, quantitative proteomics and phosphoproteomics assays on VSMCs with and without siRNA-induced FES knockdown. The phosphoproteomics analysis identified a number of proteins whose phosphorylation status might be influenced by FES knockdown (Supplementary Data 26) and a pathway analysis of these proteins suggested that they were enriched in several pathways including the epidermal growth factor (EGF)-, VEGF-, and CCKR (cholecystokinin A receptor)-signaling pathways (Figure 6A). The RNA-seq and quantitative proteomics analyses indicated that FES knockdown could potentially influence the production of a number of genes/proteins, including the extracellular matrix protein-degrading enzymes matrix metalloproteinase-1 (MMP1) and MMP3 (Figure 6B&C, Supplementary Data 27-29). Reverse transcription-polymerase chain reaction (RT-PCR) and Western blot analyses confirmed increased MMP1 and MMP3 production in VSMCs with FES knockdown (Figure 7A-C) and additionally showed that FES knockdown decreased the levels of contractile VSMC phenotype markers (Figure 7D&E).

Effect of FES on atherosclerosis development and blood pressure in mouse models
In the colocalization analysis described early, FES was identified as a pleiotropic gene for both CAD and hypertension.

We previously showed that Fes-/-/Apoe-/- mice had larger aortic-root atherosclerotic lesions with a higher content of VSMCs and macrophages, compared with Fes+/+/Apoe-/- littermates, after a high-fat diet for 12 weeks28. To further assess the effect of Fes on atherogenesis, we performed en face Oil Red O staining on aortae taken from the Fes-/-/Apoe-/- mice and their Fes+/+/Apoe-/- littermates, and found that Fes-/-/Apoe-/- mice had significantly larger areas of atherosclerotic lesions than Fes+/+/Apoe-/- littermates (mean ± standard deviation: 8.34±2.54 versus 6.06±2.35; P=0.013) (Figure 8A), which was observed in male mice but not in female mice when data from male and female mice were analyzed separately (Supplementary Figure 10).

To assess the impact of FES on blood pressure, we performed hemodynamic measurements in unconscious Fes-/-/Apoe-/- mice and their Fes+/+/Apoe-/- littermates, both of which had been on chow diet. At baseline, Fes-/-/Apoe-/- mice had a significant elevation in both systolic (88.0±10.1mmHg versus 104.4±6.7mmHg, Fes+/+/Apoe-/- versus Fes-/-/Apoe-/-, P=0.042) and diastolic (58.8±8.9mmHg versus 74.9±9.0mmHg, Fes+/+/Apoe-/- versus Fes-/-/Apoe-/-, P=0.042) blood pressure (Figure 8B). The increase in blood pressure in Fes-/-/Apoe-/- mice did not lead to a compensatory change in heart rate, with no difference observed in heart rate at baseline between the two groups (Supplementary Figure 11A). As expected, acetylcholine caused a reduction in both systolic and diastolic blood pressure in both groups. However, in Fes-/-/Apoe-/- mice this response was significantly blunted with systolic blood pressure reduced by only 14.6±3.8mmHg in Fes-/-/Apoe-/- mice versus 22.2±4.3mmHg in Fes+/+/Apoe-/- mice (P=0.027) (Figure 8C). A similar result was observed in diastolic responses with a greater reduction in diastolic blood pressure observed in Fes+/+/Apoe-/- mice compared with Fes-/-/Apoe-/- (P=0.014) (Figure 8C). In contrast, no difference was observed in either systolic or diastolic blood pressure between the two groups in response to phenylephrine (Supplementary Figure 11B).

Association of FES gene mutations with susceptibility to CAD and hypertension in humans
In addition to the above mouse model experiments, through the AstraZeneca PheWAS Portal25, we collated human data of deleterious genetic variants in the protein-coding regions of the human FES gene in relation to blood pressure, hypertension, and the CAD traits angina and myocardial infarction, based on whole genome sequencing and phenotypic measurements of European participants in the UK Biobank. Collapsing analyses of FES protein truncating variants showed that, collectively, they were significantly associated with elevated systolic and diastolic blood pressure (P=4.7010-5 for systolic blood pressure and 2.7010-4 for diastolic blood pressure)(Table 1), and a higher likelihood of hypertension [odds ratio (95% confidence interval) = 1.93 (1.30-2.86)](Table 2), as well as a >2-fold increased risk of myocardial infarction [2.30 (1.19-4.44)] and angina [2.05 (1.16-3.61)]25 (Table 2).

Methods
Generation of a VSMC eQTL catalogue
The study was approved by the East Midlands - Derby Research Ethics Committee (20/EM/0028), and the parents of the umbilical cord donors gave written informed consent.

The collection of VSMCs from different individuals and the methods of genotyping, imputation, RNA-Seq, eQTL analysis, and VSMC behavior assays, have been described previously16. In brief, VSMCs were isolated from the artery of umbilical cords from 2,114 different donors, with the use of a well-established method61. DNA from 1,992 samples was genotyped for 760,000 variants using the Infinium Global Screening Array-24 2.0 BeadChips (Illumina), and imputation was subsequently performed to obtain genotypic information of 7,334,165 variants. RNA-seq was conducted on total RNA extracted from passage 3 VSMCs from 1,499 donors. For RNA-seq, a strand-specific library with rRNA removal was prepared from total RNA of each of the 1,499 VSMC samples, and 150bp paired-end sequencing, at a read depth of 30 million, was performed using the Illumina platform. An eQTL analysis was carried out on 1,486 VSMC samples that had both genotype and RNA-Seq data, applying the eigenMT-BH method62 for multiple testing correction.

Compilation of tagging SNPs that marked the genetic variants/haplotypes reported to be associated with CAD, blood pressure/hypertension, stroke, and AAA, respectively
We previously identified 424 tagging SNPs which marked the genetic variants/haplotypes that had been reported to be associated with CAD at the genome-wide significance level (P < 5×10-8) in GWAS1, 3, 4, 63, 64, did not have significant linkage disequilibrium (LD) between them, and were typed in the abovementioned VSMC collection either directly by using the Global Screening Array or indirectly by imputation16. In the present study, we further compiled lists of tagging SNPs which marked the genetic variants/haplotypes that had been reported to be associated with blood pressure/hypertension5, 19, stroke5, 6, 7, and AAA5, 8, respectively, at the genome-wide significance level (P < 5×10-8) in GWAS, did not have significant LD between them, and were typed in the abovementioned VSMC collection either directly by using the Global Screening Array or indirectly by imputation16. LD between the SNPs was assessed using LDlink’s SNPclip program, which removed SNPs based on LD in a given population and kept the first SNP (based on genomic position). We used an R2 threshold of 0.8 and a MAF threshold of 0 with the “European” option. If there was available LD data, SNPs were included/excluded as required but if not, the SNPs were kept in the list and assumed to be independent signals. In total, 1402 independent SNPs were identified for blood pressure/hypertension (Supplementary Data 30), 139 for stroke (Supplementary Data 31), and 157 for AAA (Supplementary Data 32). Of these, 1203 for blood pressure/hypertension, 119 for stroke, and 136 for AAA, were available in this study (either directly or via proxy SNPs with R2 > 0.8), which were typed in the abovementioned VSMC collection16 by the Global Screening Array or imputed, and were not filtered out (missingness < 0.05, MAF > 0.01; HWE P-value > 1x10-6).

Analyses of colocalization between disease GWAS and VSMC eQTL signals
Source and quality control of CAD GWAS datasets
CAD GWAS summary statistics from Nelson et al.2, van der Harst and Verwei3, Howson et al.4, and Aragam et al1. were downloaded from http://www.cardiogramplusc4d.org (accessed on February 18, 2021), ftp://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST005001-GCST006000/GCST005194 (accessed on February 18, 2021), https://graspnhlbinihgov.ezproxy3.lib.le.ac.uk/FullResults.aspx (accessed on March 29, 2021), and http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST90132001-GCST90133000/GCST90132314 (accessed December 13, 2022), respectively. Blood pressure GWAS summary statistics from Evangelou et al.19 (GCST006624, GCST006630) were downloaded from http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST006001-GCST007000 (accessed on February 02, 2022), and hypertension GWAS summary statistics from Neale5 were downloaded from http://www.nealelab.is/uk-biobank. Stroke GWAS summary statistics from Malik et al.6 and Mishra et al.7 (GCST006909, GCST006907, GCST006910, GCST006906, GCST90104543, GCST90104542, GCST90104541, GCST90104539) were downloaded from GWAS catalog https://www.ebi.ac.uk/gwas/ (accessed April 02, 2022 and December 13, 2022), and AAA GWAS summary statistics from Roychowdhury et al.8 were obtained from the study authors, respectively.

Cleaning and quality control steps were applied to the raw summary statistics data before they were used. Genome coordinates were converted to hg38 build with CrossMap65 and SNP marker names were updated with the new coordinates. If absent, variance, z-scores and minor allele frequencies were calculated in R v3.6.1 (https://www.R-project.org).

eCAVIAR analyses
We used eCAVIAR20 to test for colocalization of disease GWAS with VSMC cis-eQTL. The eCAVIAR analysis was performed on SNPs that had been reported to associate with CAD1, 2, 3, 4, hypertension5, stroke6, 7 or AAA8, and QTL data from the abovementioned VSMC eQTL catalogue. The top eQTL SNP (i.e. SNPs with the lowest P-value) of genes were selected and SNPs within 500kb of the top SNP were overlapped with GWAS summary data. We analyzed 500kb windows as in several other studies16, 59, 66, to include most, if not all, SNPs in LD with the top SNPs, since significant LD between SNPs typically decays within 100 kb in outbreed human populations67, 68, 69. Supplementary Figure 12 shows a locus zoom plot of a CAD-associated locus on chromosome 15, at which all CAD-associated SNPs are located within 100kb. 500kb windows were more extensive than 200kb windows used in the originally reported eCAVIAR method20; nevertheless, fixed windows of any size have the limitation of not accounting for between-region differences in LD structure. The eCAVIAR colocalization test was performed for loci that had 5 or > 5 overlapping SNPs present in both the eQTL and GWAS datasets, to reduce potential false positives due to overlaps by chance. We estimated LD for overlapping GWAS SNPs from individuals of European ancestry in the 1000 Genomes Phase 3 using PLINK70. LD of cis-eQTL overlapping SNPs was estimated from VSMC genotype data. The number of causal SNPs was set to be up to 2 per locus, and colocalization events with > 0.05 colocalization posterior probability (CLPP) were considered significant.

The above analyses were performed on SNPs reported to be associated with CAD1, 2, 3, 4, hypertension5, or AAA8, at the genome-wide significant level (P < 5 × 10-8), and on SNPs reported to be associated with stroke6, 7 at P < 5 × 10-5 as the number of SNPs reported to be associated with stroke at P < 5 × 10-8 was small. However, only the SNPs with both CLPP > 0.05 in eCAVIAR and P < 5 × 10-8 in stroke GWAS6, 7 were considered to be likely causal SNPs for stroke.

SMR/HEIDI analyses
We also used SMR/HEIDI21 to test for colocalization between disease GWAS and VSMC cis-eQTL. We performed SMR/HEIDI analyses with default settings. LD was estimated with individuals of European ancestry in the 1000 Genomes Phase 3 as reference. Although we ran transcriptome-wide SMR/HEIDI analysis, we focused only on SNPs that had been reported to associate with CAD1, 2, 3, 4, hypertension5, stroke6, 7, or AAA8, at the genome-wide significance level (P-value < 5 x 10−8) in GWAS. We applied a threshold of p < 0.05 & p > 0.05 to identify significant colocalizations of CAD GWAS signals with SMR HEIDI VSMC eQTL signals.

Analyses of colocalization between disease GWAS and arterial tissue eQTL signals
With the use of LocusCompare V2 (https://www.locuscompare2.com/) and its default settings, additional colocalization analyses were performed on the abovementioned summary statistics of GWASs of CAD1, 2, 3, 4, hypertension5, stroke6, 7, and AAA8, respectively, in conjunction with eQTL data from coronary artery tissues and aortic artery tissues, respectively, from the GTEx Portal. eCAVIAR CLPP > 0.05, SMR P-value <10-6, Coloc (H4) > 0.8, PrediXcan P-value <10-6, fastEnloc GLCP > 0.05, and/or FUSION P-value <10-6, were considered significant.

ATAC-seq
ATAC-seq was performed on 3 biological replicates of VSMCs derived from umbilical arteries from different individuals, as described previously71. In brief, 5×104 VSMCs were lysed with cold lysis buffer, and then the nuclei were incubated with a transposition reaction buffer containing Tn5 transposase (Illumina, FC-121-1030) for 30 minutes at 37°C. The resulting libraries were subjected to polymerase chain reaction (PCR) with high fidelity polymerase (New England Labs, M0541) and Nextera barcodes. The libraries were then multiplexed and sequenced using the Novaseq 6000 SP Flow Cell (Illumina) with >100 million reads (50bp pair end) per individual indexed library.

ATAC-seq data were processed using Galaxy (https://usegalaxy.eu and https://usegalaxy.org)72, 73. In brief, the quality of the raw data was evaluated with FastQC (v0.11.9). Adapters were removed using trimmomatic (v0.38), and paired-end reads aligned to the human genome (hg38) using Bowtie2 (v2.4.2) with the --very-sensitive, --dovetail and --X 1000 maximum insert size parameters. Mitochondrial and duplicate reads were removed using bamtools (v2.4.0) and Picard MarkDuplicates (v2.18.2), resulting in an average of 45 million filtered reads per sample for downstream analyses. Peaks were called using MACS2 (v2.1.1.20160309) with the start sites of the reads extending by 200bp (100bp in each direction) to assess coverage and a P-value 0.05 cutoff to reveal open chromatin peaks. The intersect function of BedTools v2.30.0 was used to identify overlaps of MACS2 peaks with the chromosome 7p15.3 and 7q23.3 loci at which variants were found to be associated with VSMC apoptosis/ACD in this study. Pygenometracks (v3.6) was used for visualization of genomic data tracks with publicly available ENCODE ChIP-seq SMC embryo-origin datasets (ENCSR116JEF: H3K27ac experiment ENCSR210ZPC, H3K4me3 experiment ENCSR515PKY and H3K27me3 experiment ENCSR143RMH) and the generation of MACS2 peak and coverage files.

H3K27ac HiChIP-seq
H3K27ac HiChIP-seq was performed as described previously74, 75, with some modifications. Two independent H3K27ac ChIP-seq on human coronary artery smooth muscle cells (>1107) (ThermoFisher Scientific, C-017-5C) were performed, prior to enriching ligated junctions. Briefly, cells were crosslinked with 1% formaldehyde. Hi-C was performed with the use of the Arima High Coverage HiC Kit (Arima Genomics, A410110) according to the manufacturer’s protocol. Thereafter, sheared chromatin samples were subjected to immunoprecipitation by an incubation with 5μg ChIP-grade anti-histone H3 (acetyl K27) antibody (Abcam, ab4729) and 50μl Dynabeads Protein G for Immunoprecipitation (ThermoFisher Scientific, 10004D) overnight at 4C. After washing, the immunoprecipitated DNA was de-crosslinked at 67C for 2 hours, eluted in a solution containing 50mM sodium bicarbonate and 1% sodium dodecyl sulphate, and enriched with the use of Dynabeads MyOne Streptavidin C1 (ThermoFisher Scientific, 65002). Thereafter, a library was prepared with the use of NEBNext Ultra II DNA Library Prep Kit for Illumina (New England Biolabs, E7645L) according to the manufacturer’s protocol. The library was subjected to PCR amplification (12 cycles) with indexed primers, and subsequently amplicons of 300-500 bp in length were selectively collected with the use of Omega Mag-Bind TotalPure NGS (Omega BIO-TEK, M1378). The library was subjected to next-generation sequencing at >300 million paired-end, 2151bp reads, on an illumina HiSeq 4000 platform.

Raw Fastq format paired-end reads were processed with HiChIP pipeline by Dovertail Genomics (https://hichip.readthedocs.io/en/latest/index.html). The hg38 human genome index generated with Burrows-Wheeler Aligner (http://arxiv.org/abs/1303.3997) was used as reference for alignments and valid pair selection. Duplicate pairs were removed using pairtools76. FitSHiChIP4 (version 11.0) was used to call significant HiChIP loops at 2.5Kb, 5Kb, 10Kb, 15Kb, and 20Kb resolution. Using ChIP-Seq peak files as input, and initial paired interaction files, high-confidence peak-to-all interactions were processed with the following parameters: loose background (UseP2PBackgrnd=0), coverage bias regression (BiasType=1), merge filtering (MergeInt=1), and 1% FDR (QVALUE=0.01). High-confidence chromatin interactions were sorted and indexed in bed format, and plotted in R with Givz77 (version 3.20).

DNA methylation analysis
DNA methylation assay was performed on 3 biological replicates of human umbilical artery smooth muscle cells (HUASMC, from different individuals) and on human aortic smooth muscle cells (HAoSMC, from 1 individual). Genomic DNA was extracted from the studied cells using the Sigma GenElute Mammalian DNA kit (Sigma Aldrich, G1N70). The DNA samples were subjected to analysis of methylation using the Illumina Infinium MethylationEPIC array. The resulting IDAT files were processed to derive CpG methylation beta values from the array data using the EPIC methylation array-specific ChAMP pipeline78, 79.

The hg38 genomic positions of the CpGs in the Infinium MethylationEPIC array were identified and the mean methylation beta value (taken from 3 HUASMC and 1 HAoSMC cell lines) was calculated for each CpG. A CpG was defined as having low methylation levels if the mean beta value was 0.25. The distance between each CpG and the likely causal variant identified from our colocalization analyses was calculated, and CpG-variant pairs with an absolute distance of ≤200bp were considered to be within close proximity of each other (for variants with more than one CpG within close proximity, the nearest CpG was selected). Of the close-proximity CpG-variant pairs identified, the proportion that had low methylation (defined above) was calculated (both separately for each vascular disease and combined).

Pathway analyses
The likely causal genes identified in the colocalization analyses were subjected to gene ontology (GO) enrichment analysis80. For each disease, the names of all of the likely causal genes were entered in the Gene Ontology browser (http://geneontology.org/) and searched for various GO aspects including molecular function, biological process, cellular component, and PANTHER pathways, with the default setting for overrepresentation analysis by Fisher’s exact test and with false discovery rate correction. For each GO aspect, the 3 most significant (with the smallest P-values and smallest false discovery rates) GO terms are presented as enrichment bubble plots which were drawn using the SRplot tool81.

Cross-trait LD score regression analysis
We assessed the genetic correlation between each of the cardiovascular diseases and traits using default settings of the software package LDSC (v1.0.1)82. The pairwise product of the SNP z statistic was regressed against LD scores of Europeans available in the LDSC software.

Investigation of phenotypic effects of deleterious protein-coding variants in candidate causal genes
To investigate if deleterious variants in the protein-coding regions of the likely causal genes identified in the colocalization analyses were associated with the disease of interest, we interrogated the candidate genes in the AstraZeneca PheWAS Portal25 (https://azphewas.com/) (UK Biobank 500k WGS (v2) Public) and PheWeb (https://r8.finngen.fi/). Each of these genes was individually interrogated in both the AstraZeneca PheWAS Portal and PheWeb, and variant-level results were recorded. The results were then filtered for genetic variants associated with the vascular disease of interest, at the genome-wide significance level (P < 110-8.7 in PheWAS and P <510-8 in PheWeb), including traits of CAD (atherosclerotic heart disease, coronary atherosclerosis, ischemic heart disease, angina pectoris, myocardial infarction, major coronary heart disease event, coronary revascularization, coronary angioplasty, coronary artery bypass grafting), blood pressure/hypertension (systolic blood pressure, diastolic blood press, hypertension, hypertension diseases), stroke, and/or AAA.

To investigate the potential functional consequences of the disease-associated variants identified in the abovementioned search in the AstraZeneca PheWAS Portal and PheWeb, we looked up the variants in the Ensembl Genome Browser 110 (www.ensembl.org) to gather information on functional consequence predictions by SIFT (sorts intolerant from tolerant substitutions)83, PolyPhen (polymorphism phenotyping)84, CADD (combined annotation dependent depletion)85, REVEL (rare exome variant ensemble learner)86, MetaLR87, and Mutation Assessor88. Each variant was individually interrogated in the Ensembl Genome Browser, and the scores of SIFT, PolyPhen, CADD, REVEL, MetaLR and Mutation Assessor were recorded. A SIFT score ≤ 0.05, a PolyPhen score ≥ 0.85, a CADD score ≥ 30, a REVEL score ≥ 0.75, and/or a MetaLR ≥ 3.5 score was considered to indicate that the variant was deleterious.

Druggability and drug-gene interaction analyses
To investigate whether any of the candidate causal genes identified in this study were potential therapeutic targets, we interrogated these genes in the Drug-Gene Interaction Database (DGIdb)26 which includes information from >30 trusted sources on the druggable genome with potentially druggable genes (genes or gene products that are predicted to be druggable, e.g. kinases) and genes with evidence of drug-gene interaction (genes or gene products that have been tested and reported to be druggable). For each disease, the names of all of the likely causal genes were entered in the search box in the search engine interface (https://dgidb.org/), and all genes indicated to be druggable in DGIdb were recorded.

Pooled CRISPR-Cas9-nuclease knockout screens
Pooled CRISPR-Cas9-nuclease knockout screens were performed on the genes that were identified in the colocalization analyses to be likely causal for CAD, hypertension, stroke, and/or AAA, and that were protein-coding genes because the pooled CRISPR-Cas9-nuclease screening technique is unable to screen non-coding RNA genes. A single-guide RNA (sgRNA) library was constructed, which included 4 sgRNAs for each of the target genes and 1,000 non-targeting control sgRNAs. The sequences of the sgRNAs were selected from the Human CRISPR knockout Pooled Library (GeCKO v2 or Brunello). Corresponding oligonucleotides were synthetized by GenScript, and cloned into the lentiCRISPRv2 plasmid (Addgene, #52961) as described89. The quality of the library was analyzed by next-generation sequencing and MAGeCK (Model-based Analysis of Genome-wide CRISPR-Cas9 Knockout)90.

Human aortic smooth muscle cells (Lonza, CC-2571) were cultured in 10 plates (15cm diameter) for the proliferation experiment and 5 plates for the migration experiment, with 3 technical replicates in each experiment. At 80% confluency, cells were transduced with the above-mentioned sgRNA library at a multiplicity of infection (MOI) of 0.2-0.3 as determined by titration. Two days post-transduction, cells were treated with 1 µg/mL puromycin and selected over the course of 8 days to enrich for transduced cells. After selection, media was replaced with Human Vascular Smooth Muscle Cell Basal Medium (ThermoFisher Scientific, M231500) with Smooth Muscle Growth Supplement (ThermoFisher Scientific, S00725), 2% fetal bovine serum, and 1 penicillin-streptomycin.

For proliferation experiments, genomic DNA was prepared from 5 plates on day 5 and from another 5 plates on day 28, post-puromycin treatment. Genomic DNA was prepared using the PureLink Genomic DNA Mini Kit (ThermoFisher Scientific, K182002).

For migration experiments, on day 12 post-puromycin treatment, 8106 cells in 1mL Human Vascular Smooth Muscle Cell Basal Medium (ThermoFisher Scientific, M231500) without supplements, were seeded at 2.5106 per 8µm pore polycarbonate insert in 6-well (24mm) transwell plates (Corning, 3428). The lower wells contained 2mL Human Vascular Smooth Muscle Cell Basal Medium (ThermoFisher Scientific, M231500) with 100ng/mL platelet-derived growth factor-BB (Sigma-Aldrich, P3201-50UG). After 24 hours, cells from both the top and bottom surfaces of the insert were harvested separately with trypsinization and then subjected to genomic DNA extraction with the use of the PureLink Genomic DNA Mini Kit (ThermoFisher Scientific, K182002).

PCR with the use of KAPA HiFi HotStart Ready Mix (Roche, 07958927001) was performed on the genomic DNA samples (32µg genomic DNA per sample) from the above-described proliferation and migration experiments. The sequences of the PCR primers were as described by Joung et al.89 (Supplementary Data 33). The PCR cycling conditions were as follows: 95oC for 5 minutes, then 24-26 cycles of 98oC for 20 seconds, 60oC for 15 seconds and 72oC for 15 seconds, and finally 72oC for 1 min. The PCR products were pooled and purified using KAPA HyperPure Beads (Roche, 08963843001) with a PCR product-to-bead ratio of 1:0.55. After collecting the supernatant an additional 0.35 volume of beads were added to achieve a final ratio of 1:0.9. Each purified sample was dissolved in 20µL nuclease-free water, and the concentrations were quantified with the Qubit dsDNA HS Assay Kit (ThermoFisher Scientific, Q32851) and the quality assessed with DNA 1000 kit (Agilent, 5067-1504) on the 2100 Bioanalyzer System (Agilent). Subsequently, libraries with barcoded Rev-1 to Rev-4 were pooled (500ng each sample, combined into a total volume of 40µL) and further quantified by quantitative PCR using standards from the NEBNext Library Quant Kit for Illumina (New England Biolabs, E7630S). The combined libraries were sequenced on the Illumina NovaSeq X platform, at 6 million 2150bp paired-end reads per sample.

Raw reads files were processed with Cutadapt91 for adapter trimming with custom scripts. Subsequently, MAGeCK90 (version 0.5.9.5) count and test algorithms were applied to the migration and proliferation datasets to identify positively-selected sgRNAs and negatively-selected sgRNAs, through paired analyses with control sgRNAs normalization. For the proliferation experiment, data from day 28 were compared with data from day 5, post-puromycin treatment. For the migration experiment, data from the bottom surface of the insert were compared with data from the top surface of the insert. Data from the 3 replicates of the proliferation experiment had similar distributions and corrections, so did data from the 3 replicates of the migration experiment, indicating high reproductivity (Supplementary Figure 13). Volcano plots were generated with the use of SRplot81.

Vascular smooth muscle cell proliferation assay
Human coronary artery SMCs (Cell Applications, 350K-05a) were transfected with either CARF siRNA (ThermoFisher Scientific, HSS128878) or control siRNA (ThermoFisher Scientific, 4390843), and then a proliferation assay was performed using Cell Counting Kit-8 (MedChemExpress, HY-K0301), with 4 technical replicates. In brief, cells were cultured in 96-well plates (5×104 cells/well) for 48 hours, and a 2-(2-methoxy-4-nitrophenyl)-3-(4-nitrophenyl)-5-(2,4disulfophenyl)-2H-tetrazolium solution was added into each well. At 2 hours after adding the reagent, a colorimetric assay was carried out using a microplate reader which measured the absorbance at 450nm.

Human aortic smooth muscle cells (Lonza, CC-2571) were transfected with either BCAR1 DsiRNA (Integrated DNA Technologies, sequence described in Supplementary 33) or negative control DsiRNA (Integrated DNA Technologies, 51-01-14-04), with 3 technical replicates. At 0, 24, and 48 hours post-transfection, cells were subjected to a proliferation assay with the use of Cell Counting Kit-8 (Abcam, ab228554)

Human aortic smooth muscle cells (Lonza, CC-2571) were transfected with either SMARCA4 siRNA (ThermoFisher Scientific, HSS110005C) or non-targeting control siRNA (ThermoFisher Scientific, 12935300l), with 8 technical replicates. At 0, 24, 48, 72, and 96 hours post-transfection, cells were subjected to a proliferation assay using Cell Counting Kit-8 (Abcam, ab228554). A serial dilution starting from 25,000 cells was generated to serve as a standard curve for analyzing the results of the proliferation assay.

Vascular smooth muscle cell migration assay
Human aortic smooth muscle cells (Lonza, CC-2571) were transfected with either FES siRNA (ThermoFisher Scientific, HSS103636), control siRNA (ThermoFisher Scientific, 12935300), CSNK2B DsiRNA (Integrated DNA Technologies, sequence described in Supplementary 33) or negative control DsiRNA (Integrated DNA Technologies, 51-01-14-04), with the use of Lipofectamine RNAiMAX Transfection Reagent (ThermoFisher Scientific, 13778150). At 47 hours post transfection, cells were incubated with 1g/mL mitomycin C (Sigma-Aldrich, M7949-2MG) for 1 hour and then 4104 cells were seeded in each well of a Corning Transwell 24-well plate with 8m pores (Sigma-Aldrich, CLS3422-48EA), with 6 technical replicates per condition. 24 hours later, cells were incubated with 4% formaldehyde (Sigma-Aldrich, F8775-500ML) for 10 minutes, 5% Triton X-100 (Sigma-Aldrich, T8787) for 5 minutes, and then 1g/mL DAPI (4',6-diamidino-2-phenylindole) (ThermoFisher Scientific, 62248) for 10 minutes. Subsequently, 3 images of each well were taken using a fluorescence microscope with a 10 objective lens and a digital camera. Thereafter, cells on the upper aspect of the transwell were wiped off, and 3 images of the lower aspect were taken. Cells in each image were counted with the use of ImageJ (version2.14.0) software, and the cell counts in each set of 3 images were averaged.

RNA-seq
Human coronary artery smooth muscle cells (Cell Applications, 350K-05a) transfected with either FES siRNA (ThermoFisher Scientific, HSS103636) or control siRNA (ThermoFisher Scientific, 12935300), with 5 technical replicates per condition, were subjected to RNA-seq. A strand-specific library with rRNA removal was prepared from total RNA of each sample, and 150bp paired-end sequencing at a >30 million read depth (12G raw data per sample) was performed with the Illumina NovaSeq 6000 Sequencing System. Raw data (raw reads) in FASTQ format were processed as follows: (1) removing reads with 5’ adapters, reads without 3’ adapter or insert sequence, reads with > 10% undetermined nucleotides, reads with >50% nucleotides with Qphred<=20, and reads with ploy A/T/G/C; (2) removing adapter sequences from the 3’ ends of reads. Thereafter, the processed clean reads from each sample were mapped to the human reference genome with HISAT2 software, and subsequently transcripts were assembled with the StringTie program. For quantification of the transcripts and genes, RPKM (Reads Per Kilobase of transcript per Million mapped reads) values were determined with the use of StringTie. A differential expression analysis was performed with the use of Cuffdiff, and P-values were adjusted using the Benjamini and Hochberg method. A volcano plot of differentially-expressed genes (adjusted P < 0.05) was generated with the use of SPplot81.

Proteomics and phosphoproteomics analyses
Human coronary artery smooth muscle cells (Cell Applications, 350K-05a) transfected with either FES siRNA (ThermoFisher Scientific, HSS103636) or control siRNA (ThermoFisher Scientific, 12935300), with 5 technical replicates per condition, were subjected to quantitative proteomics and phosphoproteomics analysis. In brief, protein extracts from cells were reduced with dithiothreitol, alkylated with iodoacetamide, washed with acetone, and then dissolved in a solution containing 0.1M triethylammonium bicarbonate (pH 8.5) and 6M urea. The concentration of total protein in the samples was quantified using the Bradford method, and protein quality was assessed using sodium dodecyl sulfate-polyacrylamide gel electrophoresis with Coomassie Brilliant Blue R-250 staining. The protein samples were digested by incubation with trypsin and subjected to tandem mass tag (TMT) labeling using a mass tagging kit (ThermoFisher Scientific, 90113). Phosphopeptides in the samples were enriched using PHOS-Select Iron Affinity Gel (Sigma-Aldrich, P9740). Subsequently, liquid chromatography-mass spectrometry was performed with the use of an EASY-nLCTM 1200 System (ThermoFisher Scientific) and an Q Exactive HF-X mass spectrometer (ThermoFisher Scientific).

The liquid chromatography-mass spectrometry data were analyzed using Proteome Discoverer Software 2.4 (ThermoFisher Scientific) with the following parameters: a mass tolerance of 10 ppm for precursor ion scans and a mass tolerance of 0.02 Da for the product ion scans. Phosphorylation of serine (S)/threonine (T)/tyrosine (Y), oxidation of methionine (M), and tandem mass tags (TMT)-plex of lysine (K) were specified as dynamic modifications. Carbamidomethyl was set as a fixed modification. TMT-plex, acetylation, and loss of methylation were specified as N8 terminal modifications. The motif-x algorithm was used to identify motifs enriched within a set of phosphosites. All enrichments were carried out for 7 residues surrounding the central residue with occurrences > 20 and P < 10-6. WebLogo was used to build the motif figures. To predict kinase-substrate relationships, all identified serine, threonine and tyrosine phosphorylation sites (pS/T/Y) were scored with the NetPhorest algorithm.

Each protein was identified with at least 1 unique peptide at a false discovery rate (FDR) < 1%. Proteins with similar peptides that could not be distinguished based on the mass spectrometry data were combined as protein groups. Quantification based on intensity was performed.

A GO enrichment analysis was conducted on proteins that showed suggestive differences in modification between cells with siRNA-induced FES knockdown and cells transfected with the control siRNA (P < 0.05 by t-test), using the default setting of Gene Ontology (http://geneontology.org). A bubble plot of enriched pathways was generated with the use of SPplot81.

Any difference in the quantity of each protein between cells with FES knockdown and cells transfected with the control siRNA was ascertained by t-test. A volcano plot of differentially expressed proteins (P value < 0.05 and >1.2fold) was generated with the use of SPplot81.

RT-PCR
Cells were lysed using TRIzol reagent (ThermoFisher Scientific, 15596026), and thereafter total RNA was extracted using the Direct-zol RNA MiniPrep kit (Zymo Research, R2050S) according to the manufacturer’s protocol. RNA was then reverse transcribed into cDNA with the use of iScript Reverse Transcription Supermix (Bio-Rad, 1708841). Subsequently, cDNA of the genes of interest was amplified using iTaq Universal SYBR Green Supermix (Bio-Rad, 1725124) and the primers described in Supplementary Data 35. The PCR thermocycling conditions were as follows: 95°C for 2 minutes, followed by 40 cycles of 95°C (denaturation) for 5 seconds and 60°C (annealing and extension) for 30 seconds. Differences in expression levels of the genes of interest between cells transfected with the target gene siRNA and cells transfected with the control RNA were determined using the ΔΔCt method, with the housekeeping gene ACTB as a reference.

Western blot analyses
Protein extracts from transfected cells were prepared using RIPA (radioimmunoprecipitation assay) lysis buffer (ThermoFisher Scientific, #89901), and protein concentrations in the extracts were quantified using Pierce BCA Protein Assay Kit (ThermoFisher Scientific, #23327). Equal amounts of protein from each sample were subjected to sodium dodecyl-sulfate polyacrylamide gel electrophoresis and thereafter transferred onto polyvinylidene difluoride membranes. The membranes were then blocked with 5% bovine serum albumin in Tris-buffered saline with Tween-20 for 2 hours at room temperature, and subsequently incubated with a primary antibody (Supplementary Data 35) at 4C overnight. After washing, the membranes were incubated with a secondary antibody conjugated with horse-radish peroxidase (Supplementary Data 36) for 1 hour at room temperature. The bands on the membrane were detected using a ChemiDoc Touch Imaging system (Bio-Rad).

Animal study
The study was conducted in accordance with the UK Animals (Scientific Procedures) Act 1986, under a Home Office Project License (60/4332), and following the guidelines of ARRIVE (Animal Research: Reporting of In Vivo Experiments), the University of Leicester and the University of Oxford.

Animals
The generation of Fes-/-/Apoe-/- mice and Fes+/+/Apoe-/-control littermates has been described previously28. In brief, Fes-/- mice were backcrossed onto a C57BL6/J background for seven generations, and subsequently intercrossed with Apoe-/- mice (B6.129P2-Apoetm1Unc/J purchased from Jackson Laboratory) to generate Fes-/-/Apoe-/- mice and Fes+/+/Apoe-/-control littermates. Mice were housed in a pathogen-free environment. Animals were maintained in ventilated cages with a controlled 12:12-hour light/dark cycle (lights on at 07:00 hour). The ambient room temperature was maintained at 22 ± 2°C with a relative humidity of 55 ± 10%.

Study of atherosclerosis
Fes-/-/Apoe-/- and littermate control (Fes+/+/Apoe-/-) mice of 6 weeks of age were fed a high fat diet for 12 weeks. The extent of aortic atherosclerotic lesions in the mice was analyzed by en face staining of aortae with Oil Red O, as previously described92. In brief, the aorta was opened longitudinally along the ventral midline from the iliac arteries to the aortic root, pinned out flat on a surface, and stained en face with Oil Red O. The stained aortas were photographed using a digital camera, and the Oil Red O-stained areas analyzed by computer-assisted quantification using the Image-Pro software.

Arterial pressure monitoring
Fes-/-/Apoe-/- and littermate control (Fes+/+/Apoe-/-) mice, at 8 weeks of age, were anesthetized with 4% isoflurane in medical O and maintained on a nose cone at 1.5-1% isoflurane for measurements of blood pressure and heart rate. The aorta was cannulated via the right common carotid artery using a 1.4 French blood pressure probe and transducer (Millar Instruments Inc. Houston, TX) coupled to a Powerlab/4SP data acquisition system (ADInstruments Pty Ltd., New Castle, Australia). Measurements were obtained after a 15-minute equilibrium period; phenylephrine was dosed via a catheter placed in the jugular vein. Blood pressure and heart rate measurements were taken for 30 seconds and average recordings during this time taken. Increasing doses of phenylephrine (5µg/kg and 25µg/kg) were administered with a 5-minute wash out period between doses. After a further 10-minute wash out period, acetylcholine (5µg/kg) was administered. Delta change in blood pressure was measured by subtracting baseline measurements from blood pressure after drug administration. A total of n=7 Fes+/+/Apoe-/- and 5 Fes-/-/Apoe-/- mice per group were used in the study, with procedures performed blinded. One Fes-/-/Apoe-/- mouse was excluded due to a technical issue with the catheterization. Two hemodynamic measurements were excluded in the wild type group due to technical problems with the traces (one 5µg/kg phenylephrine and one acetylcholine) and one wild type measurement at 5µg/kg was missed due to a problem with the injection.

Collapsing analyses of human FES protein truncating variants in relation to blood pressure and CAD traits
Through the AstraZeneca PheWAS Portal25, we collated human data of deleterious genetic variants in the protein-coding regions of the human FES gene in relation to blood pressure (data fields 4079 and 4080), hypertension (I10), and the CAD traits angina (I20) and myocardial infarction (I21), based on whole genome sequencing and phenotypic measurements of European participants in the UK Biobank. Collapsing analyses of the human FES protein truncating variants in relation to the blood pressure and CAD traits, were performed.

Statistical analyses
The Mann-Whitney test or t-test was used to ascertain differences between experimental groups in the VSMC proliferation assays, VSMC migration assays, RT-PCR analyses, standardized band intensity in the Western blot analyses, and atherosclerotic lesion areas and blood pressure in the animal studies, respectively.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Solomon et al. conducted a series of multi-step analyses to thoroughly examine how risk variants and their associated genes contribute to the development of vascular diseases, including coronary artery disease (CAD), hypertension, systolic blood pressure (SBP), diastolic blood pressure (DBP), and abdominal aortic aneurysm (AAA). Initially, the authors created a cohort of 1,846 samples in a previous study and performed genotyping alongside whole-genome RNA sequencing to produce eQTL datasets. They incorporated GWAS summary statistics for traits such as CAD, hypertension, SBP, DBP, and AAA to pinpoint causal loci and genes. Following this, the authors selected SNPs and analyzed the colocalization between eQTL data from smooth muscle cells and GWAS findings, identifying several pleiotropic genes associated with these traits. To validate their results, the authors conducted various analyses, including ATAC-seq, H3K27ac HiChIP-seq, and DNA methylation assessments. The authors proceeded with pathway enrichment analyses, identified harmful protein-coding variants, evaluated the druggability of likely causal genes, and performed CRISPR-Cas9 nuclease screening. Ultimately, the authors concentrated on the FES gene and carried out assays to evaluate smooth muscle cell proliferation and migration. These included RNA sequencing, proteomics, phosphoproteomics, RT-PCR, and western blot analyses to determine the function of FES, particularly focusing on the effects of its absence. Furthermore, knocking out the Fes gene in mice leads to increased atherosclerosis and elevated blood pressure.\nThe experimental design of this paper appears comprehensive and structured; however, the following comments and suggestions still need to be addressed:\nMajor comments:\nUse of umbilical cord VSMCs for eQTL analysis:\nIt’s unclear why umbilical cord-derived VSMCs were used for eQTL mapping in the context of adult cardiovascular disease. Neonatal cells differ substantially from adult cells in both gene expression and regulatory landscape. The authors should justify this choice and discuss it’s limitations.\nSelection of GWAS SNPs based solely on genomic position (Line 339):\nThe strategy of keeping only the first SNP by genomic position within LD blocks (R2 > 0.8) assumes that this SNP is the most representative, which is questionable. It would be helpful for the authors to clarify why this approach was used and discuss it’s limitations as well.\nColocalization strategy (Line 385):\nUsing a fixed 500kb window across all loci doesn’t account for the differences in LD structure or regulatory architecture between regions. It’s not clear why this specific window size was applied uniformly, as the window from locus to locus can vary. Also, the decision to retain only genes with more than 4 signals isn’t explained, the authors should clarify the reasoning behind this threshold. This reviewer questions why the ones with less than 4 were not retained.\nDeleterious protein-coding variants in likely causal genes (Lines 152-165):\nThis reviewer found this approach to be very interesting. Could the authors clarify why the same gene (supplementary table 18) exhibits both beneficial and detrimental effects concerning CAD or blood pressure for certain mutations?\nGene selection for validation (Lines 182–190):\nThe criteria for selecting CARF, BCAR1, and FES for experimental validation are not explained. In addition, how did the focus suddenly shift to just FES. If the others did not reach significance, this should be mentioned and the negative results made available in supplementary materials.\nFes knockout model and sex bias in in-vivo experiments:\nThere’s a disconnect between the in vitro and in vivo data for FES. Knockout in VSMCs improved proliferation and migration, which can be beneficial in vascular repair and fibrous cap stability, yet the whole-body knockout in mice led to increased atherosclerosis. This contradiction isn’t addressed. Additionally, FES is broadly expressed across multiple cell types; the plaque phenotype likely reflects cumulative effects beyond VSMCs. To resolve this, a VSMC-specific knockout will clarify if the effect is truly VSMC-driven. Also, only male mice were used. If female mice were tested and showed no effect, that should be reported. If not tested at all, that’s a major limitation; sex differences in vascular disease are well established and should be considered. Lastly, it has been shown that FES has a sex-biased effect on Blood pressure (PMID: 38459180).\nMinor comments:\nLack of detail in key results: Several important findings are mentioned only briefly without sufficient interpretation. The results section can benefit from more throughout reporting.\nFigure referencing issues: There are several incorrect figure references in the results section (eg. Lines 193, 200, 203, 205, 217, 223, 229, 231). These should be checked and corrected.\nSupplementary Figure 10: P-value is missing.\nLine 38: The introduction of FES is too abrupt, a brief transition of context will help improve the flow. Also, the sentence “FES is a pleotropic for both …” is imcomplete, pleitropic what? Please clarify.\nLine 202: Please explain briefly the relevance of the two genes MMP1 and MMP3.\nLine 202: The full name “epidermal growth factor” should be written out before introducing the abbreviations (EGF), not the opposite.", "reviewer_2": "Solomon and colleagues presented their work characterizing the causative genes associated with vascular disease. They applied multi-omics experiments, including ATAC-seq, H3K27ac HiChIP-seq, and DNA methylation profiling, to explore the functional genetic variants identified in their previous eQTL/GWAS study. They identified 23 genes that were commonly significant for more than one vascular disease. These genes were further examined using the UK Biobank and Finnish Biobank datasets. The authors also utilized CRISPR screens to identify important genes associated with CSMC migration or proliferation, selecting FES for further functional validation. The study demonstrated the function of FES in human vascular disease through the integration of distinct omics data, providing a valuable strategy and resources to the scientific community. However, the manuscript overall lacks detail, and most of the analyses do not seem to have been comprehensively performed. Here are my concerns.\n1. he manuscript lacks clarity on how many samples or replicates were performed for each omics experiment. The bioinformatics analysis is not sufficiently detailed, providing only conclusions without describing the methodologies and data quality. It is also noted that the data is not open-access. For example, the authors mention that many variants are located in open chromatin regions (OCRs) with low methylation, but how are these lowly methylated regions defined? What criteria and number of replicates were used? Were any differential methylation or chromatin accessibility analyses performed? Similar issues are present in the HiChIP-seq data analysis. How does H3K27ac correlate with ATAC-seq and DNA methylation at significant genetic variants?\n2. While the approaches were used to identify causal genes, the identified genes do not seem significant in the eQTL/GWAS analysis. This might suggest that the regulation of these causal genes is more critical to human vascular disease. However, the current results do not explore the regulatory relationships between genetic variants and their target genes. Additionally, it is unclear how these regulatory relationships are distributed within the human population to connect with vascular disease.\n3. The CRISPR screening identified important gene candidates, but the log fold change appears relatively small in this analysis. It is also unclear whether the y-axis represents p-value or adjusted p-value, and what the stringency of this analysis was. In Figure 5B, the blue and pink dots are marked as up in non-migrated cells, but further clarification is needed.\n4. The animal experiments indicate that downregulated expression of FES is associated with increased atherosclerotic lesions and hypertension. Are these results consistent with human data?"}, "author_response": "Response to Reviewers’ Comments\nWe thank the reviewers for their highly valuable comments and constructive advice. In the revised\nmanuscript, the reviewers’ points have been addressed as detailed below. Reviewers’ comments are\nin bold font, followed by authors’ response in regular font.\nReviewer 1\nSolomon et al. conducted a series of multi-step analyses to thoroughly examine how risk variants\nand their associated genes contribute to the development of vascular diseases, including coronary\nartery disease (CAD), hypertension, systolic blood pressure (SBP), diastolic blood pressure (DBP),\nand abdominal aortic aneurysm (AAA). Initially, the authors created a cohort of 1,846 samples in a\nprevious study and performed genotyping alongside whole-genome RNA sequencing to produce\neQTL datasets. They incorporated GWAS summary statistics for traits such as CAD, hypertension,\nSBP, DBP, and AAA to pinpoint causal loci and genes. Following this, the authors selected SNPs and\nanalyzed the colocalization between eQTL data from smooth muscle cells and GWAS findings,\nidentifying several pleiotropic genes associated with these traits. To validate their results, the\nauthors conducted various analyses, including ATAC-seq, H3K27ac HiChIP-seq, and DNA\nmethylation assessments. The authors proceeded with pathway enrichment analyses, identified\nharmful protein-coding variants, evaluated the druggability of likely causal genes, and performed\nCRISPR-Cas9 nuclease screening. Ultimately, the authors concentrated on the FES gene and carried\nout assays to evaluate smooth muscle cell proliferation and migration. These included RNA\nsequencing, proteomics, phosphoproteomics, RT-PCR, and western blot analyses to determine the\nfunction of FES, particularly focusing on the effects of its absence. Furthermore, knocking out the\nFes gene in mice leads to increased atherosclerosis and elevated blood pressure. The experimental\ndesign of this paper appears comprehensive and structured; however, the following comments\nand suggestions still need to be addressed:\nMajor comments:\nUse of umbilical cord VSMCs for eQTL analysis:\nIt’s unclear why umbilical cord-derived VSMCs were used for eQTL mapping in the context of adult\ncardiovascular disease. Neonatal cells differ substantially from adult cells in both gene expression\nand regulatory landscape. The authors should justify this choice and discuss it’s limitations.\nWe thank the reviewer for the constructive comment and advice. We have added the following text\nin the Discussion:\n“We note some limitations of our work. First, our study used human umbilical cord-derived VSMCs\nfor eQTL mapping in the context of adult cardiovascular disease. At present, a large collection of\nadult VSMCs is unavailable. Therefore, in this study we utilized a large collection of human umbilical\ncord-derived VSMCs (VSMC eQTL data from 1,486 individuals) to provide high statistical power for\nthe comprehensive genome-wide eQTL analysis. As the cells were isolated from umbilical cords from\nindividuals of the same biological age, the analysis also benefited from circumventing age-related\nconfounding. However, there is a caveat that neonatal cells can differ from adult cells in gene\nregulation and expression. Notwithstanding, in a recent work16, we compared the transcriptomes of\nthe umbilical cord-derived VSMCs utilized in this study with reported transcriptomic data from other\ntypes of cell/tissue58, 59, and found that the gene expression profile of the human umbilical cord-\nderived VSMCs mapped closer to adult human coronary artery smooth muscle cells than any other\ncell/tissue types16. …” (page 10/paragraph 3/line 1-12).\nSelection of GWAS SNPs based solely on genomic position (Line 339):\nThe strategy of keeping only the first SNP by genomic position within LD blocks (R2 > 0.8) assumes\n1\nthat this SNP is the most representative, which is questionable. It would be helpful for the authors\nto clarify why this approach was used and discuss it’s limitations as well.\nWe apologize for the lack of clarity in our original manuscript regarding the specific purpose that\nthese SNPs were selected for. These SNPs were selected to tag the genetic variants/haplotypes\nwhich had been reported to be associated with CAD, blood pressure, hypertension, stroke, and AAA,\nrespectively, in GWAS. These tagging SNPs were then looked up in the VSMC eQTL catalogue\ngenerated in our study, so as to investigate which of the tagging SNPs (or SNPs in LD with these\ntagging SNPs) showed eQTL effect in the VSMC eQTL catalogue. This part of the study was not\nintended to identify the causal variants in the disease-associated LD blocks. Therefore, this was\nfollowed by the colocalization analyses to identify likely causal variants. The colocalization analyses\n(eCAVIAR, and SMR/HEIDI) included all SNPs that have been reported to be associated with the\nrelevant disease (CAD, hypertension, stroke, and AAA, respectively) in GWAS, rather than just the\nabove selected tagging SNPs.\nIn the revised manuscript, we have tried to clarify this by revising the relevant texts to read as\nfollows:\n“We recently established a large collection of VSMCs from different individuals (n = 1,486), and on\nthis cell bank we performed genome-wide genotyping and whole-genome RNA-sequencing (RNA-\nseq) to generate a comprehensive catalogue of expression quantitative trait loci (eQTL) with\nstringent statistical significance16. We compiled a list of tagging single nucleotide polymorphisms\n(SNPs) that marked the genetic variants/haplotypes reported to be associated with CAD in GWAS1, 2,\n3, 4 and then interrogated these tagging SNPs in the VSMC eQTL catalogue. As reported previously16,\nwe observed that ~60% of the CAD-associated SNPs had an eQTL effect on gene expression, with\ntwo-thirds of them affecting more than one gene” (page 4/paragraph 1).\n“Here we compiled lists of tagging SNPs that marked the genetic variants/haplotypes reported to be\nassociated with blood pressure/hypertension5, 19, stroke5, 6, 7 and/or AAA5, 8 in GWAS, and\ninterrogated these tagging SNPs in the abovementioned VSMC eQTL catalogue. We found that ~59%\nof the blood pressure/hypertension-associated SNPs, ~55% of the stroke-associated SNPs, and ~59%\nof the AAA-associated SNPs, had eQTL effects in VSMCs (Supplementary Table 1-3 and\nSupplementary Fig. 1). Of these SNPs, ~66% showed eQTL effects on more than one gene. Of the\nassociated genes, ~77% were protein-coding and ~15% were long non-coding RNAs (lncRNAs)\n(highlighted in green and yellow, respectively, in Supplementary Table 1-3).” (page 4/paragraph\n2/line 1-8).\n“To explore causal variants and related causal genes for each of the vascular diseases mentioned\nabove, we performed colocalization tests, including eCAVIAR (eQTL and GWAS CAusal Variant\nIdentification in Associated Regions)20 and SMR/HEIDI (summary-data-based Mendelian\nRandomization/Heterogeneity in Dependent Instruments)21, utilizing reported disease GWAS\nsummary statistics in conjunction with the above-described VSMC eQTL dataset. These analyses\ndetected panels of likely causal variants and likely causal genes for each of these vascular diseases.\nSupplementary Tables 5-8 show the results of these colocalization analyses, including the identified\nlikely causal variants (columns A-F), the identified likely causal genes together with eQTL data in\nrelation to the likely causal variants (columns G-M), summary statistics of reported associations of\nthe likely causal variants with the relevant vascular diseases (columns N-R), and the statistics of the\neCAVIAR and SMR/HEIDI analyses (columns S-Y).” (page 4/paragraph 3).\n“Compilation of tagging SNPs that marked the genetic variants/haplotypes reported to be associated\nwith CAD, blood pressure/hypertension, stroke, and AAA, respectively\nWe previously identified 424 tagging SNPs which marked the genetic variants/haplotypes that had\nbeen reported to be associated with CAD at the genome-wide significance level (P < 5×10-8) in\nGWAS1, 3, 4, 62, 63, did not have significant linkage disequilibrium (LD) between them, and were typed in\n2\nthe abovementioned VSMC collection either directly by using the Global Screening Array or\nindirectly by imputation16. In the present study, we further compiled lists of tagging SNPs which\nmarked the genetic variants/haplotypes that had been reported to be associated with blood\npressure/hypertension5, 19, stroke5, 6, 7, and AAA5, 8, respectively, at the genome-wide significance\nlevel (P < 5×10-8) in GWAS, did not have significant LD between them, and were typed in the\nabovementioned VSMC collection either directly by using the Global Screening Array or indirectly by\nimputation16. LD between the SNPs was assessed using LDlink’s SNPclip program, which removed\nSNPs based on LD in a given population and kept the first SNP (based on genomic position). We used\nan R2 threshold of 0.8 and a MAF threshold of 0 with the “European” option. If there was available\nLD data, SNPs were included/excluded as required but if not, the SNPs were kept in the list and\nassumed to be independent signals. In total, 1402 independent SNPs were identified for blood\npressure/hypertension (Supplementary Table 29), 139 for stroke (Supplementary Table 30), and 157\nfor AAA (Supplementary Table 31). Of these, 1203 for blood pressure/hypertension, 119 for stroke,\nand 136 for AAA, were available in this study (either directly or via proxy SNPs with R2 > 0.8), which\nwere typed in the abovementioned VSMC collection16 by the Global Screening Array or imputed, and\nwere not filtered out (missingness < 0.05, MAF > 0.01; HWE P-value > 1x10-6).” (page 11/paragraph\n3).\nColocalization strategy (Line 385):\nUsing a fixed 500kb window across all loci doesn’t account for the differences in LD structure or\nregulatory architecture between regions. It’s not clear why this specific window size was applied\nuniformly, as the window from locus to locus can vary.\nThank you for pointing this out. We have added the following text in the revised manuscript to\ndescribe the reason and the limitation:\n“We analyzed 500kb windows as in several other studies16, 58, 65. 500kb windows were more\nextensive than 200kb windows used in the originally reported eCAVIAR method20; nevertheless,\nfixed windows of any size have the limitation of not accounting for between-region differences in LD\nstructure.” (page 12/paragraph 3/line 5-8).\nAlso, the decision to retain only genes with more than 4 signals isn’t explained, the authors should\nclarify the reasoning behind this threshold. This reviewer questions why the ones with less than 4\nwere not retained.\nWe have realized that the original text “The eCAVIAR colocalization test was performed for genes\nthat had > 4 overlapping SNPs with GWAS summary data” is unclear and misleading. We have\namended it to read as follows:\n“The eCAVIAR colocalization test was performed for loci that had 5 or > 5 overlapping SNPs present\nin both the eQTL and GWAS datasets, to reduce potential false positives due to overlaps by chance.”\n(page 12/paragraph 3/line 8-10).\nDue to the very large sizes of the eQTL and GWAS datasets each containing multiple millions of SNPs,\nthe eCAVIAR analysis to look for overlaps of SNPs between the two datasets can detect overlaps of\nSNPs by chance. In a preliminary analysis, we observed that at loci where there was only 1\noverlapping SNP when comparing the two datasets, the overlapping SNP was considered to be\nsignificant [colocalization posterior probability (CLPP) > 0.05 in the eCAVIAR analysis] at all (100%) of\nthese loci and at least one eQTL gene (associated with this overlapping SNP) was considered to be\nsignificant (CLPP > 0.05) at nearly all (95%) of these loci (please refer to the plots below). It is unlikely\nthat these are all true positive findings but likely that a proportion of them are false positives.\nHowever, if the analysis focused on loci where there were 5 or >5 overlapping SNPs between the\ntwo datasets, only ~10% of the eQTL SNPs and ~10% of the eQTL genes were considered to be\nsignificant (CLPP > 0.05) in the eCAVIAR analysis (please refer to the plots below). Therefore, we\n3\nfocused the eCAVIAR analysis of our study on loci that had 5 or > 5 overlapping SNPs present in both\nthe eQTL and GWAS datasets, assuming that ~10% of eQTL SNPs are causal as such assumption\n(10%) has been applied in other studies [PMID: 29942083]. The higher likelihood of detecting\npotential false positives at loci where there are less than 5 overlapping SNPs in colocalization\nanalyses have been noted by other investigators (https://platform-\ndocs.opentargets.org/gentropy/colocalisation).\n100\n90\n80\n70\n60\n50\n40\n30\n20\n10\n0\n1 and >1 2 and >2 3 and >3 4 and >4 5 and >5\nDeleterious protein-coding variants in likely causal genes (Lines 152-165):\nThis reviewer found this approach to be very interesting. Could the authors clarify why the same\ngene (supplementary table 18) exhibits both beneficial and detrimental effects concerning CAD or\nblood pressure for certain mutations?\nWe have realized that the word “Benign” in green color in the “Predicted consequences” column of\nthis table (Supplementary Table 18 in the original manuscript; Supplementary Table 15 in the revised\nmanuscript) may mislead and be interpreted as being beneficial, leading to the impression that some\nof the genes have both beneficial and detrimental mutations, although that is not the case.\nTherefore, we have changed the word “Benign” to “Non-deleterious missense variant” in this table,\nwith the aim to make it clearer that the variant is predicted not to cause a deleterious effect on the\nfunction of the protein encoded by the gene.\nGene selection for validation (Lines 182–190):\nThe criteria for selecting CARF, BCAR1, and FES for experimental validation are not explained. In\n4\nsenegLTQe\nfo\n)%(\negatnecreP\n)50.0>\n*PPLC(\ntnacifingis\nderedisnoc\nsisylanaRAIVACe\nni\n100\n90\n80\n70\n60\n50\n40\n30\n20\n10\n0\n1 2 3 4 5\nNumber of overlapping SNPs per locus,\npresent in both the eQTLand GWAS datasets\nsPNSLTQe\nfo\n)%(\negatnecreP\n)50.0>\n*PPLC(\ntnacifingis\nderedisnoc\nsisylanaRAIVACe\nni\nNumber of overlapping SNPs per locus,\npresent in both the eQTLand GWAS datasets\nCLPP*: colocalization posterior probability\naddition, how did the focus suddenly shift to just FES. If the others did not reach significance, this\nshould be mentioned and the negative results made available in supplementary materials.\nMany thanks for the constructive comment and advice. We have revised the text accordingly to read\nas follows and added the negative result in Supplemental Fig. 10C.\n“To assess possible effects of the likely causal genes on VSMC behavior, we performed high-\nthroughput pooled CRISPR-Cas9-nuclease knockout screens targeting the protein-coding genes\namong these likely causal genes (this technique is unable to test non-coding RNA genes). The screens\nidentified multiple genes that affected VSMC proliferation or migratory ability (Supplementary Table\n24&25, and Fig. 5A&B). A selection of these genes was subjected to validation experiments, with the\nfollowing selection criteria: 1) among the top 10 genes ranked by score in the pooled CRISPR-Cas9-\nnuclease knockout screens; 2) considered to be a pleiotropic gene for more than one vascular\ndisease in the aforementioned cross-trait analysis; and 3) likely to be druggable in the\nabovementioned druggability analysis. In the validation experiments, VSMCs were transfected with\neither siRNA for the selected gene or negative control siRNA, followed by VSMC proliferation or\nmigration assay. The following genes met the selection criteria and were tested in the validation\nexperiments: BCAR1, CARF, and SMARCA4 for VSMC proliferation, and CSNK2B and FES for VSMC\nmigration. The validation experiments confirmed the CRISPR-Cas9-nuclease knockout screen findings\nfor BCAR1, CARF, FES, and SMARCA4, with the same direction of effect in both the screen and\nvalidation experiments (Fig. 5C&D, and Supplementary Fig. 9A&9B), but not for CSNK2B\n(Supplementary Fig. 9C).” (page 6/paragraph 3/line 1-16).\nFes knockout model and sex bias in in-vivo experiments:\nThere’s a disconnect between the in vitro and in vivo data for FES. Knockout in VSMCs improved\nproliferation and migration, which can be beneficial in vascular repair and fibrous cap stability, yet\nthe whole-body knockout in mice led to increased atherosclerosis. This contradiction isn’t\naddressed.\nWe appreciate the reviewer’s comment regarding the divergent roles of VSMCs in atherosclerosis,\nand have added the follow text in the Discussion:\n“It has long been documented and is recently re-emphasized50, 51 that VSMC dedifferentiation and\ntheir accumulation significantly contribute to atherosclerotic plaque formation and enlargement,\nalthough VSMCs in the fibrotic cap are beneficial for plaque stability.” (page 10/paragraph 1/line 2-\n4).\nAdditionally, FES is broadly expressed across multiple cell types; the plaque phenotype likely\nreflects cumulative effects beyond VSMCs. To resolve this, a VSMC-specific knockout will clarify if\nthe effect is truly VSMC-driven.\nWe appreciate the reviewer’s comment and have added the follow text in the limitations paragraph\nin the Discussion:\n“… as FES is expressed in not only VSMCs but also some other cell types such as monocytes and\nendothelial cells, the total atherogenic contribution of FES is expected to derive from its effect on\nthe various cell types, including VSMCs as suggested here and monocytes as indicated in our recent\nstudy28. The relative contributions from the different cell types are yet to be established.” (page\n10/paragraph 3/line 12-16).\nAlso, only male mice were used. If female mice were tested and showed no effect, that should be\nreported. If not tested at all, that’s a major limitation; sex differences in vascular disease are well\nestablished and should be considered. Lastly, it has been shown that FES has a sex-biased effect on\nBlood pressure (PMID: 38459180).\nmice on atherosclerosis and blood pressure in Figure 8 (8A, 8B & 8C) and Supplementary Figure 10\n(10A & 10B).\nMinor comments:\nLack of detail in key results: Several important findings are mentioned only briefly without\nsufficient interpretation. The results section can benefit from more throughout reporting.\nThank you for your constructive comment and advice. We have substantially revised the text of the\nResults section, with the intention to elaborate the key findings with more details and\ninterpretation.\nFigure referencing issues: There are several incorrect figure references in the results section (eg.\nLines 193, 200, 203, 205, 217, 223, 229, 231). These should be checked and corrected.\nMany thanks for pointing these out. We have checked and made corrections accordingly.\nSupplementary Figure 10: P-value is missing.\nMany thanks. P-values have been added to the Figure (This Supplementary Figure is labeled\nSupplementary Figure 9 in the revised manuscript).\nLine 38: The introduction of FES is too abrupt, a brief transition of context will help improve the\nflow. Also, the sentence “FES is a pleotropic for both …” is imcomplete, pleitropic what? Please\nclarify.\nThank you for your constructive comment and valuable advice. As advised, we have revised the text\nto read as follows:\n“Pooled CRISPR screen analyses of these likely causal genes indicate that many of them influence\nvascular smooth muscle cell behavior, and validation experiments of selected genes confirm that\nFES, BCAR1, CARF and SMARCA4 exert such effects. Further functional experiments focusing on FES,\na pleiotropic gene for both coronary artery disease and hypertension, show that …” (Abstract, line 8-\n11).\nLine 202: Please explain briefly the relevance of the two genes MMP1 and MMP3.\nWe have revised the text to read as follows:\n“…, including increased expression of the extracellular matrix protein-degrading enzymes matrix\nmetalloproteinase-1 (MMP1) and MMP3 (Fig. 6B&C, Supplementary Table 27&28).” (page\n7/paragraph 1/line 9-10).\nAdditionally, the Discussion includes the following text:\n“… both MMP1 and MMP3 are capable of degrading a variety of extracellular proteins in the vascular\nwall and have been shown to facilitate VSMC migration, matrix degradation and vascular\nremodeling52. These processes are instrumental in the pathogeneses of both atherosclerosis and\nhypertension52, 53, 54.” (page 10/paragraph 1/line 5-8)\nLine 202: The full name “epidermal growth factor” should be written out before introducing the\nabbreviations (EGF), not the opposite.\nThank you. As advised, we have changed the text to “epidermal growth factor (EGF)” (page\n7/paragraph 1/line 6).\nSolomon and colleagues presented their work characterizing the causative genes associated with\nvascular disease. They applied multi-omics experiments, including ATAC-seq, H3K27ac HiChIP-seq,\nand DNA methylation profiling, to explore the functional genetic variants identified in their\nprevious eQTL/GWAS study. They identified 23 genes that were commonly significant for more\nthan one vascular disease. These genes were further examined using the UK Biobank and Finnish\nBiobank datasets. The authors also utilized CRISPR screens to identify important genes associated\nwith CSMC migration or proliferation, selecting FES for further functional validation. The study\ndemonstrated the function of FES in human vascular disease through the integration of distinct\nomics data, providing a valuable strategy and resources to the scientific community. However, the\nmanuscript overall lacks detail, and most of the analyses do not seem to have been\ncomprehensively performed. Here are my concerns.\n1. The manuscript lacks clarity on how many samples or replicates were performed for each omics\nexperiment.\nWe thank the reviewer for the constructive comment. We have added such information in the\nMethods section (page 13/paragraph 2/line 1, page 13/paragraph 2/line 1-3, page 14/paragraph\n2/line 1, page 17/paragraph 5/line 3, page 18/paragraph 2/line 3).\nThe bioinformatics analysis is not sufficiently detailed, providing only conclusions without\ndescribing the methodologies and data quality.\nThank you for the constructive comment. We have added relevant details in the Methods section\n(page 14/paragraph 4, page 14/paragraph 6, page 15/paragraph 2, page 15/paragraph 3).\nIt is also noted that the data is not open-access.\nThe data that support the findings of this study are available from the corresponding author upon\nreasonable request. Source data are provided with this paper. The RNA-Seq data and H3K27ac\nHiChIP-seq data are available from Gene Expression Omnibus with the accession number GSE189300\nand GSE282557. The DNA methylation data are available from ArrayExpress with the accession ID E-\nMTAB-15426. The proteomics data and phosphoproteomics data are available from MassIVE with\nthe accession IDs PXD061984 and PXD061992, respectively. (page 21).\nWe are not permitted to deposit the ATAC-seq data as they contain genetic sequences of donors of\nhuman umbilical cords, that are ethically considered sensitive personal data.\nFor example, the authors mention that many variants are located in open chromatin regions\n(OCRs) with low methylation, but how are these lowly methylated regions defined? What criteria\nand number of replicates were used?\nThank you for your constructive comment. We have expanded the DNA methylation analysis method\nsection to include information regarding replicates and how low methylation was defined, as\nfollows:\n“DNA methylation assay was performed on 3 biological replicates of human umbilical artery smooth\nmuscle cells (HUASMC, from different individuals) and on human aortic smooth muscle cells\n(HAoSMC, from 1 individual). ...” (page 14/paragraph 2/line 1-3).\n“The hg38 genomic positions of the CpGs in the Infinium MethylationEPIC array were identified and\nthe mean methylation beta value (taken from 3 HUASMC and 1 HAoSMC cell lines) was calculated\nfor each CpG. A CpG was defined as having low methylation levels if the mean beta value was ≤0.25.\nThe distance between each CpG and the likely causal variant identified from our colocalization\n7\nanalyses was calculated, and CpG-variant pairs with an absolute distance of ≤200bp were considered\nto be within close proximity of each other (for variants with more than one CpG within close\nproximity, the nearest CpG was selected). Of the close-proximity CpG-variant pairs identified, the\nproportion that had low methylation (defined above) was calculated (both separately for each\nvascular disease and combined).” (page 14/paragraph 3).\nWere any differential methylation or chromatin accessibility analyses performed?\nThe methylation and chromatin accessibility assays were performed on VSMCs from several\nindividuals, which were intended to identify regions of low methylation and high chromatin\naccessibility, but were not powered for differential analyses. Therefore, differential analyses were\nnot performed.\nSimilar issues are present in the HiChIP-seq data analysis. How does H3K27ac correlate with\nATACseq and DNA methylation at significant genetic variants?\nThank you for your valuable comment. We have now placed the results of H3K27ac, ATAC-seq and\nDNA methylation, side by side, for each of the significant genetic variants in Supplementary Table 12,\nwhich shows that a large proportion of the significant genetic variants are located in regions with\nH3K27ac as well as ATAC peaks and/or low DNA methylation.\nAdditionally, we have added the following text in the Results section:\n“To investigate if any of the likely causal variants identified in the above colocalization analyses were\nlocated in transcriptionally active regions of the genome, we performed VSMC ATAC-seq (assay for\ntransposase-accessible chromatin with sequencing) to delineate open chromatin regions, as well as\nDNA methylation assay and H3K27ac HiChIP-seq (histone H3 acetylation at lysine 27 high throughput\nchromosome conformation capture with chromatin immunoprecipitation with sequencing) since\nDNA demethylation and H3K27 acetylation upregulate gene expression. We interrogated the likely\ncausal variants in regions of ATAC-seq peaks, low DNA methylation and H3K27ac, respectively. The\nanalyses showed that a large proportion of the likely causal variants were located in regions of\nATAC-seq open chromatin (Supplementary Table 9), low DNA-methylation (variants highlighted in\nyellow in Supplementary Table 10), and/or H3K27ac (Supplementary Table 11). The likely causal\nvariants in relation to peaks of ATAC-seq, low DNA methylation and H3K27ac, as well as their\nassociated eQTL genes and vascular diseases, are summarized in Supplementary Table 12. As an\nexample, we found that the CAD associated variant rs1894401 in the FES gene resided in an ATAC-\nseq open chromatin region with lessened DNA-methylation (Supplementary Fig. 2&3) and with\nH3K27ac (Supplementary Fig. 4).” (page 4/paragraph 4).\n2. While the approaches were used to identify causal genes, the identified genes do not seem\nsignificant in the eQTL/GWAS analysis. This might suggest that the regulation of these causal\ngenes is more critical to human vascular disease. However, the current results do not explore the\nregulatory relationships between genetic variants and their target genes. Additionally, it is unclear\nhow these regulatory relationships are distributed within the human population to connect with\nvascular disease.\nMany thanks for your valuable and constructive comment. We have now removed some of the\nvariants, as they are not considered to be significant at the genome-wide significance threshold. In\nthe revised manuscript, all the likely causal variants and their associated likely causal genes shown\nare significant in both GWAS and eQTL.\nTo show the relationship of the genetic variants with their target genes and human vascular disease,\nwe have re-arranged the data in the relevant tables as follows:\n8\n• In Supplementary Tables 5-8, the names of the causal SNPs, their GWAS summary statistics\n(including P-values), the names of the causal genes, and their eQTLs summary statistics (including\nP-values), are shown one after the other.\n• In Supplementary Table 12, information of regulatory variants, ATAC-seq peaks, H3K27ac peaks,\nlow DNA methylation regions, target gene eQTL summary statistics and disease GWAS summary\nstatistics, are placed one after the other.\nAdditionally, as mentioned above, we have added the following text in the Results:\n“To investigate if any of the likely causal variants identified in the above colocalization analyses were\nlocated in transcriptionally active regions of the genome, we performed VSMC ATAC-seq (assay for\ntransposase-accessible chromatin with sequencing) to delineate open chromatin regions, as well as\nDNA methylation assay and H3K27ac HiChIP-seq (histone H3 acetylation at lysine 27 high throughput\nchromosome conformation capture with chromatin immunoprecipitation with sequencing) since\nDNA demethylation and H3K27 acetylation upregulate gene expression. We interrogated the likely\ncausal variants in regions of ATAC-seq peaks, low DNA methylation and H3K27ac, respectively. The\nanalyses showed that a large proportion of the likely causal variants were located in regions of\nATAC-seq open chromatin (Supplementary Table 9), low DNA-methylation (variants highlighted in\nyellow in Supplementary Table 10), and/or H3K27ac (Supplementary Table 11). The likely causal\nvariants in relation to peaks of ATAC-seq, low DNA methylation and H3K27ac, as well as their\nassociated eQTL genes and vascular diseases, are summarized in Supplementary Table 12. As an\nexample, we found that the CAD associated variant rs1894401 in the FES gene resided in an ATAC-\nseq open chromatin region with lessened DNA-methylation (Supplementary Fig. 2&3) and with\nH3K27ac (Supplementary Fig. 4).” (page 4/paragraph 4).\n3. The CRISPR screening identified important gene candidates, but the log fold change appears\nrelatively small in this analysis.\nAs the reviewer rightly pointed out, the log fold change was relatively small from some of the genes.\nAs the CRISPR screening is an in vitro experiment, it is unclear whether or not the effect sizes\nobserved in this experimental setting are equivalent to the magnitudes of effects of these genes in\nvivo. However, as the pooled CRISPR screening can test multiple genes simultaneously under the\nsame condition, it provides a ranking of the effects of the tested genes, based on a scoring method\nthat has been widely used in many studies. We utilized this ranking to prioritize genes for validation\nexperiments. We prioritized genes for validation using the following selection criteria: 1) among the\ntop 10 genes ranked by score in the pooled CRISPR-Cas9-nuclease knockout screens; 2) considered\nto be a pleiotropic gene for more than one vascular disease in the cross-trait analysis; and 3) likely to\nbe druggable in the druggability analysis (page 6/paragraph 3/line 6-9).\nIt is also unclear whether the y-axis represents p-value or adjusted p-value, and what the\nstringency of this analysis was.\nThe plots (Figure 5A&5B) show nominal p-values. We thank the reviewer for this comment and have\nmodified the figure legends to clarify this. Adjusted p-values are included in Supplementary Table 24\n& 25.\nIn Figure 5B, the blue and pink dots are marked as up in non-migrated cells, but further\nclarification is needed.\nWe apologize for the typo – the pink dots should have been marked as up in “migration cells” rather\nthan “non-migrated cells”. We have corrected this typo.\n4. The animal experiments indicate that downregulated expression of FES is associated with\nincreased atherosclerotic lesions and hypertension. Are these results consistent with human data?\ndeficiency promotes atherosclerosis and hypertension. Specifically, the animal experiments showed\nthat Fes knockout increased atherosclerotic lesion sizes and blood pressure (Figure 8), and the\nhuman data showed that FES gene deleterious variants are associated with increased risk of\nmyocardial infarction and elevated blood pressure (Table 1, and page 7/paragraph 5).\nTo highlight this, we have revised the following text in the Discussion to read as follows:\n“we demonstrated that knockout of FES, one of the pleiotropic genes, promotes atherosclerosis and\nraises blood pressure in mouse models, and additionally gathered evidence of FES gene deleterious\nvariants significantly increasing the risk of both hypertension and CAD in humans among UK Biobank\nparticipants.” (page 9/paragraph 5/line 1-4).\n10"}, {"round": 2, "reviewer_comments": {"reviewer_1": "Colocalization strategy\nFor eCAVIAR and SMR/HEIDI analyses, can the author create a locus zoom window using the GWAS Tag SNP by building 200 KB ( as a starting point) around significant SNPs based on LD, then adding 100 or 200 kb left or right until no SNP is in LD?\nFes knockout model and sex bias in in-vivo experiments:\nSince in vitro FES gene analyses were conducted in VSMCs, unlike the in vivo mouse model with a whole-body knockout, it is difficult to determine the cell type source of the effect, making it hard to draw conclusive results. Therefore, it would be beneficial to have VSMC-specific KO of Fes and study atherosclerosis.\nAdditionally, combining data from both males and females with a small sample size, especially in females, for the atherosclerosis study, is a concern and makes it hard to identify if there is a sex-specific effect.", "reviewer_2": "I appreciate that the authors addressed all my concerns.", "reviewer_3": "Report\nReport focusing on the Fes knockout mouse experiments and the in-vitro validation data:\nThe group has previously generated an eQTL catalogue for human umbilical cord smooth muscle cells (SMC) and found co-localisation of SMC eQTLs with coronary artery disease (CAD)-associated risk loci. Here, they report overlap of hypertension, stroke and abdominal aortic aneurysm risk variants with SMC eQTLs and identify variant shared for multiple conditions. Co-localisation analysis and epigenetic profiling data is leveraged to identify candidate causal genes and associated mechanisms.The analysis suggests that some genes are linked to multiple conditions suggesting pleiotropic effects or common disease pathways for the conditions. Furthermore, database analysis highlights many of the identified genes as potential drug targets.\nPooled CRISPR screens of genes regulated by eQTLs colocalizalising with risk variants was done in human aortic smooth muscle cells to reveal impact on proliferation (enrichment at day 28 vs d 5) and migration (transwell migration). Validation experiments confirmed changes for 4 out of 5 genes selected based on suggested to have pleiotropic effects and top ranked in the CRISPR screen.\n• Significance (grey dots in Figure 5A) in the CRISPR screen appears to be evaluated without adjusting for multiple testing, which is problematic. The screen was done in triplicate, but it is not clear to me whether cells from different donors were used for the three screen replicates and how the replicates were considered for ranking of genes.\n• For the CRISPR screen validation experiments, it is unclear how experimental replication is achieved; the legend to figure 5 states group sizes of 4 or 6, but methods section suggest that these are technical repeats. For experiments shown in supplementary data, there are varying group sizes and timings (e.g. SMARCA evaluated at up to 96h). Ideally cells from different donors should be used for each experiment for datapoints to be considered independent.\nFES, which showed an impact on migration and genetic association with CAD, was selected for further analysis. The group has previously shown increased atherosclerosis in Fes knockout (KO) animals (PMID: 36321446) and this was here confirmed used an alternative measurement in the same experimental atherosclerosis model. Blood pressure measurements in non-atherogenic mice suggest that Fes-KO impacts both base line pressures and the response to acetylcholine. Furthermore, genetic variants resulting in truncated FES protein was associated with elevated blood pressure and likelihood of hypertension, angina and myocardial infarction in human.\n• The group size for blood pressure analysis is low, with only 4 animals in the KO group. This is a concern as the pressure is measured under anaesthesia (isoflurane, which can cause peripheral vasoconstriction), which is more variable. The low animal numbers also preclude comparison of effects in male and females.\n• The analysis of human data is not stratified by sex or analysed for confounding factors\nMolecular changes of siRNA-mediated FES knockdown in cultured SMCs are recorded by RNA-seq and proteomics analysis. This reveals changes in pathways and specific proteins, including MMP1.\n• The proteomics and phospho-proteomics only provide uncorrected p-values, with little or no, experimental validation\nIt is not clear whether and how these changes are related to the in vivo phenotypes in mouse (or human traits), nor is the phenotype directly linked to an effect of FES on SMCs."}, "author_response": "Response to Reviewers’ Comments\nWe thank the reviewers for their highly valuable comments and constructive advice. In the revised\nmanuscript, the reviewers’ points have been addressed as detailed below. Reviewers’ comments are\nin bold font, followed by authors’ response in regular font.\nReviewer 1\nColocalization strategy\nFor eCAVIAR and SMR/HEIDI analyses, can the author create a locus zoom window using the\nGWAS Tag SNP by building 200 KB (as a starting point) around significant SNPs based on LD, then\nadding 100 or 200 kb left or right until no SNP is in LD?\nAs advised, we have presented a locus zoom plot (Supplementary Figure 12). The plot shows that the\nGWAS tag SNP associated with CAD and all significant SNPs in LD with the GWAS tag SNP are located\nwithin a 100kb interval.\nFes knockout model and sex bias in in-vivo experiments:\nSince in vitro FES gene analyses were conducted in VSMCs, unlike the in vivo mouse model with a\nwhole-body knockout, it is difficult to determine the cell type source of the effect, making it hard\nto draw conclusive results. Therefore, it would be beneficial to have VSMC specific KO of Fes and\nstudy atherosclerosis.\nWe appreciate the reviewer’s comment. However, the primary aim of our present study is not to\ntest a cell-type specific effect on atherosclerosis and blood pressure, but to identify causal genes at\nthe genomic loci that have been found to be associated with CAD, hypertension, stroke and/or AAA\nin GWAS. We have now also highlighted this in the Discussion as follows:\n“… as FES is expressed in not only VSMCs but also some other cell types such as monocytes and\nendothelial cells, the total atherogenic contribution of FES is expected to derive from its effect on\nthe various cell types, including VSMCs as suggested here and monocytes as indicated in our recent\nstudy28. The relative contributions from the different cell types are yet to be established.\nNotwithstanding, the primary aim of our present study is not to test a cell-type specific effect on\nvascular diseases, but to identify causal genes at the genomic loci that have been found to be\nassociated with CAD, hypertension, stroke and/or AAA in GWAS.” (page 10/paragraph 3/line 12-18).\nAdditionally, combining data from both males and females with a small sample size, especially in\nfemales, for the atherosclerosis study, is a concern and makes it hard to identify if there is a sex-\nspecific effect.\nWe thank the reviewer for the constructive comment. We have added a supplementary figure\nshowing the data from males and females separately. (Supplementary Figure 10)\nReviewer 2\nI appreciate that the authors addressed all my concerns.\nWe thank the reviewer for their time.\nReviewer 3\nReport\nReport focusing on the Fes knockout mouse experiments and the in-vitro validation data: The\ngroup has previously generated an eQTL catalogue for human umbilical cord smooth muscle cells\n1\n(SMC) and found co-localisation of SMC eQTLs with coronary artery disease (CAD)-associated risk\nloci. Here, they report overlap of hypertension, stroke and abdominal aortic aneurysm risk\nvariants with SMC eQTLs and identify variant shared for multiple conditions. Co-localisation\nanalysis and epigenetic profiling data is leveraged to identify candidate causal genes and\nassociated mechanisms. The analysis suggests that some genes are linked to multiple conditions\nsuggesting pleiotropic effects or common disease pathways for the conditions. The analysis suggests\nthat some genes are linked to multiple conditions suggesting pleiotropic effects or common disease\npathways for the conditions. Furthermore, database analysis highlights many of the identified\ngenes as potential drug targets.\nPooled CRISPR screens of genes regulated by eQTLs colocalizalising with risk variants was done in\nhuman aortic smooth muscle cells to reveal impact on proliferation (enrichment at day 28 vs d 5)\nand migration (transwell migration). Validation experiments confirmed changes for 4 out of 5\ngenes selected based on suggested to have pleiotropic effects and top ranked in the CRISPR\nscreen.\n• Significance (grey dots in Figure 5A) in the CRISPR screen appears to be evaluated without\nadjusting for multiple testing, which is problematic. The screen was done in triplicate, but it is not\nclear to me whether cells from different donors were used for the three screen replicates and how\nthe replicates were considered for ranking of genes.\nWe appreciate the reviewer’s comment. FDR-adjusted P-values for the CRISPR knockout screen\nresults are provided in Supplementary Tables 24 and 25. We would like to clarify that we performed\nthese experiments on all the candidate genes simultaneously under the same condition, with the\nintention to prioritize some of these genes for further study. The prioritization was based on ranking\nof the tested genes. The ranking (further described below) was provided by the MAGeCK algorithm\n(Li et al., Genome Biol 2014, PMID: 25476604) which we applied to analyze the pooled CRISPR\nknockout screen data from this study and has been widely used in many other studies for analyzing\npooled CRISPR knockout screen data.\nAs described in the Methods (page 16/paragraph 1/line 2), the pooled CRISPR knockout screening\nexperiments had 3 technical replicates. In these experiments, we included technical replicates\n(rather than biological replicates of cells from different donors), a strategy used in many other\nstudies in the literature where pooled CRISPR screen experiments were performed using a single cell\nline in a study (e.g. Shalem et al., Science 2014, PMID: 24336571; Wang et al., Science 2014, PMID:\n24336569; Konermann et al., Nature 2015, PMID: 25494202; Joung et al., Nat Protoc 2017, PMID:\n28333914).\nThe MAGeCK algorithm integrates all the replicates in its statistical framework for gene ranking. The\nanalysis begins by using the replicates to model the data with a Negative Binomial (NB) distribution\nand estimate the mean abundance, variance, and dispersion of sgRNA read counts. The algorithm\nuses the reads of non-targeting sgRNAs to normalize each replicate independently. The replicate-\nderived parameters are incorporated in the calculation of the log-fold change and P-value for each\nindividual sgRNA. Thereafter, gene-level ranking is determined by the Robust Rank Aggregation\n(RRA) algorithm, which assesses the data across replicates by checking whether a gene's multiple\nsgRNAs are consistently and reproducibly ranked among the most enriched or depleted hits. In\nsummary, replicates are not pooled but used independently to build a statistical reference for the\ninitial sgRNA ranking, which later feed into RRA for final gene ranking.\n• For the CRISPR screen validation experiments, it is unclear how experimental replication is\nachieved; the legend to figure 5 states group sizes of 4 or 6, but methods section suggest that\nthese are technical repeats.\nWe apologize for unclearness of the text in the original figure legend and the confusion caused. As\ndescribed in the Methods (page 17/paragraphs 1-4), they were technical replicates. We have now\nrevised the figure legend to state that they are technical replicates.\nSMARCA evaluated at up to 96h). Ideally cells from different donors should be used for each\nexperiment for datapoints to be considered independent.\nFor the prioritized genes, we used a different technique to validate the findings of the pooled CRISPR\nknockout screens. In the pooled CRISPR screens, each of the tested genes was knocked out which\nwas mediated by CRISPR/Cas single-guide RNAs, and all of the studied genes were tested\nconcurrently. In the validation experiments, each of the tested genes was knocked down by siRNA\nand each gene was tested individually. Thus, although the same source of cells was used in both the\npooled CRISPR screens and validation experiments, a different technique was used in the validation\nexperiment to assess the finding from the screen.\nFES, which showed an impact on migration and genetic association with CAD, was selected for\nfurther analysis. The group has previously shown increased atherosclerosis in Fes knockout (KO)\nanimals (PMID: 36321446) and this was here confirmed used an alternative measurement in the\nsame experimental atherosclerosis model. Blood pressure measurements in non-atherogenic mice\nsuggest that Fes-KO impacts both base line pressures and the response to acetylcholine.\nFurthermore, genetic variants resulting in truncated FES protein was associated with elevated\nblood pressure and likelihood of hypertension, angina and myocardial infarction in human.\n• The group size for blood pressure analysis is low, with only 4 animals in the KO group. This is a\nconcern as the pressure is measured under anaesthesia (isoflurane, which can cause peripheral\nvasoconstriction), which is more variable. The low animal numbers also preclude comparison of\neffects in male and females.\nWe appreciate the reviewer’s comment. We have added the following text in the paragraph\ndiscussing limitations of our study:\n“…, the experiments of Fes knockout mice were not intended and powered to test possible sex-\nspecific effects of Fes on atherosclerosis and blood pressure”. (page 10/paragraph 3/line 18-20).\n• The analysis of human data is not stratified by sex or analysed for confounding factors.\nThe human genetic results used in our study were obtained from the AstraZeneca PheWAS Portal,\nwhich are based on the UK biobank cohort. These analyses are performed using established\nregression models that already adjust for key factors including sex (Backman et al., Nature 2021,\nPMID: 34662886). The data we accessed are provided at the summary-statistic level, meaning that\nresults are already pre-adjusted and further sex-stratified analyses cannot be carried out directly.\nMolecular changes of siRNA-mediated FES knockdown in cultured SMCs are recorded by RNA-seq\nand proteomics analysis. This reveals changes in pathways and specific proteins, including MMP1.\n• The proteomics and phospho-proteomics only provide uncorrected p-values, with little or no,\nexperimental validation.\nWe appreciate the reviewer’s comment and have added Benjamini-Hochberg adjusted P-values in\nSupplementary Tables 26 & 29.\n• It is not clear whether and how these changes are related to the in vivo phenotypes in mouse (or\nhuman traits), nor is the phenotype directly linked to an effect of FES on SMCs.\nWe appreciate the reviewer’s comment. While clarifying these points requires much further\nresearch, in this manuscript, we leverage these in vitro experimental results to help generate a\nmechanistic hypothesis.\n3"}, {"round": 3, "reviewer_comments": {"reviewer_1": "The authors addressed all my concerns.", "reviewer_3": "The revised manuscript have responded to most of the queries raised in my report, but the critique regarding group sizes for the in vivo analysis is not addressed (listed below). The new information about replication and the appropriate statistical analysis raise some concerns regarding the robustness and reproducibility about the findings from the experimental analysis. The revised manuscript state that the study does not aim to test whether the effects in VSMCs account for detected risk heritability, but to add a mechanistic hypothesis.\nAdditions to the revised manuscript\n1. Appropriate statistical analysis (adjusted p-values) has been included for the crisp screen.\nAdjusted p-values suggest that most observed changes are unlikely to be due to actual biological differences.While FES scores 2nd in the migration screen, the adjusted p-value is 0.92\n2. The confusion regarding the nature of the replication has been resolved.\nThis reveals that all CRISPR screen replicates and the follow up experiments were done in cells from a single donor. These two sets of experiments were done using different loss-of-function technologies. However, using a single genetic background means that the robustness of the observation is not tested.\n3. The authors clarify that comparison between males and females is not intended. However, this does not address the concern regarding small group sizes\n4. Concern regarding confounding factors has been appropriately addressed\n5. Appropriate statistical analysis (adjusted p-values) has been included for the proteomics analysis. This reveals that most observed changes are unlikely to be due to actual biological differences, which does not justify the statement that “The phosphoproteomics analysis detected many differentially phosphorylated proteins (Supplementary Table 26)…\". However, the text has been revised to tone down the conclusions based on this data.\n6. The discussion states that the manuscript does not intend to assess cell-type specificity."}, "author_response": "Response to Reviewers’ Comments\nWe thank the reviewers for their highly valuable comments and constructive advice. In the revised\nmanuscript, the reviewers’ points have been addressed as detailed below. Reviewers’ comments are\nin bold font, followed by authors’ response in regular font.\nReviewer 1\nThe authors addressed all my concerns.\nWe thank the reviewer for their time.\nReviewer 3\nThe revised manuscript have responded to most of the queries raised in my report, but the\ncritique regarding group sizes for the in vivo analysis is not addressed (listed below).\nWe appreciate the reviewer’s comment. In the revised manuscript, we have added the following text\nto explicitly acknowledge this limitation:\n“Third, the in vivo experiments of Fes knockout were performed on small numbers of mice and were\nnot powered to test possible sex-specific effects of Fes on atherosclerosis and blood pressure.” (page\n10, paragraph 3, lines 18-20)\nThe new information about replication and the appropriate statistical analysis raise some\nconcerns regarding the robustness and reproducibility about the findings from the experimental\nanalysis.\nWe appreciate the reviewer’s comment. In the revised manuscript, we have added the following\ntext:\n“Fourth, the CRISPR knockout screens and follow-up experiments were carried out on VSMCs from a\nsingle donor and so could not account for potential inter-individual biological variations. Therefore,\nthe findings from these experiments are exploratory and for hypothesis generation, rather than\ndefinitive.” (page 10, paragraph 3, lines 20-23)\nThe revised manuscript state that the study does not aim to test whether the effects in VSMCs\naccount for detected risk heritability, but to add a mechanistic hypothesis.\nWe understand that the reviewer considers this statement appropriate.\nAdditions to the revised manuscript\n1. Appropriate statistical analysis (adjusted p-values) has been included for the crisp screen.\nAdjusted p-values suggest that most observed changes are unlikely to be due to actual\nbiological differences. While FES scores 2nd in the migration screen, the adjusted p-value is\n0.92.\nWe have modified the original text “The screens identified multiple genes that affected VSMC\nproliferation or migratory ability (Supplementary Table 24&25, and Fig. 5A&B).” which now reads\n“The results of these screens suggested that some of these genes might influence VSMC\nproliferation or migratory ability (Supplementary Table 24&25, and Fig. 5A&B).” (page 6, paragraph\n3, lines 3-5)\n2. The confusion regarding the nature of the replication has been resolved.\nThis reveals that all CRISPR screen replicates and the follow up experiments were done in\n1\ncells from a single donor. These two sets of experiments were done using different loss-of-\nfunction technologies. However, using a single genetic background means that the robustness\nof the observation is not tested.\nWe have added the following text to acknowledge this limitation:\n“Fourth, the CRISPR knockout screens and follow-up experiments were carried out on VSMCs from a\nsingle donor and so could not account for potential inter-individual biological variations. Therefore,\nthe findings from these experiments are exploratory and for hypothesis generation, rather than\ndefinitive.” (page 10, paragraph 3, lines 20-23)\n3. The authors clarify that comparison between males and females is not intended. However,\nthis does not address the concern regarding small group sizes.\nWe have added the following text to acknowledge this limitation:\n“Third, the in vivo experiments of Fes knockout were performed on small numbers of mice and were\nnot powered to test possible sex-specific effects of Fes on atherosclerosis and blood pressure.” (page\n10, paragraph 3, lines 18-20)\n4. Concern regarding confounding factors has been appropriately addressed.\n5. Appropriate statistical analysis (adjusted p-values) has been included for the proteomics\nanalysis. This reveals that most observed changes are unlikely to be due to actual biological\ndifferences, which does not justify the statement that “The phosphoproteomics analysis\ndetected many differentially phosphorylated proteins (Supplementary Table 26)…\". However,\nthe text has been revised to tone down the conclusions based on this data.\nThe original text “The phosphoproteomics analysis detected many differentially phosphorylated\nproteins (Supplementary Table 26) and a pathway analysis of these proteins suggested that they\nwere enriched in several pathways including the epidermal growth factor (EGF)-, VEGF-, and CCKR\n(cholecystokinin A receptor)-signaling pathways (Fig. 6A). The RNA-seq and quantitative proteomics\nanalyses indicated that FES knockdown led to changes in the production of multiple genes/proteins,\nincluding increased expression of the extracellular matrix protein-degrading enzymes matrix\nmetalloproteinase-1 (MMP1) and MMP3 (Fig. 6B&C, Supplementary Table 27-29).” have been\nrevised and now reads “The phosphoproteomics analysis identified a number of proteins whose\nphosphorylation status might be influenced by FES knockdown (Supplementary Table 26) and a\npathway analysis of these proteins suggested that they were enriched in several pathways including\nthe epidermal growth factor (EGF)-, VEGF-, and CCKR (cholecystokinin A receptor)-signaling\npathways (Fig. 6A). The RNA-seq and quantitative proteomics analyses indicated that FES knockdown\ncould potentially influence the production of a number of genes/proteins, including the extracellular\nmatrix protein-degrading enzymes matrix metalloproteinase-1 (MMP1) and MMP3 (Fig. 6B&C,\nSupplementary Table 27-29).” (page 7, paragraph 1, lines 5-12)\nAdditionally, we have added the following text in the limitations paragraph in the Discussion:\n“Therefore, the findings from these experiments are exploratory and for hypothesis generation,\nrather than definitive.” (page 10, paragraph 3, lines 22-23)\n6. The discussion states that the manuscript does not intend to assess cell-type specificity.\nWe understand that the reviewer considers this statement appropriate.\n2"}, {"round": 4, "reviewer_comments": {"reviewer_3": "I have no further comments"}, "author_response": ""}]

Decision letter