Genome-wide association meta-analysis identifies 126 novel loci for diverticular disease and implicates connective tissue and colonic motility

Genome-wide association meta-analysis identifies 126 novel loci for diverticular disease and implicates connective tissue and colonic motility | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Genome-wide association meta-analysis identifies 126 novel loci for diverticular disease and implicates connective tissue and colonic motility Christopher J. Neylan , View ORCID Profile Michael G. Levin , Katherine Hartmann , Katherine Beigel , Sam Khodursky , View ORCID Profile John S. DePaolo , Sarah Abramowitz , Emma E. Furth , Robert O. Heuckeroth , View ORCID Profile Scott M. Damrauer , Lillias H. Maguire doi: https://doi.org/10.1101/2025.03.27.25324777 Christopher J. Neylan 1 Department of Surgery, Perelman School of Medicine, University of Pennsylvania , Philadelphia, PA 19104 M.D. Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: Christopher.neylan{at}pennmedicine.upenn.edu Michael G. Levin 2 Cardiovascular Institute, Department of Medicine, Perelman School of Medicine, University of Pennsylvania , Philadelphia, PA 19104 3 Corporal Michael Crescenz VA Medical Center , Philadelphia, PA 19104 M.D. Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Michael G. Levin Katherine Hartmann 4 Department of Radiology, Perelman School of Medicine, University of Pennsylvania , Philadelphia, PA 19104 M.D. Ph.D. Find this author on Google Scholar Find this author on PubMed Search for this author on this site Katherine Beigel 5 Department of Biomedical and Health Informatics (DBHi), Children’s Hospital of Philadelphia , Philadelphia, PA 19104 M.S. Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sam Khodursky 1 Department of Surgery, Perelman School of Medicine, University of Pennsylvania , Philadelphia, PA 19104 Ph.D. Find this author on Google Scholar Find this author on PubMed Search for this author on this site John S. DePaolo 1 Department of Surgery, Perelman School of Medicine, University of Pennsylvania , Philadelphia, PA 19104 M.D. Ph.D. Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for John S. DePaolo Sarah Abramowitz 1 Department of Surgery, Perelman School of Medicine, University of Pennsylvania , Philadelphia, PA 19104 6 The Donald and Barbara Zucker School of Medicine at Hofstra/Northwell , Hempstead, NY 11549 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Emma E. Furth 7 Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania , Philadelphia, PA, 19104 M.D. Find this author on Google Scholar Find this author on PubMed Search for this author on this site Robert O. Heuckeroth 8 Division of Gastroenterology, Hepatology and Nutrition, Children’s Hospital of Philadelphia, 3401 Civic Center Blvd , Philadelphia, PA 19104 9 The Children’s Hospital of Philadelphia Research Institute and Abramson Research Center, 3615 Civic Center Blvd , Philadelphia, PA 19104, USA 10 Perelman School of Medicine at the University of Pennsylvania, 3400 Civic Center Boulevard , Philadelphia, PA 19104 M.D. Ph.D. Find this author on Google Scholar Find this author on PubMed Search for this author on this site Scott M. Damrauer 1 Department of Surgery, Perelman School of Medicine, University of Pennsylvania , Philadelphia, PA 19104 2 Cardiovascular Institute, Department of Medicine, Perelman School of Medicine, University of Pennsylvania , Philadelphia, PA 19104 3 Corporal Michael Crescenz VA Medical Center , Philadelphia, PA 19104 11 Department of Genetics, Perelman School of Medicine, University of Pennsylvania , Philadelphia, PA 19104 M.D. Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Scott M. Damrauer Lillias H. Maguire 3 Corporal Michael Crescenz VA Medical Center , Philadelphia, PA 19104 12 Division of Colon and Rectal Surgery, Department of Surgery, Perelman School of Medicine, University of Pennsylvania , Philadelphia, PA 19104 M.D. Find this author on Google Scholar Find this author on PubMed Search for this author on this site Abstract Full Text Info/History Metrics Data/Code Preview PDF ABSTRACT Diverticular disease is a common and morbid complex phenotype influenced by both innate and environmental risk factors. We performed the largest genome-wide association study meta-analysis for diverticular disease, identifying 126 novel loci. Employing multiple downstream analytic strategies, including tissue and pathway enrichment, statistical fine-mapping, allele-specific expression, protein quantitative trait loci and drug-target investigations, and linkage disequilibrium score regression, we prioritized causal genes and produced several lines of evidence linking diverticular disease to connective tissue biology and colonic motility. We substantiated these findings by integrating single-cell RNA sequencing data, showing that prioritized diverticular disease-associated genes are enriched for expression in colonic smooth muscle, fibroblasts, and interstitial cells of Cajal. In quantitative analysis of surgical specimens, we found a substantial reduction in the density of elastin present in the sigmoid colon in severe diverticulitis. INTRODUCTION Diverticula are protrusions of the mucosa and submucosa through the muscularis propria in the human colon. Diverticulosis (the asymptomatic presence of diverticula) occurs in the sigmoid colon of over 60% of Americans by the age of 60 years. 1 – 6 Diverticulitis, the inflammation of a diverticular colon, occurs in a minority of patients with diverticulosis and manifests with symptoms ranging from mild abdominal pain to fatal bowel perforation. Diverticulitis recurs after the first episode in 20-30% of people and can have sequela such as fistulization and abscess. Due to the ubiquity of the precursor lesion, the burden of disease caused by diverticulitis is high: in aggregate, diverticulitis is annually responsible for nearly 300,00 inpatient admissions and $2 billion of direct costs in the United States. 7 – 13 Diverticulitis has historically been understood as an acquired disease attributable to environmental risk factors. 14 Generations of healthcare providers attributed diverticulitis to constipation and food (e.g. nuts and seeds) lodging in a diverticulum leading to inflammation and physical disruption of the colon. 15 While environmental factors are important, evidence now suggests that these hypotheses are overly simplistic, and a more thorough understanding of the etiology of diverticular disease (DD) is greatly needed. 16 – 21 Genetic liability to diverticular disease (an umbrella term for diverticulosis and diverticulitis) has recently been demonstrated. Twin studies indicate 40-53% heritability. Several genome wide association studies (GWASs) have been performed. 22 – 26 The largest study to date included over 724,000 individuals and identified 150 loci associated with DD. 25 , 27 , 28 However, in these studies, most participants were genetically similar to the European reference population. 25 , 26 In the current study, we 1) perform the largest and most diverse GWAS meta-analysis for diverticular disease to date identifying 126 novel loci and 159 novel genes, 2) prioritize causal genes, pathways, and involved proteins, including protein targets for drug repurposing, 3) integrate single cell RNA sequencing data to localize gene expression to cell types, 4) investigate the potentially causal role of the gut microbiome with Mendelian randomization, and 5) analyze the extracellular matrix in surgical specimens of patients with diverticulitis. Multiple lines of evidence in our study indicate that DD may best be understood as a complex phenotype strongly linked to structural factors (e.g. connective tissue such as elastin, as well as smooth muscle) and colonic motility. METHODS GWAS meta-analysis To identify genetic variants associated with diverticular disease, we performed a meta-analysis of two publicly available genome-wide association studies (GWASs) conducted in separate discovery cohorts (the Million Veteran Program (MVP) and the United Kingdom Biobank (UKB)) ( Figure 1 ). 29 , 30 The Department of Veterans Affairs (VA) MVP is a longitudinal health cohort that has enrolled > 1 million Veteran volunteers. It utilizes a harmonized ancestry and race/ethnicity (HARE) approach to integrate self-identified racial background information with genetic data. 29 , 31 The MVP-HARE cohort consisted of 638,797 participants: 457,880 in the European American population, 121,703 in the African American population, 51,153 in the Hispanic/Latino American population, and 8,061 in the Asian American population. 29 , 31 The UKB is a collection of >500,000 genotyped individuals that links genetic and phenotypic information in a large-scale biomedical database, and the Pan-UKB is an initiative that performs pan-ancestry genetic analysis within the UKB. 32 Genetic similarity to reference populations was determined in the source GWAS and is described elsewhere. 30 The Pan-UKB cohort consisted of 381,541 individuals in the following populations: 365,887 European (EUR), 7,587 Central or south Asian (CSA), 5,835 African (AFR), 1,370 middle eastern (MID), and 862 admixed American (AMR). 33 Population-specific meta-analyses of the MVP-HARE and Pan-UKB cohorts were performed for each of the above populations (data not shown) in addition to a meta-analysis of the overall cohort. MVP-HARE populations were mapped to Pan-UKB populations in the following manner: European American to EUR, African American to AFR, Hispanic/Latino to AMR, and Asian American to CSA. In each source GWAS, diverticular disease was identified by phecode 562, a parent code for a group of nine International Classification of Disease (ICD)-10 codes and nineteen ICD-9 codes specific to diverticulosis and diverticulitis. 34 Download figure Open in new tab Figure 1. Study overview. a, Flowchart of GWAS and gene prioritization. b, Manhattan plot demonstrates results of GWAS meta-analysis. Quality control was performed on each set of summary statistics to remove variants with poor imputation scores ( r 2 1 or < 0). Meta-analysis was performed with a fixed-effects, inverse-variance weighted model using METAL. 35 Meta-analyzed data was then quality-controlled and cleaned using GWASinspector (Supplemental Figure 1). 36 SNPs with a minor allele frequency (MAF) < 0.001 were discarded, strand flips were corrected, and variants were removed if they were missing crucial values, duplicated, or ambiguous. Definition of loci and secondary signals From the meta-analyzed overall cohort, loci were defined around genome-wide significant variants (p < 5 x 10 - 8 ) via local clumping with a 500kb window and an r 2 threshold of 0.01. A single lead variant (or index variant) was retained per locus (Supplemental Table 1). To identify additional, independently significant variants within a locus (secondary signals), we performed statistical fine-mapping using CARMA. 37 A fine-mapped variant was considered a secondary signal if it fell within a defined credible set and had the highest PIP within its credible set. Index variants and secondary signals were then carried forward for further analysis. Loci were additionally defined in each population-specific meta-analysis (EUR, CSA, AFR, MID, AMR). Gene mapping and prioritization An ensemble approach considering several methods was used to map each signal to a gene. First, the nearest gene to the signal was identified using gwasRtools package in R. 38 Next, MAGMA was run using the magmaR package to identify a gene associated with the signal based on the 1000 Genomes Project Phase 3 EUR imputation panel. 39 Third, OpenTargets annotations were analyzed to determine whether the signal lies in a known coding region or regulatory region for a gene. 40 Finally, multi-trait colocalization was performed using HyPrColoc to identify colocalization between the signals and eQTLs in the eQTLGen Phase I cis-eQTL blood data. 41 All genes mapped to each signal by these methods were then tabulated and a single gene was prioritized for each signal based on a process that prioritized coding variants over non-coding variants, and gave preference to genes identified by multiple methods at the same signal (Supplemental Table 2). Enrichment analysis The list of genes to which signals were mapped was then subjected to a suite of enrichment analyses to gain insight into the biological function of the genes. First, utilizing Enrichr, the set genes was analyzed for enrichment against numerous gene set libraries (e.g. the Gene Ontology Cellular Component dataset) ( Figure 2a , b). 42 – 44 Then, FUMA ( GENE2FUNC feature) was used to analyze tissue expression with the following parameters: all background genes, GTEx v8 tissue types (excluding MHC region), FDR multiple test correction (adjusted p < 0.05) ( Figure 2c ). 45 Download figure Open in new tab Figure 2. Enrichment analysis. Prioritized gene enrichment in a, Reactome Pathway (2024), b, GO Cellular Component (2023), and c, GTEx (v8) tissues. In volcano plots (a and b), each dot represents a term from target gene set in which the prioritized genes were enriched; blue dots are significant (p-value < 0.05); ECM = extracellular matrix. In c, significance is determined by adjusted p-value < 0.05. Linkage Disequilibrium Score Regression (LDSC) LDSC was used to estimate the heritability of diverticular disease and genetic correlation with select phenotypes. 36 LD scores derived from the 1000 Genomes Project European samples were utilized. SNP heritability was transformed to the liability scale using a sample prevalence of 12.3% (the calculated prevalence in our data) and a population prevalence of 30% (the approximated epidemiologic prevalence of disease). 46 To account for the potential inflationary effect of assumed population prevalence on liability-transformed SNP heritability, we also report the unadjusted, observed h 2 as well as the unbiased h 2 -Z score. Publicly available summary statistics were used for phenotypes other than diverticular disease, and sources are listed in Supplemental Table 3. Microbiome Mendelian randomization (MR) To investigate whether a potentially causal relationship between the microbiome and diverticular disease exists, we performed univariable two-sample MR and bidirectional MR, using the TwoSampleMR package (v. 0.6.8) in R. 47 Inverse variance weighting was used for MR estimation in cases where the instrument comprised > 1 SNP; otherwise Wald ratios were utilized. Genetic instruments for microbiome exposures were taken from a GWAS (listed in Supplemental Table 3) of host genetic variation effects on gut microbiome composition among 18,340 participants, which revealed significant associations between host genetics and 385 bacterial genera. 48 The outcome of diverticular disease was instrumented using variants discovered in our full cohort GWAS meta-analysis. The two sets of summary statistics were harmonized prior to running MR, and variants with missing data were excluded. We filtered variants in both sets of summary statistics to a p-value threshold of p < 5 x 10 - 6 , with an r 2 threshold of 0.001. This p-value threshold is often used in Mendelian randomization and fulfills the instrumental variable assumption of relevance. 49 , 50 This MR analysis meets the three core instrumental variable assumptions of relevance, independence, and exclusion. Sensitivity analysis was performed for all exposure instruments via leave-one-out analysis. Allele-specific expression At heterozygous DNA positions, allele-specific expression can quantify the RNA expression of one allele relative to the other and thereby interrogate cis- regulatory variation. 51 We analyzed allele-specific expression at each independent index variant and secondary signal across all GTEx (v8) dbGaP tissue types, filtering to variant-person-tissue combinations with at least five reference and five alternate alleles. Protein drug targets We performed a proteome-wide plasma Mendelian randomization screen to identify proteins with putatively causal effects on the development of diverticular disease, which could serve as targets for the repurposing of existing drugs. First, we identified genetic variants associated with plasma protein levels (protein quantitative trait loci, or pQTLs) in two large-scale plasma-proteomics studies (deCODE and UK Biobank Pharma Proteomics Project, hereafter UKB-PPP). 52 , 53 Then we performed Mendelian randomization using pQTLs as instrumental variables and our diverticular disease GWAS meta-analysis summary statistics to instrument the outcome of diverticular disease. Instrumental variables were selected if they met the following criteria: genome-wide significance (p < 5 x 10 - 8 ), maximum linkage disequilibrium r 2 of 0.01, and cis variant within a 500kb window of the respective gene. Wald ratios were used for instruments comprised of a single SNP, and inverse-variance weighted MR was used otherwise. Proteins were considered significant if the adjusted p-value was < 0.05, according to the Benjamini-Hochberg correction to maintain FDR < 0.05. Using a previously-generated list of 1,263 actionable targets, defined as proteins targeted by approved drugs or drugs in the clinical phase of development, we then identified actionable targets within our own results. 54 To understand the biological implication of the significant proteins, we performed enrichment analysis of the identified proteins in the Reactome pathway database. Immunohistochemistry Formalin-fixed, paraffin embedded tissue blocks from surgical resection of sigmoid colons were obtained from the Pathology Department of the Hospital of the University of Pennsylvania. Patient data were reviewed in the electronic medical record (chart review) to classify the indication for surgery as incidental (sigmoidectomy for reasons unrelated to diverticular disease, such as iatrogenic injury to the colon), elective quiescent (patient with recurrent diverticulitis, but asymptomatic at the time of surgery), acutely symptomatic (patient with symptomatic diverticulitis at the time of surgery), colonic fistulization, or emergent colonic perforation. These indications were then collapsed into a variable termed ‘Patient Classification’ which took the values of ‘No disease’ for an incidental indication in which the patient did not have diverticular disease, ‘Diverticulosis’ for an incidental indication in which the patient had diverticulosis but not diverticulitis, ‘Emergent diverticulitis’ for an indication of emergent colonic perforation, or ‘Non-emergent diverticulitis’ for any of the following indications: acutely symptomatic diverticulitis, elective quiescent diverticulitis, or colonic fistulization. Patients with known connective tissue disorders or confounding colonic pathology (e.g. Crohn’s disease or ulcerative colitis) were excluded. A subset of patients with an incidental indication were undergoing colectomy for colon cancer and did not have DD; these were used as controls. For those with DD, slides were prepared from tissue samples taken from areas of grossly apparent disease and from the margins of the surgical specimen (typically the area, in the estimation of the operating surgeon, furthest from disease). All slides from patients without DD were considered normal tissue. Stains for elastin, the smooth muscle marker MYH11, and the collagen stain Sirius Red were applied using the protocols given in Supplemental Figure 2. After staining, to quantify the amount of elastin and collagen present, stained sections were imaged at ×40 magnification using Hamamatsu NanoZoomerS360 to generate whole slide digital images, which were uploaded to QuPath 5.1 for analyses. 55 We developed pixel classifiers for the elastin and collagen stains through several rounds of annotation and training using the Random Forests algorithm from a compiled region image set from the cohort. This classifier was then applied to scanned images of the annotated inner and outer muscularis propria for each section to determine the percentage of elastin and collagen per square millimeter (mm) of muscularis propria in the region adjacent to the diverticulum. The color intensity per square mm was derived by running the “Compute intensity features” application and choosing the diaminobenzidine (color deconvoluted) channel. The average percent of elastin and collagen per square mm of muscularis propria on each slide was calculated by averaging the percentage of elastin or collagen per square mm of inner and outer muscularis propria layers. A multivariable linear regression was then constructed with average elastin as the dependent variable and patient classification, age, and sex as the independent variables. Single cell RNAseq analysis Cell-level gene expression was ascertained from single cell RNA-sequencing (RNAseq) of the human colon in two previously published datasets. 56 , 57 Hickey et al. (2023) used the 10x Genomics platform to process samples surgically removed from sites throughout the human small and large intestine (see Supplemental Table 3 for data availability of both studies). In this dataset, we restricted our analysis to samples from the sigmoid colon and re-annotated “Myofibroblasts/SM 1,” “Myofibroblasts/SM 2,” and “Myofibroblasts/SM 3” as ‘Myofibroblasts’ for our analyses. 56 Wright et al. (2021) was produced from manual dissection of the human colonic myenteric plexus, resulting in RNA sequencing of 3798 glia, 5568 smooth muscle cells, 377 interstitial cells of Cajal, and 2153 macrophages. 57 In both datasets, we employed the single-cell disease-relevance score (scDRS) method to evaluate expression of our GWAS-associated genes in individual cells. scDRS is an analytic method that identifies differential expression of GWAS-implicated genes at single cell level. 58 We ran scDRS on each published dataset with our list of GWAS-identified genes. scDRS was run using the python (v3.10) package scdrs (v1.0.2). To determine whether GWAS-identified genes are expressed in specific cell types, for the Wright et al. and the Hickey et al. datasets, we applied k-means clustering and generated clustered dot plots using the R package scCustomize. 59 The k-means clustering and relative expression were used to assess patterns of gene expression, and we identified specific k-means clusters of genes that were more predominantly expressed in specific cell types. We visually identified clusters of genes with enriched expression in any cell type noted to be significant in scDRS, and then generated heatmaps of gene expression for all genes that comprised the relevant clusters. We finally performed pathway analysis of the genes the comprised the relevant clusters, using Enrichr. RESULTS GWAS meta-analysis identified 236 loci The meta-analysis consisted of 934,030 individuals in the combined MVP and UKB cohorts, of whom 114,806 (12%) had diverticular disease and 819,224 (88%) did not ( Figure 1 ). In the overall cohort, 763,970 (82%) individuals were genetically similar to the European reference population (EUR); 114,236 (12%) to the African reference population (AFR); 46,867 (5.0%) to the admixed American population (AMR); 7,587 (0.81%) to the Central/South Asian (CSA) population; and 1,370 (0.15%) to the Middle Eastern (MID) population. We obtained single-variant association statistics for a total of 45,061,282 SNPs, of which 99.8% passed GWASinspector variant processing, with λ=1.16 (Supplemental Figure 1). We identified 236 loci defined around index variants that were genome-wide significant (p < 5 x 10 - 8 ) for their association with diverticular disease, of which 126 (53%) were novel (Supplemental Table 1). Statistical fine-mapping identified an additional 105 secondary signals, for a total of 341 independent signals (Supplemental Table 2). Population-specific meta-analyses revealed 22 loci present in the overall cohort meta-analysis that were not present in the EUR population meta-analysis. However, all loci present in a non-EUR population were also present in the EUR population (i.e. there were no loci unique to non-EUR populations). Gene mapping and biological function Of 341 independent signals, gene mapping was successful for 309; several signals mapped to the same gene, and ultimately the 341 signals mapped to 214 distinct genes. Of the 214 genes, 7 (3%) were implicated in human disease by damaging coding variants (missense or stop-gain), while 20 (9%) were prioritized by coding or well-annotated regulatory variants (Supplemental Table 2). Interrogation of the Reactome (2024) pathway database showed the prioritized genes were significantly enriched in “extracellular matrix organization” and “elastic fiber formation”, as well as “disorders of nervous system development” ( Figure 2a ), with similar results seen in gene ontology analysis ( Figure 2b ). Specific mapped genes involved in extracellular matrix remodeling include ELN , LTBP4 , LOXL1, COL family and ADAM family genes, while examples of mapped genes involved in smooth muscle function, which implicates both structure and motility, included ANO1, RYR2, and VIPR1 . GTEx (v8) analysis performed using FUMA revealed significant enrichment of gene expression for mapped DD-associated genes in visceral smooth muscle containing organs (e.g. sigmoid colon, transverse colon, esophagus, bladder, uterus), as well as to arteries, the tibial nerve and visceral adipose ( Figure 2c ). Linkage Disequilibrium Score Regression The liability-scale SNP heritability ( h 2 SNP ) was 0.33 for diverticular disease. The unadjusted observed h 2 was 0.10, while the h 2 -Z score was 15.7. Moderate genetic correlation was observed with connective tissue phenotypes such as hernia and pelvic organ prolapse, weak genetic correlation with abdominal aortic aneurysm (AAA), and negligible correlation with ulcerative colitis and Crohn’s disease ( Figure 3 ). Download figure Open in new tab Figure 3. LD score regression shows genetic correlation between diverticular disease and other putative connective tissue disease phenotypes. Microbiome Mendelian randomization Dietary factors, such as low fiber intake or high red meat consumption, are established risk factors for diverticulitis, but it is unclear whether these associations are causative or correlative. 17 – 19 , 60 Additionally, recent evidence suggested that microbiome composition may be implicated in the development of diverticular disease. 20 , 21 We hypothesized that dietary factors may mediate diverticulitis risk through changes in the microbiome, and utilized Mendelian randomization to elucidate the relationship between microbiome composition and diverticular disease based on a GWAS that links host genetic variants to specific gut microbes. Genetically predicted increases in the abundances of the phylum Actinobacteria, the genera Desulfovibrio and Intestinibacter , and several other taxa in the gut microbiome were associated with decreased genetic liability to diverticular disease, while genetically predicted increases in the genera Flavonifractor, Lachnoclostridium , Butyricimonas , and other taxa were associated with increased liability ( Figure 4 ). Significant effects were not seen in the reverse direction, suggesting a causal relationship between bacterial abundance and diverticular disease. The most intriguing result came from Ruminococcus , which was associated with both increased and decreased liability to diverticular disease depending upon the particular SNPs used to instrument the exposure. A possible explanation is that Ruminococcus exerts a species-specific effect, whereby some Ruminococcus species increase the risk of diverticular disease while others are protective against it. The putative Ruminococcus -mediated increase in DD risk may be attributable to Ruminoccocus gnavus, which produces a pro-inflammatory polysaccharide that is hypothesized to change the intraluminal milieu in a way that increases susceptibility to diverticular disease. 61 Indeed, several reports in the literature confirm this disease-promoting effect of Ruminococcus gnavus and even demonstrate decreased Ruminococcus gnavus in response to the initiation of a high-fiber diet. 60 – 62 Taken together, these data suggest the possibility that the protective effect of a high-fiber diet is mediated through a decrease in Ruminococcus gnavus . Our study provides only limited evidence for this hypothesis, and the protective effect of Ruminococcus that is simultaneously seen in MR is not concordant, though this result may be confounded by pleiotropy in addition to multiple Ruminococcus species. Download figure Open in new tab Figure 4. Microbiome Mendelian randomization (MR) results. In all cases, inverse variance weighted MR was performed for the outcome of diverticular disease. Odds ratio > 1 indicates association with increased diverticular disease liability. Allele specific expression At heterozygous DNA sites in diploid individuals, equal expression of haplotypes is expected except in cases of cis- regulatory variation (e.g. expression or splicing quantitative trait loci) and certain other rare regulatory phenomena (e.g. nonsense-mediated decay or parent-of-origin epigenetic imprinting). 44 , 63 Using RNA sequencing data, the quantity of RNA representing each nucleotide can be measured to detect allele-specific expression, which thus implies a functional regulatory variant. 64 We investigated allele specific expression data available in GTEx RNA sequencing data and found data for 64 (19%) of 341 SNPs representing independent GWAS signals (most variants identified by GWAS are non-coding and thus not well-captured by RNA sequencing data). One variant, rs34093919 which is a missense variant mapped to LTBP4, demonstrated clear patterns of imbalanced expression ( Figure 5 ) in the sigmoid colon of all observed individuals. The dramatically imbalanced expression is suggestive of (likely cis -) regulatory variation, providing evidence that this variant or those in near perfect linkage disequilibrium are functional regulatory variants of LTBP4 . An additional SNP, rs1705003, which is a missense variant mapped to CUTA , demonstrated imbalanced expression in only some individuals suggesting that the variant itself is not functional but rather serves as a marker of a functional variant(s) in the locus. That we have specific evidence of this imbalanced expression in the colon is consistent with a potential role of these genes in the development of diverticular disease. Download figure Open in new tab Figure 5. Allele specific expression in GTEx of two genetic variants ( a and b ) across multiple tissues. Each bar in the graph shows expression in a single individual. Y-axis reveals the relative abundance of expression (range -0.5, 0.5) where |0.5| demonstrates exclusive expression of one allele. Protein drug targets Proteome-wide Mendelian randomization analysis using deCODE and UKB-PPP pQTLs to link diverticular disease GWAS associations to serum protein levels identified a total of 157 distinct proteins significantly associated with diverticular disease ( Figure 6 ), from a possible 1703 proteins in deCODE and 1963 proteins in UKB-PPP (see Supplemental Tables 4 and 5 for full results). Many of these proteins whose abundance in plasma is associated with diverticular disease-associated SNPs directly overlapped with our independently mapped genes in GWAS analysis, including ELN, LTBP4, COL15A1 , and COL6A1 , while other proteins corresponded to the same sub-families as mapped genes such as LOXL1/LOXL3 and SEMA3G/SEMA3D/SEMA3E/SEMA3F . Analysis of the 157 proteins using the Reactome 2024 database (via Enrichr) revealed enrichment in the extracellular matrix proteins and elastin-forming processes, mirroring the enrichment analysis for our mapped genes (Supplemental Figure 3). It should be noted that this plasma MR experiment identifies circulating proteins, not proteins measured in colonic tissue. However, extracellular matrix proteins are often released into circulation during disease states, making a segment of colon with diverticulitis a plausible source of the observed proteins. 65 Moreover, even if released into circulation from other tissues, the observed plasma levels may reflect a global process that would also occur in colonic tissue. Download figure Open in new tab Figure 6. pQTL Mendelian randomization identifies 157 proteins associated with diverticular disease in the a, deCODE and b, UKB-PPP databases. MR effect estimate is the natural logarithm of the odds ratio. Positive beta means increase serum protein abundance is positively associated with diverticular disease. Of the 157 proteins found to associate with diverticular disease, 21 (13.4%) were known drug targets, including ELN and TNF products ( Figure 6 ). These provide the possibility of actionable targets, allowing the repurposing of existing drugs against these gene products. For example, the mTOR inhibitor everolimus improves in vitro smooth muscle cell differentiation of stem cells derived from individuals with elastin insufficiency because of ELN mutations. 66 Our results suggest that targeting DD with everolimus is a compelling topic of future research. Immunohistochemistry To further define anatomic findings in diverticular disease, assess the plausibility of links to elastin, and appreciate differences between individuals with diverticulitis relative to individuals with diverticulosis, we analyzed colon surgical resections from 24 patients (78 slides total). Of the 24 people, 13 (54%) has diverticulitis, 5 (21%) had diverticulosis without diverticulitis, 6 (25%) were without diverticular disease. Among the 13 individuals with diverticulitis, 3 (23%) had life-threatening diverticulitis requiring emergency surgery, while 10 required surgery but disease was not life-threatening and surgery timing was elective. A representative MYH11 antibody-stained slide from a non-emergency diverticulitis patient demonstrates severe attenuation of the muscularis propria overlying the diverticulum ( Figure 7 ). The slide does not depict the diverticulitis-causing (i.e. perforated) diverticulum, but rather an asymptomatic diverticulum in the region resected. Because the observed diverticulum is not at the exact site of perforation, it is less likely that attenuated muscularis propria is secondary to the perforation (or its associated inflammation), and more likely that it was present in the tissue prior to the perforation, raising the possibility that this muscular change is present in many regions of the sigmoid colon of the sampled patient. Download figure Open in new tab Figure 7. MYH11 antibody staining (brown) of colonic tissue resected during surgery from a patient with diverticular disease, demonstrating thinning of the muscularis propria overlying the diverticulum at a, 1x magnification, b, 5x magnification, and c, 20x magnification. The area outlined with dashed lines in panel a shows the borders of panel b, and the area outlined with dashed lines in panel b shows the borders of panel c. Hematoxylin (purple) was used as a counter-stain. To test the hypothesis that weakness of the elastin extracellular matrix could predispose to diverticulitis, as suggested by our GWAS results, we analyzed elastin fibers in muscularis propria of sigmoid colon resections. Several areas were sampled for analysis: in diverticulitis patients, samples were taken from areas around a diverticulum in a region of diverticulitis (i.e. where the perforated diverticulum lies), and separately samples were taken from areas around a diverticulum at the surgical margin (i.e. the region furthest from the point of diverticulitis); in diverticulosis patients, samples were taken from areas around a diverticulum (i.e. areas of gross disease) and separate samples were taken from grossly healthy areas (i.e. areas without diverticulosis); in patients without diverticular disease, samples were taken from areas that appeared grossly healthy (sample sites are depicted in Supplemental Figure 4). Across all samples, the density of elastin fibers in the muscularis propria was markedly decreased among individuals who required emergency surgery for diverticulitis compared to all other groups ( Figure 8a ). In attempt to exclude the local effects of diverticulitis-associated inflammation and uncover potentially genetically-mediated differences between individuals with and without diverticulitis, we compared the density of elastin present at the surgical margin in patients with emergency diverticulitis (location 5 in Supplemental Figure 4) to a) the density of elastin present immediately adjacent to diverticula in individuals with diverticulosis (location 2 in Supplemental Figure 4), and b) the density of elastin present in individuals without diverticular disease (location 1 in Supplemental Figure 4). These are the appropriate anatomic sites for comparison because they create the most stringent contrast: the site most likely to have normal elastin density in individuals with diverticulitis (the site furthest from the perforation) compared to the site least likely to have normal elastin density in individuals with diverticulosis (the site immediately adjacent to the diverticulum). To perform these comparisons, we constructed a multivariable linear regression (controlling for age and sex), which revealed that those requiring emergency surgery for diverticulitis had a 41% reduction (95% CI -66% to -17%, p = 0.001; Figure 8b ) in elastin density in the muscularis propria (sampled at location 5) relative to patients without diverticular disease (sampled at location 1), and a 43% reduction (95% CI -69% to -17%, p = 0.002) in elastin density compared to those with diverticulosis (sampled at location 2). These analyses were re-run for percent of collagen, but no significant differences were observed between groups (Supplemental Figure 5). Download figure Open in new tab Figure 8. Elastin quantification. a, Each dot represents the measured elastin density on a prepared slide, with boxplot showing median elastin density (horizontal line) by patient class, interquartile range (IQR; the borders of the box) and whiskers extending 1.5*IQR from the end of the box. b, Multivariable linear regression showing a 41% reduction in elastin density among patients undergoing emergency diverticulitis surgery compared to those without disease. Peri-diverticular area corresponds to location 2 in Extended Data Fig.6; surgical margin corresponds to location 5 in Extended Data Fig.6. Single cell RNAseq analysis To identify the cell types in which diverticular disease-associated genes are normally expressed, we employed scDRS to analyze two single cell RNAseq datasets (Hickey et al. and Wright et al.) which collected data from patients without diverticular disease (Hickey et al.) or without an indication of diverticulitis for resection (but undocumented diverticular disease history, Wright et al.). In the Hickey et al. dataset, DD-associated genes in the sigmoid colon were most differentially abundant in WNT5B+ fibroblasts (also referred to as “villus fibroblasts WNT5B+”) and the interstitial cells of Cajal (ICC) ( Figure 9 ). The analysis was repeated across immune, epithelial, secretory, and enteroendocrine cells without any without identifying statistically significant single cell disease relevance scores in any of these cells. In the Wright et al. dataset, a similar analysis identified increased expression of DD-associated genes in colon to smooth muscle cells and ICC ( Figure 10 ). Download figure Open in new tab Figure 9. Cell-level localization of diverticular disease-associated genes obtained with scDRS in the Hickey et al. single-cell RNAseq dataset showing a) Dimensional reduction of analyzed cells, b) Relative cell-level expression of diverticular-disease associated genes, c) Cell type-gene expression associations, with X denoting significantly (FDR < 0.01) increased expression of diverticular disease (DivDz)-associated genes in the indicated cell type. Boxes in white do not have a high proportion of cells with significant (Prop. of sig. cells) gene expression enrichment. Download figure Open in new tab Figure 10. Cell-level localization of diverticular disease-associated genes obtained with scDRS in the Wright et al. single-cell RNAseq dataset showing a) Dimensional reduction of analyzed cells, b) Relative cell-level expression of diverticular-disease associated genes, c) Cell type-gene expression associations, with X denoting significantly (FDR < 0.01) increased expression of diverticular disease (DivDz)-associated genes in the indicated cell type. Boxes in white do not have a high proportion of cells with significant (Prop. of sig. cells) gene expression enrichment. To more specifically understand which of the 214 DD-associated genes were enriched in these cell types, we constructed dot plots with k-means clustering for each dataset, which revealed in the Hickey et al. dot plot the enrichment of cluster 3 genes in ICC cells and cluster 10 genes in WNT5B+ fibroblasts, and in the Wright et al. dot plot the enrichment of cluster 7 genes in ICC and cluster 9 genes in smooth muscle cells ( Figure 11 ). Heatmaps constructed for the genes that comprise these clusters revealed marked expression of several DD-associated genes, including ANO1 , in ICC cells, as well as CACNB2 and RYR2 in smooth muscle cells ( Figure 12 ). ANO1 is a calcium activated chloride channel that plays a role in pacemaker activity of the intestine, 67 while CACNB2 and RYR2 are both calcium channels involved in muscle contraction 44 , 68 . These observations suggest some diverticular disease-associated genes have critical roles in colonic motility. Cluster-level pathway analysis of DD-associated genes also implicated biological processes of nerve conduction (e.g. cytoskeleton of the presynaptic active zone), muscle contraction (e.g. sarcoplasmic reticulum), and extracellular matrix ( Figure 13 ). Download figure Open in new tab Figure 11. Dot plots showing expression patterns of diverticular disease-associated genes with k-means clustering applied, in the a, Hickey et al. and b, Wright et al. scRNAseq datasets. Mean expression for each gene across all samples is set to zero, and scaled expression is shown as standard deviations above or below the mean. Download figure Open in new tab Figure 12. Heatmaps showing expression of clusters of genes in a, Hickey et al. WNT5B+ fibroblasts (cluster 10) and ICC (cluster 3) and b, Wright et al. smooth muscle cells (cluster 9) and ICC (cluster 7). Mean expression for each gene across all samples is set to zero, and scaled expression is shown as standard deviations above or below the mean. Download figure Open in new tab Figure 13. Pathway analysis of clusters of genes enriched in a, smooth muscle cells (cluster 9, Wright et al.), in gene ontology (GO) cellular component 2023, b, interstitial cells of Cajal (ICC) (cluster 7, Wright et al.), in GO cellular component 2023, c, ICC (cluster 3, Hickey et al.), in the MGI mammalian phenotype enrichr pathway, d, WNT5B+ fibroblasts (cluster 10, Hickey et al.), in the Jensen compartments enrichr pathway These single cell RNAseq analyses suggest that many genes associated with DD identified by our GWAS are expressed at relatively high levels in ICC, smooth muscle, and fibroblasts in the colon, and have important roles in gastrointestinal motility and extracellular matrix formation or maintenance. This implies that genetic variation that influences colonic motility and/or extracellular matrix composition may contribute to the formation of diverticular disease. These proposed biological mechanisms are illustrated in Figure 14 . Download figure Open in new tab Figure 14. Proposed biological changes through which the genes identified in this GWAS may increase liability to diverticular disease. DISCUSSION The current study offers multiple lines of evidence that diverticular disease (DD) is predominantly a disorder of connective tissue biology and colonic motility, in contrast to historical hypotheses that DD is an infectious or inflammatory disease. This assertion is concordant with prior clinical associations of DD with rare connective tissue disorders; common diseases such as hernia, prolapse, and colonic dysmotility; and more recent genomic work. 25 , 69 We began with the largest and most diverse GWAS meta-analysis for diverticular disease (DD) to date and identified 126 novel loci, nearly doubling the number reported in the literature. These loci were mapped to genes that were enriched for expression in the sigmoid colon (the site of the vast majority of diverticular disease) and are involved in biological processes related to extracellular matrix maintenance (particularly elastin synthesis and degradation) and colonic motility. By integrating our results with single cell RNAseq data from the sigmoid colon, we localized expression of diverticulitis-associated gene clusters to fibroblasts, interstitial cells of Cajal, and smooth muscle cells (which generate force needed for bowel motility and to resist stretching when intraluminal pressure is high). Finally, we found direct evidence of reduced elastin in the sigmoid colon of people with severe diverticulitis. These findings have important consequences, implicating novel cell types, suggesting a critical role for elastin and motility in DD, and providing a prioritized list of potential targets for medical therapy for a common disease with no effective medical treatment. Analysis of tissue and pathway enrichment revealed two important findings. The first is that the genes at loci associated with DD are enriched not only in the sigmoid colon but also in hollow organs (e.g. artery, bladder, uterus) with prominent smooth muscle and extracellular matrix (ECM) components. The second is that many biological processes in which DD- associated genes are enriched relate to ECM formation (such as elastin fiber production) or degradation, as well as gastrointestinal motility; and that cells in which DD genes are enriched (fibroblasts, smooth muscle, and ICC) are responsible for these biological processes. For example, the interpretation of enrichment of DD-associated genes in ICC suggests the possibility of a motility-mediated component of these genes, which is reflected by the same genes being involved in neuronal transmission and digestive physiology. Alterations in motility and in multiple colonic cell types (nerves, glia, neuroendocrine cells, etc) have long been appreciated in DD patients and specimens. 70 However, these clinical and histological studies are purely correlative and investigators have not been able to determine whether the associated changes are causes or effects of DD. In this context, these genetic analyses are important because they implicate specific cell types and do not suffer from reverse causation. Taken together, our findings suggest a structural (ECM and smooth muscle) and functional (ICC and smooth muscle) basis for DD. This interpretation is further supported by our LDSC results showing shared genetic liability to other connective tissue and smooth muscle diseases (e.g. hernia, pelvic organ prolapse, and abdominal aortic aneurysm). Interestingly, we did not see an association on LDSC with inflammatory bowel disease, again suggesting that etiopathogenesis of diverticular disease is more similar to these connective tissue or muscle layer disorders than to inflammatory diseases of the bowel. The hypothesis that DD stems from alterations in connective tissue biology was further supported and refined by our analysis of intraoperatively collected sigmoid colonic tissue. Specifically, our finding of dramatically reduced elastin density (41% decrease, adjusted for age and sex) in the sigmoid colon muscularis propria of people requiring emergency surgery for severe diverticulitis compared to controls without diverticular disease. Most interestingly, similar reductions in muscularis elastin density (43% decrease, adjusted for age and sex) were present in those needing emergency surgery when compared to those with diverticulosis, leading us to conclude that elastin reductions are associated with diverticulitis per se rather than solely DD writ large. This supports the possibility that loss of elastin in the muscularis propria increases risk of diverticulitis, but not to diverticulosis, an unexpected correlation. Given that diverticulitis is the most clinically relevant manifestation of DD, this has potentially profound clinical implications by providing insight into the biological features that distinguish diverticulosis patients from diverticulitis patients. Such a finding heightens the potential that correcting elastin deficiencies may prevent diverticulitis. One potential therapy would be everolimus, a mammalian target of rapamycin (mTOR) antagonist that was shown to rescue defective elastin phenotypes in stem cell-derived vascular smooth muscle cells in individuals with ELN gene mutations that caused elastin insufficiency. 66 It should be noted that the direction of causality in the association of diverticulitis and decreased elastin is not definitively established in our analyses: it is possible chronic inflammation in individuals with diverticulitis leads to elastin degradation. 71 However, none of the individuals in our analysis who had emergency diverticulitis surgery had prior episodes of diverticulitis or gastrointestinal inflammatory conditions, leading us to believe it is more likely that decreased elastin density led to diverticulitis rather than the reverse. One of the main limitations of this study is that it relies for the identification of diverticular disease on electronic medical record coding using Phecodes that do not distinguish between diverticulosis and diverticulitis. This is mitigated by the fact that diverticulosis is an asymptomatic and therefore underdiagnosed disease; DD cases come to light primarily through symptomatic diverticulitis (approximately 4% of individuals with diverticulosis) 72 or through screening colonoscopy (for which the rates are well below 100% of recommended individuals, and which is an imperfect tool to diagnose diverticulosis). 73 , 74 Therefore, we hypothesize that our identified DD cases are enriched for symptomatic disease, which explains the 12% DD prevalence in our population, which is substantially lower than population-level estimates of asymptomatic DD. 4 Nevertheless, disentangling the genetic etiology of symptomatic from asymptomatic DD remains a challenge. A further limitation is that the largest contributor to our meta-analysis was a GWAS in the VA Million Veteran Program, which is overwhelmingly male, 75 and that the current study does not include significant numbers of individuals of Asian ancestry, whose anatomically right-sided diverticular disease may stem from a different genetic etiology. 76 Data Availability All data produced in the present study are available upon reasonable request to the authors SOURCES OF FUNDING C.J.N is supported by the American Society of Colon and Rectal Surgeons General Surgery Resident Research Initiation Grant, the Institute for Translational Medicine and Therapeutics of the Perelman School of Medicine at the University of Pennsylvania, and the NIH National Center for Advancing Translational Sciences (TL1TR001880). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. M.G.L is supported by the Doris Duke Foundation (Grant 2023-0224) J.S.D is supported by the American Heart Association (23POST1011251) S.A.A is supported by the Sarnoff Cardiovascular Research Foundation R.O.H is supported by the Irma and Norman Braman Endowment, Lustgarten Center Endowment, and NIH R01 DK128282. S.M.D is supported by the US Department of Veterans Affairs Clinical Research and Development Award IK2-CX001780. This publication does not represent the views of the Department of Veterans Affairs or the US Government. L.H.M is supported by NIH K08 DK124687 SUPPLEMENTAL DATA Download figure Open in new tab Supplemental Figure 1. GWASinspector results showing quality control metrics for the GWAS meta-analysis. Download figure Open in new tab Supplemental Figure 2. Immunohistochemistry staining protocols. (Note: only first page displayed.) Download figure Open in new tab Supplemental Figure 3. Enrichment analysis of significant pQTL MR proteins in the Reactome database. Blue dots are significant (p-value < 0.05). Download figure Open in new tab Supplemental Figure 4. Locations of samples taken from surgical specimens for immunohistochemistry staining and elastin quantification. Download figure Open in new tab Supplemental Figure 5. Collagen quantification a, Each dot represents the measured collagen density on a prepared slide, with boxplot showing median collagen density (horizontal line) by patient class, interquartile range (IQR; the borders of the box) and whiskers extending 1.5*IQR from the end of the box. b, Multivariable linear regression showing no significant difference in collagen density among patients undergoing emergency diverticulitis surgery compared to those without disease. Peri-diverticular area corresponds to location 2 in Extended Data Fig.6; surgical margin corresponds to location 5 in Extended Data Fig.6. View this table: View inline View popup Download powerpoint Supplemental Table 1. 236 genome-wide significant loci disocvered in the genome-wide association study meta-analysis. (Note: only first page displayed.) View this table: View inline View popup Download powerpoint Supplemental Table 2. Genes prioritized for each signal (index variants and secondary signals). Locus column displays the locus in which the signal resides. (Note: only first page displayed.) View this table: View inline View popup Download powerpoint Supplemental Table 3. Input data sources. View this table: View inline View popup Download powerpoint Supplemental Table 4. Full results of pQTL MR using deCODE data. (Note: only first page displayed.) View this table: View inline View popup Download powerpoint Supplemental Table 5. Full results of pQTL MR using UK Biobank Pharma Proteomics Project (UKB-PPP) data. (Note: only first page displayed.) ACKNOWLEDGEMENTS & STATEMENTS The authors would like express their gratitude for the participants of the Penn Medicine Biobank. The PMBB is supported by Perelman School of Medicine at University of Pennsylvania, a gift from the Smilow family, and the National Center for Advancing Translational Sciences of the National Institutes of Health under CTSA award number UL1TR001878. The authors thank Million Veteran Program (MVP) staff, researchers, and volunteers, who have contributed to MVP, and especially participants who previously served their country in the military and now generously agreed to enroll in the study. 75 (See https://www.research.va.gov/mvp/ for more details). This research is based on data from the Million Veteran Program, Office of Research and Development, Veterans Health Administration, and was supported by the Veterans Administration (VA) Million Veteran Program (MVP) award #000. This research is based on dbGaP data accession number phs001672.v11.p1. 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