Multi-ancestry Genome-wide Association Study of Inpatient Opioid Dosing Following Knee or Hip Arthroplasty

Multi-ancestry Genome-wide Association Study of Inpatient Opioid Dosing Following Knee or Hip Arthroplasty | 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 Multi-ancestry Genome-wide Association Study of Inpatient Opioid Dosing Following Knee or Hip Arthroplasty View ORCID Profile Zeal Jinwala , View ORCID Profile Christal N. Davis , View ORCID Profile Jackson F. SooHoo , Million Veteran Program , Penn Medicine Biobank , View ORCID Profile Joel Gelernter , View ORCID Profile Rachel L. Kember , View ORCID Profile Rachel Vickers-Smith , View ORCID Profile Henry R. Kranzler doi: https://doi.org/10.1101/2025.07.23.25331996 Zeal Jinwala 1 Mental Illness Research, Education and Clinical Center, Crescenz Veterans Affairs Medical Center , Philadelphia, PA, USA 2 Department of Psychiatry, University of Pennsylvania Perelman School of Medicine , Philadelphia, PA, USA B.S. Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Zeal Jinwala Christal N. Davis 1 Mental Illness Research, Education and Clinical Center, Crescenz Veterans Affairs Medical Center , Philadelphia, PA, USA 2 Department of Psychiatry, University of Pennsylvania Perelman School of Medicine , Philadelphia, PA, USA Ph.D. Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Christal N. Davis Jackson F. SooHoo 2 Department of Psychiatry, University of Pennsylvania Perelman School of Medicine , Philadelphia, PA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jackson F. SooHoo Joel Gelernter 4 VA Connecticut Healthcare Center , West Haven, CT, USA 5 Departments of Psychiatry , Genetics, and Neuroscience, Yale University School of Medicine , New Haven, CT, USA M.D. Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Joel Gelernter Rachel L. Kember 1 Mental Illness Research, Education and Clinical Center, Crescenz Veterans Affairs Medical Center , Philadelphia, PA, USA 2 Department of Psychiatry, University of Pennsylvania Perelman School of Medicine , Philadelphia, PA, USA Ph.D. Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Rachel L. Kember Rachel Vickers-Smith 3 Department of Epidemiology and Environmental Health, University of Kentucky College of Public Health , Lexington, KY, USA Ph.D. Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Rachel Vickers-Smith Henry R. Kranzler 1 Mental Illness Research, Education and Clinical Center, Crescenz Veterans Affairs Medical Center , Philadelphia, PA, USA 2 Department of Psychiatry, University of Pennsylvania Perelman School of Medicine , Philadelphia, PA, USA M.D. Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Henry R. Kranzler For correspondence: kranzler{at}pennmedicine.upenn.edu Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Opioid analgesics are commonly prescribed to manage acute postoperative pain. However, individuals vary in their opioid dosing needs, with no validated biomarkers to guide prescribing. We used electronic health records data from the Million Veteran Program sample to investigate individual differences in opioid analgesic dosing following knee (n=18,540) or hip (n=9,363) arthroplasty in a predominantly male (93%) veteran sample. We extracted data from pharmacy records on inpatient opioid medications dispensed to estimate each patient’s average daily morphine milligram equivalent (MME) doses. We used genome-wide association studies (GWAS) stratified by arthroplasty type (i.e., knee or hip) and genetically inferred ancestry group (i.e., European-like (EUR), African-like (AFR), and Admixed American-like (AMR) to identify genetic variants associated with daily MME doses. Within-ancestry summary statistics from the knee and hip arthroplasty GWAS were meta-analyzed, followed by a cross-ancestry GWAS meta-analysis. We identified two genome-wide significant loci in the AFR hip arthroplasty GWAS and three in the AMR GWAS, through no variants were genome-wide significant in the cross-ancestry meta-analysis. Genes mapped to these loci were implicated in a wide range of biological functions, including T-cell activation, chromatin organization and epigenetic regulation, epidermal differentiation and cell adhesion, and cell growth and organ development. Tissue enrichment analysis revealed significant enrichment in brain tissue from early infancy developmental stage. This study provides a proof-of-concept framework for the use of electronic health records data to examine the genetics of opioid dosing in the management of post-operative pain. Summary Using electronic health record data from the Million Veteran Program, we identified genetic variants associated with opioid dosing needs following knee and hip arthroplasty procedures. Introduction Opioid analgesics are commonly prescribed to manage acute postoperative pain. Although inter-individual variation in pain tolerance and opioid response contribute to decisions regarding which analgesic to use and at what dosage, there are no validated biomarkers to guide prescribing. This limits optimal dosing and analgesic efficacy. In addition to its potential to improve clinical care, more precise opioid analgesic dosing could reduce the risk of opioid use disorder (OUD). The drive to manage pain aggressively between 1990 and 2010 produced a sharp increase in U.S. opioid prescriptions, one factor that fueled an epidemic of opioid misuse, dependence, and overdose deaths [ 39 , 43 , 8 , 45 ]. In the Veterans Health Administration (VHA), opioid prescribing nearly doubled over a decade [ 36 ] and was accompanied by a substantial rise in opioid-related overdose deaths [ 5 ]. Despite a recent widespread decline in opioid overdose deaths [ 1 ], the ongoing availability of illicit synthetic opioids, such as fentanyl, continues to pose a serious public health challenge [ 44 ]. Thus, there is a critical need for empirically-based, individualized opioid prescribing, particularly in acute care settings where individuals are often first exposed to opioids [ 32 ]. Despite well-recognized individual differences in opioid analgesic sensitivity, there is little known regarding the biological basis of this variability [ 24 ]. Candidate gene studies have yielded inconsistent findings [ 13 , 17 , 24 , 31 , 38 ]. Genome-wide association studies (GWAS) that have implicated genes such as CREB1 , TAOK3 , and TRPC3 , were conducted in small samples ( n s<650) and remain to be replicated [ 3 , 11 , 37 ]. Recent studies evaluating the utility of a machine learning algorithm to predict OUD based on candidate gene findings concluded that genetic variants in the algorithm are ineffective at predicting OUD [ 14 , 23 ]. A recent GWAS of the number of codeine prescriptions provided to patients identified 9 genome-wide significant (GWS) loci. There was also a significant correlation between genetic liability for OUD and the number of codeine prescriptions. However, the study was limited by its focus on a single opioid—codeine, lack of consideration of dosage, and the inclusion of only European-like (EUR) individuals [ 47 ]. Thus, larger GWAS of opioid sensitivity in multiple genetically inferred ancestry groups are needed to identify genetic predictors of opioid dosing for postoperative pain management. We used electronic health records (EHR) from the Million Veteran Program (MVP) sample to investigate individual differences in opioid analgesic dosing following knee or hip arthroplasty. These common orthopedic procedures typically cause moderate-to-severe postoperative pain, enabling the study of genetic variation underlying opioid dosing. We aimed to (1) characterize the genetics of inpatient opioid dosing among arthroplasty patients and (2) examine how polygenic scores based on the GWAS are associated with psychiatric, medical, and behavioral phenotypes. We conducted a GWAS of estimated average daily morphine milligram equivalent (MME) doses prescribed to 27,903 patients who underwent knee or hip arthroplasty. We limited the study to opioids prescribed during the inpatient stay to characterize individual differences in acute post-operative pain management needs. Identifying genetic variation associated with opioid dosing could support more personalized prescribing guidelines and reduce the risk of inadequate pain control and adverse effects, including OUD. Methods All study procedures were approved by both the VA Central Institutional Review Board (IRB) and all local IRBs. Participants provided written informed consent and were not paid to participate. Phenotyping We identified patients who underwent knee or hip arthroplasty procedures using International Classification of Diseases (ICD)-9 and ICD-10 diagnoses and Current Procedural Terminology (CPT) codes recorded in the EHR linked to the MVP (see Supplementary Table S1 for a list of diagnosis codes). We excluded arthroplasty procedures performed before 1999 to ensure adequate availability of EHR data. For patients who underwent multiple arthroplasties, we included only the first in the analysis. The length of the hospital stay (in hours) was calculated as the difference between the times of admission and discharge. See Figure 1 for a schematic of the phenotype workflow. Download figure Open in new tab Figure 1: Schematic of the phenotype workflow We identified a subset of arthroplasty patients with OUD based on a medical history of at least one inpatient or two outpatient ICD-9 or ICD-10 diagnostic codes for the disorder (Supplementary Table S1). Individuals were considered to have pre-existing OUD if they received the diagnosis prior to or during the hospitalization for arthroplasty. Data on inpatient opioid medications were extracted from pharmacy records using drug class codes “CN101” and “RE301” from the National Drug Code classification system used by the VA (Supplementary Table S1). We also ascertained whether patients received prescription opioids during the 30 days prior to the arthroplasty admission date for inclusion as a covariate. We excluded prescriptions with missing drug names or drug class information. For each patient, we estimated the average daily morphine milligram equivalent (MME) doses using the following formula: We used the number of doses rather than the drug dosage, as the latter value could not always be accurately ascertained from the EHR. We used a publicly available MME conversion table ( https://pain.ucsf.edu/opioid-analgesics/calculation-oral-morphine-equivalents-ome ) to estimate the MME for each drug type. Because accurate application of the conversion factor required knowledge of both the number of doses and the drug formulation, prescriptions missing either of these were excluded. We summed up the opioid MME doses administered across the entire inpatient stay for each patient and divided this by the duration of the stay in hours to obtain a single average MME doses per hour for each patient, which we multiplied by 24 to get the mean daily MME doses, the phenotype used in GWAS. Genotyping and Imputation Samples were genotyped using a custom Affymetrix Axiom Biobank Array. Quality control (QC) and imputation of genotype data were performed by the MVP working group [ 27 ]. QC included the removal of samples for participants whose genetic and phenotypic sex did not match, who had seven or more relatives in MVP (kinship > 0.08), or who demonstrated excessive heterozygosity or a genotype call rate <98.5%. Monomorphic variants, variants with a Hardy-Weinberg equilibrium (HWE) P-value <1 × 10 -6 or a high missingness (call rate <0.95) were removed. Genotypes were phased using SHAPEIT4 [ 15 ] and imputed using Minimac4 software [ 16 ]. A combination of the 1000 Genomes Phase 3 panel and the African Genome Resources reference panel was used to impute biallelic SNPs. Genetic ancestry was determined by calculating GIA composition with principal component analysis, as previously outlined in Hunter-Zinck et al. (2020) [ 27 ]. Genome-wide association studies We performed GWAS stratified by arthroplasty type (i.e., knee or hip) because these procedures differ in surgical approach, recovery time, and patient outcomes [ 12 ]. Analyses were also separated by ancestry group (i.e., European-like (EUR), African-like (AFR), and Admixed American-like (AMR)). Linear regression models were implemented using PLINK v.2.0, with age, sex, the first 10 within-ancestry principal components (PCs), and pre-existing OUD diagnosis included as covariates. Variants with an imputation quality (INFO) score <0.8 were excluded. A minor allele frequency (MAF) threshold of 5% was applied to the AFR and AMR GWAS because of their smaller sample sizes, with an MAF of 1% used for the EUR GWAS. To account for relatedness, we randomly removed one individual from each pair of related individuals (kinship >0.08). Lead SNPs were identified using a two-step linkage disequilibrium (LD) clumping process in the Functional Mapping and Annotation of Genome-Wide Association Studies (FUMA) interface [ 50 ]. First, SNPs with P-value ≤5 × 10 -8 and independent at r 2 <0.6 were selected as independent significant SNPs, with lead SNPs defined as those independent from each other at r 2 <0.1, and candidate SNPs defined as those in LD (r 2 ≥0.6) with lead SNPs and with a P-value ≤0.05. We used the 1000 Genomes reference panel to estimate the r 2 between SNPs. [ 4 ] Cross-ancestry and cross-phenotype meta-analyses Summary statistics from the within-ancestry knee and hip arthroplasty GWAS were meta-analyzed (e.g., EUR hip and EUR knee) using the inverse-variance weighted approach in METAL [ 51 ], followed by a cross-ancestry GWAS meta-analysis (i.e., AFR hip/knee meta- analysis with AMR and EUR hip/knee meta-analyses). Biological characterization Findings from each GWAS were characterized biologically using an ensemble of prioritization tools available in FUMA. Lead SNPs were mapped to genes using both positional proximity and functional annotations, including eQTL and chromatin interaction data, to identify connections between genomic regions. [ 6 , 33 , 42 ]. We used Multi-marker Analysis of GenoMic Annotation (MAGMA) to conduct gene-based enrichment analysis and test for associations between GWAS signals and gene expression data. Following biological characterization, we identified prior associations of the SNPs in the GWAS catalog [ 9 ]. Polygenic scores and phenome-wide association studies Using PRS-CS software [ 19 ], we calculated ancestry-specific polygenic scores (PGS) in the Yale-Penn [ 28 ] and Penn Medicine Biobank (PMBB) [ 49 ] samples. We did not calculate PGS for AMR individuals because of the small sample sizes of this group in the target cohorts. In PRS-CS, default values were used to estimate shrinkage parameters, except for phi , the global shrinkage parameter, which was learned from the datasets. We conducted phenome- wide association studies (PheWAS) using linear and logistic regression models to examine associations between the average daily MME doses PGS and available psychiatric, behavioral, and medical phenotypes, with age, sex, and the first 10 PCs as covariates. We examined the association of PGS with 691 phenotypes (P Bonferroni = 7.23 × 10 -5 ) in the Yale-Penn sample, and 1,151 phecodes (P Bonferroni = 4.34 × 10 -5 ) for AFR individuals and 1,370 phecodes (P Bonferroni = 3.65 × 10 -5 ) for EUR individuals in the PMBB. Fewer phecodes were available for use in AFR individuals because of the requirement that phecodes have > 100 cases to be included. The Yale-Penn and PMBB samples and genotyping procedures are provided elsewhere [ 22 , 28 , 41 ]. Results Sample characteristics The combined sample comprised 27,903 individuals who were predominantly male (92.66%) with a mean (SD) age of 65.95 (9.02). The knee arthroplasty sample comprised 18,540 individuals (mean (SD) age = 66.00 (8.63), 92.20% males), while the hip arthroplasty sample included 9,363 individuals (mean (SD) age = 66.11 (9.74), 93.56% males). The knee arthroplasty sample included 14,088 EUR, 2,846 AFR, and 1,829 AMR individuals. For hip arthroplasty, the corresponding numbers of individuals were 7,012 EUR, 1,606 AFR, and 522 AMR. The overall percentage of individuals with a lifetime diagnosis of OUD was 4.15%, with 3.77% and 4.90% in the knee and hip arthroplasty samples, respectively. Sample characteristics for each genetically inferred ancestry group are presented in Table 1 . View this table: View inline View popup Download powerpoint Table 1: Sample characteristics GWAS results In the overall cross-ancestry GWAS meta-analysis, although no loci reached genome- wide significance (GWS; P <5.0 × 10 -8 ), 14 loci surpassed the nominal significance threshold (P <5 × 10 ⁻6 ). The most significant association was at rs114400205 (P = 8.42 × 10 -7 ; see Table 2 and Figure 2 ). Download figure Open in new tab Figure 2: Cross-ancestry and cross-phenotype genome-wide association results for estimated average MME opioid doses in who had a knee or hip arthroplasty. The Manhattan plot displays the –log₁₀(P) values of SNP associations across the genome. The red horizontal line indicates the genome-wide significance threshold (P = 5 × 10 -8 ), and the blue line marks the suggestive significance threshold (P = 1 × 10⁻). SNPs exceeding the suggestive threshold are labeled with their rsIDs. The plot in the upper right corner shows the quantile– quantile (QQ) plot of observed versus expected P-values. View this table: View inline View popup Download powerpoint Table 2: Summary of SNPs reaching nominal significance (p < 5 × 10 -6 ) in the cross-ancestry meta-analysis In the EUR GWAS (Supplementary Table S3, Supplementary Figure 2), no loci were GWS for either phenotype. For the knee arthroplasty GWAS, 11 independent loci were nominally significant, with the lead variant being rs117515502 (P = 2.29 × 10 -7 . For the hip arthroplasty GWAS, 21 loci were nominally significant, with the top SNP being rs115692385 (P = 2.87 × 10 -7 ). Although in the AFR knee arthroplasty GWAS (Supplementary Table S4, Supplementary Figure 3) no variants were GWS, 8 loci were nominally significant (P <5 × 10 ⁻6 ), with the lead SNP being rs6727808 (P = 1.63E × 10 -7 ). Two loci were GWS in the AFR hip arthroplasty GWAS: one on chromosome 2 (rs116314843, P = 7.59 × 10 -9 ) and one on chromosome 3 (rs151228165, P = 2.58 × 10 -9 ). In the AMR knee arthroplasty GWAS, although no loci were GWS, 4 were nominally significant (P < 5 × 10 ⁻6 ) (Supplementary Table S5, Supplementary Figure 5). In the hip arthroplasty GWAS, three variants were GWS: rs72773845 (P = 2.97E × 10 -9 ), rs6563008 (P=2.49E × 10 -8 ) and rs11022449 (P=4.85 × 10 -8 ). These were on chromosomes 10, 13, and 11, respectively. Biological characterization In AFR individuals, a SNP that was GWS in the hip arthroplasty GWAS, rs151228165, maps to GM2AP. The gene encodes a GM2 activator protein that enables the degradation of GM2 ganglioside, a lipid contained in neuronal membranes [ 25 ]. The second GWS SNP, rs116314843, maps to transcript AC125238.3 of the CD28 gene, which encodes a protein that plays a vital role in T-cell activation, proliferation, survival, and the maintenance of immune homeostasis. Of the three SNPs that were GWS in the AMR hip arthroplasty GWAS, rs72773845 maps to SFMBT2 , which encodes a protein involved in chromatin organization and epigenetic regulation [ 52 ]. The second SNP, rs6563008, maps to SCEL , whose protein product is involved in epidermal differentiation and intercellular adhesion of epithelial cells. The third SNP, rs11022449, maps to TEAD1 , which encodes a transcription factor known for its role in cell growth and the development of organs, including the heart, skeletal muscle, and nervous system (Gessler et al., 2024; Pelenyi et al., 2024; Song et al., 2024). An examination of nominally significant associations showed several SNPs that map to genes of biological relevance. For example, rs12722489, nominally significant in the AMR knee arthroplasty GWAS, maps to IL2RA , which encodes a protein that plays a role in immune tolerance [ 35 ]. Using MAGMA gene property analysis, we examined the relationship between tissue-specific gene expression and gene-level association signals from the cross-ancestry meta-GWAS. The only significant association in the datasets tested was in brain tissue from the early infancy developmental stage in the BrainSpan data (P = 1.09 × 10 -03 ; Supplementary Table S7, Supplementary Figure 7). PheWAS in the PMBB and Yale-Penn Sample PheWAS conducted using summary statistics from the EUR and AFR GWAS to calculate PGs in the target samples yielded several associations, though none survived Bonferroni correction. The EUR knee arthroplasty PGS was nominally associated with 79 traits in the PMBB. Among these, higher PGS were nominally associated with staphylococcal infections (betaL=L0.235, SEL=L0.061, p L=L1.02 × 10 -4 ), major depressive disorder (betaL=L0.092, SEL=L0.025, p L=L2.33 × 10 -4 ) and anxiety disorders (betaL=L0.061, SEL=L0.019, p L=L1.41 × 10 -3 ). For the EUR hip arthroplasty PGS, 65 associations were nominally significant in the PMBB. In AFR PMBB participants, knee and hip arthroplasty PGS were nominally associated with 41 and 63 traits, respectively (Supplementary Table S8, Supplementary Figure 8). In the Yale-Penn sample, 14 phenotypes were nominally associated with knee arthroplasty PGS in EUR individuals, eight of which were substance related. Other psychiatric traits were also nominally associated with the knee arthroplasty PGS in EUR individuals, including lifetime trauma assessment (betaL=L-0.079, SEL=L0.029, p L=L6.90 × 10 -3 ) and trouble functioning due to depression (betaL=L0.061, SEL=L0.029, p L=L3.77 × 10 -2 ). There were 49 nominal associations with the hip arthroplasty PGS in EUR Yale-Penn participants, 36 of which were substance related. In the AFR Yale-Penn participants, there were 42 nominal associations for the knee arthroplasty PGS and 28 for the hip arthroplasty PGS (Supplementary Table S9, Supplementary Figure 9). Discussion This study is the first genome-wide investigation of variation in inpatient opioid dosing following knee or hip arthroplasty. Utilizing data from the MVP cohort, a large, diverse biobank comprising >600,000 genotyped individuals with EHR data, we identified 27,903 individuals who underwent one of these surgeries. The findings enhance our understanding of the genetic underpinnings of individuals’ opioid analgesic dosing needs and advance the field beyond traditional candidate gene approaches [ 7 , 13 , 17 , 31 , 38 ] and small-sample GWAS (Aoki et al., 2015; Cook-Sather et al., 2014; Nishizawa et al., 2014) that have been used to study these effects. We identified 5 GWS loci, three in AMR individuals and two in AFR individuals. Two of the three loci in the AMR hip arthroplasty GWAS mapped to genes previously implicated in a GWAS of opioid dependence: SFMBT2 and RPL17P35 [ 10 , 18 ]. Although there were no GWS loci identified in either the cross-ancestry meta-analysis or the EUR GWAS, these analyses yielded many nominally significant loci. Of the genes mapped to nominally significant loci across all the GWAS that we conducted, 19 were previously associated with traits related to pain intensity, response to opioids, osteoarthritis, or the revision of knee or hip arthroplasties. For example, four genes identified in our study, GRIA1 , NRXN1 , ZNF423 , and ROBO2 , were also identified in a GWAS of pain intensity in the MVP sample [ 48 ]. These genes’ associations with pain intensity may contribute to their link to opioid dosing. Notably, tissue enrichment analysis showed significant enrichment in developmental tissue from early infancy, suggesting that genetic variation related to opioid dosing may also be involved in mechanisms that support the structural and functional development of the brain, as has previously been reported [ 21 ]. Specifically, the anterior cingulate cortex (ACC) area BA24 was the most enriched tissue type, consistent with prior studies highlighting the role of this brain region in pain transmission and processing [ 2 , 30 ]. As with other complex traits, post-operative opioid dosing is likely highly polygenic, with small effects distributed across the genome. Detecting such effects will require larger samples than those used here. Another limitation of the present study is the phenotype used—an estimate of MME dosage based on the number of doses of different opioids administered during a discrete period, but not the dosage of each. Because this approach, necessitated by the distributed nature of pharmacy records in the VA EHR, does not fully capture the variation in opioid dosing, efforts are needed to capture more detailed opioid dosing information. Third, the impact of other factors, such as recent changes in opioid prescribing patterns, provider preferences, patient perception of pain, and social biases, may also influence which opioids are prescribed to patients and their dosage following surgery. Thus, studies that consider these factors’ effects on opioid dosing are also needed [ 26 , 29 , 32 , 34 ]. A potential limitation of our findings is collider bias. Because all individuals in our sample underwent a knee or hip arthroplasty, the genetic and phenotypic associations may partially reflect factors related to the need for an arthroplasty in addition to opioid dosing requirements specifically. We also saw key differences in the phenotypes associated with the arthroplasty PGS between the PMBB and Yale-Penn sample. These differences are likely because the PMBB is a biobank linked to an EHR with a broad swath of diagnostic and procedural data from both inpatients and outpatients, while the Yale-Penn sample was ascertained and deeply phenotyped in the context of genetic studies of substance use disorders. Despite these limitations, our study has several key strengths. It is the first to use GWAS to identify genetic predictors of opioid dosing in the period following arthroplasty, a surgical procedure associated with high levels of pain. To do this, we leveraged a large and genetically diverse biobank—MVP—with the goal of characterizing opioid prescribing patterns in an inpatient setting. Whereas MVP patients were prescribed a wide variety of opioids comprising multiple drug formulations, we maximized the available information using an MME conversion factor to yield a consistent measure across individuals. Following GWAS, we performed biological annotation to gain insights into the functional importance of identified loci and evaluated the predictive performance of our findings by generating PGS in two distinct, independent samples. These results provide a foundation for future work aimed at advancing the precision treatment of post-operative pain and highlight the importance of large, diverse datasets for uncovering genetic contributors to prescription opioid dosing. Data Availability The cross-ancestry and within-ancestry GWAS and meta-analysis summary-level association data will be available in the database of Genotypes and Phenotypes (dbGaP) ( https://www.ncbi.nlm.nih.gov/gap/ ) under accession phs001672 “Veterans Administration (VA) MVP Summary Results from Omics Studies.” Registration and approval are needed following dbGaP's data access process. Acknowledgements The views expressed in this article are those of the authors and do not necessarily represent the position or policy of the Department of Veterans Affairs or the US Government. The MVP Core Acknowledgment is in Supplementary Material. This research is based on data from the Million Veteran Program, Office of Research and Development, Veterans Health Administration, and supported by MVP000, MVP004, and Merit Review Awards I01 CX001734 and I01 BX004820; the VISN 4 Mental Illness Research, Education and Clinical Center; NIDA grant P30 DA046345 and NIAAA grant K01 AA028292 (to RLK). The authors thank the Million Veteran Program staff, researchers, and volunteers who have contributed to MVP, and especially those who served their country in the military and agreed to enroll in the study ( mvp.va.gov ). We acknowledge the Penn Medicine Biobank (PMBB) for providing data and thank the patient-participants of Penn Medicine who consented to participate in this research program. We would also like to thank the Penn Medicine Biobank team and Regeneron Genetics Center for providing genetic variant data for analysis. The PMBB is approved under IRB protocol #813913 and supported by the Perelman School of Medicine at the 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. Scott Damrauer, M.D. provided helpful comments on the manuscript. Footnotes ↵ ** Full lists of these consortium members are provided in the supplemental information Disclosures: Dr. Kranzler is a member of advisory boards for Altimmune and Clearmind Medicine; a consultant to Sobrera Pharmaceuticals, Altimmune, and Lilly; the recipient of research funding and medication supplies for an investigator-initiated study from Alkermes and a company-initiated study by Altimmune; and an inventor on U.S. provisional patent “Multi- ancestry Genome-wide Association Meta-analysis of Buprenorphine Treatment Response.” The other authors have no disclosures. Data availability : The cross-ancestry and within-ancestry GWAS and meta-analysis summary- level association data will be available in the database of Genotypes and Phenotypes (dbGaP) ( https://www.ncbi.nlm.nih.gov/gap/ ) under accession phs001672 “Veterans Administration (VA) MVP Summary Results from Omics Studies.” Registration and approval are needed following dbGaP’s data access process. References [1]. ↵ Ahmad F , Cisewski J , Rossen L , Sutton P . Provisional Drug Overdose Death Counts . Centers for Disease Control and Prevention (U.S.). National Center for Health Statistics , 2025 doi: 10.15620/cdc/20250305008 . OpenUrl CrossRef [2]. ↵ Ambron R . Synaptic sensitization in the anterior cingulate cortex sustains the consciousness of pain via synchronized oscillating electromagnetic waves . Front Hum Neurosci 2024 ; 18 . doi: 10.3389/fnhum.2024.1462211 . OpenUrl CrossRef [3]. ↵ Aoki Y , Nishizawa D , Hasegawa J , Kasai S , Yoshida K , Koukita Y , Ichinohe T , Nagashima M , Katoh R , Satoh Y , Tagami M , Hayashida M , Fukuda K , Ikeda K . Association between the rs1465040 single-nucleotide polymorphism close to the transient receptor potential subfamily C member 3 (TRPC3) gene and postoperative analgesic requirements . J Pharmacol Sci 2015 ; 127 : 391 – 393 . OpenUrl [4]. ↵ Auton A , Abecasis GR , Altshuler DM , Durbin RM , Abecasis GR , Bentley DR , Chakravarti A , Clark AG , Donnelly P , Eichler EE , Flicek P , Gabriel SB , Gibbs RA , Green ED , Hurles ME , Knoppers BM , Korbel JO , Lander ES , Lee C , Lehrach H , Mardis ER , Marth GT , McVean GA , Nickerson DA , Schmidt JP , Sherry ST , Wang J , Wilson RK , Gibbs RA , Boerwinkle E , Doddapaneni H , Han Y , Korchina V , Kovar C , Lee S , Muzny D , Reid JG , Zhu Y , Wang J , Chang Y , Feng Q , Fang X , Guo X , Jian M , Jiang H , Jin X , Lan T , Li G , Li J , Li Y , Liu S , Liu X , Lu Y , Ma X , Tang M , Wang B , Wang G , Wu H , Wu R , Xu X , Yin Y , Zhang D , Zhang W , Zhao J , Zhao M , Zheng X , Lander ES , Altshuler DM , Gabriel SB , Gupta N , Gharani N , Toji LH , Gerry NP , Resch AM , Flicek P , Barker J , Clarke L , Gil L , Hunt SE , Kelman G , Kulesha E , Leinonen R , McLaren WM , Radhakrishnan R , Roa A , Smirnov D , Smith RE , Streeter I , Thormann A , Toneva I , Vaughan B , Zheng-Bradley X , Bentley DR , Grocock R , Humphray S , James T , Kingsbury Z , Lehrach H , Sudbrak R , Albrecht MW , Amstislavskiy VS , Borodina TA , Lienhard M , Mertes F , Sultan M , Timmermann B , Yaspo M-L , Mardis ER , Wilson RK , Fulton L , Fulton R , Sherry ST , Ananiev V , Belaia Z , Beloslyudtsev D , Bouk N , Chen C , Church D , Cohen R , Cook C , Garner J , Hefferon T , Kimelman M , Liu C , Lopez J , Meric P , O’Sullivan C , Ostapchuk Y , Phan L , Ponomarov S , Schneider V , Shekhtman E , Sirotkin K , Slotta D , Zhang H , McVean GA , Durbin RM , Balasubramaniam S , Burton J , Danecek P , Keane TM , Kolb- Kokocinski A , McCarthy S , Stalker J , Quail M , Schmidt JP , Davies CJ , Gollub J , Webster T , Wong B , Zhan Y , Auton A , Campbell CL , Kong Y , Marcketta A , Gibbs RA , Yu F , Antunes L , Bainbridge M , Muzny D , Sabo A , Huang Z , Wang J , Coin LJM , Fang L , Guo X , Jin X , Li G , Li Q , Li Y , Li Z , Lin H , Liu B , Luo R , Shao H , Xie Y , Ye C , Yu C , Zhang F , Zheng H , Zhu H , Alkan C , Dal E , Kahveci F , Marth GT , Garrison EP , Kural D , Lee W-P , Fung Leong W , Stromberg M , Ward AN , Wu J , Zhang M , Daly MJ , DePristo MA , Handsaker RE , Altshuler DM , Banks E , Bhatia G , del Angel G , Gabriel SB , Genovese G , Gupta N , Li H , Kashin S , Lander ES , McCarroll SA , Nemesh JC , Poplin RE , Yoon SC , Lihm J , Makarov V , Clark AG , Gottipati S , Keinan A , Rodriguez-Flores JL , Korbel JO , Rausch T , Fritz MH , Stütz AM , Flicek P , Beal K , Clarke L , Datta A , Herrero J , McLaren WM , Ritchie GRS , Smith RE , Zerbino D , Zheng-Bradley X , Sabeti PC , Shlyakhter I , Schaffner SF , Vitti J , Cooper DN , Ball EV , Stenson PD , Bentley DR , Barnes B , Bauer M , Keira Cheetham R , Cox A , Eberle M , Humphray S , Kahn S , Murray L , Peden J , Shaw R , Kenny EE , Batzer MA , Konkel MK , Walker JA , MacArthur DG , Lek M , Sudbrak R , Amstislavskiy VS , Herwig R , Mardis ER , Ding L , Koboldt DC , Larson D , Ye K , Gravel S , The 1000 Genomes Project Consortium, Corresponding authors, Steering committee, Production group, Baylor College of Medicine, BGI-Shenzhen, Broad Institute of MIT and Harvard, Coriell Institute for Medical Research, European Molecular Biology Laboratory EBI, Illumina, Max Planck Institute for Molecular Genetics, McDonnell Genome Institute at Washington University, US National Institutes of Health, University of Oxford, Wellcome Trust Sanger Institute, Analysis group, Affymetrix, Albert Einstein College of Medicine, Bilkent University, Boston College, Cold Spring Harbor Laboratory, Cornell University, European Molecular Biology Laboratory, Harvard University, Human Gene Mutation Database, Icahn School of Medicine at Mount Sinai, Louisiana State University, Massachusetts General Hospital, McGill University, National Eye Institute N. A global reference for human genetic variation . Nature 2015 ; 526 : 68 – 74 . OpenUrl CrossRef PubMed [5]. ↵ Bohnert ASB , Ilgen MA , Trafton JA , Kerns RD , Eisenberg A , Ganoczy D , Blow FC . Trends and regional variation in opioid overdose mortality among Veterans Health Administration patients, fiscal year 2001 to 2009 . Clin J Pain 2014 ; 30 : 605 – 612 . OpenUrl CrossRef PubMed [6]. ↵ Boyle AP , Hong EL , Hariharan M , Cheng Y , Schaub MA , Kasowski M , Karczewski KJ , Park J , Hitz BC , Weng S , Cherry JM , Snyder M . Annotation of functional variation in personal genomes using RegulomeDB . Genome Res 2012 ; 22 : 1790 – 1797 . OpenUrl Abstract / FREE Full Text [7]. ↵ Candiotti KA , Yang Z , Morris R , Yang J , Crescimone NA , Sanchez GC , Bird V , Leveillee R , Rodriguez Y , Liu H , Zhang YD , Bethea JR , Gitlin MC . Polymorphism in the interleukin- 1 receptor antagonist gene is associated with serum interleukin-1 receptor antagonist concentrations and postoperative opioid consumption . Anesthesiology 2011 ; 114 : 1162 – 1168 . OpenUrl CrossRef PubMed Web of Science [8]. ↵ Centers for Disease Control and Prevention (CDC) . Vital signs: overdoses of prescription opioid pain relievers---United States, 1999--2008 . MMWR Morb Mortal Wkly Rep 2011 ; 60 : 1487 – 1492 . OpenUrl PubMed [9]. ↵ Cerezo M , Sollis E , Ji Y , Lewis E , Abid A , Bircan KO , Hall P , Hayhurst J , John S , Mosaku A , Ramachandran S , Foreman A , Ibrahim A , McLaughlin J , Pendlington Z , Stefancsik R , Lambert SA , McMahon A , Morales J , Keane T , Inouye M , Parkinson H , Harris LW . The NHGRI-EBI GWAS Catalog: standards for reusability, sustainability and diversity . Nucleic Acids Res 2025 ; 53 : D998 – D1005 . OpenUrl CrossRef PubMed [10]. ↵ Cheng Z , Zhou H , Sherva R , Farrer LA , Kranzler HR , Gelernter J . Genome-wide Association Study Identifies a Regulatory Variant of RGMA Associated With Opioid Dependence in European Americans . Biol Psychiatry 2018 ; 84 : 762 – 770 . OpenUrl CrossRef PubMed [11]. ↵ Cook-Sather SD , Li J , Goebel TK , Sussman EM , Rehman MA , Hakonarson H . TAOK3, a novel genome-wide association study locus associated with morphine requirement and postoperative pain in a retrospective pediatric day surgery population . Pain 2014 ; 155 : 1773 – 1783 . OpenUrl [12]. ↵ Cooper GM , Bayram JM , Clement ND . The functional and psychological impact of delayed hip and knee arthroplasty: a systematic review and meta-analysis of 89,996 patients . Sci Rep 2024 ; 14 : 8032 . OpenUrl PubMed [13]. ↵ Coulbault L , Beaussier M , Verstuyft C , Weickmans H , Dubert L , Trégouet D , Descot C , Parc Y , Lienhart A , Jaillon P , Becquemont L . Environmental and genetic factors associated with morphine response in the postoperative period . Clin Pharmacol Ther 2006 ; 79 : 316 – 324 . OpenUrl CrossRef PubMed Web of Science [14]. ↵ Davis CN , Jinwala Z , Hatoum AS , Toikumo S , Agrawal A , Rentsch CT , Edenberg HJ , Baurley JW , Hartwell EE , Crist RC , Gray JC , Justice AC , Gelernter J , Kember RL , Kranzler HR . Candidate Genes from an FDA-Approved Algorithm Fail to Predict Opioid Use Disorder Risk in Over 450,000 Veterans . 2024 :2024.05.16.24307486. doi: 10.1101/2024.05.16.24307486 . OpenUrl Abstract / FREE Full Text [15]. ↵ Delaneau O , Zagury J-F , Robinson MR , Marchini JL , Dermitzakis ET . Accurate, scalable and integrative haplotype estimation . Nat Commun 2019 ; 10 : 5436 . OpenUrl CrossRef PubMed [16]. ↵ Fuchsberger C , Abecasis GR , Hinds DA . minimac2: faster genotype imputation . Bioinformatics 2015 ; 31 : 782 – 784 . OpenUrl CrossRef PubMed [17]. ↵ Fukuda K , Hayashida M , Ide S , Saita N , Kokita Y , Kasai S , Nishizawa D , Ogai Y , Hasegawa J , Nagashima M , Tagami M , Komatsu H , Sora I , Koga H , Kaneko Y , Ikeda K . Association between OPRM1 gene polymorphisms and fentanyl sensitivity in patients undergoing painful cosmetic surgery . Pain 2009 ; 147 : 194 – 201 . OpenUrl CrossRef PubMed Web of Science [18]. ↵ Gaddis N , Mathur R , Marks J , Zhou L , Quach B , Waldrop A , Levran O , Agrawal A , Randesi M , Adelson M , Jeffries PW , Martin NG , Degenhardt L , Montgomery GW , Wetherill L , Lai D , Bucholz K , Foroud T , Porjesz B , Runarsdottir V , Tyrfingsson T , Einarsson G , Gudbjartsson DF , Webb BT , Crist RC , Kranzler HR , Sherva R , Zhou H , Hulse G , Wildenauer D , Kelty E , Attia J , Holliday EG , McEvoy M , Scott RJ , Schwab SG , Maher BS , Gruza R , Kreek MJ , Nelson EC , Thorgeirsson T , Stefansson K , Berrettini WH , Gelernter J , Edenberg HJ , Bierut L , Hancock DB , Johnson EO . Multi-trait genome-wide association study of opioid addiction: OPRM1 and beyond . Sci Rep 2022 ; 12 : 16873 . OpenUrl CrossRef PubMed [19]. ↵ Ge T , Chen C-Y , Ni Y , Feng Y-CA , Smoller JW . Polygenic prediction via Bayesian regression and continuous shrinkage priors . Nat Commun 2019 ; 10 : 1776 . OpenUrl CrossRef PubMed [20]. Gessler L , Huraskin D , Jian Y , Eiber N , Hu Z , Prószyński TJ , Hashemolhosseini S . The YAP1/TAZ-TEAD transcriptional network regulates gene expression at neuromuscular junctions in skeletal muscle fibers . Nucleic Acids Res 2024 ; 52 : 600 – 624 . OpenUrl [21]. ↵ Grotheer M , Rosenke M , Wu H , Kular H , Querdasi FR , Natu VS , Yeatman JD , Grill- Spector K . White matter myelination during early infancy is linked to spatial gradients and myelin content at birth . Nat Commun 2022 ; 13 : 997 . OpenUrl CrossRef PubMed [22]. ↵ Hartwell EE , Jinwala Z , Milone J , Ramirez S , Gelernter J , Kranzler HR , Kember RL . Application of polygenic scores to a deeply phenotyped sample enriched for substance use disorders reveals extensive pleiotropy with psychiatric and somatic traits . Neuropsychopharmacol Off Publ Am Coll Neuropsychopharmacol 2024 ; 49 : 1958 – 1967 . OpenUrl [23]. ↵ Hatoum AS , Wendt FR , Galimberti M , Polimanti R , Neale B , Kranzler HR , Gelernter J , Edenberg HJ , Agrawal A . Ancestry may confound genetic machine learning: Candidate- gene prediction of opioid use disorder as an example . Drug Alcohol Depend 2021 ; 229 : 109115 . OpenUrl CrossRef [24]. ↵ Hoofwijk DMN , van Reij RRI , Rutten BP , Kenis G , Buhre WF , Joosten EA . Genetic polymorphisms and their association with the prevalence and severity of chronic postsurgical pain: a systematic review . Br J Anaesth 2016 ; 117 : 708 – 719 . OpenUrl CrossRef PubMed [25]. ↵ Hsieh Y-C , Negri J , He A , Pearse RV , Liu L , Duong DM , Chibnik LB , Bennett DA , Seyfried NT , Young-Pearse TL . Elevated ganglioside GM2 activator (GM2A) in human brain tissue reduces neurite integrity and spontaneous neuronal activity . Mol Neurodegener 2022 ; 17 : 61 . OpenUrl PubMed [26]. ↵ Hu J-C , Chakraborty P , Zhang H , Portenoy R , Rosa WE , Zhang Y , Reid MC , Tamimi RM , Zhang F , Bruera E , Paice JA , Bao Y. Distribution of opioid analgesics by community racial/ethnic and socioeconomic profiles, 2011-2021 . PAIN 2022 : doi: 10.1097/j.pain.0000000000003511 . OpenUrl CrossRef [27]. ↵ Hunter-Zinck H , Shi Y , Li M , Gorman BR , Ji S-G , Sun N , Webster T , Liem A , Hsieh P , Devineni P , Karnam P , Gong X , Radhakrishnan L , Schmidt J , Assimes TL , Huang J , Pan C , Humphries D , Brophy M , Moser J , Muralidhar S , Huang GD , Przygodzki R , Concato J , Gaziano JM , Gelernter J , O’Donnell CJ , Hauser ER , Zhao H , O’Leary TJ , VA Million Veteran Program, Tsao PS , Pyarajan S. Genotyping Array Design and Data Quality Control in the Million Veteran Program. Am J Hum Genet 2020 ; 106 : 535 – 548 . OpenUrl [28]. ↵ Kember RL , Hartwell EE , Xu H , Rotenberg J , Almasy L , Zhou H , Gelernter J , Kranzler HR . Phenome-wide Association Analysis of Substance Use Disorders in a Deeply Phenotyped Sample . Biol Psychiatry 2023 ; 93 : 536 – 545 . OpenUrl [29]. ↵ Kiang MV , Humphreys K , Cullen MR , Basu S. Opioid prescribing patterns among medical providers in the United States, 2003-17: retrospective, observational study . BMJ 2020 ; 368 : l6968 . OpenUrl Abstract / FREE Full Text [30]. ↵ Ko H-G , Jung H , Han S , Choi DI , Lee C , Choi JE , Oh J , Kwak C , Han DH , Kim J-N , Ye S , Lee J , Lee J , Lee K , Lee J-H , Zhuo M , Kaang B-K . Processing of pain and itch information by modality-specific neurons within the anterior cingulate cortex in mice . Nat Commun 2025 ; 16 : 2137 . OpenUrl [31]. ↵ Kumar D , Deb I , Chakraborty J , Mukhopadhyay S , Das S . A polymorphism of the CREB binding protein (CREBBP) gene is a risk factor for addiction . Brain Res 2011 ; 1406 : 59 – 64 . OpenUrl CrossRef PubMed [32]. ↵ Ladha KS , Neuman MD , Broms G , Bethell J , Bateman BT , Wijeysundera DN , Bell M , Hallqvist L , Svensson T , Newcomb CW , Brensinger CM , Gaskins LJ , Wunsch H . Opioid Prescribing After Surgery in the United States , Canada, and Sweden. JAMA Netw Open 2019 ; 2 : e1910734 . OpenUrl [33]. ↵ Lek M , Karczewski KJ , Minikel EV , Samocha KE , Banks E , Fennell T , O’Donnell-Luria AH , Ware JS , Hill AJ , Cummings BB , Tukiainen T , Birnbaum DP , Kosmicki JA , Duncan LE , Estrada K , Zhao F , Zou J , Pierce-Hoffman E , Berghout J , Cooper DN , Deflaux N , DePristo M , Do R , Flannick J , Fromer M , Gauthier L , Goldstein J , Gupta N , Howrigan D , Kiezun A , Kurki MI , Moonshine AL , Natarajan P , Orozco L , Peloso GM , Poplin R , Rivas MA , Ruano-Rubio V , Rose SA , Ruderfer DM , Shakir K , Stenson PD , Stevens C , Thomas BP , Tiao G , Tusie-Luna MT , Weisburd B , Won H-H , Yu D , Altshuler DM , Ardissino D , Boehnke M , Danesh J , Donnelly S , Elosua R , Florez JC , Gabriel SB , Getz G , Glatt SJ , Hultman CM , Kathiresan S , Laakso M , McCarroll S , McCarthy MI , McGovern D , McPherson R , Neale BM , Palotie A , Purcell SM , Saleheen D , Scharf JM , Sklar P , Sullivan PF , Tuomilehto J , Tsuang MT , Watkins HC , Wilson JG , Daly MJ , MacArthur DG , Exome Aggregation Consortium . Analysis of protein-coding genetic variation in 60,706 humans . Nature 2016 ; 536 : 285 – 291 . OpenUrl CrossRef PubMed Web of Science [34]. ↵ Luby AO , Alessio-Bilowus D , Hu HM , Brummett CM , Waljee JF , Bicket MC . Trends in Opioid Prescribing and New Persistent Opioid Use After Surgery in the United States . Ann Surg 2025 ; 281 : 347 – 352 . OpenUrl [35]. ↵ Malek TR , Castro I . Interleukin-2 Receptor Signaling: At the Interface between Tolerance and Immunity . Immunity 2010 ; 33 : 153 – 165 . OpenUrl CrossRef PubMed Web of Science [36]. ↵ Mosher HJ , Krebs EE , Carrel M , Kaboli PJ , Weg MWV , Lund BC . Trends in prevalent and incident opioid receipt: an observational study in Veterans Health Administration 2004- 2012 . J Gen Intern Med 2015 ; 30 : 597 – 604 . OpenUrl CrossRef PubMed [37]. ↵ Nishizawa D , Fukuda K , Kasai S , Hasegawa J , Aoki Y , Nishi A , Saita N , Koukita Y , Nagashima M , Katoh R , Satoh Y , Tagami M , Higuchi S , Ujike H , Ozaki N , Inada T , Iwata N , Sora I , Iyo M , Kondo N , Won M-J , Naruse N , Uehara-Aoyama K , Itokawa M , Koga M , Arinami T , Kaneko Y , Hayashida M , Ikeda K . Genome-wide association study identifies a potent locus associated with human opioid sensitivity . Mol Psychiatry 2014 ; 19 : 55 – 62 . OpenUrl CrossRef PubMed [38]. ↵ Nishizawa D , Nagashima M , Katoh R , Satoh Y , Tagami M , Kasai S , Ogai Y , Han W , Hasegawa J , Shimoyama N , Sora I , Hayashida M , Ikeda K . Association between KCNJ6 (GIRK2) gene polymorphisms and postoperative analgesic requirements after major abdominal surgery . PloS One 2009 ; 4 : e7060 . OpenUrl PubMed [39]. ↵ Okie S . A flood of opioids, a rising tide of deaths . N Engl J Med 2010 ; 363 : 1981 – 1985 . OpenUrl CrossRef PubMed Web of Science [40]. Pelenyi A , Atterton C , Jones J , Currey L , Al-Khalily M , Wright L , Kurniawan ND , Thor S , Piper M . Expression of the Hippo pathway effector, TEAD1, within the developing murine forebrain . Gene Expr Patterns 2024 ; 54 : 119384 . OpenUrl [41]. ↵ Pierucci-Lagha A , Gelernter J , Feinn R , Cubells JF , Pearson D , Pollastri A , Farrer L , Kranzler HR . Diagnostic reliability of the Semi-structured Assessment for Drug Dependence and Alcoholism (SSADDA) . Drug Alcohol Depend 2005 ; 80 : 303 – 312 . OpenUrl CrossRef PubMed Web of Science [42]. ↵ Rentzsch P , Witten D , Cooper GM , Shendure J , Kircher M . CADD: predicting the deleteriousness of variants throughout the human genome . Nucleic Acids Res 2019 ; 47 : D886 – D894 . OpenUrl CrossRef PubMed [43]. ↵ Rudd RA , Aleshire N , Zibbell JE , Gladden RM . Increases in Drug and Opioid Overdose Deaths--United States, 2000-2014 . MMWR Morb Mortal Wkly Rep 2016 ; 64 : 1378 – 1382 . OpenUrl CrossRef PubMed [44]. ↵ SAMSHA . 2023 NSDUH Annual National Report | CBHSQ Data . 2024 . Available: https://www.samhsa.gov/data/report/2023-nsduh-annual-national-report . Accessed 2 Jun 2025 . [45]. ↵ SAMSHA . Treatment Episode Data Set (TEDS) 2001-2011 National Admissions to Substance Abuse Treatment Services | CBHSQ Data . 201 3. Available: https://www.samhsa.gov/data/report/treatment-episode-data-set-teds-2001-2011-national-admissions-substance-abuse-treatment . Accessed 28 May 2025 . [46]. Song S , Zhang X , Huang Z , Zhao Y , Lu S , Zeng L , Cai F , Wang T , Pei Z , Weng X , Luo W , Lu H , Wei Z , Wu J , Yu P , Shen L , Zhang X , Sun A , Ge J . TEA domain transcription factor 1 (TEAD1) induces cardiac fibroblasts cells remodeling through BRD4/Wnt4 pathway . Signal Transduct Target Ther 2024 ; 9 : 1 – 12 . OpenUrl CrossRef PubMed [47]. ↵ Song W , Lam M , Liu R , Simona A , Weiner SG , Urman RD , Mukamal KJ , Wright A , Bates DW . A genome-wide Association study of the Count of Codeine prescriptions . Sci Rep 2024 ; 14 : 22780 . OpenUrl [48]. ↵ Toikumo S , Vickers-Smith R , Jinwala Z , Xu H , Saini D , Hartwell EE , Pavicic M , Sullivan KA , Xu K , Jacobson DA , Gelernter J , Rentsch CT , Pavicic M , Stahl E , Cheatle M , Zhou H , Waxman SG , Justice AC , Kember RL , Kranzler HR . A multi-ancestry genetic study of pain intensity in 598,339 veterans . Nat Med 2024 ; 30 : 1075 – 1084 . OpenUrl CrossRef PubMed [49]. ↵ Verma A , Damrauer SM , Naseer N , Weaver J , Kripke CM , Guare L , Sirugo G , Kember RL , Drivas TG , Dudek SM , Bradford Y , Lucas A , Judy R , Verma SS , Meagher E , Nathanson KL , Feldman M , Ritchie MD , Rader DJ , For The Penn Medicine BioBank null. The Penn Medicine BioBank: Towards a Genomics-Enabled Learning Healthcare System to Accelerate Precision Medicine in a Diverse Population . J Pers Med 2022 ; 12 : 1974 . OpenUrl [50]. ↵ Watanabe K , Taskesen E , van Bochoven A , Posthuma D . Functional mapping and annotation of genetic associations with FUMA . Nat Commun 2017 ; 8 : 1826 . OpenUrl CrossRef PubMed [51]. ↵ Willer CJ , Li Y , Abecasis GR . METAL: fast and efficient meta-analysis of genomewide association scans . Bioinforma Oxf Engl 2010 ; 26 : 2190 – 2191 . OpenUrl [52]. ↵ Wu S , Trievel RC , Rice JC . Human SFMBT is a transcriptional repressor protein that selectively binds the N-terminal tail of histone H3 . FEBS Lett 2007 ; 581 : 3289 – 3296 . OpenUrl CrossRef PubMed Web of Science View the discussion thread. Back to top Previous Next Posted July 24, 2025. Download PDF Supplementary Material Data/Code Email Thank you for your interest in spreading the word about medRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. 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