Genome-Wide Association Study Identifies a Potential Genomic Risk Locus at Chr11q13.1 for Acute Kidney Injury in Patients Undergoing Off-Pump Coronary Artery Bypass Grafting

Genome-Wide Association Study Identifies a Potential Genomic Risk Locus at Chr11q13.1 for Acute Kidney Injury in Patients Undergoing Off-Pump Coronary Artery Bypass Grafting | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (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],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Genome-Wide Association Study Identifies a Potential Genomic Risk Locus at Chr11q13.1 for Acute Kidney Injury in Patients Undergoing Off-Pump Coronary Artery Bypass Grafting Muralidhar Kanchi, Varun Shetty, Maessen Jos, Avinash Arvind Rasalkar, and 11 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6170946/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract One of the most frequent perioperative complications in heart surgery is acute kidney damage (AKI). We conducted a genome-wide association study (GWAS) to investigate genetic predispositions for AKI and the impact of genome-wide polygenic risk scores for coronary artery disease (GPSCAD) in patients undergoing off-pump coronary artery bypass grafting (OP-CABG) in a South Asian population. Patients were categorized into three groups: (A) Patients undergoing OP-CABG who have normal renal function, (B) patients undergoing OP-CABG with pre-existing renal dysfunction, and (C) age-matched healthy controls. GWAS analysis was performed using logistic regression with age, gender, and top 10 principal components as covariates. Postoperative AKI was defined using KDIGO (The Kidney Disease: Improving Global Outcomes) criteria. Among 746 patients in group A, 80(10.7%) developed AKI and of 255 patients in group B, 167(65.5%) exhibited deterioration of renal function. GWAS identified significant single nucleotide polymorphisms (SNPs) on chr11q13.1 (rs11231649, p = 2.15E-08, rs60668438, p = 7.40E-08 and rs114977339, p = 8.98E-08). No significant GPS CAD differences were observed between AKI and non-AKI groups in group B. This study highlights significant genetic associations with AKI on chr11q13.1 in patients undergoing OP-CABG without pre-existing renal dysfunction. GPS CAD did not show an impact on AKI incidence. These findings warrant further validation in larger cohorts. Biological sciences/Genetics Biological sciences/Genetics/Genetic association study Biological sciences/Genetics/Genomics coronary artery bypass grafting genome-wide association renal function genetic predisposition Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Rapid or abrupt deterioration in kidney function, followed by excessive retention of nitrogenous waste and dysregulation of body electrolytes and volume, is a clinical phenomenon known as acute kidney injury (AKI). 1 AKI affects millions annually, contributing to significant morbidity and mortality. 2 The risk of AKI from cardiac surgery is high; depending on the operation, the incidence might range from 19–29%. 3 In cardiac surgery, AKI is one of the most common serious complication, 4 with incidences ranging from 5–30% following CABG. 5 Approximately 77,000 patients who undergo cardiac surgery each year experience postoperative AKI, with 14,000 of them requiring dialysis for the first time. 6 Renal Replacement Therapy (RRT) is necessary for about 3% of patients who develop AKI after CABG, and up to 60% of these patients pass away before being released from the hospital. Survivors of CABG continue to have chronic renal failure, either with or without dialysis. 7 Patients with AKI also have a significantly greater incidence of periprocedural complications and have an extended hospital duration of stay. These patients are more likely to experience long-term negative consequences, such as the onset of end-stage renal disease, chronic kidney disease, or even death. 8 – 11 Several AKI risk factors have been identified in cardiac surgery cohorts which includes advanced age, obesity, diabetes, chronic kidney disease (CKD), poor ventricular function, hypertension, use of nephrotic medications, severe anemia, and certain surgical procedures. 12 , 13 Given the multifactor-driven pathophysiology of AKI linked to cardiac surgery (CSA-AKI), the lack of a trustworthy and consistent relationship is a significant restriction. These limitations could be overcome through GWAS (genome-wide association studies) to identify risk variants for the development of coronary artery disease associated with AKI (CAD-AKI). Identifying genetic risk factors could improve risk stratification and guide preoperative strategies towards reducing the risk of AKI. A quantitative genetic risk score, the GPS CAD (genome-wide polygenic score for CAD) combines data from numerous prevalent DNA variations into a single figure. 14 With 6.6 million markers, the GPS CAD offers a generalizable approach for GPS evaluation based on ancestory and was created and tested in the South Asian population. 15 A risk estimator called GPS CAD is accessible from birth and divides people into several clinical risk trajectories for CAD. By using GPS CAD , it may be possible to identify high-risk individuals decades before their risk factors or diseases become apparent. 16 A GWAS is used to find out genomic variants that are statistically linked with a risk for a disease or a particular trait. 17 As cardiac surgery-associated AKI cannot be wholly explained by known risk factors, it is hypothesized that there is a genetic predisposition to renal injury in patients undergoing cardiac surgery, and can be tested using GWAS approach. Hence, this study investigates genetic predispositions to AKI using GWAS and evaluates the role of GPS CAD in an Indian cohort undergoing OP-CABG. Methods Samples/patient recruitment The study was approved by institutional ethics committee (NH/IRB-CL-2014-224) and all procedures were performed in accordance with ethical standards from the Helsinki Declaration of 1975, as revised in 2013. Informed written consent was collected from the patients participating in the study. In this we included patients from cardiology department who were undergoing OP-CABG with or without renal dysfunction. In this study we excluded the patients who underwent elective OP-CABG, patients receiving dialysis for stage 5 chronic kidney disease (CKD) or those requiring emergency surgery. To prevent confounding problems from contrast-induced nephropathy, the study excluded patients who had coronary angiography within 48 hours of scheduled surgery. Each participant provided demographic information, clinical history, clinical diagnosis, and family history. Serum creatinine was estimated and recorded postoperatively for 5 days and on the day of discharge and AKI was confirmed based on the KDIGO criteria. To verify the integrity and purity, these blood samples were used to extract and quantify DNA. Study participants were grouped as follows: Group A: patients with normal renal function (serum creatinine 60ml/min/m2), Group B: patients with pre-existing renal dysfunction (serum creatinine > 1.3mg/dl and/or eGFR < 60ml/min/m2) and Group C: healthy controls. Anaesthesia and Surgical Technique for groups A and B : After a comprehensive preoperative assessment and anesthesia review, all antihypertensive and antianginal drugs were continued for patients in groups A and B until the morning of surgery, with the exception of angiotensin-converting enzyme (ACE) inhibitors and angiotensin receptor blockers. Comorbidities, such as, diabetes mellitus, were optimally controlled and handled. The night before surgery, patients were given oral alprazolam as a sedative. A five-lead electrocardiogram (ECG) with simultaneous display of leads II and V5, automated ST segment analysis, pulse oximetry, and direct arterial blood pressure measurement were all part of the intraoperative monitoring. Following a preoxygenation period of three to five minutes, intravenous fentanyl (3 to 5 µg/kg), midazolam (0.05 to 0.1 mg/kg), and propofol (0.5 to 1 mg/kg) were used to induce anesthesia. The anesthesiologist chose either pancuronium or vecuronium to relax the muscles, followed by endotracheal intubation. A combination of oxygen, air, and 1% end-tidal isoflurane concentration was used to maintain anesthesia. A total fentanyl dose of 10 to 15 µg/kg was administered throughout the surgery. The perioperative strategy prioritized fast-tracking and early extubation. Heparin was administered at a dose of 300 units/kg before grafting, with additional doses of 100 units/kg given hourly until grafting was completed, ensuring an activated coagulation time of more than 300 seconds. At the end of the grafting process, heparin reversal was accomplished using a dose of 1 to 2 mg/kg of protamine. ECG, central venous pressure monitoring, and invasive arterial pressure measurement were all part of the hemodynamic monitoring. For patients with left ventricular dysfunction (ejection fraction < 40%), a pulmonary artery catheter was used. To check volume status, valve functioning, and heart function, transesophageal echocardiography was performed. Additionally atheromatous lesions in the ascending aorta, if any, were graded. Urine output was measured hourly. Off-pump coronary artery bypass grafting (OPCABG) with a myocardial stabilizer (Octopus) for distal coronary anastomosis was carried out by midline sternotomy. Unless otherwise specified, proximal anastomosis on the aorta was carried out at a systolic arterial pressure (SAP) of 80 to 90 mmHg, and distal anastomosis was carried out at a SAP of 110 to 120 mmHg. With the exception of times when modifications were necessary, throughout the procedure, a mean arterial pressure of 70 mmHg was maintained. Patients were electively ventilated for two to four hours after the surgery until they were hemodynamically stable, fully awake, and not bleeding. After a brief attempt of spontaneous breathing, extubation was performed. 18 DNA extraction and genotyping Genomic DNA was isolated using QIA Symphony (QIAGEN). Quantification of genomic DNA was done on Qubit and Qiaxpert. DNA quality was assessed using an agarose gel. Genotyping was conducted using Global Screening Array chip (Illumina), containing probes that covers ~ 640k markers. Two hundred nanograms of genomic DNA was isothermally amplified at 37°C for 20–24 hours in Illumina hybridization oven. The bead chips were placed into the hybridisation chambers and incubated for 16–24 hours at 48°C in an Illumina Hybridization oven. After hybridisation, chips were cleaned and prepared for single-base extension. After single-base extension, the chips were stained with fluorescent dyes (biotin for G and C nucleotides and DNP for A and T nucleotides) and scanned using the iScan system. Genotyping data analysis, imputation, GWAS and Polygenic Risk Score (PRS) generation The genotype data were exported using Genome Studio v2.0 (Illumina Inc.). Further, 488,765 common sites of genotyping data with South Asian whole-genome sequencing (WGS) data were obtained considering < 5% sample level missing rate, < 5% site level missing rate, Hardy Weinberg Equilibrium p-value of < 1e-10, and MAF cut-off 0.001. The combined data was pruned for estimating genetic relationship and principal component analysis (PCA). The genetic relationship of the samples was estimated using PLINK 1.9 19 , and the related samples were removed (Kinship cut-off 0.088). WGS samples were used for PCA analysis, and genotyped samples were assigned to WGS PCA space. Beagle version 5.0 was used to perform genotype imputation 20 with Genome Asia Pilot project reference panel comprising of 1648 samples of diverse ethnicities (South Asian = 677, West Eurasian = 109, African = 101, South East Asian = 332, North East Asian = 346, Oceanic = 66, Admixed-American = 23). The reference panel is generated using whole genome sequencing at 30x average depth. There was a total of 21.4 million sites imputed using Beagle tool. Further, we queried for the 6.6 million markers from the CARDIOGRAMplusC4D study. 21 Out of 6.6 million markers, there were 5,922,374 markers present in the imputed data. The GWAS summary of 6.6 million markers is available from 'CARDIOGRAMplusC4D' ( http://www.cardiogramplusc4d.org/data-downloads/ ). The modified version of LDpred algorithm was obtained from https://github.com/wavefancy/LDpredChrByChr 22 . There were 503 European ancestry samples from 1000 Genome phase III dataset, which were used to extract 6.6 million CAD related markers (The 1000 Genomes Project Consortium). This dataset was used as the LD reference panel to generate the LD structure file. LDpred’s Gibbs sampling option is used to get the re-weighted SNP effect size. PLINK version 2.0 was used to generate the polygenetic risk score . 22 Using FlashPCA tool, the genotyping data were projected to South Asian WGS PCA space and derived the residuals to normalize the PRS. GWAS analysis was performed using age, genders and top10 principal components as covariates with logistic regression by using PLINK 2.0. The results were annotated using functional mapping and annotation (FUMA) web server. The overall workflow of the GWAS study is shown in Fig. 1 . In this study we considered the widely accepted p-value threshold of 1 × 10 − 8 as genome-wide significant. Fisher's test was used to compare the categorical data, and the t-test was utilized to compare the numerical data between the groups. Gene-Based Analysis and Gene-Set Analysis Multi-marker analysis of genomic annotation (MAGMA) v1.07, which is integrated into FUMA, was used to analyze the gene set. 23 Several genetic markers can be analyzed together using this method to ascertain their combined impact. Protein-coding genes were mapped to input SNPs. MAGMA-based gene analyses, which apply an SNP-wide mean model to GWAS summary results, were performed with default parameters. Similar procedures were used for gene-set studies, which limited the sets being examined to those that are a component of the biological processes described by Gene Ontology. We used gene-set analysis by MAGMA, to understand the biological enriched pathways related with the AKI. Gene-set enrichments were considered significant at an adjusted ‘p’ value level < 0.05. Results 1825 participants were enrolled in the study. In group A there were 746 patients of which, 80 patients developed AKI postoperatively and 666 patients did not develop AKI. In group B there were 255 patients, 167 patients developed AKI and 88 did not develop AKI. In group C there were 824 age-matched healthy control population. Figure 2 summarizes the study participants in each group. Characteristics of the participant population The baseline clinical and demographic characteristics of the participants are shown in Table 1 . In group A, the mean age was 58 ± 9.2 years who developed AKI and 56 ± 9.5 years who did not develop AKI. In Group A 10.7% patients developed AKI. In group B, the mean age was 60 ± 8.4 years for group B-AKI and 58 ± 8.3 years for group B-no-AKI. In Group B 65.5% patients experienced renal deterioration. In groups C, the mean age was 52 ± 6.6 years and there were healthy control in this group. Male patients in group A who developed AKI were 92.5% and who did not develop AKI were 87.69%, in group B who developed AKI were 94.01% and who did not developed AKI were 95.45% and in group C were 85.35%. The details of comorbidities in patients of all groups were given in Table 1 . There was statistical significance of age, serum creatinine, GFR and urine output in patients of group A who developed AKI and who does not developed AKI after OP-CABG (P values = 0.0009, 1.4733E-12, 2.0942E-09 and 0.015, respectively). There was statistical significance of serum creatinine and GFR in patients of group B who developed AKI and who does not developed AKI after OP-CABG (P values = 0.0044 and 0.0369, respectively). Table 1 Baseline characteristics of study groups. Group A Group B Group C (n = 824) AKI (n = 80) No AKI (n = 666) P value AKI (n = 167) No AKI (n = 88) P value Age 58 (± 9.2) 56 (± 9.5) 0.0009 60 (± 8.4) 58 (± 8.3) 0.1865 52 (± 6.6) Male (%) 92.5 87.69 0.9731 94.01 95.45 0.4350 85.35 BMI, kg/m2 24.72 (± 3.2) 24.71 (± 3.4) 0.4456 24.375 (± 3.5) 24.82 (± 3.4) 0.1263 26.47 (± 4) Serum creatinine (mg/dl) 1.16 (± 0.1) 0.98 (± 0.2) 1.4733E-12 1.46 (± 0.7) 1.33 (± 0.3) 0.0044 0.88 (± 0.2) LDL-C level (mg/dl) 76 (± 33.7) 78 (± 40.5) 0.8964 77 (± 35.7) 66.5 (± 32.4) 0.2986 124 (± 34.4) Total cholesterol (mg/dl) 132 (± 36.4) 129.5 (± 47) 0.9264 133 (± 39.2) 117 (± 37.3) 0.2671 184 (± 39.2) Hypertension (%) 72.5% 69.97% 0.8029 83.23 71.59 0.9946 33.05 Diabetes (%) 65% 63.51% 0.7339 70.66 63.64 0.9004 31.84 Previous MI (%) 26.25% 22.50% 0.3796 27.50 22.50 0.3566 NA GFR ( ml/min/m2) 69.58 (± 15.09) 82.74 (± 18.66) 2.0942E-09 46.32(± 10.35) 51.82(± 8.56) 0.0369 NA Urine output (ml/hour) 140.91 (± 49.39) 159.88 (± 49.28) 0.0159 126.09(± 45.25) 136.10 (± 41.97) 0.1596 NA GWAS for AKI among Group A In Fig. 3 B, Manhattan plots show the GWAS results for every SNP associated with the risk of AKI. The top 25 significant markers are shown in supplementary table 1 . The supplemental Fig. 1, displays the Q-Q plot. The measured genomic inflation factor (λ) of 1.0 indicates that the association analysis should not be significantly impacted by the population substructure. Further, the association analysis identified 607 SNPs with suggestive significance p-value threshold of 1 × 10 − 4 (Supplementary Table 2). However, none of the markers remained significant after multiple testing corrections using Benjamini-Hochberg. 24 SNPs found for Genome wide association was computed as explained in the methodology and presented in the Manhattan Plot, Fig. 3 B, that reported the strong association of 3 SNPs located on chromosome 11q13.1, viz, rs11231649, rs60668438 and rs114977339 with AKI, Fig. 3 B. To confirm the involvement of these 3 SNPs we calculated Odds Ratio (OR) that reported chr11: 63710016G > A (rs11231649; OR = 3.7, P = 0.000000021), and chr 11: 63706276G > A, (rs60668438; OR = 3.63; P = 0.000000074) and chr 11: 63716240C > T (rs114977339; OR = 3.56; P = 0.000000089). (Fig. 3 A). Figure 3 C, shows the locus zoom plot of the significant region in chromosome 11. GWAS for AKI among Group B Figure 4 A, represents the Manhattan plot for Group B which indicates that there were very few alterations above the threshold of 4x10 − 8 . To further access the involvement of the identified loci on chr11q13.1 we analyzed these three SNPs in individuals of group B, who had pre- existing renal dysfunction, in that we found, chr11: 63710016G > A (rs11231649; OR = 1.1, p = 0.52), chr 11: 63706276G > A, (rs60668438; OR = 1.2, p = 0.4) and chr 11: 63716240C > T (rs114977339; OR = 0.9; P = 0.93) (Fig. 4 B). Genetic Data Analysis We observed major difference between allele frequency in individuals who developed AKI and individuals who did not develop AKI in group A for SNPs: 11:63710016 it was 0.23 and 0.08, respectively, for 11:63706276 it was 0.23 and 0.09, respectively and for 11:63716240 it was 0.2 and 0.06, respectively. For group B the less difference was seen in allele frequency in individuals who developed AKI and individuals who did not develop AKI for SNPs: 11:63710016 it was 0.14 and 0.11, respectively, for 11:63706276 it was 0.12 and 0.09, respectively and for 11:63716240 it was 0.10 and 0.09, respectively. Allele frequency for group C for SNPs are, 11:63710016 it was 0.11 for 11:63706276 it was 0.11 and for 11:63716240 it was 0.08. (Fig. 5 ). Gene-based enrichment analysis on AKI The key genes from the MAGMA gene-based test included MACROD2 (P = 3.20E-10), GPC6 (P = 4.77E-10) and MAGI2 (P = 2.45E-09) (Fig. 6 ). The top 25 most significant genes are summarised in Supplementary table 1 . Gene-set enrichment analysis on AKI Using the association analysis summary data, we subsequently utilized gene-set analysis to identify the major enriched gene sets associated with AKI. Six gene-sets satisfied the statistical significance with adjusted p < 0.05 which are shown in Table 2 . The top 3 most significant gene-sets were nedd8_specific_protease_activity (Gene-set size: 3; adjusted P value 3.85E-06), followed by negative_regulation_of_cell_ cycle_checkpoint (Gene-set size: 2; adjusted P value 0.00573) and negative_regulation_of_DNA_damage_checkpoint (Gene-set size: 2; adjusted P value 0.00573). Top gene sets linked to cell cycle regulation and DNA damage repair. Table 2 Gene-set enrichment results Gene sets N genes Raw p vale Adjusted p vale GO_mf:go_nedd8_specific_protease_activity 3 2.63E-10 3.85E-06 GO_bp:go_negative_regulation_of_cell_cycle_checkpoint 2 3.91E-07 0.00573 GO_bp:go_negative_regulation_of_dna_damage_checkpoint 2 3.91E-07 0.00573 Curated_gene_sets:lei_hoxc8_targets_dn 7 5.27E-07 0.007721 GO_bp:go_histone_h2a_k63_linked_ubiquitination 2 2.14E-06 0.031387 GO_bp:go_positive_regulation_of_hair_cycle 7 3.20E-06 0.046831 GPS CAD and correlation with AKI We further investigated the influence of GPS CAD on the development of postoperative AKI. Figure 7 A, shows the CAD PRS model generation and validation process. The GPS CAD was generated for all samples in the three groups and were binned into 10 deciles in the increasing order of GPS CAD . We observed an increase of GPS CAD across deciles 1 to 10 in the group A which consisted of patients undergoing OP-CABG with normal renal function. The first decile (lowest GPS CAD bin) comprises of ~ 59% of healthy controls (group C), 22% group A and 12% group B. Comparatively, the last decile (highest GPS CAD bin) comprises of ~ 25% of healthy controls, 56% group A and 15% group B (Fig. 7 B; Supplementary table 3). The healthy controls (group C) had a significantly lower GPS CAD than other groups. No difference was observed in GPS CAD between AKI and no-AKI samples belonging to the group A (Fig. 7 C; Supplementary table 4). The top decile consists of 22.5% of AKI and 23.2% no-AKI subjects. Based on our findings in the tested sample set, there is no significant association between GPS CAD and AKI across groups. Discussion In order to test for genetic variations linked to AKI after CABG surgery without the use of Cardiopulmonary Bypass (CPB), we provided a genome-wide analysis in this study. In group A among all the chromosomes we found some alterations which were little above the threshold and few were very above the threshold, which has shown the significant association with the risk of AKI. The top three SNPs were found on chr11q13.1 - chr11: 63710016, chr 11: 63706276, and chr 11: 63716240. These SNPs were statistically significant for group A and were not statistically significant with group B. There were major differences in the allele frequencies between individuals who developed acute kidney injury (AKI) compared to those who did not in group A, while for group B the less difference was seen in allele frequency for these SNPs. Additionally, we looked into the relationship between a GPSCAD and the onset of AKI following OP-CABG. Our study found no evidence to support a suggestion that patients with a high GPS CAD have an increased likelihood of developing AKI after OP-CABG. These results do not provide evidence that GPS CAD is associated with AKI. Till date, only two GWAS for AKI have been reported in patients who were undergoing CABG. 12 , 25 The first study conducted in European population, used a discovery-replication analysis approach. Nine SNPs showed significant association in the discovery of data set among which rs13317787 in GRM7/LMCD1-AS1 intergenic region (3p21.6) and rs10262995 in BBS9 (7p14.3) that remained significant in replication data set as well as meta-analysis. Subsequent fine-mapping with imputation across these two regions and meta-analysis revealed substantial connection at the BBS9 gene and genome-wide significance at GRM7/LMCD1-AS1 locus. 25 The second study, conducted in subjects with Caucasian ethnicity, revealed association between AKI and four SNPs. These included rs6234163 and rs62341657 on chr4q35.1 near APOL1-regulator IRF2, and rs9617814 and rs10854554 on chr22q11.21 near acute kidney injury–related gene TBX1. 12 In this study, we conducted GWAS to identify genomic predispositions for AKI developed post-OP-CABG and observed a significant signal (association) at 11q13.1. However, this signal did not pass the multiple corrections (Benjamini-Hochberg). This work is the first that we are aware of that uses GWAS to find SNPs associated with AKI in an Indian population. Subsequently, we assessed the association of GPS CAD with the development of AKI post OP-CABG and did not find any significant association. P Bhatraju et al. recently conducted a single genome-wide association study only for AKI. In this, Assessment, Serial Evaluation, and Subsequent Sequelae of AKI did not show any genome-wide significant association with AKI risk (P < 5×10 − 8). 26 In our study we found the significant association of AKI in the patients who were undergoing CABG without renal dysfunction and in patients with pre-existing renal function we did not found any significant genome wide association. Although, GWAS in this study was performed with relatively small number of subjects, the results are indicative of identifying the potential risk variants. The significant marker was located on chr11q31.1 locus. This region has not been reported in literature in association with AKI. However, this region has been reported in kidney disease-related trait, uric acid levels, in a GWAS study in Korean population. 27 Ten SNPs in six genetic loci, which included OTUB1, NRXN2/SLC22A12, CDC42BPG, RPS6KA4, SLC22A9, and MAP4K2, were shown to be associated with uric acid on chromosome 11. Our top significant marker (rs11231649) is located in the intronic region on N-alpha-acetyltransferase 40 (NAA40). The second and third significant SNP (rs60668438 and rs114977339) is located upstream and intronic region of NAA40, respectively, which contribute to the pathogenesis of colon cancer. 28 Findings on chr11q13.1 align with known roles of NAA40 and related pathways. Our gene-based analyses identified MARCRO2 (mono-ADP ribosylhydrolase 2) was the most significant gene. An intragenic deletion has been reported in a family with complex phenotype which also included renal dysfunction. 29 It is also known to be associated with congenital heart disease, obesity and physical activity, and autistic-like traits. 30 – 32 Another top-hit gene MAGI2 (membrane-associated guanylate kinase, WW, and PDZ domain containing 2) is known to harbour mutations causing Nephrotic syndrome, type 15 (NPHS15). 32 , 33 It is also known to be associated with cognitive impairment. 34 The results of the gene-based analyses need to be replicated in larger and other data sets to get more insights into these genes as causal factors for AKI. This is a large study looking into genetic predisposition to AKI in patients undergoing OP-CABG in the Indian subcontinent, as per our knowledge. A number of important limitations should be taken into account when interpreting the study's findings. The limitation of the present study include low sample size and lack of replication datasets. Further, validation of our results was not possible due to the unavailability of a validation sample set, with individuals undergone CABG and developed AKI. The low-frequency and rare variants could have been overlooked and a sequencing-based approach might identify the rare variants missed by our study. This study's strength is the use of a GWAS design, which enables a thorough analysis of genetic variants linked to acute kidney damage (AKI) in a particular cohort of patients having off-pump coronary artery bypass grafting (OP-CABG). This approach enables the detection of previously unrecognized genetic factors, which may lead to more personalized risk prediction and management strategies for AKI in CABG patients. However, future research needs to concentrate on confirming these loci's functionality. Conclusion This study is the first GWAS to analyze predispositions for AKI in the Indian population undergoing OPCABG. The top three SNPs were found on chr11q13.1 - chr11: 63710016, chr 11: 63706276, and chr 11: 63716240. Our study identifies chr11q13.1 as a potential genomic risk locus for AKI in OP-CABG patients without pre-existing renal dysfunction. Additionally, there was no significant correlation between GPS CAD and the development of AKI. These findings provide a basis for further genetic investigations and risk stratification strategies on chr11q13.1 for these SNPs on broader scale. Declarations Author contributions: MK, VS, GMMR and MJ contributed to conceptualization, study design, manuscript drafting, and critical review. AAR and GC were responsible for imaging analysis, genetic analysis, data interpretation, and manuscript drafting. AC and VLR provided bioinformatics support, genetic counseling, and manuscript review. RMB and RMA were involved in clinical evaluation, clinical support, and manuscript review. L and RG contributed to data validation, literature review, and manuscript drafting. SM analyzed the historical context and participated in manuscript review. RM and GMMR handled data curation, statistical validation, and manuscript review. All authors conducted the final manuscript review and approval. Data availability statement : The datasets used or analyzed during the current study available from the corresponding author on reasonable request. Acknowledgments : The authors are grateful to the following research assistants: Ms Hilda, Ms Esaimalar, Mr Dheeraj Maajhi, Mr Sashank Viswanathan, Mr Akshay Bhati, Dr Gokul and Dr. Vaishnav Funding : This project was funded by the Indian Council of Medical Research (ICMR): No. BMS/ADHOC/GEN/2015-0567/JUN-16/3/KA/PVT Conflict of Interest : No Conflict of Interest References Acute Kidney Injury (AKI): Practice Essentials, Background, Pathophysiology. (2024). https:// Scurt, F. G., Bose, K., Mertens, P. R., Chatzikyrkou, C. & Herzog, C. 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Khera, A. V. et al. Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat. Genet. 50 , 1219 (2018). Wang, M. et al. Developing Genome-wide Polygenic Risk Scores for Coronary Artery Disease in South Asians. J. Am. Coll. Cardiol. 76 , 703 (2020). Hindy, G. et al. Genome-Wide Polygenic Score, Clinical Risk Factors, and Long-Term Trajectories of Coronary Artery Disease. Arterioscler. Thromb. Vasc. Biol. 40 , 2738–2746 (2020). Genome-Wide Association Studies (GWAS). https://www.genome.gov/genetics-glossary/Genome-Wide-Association-Studies-GWAS Kanchi, M., Menon, R., Mishra, S. & Vedam, R. Genome wide association study for acute kidney injury in patients undergoing off-pump coronary artery bypass graft surgery. Circulation 144 , (2021). Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81 , 559–575 (2007). Browning, B. L., Zhou, Y. & Browning, S. R. A One-Penny Imputed Genome from Next-Generation Reference Panels. Am. J. Hum. Genet. 103 , 338–348 (2018). Nikpay, M. et al. A comprehensive 1,000 Genomes-based genome-wide association meta-analysis of coronary artery disease. Nat. Genet. 47 , 1121–1130 (2015). Vilhjálmsson, B. J. et al. Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores. Am. J. Hum. Genet. 97 , 576–592 (2015). de Leeuw, C. A., Mooij, J. M., Heskes, T. & Posthuma, D. MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput. Biol. 11 , e1004219 (2015). Benjamini, Y. & Hochberg, Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. J. Royal Stat. Soc. Ser. B (Methodological) . 57 , 289–300 (1995). Zhao, B. et al. A Genome-Wide Association Study to Identify Single-Nucleotide Polymorphisms for Acute Kidney Injury. Am. J. Respir Crit. Care Med. 195 , 482–490 (2017). Bhatraju, P. K. et al. Genome-wide Association Study for AKI. Kidney360 4, 870–880 (2023). Lee, J. et al. Genome-wide association analysis identifies multiple loci associated with kidney disease-related traits in Korean populations. PLoS One . 13 , e0194044 (2018). Demetriadou, C. et al. NAA40 contributes to colorectal cancer growth by controlling PRMT5 expression. Cell. Death Dis. 10 , 236 (2019). Lombardo, B. et al. Intragenic Deletion in MACROD2: A Family with Complex Phenotypes Including Microcephaly, Intellectual Disability, Polydactyly, Renal and Pancreatic Malformations. Cytogenet. Genome Res. 158 , 25–31 (2019). Lahm, H. et al. Congenital heart disease risk loci identified by genome-wide association study in European patients. J Clin Invest 131, e141837, 141837 (2021). Kim, H. R., Jin, H. S. & Eom, Y. B. Association of MACROD2 gene variants with obesity and physical activity in a Korean population. Mol. Genet. Genomic Med. 9 , e1635 (2021). Jones, R. M. et al. MACROD2 gene associated with autistic-like traits in a general population sample. Psychiatr Genet. 24 , 241–248 (2014). Bierzynska, A. et al. MAGI2 Mutations Cause Congenital Nephrotic Syndrome. J. Am. Soc. Nephrol. 28 , 1614–1621 (2017). Ashraf, S. et al. Mutations in six nephrosis genes delineate a pathogenic pathway amenable to treatment. Nat. Commun. 9 , 1960 (2018). Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6170946","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":477503778,"identity":"9413e77c-d611-4c6a-b4f4-09434aa7be64","order_by":0,"name":"Muralidhar Kanchi","email":"data:image/png;base64,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","orcid":"","institution":"Narayana Institute of Cardiac Sciences, Narayana Health","correspondingAuthor":true,"prefix":"","firstName":"Muralidhar","middleName":"","lastName":"Kanchi","suffix":""},{"id":477503779,"identity":"56997ea1-b4f6-46f5-b95f-58877d6835cb","order_by":1,"name":"Varun Shetty","email":"","orcid":"","institution":"Narayana 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Health","correspondingAuthor":false,"prefix":"","firstName":"Limesh","middleName":"","lastName":"Marisiddappa","suffix":""},{"id":477503786,"identity":"dfffe901-e72b-4aa4-a5cb-05db56ca3056","order_by":8,"name":"Vedam L Ramprasad","email":"","orcid":"","institution":"MedGenome Labs","correspondingAuthor":false,"prefix":"","firstName":"Vedam","middleName":"L","lastName":"Ramprasad","suffix":""},{"id":477503787,"identity":"1d2c91ac-baf6-41bc-8653-a84b913e8682","order_by":9,"name":"Ravi Gupta","email":"","orcid":"","institution":"MedGenome Lab Ltd","correspondingAuthor":false,"prefix":"","firstName":"Ravi","middleName":"","lastName":"Gupta","suffix":""},{"id":477503788,"identity":"b57cc72f-bdd9-4ed2-b03a-c875708e9840","order_by":10,"name":"Sanghamitra Mishra","email":"","orcid":"","institution":"MedGenome Lab Ltd","correspondingAuthor":false,"prefix":"","firstName":"Sanghamitra","middleName":"","lastName":"Mishra","suffix":""},{"id":477503789,"identity":"aecab340-6f64-4970-bb7d-60c968544465","order_by":11,"name":"Ram Murthy Anjanappa","email":"","orcid":"","institution":"MedGenome Lab Ltd","correspondingAuthor":false,"prefix":"","firstName":"Ram","middleName":"Murthy","lastName":"Anjanappa","suffix":""},{"id":477503790,"identity":"6eeb55b1-f677-4ea4-bc08-c11b65e7056c","order_by":12,"name":"Ramesh Menon","email":"","orcid":"","institution":"MedGenome Lab Ltd","correspondingAuthor":false,"prefix":"","firstName":"Ramesh","middleName":"","lastName":"Menon","suffix":""},{"id":477503791,"identity":"60cc98ac-1e43-4a95-badb-16767f772218","order_by":13,"name":"Gopireddy Murali Mohan Reddy","email":"","orcid":"","institution":"coGuide Academy","correspondingAuthor":false,"prefix":"","firstName":"Gopireddy","middleName":"Murali Mohan","lastName":"Reddy","suffix":""},{"id":477503792,"identity":"dbcfcdbb-2b2c-4145-aa69-49f1f7771680","order_by":14,"name":"Kumar Belani","email":"","orcid":"","institution":"University of Minnesota","correspondingAuthor":false,"prefix":"","firstName":"Kumar","middleName":"","lastName":"Belani","suffix":""}],"badges":[],"createdAt":"2025-03-06 13:23:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6170946/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6170946/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85828873,"identity":"b28507be-fabf-4ef9-b98e-c9f8f2e069ba","added_by":"auto","created_at":"2025-07-02 07:32:12","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":65653,"visible":true,"origin":"","legend":"\u003cp\u003eGWAS Workflow\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6170946/v1/e40f0f2c97119472ca4998fc.jpeg"},{"id":85830531,"identity":"e82e9b10-e430-4b29-a430-c490cb900b99","added_by":"auto","created_at":"2025-07-02 07:40:12","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":208609,"visible":true,"origin":"","legend":"\u003cp\u003eRepresentation of the sample details.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6170946/v1/81dad1f58846d907101524f6.jpeg"},{"id":85831159,"identity":"610ee260-2c77-4596-8850-bb018411a21b","added_by":"auto","created_at":"2025-07-02 07:48:12","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":93680,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA: \u003c/strong\u003eRisk of different SNPs of chromosome 11 in patients of Group A \u003cstrong\u003eB. \u003c/strong\u003eManhattan plot highlighting chr11q13.1. \u003cstrong\u003eC. \u003c/strong\u003eshows the locus zoom plot of the significant region in chromosome 11.\u003c/p\u003e\n\u003cp\u003e(GAsPh2- GenomeAsia phase 2; GSA- Global screening array; MAF- Minor allele frequency; HWE – Hardy-Weinberg equilibrium.)\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6170946/v1/b7113f8ec49f1bb2989792ef.jpeg"},{"id":85828878,"identity":"6fc19105-eeb8-45ae-97d2-f5d37b260735","added_by":"auto","created_at":"2025-07-02 07:32:12","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":74373,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA: \u003c/strong\u003eManhattan plot for Group A \u003cstrong\u003eB: \u003c/strong\u003eRisk of different SNPs of chromosome 11 in patients of Group A\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6170946/v1/3ee9063b75057045eaf03f95.jpeg"},{"id":85830537,"identity":"a30ce447-d544-452a-9102-779281545ddd","added_by":"auto","created_at":"2025-07-02 07:40:12","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":74200,"visible":true,"origin":"","legend":"\u003cp\u003eAllele frequency comparison of most significant markers rs11231649 (chr11: 63710016), rs60668438 (chr 11: 63706276) and rs114977339 (chr 11: 63716240) in AKI vs no-AKI from group A and group B and for group C and 1000 Genome South Asian (SAS).\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6170946/v1/3cf64fc77107998eb284ecab.jpeg"},{"id":85828884,"identity":"d3857164-9f56-4396-96f4-9aab0d3f492c","added_by":"auto","created_at":"2025-07-02 07:32:12","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":188423,"visible":true,"origin":"","legend":"\u003cp\u003eSignificant genes in the gene-based association test in MAGMA after multiple correction. The x-axis represents the chromosome number, y-axis shows the negative log10-transformed gene-based P-value and the red dotted line indicates significance level. The significant genes are annotated with the corresponding gene symbols.\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6170946/v1/14c7f0b65ac486dbf13504a7.jpeg"},{"id":85831160,"identity":"ddedb07d-b6ae-4296-b8c6-5fbefb3f23b7","added_by":"auto","created_at":"2025-07-02 07:48:12","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":219273,"visible":true,"origin":"","legend":"\u003cp\u003eA. Flowchart showing CAD PRS model generation and validation. B. Graphical representation of various groups (A and B), and AKI vs No-AKI (Panel C) in different decile bins graded according to the CAD\u003csub\u003eGPS\u003c/sub\u003e score.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-6170946/v1/5399764429330aa8b780499b.png"},{"id":88088461,"identity":"dd064693-b621-4f43-80f0-9e1327923275","added_by":"auto","created_at":"2025-08-01 09:47:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1898927,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6170946/v1/676fd1c7-a53f-47b6-a754-a47079f589c5.pdf"},{"id":85830534,"identity":"9156f173-7fae-4547-ba37-0fce93382f8e","added_by":"auto","created_at":"2025-07-02 07:40:12","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":184924,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-6170946/v1/5700ff55fd0c58eea34638fa.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eGenome-Wide Association Study Identifies a Potential Genomic Risk Locus at Chr11q13.1 for Acute Kidney Injury in Patients Undergoing Off-Pump Coronary Artery Bypass Grafting\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRapid or abrupt deterioration in kidney function, followed by excessive retention of nitrogenous waste and dysregulation of body electrolytes and volume, is a clinical phenomenon known as acute kidney injury (AKI).\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e AKI affects millions annually, contributing to significant morbidity and mortality.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe risk of AKI from cardiac surgery is high; depending on the operation, the incidence might range from 19\u0026ndash;29%.\u003csup\u003e3\u003c/sup\u003e In cardiac surgery, AKI is one of the most common serious complication,\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e with incidences ranging from 5\u0026ndash;30% following CABG.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e Approximately 77,000 patients who undergo cardiac surgery each year experience postoperative AKI, with 14,000 of them requiring dialysis for the first time.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e Renal Replacement Therapy (RRT) is necessary for about 3% of patients who develop AKI after CABG, and up to 60% of these patients pass away before being released from the hospital. Survivors of CABG continue to have chronic renal failure, either with or without dialysis.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e Patients with AKI also have a significantly greater incidence of periprocedural complications and have an extended hospital duration of stay. These patients are more likely to experience long-term negative consequences, such as the onset of end-stage renal disease, chronic kidney disease, or even death.\u003csup\u003e\u003cspan additionalcitationids=\"CR9 CR10\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e Several AKI risk factors have been identified in cardiac surgery cohorts which includes advanced age, obesity, diabetes, chronic kidney disease (CKD), poor ventricular function, hypertension, use of nephrotic medications, severe anemia, and certain surgical procedures.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eGiven the multifactor-driven pathophysiology of AKI linked to cardiac surgery (CSA-AKI), the lack of a trustworthy and consistent relationship is a significant restriction. These limitations could be overcome through GWAS (genome-wide association studies) to identify risk variants for the development of coronary artery disease associated with AKI (CAD-AKI). Identifying genetic risk factors could improve risk stratification and guide preoperative strategies towards reducing the risk of AKI. A quantitative genetic risk score, the GPS\u003csub\u003eCAD\u003c/sub\u003e (genome-wide polygenic score for CAD) combines data from numerous prevalent DNA variations into a single figure.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e With 6.6\u0026nbsp;million markers, the GPS\u003csub\u003eCAD\u003c/sub\u003e offers a generalizable approach for GPS evaluation based on ancestory and was created and tested in the South Asian population.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e A risk estimator called GPS\u003csub\u003eCAD\u003c/sub\u003e is accessible from birth and divides people into several clinical risk trajectories for CAD. By using GPS\u003csub\u003eCAD\u003c/sub\u003e, it may be possible to identify high-risk individuals decades before their risk factors or diseases become apparent.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eA GWAS is used to find out genomic variants that are statistically linked with a risk for a disease or a particular trait.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e As cardiac surgery-associated AKI cannot be wholly explained by known risk factors, it is hypothesized that there is a genetic predisposition to renal injury in patients undergoing cardiac surgery, and can be tested using GWAS approach. Hence, this study investigates genetic predispositions to AKI using GWAS and evaluates the role of GPS\u003csub\u003eCAD\u003c/sub\u003e in an Indian cohort undergoing OP-CABG.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSamples/patient recruitment\u003c/h2\u003e \u003cp\u003e The study was approved by institutional ethics committee (NH/IRB-CL-2014-224) and all procedures were performed in accordance with ethical standards from the Helsinki Declaration of 1975, as revised in 2013. Informed written consent was collected from the patients participating in the study. In this we included patients from cardiology department who were undergoing OP-CABG with or without renal dysfunction. In this study we excluded the patients who underwent elective OP-CABG, patients receiving dialysis for stage 5 chronic kidney disease (CKD) or those requiring emergency surgery. To prevent confounding problems from contrast-induced nephropathy, the study excluded patients who had coronary angiography within 48 hours of scheduled surgery. Each participant provided demographic information, clinical history, clinical diagnosis, and family history. Serum creatinine was estimated and recorded postoperatively for 5 days and on the day of discharge and AKI was confirmed based on the KDIGO criteria. To verify the integrity and purity, these blood samples were used to extract and quantify DNA.\u003c/p\u003e \u003cp\u003eStudy participants were grouped as follows: Group A: patients with normal renal function (serum creatinine\u0026thinsp;\u0026lt;\u0026thinsp;1.3mg/dl and/or eGFR\u0026thinsp;\u0026gt;\u0026thinsp;60ml/min/m2), Group B: patients with pre-existing renal dysfunction (serum creatinine\u0026thinsp;\u0026gt;\u0026thinsp;1.3mg/dl and/or eGFR\u0026thinsp;\u0026lt;\u0026thinsp;60ml/min/m2) and Group C: healthy controls.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e\u003c/h3\u003e\n\u003cdiv class=\"Heading\"\u003e\u003cb\u003eAnaesthesia and Surgical Technique for groups A and B\u003c/b\u003e:\u003c/div\u003e \u003cp\u003eAfter a comprehensive preoperative assessment and anesthesia review, all antihypertensive and antianginal drugs were continued for patients in groups A and B until the morning of surgery, with the exception of angiotensin-converting enzyme (ACE) inhibitors and angiotensin receptor blockers. Comorbidities, such as, diabetes mellitus, were optimally controlled and handled. The night before surgery, patients were given oral alprazolam as a sedative.\u003c/p\u003e \u003cp\u003eA five-lead electrocardiogram (ECG) with simultaneous display of leads II and V5, automated ST segment analysis, pulse oximetry, and direct arterial blood pressure measurement were all part of the intraoperative monitoring. Following a preoxygenation period of three to five minutes, intravenous fentanyl (3 to 5 \u0026micro;g/kg), midazolam (0.05 to 0.1 mg/kg), and propofol (0.5 to 1 mg/kg) were used to induce anesthesia. The anesthesiologist chose either pancuronium or vecuronium to relax the muscles, followed by endotracheal intubation. A combination of oxygen, air, and 1% end-tidal isoflurane concentration was used to maintain anesthesia. A total fentanyl dose of 10 to 15 \u0026micro;g/kg was administered throughout the surgery. The perioperative strategy prioritized fast-tracking and early extubation. Heparin was administered at a dose of 300 units/kg before grafting, with additional doses of 100 units/kg given hourly until grafting was completed, ensuring an activated coagulation time of more than 300 seconds. At the end of the grafting process, heparin reversal was accomplished using a dose of 1 to 2 mg/kg of protamine. ECG, central venous pressure monitoring, and invasive arterial pressure measurement were all part of the hemodynamic monitoring. For patients with left ventricular dysfunction (ejection fraction\u0026thinsp;\u0026lt;\u0026thinsp;40%), a pulmonary artery catheter was used. To check volume status, valve functioning, and heart function, transesophageal echocardiography was performed. Additionally atheromatous lesions in the ascending aorta, if any, were graded. Urine output was measured hourly. Off-pump coronary artery bypass grafting (OPCABG) with a myocardial stabilizer (Octopus) for distal coronary anastomosis was carried out by midline sternotomy. Unless otherwise specified, proximal anastomosis on the aorta was carried out at a systolic arterial pressure (SAP) of 80 to 90 mmHg, and distal anastomosis was carried out at a SAP of 110 to 120 mmHg. With the exception of times when modifications were necessary, throughout the procedure, a mean arterial pressure of 70 mmHg was maintained. Patients were electively ventilated for two to four hours after the surgery until they were hemodynamically stable, fully awake, and not bleeding. After a brief attempt of spontaneous breathing, extubation was performed.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003ch3\u003eDNA extraction and genotyping\u003c/h3\u003e\n\u003cp\u003eGenomic DNA was isolated using QIA Symphony (QIAGEN). Quantification of genomic DNA was done on Qubit and Qiaxpert. DNA quality was assessed using an agarose gel. Genotyping was conducted using Global Screening Array chip (Illumina), containing probes that covers\u0026thinsp;~\u0026thinsp;640k markers. Two hundred nanograms of genomic DNA was isothermally amplified at 37\u0026deg;C for 20\u0026ndash;24 hours in Illumina hybridization oven. The bead chips were placed into the hybridisation chambers and incubated for 16\u0026ndash;24 hours at 48\u0026deg;C in an Illumina Hybridization oven. After hybridisation, chips were cleaned and prepared for single-base extension. After single-base extension, the chips were stained with fluorescent dyes (biotin for G and C nucleotides and DNP for A and T nucleotides) and scanned using the iScan system.\u003c/p\u003e\n\u003ch3\u003eGenotyping data analysis, imputation, GWAS and Polygenic Risk Score (PRS) generation\u003c/h3\u003e\n\u003cp\u003eThe genotype data were exported using Genome Studio v2.0 (Illumina Inc.). Further, 488,765 common sites of genotyping data with South Asian whole-genome sequencing (WGS) data were obtained considering\u0026thinsp;\u0026lt;\u0026thinsp;5% sample level missing rate, \u0026lt;\u0026thinsp;5% site level missing rate, Hardy Weinberg Equilibrium p-value of \u0026lt;\u0026thinsp;1e-10, and MAF cut-off 0.001. The combined data was pruned for estimating genetic relationship and principal component analysis (PCA). The genetic relationship of the samples was estimated using PLINK 1.9\u003csup\u003e19\u003c/sup\u003e, and the related samples were removed (Kinship cut-off 0.088). WGS samples were used for PCA analysis, and genotyped samples were assigned to WGS PCA space.\u003c/p\u003e \u003cp\u003eBeagle version 5.0 was used to perform genotype imputation \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e with Genome Asia Pilot project reference panel comprising of 1648 samples of diverse ethnicities (South Asian\u0026thinsp;=\u0026thinsp;677, West Eurasian\u0026thinsp;=\u0026thinsp;109, African\u0026thinsp;=\u0026thinsp;101, South East Asian\u0026thinsp;=\u0026thinsp;332, North East Asian\u0026thinsp;=\u0026thinsp;346, Oceanic\u0026thinsp;=\u0026thinsp;66, Admixed-American\u0026thinsp;=\u0026thinsp;23). The reference panel is generated using whole genome sequencing at 30x average depth. There was a total of 21.4\u0026nbsp;million sites imputed using Beagle tool. Further, we queried for the 6.6\u0026nbsp;million markers from the CARDIOGRAMplusC4D study.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e Out of 6.6\u0026nbsp;million markers, there were 5,922,374 markers present in the imputed data. The GWAS summary of 6.6\u0026nbsp;million markers is available from 'CARDIOGRAMplusC4D' (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.cardiogramplusc4d.org/data-downloads/\u003c/span\u003e\u003cspan address=\"http://www.cardiogramplusc4d.org/data-downloads/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe modified version of LDpred algorithm was obtained from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/wavefancy/LDpredChrByChr\u003c/span\u003e\u003cspan address=\"https://github.com/wavefancy/LDpredChrByChr\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003csup\u003e22\u003c/sup\u003e. There were 503 European ancestry samples from 1000 Genome phase III dataset, which were used to extract 6.6\u0026nbsp;million CAD related markers (The 1000 Genomes Project Consortium). This dataset was used as the LD reference panel to generate the LD structure file. LDpred\u0026rsquo;s Gibbs sampling option is used to get the re-weighted SNP effect size. PLINK version 2.0 was used to generate the polygenetic risk score .\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e Using FlashPCA tool, the genotyping data were projected to South Asian WGS PCA space and derived the residuals to normalize the PRS.\u003c/p\u003e \u003cp\u003eGWAS analysis was performed using age, genders and top10 principal components as covariates with logistic regression by using PLINK 2.0. The results were annotated using functional mapping and annotation (FUMA) web server. The overall workflow of the GWAS study is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. In this study we considered the widely accepted p-value threshold of 1 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e as genome-wide significant. Fisher's test was used to compare the categorical data, and the t-test was utilized to compare the numerical data between the groups.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eGene-Based Analysis and Gene-Set Analysis\u003c/h3\u003e\n\u003cp\u003eMulti-marker analysis of genomic annotation (MAGMA) v1.07, which is integrated into FUMA, was used to analyze the gene set.\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e Several genetic markers can be analyzed together using this method to ascertain their combined impact. Protein-coding genes were mapped to input SNPs. MAGMA-based gene analyses, which apply an SNP-wide mean model to GWAS summary results, were performed with default parameters. Similar procedures were used for gene-set studies, which limited the sets being examined to those that are a component of the biological processes described by Gene Ontology. We used gene-set analysis by MAGMA, to understand the biological enriched pathways related with the AKI. Gene-set enrichments were considered significant at an adjusted \u0026lsquo;p\u0026rsquo; value level\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e1825 participants were enrolled in the study. In group A there were 746 patients of which, 80 patients developed AKI postoperatively and 666 patients did not develop AKI. In group B there were 255 patients, 167 patients developed AKI and 88 did not develop AKI. In group C there were 824 age-matched healthy control population. Figure \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e summarizes the study participants in each group.\u003c/p\u003e\n\u003ch3\u003eCharacteristics of the participant population\u003c/h3\u003e\n\u003cp\u003eThe baseline clinical and demographic characteristics of the participants are shown in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. In group A, the mean age was 58\u0026thinsp;\u0026plusmn;\u0026thinsp;9.2 years who developed AKI and 56\u0026thinsp;\u0026plusmn;\u0026thinsp;9.5 years who did not develop AKI. In Group A 10.7% patients developed AKI. In group B, the mean age was 60\u0026thinsp;\u0026plusmn;\u0026thinsp;8.4 years for group B-AKI and 58\u0026thinsp;\u0026plusmn;\u0026thinsp;8.3 years for group B-no-AKI. In Group B 65.5% patients experienced renal deterioration. In groups C, the mean age was 52\u0026thinsp;\u0026plusmn;\u0026thinsp;6.6 years and there were healthy control in this group. Male patients in group A who developed AKI were 92.5% and who did not develop AKI were 87.69%, in group B who developed AKI were 94.01% and who did not developed AKI were 95.45% and in group C were 85.35%. The details of comorbidities in patients of all groups were given in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. There was statistical significance of age, serum creatinine, GFR and urine output in patients of group A who developed AKI and who does not developed AKI after OP-CABG (P values\u0026thinsp;=\u0026thinsp;0.0009, 1.4733E-12, 2.0942E-09 and 0.015, respectively). There was statistical significance of serum creatinine and GFR in patients of group B who developed AKI and who does not developed AKI after OP-CABG (P values\u0026thinsp;=\u0026thinsp;0.0044 and 0.0369, respectively).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBaseline characteristics of study groups.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eGroup A\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eGroup B\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eGroup C\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;824)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAKI\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;80)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo AKI\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;666)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAKI\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;167)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo AKI\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;88)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58 (\u0026plusmn;\u0026thinsp;9.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56 (\u0026plusmn;\u0026thinsp;9.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60 (\u0026plusmn;\u0026thinsp;8.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58 (\u0026plusmn;\u0026thinsp;8.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1865\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52 (\u0026plusmn;\u0026thinsp;6.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9731\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.4350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI, kg/m2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.72 (\u0026plusmn;\u0026thinsp;3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.71 (\u0026plusmn;\u0026thinsp;3.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4456\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.375 (\u0026plusmn;\u0026thinsp;3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.82 (\u0026plusmn;\u0026thinsp;3.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1263\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.47 (\u0026plusmn;\u0026thinsp;4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSerum creatinine (mg/dl)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.16 (\u0026plusmn;\u0026thinsp;0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98 (\u0026plusmn;\u0026thinsp;0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.4733E-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.46 (\u0026plusmn;\u0026thinsp;0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.33 (\u0026plusmn;\u0026thinsp;0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.88 (\u0026plusmn;\u0026thinsp;0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLDL-C level (mg/dl)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76 (\u0026plusmn;\u0026thinsp;33.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78 (\u0026plusmn;\u0026thinsp;40.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8964\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77 (\u0026plusmn;\u0026thinsp;35.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66.5 (\u0026plusmn;\u0026thinsp;32.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2986\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e124 (\u0026plusmn;\u0026thinsp;34.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal cholesterol (mg/dl)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e132 (\u0026plusmn;\u0026thinsp;36.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e129.5 (\u0026plusmn;\u0026thinsp;47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e133 (\u0026plusmn;\u0026thinsp;39.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e117 (\u0026plusmn;\u0026thinsp;37.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2671\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e184 (\u0026plusmn;\u0026thinsp;39.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHypertension (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69.97%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.9946\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiabetes (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63.51%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7339\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.9004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.84\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrevious MI (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.25%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3796\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3566\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGFR (\u003c/strong\u003eml/min/m2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69.58 (\u0026plusmn;\u0026thinsp;15.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82.74 (\u0026plusmn;\u0026thinsp;18.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.0942E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.32(\u0026plusmn;\u0026nbsp;10.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51.82(\u0026plusmn;\u0026thinsp;8.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0369\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eUrine output (ml/hour)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e140.91 (\u0026plusmn;\u0026thinsp;49.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e159.88 (\u0026plusmn;\u0026thinsp;49.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e126.09(\u0026plusmn;\u0026thinsp;45.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e136.10 (\u0026plusmn;\u0026nbsp;41.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1596\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003ch3\u003eGWAS for AKI among Group A\u003c/h3\u003e\n\u003cp\u003eIn Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB, Manhattan plots show the GWAS results for every SNP associated with the risk of AKI. The top 25 significant markers are shown in supplementary table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. The supplemental Fig.\u0026nbsp;1, displays the Q-Q plot. The measured genomic inflation factor (\u0026lambda;) of 1.0 indicates that the association analysis should not be significantly impacted by the population substructure. Further, the association analysis identified 607 SNPs with suggestive significance p-value threshold of 1 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e (Supplementary Table 2). However, none of the markers remained significant after multiple testing corrections using Benjamini-Hochberg.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e SNPs found for Genome wide association was computed as explained in the methodology and presented in the Manhattan Plot, Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB, that reported the strong association of 3 SNPs located on chromosome 11q13.1, viz, rs11231649, rs60668438 and rs114977339 with AKI, Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB. To confirm the involvement of these 3 SNPs we calculated Odds Ratio (OR) that reported chr11: 63710016G\u0026thinsp;\u0026gt;\u0026thinsp;A (rs11231649; OR\u0026thinsp;=\u0026thinsp;3.7, P\u0026thinsp;=\u0026thinsp;0.000000021), and chr 11: 63706276G\u0026thinsp;\u0026gt;\u0026thinsp;A, (rs60668438; OR\u0026thinsp;=\u0026thinsp;3.63; P\u0026thinsp;=\u0026thinsp;0.000000074) and chr 11: 63716240C\u0026thinsp;\u0026gt;\u0026thinsp;T (rs114977339; OR\u0026thinsp;=\u0026thinsp;3.56; P\u0026thinsp;=\u0026thinsp;0.000000089). (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA). Figure \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eC, shows the locus zoom plot of the significant region in chromosome 11.\u003c/p\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eGWAS for AKI among Group B\u003c/h2\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA, represents the Manhattan plot for Group B which indicates that there were very few alterations above the threshold of 4x10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e. To further access the involvement of the identified loci on chr11q13.1 we analyzed these three SNPs in individuals of group B, who had pre- existing renal dysfunction, in that we found, chr11: 63710016G\u0026thinsp;\u0026gt;\u0026thinsp;A (rs11231649; OR\u0026thinsp;=\u0026thinsp;1.1, p\u0026thinsp;=\u0026thinsp;0.52), chr 11: 63706276G\u0026thinsp;\u0026gt;\u0026thinsp;A, (rs60668438; OR\u0026thinsp;=\u0026thinsp;1.2, p\u0026thinsp;=\u0026thinsp;0.4) and chr 11: 63716240C\u0026thinsp;\u0026gt;\u0026thinsp;T (rs114977339; OR\u0026thinsp;=\u0026thinsp;0.9; P\u0026thinsp;=\u0026thinsp;0.93) (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eGenetic Data Analysis\u003c/h2\u003e\n \u003cp\u003eWe observed major difference between allele frequency in individuals who developed AKI and individuals who did not develop AKI in group A for SNPs: 11:63710016 it was 0.23 and 0.08, respectively, for 11:63706276 it was 0.23 and 0.09, respectively and for 11:63716240 it was 0.2 and 0.06, respectively. For group B the less difference was seen in allele frequency in individuals who developed AKI and individuals who did not develop AKI for SNPs: 11:63710016 it was 0.14 and 0.11, respectively, for 11:63706276 it was 0.12 and 0.09, respectively and for 11:63716240 it was 0.10 and 0.09, respectively. Allele frequency for group C for SNPs are, 11:63710016 it was 0.11 for 11:63706276 it was 0.11 and for 11:63716240 it was 0.08. (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eGene-based enrichment analysis on AKI\u003c/h2\u003e\n \u003cp\u003eThe key genes from the MAGMA gene-based test included MACROD2 (P\u0026thinsp;=\u0026thinsp;3.20E-10), GPC6 (P\u0026thinsp;=\u0026thinsp;4.77E-10) and MAGI2 (P\u0026thinsp;=\u0026thinsp;2.45E-09) (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). The top 25 most significant genes are summarised in Supplementary table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003eGene-set enrichment analysis on AKI\u003c/h2\u003e\n \u003cp\u003eUsing the association analysis summary data, we subsequently utilized gene-set analysis to identify the major enriched gene sets associated with AKI. Six gene-sets satisfied the statistical significance with adjusted p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 which are shown in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. The top 3 most significant gene-sets were nedd8_specific_protease_activity (Gene-set size: 3; adjusted P value 3.85E-06), followed by negative_regulation_of_cell_ cycle_checkpoint (Gene-set size: 2; adjusted P value 0.00573) and negative_regulation_of_DNA_damage_checkpoint (Gene-set size: 2; adjusted P value 0.00573). Top gene sets linked to cell cycle regulation and DNA damage repair.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eGene-set enrichment results\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGene sets\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eN genes\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRaw p vale\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAdjusted p vale\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO_mf:go_nedd8_specific_protease_activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.63E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.85E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO_bp:go_negative_regulation_of_cell_cycle_checkpoint\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.91E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00573\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO_bp:go_negative_regulation_of_dna_damage_checkpoint\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.91E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00573\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCurated_gene_sets:lei_hoxc8_targets_dn\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.27E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007721\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO_bp:go_histone_h2a_k63_linked_ubiquitination\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.14E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.031387\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO_bp:go_positive_regulation_of_hair_cycle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.20E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.046831\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003eGPS\u003csub\u003eCAD\u003c/sub\u003e and correlation with AKI\u003c/h2\u003e\n \u003cp\u003eWe further investigated the influence of GPS\u003csub\u003eCAD\u003c/sub\u003e on the development of postoperative AKI. Figure \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eA, shows the CAD PRS model generation and validation process. The GPS\u003csub\u003eCAD\u003c/sub\u003e was generated for all samples in the three groups and were binned into 10 deciles in the increasing order of GPS\u003csub\u003eCAD\u003c/sub\u003e. We observed an increase of GPS\u003csub\u003eCAD\u003c/sub\u003e across deciles 1 to 10 in the group A which consisted of patients undergoing OP-CABG with normal renal function. The first decile (lowest GPS\u003csub\u003eCAD\u003c/sub\u003e bin) comprises of ~\u0026thinsp;59% of healthy controls (group C), 22% group A and 12% group B. Comparatively, the last decile (highest GPS\u003csub\u003eCAD\u003c/sub\u003e bin) comprises of ~\u0026thinsp;25% of healthy controls, 56% group A and 15% group B (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eB; Supplementary table 3). The healthy controls (group C) had a significantly lower GPS\u003csub\u003eCAD\u003c/sub\u003e than other groups. No difference was observed in GPS\u003csub\u003eCAD\u003c/sub\u003e between AKI and no-AKI samples belonging to the group A (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eC; Supplementary table 4). The top decile consists of 22.5% of AKI and 23.2% no-AKI subjects. Based on our findings in the tested sample set, there is no significant association between GPS\u003csub\u003eCAD\u003c/sub\u003e and AKI across groups.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn order to test for genetic variations linked to AKI after CABG surgery without the use of Cardiopulmonary Bypass (CPB), we provided a genome-wide analysis in this study. In group A among all the chromosomes we found some alterations which were little above the threshold and few were very above the threshold, which has shown the significant association with the risk of AKI. The top three SNPs were found on chr11q13.1 - chr11: 63710016, chr 11: 63706276, and chr 11: 63716240. These SNPs were statistically significant for group A and were not statistically significant with group B. There were major differences in the allele frequencies between individuals who developed acute kidney injury (AKI) compared to those who did not in group A, while for group B the less difference was seen in allele frequency for these SNPs. Additionally, we looked into the relationship between a GPSCAD and the onset of AKI following OP-CABG. Our study found no evidence to support a suggestion that patients with a high GPS\u003csub\u003eCAD\u003c/sub\u003e have an increased likelihood of developing AKI after OP-CABG. These results do not provide evidence that GPS\u003csub\u003eCAD\u003c/sub\u003e is associated with AKI.\u003c/p\u003e \u003cp\u003eTill date, only two GWAS for AKI have been reported in patients who were undergoing CABG.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e The first study conducted in European population, used a discovery-replication analysis approach. Nine SNPs showed significant association in the discovery of data set among which rs13317787 in GRM7/LMCD1-AS1 intergenic region (3p21.6) and rs10262995 in BBS9 (7p14.3) that remained significant in replication data set as well as meta-analysis. Subsequent fine-mapping with imputation across these two regions and meta-analysis revealed substantial connection at the BBS9 gene and genome-wide significance at GRM7/LMCD1-AS1 locus.\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e The second study, conducted in subjects with Caucasian ethnicity, revealed association between AKI and four SNPs. These included rs6234163 and rs62341657 on chr4q35.1 near APOL1-regulator IRF2, and rs9617814 and rs10854554 on chr22q11.21 near acute kidney injury\u0026ndash;related gene TBX1.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e In this study, we conducted GWAS to identify genomic predispositions for AKI developed post-OP-CABG and observed a significant signal (association) at 11q13.1. However, this signal did not pass the multiple corrections (Benjamini-Hochberg). This work is the first that we are aware of that uses GWAS to find SNPs associated with AKI in an Indian population. Subsequently, we assessed the association of GPS\u003csub\u003eCAD\u003c/sub\u003e with the development of AKI post OP-CABG and did not find any significant association.\u003c/p\u003e \u003cp\u003eP Bhatraju et al. recently conducted a single genome-wide association study only for AKI. In this, Assessment, Serial Evaluation, and Subsequent Sequelae of AKI did not show any genome-wide significant association with AKI risk (P\u0026thinsp;\u0026lt;\u0026thinsp;5\u0026times;10\u0026thinsp;\u0026minus;\u0026thinsp;8).\u003csup\u003e26\u003c/sup\u003e In our study we found the significant association of AKI in the patients who were undergoing CABG without renal dysfunction and in patients with pre-existing renal function we did not found any significant genome wide association.\u003c/p\u003e \u003cp\u003eAlthough, GWAS in this study was performed with relatively small number of subjects, the results are indicative of identifying the potential risk variants. The significant marker was located on chr11q31.1 locus. This region has not been reported in literature in association with AKI. However, this region has been reported in kidney disease-related trait, uric acid levels, in a GWAS study in Korean population.\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e Ten SNPs in six genetic loci, which included OTUB1, NRXN2/SLC22A12, CDC42BPG, RPS6KA4, SLC22A9, and MAP4K2, were shown to be associated with uric acid on chromosome 11. Our top significant marker (rs11231649) is located in the intronic region on N-alpha-acetyltransferase 40 (NAA40). The second and third significant SNP (rs60668438 and rs114977339) is located upstream and intronic region of NAA40, respectively, which contribute to the pathogenesis of colon cancer.\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e Findings on chr11q13.1 align with known roles of NAA40 and related pathways.\u003c/p\u003e \u003cp\u003eOur gene-based analyses identified MARCRO2 (mono-ADP ribosylhydrolase 2) was the most significant gene. An intragenic deletion has been reported in a family with complex phenotype which also included renal dysfunction.\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e It is also known to be associated with congenital heart disease, obesity and physical activity, and autistic-like traits.\u003csup\u003e\u003cspan additionalcitationids=\"CR31\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e Another top-hit gene MAGI2 (membrane-associated guanylate kinase, WW, and PDZ domain containing 2) is known to harbour mutations causing Nephrotic syndrome, type 15 (NPHS15).\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e It is also known to be associated with cognitive impairment.\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e The results of the gene-based analyses need to be replicated in larger and other data sets to get more insights into these genes as causal factors for AKI.\u003c/p\u003e \u003cp\u003eThis is a large study looking into genetic predisposition to AKI in patients undergoing OP-CABG in the Indian subcontinent, as per our knowledge. A number of important limitations should be taken into account when interpreting the study's findings. The limitation of the present study include low sample size and lack of replication datasets. Further, validation of our results was not possible due to the unavailability of a validation sample set, with individuals undergone CABG and developed AKI. The low-frequency and rare variants could have been overlooked and a sequencing-based approach might identify the rare variants missed by our study. This study's strength is the use of a GWAS design, which enables a thorough analysis of genetic variants linked to acute kidney damage (AKI) in a particular cohort of patients having off-pump coronary artery bypass grafting (OP-CABG). This approach enables the detection of previously unrecognized genetic factors, which may lead to more personalized risk prediction and management strategies for AKI in CABG patients. However, future research needs to concentrate on confirming these loci's functionality.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study is the first GWAS to analyze predispositions for AKI in the Indian population undergoing OPCABG. The top three SNPs were found on chr11q13.1 - chr11: 63710016, chr 11: 63706276, and chr 11: 63716240. Our study identifies chr11q13.1 as a potential genomic risk locus for AKI in OP-CABG patients without pre-existing renal dysfunction. Additionally, there was no significant correlation between GPS\u003csub\u003eCAD\u003c/sub\u003e and the development of AKI. These findings provide a basis for further genetic investigations and risk stratification strategies on chr11q13.1 for these SNPs on broader scale.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u003c/strong\u003e MK, VS, GMMR and MJ contributed to conceptualization, study design, manuscript drafting, and critical review. AAR and GC were responsible for imaging analysis, genetic analysis, data interpretation, and manuscript drafting. AC and VLR provided bioinformatics support, genetic counseling, and manuscript review. RMB and RMA were involved in clinical evaluation, clinical support, and manuscript review. L and RG contributed to data validation, literature review, and manuscript drafting. SM analyzed the historical context and participated in manuscript review. RM and GMMR handled data curation, statistical validation, and manuscript review. All authors conducted the final manuscript review and approval.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e: The datasets used or analyzed during the current study available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e: The authors are grateful to the following research assistants: Ms Hilda, Ms Esaimalar, Mr Dheeraj Maajhi, Mr Sashank Viswanathan, Mr Akshay Bhati, Dr Gokul and Dr. Vaishnav\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e: This project was funded by the Indian Council of Medical Research (ICMR): No. BMS/ADHOC/GEN/2015-0567/JUN-16/3/KA/PVT\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e: No Conflict of Interest\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAcute Kidney Injury (AKI): Practice Essentials, Background, Pathophysiology. 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Commun.\u003c/em\u003e \u003cb\u003e9\u003c/b\u003e, 1960 (2018).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"coronary artery bypass grafting, genome-wide association, renal function, genetic predisposition","lastPublishedDoi":"10.21203/rs.3.rs-6170946/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6170946/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eOne of the most frequent perioperative complications in heart surgery is acute kidney damage (AKI). We conducted a genome-wide association study (GWAS) to investigate genetic predispositions for AKI and the impact of genome-wide polygenic risk scores for coronary artery disease (GPSCAD) in patients undergoing off-pump coronary artery bypass grafting (OP-CABG) in a South Asian population. Patients were categorized into three groups: (A) Patients undergoing OP-CABG who have normal renal function, (B) patients undergoing OP-CABG with pre-existing renal dysfunction, and (C) age-matched healthy controls. GWAS analysis was performed using logistic regression with age, gender, and top 10 principal components as covariates. Postoperative AKI was defined using KDIGO (The Kidney Disease: Improving Global Outcomes) criteria. Among 746 patients in group A, 80(10.7%) developed AKI and of 255 patients in group B, 167(65.5%) exhibited deterioration of renal function. GWAS identified significant single nucleotide polymorphisms (SNPs) on chr11q13.1 (rs11231649, p\u0026thinsp;=\u0026thinsp;2.15E-08, rs60668438, p\u0026thinsp;=\u0026thinsp;7.40E-08 and rs114977339, p\u0026thinsp;=\u0026thinsp;8.98E-08). No significant GPS\u003csub\u003eCAD\u003c/sub\u003e differences were observed between AKI and non-AKI groups in group B. This study highlights significant genetic associations with AKI on chr11q13.1 in patients undergoing OP-CABG without pre-existing renal dysfunction. GPS\u003csub\u003eCAD\u003c/sub\u003e did not show an impact on AKI incidence. These findings warrant further validation in larger cohorts.\u003c/p\u003e","manuscriptTitle":"Genome-Wide Association Study Identifies a Potential Genomic Risk Locus at Chr11q13.1 for Acute Kidney Injury in Patients Undergoing Off-Pump Coronary Artery Bypass Grafting","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-02 07:32:08","doi":"10.21203/rs.3.rs-6170946/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f35ece32-4099-41ce-bff6-2bc2301d889a","owner":[],"postedDate":"July 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":50884148,"name":"Biological sciences/Genetics"},{"id":50884149,"name":"Biological sciences/Genetics/Genetic association study"},{"id":50884150,"name":"Biological sciences/Genetics/Genomics"}],"tags":[],"updatedAt":"2025-08-01T09:39:13+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-02 07:32:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6170946","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6170946","identity":"rs-6170946","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}