Genome-wide association study of pediatric bacteremia and sepsis

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Thorball , Zhi Ming Xu , Alessandro Borghesi , Eric Giannoni , View ORCID Profile Johannes Trück , Martin Stocker , Klara M Posfay-Barbe , Ulrich Heininger , Sara Bernhard-Stirnemann , Anita Niederer-Loher , Christian R. Kahlert , Giancarlo Natalucci , Christa Relly , Christoph Berger , Thomas Riedel , Christoph Aebi , Philipp Agyeman , Jacques Fellay , Luregn J. Schlapbach the Swiss Pediatric Sepsis Study doi: https://doi.org/10.1101/2025.06.12.25329506 Dylan Lawless 1 Department of Intensive Care and Neonatology, University Children’s Hospital Zürich, University of Zürich , Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Dylan Lawless For correspondence: dylan.lawless{at}kispi.uzh.ch Flavia Aurelia Hodel 2 Precision Medicine Unit, Biomedical Data Science Center, Lausanne University Hospital and University of Lausanne , Lausanne, Switzerland 3 Global Health Institute, School of Life Sciences, École Polytechnique Fédérale de Lausanne , Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Christian W. Thorball 2 Precision Medicine Unit, Biomedical Data Science Center, Lausanne University Hospital and University of Lausanne , Lausanne, Switzerland 3 Global Health Institute, School of Life Sciences, École Polytechnique Fédérale de Lausanne , Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Zhi Ming Xu 3 Global Health Institute, School of Life Sciences, École Polytechnique Fédérale de Lausanne , Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Alessandro Borghesi 3 Global Health Institute, School of Life Sciences, École Polytechnique Fédérale de Lausanne , Switzerland 4 Neonatal Intensive Care Unit, San Matteo Research Hospital , Pavia, Italy 5 Neonatal Unit, Geneva University Hospitals, Mother, Child and Adolescent Department , Geneva, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Eric Giannoni 6 Clinic of Neonatology, Department Woman-Mother-Child, Lausanne University Hospital and University of Lausanne , Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Johannes Trück 7 Divisions of Allergy and Immunology, University Children’s Hospital Zurich and Children’s Research Center, University of Zurich (UZH) , Zurich, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Johannes Trück Martin Stocker 8 Pediatric Intensive Care and Neonatology, Children’s Hospital of Central Switzerland and University of Lucerne , Lucerne, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Klara M Posfay-Barbe 9 Pediatric Infectious Diseases Unit, Children’s Hospital of Geneva, University Hospitals of Geneva , Geneva, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ulrich Heininger 10 Infectious Diseases and Vaccinology, University of Basel Children’s Hospital , Basel, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sara Bernhard-Stirnemann 11 Children’s Hospital Aarau , Aarau, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Anita Niederer-Loher 12 Division of Infectious Diseases and Infection Control, Children’s Hospital of Eastern Switzerland St. Gallen , St. Gallen, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Christian R. Kahlert 12 Division of Infectious Diseases and Infection Control, Children’s Hospital of Eastern Switzerland St. Gallen , St. Gallen, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Giancarlo Natalucci 13 Department of Neonatology, University Hospital Zurich , Zurich, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Christa Relly 14 Division of Infectious Diseases and Hospital Epidemiology, and Children’s Research Center, University Children’s Hospital Zurich , Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Christoph Berger 14 Division of Infectious Diseases and Hospital Epidemiology, and Children’s Research Center, University Children’s Hospital Zurich , Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Thomas Riedel 15 Division of Infectious Disease, Department of Pediatrics, Inselspital, Bern University Hospital, University of Bern , Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Christoph Aebi 15 Division of Infectious Disease, Department of Pediatrics, Inselspital, Bern University Hospital, University of Bern , Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Philipp Agyeman 15 Division of Infectious Disease, Department of Pediatrics, Inselspital, Bern University Hospital, University of Bern , Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jacques Fellay 2 Precision Medicine Unit, Biomedical Data Science Center, Lausanne University Hospital and University of Lausanne , Lausanne, Switzerland 3 Global Health Institute, School of Life Sciences, École Polytechnique Fédérale de Lausanne , Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Luregn J. Schlapbach 1 Department of Intensive Care and Neonatology, University Children’s Hospital Zürich, University of Zürich , Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Background Sepsis is defined as a dysregulated host response to infection leading to organ dysfunction. It represents a major global health concern, particularly in childhood. The precise underlying pathophysiological mechanisms remain poorly understood. Methods Using samples and clinical data from 650 children enrolled in the Swiss Pediatric Sepsis Study, a national multicenter cohort study for culture-proven bacterial sepsis, and from 1395 controls, we explored the human genetic determinants of inter-individual variability in sepsis-related outcomes. We used a case-control study design to search for associations between genome-wide polymorphisms and susceptibility to sepsis, and case-only analysis for specific characteristics of the disease. Findings We identified one locus significantly associated with sepsis susceptibility, encompassing the CTNNAL1 and ELP1 genes. Interpretation Our results suggest a genetic modulator of sepsis susceptibility in children. Funding This study was funded by grants from the Swiss National Science Foundation (320030_201060 and 342730 153158/1), the Swiss Society of Intensive Care, the Bangerter Foundation, the Vinetum and Borer Foundation, and the Foundation for the Health of Children and Adolescents. Evidence before this study Blood culture-confirmed bacterial sepsis in children remains an important cause of morbidity and mortality. Several prospective studies from the Swiss Pediatric Sepsis Study (SPSS) have defined its incidence, clinical features, and outcomes in Switzerland. A 2017 nationwide cohort study reported an incidence of 25.1 per 100,000 children annually and a 30-day mortality rate of 7%, with risk of death closely associated with the presence and number of organ dysfunctions. A neonatal-focused analysis identified three distinct clinical subgroups (early-onset, hospital-acquired, and community-acquired late-onset sepsis), each with specific pathogens, risk profiles, and outcomes. Hospital-acquired infections were most frequent and carried the highest case fatality. In a separate cohort of previously healthy children with community-acquired sepsis, rare variants in primary immunodeficiency genes were identified in 20% of cases, although most were of uncertain significance. Another SPSS study assessed organ dysfunction scores in paediatric sepsis and found similar performance in predicting 30-day mortality. Cardiovascular, respiratory, and neurologic dysfunctions were most strongly associated with death, suggesting that simplified assessment may be feasible. Added value of this study This is the first genome-wide association study of paediatric bacterial sepsis based on a national cohort with blood culture-confirmed diagnoses. The cohort included 650 sepsis cases and 1395 population-based controls. A genome-wide significant susceptibility locus was identified on chromosome 9 (CTNNAL1/ELP1). Some biological relevance of this locus has previously been demonstrated independently. The study expands previous SPSS research by integrating epidemiological data with genome-wide genetics and by applying stringent ancestry correction and imputation within the clinical phenotype. The findings offer new avenues for investigating genetic contributions to paediatric sepsis susceptibility. Implications of all the available evidence Combined with earlier SPSS studies, these findings support a model in which both genetic and clinical factors contribute to sepsis susceptibility and outcome in children. The identification of a susceptibility locus near CTNNAL1 and ELP1 provides a new basis for mechanistic studies and implicates transcriptional regulation and NF-κB signalling pathways. Use of a microbiologically confirmed phenotype enhances interpretability and relevance for translational research. These results provide a foundation for future work on host-pathogen interactions, genetic risk stratification, and targeted interventions in paediatric sepsis. Introduction Sepsis represents a leading cause of death and disability in adults and children [ 1 ], [ 2 ], [ 3 ]. Globally, pediatric sepsis results in over one million annual deaths [ 4 ], [ 5 ]. Even in countries with low childhood mortality, sepsis contributes to 10 to 20% of childhood deaths, and to one out of four deaths in pediatric intensive care units (PICU) [ 6 ], [ 7 ]. Sepsis is the result of a dysregulated host response to infection leading to organ dysfunction, yet the biomolecular mechanisms and host-pathogen interactions involved remain poorly understood [ 8 ], [ 9 ], [ 10 ]. A key feature of sepsis is that it usually develops after an insidious onset of non-specific signs common to many mild infections before progressing to organ dysfunction. In clinical practice, recognizing this transition from an infected child who may solely require antibiotics to a septic child progressing to multiorgan failure (MOF) and death, is extremely challenging. The Swiss Pediatric Sepsis Study (SPSS) is a national observational multicenter cohort study investigating blood culture-proven bacterial sepsis in children in Switzerland. Clinical and laboratory data have been have been previously reported [ 2 ], [ 11 ], [ 12 ]. We have also previously reported population-based incidence estimates and findings on the epidemiology of sepsis [ 1 ]. Based on our data, 4.4% (95%-CI 3.5-5.4) of all childhood deaths in Switzerland were associated with blood culture-proven bacterial sepsis, ranking 5 th as the cause of all childhood deaths. The age-standardized incidence of sepsis was 25.1 per 100’000 children per year with an average 30-day in-hospital mortality of 6.9% (95%-CI 5.6-8.6), with highest rates observed in children under five years of age. The incidence of sepsis is highest during early life [ 13 ], a vulnerable period of adaptive immunity development [ 14 ], [ 15 ], [ 16 ]. The mechanisms underlying susceptibility and resistance to severe infectious diseases in otherwise healthy patients remains largely unknown, however epidemiological studies suggest a strong genetic contribution [ 17 ], [ 18 ]. Previous studies have used mostly candidate gene and linkage analyses [ 19 ]. Genome-wide association studies (GWAS), which allow a comprehensive interrogation of potential associations throughout the genome, have identified common genetic variants that are associated with many infectious diseases [ 20 ]. Understanding why a minority of children become very ill during the course of an infection remains one of the unsolved riddles in host genetic research, and may harbor clues for future targeted interventions. We here use a GWAS approach to elucidate host genetic determinants of susceptibility to pediatric sepsis and disease characteristics. Methods Sample collection The SPSS ran prospectively from September 1, 2011, to December 31, 2015. During the recruitment period, participating centers accounted for 77.6% of all hospital admissions and 97.8% of all pediatric intensive care unit admissions with an ICD-10 code for pathogen-specific sepsis in children below the age of 17 years in Switzerland. A total of 1269 blood culture-proven sepsis episodes were recorded in 1164 children. Detailed clinical and laboratory data were collected to assess predictors including pediatric organ dysfunction scores [ 2 ], [ 3 ], [ 4 ]. Genotype quality control and imputation Genomic DNA was extracted from whole blood of 704 participants. From these, 670 were patients with clinical features and genomic data suitable for our analysis. Samples were genotyped using Illumina OmniExpressExome-8 v1.4 genotyping array and genotypes were called using Illumina GenomeStudio. An in-house control cohort of 1395 Swiss population samples was included. Study participants were excluded based on a missing genotype call rate of 10%. Subject independence was assessed using KING; nine samples were removed due to a high degree of kinship or duplication (pairwise identify-by-state (IBS) estimated kinship coefficient > 0.18) [ 21 ]. From the distribution of genetic distance, no exclusion of outliers was necessary. One sample was removed based on retraction of genetic consent. Variants were removed for minor allele frequencies 0.1, and additionally for controls, Hardy-Weinberg Equilibrium (HWE) P < 1E-6. Reported and estimated sex was examined for discrepancy. We compared the genetic ancestry in cases to self-reported ethnicity to check for mislabeling. After quality control (QC), 650 sepsis case samples and 1395 controls remained. Genotyping data was phased (SHAPEIT2) and imputed (IMPUTE2) using the 1000 Genomes Project phase 3 reference panel. The reference genome build and LD population used was hg19/1000G Nov2014 EUR. Imputation quality was assessed and SNPs with an information score of < 0.8 or minor allele frequency < 0.05 were removed ( Figure S2 ). Association testing GCTA was used to calculate the genetic relationship matrix (GRM) and to perform principal component analysis (PCA) to quantify population structure [ 22 ]. For case-control analysis of sepsis susceptibility, population structure/stratification was alleviated by sub-setting the dataset to remove outlying clusters as evident from PCA ( Figure S3 ). This removed 140 cases and 401 controls, leaving a case-control subset of 510 cases and 994 controls of European ancestry. Datasets were merged using PLINK v1.9. SNP positions and identifiers were updated according to dbNSFP4.0a (hg19) [ 23 ]. QC was repeated after merging cases and controls for combined cohort-specific frequencies and GCTA mixed linear model-based association (MLMA) with leaving-one-chromosome-out (LOCO) was used for analysis. SNP QC filters and association model summaries are listed in Tables S1 and S2 , respectively. GCTA MLMA-LOCO was used for within-cohort (case only) analyses of several phenotypes as listed in Table 1 and S2 [ 24 ]. Population structure was controlled by GRM eigenvectors and analysis covariates consisted of sex, age, and study site. View this table: View inline View popup Table 1: Demographic and clinical characteristics of 650 Swiss Pediatric Sepsis Study GWAS participants. Categorical variables are presented as frequencies (%) and continuous variables as median (IQR). Column percentages are presented based on available data for each variable. a Missing in 19, b Missing in 1, c Missing in 1, d Missing in 3, e Missing in 9, f In 3 cases laboratory and physiological parameters needed to define organ dysfunctions according to the 2005 consensus definition were missing, but according to the initial investigator-based classification these patients did not suffer from any organ dysfunction. CVAD, central venous access device; PICU, paediatric intensive care unit; NICU, neonatal intensive care unit. Testing for overlap with an independent mouse model study We identified ELP1 and CTNNAL1 as candidate genes for susceptibility to paediatric sepsis. These genes ( Elp1 and Ctnnal1 in mice) were independently reported by Vered et al. [ 26 ] as part of the Collaborative Cross (CC), a systems genetics resource for host-pathogen interaction studies using a multi-parental mouse genetic reference population, as reviewed by Noll et al. [ 25 ]. Vered et al. [ 26 ] challenged 73 pre-CC mouse lines with Klebsiella pneumoniae and identified 2 and 6 significantly associated intronic SNPs in Elp1 and Ctnnal1, respectively. To test whether the overlap of these genes across the two studies exceeded that expected by chance, we applied a weighted Z-test and Fisher’s combined probability test using the reported study sizes (n=1504 for SPSS and n=328 for CC). Ethics statement The study was approved by the respective ethics committees of all participating centers (Cantonal Ethics Committee Bern, approval number KEK-029/11) and the study was conducted in accordance with the Declaration of Helsinki. Participants or their legally acceptable representatives provided their written consent to participate in the study after having been informed about the nature and purpose of the study, participation/termination conditions, and risks and benefits of treatment. Role of funders The funders were not involved in study design, data collection and analysis, interpretation, and writing of the manuscript. Results The demographic and clinical characteristics of the 650 study participants for whom within-cohort analysis was performed are shown in Table 1 and Figure S1 . Within-cohort (case only) association testing was performed on all 650 case samples. We found no association with specific sepsis characteristics, including site or type of infection, cardiovascular failure, comorbidities, pathogens identified, case fatality, PICU admission, hospital-acquired sepsis, length of PICU stay, time to death from sepsis onset, number of organ dysfunctions, length of stay after sepsis onset, invasive ventilation, and age groups ( Table S2 ). The sub-group summaries are listed with demographic and clinical characteristics in Table 1 and Figure S1 . A subset of 510 cases and 994 Swiss population-matched controls were included in a case-control GWAS for sepsis susceptibiliy as shown in Figure 1 . This analysis highlighted one significantly associated region of chromosome 9 with 13 genome-wide significant SNPs and rs28361152 as the top-associated variant ( Table 2 and Figure 2A ). Conditional analysis with GCTA-COJO showed that all SNPs reflect the same association signal. All 13 SNPs were intronic and did not initially appear to affect protein structure, but deeper analyses revealed their functional effects as expression Quantitative Trait Loci (eQTL). The QQ-plot illustrated in Figure 2B indicates that genomic inflation was controlled. Download figure Open in new tab Figure 1: Cohort analysis summary. Swiss Pediatric Sepsis cohort episodes, genotyping, and quality control. Within-cohort analysis are listed in Table S2. Case-control analysis subset to suitably match cohorts. Download figure Open in new tab Figure 2: GWAS Manhattan plot and QQ plot. (A) Manhattan plot of association shows significant association signals in chromosome 9 (significant threshold P-value = 5e −08 ). (B) QQ plot demonstrates that the observed distribution of P-values corresponds to the expected distribution under the null hypothesis, indicating that potential confounders are well controlled, λ=1.019. View this table: View inline View popup Table 2. Significant associations identified for case and control susceptibility. Lead SNP rs28361152 is a non-coding variant that falls within CTNNAL1 . The remaining associated SNPs in LD (r 2 ≥ 0.8) span ELP1 and FAM206A . The lead SNP, associated variants and gene positions in this region are shown in a LocusZoom plot in Figure 3 . The strength and extent of the association signal relative to genomic position, local LD and recombination patterns indicate five genes of potential interest: ELP1 (elongator acetyltransferase complex subunit 1), FAM206A (family with sequence similarity 206, member A), CTNNAL1 (catenin alpha-like 1 gene), TMEM245 (Transmembrane protein 245), and FRRS1L (DOMON domain-containing protein FRRS1L). Using GTEx v.8 data ( Figure S4 ), we observed that the top associated variant is a significant cis-eQTL for all five genes in the LD block ( Figure 3 ). Download figure Open in new tab Figure 3: A LocusZoom plot for SNPs in the region flanking 200kb on either side of the selected SNP rs28361152 on chromosome 9. P-values in –log10 scale are shown on the left vertical axis, the recombination rates are on the right vertical axis, and the chromosomal positions are on the horizontal axis. LD is shown by r 2 and represented by color. The lead SNP is in strong LD with nearest gene CTNNAL1 , FAM206A, and ELP1. The LD block extends to TMEM245 and FRRS1L, but no associated with disease is evident within the SPSS cohort. The details of all coding and non-coding variants in LD are provided in Table S3 and Table S4 , respectively. Table S5 shows all significant eQTLs for the 13 SNPs associated with susceptibility to sepsis in our study. The strongest association between rs28361152 and mRNA expression differences was found for FRRS1L , approximately 165kb downstream of rs28361152 with increased expression in presence of the minor allele. rs28361152 was also found to act as a splicing Quantitative Trait Locus (sQTL) in ELP1 for intron ID 108906485:108908305:clu_63656 (GRCh38) affecting exon 14 ENSE00000983463 and intron 14-15 ( Table S6 ). All genes in the region are highly expressed in most tissues, with the exception of FRRS1L , which has low expression globally ( Figure S4 ). ELP1 and CTNNAL1 (and murine Elp1 and Ctnnal1 ) were identified as candidate causal genes in both our paediatric sepsis susceptibility scan and in a previously reported independent scan of 73 mouse lines challenged with K. pneumoniae by Vered et al. [ 26 ], in the CC study [ 25 ]. K. pneumoniae causes severe pneumonia and sepsis in immunocompromised individuals. We tested the probability of identifying the same two genes in the two independent scans and found it highly unlikely to be due to chance: P-value=1.28E-13 using weighted Z -test (study sizes 1504 and 328 for SPSS and CC, respectively), and 1.53E-17 using Fisher’s combined probability test. Infection with Klebsiella spp. was reported in 28 of our 510 sepsis cases. The direction and strength of the association with rs28361152 was comparable between sepsis due to Klebsiella spp. (OR 0.68, 95% CI 0.3-1.56) and sepsis due to other pathogens (OR 0.46, 95% CI 0.37-0.58). Discussion As part of the Swiss Pediatric Sepsis Study, we explored culture-proven bacteremia and sepsis-related outcomes for 650 cases and performed case-control analysis for 510 cases and 994 controls of European ancestry. Our search for human genetic determinants of susceptibility to pediatric sepsis identified a locus of interest on chromosome 9 with 13 genome-wide significant SNPs. The top associated variant, rs28361152, is an eQTL for five genes in the immediate vicinity - ELP1, FAM206A , CTNNAL1 , TMEM245, and FRRS1L . It is also an sQTL for ELP1 . Of note, elp1 and ctnnal1 have been previously reported as top candidate genes in a murine model of sepsis [ 26 ]. CTNNAL1 encodes alpha-catulin, a regulatory protein that is associated with inflammatory and immune pathways through modulation of the NF-kB pathway activity [ 27 ]. Murine Ctnnal1 is also important to cell adhesion, in response to bacterial triggers, to eliminate infection [ 28 ], [ 29 ]. ELP1 encodes the elongator complex protein 1, a component of the RNA polymerase II elongator complex, which is involved in transcriptional elongation. This complex catalyzes the formation of carboxymethyluridine in the wobble base at position 34 in tRNAs [ 30 ]. Loss of mouse Elp1 is embryonic lethal and can be rescued by human ELP1 [ 31 ]. The Swiss Pediatric Sepsis Study addressed the pressing need for comprehensive, longitudinal, population-based cohorts in paediatric sepsis - a condition that remained a leading cause of death and disability globally [ 5 ]. The relatively small sample size limited the power of some analyses. In particular, the case-only analyses were hindered by insufficient sample numbers, and the case-control analysis was constrained by the limited availability of well-matched controls for non-European genetic backgrounds. Differences in allele frequencies further highlighted the challenges in this research area. The in-house Swiss population control cohort revealed a minor allele frequency of approximately 0.3 for the lead variant, consistent with data from gnomAD and FinnGen. In contrast, African and South Asian populations exhibited much lower frequencies, 0.05 and 0.0, respectively, suggesting that sepsis susceptibility might have varied significantly with ancestry. Increasing sample sizes and increasing the controls in future studies could enhance the ability to detect additional genetic associations more effectively. Although GWAS provided valuable statistical associations, they fall short of pinpointing causal variants, especially in non-coding regions where genomic complexity is a major hurdle. Replication studies with independent cohorts, along with functional assays and fine mapping, are preferable to validate these associations and to provide a definitive explanation of the underlying mechanisms. Large-scale genomic approaches, such as targeted exome and whole genome sequencing (WGS), offer promising avenues for uncovering novel candidate genes and clarifying the biological mechanisms of host susceptibility and host-pathogen interactions. These insights may ultimately pave the way for targeted interventions, even though current clinical guidelines do not yet incorporate these genomic findings [ 32 ], [ 33 ]. With increased power in future studies, GWAS or WGS approaches could be employed for host-pathogen interaction analyses that reveal avenues for targeted interventions. Through genomic analysis as part of a national multicenter cohort study for culture-proven bacterial sepsis in children in Switzerland, we identified a genomic region significantly associated with individual susceptibility to sepsis. This offers a potential route for improving the prognosis for those affected and uncovering pathogenic mechanisms involved in immune responses and host-pathogen interactions. Conclusion This genome-wide analysis in the Swiss Pediatric Sepsis Study identified a significantly associated locus on chromosome 9, centred around CTNNAL1 and ELP1 , considered to modulate paediatric sepsis susceptibility. These findings provide a potential avenue for improving prognosis and shed light on the underlying host-pathogen interactions, offering a foundation for future targeted interventions. Data Availability All summary statistic data are present in the work. Raw genomic data is stored under restricted access according to ethics agreements. All code is published. Weblinks FinnGen https://r4.finngen.fi FUMA https://fuma.ctglab.nl GCTA https://cnsgenomics.com/software/gcta/ GTEx https://www.gtexportal.org/home/ HaploReg https://pubs.broadinstitute.org/mammals/haploreg/haploreg.php IMPUTE2 https://mathgen.stats.ox.ac.uk/impute/impute_v2.html KING https://people.virginia.edu/~wc9c/KING/ LDLink https://ldlink.nci.nih.gov LocusZoom http://locuszoom.org PLINK http://zzz.bwh.harvard.edu/plink/ SHAPEIT2 https://mathgen.stats.ox.ac.uk/genetics_software/shapeit/shapeit.html Contributors Dylan Lawless performed the genomic analysis and wrote the manuscript. Flavia Aurelia Hodel, Zhi Ming Xu, Christian W. Thorball, Alessandro Borghesi, Eric Giannoni, Johannes Trück, Martin Stocker, Klara M Posfay-Barbe, Ulrich Heininger, Sara Bernhard-Stirnemann, Anita Niederer-Loher, Christian R. Kahlert, Giancarlo Natalucci, Christa Relly, Christoph Berger, Thomas Riedel, Christoph Aebi, and Philipp Agyeman contributed to data collection and manuscript drafting. Jacques Fellay and Luregn J. Schlapbach led the project and supervised the study. Declaration of Interest The authors declare no conflict of interest. Data Sharing Statement Summary statistic data is available from the public repository. Access to individual-level genetic data is restricted due to research use agreements. Researchers may contact the corresponding authors (L.J.S. and J.F.) to request access, subject to institutional and ethical approvals. Supplemental Table S1: SNP quality control filters. Samples were genotyped using Illumina OmniExpressExome-8 v1.4 genotyping array, phased with (SHAPEIT2) and imputed (IMPUTE2) using the 1000 Genomes Project phase 3 reference panel. Filtering included missing genotype call rate 10%, minor allele frequencies < 0.05, control HWE 1e −6 , and subject independence by identify-by-state (IBS). Geno, genotype call rate; HWE, Hardy-Weinberg Equilibrium; MAF, minor allele frequency; LD, linkage disequilibrium. Table S2: Association model summary. Covariates were included to account for age, sex, study sites. Ancestry was accounted for by GRM. Case-only analysis was also performed for key criteria of clinical characteristics that may be used to assess sepsis. Table S3. List of protein-altering variants in LD with lead SNP Genomic coordinates are based on the GRCh 37 build. LD estimates are based on the HRC reference panel. Haploreg v4 was used to assess the protein-altering functional annotation of the proxy SNPs. Table S4. Extended list of all variants in LD with lead SNP The extended list of variants in LD with the lead SNP contain 1519 variants, with ranges in their MAF 0.1-0.5; D’ 0.1-1.0; and R 2 0.0-1.0. Table S5. eQTL associations for associated SNPs in GTEx v8 Genome wide significant SNPs in sepsis susceptibility case-control analysis were used to query for eQTL associations in GTEx v8. Results for the lead SNP rs28361152 are shown. P-values included are below the gene-level significant threshold of variant-gene pairs. Genomic coordinates are based on the GRCh 38 build. NES, normalized effect size; sQTL, splicing Quantitative Trait Loci. Table S6. sQTL associations for associated SNPs in GTEx v8 Genome wide significant SNPs in sepsis susceptibility case-control analysis were used to query for eQTL associations in GTEx v8. Results for the lead SNP rs28361152 are shown. P-values included are below the gene-level significant threshold of variant-gene pairs. Genomic coordinates are based on the GRCh 38 build. eQTL, expression Quantitative Trait Loci; NES, normalized effect size. Download figure Open in new tab Figure S1: Patient cohort characteristics. (A) categorical and (B) continuous variables for patient cohort. Our cohort consisted of 650 cases after QC (quality control). Color scale low (blue) to high (yellow). Download figure Open in new tab Figure S2: Imputation quality. The information score is plotted for illustration of a randomly selected region of 5e 6 SNPs. High quality imputation is present for variants with scores above 0.7-0.8 as shown for (A) total count and (B) cumulative sum. INFO score >0.8 was used for analysis. Download figure Open in new tab Figure S3: Principal component analysis. (A) The Swiss Pediatric Sepsis Study cohort is predominantly made up of individuals of European ancestry (83%, Table 1 ). Within-cohort association was tested for all samples with eight eigenvalues included as quantitative covariates to control for population structure in a mixed model. (B) For case-control analysis population structure required pruning to European only ancestry. The first twenty PCs are shown in (C). Download figure Open in new tab Figure S4: Gene expression in candidate gene locus Gene expression heatmap from 5 genes of interest on Chr 9 in association with sepsis. Annotated for biological context based on Functional Mapping and Annotation of Genome-Wide Association Studies (FUMA). Significantly associated SNPs overlap ELP1, FAM206A , and CTNNAL1 and all five genes were identified as expression Quantitative Trait Loci. View this table: View inline View popup STROBE Statement—Checklist of items that should be included in reports of cohort studies Acknowledgements We gratefully acknowledge the participation of all volunteers, and thank the medical centres and staff for their contribution. Footnotes jacques.fellay{at}epfl.ch , Luregn.Schlapbach{at}kispi.uzh.ch Formatted to include ethics and funder information in the methods, funding statement in the structured abstract, and a "Research in context" summary. Abbreviations Actl7a and Actl7b actin-like 7a and 7b CC Collaborative Cross CTNNAL1 catenin alpha-like 1 gene CVAD central venous access device DALYs Disability Adjusted Life Years DEG differentially expressed gene ELP1 elongator complex protein 1 eQTL expression Quantitative Trait Locus EUCLIDS EU Childhood Life-threatening Infectious Disease Study FAM206A family with sequence similarity 206, member A GRM genetic relationship matrix GWAS genome-wide association study HWE Hardy-Weinberg Equilibrium IBS identify-by-state ELP1 elongator acetyltransferase complex subunit 1 IKK IkB enzyme kinase LD linkage disequilibrium LOCO leave-one-chromosome-out MLMA mixed linear model based asso-ciation MOF multiorgan failure NICU neonatal intensive care unit PC principal component PCA principal component analysis PICU Pediatric Intensive Care Unit SNP single nucleotide polymorphism sQTL splicing Quantitative Trait Locus WGS whole genome sequencing References [1]. ↵ P. K. A. 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Thorball , Zhi Ming Xu , Alessandro Borghesi , Eric Giannoni , Johannes Trück , Martin Stocker , Klara M Posfay-Barbe , Ulrich Heininger , Sara Bernhard-Stirnemann , Anita Niederer-Loher , Christian R. Kahlert , Giancarlo Natalucci , Christa Relly , Christoph Berger , Thomas Riedel , Christoph Aebi , Philipp Agyeman , Jacques Fellay , Luregn J. Schlapbach medRxiv 2025.06.12.25329506; doi: https://doi.org/10.1101/2025.06.12.25329506 Share This Article: Copy Citation Tools Genome-wide association study of pediatric bacteremia and sepsis Dylan Lawless , Flavia Aurelia Hodel , Christian W. Thorball , Zhi Ming Xu , Alessandro Borghesi , Eric Giannoni , Johannes Trück , Martin Stocker , Klara M Posfay-Barbe , Ulrich Heininger , Sara Bernhard-Stirnemann , Anita Niederer-Loher , Christian R. Kahlert , Giancarlo Natalucci , Christa Relly , Christoph Berger , Thomas Riedel , Christoph Aebi , Philipp Agyeman , Jacques Fellay , Luregn J. 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