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GWAS Identifies LINC01184/SLC12A2 as a Risk Locus for Skin and Soft Tissue Infections

2021; Elsevier BV; Volume: 141; Issue: 8 Linguagem: Inglês

10.1016/j.jid.2021.01.020

ISSN

1523-1747

Autores

Tormod Rogne, Kristin Vardheim Liyanarachi, Humaira Rasheed, Laurent F. Thomas, Helene M. Flatby, Jørgen Stenvik, Mari Løset, Dipender Gill, Stephen Burgess, Cristen J. Willer, Kristian Hveem, Bjørn Olav Åsvold, Ben Brumpton, Andrew T. DeWan, Erik Solligård, Jan Kristian Damås,

Tópico(s)

Diabetes and associated disorders

Resumo

Microbial invasion of the skin and underlying soft tissues, known as skin and soft tissue infections (SSTIs), contribute to a considerable burden of disease worldwide (Kaye et al., 2019Kaye K.S. Petty L.A. Shorr A.F. Zilberberg M.D. Current epidemiology, etiology, and burden of acute skin infections in the United States.Clin Infect Dis. 2019; 68: S193-S199Crossref PubMed Scopus (40) Google Scholar; Lozano et al., 2012Lozano R. Naghavi M. Foreman K. Lim S. Shibuya K. Aboyans V. et al.Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: a systematic analysis for the Global Burden of Disease Study 2010.Lancet. 2012; 380: 2095-2128Abstract Full Text Full Text PDF PubMed Scopus (9154) Google Scholar). Knowledge about host factors contributing to SSTI risk is important to prevent the SSTIs. The genetics of SSTI susceptibility remain largely unknown, and the only previously published genome-wide study on SSTIs is a small family-based linkage study that did not identify significant linkage to any genes for erysipelas or cellulitis susceptibility (Hannula-Jouppi et al., 2013Hannula-Jouppi K. Massinen S. Siljander T. Mäkelä S. Kivinen K. Leinonen R. et al.Genetic susceptibility to non-necrotizing erysipelas/cellulitis.PLoS One. 2013; 8: e56225Crossref PubMed Scopus (10) Google Scholar). A range of cardiometabolic risk factors has been associated with SSTIs (Butler-Laporte et al., 2020Butler-Laporte G. Harroud A. Forgetta V. Richards J.B. Elevated body mass index is associated with an increased risk of infectious disease admissions and mortality: a Mendelian randomization study [e-pub ahead of print].Clin Microbiol Infect. 2020; (accessed 1 September 2020)https://doi.org/10.1016/j.cmi.2020.06.014Abstract Full Text Full Text PDF Scopus (4) Google Scholar; Kaye et al., 2019Kaye K.S. Petty L.A. Shorr A.F. Zilberberg M.D. Current epidemiology, etiology, and burden of acute skin infections in the United States.Clin Infect Dis. 2019; 68: S193-S199Crossref PubMed Scopus (40) Google Scholar; Winter-Jensen et al., 2020Winter-Jensen M. Afzal S. Jess T. Nordestgaard B.G. Allin K.H. Body mass index and risk of infections: a Mendelian randomization study of 101,447 individuals.Eur J Epidemiol. 2020; 35: 347-354Crossref PubMed Scopus (12) Google Scholar). Few studies have used genetic variants as instrumental variables (Mendelian randomization [MR]) to assess causality, which may reduce bias owing to reverse causation and confounding (Davies et al., 2018Davies N.M. Holmes M.V. Davey Smith G. Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians.BMJ. 2018; 362: k601Crossref PubMed Scopus (471) Google Scholar). Increasing body mass index has been found to increase the risk of SSTIs in such a framework (Butler-Laporte et al., 2020Butler-Laporte G. Harroud A. Forgetta V. Richards J.B. Elevated body mass index is associated with an increased risk of infectious disease admissions and mortality: a Mendelian randomization study [e-pub ahead of print].Clin Microbiol Infect. 2020; (accessed 1 September 2020)https://doi.org/10.1016/j.cmi.2020.06.014Abstract Full Text Full Text PDF Scopus (4) Google Scholar; Winter-Jensen et al., 2020Winter-Jensen M. Afzal S. Jess T. Nordestgaard B.G. Allin K.H. Body mass index and risk of infections: a Mendelian randomization study of 101,447 individuals.Eur J Epidemiol. 2020; 35: 347-354Crossref PubMed Scopus (12) Google Scholar), but other cardiometabolic risk factors have, to our knowledge, not been explored. The aims of this study were to conduct a GWAS on susceptibility to SSTIs, explore possible biological pathways through transcriptome-wide association analyses, and perform MR analyses to investigate the potential causal relationships of cardiometabolic risk factors with SSTIs. We used two independent cohorts: UK Biobank and Trøndelag Health Study (HUNT), where the UK Biobank served as the discovery cohort in the genome-wide association analyses and the HUNT as the replication cohort. Subjects who had been hospitalized with a primary diagnosis of SSTI served as cases, whereas those who had not been hospitalized with a primary or secondary diagnosis of SSTI were considered controls (Supplementary Material and Methods). Genome-wide association analyses were conducted using scalable and accurate implementation of generalized mixed model, with age, sex, genotype chip, and ancestry-informative principal components as covariates (Zhou et al., 2018Zhou W. Nielsen J.B. Fritsche L.G. Dey R. Gabrielsen M.E. Wolford B.N. et al.Efficiently controlling for case-control imbalance and sample relatedness in large-scale genetic association studies.Nat Genet. 2018; 50: 1335-1341Crossref PubMed Scopus (141) Google Scholar), and meta-analyses were conducted using METAL (Supplementary Materials and Methods). Associations with P < 1e-6 and P < 5e-8 were considered genome-wide suggestive and significant, respectively. We used FUSION to perform transcriptome-wide association analyses by combining the summary statistics from the genome-wide meta-analysis with linkage disequilibrium (European ancestry in 1000 Genomes Project) and reference gene expression panels (Genotype-Tissue Expression, version 7) to estimate the gene expression patterns associated with SSTIs (Gusev et al., 2016Gusev A. Ko A. Shi H. Bhatia G. Chung W. Penninx B.W. et al.Integrative approaches for large-scale transcriptome-wide association studies.Nat Genet. 2016; 48: 245-252Crossref PubMed Scopus (626) Google Scholar). Sun-exposed skin (lower legs) was the tissue of interest for the transcriptome-wide analyses (8,609 genes tested), whereas all the 48 general tissues from Genotype-Tissue Expression, version 7, were analyzed for the chromosome with genome-wide‒significant hits (10,518 tests). Bonferroni-corrected threshold for genome-wide significance was P < 2.6e-6. Two-sample MR analyses were conducted separately for the results from the meta-analysis, UK Biobank, and HUNT. Genetic instruments for body mass index, type-2 diabetes mellitus, low-density lipoprotein cholesterol, systolic blood pressure, lifetime smoking, and sedentary lifestyle were extracted from relevant published GWASs (Supplementary Table S1). The TwoSampleMR R package (version 0.5.0) (Hemani et al., 2018Hemani G. Zheng J. Elsworth B. Wade K.H. Haberland V. Baird D. et al.The MR-base platform supports systematic causal inference across the human phenome.Elife. 2018; 7: e34408Crossref PubMed Scopus (848) Google Scholar) was used to carry out inverse-variance weighted MR analyses (main analyses), along with statistical tests for heterogeneity, simple median, weighted median, and MR Egger (sensitivity analyses). In both UK Biobank and HUNT, cases at baseline were older, had higher body mass index and systolic blood pressure, were more likely to be male, were more likely to be ever smoker, and were more likely to self-report as diabetic (Supplementary Table S2) than the controls. The GWAS included 6,107 cases and 399,239 controls from UK Biobank and 1,657 cases and 67,522 controls from HUNT. UK Biobank yielded seven suggestive loci (Supplementary Table S3 and Supplementary Figure S1), of which one was replicated in HUNT: rs3749748 in the LINC01184/SLC12A2-gene region on chromosome 5 (Supplementary Figures S2 and S3). In the meta-analysis of 7,764 cases and 466,761 controls, only the locus in LINC01184/SLC12A2 reached genome-wide significance (Figure 1), whereas two additional loci were close to genome-wide significance: PSMA1 on chromosome 11 and GAN on chromosome 16 (Supplementary Table S3). There was no indication of genomic inflation (Figure 1 and Supplementary Figures S1 and S2). LINC01184 is part of the long intervening noncoding RNA class of genes that do not encode for proteins but have still been found to modulate inflammation and infection risk (Atianand et al., 2016Atianand M.K. Hu W. Satpathy A.T. Shen Y. Ricci E.P. Alvarez-Dominguez J.R. et al.A long noncoding RNA lincRNA-EPS acts as a transcriptional brake to restrain inflammation.Cell. 2016; 165: 1672-1685Abstract Full Text Full Text PDF PubMed Scopus (260) Google Scholar; Carpenter et al., 2013Carpenter S. Aiello D. Atianand M.K. Ricci E.P. Gandhi P. Hall L.L. et al.A long noncoding RNA mediates both activation and repression of immune response genes.Science. 2013; 341: 789-792Crossref PubMed Scopus (646) Google Scholar). SLC12A2 encodes for the protein NKCC1, which regulates the transportation of chloride, potassium, and sodium across cell membranes, and is key in modulating ion movement across the epithelium, the volume of cells, and antimicrobial activity (Matthay and Su, 2007Matthay M.A. Su X. Pulmonary barriers to pneumonia and sepsis.Nat Med. 2007; 13: 780-781Crossref PubMed Scopus (17) Google Scholar; Yang et al., 2020Yang X. Wang Q. Cao E. Structure of the human cation–chloride cotransporter NKCC1 determined by single-particle electron cryo-microscopy.Nat Commun. 2020; 11: 1016Crossref PubMed Scopus (14) Google Scholar). In the transcriptome-wide association analysis of the skin on the lower legs, the only gene that was statistically significantly associated with SSTIs was LINC01184 (Supplementary Figure S4). A reduced expression of LINC01184 was associated with an increased risk of SSTIs. The same association was observed in all tissues but less pronounced in the brain (Supplementary Figure S4). An increase in genetically predicted body mass index, systolic blood pressure, and smoking increased the risk of SSTIs, whereas increasing low-density lipoprotein cholesterol was associated with a reduced risk of SSTIs (Figure 2). Sensitivity analyses supported the findings from the inverse-variance weighted analyses (Supplementary Table S4). To our knowledge, this study is a GWAS published on SSTIs, with a large number of cases and controls. We were able to identify a locus—LINC01184/SLC12A2—robustly associated with SSTIs in the discovery cohort and the independent replication cohort. A limitation of our study is that we did not have the power to identify more than one genome-wide‒significant locus, which in part may be due to the nondifferential misclassification of the outcome, and we thus encourage a replication with meta-analysis in independent cohorts. Of note, whereas the minor allele frequency of rs3749748 in the North-Western European populations is around 23%, it is only 4% in African American populations (Karczewski et al., 2020Karczewski K.J. Francioli L.C. Tiao G. Cummings B.B. Wang Q. Collins R.L. et al.The mutational constraint spectrum quantified from variation in 141,456 humans [published correction appears in Nature 2021;590:E53].Nature. 2020; 581: 434-443Crossref PubMed Scopus (1711) Google Scholar). It is therefore important to evaluate the populations of different ancestries other than the one currently considered. In conclusion, we have identified genetic variation in LINC01184/SLC12A2 to be strongly associated with the risk of SSTIs. Interventions to reduce smoking, hypertension, overweight, and obesity in the population will likely reduce the disease burden of SSTIs. Data from the Trøndelag Health Study and UK Biobank are available on application. Gene expression data are available through the FUSION website (http://gusevlab.org/projects/fusion/). Summary statistics are available at the GWAS Catalog (https://www.ebi.ac.uk/gwas) under identification number GCST90013411. Tormod Rogne: http://orcid.org/0000-0002-9581-7384 Kristin V. Liyanarachi: http://orcid.org/0000-0001-5499-9196 Humaira Rasheed: http://orcid.org/0000-0002-3331-5864 Laurent F. Thomas: http://orcid.org/0000-0003-0548-2486 Helene M. Flatby: http://orcid.org/0000-0002-5700-020X Jørgen Stenvik: http://orcid.org/0000-0002-1051-9258 Mari Løset: http://orcid.org/0000-0003-3736-6551 Dipender Gill: http://orcid.org/0000-0001-7312-7078 Stephen Burgess: http://orcid.org/0000-0001-5365-8760 Cristen J. Willer: http://orcid.org/0000-0001-5645-4966 Kristian Hveem: http://orcid.org/0000-0001-8157-9744 Bjørn O. Åsvold: http://orcid.org/0000-0003-3837-2101 Ben M. Brumpton: http://orcid.org/0000-0002-3058-1059 Andrew T. DeWan: http://orcid.org/0000-0002-7679-8704 Erik Solligård: http://orcid.org/0000-0001-6173-3580 Jan K. Damås: http://orcid.org/0000-0003-4268-671X DG is employed part-time by Novo Nordisk, outside of the submitted work. The remaining authors state no conflict of interest. This study was in part funded by Samarbeidsorganet Helse Midt-Norge, NTNU Norwegian University of Science and Technology (Trondheim, Norway), and The Research Council of Norway (grant 299765). The first author was funded in part by a Fulbright Scholarship by the United States-Norway Fulbright Foundation. BMB, HR, LFT, ML, KH, and BOÅ work in a research unit funded by Stiftelsen Kristian Gerhard Jebsen; Faculty of Medicine and Health Sciences, NTNU Norwegian University of Science and Technology; The Liaison Committee for education, research, and innovation in Central Norway; the Joint Research Committee between St. Olavs Hospital (Trondheim, Norway) and the Faculty of Medicine and Health Sciences, NTNU Norwegian University of Science and Technology; and the Medical Research Council Integrative Epidemiology Unit at the University of Bristol (United Kingdom), which is supported by the Medical Research Council and the University of Bristol (MC_UU_12013/1). All authors had full access to all the data in the study and can take responsibility for the integrity of the data and the accuracy of the data analysis. All authors made substantial contributions to the interpretation of the data and critically revised the manuscript. All authors have approved the submitted version and are personally accountable for the author's own contributions. The Trøndelag Health Study is a collaboration between the Trøndelag Health Study Research Centre (Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology), the Trøndelag County Council, the Central Norway Regional Health Authority, and the Norwegian Institute of Public Health. This research has been conducted using the UK Biobank Resource under application number 40135. Conceptualization: TR, KVL, ES, JKD, ATD, HMF; Data Curation: TR, HMF, BMB, HR, LFT, CJW, KH, BOÅ; Formal Analysis: TR, HR, LFT; Funding Acquisition: TR, ES, JKD, KH, CJW, BOÅ, JS, ATD, BMB; Investigation: TR, HR, LFT, ES, JKD, KH, CJW, BOÅ, JS, ATD, ML, BMB; Methodology: TR, HR, LFT, DG, SB, ATD, BMB; Project Administration: TR, ES, JKD, BOÅ, KH, CJW, ATD, BMB, ML; Resources: ES, JKD, BOÅ, KH, ATD, JS; Software: DG, SB, BMB, HR, LFT; Supervision: TR, ES, JKD, ATD, BMB, DG, SB, BOÅ, ML, CJW; Validation: HR, LFT, BMB, JS; Visualization: TR, HR, LFT; Writing - Original Draft Preparation: TR; Writing - Review and Editing: TR, KVL, HR, LFT, HMF, JS, ML, DG, SB, CJW, KH, BOÅ, BMB, ATD, ES, JKD The funding sources had no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; nor in the decision to submit the article for publication. The researchers were independent of the funders. Details about the UK Biobank have previously been described (Bycroft et al., 2018Bycroft C. Freeman C. Petkova D. Band G. Elliott L.T. Sharp K. et al.The UK Biobank Resource with deep phenotyping and genomic data.Nature. 2018; 562: 203-209Crossref PubMed Scopus (1419) Google Scholar). In brief, the cohort consists of 503,325 subjects enrolled between 2006 and 2010 throughout the United Kingdom. Age at baseline was between 38 and 73 years, and 94% of the subjects self-reported being of European ancestry. At baseline, genome-wide genotyping was done on 488,377 individuals, where 84% self-reported that they were of white-British ancestry with European genetic ethnicity. Information on self-reported health and lifestyle was collected, along with measurements such as height and weight. Inpatient hospital data on all the participants were available through electronic record linkage. The Trøndelag Health Study (HUNT) study is a series of surveys conducted in the Nord-Trøndelag region in Norway (∼130,000 inhabitants) between 1984 and 2019 on subjects aged ≥20 years (Krokstad et al., 2013Krokstad S. Langhammer A. Hveem K. Holmen T.L. Midthjell K. Stene T.R. et al.Cohort profile: the HUNT study, Norway.Int J Epidemiol. 2013; 42: 968-977Crossref PubMed Scopus (619) Google Scholar). We used data from HUNT2 (1995–1997) and HUNT3 (2006–2008), in which 78,973 subjects representative of the adult Norwegian population participated (Krokstad et al., 2013Krokstad S. Langhammer A. Hveem K. Holmen T.L. Midthjell K. Stene T.R. et al.Cohort profile: the HUNT study, Norway.Int J Epidemiol. 2013; 42: 968-977Crossref PubMed Scopus (619) Google Scholar). Baseline characteristics were collected at study enrollment, and selected measurements were carried out, including height and weight measurement. Information on all hospitalizations in the county and to the regional tertiary care hospital were linked to the study subjects. Through linkage with the Norwegian population registry, we retrieved data on the date of emigration out of the study region and date of death. Cases and controls were defined similarly to the definitions in the UK Biobank and HUNT. The following International Classification of Diseases (ICD)-9 and ICD-10 codes were considered as skin and soft tissue infection (SSTI) codes: 035 (erysipelas; ICD-9), 729.4 (fasciitis, unspecified; ICD-9), A46 (erysipelas; ICD-10), L03 (cellulitis and acute lymphangitis; ICD-10), and M72.6 (necrotizing fasciitis; ICD-10). These codes are used primarily for bacterial infections, and nonbacterial infections of the skin have other specific codes not considered. Our main definition of SSTI was a case that had been hospitalized with an SSTI as the primary diagnosis. In the sensitivity analysis, we included secondary diagnoses in the definition of SSTI (i.e., SSTIs not the primary cause of hospitalization). Those who had not been hospitalized with an SSTI (primary or secondary diagnosis) served as controls. The Affymetrix UK BiLEVE Axiom array was used to genotype the initial 50,000 participants, and the Affymetrix UK Biobank Axiom array was used to genotype the rest of the subjects. Directly genotyped variants were prephased using SHAPEIT3 (O'Connell et al., 2016O'Connell J. Sharp K. Shrine N. Wain L. Hall I. Tobin M. et al.Haplotype estimation for biobank-scale data sets.Nat Genet. 2016; 48: 817-820Crossref PubMed Scopus (86) Google Scholar) and imputed using Impute4 and the UK10K (UK10K Consortium et al., 2015Walter K. Min J.L. Huang J. Crooks L. Memari Y. et al.UK10K ConsortiumThe UK10K project identifies rare variants in health and disease.Nature. 2015; 526: 82-90Crossref PubMed Scopus (570) Google Scholar), Haplotype Reference Consortium (UK10K Consortium et al., 2015Walter K. Min J.L. Huang J. Crooks L. Memari Y. et al.UK10K ConsortiumThe UK10K project identifies rare variants in health and disease.Nature. 2015; 526: 82-90Crossref PubMed Scopus (570) Google Scholar), and 1000 Genomes Phase 3 (1000 Genomes Project Consortium, et al., 2015Auton A. Brooks L.D. Durbin R.M. Garrison E.P. Kang H.M. et al.1000 Genomes Project ConsortiumA global reference for human genetic variation.Nature. 2015; 526: 68-74Crossref PubMed Scopus (6929) Google Scholar) reference panels (version 3 of the imputed data). Exclusions were made for variants with imputation score r2 < 0.3. More detail is contained in a previous publication (Bycroft et al., 2018Bycroft C. Freeman C. Petkova D. Band G. Elliott L.T. Sharp K. et al.The UK Biobank Resource with deep phenotyping and genomic data.Nature. 2018; 562: 203-209Crossref PubMed Scopus (1419) Google Scholar). As previously described, three different Illumina HumanCoreExome arrays were used to genotype the study participants (HumanCoreExome12, version 1.0; HumanCoreExome12, version1.1; and UM HUNT Biobank, version1.0) (Ferreira et al., 2017Ferreira M.A. Vonk J.M. Baurecht H. Marenholz I. Tian C. Hoffman J.D. et al.Shared genetic origin of asthma, hay fever and eczema elucidates allergic disease biology.Nat Genet. 2017; 49: 1752-1757Crossref PubMed Scopus (215) Google Scholar). Samples with a call rate 2.5% as estimated with BAF Regress (Jun et al., 2012Jun G. Flickinger M. Hetrick K.N. Romm J.M. Doheny K.F. Abecasis G.R. et al.Detecting and estimating contamination of human DNA samples in sequencing and array-based genotype data.Am J Hum Genet. 2012; 91: 839-848Abstract Full Text Full Text PDF PubMed Scopus (203) Google Scholar), with genotypic and phenotypic sex discordance, and that were not of European ancestry were excluded, leaving 69,422 genotyped subjects. Genetic variants of Hardy‒Weinberg equilibrium (P < 0.0001) or with a call rate 0.3). Principal components were calculated using of TRACE (version 1.03), with 938 individuals from the Human Genome Diversity Project serving as reference (Wang et al., 2015Wang C. Zhan X. Liang L. Abecasis G.R. Lin X. Improved ancestry estimation for both genotyping and sequencing data using projection Procrustes analysis and genotype imputation.Am J Hum Genet. 2015; 96: 926-937Abstract Full Text Full Text PDF PubMed Scopus (64) Google Scholar, Wang et al., 2014Wang C. Zhan X. Bragg-Gresham J. Kang H.M. Stambolian D. Chew E.Y. et al.Ancestry estimation and control of population stratification for sequence-based association studies.Nat Genet. 2014; 46: 409-415Crossref PubMed Scopus (69) Google Scholar). Genome-wide association analysis was performed in scalable and accurate implementation of generalized mixed model (version 0.35.8.3) using a linear mixed model that accounts for cryptic relatedness and imbalance in the proportion of cases and controls (Zhou et al., 2018Zhou W. Nielsen J.B. Fritsche L.G. Dey R. Gabrielsen M.E. Wolford B.N. et al.Efficiently controlling for case-control imbalance and sample relatedness in large-scale genetic association studies.Nat Genet. 2018; 50: 1335-1341Crossref PubMed Scopus (231) Google Scholar). We included birth year, sex, genotype chip, and the first six ancestry-informative principal components as covariates. We used scalable and accurate implementation of generalized mixed model with the same settings to analyze the X chromosome, coding males as diploid. Variants with minor allele frequency >0.5% were included in the analyses, and dosages were used for imputed variants. Genome-wide association tests were carried using scalable and accurate implementation of generalized mixed model (version 0.29.4) on autosomal chromosomes (Zhou et al., 2018Zhou W. Nielsen J.B. Fritsche L.G. Dey R. Gabrielsen M.E. Wolford B.N. et al.Efficiently controlling for case-control imbalance and sample relatedness in large-scale genetic association studies.Nat Genet. 2018; 50: 1335-1341Crossref PubMed Scopus (231) Google Scholar), whereas BOLT-LMM (version 2.3.4) was used in the analysis of the X chromosome, coding males as diploid (Loh et al., 2015Loh P.R. Tucker G. Bulik-Sullivan B.K. Vilhjálmsson B.J. Finucane H.K. Salem R.M. et al.Efficient Bayesian mixed-model analysis increases association power in large cohorts.Nat Genet. 2015; 47: 284-290Crossref PubMed Scopus (523) Google Scholar). The beta-coefficients from BOLT-LMM were transformed using the formula: log OR = β/(μ ∗ (1 – μ)), where μ = case fraction. The standard errors from BOLT-LMM were transformed using the formula: SEtransformed = SEoriginal/(μ × [1 – μ]). Age, sex, genotype batch, and the five first ancestry-informative principal components were included as covariates. Variants with minor allele frequency >0.5% were included in the analyses, and dosages were used for imputed variants. We carried out a meta-analysis using METAL (version 2011-03-25), with the use of effect size estimates and standard errors as weights, adjusting for residual population stratification and relatedness through genomic control correction (Willer et al., 2010Willer C.J. Li Y. Abecasis G.R. METAL: fast and efficient meta-analysis of genomewide association scans.Bioinformatics. 2010; 26: 2190-2191Crossref PubMed Scopus (2278) Google Scholar). A total of 9,211,777 SNPs that were present in both cohorts were included in the meta-analysis. The Regional Committee for Medical Research, Health Region IV, Norway approved the HUNT study, and this project is regulated in conjunction with The Norwegian Social Science Data Services. The UK Biobank study has ethical approval from the North West Multi-centre Research Ethics Committee. Approval for individual projects is covered by the Research Tissue Bank.Supplementary Figure S2Manhattan plot of the results for the replication stage (HUNT). Axes display the −log10 transformed P-value by chromosomal position. The blue line indicates the genome-wide‒suggestive associations (P < 1e-6), and the red line indicates the genome-wide‒significant associations (P < 5e-8). Genome-wide‒suggestive loci from the discovery stage (±500 kb of lead variant) are highlighted in green. The image in the top right corner shows the quantile‒quantile plot. Axes display the observed (y-axis) and expected (x-axis) −log10 transformed P-value. The black dots represent the observed P-values, whereas the red line represents the expected P-values under the null distribution. Genomic inflation factor (λ) = 1.00. HUNT, Trøndelag Health Study; kb, kilobase.View Large Image Figure ViewerDownload Hi-res image Download (PPT)Supplementary Figure S3Regional plot of the association results of the discovery stage genome-wide‒significant locus that was replicated. Associations between genetic variants and SSTI from the meta-analysis are plotted by position (x-axis) and −log10 transformed P-values (left y-axis). rs3749748 served as sentinel variant, whereas the remaining variants are color coded in terms of the linkage disequilibrium (r2) to the sentinel variant. Estimated recombination rates are plotted as light blue lines (right y-axis). The European population from 1000 Genomes Project, November 2014 release, was used as the reference on genome build hg19. chr, chromosome; Mb, megabase; SSTI, skin and soft tissue infection.View Large Image Figure ViewerDownload Hi-res image Download (PPT)Supplementary Figure S4Manhattan plot of transcriptome-wide association analysis. Each dot represents the association between the predicted gene expressions in the skin on the lower legs with the risk of SSTIs. The red line indicates the statistically significant associations (P < 2.6e-6). The image in the top right corner shows the transcriptome association statistic for LINC01184 in all the 48 tissues from GTEx v7. BA, Brodmann area; EBV, Epstein-Barr virus; GTEx v7, Genotype-Tissue Expression, version 7; SSTI, skin and soft tissue infection.View Large Image Figure ViewerDownload Hi-res image Download (PPT)Supplementary Table S1Genetic Instruments for Cardiometabolic ExposuresTraitSample SizePopulation AncestryNumber of VariantsVariance Explained, %ReferenceBody mass index681,275European5956.0(Yengo et al., 2018Yengo L. Sidorenko J. Kemper K.E. Zheng Z. Wood A.R. Weedon M.N. et al.Meta-analysis of genome-wide association studies for height and body mass index in ∼700000 individuals of European ancestry.Hum Mol Genet. 2018; 27: 3641-3649Crossref PubMed Scopus (513) Google Scholar)Type-2 diabetes mellitus74,124 cases and 824,006 controlsEuropean20216.3(Mahajan et al., 2018Mahajan A. Taliun D. Thurner M. Robertson N.R. Torres J.M. Rayner N.W. et al.Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps.Nat Genet. 2018; 50: 1505-1513Crossref PubMed Scopus (493) Google Scholar)Low-density lipoprotein cholesterol188,577European807.9(Willer et al., 2013Willer C.J. Schmidt E.M. Sengupta S. Peloso G.M. Gustafsson S. Kanoni S. et al.Discovery and refinement of loci associated with lipid levels.Nat Genet. 2013; 45: 1274-1283Crossref PubMed Scopus (1645) Google Scholar)Systolic blood pressure318,417European1922.9(Carter et al., 2019Carter A.R. Gill D. Davies N.M. Taylor A.E. Tillmann T. Vaucher J. et al.Understanding the consequences of education inequality on cardiovascular disease: Mendelian randomisation study.BMJ. 2019; 365: l1855Crossref PubMed Scopus (65) Google Scholar)Lifetime smoking index462,690European1260.4(Wootton et al., 2019Wootton R.E. Richmond R.C. Stuijfzand B.G. Lawn R.B. Sallis H.M. Taylor G.M.J. et al.Evidence for causal effects of lifetime smoking on risk for depression and schizophrenia: a Mendelian randomisation study.Psychol Med. 2019; 50: 2435-2443Crossref PubMed Scopus (73) Google Scholar)Sedentary lifestyle91,105European40.08(Doherty et al., 2018Doherty A. Smith-Byrne K. Ferreira T. Holmes M.V. Holmes C. Pulit S.L. et al.GWAS identifies 14 loci for device-measured physical activity and sleep duration.Nat Commun. 2018; 9: 5257Crossref PubMed Scopus (94) Google Scholar)Only independent SNPs (r2 < 0.001) with P < 5e-8 in these GWASs were included. Open table in a new tab Supplementary Table S2Background Characteristics at Entry in the UK Biobank and the HUNT StudyCharacteristicUK BiobankHUNTCases (n = 6,107)Controls (n = 399,239)All (N = 405,346)Cases (n = 1,657)Controls (n = 67,522)All (N = 69,179)Female sex2,535 (41.5)216,956 (54.3)219,491 (54.1)825 (49.8)35,829 (53.1)36,654 (53.0)Age, y60 (53–65)58 (51–63)58 (51–63)55 (43–68)46 (34–60)46 (34–60)Ever smoker3,895 (63.8)240,412 (60.2)244,307 (60.3)923 (57.4)37,518 (56.6)38,441 (56.6)Sedentary lifestyle1Sedentary lifestyle: the proportion with sedentary lifestyle among all subjects in UK Biobank was estimated from none of the above from data field 6,164 (types of physical activity in the last 4 weeks) because individual-level data were unavailable; in HUNT, sedentary lifestyle was defined as a self-reported average of 0 hours of low or vigorous physical activity per week in the last year.——(7.1)192 (13.4)4,180 (7.0)4,372 (7.1)Diabetes (self-reported)115 (1.9)2,860 (0.7)2,975 (0.7)102 (6.2)2,003 (3.0)2,105 (3.1)Body mass index, kg/m230.6 (6.6)27.3 (4.7)27.4 (4.7)28.8 (5.2)26.3 (4.1)26.4 (4.2)LDL cholesterol, mmol/l3.4 (0.9)3.6 (0.9)3.6 (0.9)3.8 (1.1)3.6 (1.1)3.6 (1.1)Systolic blood pressure, mmHg141.1 (19.1)138.2 (18.6)138.2 (18.6)142.1 (22.7)134.9 (20.9)135.0 (21.0)Abbreviations: HUNT, Trøndelag Health Study; LDL, low-density lipoprotein.Data are presented as mean (SD), median (25th and 75th centile), or number (%).1 Sedentary lifestyle: the proportion with sedentary lifestyle among all subjects in UK Biobank was estimated from none of the above from data field 6,164 (types of physical activity in the last 4 weeks) because individual-level data were unavailable; in HUNT, sedentary lifestyle was defined as a self-reported average of 0 hours of low or vigorous physical activity per week in the last year. Open table in a new tab Supplementary Table S3Genetic Variants with P < 1e-6 in the Discovery Cohort or P < 1e-7 in the Meta-Analysis on the Risk of SSTIsVariant NameChrPos (hg19)Closest GeneEA/OADiscovery (UK Biobank)Replication (HUNT)Meta-AnalysisEAFOR (95% CI)P-ValueEAFOR (95% CI)P-ValueOR (95% CI)P-Valuers729899282210,196,618MAP2G/T0.0170.69 (0.60–0.79)3.5e-70.0140.95 (0.68–1.33)7.7e-10.72 (0.63–0.83)2.0e-6rs62267025387,726,132AC108749.1C/T0.0121.60 (1.33–1.92)6.0e-70.0100.92 (0.63–1.35)6.6e-11.44 (1.22–1.70)2.0e-5rs15046882957,081,850LINC02196A/G0.0091.67 (1.36–2.05)9.7e-70.0090.98 (0.67–1.42)9.0e-11.47 (1.23–1.77)2.7e-5rs37497481Suggestive variants (P < 1e-6) in the discovery cohort that replicate in the HUNT cohort (P < 7.1e-3 and β coefficient in the same direction) are presented.5127,350,549LINC01184T/C0.2481.19 (1.14–1.24)7.6e-160.2311.15 (1.06–1.25)6.3e-41.18 (1.14–1.23)4.4e-18rs115740542626,123,502H2BC4C/T0.0751.23 (1.14–1.31)7.8e-90.0911.01 (0.90–1.14)8.4e-11.17 (1.10–1.24)4.2e-7rs20073611114,662,722PSMA1G/A0.3420.93 (0.90–0.97)4.0e-40.3650.83 (0.77–0.89)4.7e-70.91 (0.88–0.94)5.1e-8rs786250381681,402,279GANCT/C0.0061.98 (1.53–2.56)2.2e-70.0061.56 (1.00–2.41)4.9e-21.86 (1.48–2.32)5.9e-8rs5910356X117,606,177WDR44T/C0.0580.84 (0.79–0.90)5.6e-70.0551.04 (0.91–1.17)5.9e-10.88 (0.83–0.94)8.1e-5Abbreviations: Chr, chromosome; CI, confidence interval; EA, effect allele; EAF, effect allele frequency; HUNT, Trøndelag Health Study; OA, other allele; Pos, chromosome position.1 Suggestive variants (P < 1e-6) in the discovery cohort that replicate in the HUNT cohort (P < 7.1e-3 and β coefficient in the same direction) are presented. Open table in a new tab Supplementary Table S4MR Sensitivity Analyses of Cardiometabolic Risk Factors on the Risk of SSTITraitUK BiobankHUNTMeta-AnalysisOR (95% CI) or QP-ValueNumber of SNPsOR (95% CI) or QP-ValueNumber of SNPsOR (95% CI) or QP-ValueNumber of SNPsLifetime smoking IVW2.51 (1.75–3.61)6.38e-71262.61 (1.31–5.17)6.11e-31252.53 (1.79–3.56)1.16e-7125 Heterogeneity IVW135.532.45e-1126125.354.49e-1125148.496.62e-2125 Simple median2.45 (1.46–4.12)7.31e-41262.92 (1.03–8.28)4.44e-21252.67 (1.67–4.28)4.03e-5125 Weighted median2.36 (1.38–4.03)1.69e-31263.16 (1.18–8.42)2.17e-21252.17 (1.34–3.52)1.71e-3125 MR Egger1.52 (0.36–6.44)5.71e-11267.17 (0.45–113.72)1.65e-11252.06 (0.52–8.06)3.04e-1125 MR Egger intercept1.01 (0.99–1.02)4.81e-11260.99 (0.97–1.02)4.60e-11251.00 (0.99–1.02)7.61e-1125Sedentary lifestyle IVW0.98 (0.31–3.11)9.75e-141.02 (0.20–5.13)9.82e-141.09 (0.33–2.96)9.83e-14 Heterogeneity IVW9.302.55e-244.891.80e-144 Simple median0.67 (0.29–1.52)3.34e-141.00 (0.21–4.81)9.99e-140.86 (0.41–1.80)6.93e-14 Weighted median0.65 (0.27–1.54)3.29e-141.01 (0.22–4.66)9.89e-140.85 (0.41–1.78)6.72e-14 MR EggerN/AN/AN/AN/AN/AN/AN/AN/AN/A MR Egger interceptN/AN/AN/AN/AN/AN/AN/AN/AN/ASystolic blood pressure IVW1.23 (1.06–1.43)5.84e-31921.25 (0.91–1.72)1.68e-11871.24 (1.08–1.42)2.05e-3187 Heterogeneity IVW182.966.49e-1192217.985.42e-2187185.374.99e-1187 Simple median1.43 (1.14–1.79)1.70e-31921.14 (0.74–1.76)5.61e-11871.21 (1.00–1.47)4.78e-2187 Weighted median1.27 (1.01–1.60)3.82e-21921.31 (0.82–2.09)2.60e-11871.10 (0.90–1.35)3.34e-1187 MR Egger0.76 (0.47–1.21)2.45e-11922.52 (0.93–6.87)7.19e-21870.99 (0.65–1.52)9.77e-1187 MR Egger intercept1.01 (1.00–1.02)3.23e-21920.99 (0.97–1.01)1.50e-11871.00 (1.00–1.01)2.87e-1187Low-density lipoprotein cholesterol IVW0.92 (0.84–1.01)9.05e-2800.90 (0.78–1.05)2.00e-1780.92 (0.85–0.99)2.14e-278 Heterogeneity IVW112.717.65e-38048.799.95e-17883.582.85e-178 Simple median0.89 (0.77–1.03)1.17e-1800.99 (0.77–1.28)9.46e-1780.87 (0.77–0.99)3.05e-278 Weighted median0.90 (0.79–1.01)7.64e-2800.98 (0.78–1.25)8.95e-1780.91 (0.82–1.01)8.14e-278 MR Egger0.89 (0.78–1.02)1.01e-1800.89 (0.71–1.12)3.13e-1780.89 (0.80–0.99)3.81e.278 MR Egger intercept1.00 (0.99–1.01)4.88e-1801.00 (0.99–1.02)8.38e-1781.00 (1.00–1.01)4.58e-178Type-2 diabetes mellitus IVW1.03 (0.98–1.09)1.81e-11991.05 (0.96–1.16)2.88e-11951.04 (0.99–1.09)1.56e-1195 Heterogeneity IVW243.511.93e-2199216.121.32e-1195263.376.75e-4195 Simple median1.05 (0.97–1.14)1.99e-11991.07 (0.92–1.23)3.85e-11951.09 (1.02–1.17)1.47e-2195 Weighted median0.96 (0.89–1.04)3.43e-11990.97 (0.81–1.16)7.39e-11950.97 (0.90–1.04)3.35e-1195 MR Egger0.90 (0.81–1.00)4.85e-21991.05 (0.85–1.29)6.54e-11950.92 (0.83–1.02)1.26e-1195 MR Egger intercept1.01 (1.00–1.02)3.61e-31991.00 (0.99–1.01)9.64e-11951.01 (1.00–1.02)1.38e-2195Body mass index IVW1.86 (1.62–2.15)1.06e-175941.68 (1.29–2.19)1.36e-45801.86 (1.64–2.12)3.22e-21580 Heterogeneity IVW658.063.26e-2594532.319.18e-1580641.163.72e-2580 Simple median1.91 (1.56–2.34)6.17e-105941.62 (1.11–2.37)1.28e-25801.92 (1.60–2.31)2.29e-12580 Weighted median1.63 (1.33–2.00)2.06e-65941.53 (1.02–2.30)4.03e-25801.83 (1.51–2.21)7.05e-10580 MR Egger1.70 (0.95–3.04)7.38e-25941.02 (0.34–3.05)9.78e-15801.41 (0.83–2.41)2.03e-1580 MR Egger intercept1.00 (0.99–1.01)7.50e-15941.01 (0.99–1.02)3.55e-15801.00 (1.00–1.01)2.96e-1580Abbreviations: CI, confidence interval; HUNT, Trøndelag Health Study; IVW, inverse-variance weighted; MR, Mendelian randomization; N/A, not applicable; SSTI, SSTI, skin and soft tissue infection.The effect estimates are presented as OR per SD increase of the genetically predicted risk factor (per unit increase in log OR for genetically proxied type-2 diabetes mellitus liability). For the heterogeneity test of the IVW analysis, the Q-statistic along with its P-value is presented. Open table in a new tab Only independent SNPs (r2 < 0.001) with P < 5e-8 in these GWASs were included. Abbreviations: HUNT, Trøndelag Health Study; LDL, low-density lipoprotein. Data are presented as mean (SD), median (25th and 75th centile), or number (%). Abbreviations: Chr, chromosome; CI, confidence interval; EA, effect allele; EAF, effect allele frequency; HUNT, Trøndelag Health Study; OA, other allele; Pos, chromosome position. Abbreviations: CI, confidence interval; HUNT, Trøndelag Health Study; IVW, inverse-variance weighted; MR, Mendelian randomization; N/A, not applicable; SSTI, SSTI, skin and soft tissue infection. The effect estimates are presented as OR per SD increase of the genetically predicted risk factor (per unit increase in log OR for genetically proxied type-2 diabetes mellitus liability). For the heterogeneity test of the IVW analysis, the Q-statistic along with its P-value is presented.

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