Sex differences in the genetics of sarcoidosis across European and African ancestry populations

fmed-10-1132799 May 4, 2023 Time: 14:33 # 1 TYPE Original Research PUBLISHED 11 May 2023 DOI 10.3389/fmed.2023.1132799 OPEN ACCESS EDITED BY Ying Zhou, Tongji University, China REVIEWED BY Humberto Garcia-Ortiz, National Institute of Genomic Medicine (INMEGEN), Mexico Ilias C. Papanikolaou, General Hospital of Corfu, Greece *CORRESPONDENCE Natalia V. Rivera [email protected] RECEIVED 27 December 2022 ACCEPTED 10 April 2023 PUBLISHED 11 May 2023 CITATION Xiong Y, Kullberg S, Garman L, Pezant N, Ellinghaus D, Vasila V, Eklund A, Rybicki BA, Iannuzzi MC, Schreiber S, Müller-Quernheim J, Montgomery CG, Grunewald J, Padyukov L and Rivera NV (2023) Sex differences in the genetics of sarcoidosis across European and African ancestry populations. Front. Med. 10:1132799. doi: 10.3389/fmed.2023.1132799 COPYRIGHT © 2023 Xiong, Kullberg, Garman, Pezant, Ellinghaus, Vasila, Eklund, Rybicki, Iannuzzi, Schreiber, Müller-Quernheim, Montgomery, Grunewald, Padyukov and Rivera. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Sex differences in the genetics of sarcoidosis across European and African ancestry populations Ying Xiong1, Susanna Kullberg1,2, Lori Garman3, Nathan Pezant3, David Ellinghaus4, Vasiliki Vasila1, Anders Eklund2, Benjamin A. Rybicki5, Michael C. Iannuzzi6, Stefan Schreiber4,7, Joachim Müller-Quernheim8, Courtney G. Montgomery3, Johan Grunewald1,2,9, Leonid Padyukov9,10 and Natalia V. Rivera1,9,10* 1Respiratory Medicine Division, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden, 2Department of Respiratory Medicine and Allergy, Theme Inflammation and Ageing, Karolinska University Hospital, Stockholm, Sweden, 3Genes and Human Disease, Oklahoma Medical Research Foundation, Oklahoma City, OK, United States, 4Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Kiel, Germany, 5Department of Public Health Sciences, Henry Ford Health System, Detroit, MI, United States, 6Zucker School of Medicine, Staten Island University Hospital, Northwell/Hofstra University, Staten Island, NY, United States, 7Clinic for Internal Medicine I, University Hospital Schleswig-Holstein, Kiel, Germany, 8Department of Pneumology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany, 9Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden, 10Division of Rheumatology, Department of Medicine Solna, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden Background: Sex differences in the susceptibility of sarcoidosis are unknown. The study aims to identify sex-dependent genetic variations in two clinical sarcoidosis phenotypes: Löfgren’s syndrome (LS) and non-Löfgren’s syndrome (non-LS). Methods: A meta-analysis of genome-wide association studies was conducted on Europeans and African Americans, totaling 10,103 individuals from three population-based cohorts, Sweden (n= 3,843), Germany (n= 3,342), and the United States (n= 2,918), followed by an SNP lookup in the UK Biobank (UKB, n= 387,945). A genome-wide association study based on Immunochip data consisting of 141,000 single nucleotide polymorphisms (SNPs) was conducted in the sex groups. The association test was based on logistic regression using the additive model in LS and non-LS sex groups independently. Additionally, gene-based analysis, gene expression, expression quantitative trait loci (eQTL) mapping, and pathway analysis were performed to discover functionally relevant mechanisms related to sarcoidosis and biological sex. Results: We identified sex-dependent genetic variations in LS and non-LS sex groups. Genetic findings in LS sex groups were explicitly located in the extended Major Histocompatibility Complex (xMHC). In non-LS, genetic differences in the sex groups were primarily located in the MHC class II subregion and ANXA11. Gene-based analysis and eQTL enrichment revealed distinct sex-specific gene expression patterns in various tissues and immune cell types. In LS sex groups, a pathway map related to antigen presentation machinery by IFN-gamma. In non-LS, pathway maps related to immune response lectin-induced complement pathway in males and related to maturation and migration of dendritic cells in skin sensitization in females were identified. Frontiers in Medicine 01 frontiersin.org fmed-10-1132799 May 4, 2023 Time: 14:33 # 2 Xiong et al. 10.3389/fmed.2023.1132799 Conclusion: Our findings provide new evidence for a sex bias underlying sarcoidosis genetic architecture, particularly in clinical phenotypes LS and nonLS. Biological sex likely plays a role in disease mechanisms in sarcoidosis. KEYWORDS sarcoidosis, genetics, genome-wide association study (GWAS), meta-analysis, single nucleotide polymorphisms, immunogenetics and HLA Background Sarcoidosis is a multi-system disease with unknown etiology, characterized by non-caseating granulomas (1). In 90% of cases, sarcoidosis affects the lungs and lymphatic system (2). Sarcoidosis is prevalent worldwide, commonly affecting individuals between 20 and 50 years of age, with males diagnosed earlier than females (3,4). On a global scale, sarcoidosis incidence and prevalence vary based on sex, age, geography, and ethnicity (4). In the United States, the prevalence is 141 per 100,000 in African–Americans and 49.8 per 100,000 in Caucasians (5). In Nordic countries such as Sweden, the prevalence of sarcoidosis was reported between 152 and 215 per 100,000 (6), whereas in Denmark was reported at 77 per 100,000 (7). The disease is heterogeneous, as different manifestations and clinical outcomes have been observed in clinical settings, particularly among sex groups (3,8,9). Interestingly, sarcoidosis is more common in females, has a late-onset, has a variable disease course depending on the affected organ, and has a higher mortality rate than men (3). Sex differences are also seen in extra-pulmonary phenotypes (10,11), including cardiac (12,13) and skin sarcoidosis. Sex hormones have been shown to play a role in sarcoidosis (14, 15). For instance, sarcoidosis has a low disease activity in pregnancy or goes into remission. However, the ameliorating effect is lost as flares occur after delivery (16,17). In the lungs, sarcoidosis is characterized by Löfgren’s syndrome (LS) and non-Löfgren’s syndrome (LS). LS is an acute form of the disease characterized by periarticular swelling around the ankles, erythema nodosum, and bilateral hilar lymphadenopathy (18). Typically, females with LS exhibit erythema nodosum (EN), while males with LS have symptoms of bilateral ankle arthritis. Patients with LS usually have a good prognosis (19). LS often occurs in individuals of European ancestry and is relatively rare in individuals of African origin. In non-LS, sarcoidosis is characterized by a heterogeneous disease course, often with an insidious onset, unrelenting disease course, and a high risk for clinical organ impairment, predominantly pulmonary fibrosis (18, 20). Patients with fibrotic sarcoidosis have markedly decreased survival compared with the general population (21,22). Abbreviations: LS, Löfgren’s syndrome; Non-LS, non-Löfgren’s syndrome; SNPs, single nucleotide polymorphisms; eQTL, expression quantitative trait loci; UK Biobank, United Kingdom Biobank; ANXA11, Annexin A11 gene; GWAS, Genome-wide association study; US-AA, United States African Americans; PC, Principal component; PCA, principal component analysis; IVW, inverse variance weighting; OR, odds ratio; CI, confidence interval. The causes for clinical differences and disease course in sarcoidosis among sex groups are unknown. However, evidencebased factors, including genetics, epigenetics, and environmental exposures, have been postulated. Regarding genetics, few studies have addressed sex differences in the genetic variation in disease, despite most studies adjusting for sex in their analyses (23). An exception is a genome-wide admixture scan conducted in African Americans stratified by sex, identifying several genetic variants associated with sarcoidosis exclusively in females (24). In this work, we sought to investigate the genetic associations in the sex groups and identify differences and commonalities in European and African population ancestries by a meta-analysis approach and high-density mapping. Materials and methods Samples and study design The samples were from three independent sarcoidosis population-based cohorts from Sweden, Germany, and the United States. All participants provided written informed consent for the study. Study protocols of all studies had been approved by respective local institutional boards. The Swedish cohort The case-control study consisted of 4,133 individuals. The study was approved by the local institutional review board in Stockholm, Sweden. All participants provided written informed consent and were permitted to use their DNA for research purposes. Baseline clinical and demographic characteristics were measured when the participants were enrolled in the study. Patients were enrolled at the time of disease investigation at the Sarcoidosis Centrum, Karolinska University Hospital Solna, Sweden. The diagnosis was established on radiographic manifestations, findings at bronchoscopy with bronchoalveolar lavage (BAL), including an elevated CD4/CD8 ratio >3.5, and positive biopsies and in accordance with the criteria outlined by the World Association of Sarcoidosis and Other Granulomatous Disorders [WASOG, Statement on sarcoidosis (27)]. Sarcoidosis patients were further characterized into two clinical phenotypes, LS and non-LS. LS was defined by typical clinical manifestations with an acute onset of the disease, including fever, bilateral hilar lymphadenopathy on chest X-ray, bilateral ankle arthritis, and/or erythema nodosum. Non-LS was defined as a heterogeneous disease course with an insidious onset, unrelenting disease course, and a Frontiers in Medicine 02 frontiersin.org fmed-10-1132799 May 4, 2023 Time: 14:33 # 3 Xiong et al. 10.3389/fmed.2023.1132799 high risk for clinical organ impairment, such as developing fibrosis in the lung. Healthy controls included 3,085 individuals who were recruited from two large-scale epidemiological cohorts. Mainly, 2,025 individuals were from the Environmental Investigation of Rheumatoid arthritis (EIRA) study described in Klareskog et al. (25) and 1,060 individuals were from the Epidemiological investigation of risk factors for Multiple Sclerosis (EIMS) study described in Hedstrom et al. (26). The German cohort The case-control study consisted of 4,975 individuals, of which 413 were non-LS (27). The description and inclusion of these patients are described elsewhere (28,29). Briefly, German control subjects (n= 4,498) were derived from Popgen (n= 2,485) (30), and the Heinz Nixdorf RECALL (HNR) study (n= 1,499) (31). Additionally, 304 control individuals of South German origin were recruited from the Bavarian Red Cross, and 210 control individuals were recruited from the Charité - Universitätsmedizin Berlin. The mean age and male percentage were 62.5 years (SD = 12.3) and 40% male in non-LS and 57.8 years (SD = 12.2), and 51% male in controls. The USA African American cohort The case-control study consisted of 1,657 individuals. The sample included 781 sarcoidosis cases characterized as non-LS and 876 healthy controls. All individuals were taken from an extensive cohort of African-American (AA) sarcoidosis patients and controls assembled from various studies. Further details are available in Refs. (32–35). Genotyping and quality control Genotyping for sarcoidosis patients in the Swedish cohort was performed using the Illumina Immunochip platform and was performed at The SNP&SEQ Technology Platform in Uppsala University, Sweden, with Illumina Infinium assay using the Immuno Bead Chip (Immunochip version 1) as described in Rivera et al. (23). Healthy controls in the EIRA cohort (n= 2,086) were genotyped on the same platform at the Genome Institute of Singapore, as described previously (36,37). Healthy controls in the EIMS cohort (n= 1,060) were genotyped with the same SNP array described elsewhere (26). Briefly, quality control filtering thresholds were applied using tools implemented in PLINK v1.09b software (38,39). SNPs at call rate <95% with minor allele frequency (MAF) >1% and call rate <99% with MAF ≤5% were filtered out. Moreover, SNPs that had Hardy–Weinberg Equilibrium (HWE) P<1×10−7(in the control group) were also excluded. Individuals with missing genotypes <97% were also removed. Quality control (QC) resulted in 141,151 SNPs and 3,842 individuals on the Illumina Immunochip array. Genotypes for the German cohort were quality controlled, as described in Fischer et al. (40). Genotyping for the African American cohort (US-AA) was performed at the OMRF using the Illumina HumanOmni1-Quad array for ∼1.1M variants across the genome. Of these, 121,988 were in common with the Immunochip platform and carried forward for analysis. Further details on genotyping and quality control filtering are described in Adrianto et al. (41). Statistical analysis Association analysis of LS and non-LS in sex groups In each sex group, we examined the association of single nucleotide polymorphisms (SNPs) in LS and non-LS. For assessing the association in LS, we applied an additive model using logistic regression adjusted for age and four principal components (PCs). PCs were derived from principal component analysis (PCA) performed using EIGENSTRAT (42) software on a pruned genotyped data set. The pruning of genotypes was performed using the pruning function and default parameters implemented in PLINK 1.90 beta software (39). For assessing the association in non-LS, we applied an additive model using logistic regression without covariate adjustment due to the lack of age information across the cohorts. In LS and non-LS, a significance threshold was defined by genomic control-corrected P<3.5 ×10−7 (0.05/141,151 quality-controlled SNPs) considering Bonferroni correction. A suggestive P<5×10−5was also considered to identify potential signals (43–46). Furthermore, to account for high linkage disequilibrium (LD) (r2≥0.8) among associated SNPs, LD pruning was performed using PriorityPruner version 0.01.4 software1with default parameters, which identified and prioritized SNPs in genomic loci and thus referred as tag-SNPs. As an exploratory analysis, to assess the effect of sex on genetics, we implemented an interaction model where the disease outcome was regressed on SNP adjusted for sex and the interaction term (SNP ×sex) as covariates. A significant threshold for the interaction term was set at P<0.05. The interaction analysis was performed using the glm function implemented in PLINK 2.0 software (39). Additionally, we also performed a heterogeneity test to assess the effects of association between males and females. Heterogeneity statistics i.e., Cochran’s Q-statistic, I2statistic (the percentage of variability in the effect sizes), and tau2(the between-study variance in our meta-analysis) were computed applying a random effects model and using the rma function implemented in the metafor R package (47). Meta-analysis of significant SNPs Meta-analysis was performed using SNP association results in non-LS. Herein, the meta-analysis was conducted using the inverse variance weighting (IVW) method implemented in the METAL software (48). For each SNP, the combined genetic effect size (defined by Beta), standard error (SE), meta-P-value (Pmeta), total variability in effect size, also known as the heterogeneity index (I2), and heterogeneity P-value were calculated. SNPs at Pmeta <5×10−8were defined as genome-wide significant. Two meta-analyses were conducted, (1) a meta-analysis considering SNP associations in the European ancestry cohorts (Sweden and Germany) and (2) a meta-analysis considering SNP associations in the multi-ethnic (Sweden, Germany, and US African American). Besides METAL computing heterogeneity index (I2), we also performed an independent heterogeneity test applying the random effects model using the rma function implemented in the metafor 1http://prioritypruner.sourceforge.net/ Frontiers in Medicine 03 frontiersin.org fmed-10-1132799 May 4, 2023 Time: 14:33 # 4 Xiong et al. 10.3389/fmed.2023.1132799 R package (47). Summary of heterogeneity statistics include Cochran’s Q-statistic, I2statistic (the percentage of variability in the effect sizes), and tau2(the between-study variance in our metaanalysis). Lookup of significant SNPs in the UK Biobank Using the UKB resource, we examined SNP associations at P<5×10−5using summary statistics of a genomewide association study (GWAS) on “doctor-diagnosed sarcoidosis” (Data-Field ID: 22133), consisting of 91,787 individuals (395 cases and 91,392 controls) in the UKBB (49). Chiefly, sexstratified summary statistics GWAS on males (189 cases and 41,045 controls) and females (206 cases and 50,347 controls) were obtained from https://github.com/Nealelab/UK_Biobank_GWAS. GWAS analysis was conducted using an additive model and logistic regression adjusted by the first 20 PCs, age, and age2. A lookup significance threshold was set at P<5×10−8. Gene-based analysis A gene-based analysis was conducted using SNP associations at P<5×10−5and MAGMA (50). Gene-based significance was set to P<2.0 ×10−6based on Bonferroni correction (0.05/24,769 genes). The number of genes was adopted from the VEGAS2 software definition, where 24,769 unique genes on the 22 autosomes were identified (51). SNP enrichment analysis via expression quantitative trait loci (eQTLs) and pathway analysis Expression quantitative trait loci enrichment was performed using SNP associations at P<5×10−5and gene expression data from 53 human tissues from the Genotype-Tissue Expression (GTEx) project version 8 available on the Functional Mapping and Annotation of genetic associations (FUMA) web tool (52). Additional eQTL enrichment using gene expression of lung tissue, whole blood, and immune cell types was conducted using EUGENE software (53,54). A significant threshold for n SNP correlated with gene expression (defined as eQTL-SNP or eSNP) was set at P<0.05. Pathway analysis was conducted based on SNP associations at P<5×10−8to elucidate disease mechanisms using MetaCoreTM software. Results The summary of phenotypes and the number of samples in the sex groups for cohorts investigated are shown in Table 1. Association results The genome-wide association analysis using high-density mapping Immunochip array was conducted in males and females independently in Swedish, German, and US-African American cohorts. TABLE 1 Sample size in association analysis in Sweden, Germany, and United States (A); sample size in replication lookup in the UK Biobank (B). (A) Cohorts LS Non-LS Males Females Males Females Swedish 166 cases vs. 849 HC 132 cases vs. 2,236 HC 263 cases vs. 855 HC 181 cases vs. 2,245 HC German 470 cases vs. 1,750 HC 732 cases vs. 1,692 HC USA-AA 322 cases vs. 497 HC 951 cases vs. 1,148 HC (B) Males Females 344 cases vs. 166,644 controls 361 cases and 193,792 controls HC, healthy controls. LS sex groups In the Swedish cohort (Supplementary Tables 1,2), 542 SNPs in LS males and 617 SNPs in LS females were identified at P<3.5 ×10−7. Rs2187668 (OR = 4.715, SE = 0.1983, P= 6.02 ×10−14) located in HLA-DQA1 was the top signal identified in males, and rs9268219 (OR = 3.667, SE = 0.1586, P= 3.33 ×10−15) located in C6orf10 was the top signal identified in females. After considering linkage disequilibrium (LD) among associated signals, 77 SNPs in males and 101 SNPs in females remained, which were defined as tag-SNPs. The top results for LS sex groups (Table 2) are illustrated in the forest plot (Figure 1). Manhattan and QQ plots are shown in Supplementary Figure 1. Heterogeneity statistics are available in Supplementary Table 2X. Conditioning for the top SNP rs2187668 in males identified 12 SNPs at P<5×10−5. The most significant SNP was rs9296079 (OR = 7.476, SE = 0.410, P= 9.51 ×10−7), located at 7.8 kb 50of HLA-DPB2. Similarly, conditioning for rs9268219 in females identified 8 SNPs at P<5×10−5. The most significant was rs29243 (OR = 10.980, SE = 0.514, P= 6.04 ×10−6) located in GABBR1. Non-MHC SNPs at P<5×10−5were identified near LRRTM4 in males and CAST, LNPEP, TNIP1, CSMD1, B4GALNT1, SBNO2, and ABCG1 in females (Supplementary Table 3). Non-LS sex groups In the Swedish cohort (Supplementary Tables 4,5), 5 SNPs in males at P<3.5 ×10−7and 1 SNP in females at P= 6.73 ×10−7 were identified. The top signals were rs1049550 (OR = 0.5231, SE = 0.1126, P= 1.82 ×10−8) located in ANXA11 in males and rs1964995 (OR = 0.5314, SE = 0.1198, P= 6.73 ×10−7) located at 36 kb of the 30of HLA-DRB5, in females. After LD assessment, 3 SNPs in males defined as tag-SNPs remained. In the German cohort (Supplementary Tables 6,7), rs1964995 (OR = 0.5452, SE = 0.08223, P= 3.40 ×10−11) in males and rs4502931 (OR = 0.5899, SE = 0.06549, P= 4.36 ×10−14) in females were identified as the top signals. In the US African American cohort (Supplementary Tables 8,9), rs9271640 (OR = 1.771, SE = 0.1097, P= 3.62 ×10−7) in males and rs1964995 (OR = 0.5452, SE = 0.08223, P= 3.40 ×10−11) in females were identified as the top signals. Frontiers in Medicine 04 frontiersin.org fmed-10-1132799 May 4, 2023 Time: 14:33 # 5 Xiong et al. 10.3389/fmed.2023.1132799 TABLE 2 The top tag-SNPs (PGC <3.5 ×10−7) associated with LS males and females in the Swedish cohort. SNP CHR BP (hg19) Alleles RefSeq genes Sex group CA CAF OR 95% CI PGC rs2187668 6 32,605,884 A/G HLA-DQA1 LS-M A 0.3557 4.7150 (3.1966, 6.9547) 6.02E-14 LS-F A 0.3557 3.4630 (2.5643, 4.6768) 6.57E-15 rs3130288 6 32,096,001 A/C ATF6B LS-M A 0.3456 4.4560 (3.0489, 6.5124) 1.29E-13 LS-F 0.3456 3.5090 (2.58, 4.7725) 1.39E-14 rs2105902 6 32,395,698 A/T 12kb 50of HLA-DRA LS-M A 0.4010 4.2330 (2.9272, 6.1214) 1.87E-13 LS-F 0.4010 3.1160 (2.32, 4.1851) 3.76E-13 rs3135382 6 32,383,441 C/A 8.5kb 50of BTNL2 LS-M C 0.4446 3.9120 (2.749, 5.567) 3.47E-13 LS-F 0.4460 2.9310 (2.2128, 3.8823) 5.50E-13 rs3131643 6 31,442,782 A/G 2.6kb 30of HCG26 LS-M A 0.3788 3.9640 (2.7471, 5.7201) 1.59E-12 LS-F 0.3788 2.8890 (2.1476, 3.8864) 1.57E-11 rs4143332 6 31,348,365 A/G 19kb 50of MICA LS-M A 0.3389 4.1600 (2.8303, 6.1145) 3.32E-12 LS-F 0.3389 3.3970 (2.5095, 4.5985) 2.66E-14 rs2071278 6 32,165,444 G/A NOTCH4 LS-M G 0.3725 3.8000 (2.6308, 5.4888) 8.55E-12 LS-F 0.3725 3.1160 (2.3032, 4.2156) 1.36E-12 rs3094049 6 30,359,360 A/G 45kb 30of RPP21 LS-M A 0.2903 4.1510 (2.7873, 6.1819) 1.78E-11 LS-F 0.2903 3.2150 (2.3198, 4.4557) 1.53E-11 rs2524069 6 31,244,789 T/A 4.9kb 50of HLA-C LS-M T 0.3540 3.6090 (2.5168, 5.1752) 2.11E-11 LS-F 0.3540 2.7470 (2.0481, 3.6845) 8.72E-11 rs2736157 6 31,600,820 G/A PRRC2A LS-M G 0.4161 3.4380 (2.4292, 4.8657) 2.22E-11 LS-F 0.4161 2.7580 (2.0587, 3.6949) 6.19E-11 rs9268219 6 32,284,108 C/A C6orf10 LS-M C 0.3356 4.4090 (3.0003, 6.4792) 4.10E-13 LS-F 0.3356 3.6670 (2.6872, 5.004) 3.33E-15 rs1634721 6 30,977,680 A/G PBMUCL1 LS-M A 0.3221 3.4810 (2.372, 5.1085) 9.45E-10 LS-F 0.3221 3.5930 (2.6382, 4.8934) 6.08E-15 rs3132449 6 31,626,013 A/G 25bp 30of APOM LS-M A 0.3418 3.9780 (2.7203, 5.8173) 8.08E-12 LS-F 0.3418 3.4680 (2.5569, 4.7038) 1.47E-14 rs1064627 6 30,698,541 G/A FLOT1 LS-M G 0.354 2.9360 (2.0263, 4.2541) 4.61E-08 LS-F 0.354 3.3110 (2.4618, 4.4532) 2.64E-14 rs9266669 6 31,348,077 A/G 19kb 50of MICA LS-M A 0.3625 3.3530 (2.3232, 4.8393) 5.56E-10 LS-F 0.3625 3.3570 (2.4872, 4.531) 2.73E-14 rs886423 6 30,782,205 G/C 70kb 50of DDR1 LS-M G 0.3322 2.8220 (1.9374, 4.1107) 2.11E-07 LS-F 0.3322 3.2850 (2.4358, 4.4303) 6.69E-14 rs3129963 6 32,380,208 G/A 5.3kb 50of BTNL2 LS-M G 0.4513 3.7770 (2.6521, 5.3791) 1.53E-12 LS-F 0.4513 3.0010 (2.2666, 3.9734) 1.61E-13 rs2233974 6 31,080,016 C/G C6orf15 LS-M C 0.3793 3.3470 (2.325, 4.8184) 4.47E-10 LS-F 0.3793 3.1680 (2.3592, 4.2542) 1.66E-13 rs3130477 6 31,428,920 G/A 2kb 50of HCP5 LS-M G 0.3389 4.0890 (2.7858, 6.0019) 5.00E-12 LS-F 0.3389 3.2220 (2.3816, 4.359) 2.91E-13 SNP, single nucleotide polymorphism; BP, chromosome base pairs are based on human genome assembly version 19 (hg19); CA, coded allele; CAF, coded allele frequency; OR, odds ratio; SE, standard error of odds ratio; PGC, genomic controlled P-value. The top association results for each cohort are summarized in Table 3. Manhattan and QQ plots for non-LS sex groups are shown in Supplementary Figures 2–4. Interaction analysis with sex Results from the interaction analysis in the LS and nonLS sex groups in the Swedish cohort showed significant SNPs interacting with the sex variable. In LS sex groups (Supplementary Table 10), the most significant interacting SNPs were rs2853973 (Pint = 3.8 ×10−4) in males and rs1470410 (Pint = 5.44 ×10−4) in females. In non-LS sex groups (Supplementary Table 11), the most significant interacting SNPs were rs10940422 (Pint = 4.18 ×10−4) in males and rs12432418 (Pint = 4.85 ×10−5) in females. Frontiers in Medicine 05 frontiersin.org fmed-10-1132799 May 4, 2023 Time: 14:33 # 6 Xiong et al. 10.3389/fmed.2023.1132799 FIGURE 1 Forest plot for the 15 top common SNPs in the LS sex groups in the Swedish cohort. Meta-analysis in non-LS Non-LS sex groups Meta-analysis in the European cohorts (Sweden and Germany) (Supplementary Tables 12,13) identified 57 SNPs in males and 112 SNPs in females at Pmeta <5×10−8. Top signal rs1964995 located 36 kb from the 30of HLA-DRB5 was the same in males (OR = 1.715, SE = 0.0656, Pmeta = 3.92 ×10−18) and females (OR = 1.7304, SE = 0.0598, Pmeta = 5 ×10−20). A forest plot illustrating the top findings is shown in Figure 2. Manhattan and Q-Q plots of the meta-analysis are shown in Supplementary Figure 5. Heterogeneity statistics in the SWE-GER are shown in Supplementary Table 13X. A comparison of associations at Pmeta <5×10−8 showed 44 SNPs shared between non-LS males and females, 17 SNPs exclusively associated with non-LS males, and 215 SNPs exclusively associated with non-LS females. Most SNPs were in the MHC region. Non-MHC SNPs include rs694739 (OR = 1.3626, SE = 0.063, Pmeta = 9.21 ×10−07) located at 7.9 kbp from the 30of PRDX5 associated with non-LS males, and rs2573346 (OR = 0.7712, SE = 0.0564, Pmeta = 4.19 ×10−06) in ANXA11 associated with nonLS females. Results from a meta-analysis in the multi-ethnic cohorts (Sweden, Germany, and US-African American) (Supplementary Tables 14,15) identified 12 SNPs in non-LS males and 49 in nonLS females (Table 3). Top SNPs were rs1964995 (OR = 1.7736, SE = 0.0587, Pmeta = 1.54 ×10−22) located 36 kb from 30of HLA-DRB5 in males, and rs2395153 (Beta = 0.6565, SE = 0.0477, Pmeta = 1.11 ×10−18) located at 5.9 kb from 50of C6orf10 in females. The top meta-SNPs are shown in Table 4. Forest plots (Figure 2) illustrate the top common meta-SNPs in the European and multi-ancestry cohort groups. Manhattan plots of meta-analysis in multi-ethnic groups are shown in Supplementary Figure 6. Heterogeneity statistics in SWE-GER-USA are shown in Supplementary Table 15X. A comparison analysis at Pmeta <5×10−8showed a shared SNP between non-LS males and females, 11 SNPs exclusively associated with non-LS males, and 48 SNPs exclusively associated with non-LS females. Most associated SNPs were in the MHC region. Non-MHC SNPs were observed in non-LS females and included rs7813186, rs1049550, and rs7133604 (Data not shown). Lookup of meta-SNPs in the UK Biobank Genome-wide association study of “Doctor diagnosed sarcoidosis” in the UKB was used as a validation step. SNPlookup showed several sex-specific SNPs in LS and non-LS at P<5×10−5. Chiefly, 223 SNPs associated with LS males and 209 SNPs associated with LS females were validated at P<5×10−8 (Supplementary Tables 16,17). In non-LS, 11 non-LS males and 31 SNPs non-LS females in the SWE-GER cohorts were validated Frontiers in Medicine 06 frontiersin.org fmed-10-1132799 May 4, 2023 Time: 14:33 # 7 Xiong et al. 10.3389/fmed.2023.1132799 TABLE 3 The top tag-SNPs (PGC <3.5 ×10−7) associated with non-LS males and females in Swedish, German, and USA-AA cohorts. Males SNP CHR BP (hg19) Alleles RefSeq genes Cohort CA CAF OR 95% CI PGC rs1049550 10 81,926,702 A/G ANXA11 SWE A 0.2962 0.5231 (0.4195, 0.6523) 1.82E-08 GER A 0.3845 0.7325 (0.6281, 0.8543) 3.63E-04 USA-AA A 0.1508 0.7724 (0.5841, 1.0215) 7.70E-02 rs2239802 6 32,411,846 C/G HLA-DRA SWE C 0.3375 1.7790 (1.4295, 2.214) 4.52E-07 GER C 0.2283 1.4480 (1.2336, 1.6997) 4.75E-05 USA-AA C 0.3321 1.5140 (1.2288, 1.8655) 1.41E-04 rs7197 6 32,412,580 A/G HLA-DRA SWE A 0.2885 1.7610 (1.4056, 2.2063) 1.50E-06 GER A 0.1901 1.4210 (1.1976, 1.6862) 2.96E-04 USA-AA A 0.2253 1.6350 (1.2895, 2.0731) 7.32E-05 rs1964995 6 32,449,411 G/A 36kb 30of HLA-DRB5 SWE G 0.2995 0.6033 (0.4872, 0.747) 5.74E-06 GER G 0.3664 0.5452 (0.464, 0.6406) 3.40E-11 USA-AA G 0.2314 0.5568 (0.431, 0.7194) 1.21E-05 rs3830135 6 32,548,464 A/G HLA-DRB1 SWE A 0.0529 0.3543 (0.2278, 0.5512) 6.71E-06 GER A 0.0980 0.4374 (0.3212, 0.5956) 2.40E-06 USA-AA C 0.0702 0.5942 (0.3897, 0.906) 1.82E-02 rs3131283 6 32,119,898 A/G 177bp 50of PRRT1 SWE A 0.1971 1.8100 (1.4054, 2.3312) 6.85E-06 GER A 0.1417 1.3790 (1.1382, 1.6707) 3.21E-03 USA-AA C 0.0256 1.7040 (0.9263, 3.1348) 9.41E-02 rs4530903 6 32,581,889 A/G 23kb 50of HLA-DQA1 SWE A 0.0529 0.3590 (0.2308, 0.5585) 8.79E-06 GER A 0.0973 0.4282 (0.3133, 0.5853) 1.74E-06 USA-AA T 0.0702 0.5942 (0.3897, 0.906) 1.82E-02 rs1800684 6 32,151,994 T/A AGER SWE T 0.1982 1.7920 (1.3917, 2.3076) 9.75E-06 GER A 0.1428 1.3810 (1.1401, 1.6729) 3.00E-03 USA-AA A 0.0293 1.6280 (0.9369, 2.8289) 9.13E-02 rs3104402 6 32,681,676 A/C 27kb 50of HLA-DQA2 SWE A 0.1050 2.0860 (1.5013, 2.8984) 1.83E-05 GER T 0.0689 1.3750 (1.0539, 1.794) 3.50E-02 USA-AA G 0.0153 2.3790 (1.0553, 5.3629) 4.12E-02 rs1044506 6 32,172,065 A/C NOTCH4 SWE A 0.2016 1.7870 (1.3878, 2.3011) 1.08E-05 GER T 0.1441 1.3560 (1.1203, 1.6413) 4.97E-03 USA-AA G 0.0263 1.9640 (1.0695, 3.6067) 3.35E-02 Females rs1964995 6 32,449,411 G/A 36kb 30of HLA-DRB5 SWE G 0.2995 0.5314 (0.4202, 0.6721) 6.73E-07 GER G 0.3611 0.5942 (0.519, 0.6804) 1.65E-12 USA-AA G 0.2408 0.8051 (0.6963, 0.9309) 3.77E-03 rs3129727 6 32,679,690 A/G 29kb 50of HLA-DQA2 SWE A 0.0474 3.0760 (2.0025, 4.7251) 1.37E-06 GER A 0.0268 1.9580 (1.3773, 2.7836) 4.53E-04 USA-AA T 0.0217 1.6270 (1.0686, 2.4773) 2.47E-02 rs3998158 6 32,681,992 G/A 27kb 50of HLA-DQA2 SWE G 0.1700 0.5047 (0.3771, 0.6755) 1.49E-05 GER C 0.1990 0.5811 (0.4908, 0.6881) 3.67E-09 USA-AA C 0.1665 1.0890 (0.925, 1.2821) 3.10E-01 rs2395153 6 32,345,595 C/G 5.9kb 50of C6orf10 SWE C 0.2748 0.5947 (0.4694, 0.7535) 5.05E-05 GER C 0.3391 0.6137 (0.5347, 0.7044) 7.74E-11 USA-AA G 0.2175 0.7410 (0.6372, 0.8618) 1.18E-04 (Continued) Frontiers in Medicine 07 frontiersin.org fmed-10-1132799 May 4, 2023 Time: 14:33 # 8 Xiong et al. 10.3389/fmed.2023.1132799 TABLE 3 (Continued) Females SNP CHR BP (hg19) Alleles RefSeq genes Cohort CA CAF OR 95% CI PGC rs2076529 6 32,363,955 G/A BTNL2 SWE G 0.3176 0.6105 (0.4859, 0.7672) 6.66E-05 GER C 0.3752 0.6458 (0.5659, 0.7371) 1.22E-09 USA-AA C 0.3056 0.8406 (0.7363, 0.9598) 1.11E-02 rs9268472 6 32,355,605 A/G 6.9kb 30of BTNL2 SWE A 0.3176 0.6107 (0.486, 0.7674) 6.71E-05 GER T 0.3754 0.6446 (0.5648, 0.7357) 1.04E-09 USA-AA A 0.3063 0.8471 (0.7421, 0.967) 1.51E-02 rs9275580 6 32,679,462 G/A 30kb 50of HLA-DQA2 SWE G 0.1791 0.5543 (0.4192, 0.7329) 9.63E-05 GER C 0.2102 0.5519 (0.4666, 0.6528) 7.77E-11 USA-AA G 0.2458 1.1700 (1.0138, 1.3503) 3.35E-02 rs3129882 6 32,409,530 G/A HLA-DRA SWE G 0.5146 1.5580 (1.263, 1.922) 9.81E-05 GER G 0.4499 1.1700 (1.0356, 1.322) 1.85E-02 USA-AA A 0.4586 1.0660 (0.9446, 1.203) 3.05E-01 SNP, single nucleotide polymorphism; BP, chromosome base pairs are based on human genome assembly version 19 (hg19); CA, coded allele; CAF, coded allele frequency; OR, odds ratio; SE, standard error of odds ratio; PGC, genomic controlled P-value. FIGURE 2 Forest plots for the 15 top common meta-SNPs in the non-LS sex groups in the European group (Swedish-German) and the multi-ethnic group (Swedish-German-United States-African American). at P<5×10−8(Supplementary Tables 18,19). In the multiethnic cohorts (SWE-GER-USA-AA), 2 SNPs in non-LS males and 8 SNPs in non-LS females were observed (Supplementary Tables 20,21). Gene-based analysis The gene-based analysis revealed significant genomic loci associated with sex groups in LS and non-LS (Supplementary Tables 22,23). The most significant genomic locus was HLA-DRA (P= 4.36 ×10−14) in LS males and FKBPL (P= 1.39 ×10−14) in LS females at a gene-based P<2.0 ×10−6(0.05/24,769 genes). In the European cohorts (SWE-GER), 15 genes associated with non-LS males and 18 genes associated with non-LS females were identified, where the most significant genomic locus was HLA-DRA (P= 5.16 ×10−15) in non-LS males and HLA-DRB1 (P= 2.42 ×10−13) in non-LS females. In the multi-ethnic cohorts (SWE-GER-USA-AA), 5 genes associated with non-LS males and 8 genes associated with non-LS females were identified. The top genes were HLA-DRA (P= 1.88 ×10−14) in non-LS males and C6orf10 (P= 6.47 ×10−12) in non-LS females. A comparison of gene-based analysis in LS and non-LS sex groups across cohorts (Supplementary Table 24) revealed differences in gene-based associations. In LS, gene-based associations showed 77 genes shared among sex groups, 11 Frontiers in Medicine 08 frontiersin.org fmed-10-1132799 May 4, 2023 Time: 14:33 # 9 Xiong et al. 10.3389/fmed.2023.1132799 TABLE 4 The top significant SNPs (Pmeta <5e-8) of GWAS meta-analysis on non-LS sex groups. Sex group (Cohorts) SNP CHR BP Alleles RefSeq_ genes CA OR 95% CI P-value Direction Het-I2Het-PVal Non-LS males (SWE-GER) rs1964995 6 32,449,411 G/A 36kb 30of HLA-DRB5 A 1.7681 (1.5548, 2.0107) 3.92E-18 ++ 0 0.4583 rs2395153 6 32,345,595 C/G 5.9kb 50of C6orf10 C 0.5861 (0.5134, 0.6692) 2.61E-15 – 0 0.5916 rs2213585 6 32,413,150 G/A 323bp 30of HLA-DRA A 0.6315 (0.5618, 0.71) 1.35E-14 – 0 0.3240 rs7195 6 32,412,539 A/G HLA-DRA A 1.5830 (1.4082, 1.7795) 1.46E-14 ++ 14.5 0.2795 rs9271588 6 32,590,953 G/A 14kb 50of HLA-DQA1 A 1.6153 (1.4291, 1.8258) 1.72E-14 ++ 0 0.5981 rs2213586 6 32,413,094 A/G 267bp 30of HLA-DRA A 1.5785 (1.4046, 1.7742) 1.93E-14 ++ 10 0.2918 rs2227139 6 32,413,459 G/A 632bp 30of HLA-DRA A 0.6335 (0.5637, 0.712) 1.93E-14 – 10 0.2918 rs7192 6 32,411,646 A/C HLA-DRA A 1.5763 (1.4023, 1.7721) 2.54E-14 ++ 0.3 0.3165 rs4373382 6 32,350,868 C/A 11kb 50of C6orf10 A 1.6291 (1.4337, 1.8512) 6.84E-14 ++ 0 0.6069 rs2076529 6 32,363,955 G/A BTNL2 A 1.6268 (1.4322, 1.8479) 7.03E-14 ++ 0 0.5625 Non-LS females (SWE-GER) rs1964995 6 32,449,411 G/A 36kb 30of HLA-DRB5 A 1.7305 (1.5391, 1.9457) 5E-20 ++ 0 0.4192 rs4502931 6 32,380,782 A/T 5.9kb 50of BTNL2 A 0.6033 (0.5401, 0.674) 3.82E-19 – 0 0.4968 rs2395153 6 32,345,595 C/G 5.9kb 50of C6orf10 C 0.6088 (0.5405, 0.6859) 3.14E-16 – 0 0.8219 rs9275582 6 32,680,070 A/G 29kb 50of HLA-DQA2 A 0.5499 (0.4757, 0.6358) 6.15E-16 – 0 0.7935 rs2647012 6 32,664,458 A/G 30kb 50of HLA-DQB1 A 1.5502 (1.3899, 1.7291) 3.74E-15 ++ 0 0.5473 rs9275393 6 32,669,439 A/G 35kb 50of HLA-DQB1 A 0.5890 (0.5156, 0.6728) 6.22E-15 – 0 0.3517 rs2294878 6 32,367,795 A/C BTNL2 A 0.6563 (0.5872, 0.7337) 1.27E-13 – 0 0.4173 rs4530903 6 32,581,889 A/G 23kb 50of HLA-DQA1 A 0.4500 (0.364, 0.5564) 1.68E-13 – 0 0.5299 rs2858332 6 32,68,1161 A/C 28kb 50of HLA-DQA2 A 0.6747 (0.6043, 0.7535) 2.83E-12 – 0 0.6205 rs2858867 6 32,575,325 G/A 18kb 50of HLA-DRB1 A 0.6810 (0.6105, 0.7597) 5.54E-12 – 0 0.7201 Non-LS males (SWE-GER-USAAA) rs1964995 6 32,449,411 G/A 36kb 30of HLA-DRB5 A 1.7736 (1.5809, 1.9899) 1.54E-22 +++ 0 0.7552 rs2239802 6 32,411,846 C/G HLA-DRA C 1.5360 (1.3764, 1.7143) 1.88E-14 +++ 19.6 0.2883 rs4530903 6 32,581,889 A/G 23kb 50of HLA-DQA1 A 0.4478 (0.36, 0.5571) 5.40E-13 — 27.7 0.2506 rs477515 6 32,569, 691 A/G 12kb 50of HLA-DRB1 A 0.6866 (0.607, 0.7767) 2.21E-09 — 0 0.4141 rs389883 6 31,947,460 C/A STK19 A 0.7073 (0.6302, 0.7939) 4.02E-09 — 0 0.3934 (Continued) Frontiers in Medicine 09 frontiersin.org fmed-10-1132799 May 4, 2023 Time: 14:33 # 16 Xiong et al. 10.3389/fmed.2023.1132799 73. 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