Abstract
Background
Type 1 diabetes (T1D) is characterized by the autoimmune destruction of the insulin-producing beta cells, and there is no cure yet for the disease. While islet autoantibodies are well-recognized biomarkers that mark the onset of islet autoimmunity (IA) and are predictors of T1D, few additional biomarkers are available to monitor disease progression. Recent studies have reported the involvement of complement system proteins in the initiation and progression of IA in the study of T1D. However, the genetic factors of complement system proteins at the time of triggering of IA is unknown.
Results
Through complement system protein quantitative trait locus (pQTL) mapping analysis of 170 participants from the Diabetes Autoimmunity Study in the Young (DAISY), we identified 240 statistically significant pQTLs (false discovery rate, FDR < 0.1) from pooled and IA case-stratified analyses. Replication analysis conducted on 385 IA cases from The Environment Determinants of Diabetes in the Young (TEDDY) study confirmed 68 significant (FDR < 0.05) pQTLs in total for C8A, C8B, CFB, C4A, and MBL2. Furthermore, all replicated pQTLs of CFB and C4A were previously reported to be associated with T1D risk.
Conclusions
We identified and replicated 68 pQTLs for five complement system proteins (C8A, C8B, CFB, C4A, and MBL2) in the young population. Among them, all replicated pQTLs of CFB and C4A are also associated with T1D risk. Our study provides evidence of complement system proteins as potential protein biomarkers underlying the development and progression of T1D.
Keywords: islet autoimmunity, T1D, complement system proteins, protein quantitative trait locus (pQTL), DAISY, TEDDY
Background
Type 1 diabetes (T1D) is a complex autoimmune disease where an autoimmune attack destroys the pancreatic islet beta cells, which eventually results in a complete dependency on exogenic insulin. There is no cure for T1D yet, and the disease progression is not well understood. Hence, the development of biomarkers that can help track disease progression and determine disease causes is important for T1D prevention and therapy. The islet antibodies are well-recognized and reliable diagnostic biomarkers that mark the onset of islet autoimmunity (IA), and persistent multi-positivity of islet autoantibodies can be used to predict individuals at high risk of developing T1D1,2. However, the genetic mechanisms of the initiation of IA, as well as the progression from IA to clinical diabetes, are still largely unknown.
The complement system, also known as complement cascade, plays an important role in the innate immune system, which helps to fight bacterial or viral infections and promotes clearance of damaged cells3. Three interrelated pathways activate the complement system, specifically the classical, lectin, and alternative pathways. These activation pathways converge at the terminal pathway, where much of the effector function resides3. The complement system also modulates adaptive immunity through activation by antibodies, including autoantibodies4. Several studies have suggested that the dysregulation of the complement system contributes to the pathogenesis of autoimmune diseases, such as systemic lupus erythematosus5, rheumatoid arthritis6, and T1D7,8. More specifically, increasing studies have reported the involvement of the complement system in the initiation and progression of IA for the study of T1D9,10. Furthermore, recent studies observed lower levels of several complement system proteins in children with islet autoantibodies compared to healthy controls11–14. However, the genetic factors that result in decreased abundance of complement system proteins at the time of triggering of IA are currently unclear15.
Circulating plasma proteins play a fundamental role in human biological processes and are frequently the targets of pharmaceutical interventions16. Protein quantitative trait locus (pQTL) mapping is an approach that can identify genetic variants underlying variation of protein expression levels. In prior studies, pQTL analysis has led to critical advances in knowledge of the genetic architecture of plasma proteins and their relationship to disease17–21.
In this study, we aim to improve our understanding of the genetic factors of complement system proteins at the time of triggering of IA and bring us closer to dissecting the genetic mechanisms underlying the development and progression of T1D. Here, we performed pQTL analysis of 170 participants in the Diabetes Autoimmunity Study in the Young (DAISY) to identify statistically significant pQTLs for complement system proteins. Complement system proteins for DAISY participants were measured by two different protein assay platforms, selected reaction monitoring (SRM)22, and Exsera Biolabs23 (hereinafter referred to as Exsera). Complement system proteins measured by two platforms were approximately overlapped in half. Hence, using proteins from both platforms provides a more comprehensive list of complement system proteins for study. Replication analysis was then conducted on 385 IA cases in the Environment Determinants of Diabetes in the Young (TEDDY) study and the proteins were measured by SRM for TEDDY participants. Finally, we used T1D Knowledge Portal to examine replicated pQTLs for their association with T1D and other disease-relevant traits.
Results
Participant characteristics
The demographic and clinical characteristics of study samples are summarized in Table 1, which includes 170 samples with 131 IA cases and 39 controls for DAISY discovery analysis, and 385 IA cases for TEDDY replication analysis after combining available proteomics and genotype data (Methods). Overall, the participants in the DAISY study had a median age of 6.2 years which was older than TEDDY participants with a median age of 1.8 years (Table 1). The percentage of first-degree relatives with T1D in DAISY was 54%, much higher than 22% from TEDDY (Table 1). For the other two clinical characteristics, female (%) and self-reported Non-Hispanic White (NHW) (%), the percentages were similar between the two studies. Females accounted for 46% and 44% in DAISY and TEDDY, respectively, and NHW was the most common population in both studies.
Table 1.
Clinical characteristics of samples in DAISY and TEDDY.
| DAISY | TEDDY | |
|---|---|---|
| Sample size | 170 (IA cases=131, controls=39) | 385 IA cases |
| Age# (year) | 6.2±3.8 | 1.8±1.2 |
| Female (%) | 78 (46%) | 168 (44%) |
| Self-reported NHW (%) | 166 (98%) | 106 (89%)* |
| First-degree relative with T1D: yes (%) | 91 (54%) | 84 (22%) |
median ± standard deviation; DAISY, the Diabetes Autoimmunity Study in the Young study; TEDDY, The Environment Determinants of Diabetes in the Young study; IA, islet autoimmunity
age matches with sample’s protein collection time; T1D, type 1 diabetes; NHW, Non-Hispanic White
TEDDY participants are from three United States (US) clinical centers (Colorado, Georgia/Florida, Washington) and three European clinical centers (Finland, Germany, Sweden), the summary statistics are from three US clinical centers as European clinical centers report many missing or unknown race/ethnicity.
cis-pQTLs discovery analysis in DAISY
We first performed cis-pQTL mapping for DAISY samples in pooled (IA cases and controls) and IA case-stratified analyses, respectively (Methods). There were 19 and 16 complement system proteins measured by the SRM and Exsera platform, respectively, for DAISY samples, and 10 proteins overlapped between the two platforms (Table 2). We observed 14 proteins with false discovery rate (FDR)-significant (FDR<0.1) cis-pQTLs (Table 2). MBL2 has the greatest number of FDR-significant cis-pQTLs with 264 and 211 cis-pQTLs identified from Exsera pooled and case-stratified analysis, respectively (Additional file 2: Table S1). However, there were no statistically significant cis-pQTLs of MBL2 from the SRM platform. Additionally, MBL2 had the cis-pQTLs with the greatest strength of statistical significance (chr10:52771475-T, chr10:52773600-G, chr10:52775077-T with p-value=3.12×10−19 and p-value=5.22×10−16 from Exsera pooled and case-stratified analysis, respectively, Additional file 2: Table S1). Among other genes, CFH and C4B had FDR-significant cis-pQTLs from both SRM and Exsera platforms (Table 2).
Table 2.
Summary of FDR-significant (FDR<0.1) cis-pQTLs identified in DAISY.
| SRM | Exsera | ||||||
|---|---|---|---|---|---|---|---|
| No. Significant cis-pQTLs | No. Significant cis-pQTLs | ||||||
|
| |||||||
| Protein | Chr | Pooled | Case-stratified | Total | Pooled | Case-stratified | Total |
| C1QC | 1 | 0 | 0 | 0 | 3 | 2 | 5 |
| CFH | 1 | 50 | 0 | 50 | 69 | 0 | 69 |
| C8A | 1 | 29 | 0 | 29 | -- | -- | -- |
| C8B | 1 | 2 | 0 | 2 | -- | -- | -- |
| CFI | 4 | 0 | 0 | 0 | 0 | 3 | 3 |
| C6 | 5 | 3 | 0 | 3 | -- | -- | -- |
| C7 | 5 | 0 | 0 | 0 | -- | -- | -- |
| C9 | 5 | 0 | 0 | 0 | -- | -- | -- |
| CFB | 6 | 26 | 0 | 26 | 0 | 0 | 0 |
| C2 | 6 | 0 | 0 | 0 | 0 | 5 | 5 |
| C4 | 6 | -- | -- | -- | 0 | 0 | 0 |
| C4A | 6 | 17 | 0 | 17 | -- | -- | -- |
| C4B | 6 | 9 | 0 | 9 | 0 | 1 | 1 |
| C5 | 9 | 0 | 0 | 0 | 0 | 0 | 0 |
| C5A | 9 | -- | -- | -- | 5 | 3 | 5 |
| C5B | 9 | -- | -- | -- | 0 | 3 | 3 |
| C8G | 9 | 0 | 0 | 0 | -- | -- | -- |
| MBL2 | 10 | 0 | 0 | 0 | 14 | 4 | 14 |
| C1R | 12 | 0 | 0 | 0 | -- | -- | -- |
| C1S | 12 | 0 | 0 | 0 | -- | -- | -- |
| CFD | 19 | -- | -- | -- | 0 | 0 | 0 |
| C3 | 19 | 0 | 0 | 0 | 0 | 0 | 0 |
| C3A | 19 | -- | -- | -- | 0 | 0 | 0 |
| C3B | 19 | -- | -- | -- | 0 | 1 | 1 |
| CFP | X | 0 | 0 | 0 | 0 | 0 | 0 |
DAISY, the Diabetes Autoimmunity Study in the Young study; 10 overlapped proteins between SRM and Exsera are highlighted in bold; SRM, selected reaction monitoring mass spectrometry assay; FDR, false discovery rate; Total, union of significant cis-pQTLs after fine mapping from pooled and case-stratified analyses; –, protein data was not available in the corresponding assay.
For six proteins that had many FDR-significant cis-pQTLs (i.e., CFH, C8A, CFB, C4A, C4B, and MBL2), we performed fine-mapping to identify potential causal cis-pQTLs for each protein (Methods). For the proteins C8A, CFB and C4B, we observed corresponding 95% credible sets (CS) containing 29, 26, and 9 fine-mapped variants, respectively, from the SRM pooled analysis. For CFH, we observed a single 95% CS with 69 fine-mapped variants in the pooled analysis of protein levels from the Exsera platform (Additional file 1: Fig. S1–4). MBL2 has fine-mapped variants from both Exsera analyses, where 14 variants were identified in two 95% CSs from pooled analysis, and 4 variants were identified in one 95% CS from case-stratified analysis (Additional file 1: Fig. S5). However, there were no fine-mapped variants for CFH and C4A from SRM pooled analysis (Additional file 2: Table S1). We used non-fine-mapped cis-pQTLs for these two proteins from the corresponding analysis. After fine-mapping analysis, we identified 240 FDR-significant (FDR<0.1) cis-pQTLs in total from DAISY discovery analysis.
Replicated cis-pQTLs in TEDDY
To follow up on our discovery pQTL analysis in DAISY, we performed replication analysis in TEDDY for the 240 cis-pQTLs reported in Additional file 2: Table S1, which maps to 14 proteins. A cis-pQTL was deemed replicated if it had FDR-corrected p-value< 0.05. Of these, we observed 68 cis-pQTLs passing the replication threshold, including 27 for C8A, 23 for CFB, 12 for C4A, 4 for MBL2, and 2 for C8B, respectively. The summary of all replicated cis-pQTLs for C8B and MBL2, and top replicated cis-pQTLs for C8A, CFB and C4A is presented in Table 3. The other replicated cis-pQTLs for C8A, CFB and C4A are presented in Additional file 3: Table S2.The replicated cis-pQTLs of CFB, C4A, and MBL2 decreased protein expression levels, while the replicated cis-pQTLs of C8A and C8B increased protein levels.All replicated cis-pQTLs had the same direction of effect between DAISY and TEDDY. However, the replicated signals in TEDDY generally achieved a lower level of statistical significance compared to DAISY. One exception to this finding was the CFB pQTL (chr10:31562639-A: Beta (SE)=−0.45 (0.11) and p-value=3.92×10−5 in TEDDY vs. Beta (SE)=−0.34 (0.08) and p-value=4.37×10−5 in DAISY; Additional file 2: Table S1;Additional file 3: Table S2).
Table 3.
Summary of replicated cis-pQTLs in TEDDY.
| DAISY | TEDDY | |
|---|---|---|
| Sample size | 170 (IA cases=131, controls=39) | 385 IA cases |
| Age# (year) | 6.2±3.8 | 1.8±1.2 |
| Female (%) | 78 (46%) | 168 (44%) |
| Self-reported NHW (%) | 166 (98%) | 106 (89%) * |
| First-degree relative with T1D: yes (%) | 91 (54%) | 84 (22%) |
Chr, chromosome; FDR, false discovery rate; Beta (SE)/P-value, cis-pQTL mapping effect size (standard error)/P-value for 385 TEDDY IA cases; TEDDY, The Environment Determinants of Diabetes in the Young study; IA, islet autoimmunity; T1D, type 1 diabetes
top pQTL variant, see Supplementary Table S2 for full replicated cis-pQTL list.
Examination of association with T1D for replicated pQTL variants
We further examined the association of replicated cis-pQTLs with T1D through publicly available resources. The T1D Knowledge Portal (https://t1d.hugeamp.org) enables searching of human genomic variants linked to T1D and other related phenotypes. We used the T1D Knowledge Portal and found that all 23 and 12 replicated cis-pQTLs for CFB and C4A respectively have been reported genome-wide significantly (p-value<5×10−8) associated with T1D risk (Additional file 3: Table S2; Additional file 1: Fig. S6–7). The T1D association p-values were obtained from a previous T1D study24, and the p-values range from 1.11×10−37 (C4A: rs9262570) to 6.29×10−235 (CFB: rs114355928) (Additional file 3: Table S2). For the other replicated cis-pQTLs, we found that they were significantly associated with highlight scatter reticulocyte count (C8A and C8B, Additional file 1: Fig. S8–9), chronic kidney disease and reticulocyte count (MBL2, Additional file 1: Fig. S10–11).
Discussion
Previous reports have examined the involvement of the complement system in the initiation and progression of IA for the study of T1D8,11,12,14, however, the genetic factors underlying the complement system proteins at the time of triggering of IA are poorly unknown. Our study investigated genetic variants that are associated with complement system proteins in DAISY, a prospective cohort of children from the general population who either had a first degree relative with T1D or had a high-risk human leukocyte antigen (HLA) genotype. From pooled and IA case-stratified analyses with 170 participants in the DAISY, 240 significant (FDR < 0.1) cis-pQTLs were associated with 14 complement system proteins. We replicated 68 cis-pQTLs with statistical significance (FDR < 0.05) in the 385 IA cases from the TEDDY study. The 68 replicated cis-pQTLs represent C8A and C8B, CFB and C4A (within the HLA region), and MBL2. The CFB and C4A cis-pQTLs are known genetic associations with T1D. However, the strong linkage disequilibrium within the region has limited our understanding of the independent contribution of complement system to T1D risk. Our study provides new insight into the role of complement system on the disease progression.
CFB (Complement Factor B) is located on chromosome 6 and between the HLA class II and class I regions7. The HLA region is the single most important genetic determinant of T1D susceptibility. The variability in the HLA region has been estimated to explain approximately 60% of the genetic influence of T1D25. CFB is a component of the alternative pathway of complement activation. The detailed function of CFB has been reported in a previous study15. T1D-associated genetic variants in CFB have been reported in European ancestry26,27 and Northern India28. The 23 replicated cis-pQTLs of CFB identified in our study, which decrease protein level, are found to be significantly associated with increasing T1D risk in a large-scale genomic study of T1D24. The T1D association p-value ranges from 5.38×10− 43 (rs115272033, EHMT2 intron) to 6.29×10− 235 (rs114355928, TSBP1 intron) (Additional file 3: Table S2).
C4A (Complement Component 4A) is also located between the HLA class II and class I regions7 and is part of the classical and lectin pathways of activating complement system. Two functionally distinct genes, C4A and C4B, code the C4 protein together. Limited T1D association studies have been focused on the structural variation of this region15. The previously reported associations of genetic variants in C4A with T1D focused on European ancestry population29,30. Our study found that the 12 replicated cis-pQTLs of C4A were previously identified to be significantly associated with increasing T1D risk24. The T1D association p-value ranges from 1.11×10− 37 (rs9262570, intergenic) to 1.33×10− 88 (rs9263822, PSORS1C3 intron) (Additional file 3: Table S2).
Our study identified three genes outside of HLA region (C8A, C8B and MBL) in addition to two genes in the HLA region. Both C8A (Complement C8 Alpha Chain) and C8B (Complement C8 Beta Chain) are located on chromosome 1 and encode Complement Component 8 (C8) protein. C8 participates in the formation of the membrane attack complex, which causes cell lysis and/or pro-inflammatory signaling15.
MBL2 (Mannose-Binding Lectin 2) is located on chromosome 10 and is the only single gene to encode human Mannose-Binding Lectin (MBL). MBL is a soluble lectin that activates the lectin complement pathway by recognizing microorganisms through the carbohydrate-recognition domain, thereby modulating inflammation31. Although our replicated cis-pQTLs of MBL2 were not associated with T1D, previous studies have identified their associations with type 2 diabetes and pneumonia. For example, the variant rs1800450 has been linked to type 2 diabetes in diverse populations, including the full-heritage Pima Indians and the Old Order Amish32, and the North Chinese Han population33. Additionally, Uysalol et al.34 reported that the rs1800450 genotypes associated with low MBL expression were significantly more common in patients with pneumonia and severe infections.
Strengths of this study include being one of the very few studies that examined pQTL mapping of complement system proteins in children with high risk of developing T1D and the use of independent study data to replicate significant pQTL signals. However, some limitations warrant mentioning. First, the statistical power of identifying significant cis-pQTLs in our study is still limited due to the sample size of both discovery and replication analyses. Second, the protein assay platform in the replication analysis is different from that in the discovery analysis for the significant findings of MBL2. We only identified statistically significant cis-pQTLs in the Exsera platform for MBL2 in the DAISY discovery analysis, however, the replication analysis in TEDDY was only available to use measured protein levels from a different protein assay, the SRM platform. Hence, future validation for the identified cis-pQTLs of MBL2 needs to be conducted on measured protein levels from the Exsera platform. Lastly, the participants in our study are predominantly composed of Non-Hispanic Whites. Hence, our findings cannot be generalized to non-European ancestry populations yet. Future research focusing on non-European or diverse ancestries will help improve the understanding of genetic mechanisms underlying the complement system and provide a more comprehensive insight into the role of complement system proteins in the etiology of T1D.
Conclusions
In summary, this study performed pQTL mapping of complement system proteins in children at high risk of developing T1D and replicated significant signals in an independent study of children with T1D. Our study confirmed that genetic variants regulating two complement system proteins CFB and C4A, located in the HLA-region, are associated with an increased risk of T1D. Further, we identified three non-HLA-region complement system proteins (C8A, C8B, and MBL2) as potential biomarkers with a putative role in the etiology of T1D.
Methods
Overview of approach
We performed cis-pQTL mapping for 170 samples (131 IA cases and 39 controls) in The Diabetes Autoimmunity Study in the Young (DAISY) as discovery analysis to identify statistically significant cis-pQTL variants for complement system proteins. To follow up on our pQTL analysis in DAISY, we conducted replication analysis for 385 IA samples from The Environmental Determinants of Diabetes in the Young (TEDDY) study. Finally, we used the T1D Knowledge Portal (https://t1d.hugeamp.org/) to examine replicated cis-pQTLs for their association with T1D and other disease-relevant traits.
DAISY: study design, proteomic profiling and genotyping
Study design: DAISY is a prospective cohort of 2,547 children from the general population who either had a first degree relative with T1D or had a high-risk human leukocyte antigen (HLA) genotype. The participants were recruited in Denver, Colorado between 1993 and 2004, and followed for up to 21 years35,36. Follow-up results are available through April 4, 2022. Written informed consent was obtained from participants and parents. The Colorado Multiple Institutional Review Board approved all protocols. The primary goal of DAISY is to learn how genes and the environment interact to cause childhood T1D35.
Proteomic profiling: The peptides of complement system proteins were measured by two different assay platforms for DAISY participants, selected reaction monitoring (SRM)-based22, and Exsera Biolabs-based (referred herein as Exsera)23. The Exsera targeted proteomics use commercial immunoassays in a College of American Pathologists/Clinical Laboratory Improvement Amendments (CAP/CLIA)-accredited laboratory. The details of two protein assay platforms for DAISY participants are described in a previous study14.
Genotype quality control and imputation: Genome-wide genotyping was performed using the custom designed Infinium TEDDY-T1D Exome array (Illumina) and was genotyped at the University of Virginia (UVA) Genome Sciences Laboratory following the manufacturer’s protocol (Illumina). The following quality control (QC) criteria were applied: 1) samples with a genotype call rate < 0.95 were removed; and 2) single nucleotide polymorphism (SNP) level QC included removal of monomorphic SNPs, and removal of SNPs that deviated from Hardy-Weiberg equilibrium (p-value < 1×10− 20 at the HLA region or p-value < 1×10− 6 otherwise). Genome-wide imputation used the Trans-Omics for Precision Medicine (TOPMed) multi-ancestry reference panel (version R2). The SNP was selected if minor allele frequency (MAF) ≥ 0.05 and imputation quality R2 ≥ 0.7. The details of QC criteria of genomic variants used in this study are provided in a previous study37.
TEDDY: study design, proteomic profiling and genotyping
Study design: TEDDY is an international prospective study that was designed to identify T1D-associated environmental factors in children who carry a high genetic risk for the disease38. The participating clinical centers include Colorado, Georgia/Florida, and Washington in the US, and Finland, Germany, and Sweden in Europe. We only reported summary statistics of self-reported Non-Hispanic White (NHW) from US centers for TEDDY as many missing or unknown race/ethnicity were reported from European centers. For TEDDY study, all procedures were approved by the ethics committees / institutional review boards including Colorado Multiple Institutional Review Board (04–0361); Medical College of Georgia Human Assurance Committee (2004–2010)/Georgia Health Sciences University Human Assurance Committee (2011–2012)/Georgia Regents University Institutional Review Board (2013–2017)/Augusta University Institutional Review Board (2017-present) (HAC 0405380); University of Florida Health Center Institutional Review Board (IRB201600277); Washington State Institutional Review Board (2004–2012)/Western Institutional Review Board (2013–present) (20130211); Ethics Committee of the Hospital District of Southwest Finland (Dnro168/2004); Bayerischen Landesärztekammer (Bavarian Medical Association) Ethics Committee (04089); and Regional Ethics Board in Lund, Section 2 (2004–2012)/Lund University Committee for Continuing Ethical Review (2013-present) (217/2004). In addition, TEDDY is monitored by an external evaluation committee formed by the National Institutes of Health, Bethesda, MD, U.S.A.
Proteomic profiling and genotyping: Both proteomics and imputed genotype data were provided by the TEDDY Data Coordinating Center. The peptides of complement system proteins for TEDDY participants were measured by SRM-based assay. The details of the protein assay for TEDDY are described in a previous study13. SNPs were genotyped using the ImmunoChip and/or the TEDDY-T1DexomeChip at the Center for Public Health Genomics at the UVA, US. GWAS imputation analysis was conducted using the TOPMed Version R2 (built from 97,256 deeply sequenced human genomes containing 308,107,085 genetic variants), the 1000 Genomes, and a subset of the TEDDY subjects (n = 1,119) with the whole-genome sequencing data as reference panels. MetaMinimac2 was used to combine genotype data imputed against these three reference panels. For imputed genotype data, we retained rare variants with MAF > 0.05 in unrelated controls with European ancestry and with imputation quality R2 > 0.50.
cis -pQTL mapping in DAISY
A cis-pQTL mapping is to test the association between measured protein levels and cis-pQTL genomic variants via statistical model. We defined a cis-pQTL genomic variant as a SNP within +/− 1Mb of the transcription start site (TSS) of the corresponding protein-coding gene. We applied a linear mixed model adjusted for age, sex, self-reported race/ethnicity, protein plate effects, first-degree relative with T1D (yes or no), the first two principal components (PCs) of genetic ancestry, and genetic relationship matrix (GRM) to perform cis-pQTL mapping in our study. The association analyses were conducted using R/GENESIS39. The mapping results were then filtered on 1) expected heterozygosity count (EHC) > 6 for WGS data, and 2) imputation quality > 0.3 and EHC > 6 for imputation genotypes by using R/EasyQC40.
We first performed cis-pQTL mapping on DAISY participants with Islet autoimmunity (IA), referred herein as IA cases. Participants were considered to have IA if they were positive for one or more islet autoantibodies tests on two or more consecutive visits or being autoantibody positive with a diagnosis of diabetes at the next visit by the American Diabetes Association criteria41. For protein levels of IA cases, 1) we averaged measured peptides if a participant had multiple peptides at one time point, and 2) we used the earliest time point if a participant had multiple visits for peptide measures. We then applied log2-transformation on the selected peptides and treated the transformed peptides as outcome variables for the linear mixed model. Due to limited IA cases in DAISY, we also conducted case-control pooled analysis for DAISY with additional group adjustment (i.e., case or control) to increase statistical power of cis-pQTL mapping. For protein levels of controls, we followed the same procedure as IA cases except for the selection of time points for controls who have multiple peptides by different visits. To obtain a similar age as IA cases for most controls, we selected the closest time point to the median age of selected visits of IA cases for controls. Finally, we applied false discovery rate (FDR) correction (Benjamini-Hochberg) at 10% on cis-pQTL mapping results to identify statistically significant cis-pQTLs in DAISY by different protein assay platforms and by different stratified analyses respectively. For proteins with many FDR-significant cis-pQTLs, we further performed fine-mapping using SuSiE42 to identify potential causal cis-pQTLs. The variant clustered in a 95% credible set (CS) is considered as a fine-mapped variant42.
Replication analysis in TEDDY
To follow-up on statistically significant cis-pQTLs identified in DAISY discovery analysis, we applied a linear mixed model adjusted for age, sex, protein plate effects, clinical centers, first-degree relative with T1D (yes or no), the first two PCs of ancestry, and GRM to test association between DAISY FDR-significant cis-pQTLs and log2-transformed peptides for TEDDY IA cases. We then applied FDR correction at 5% on TEDDY mapping results to identify replicated cis-pQTLs.
PheWAS of replicated cis-pQTLs
The T1D Knowledge Portal (https://t1d.hugeamp.org) enables searching of human genomic variants linked to T1D and other related phenotypes. We used T1D Knowledge Portal to examine replicated cis-pQTLs for their association with T1D and other disease-relevant traits.
Acknowledgements
The authors acknowledge Research Computing at The University of Virginia for providing computational resources and technical support that have contributed to the results reported within this manuscript. URL:https://rc.virginia.edu.
The authors also acknowledge the TEDDY Study Group as follows:
Colorado Clinical Center: Marian Rewers, M.D., Ph.D., PI1,4,6,9,10, Kimberly Bautista11, Judith Baxter8,911, Daniel Felipe-Morales, Brigitte I. Frohnert, M.D., Ph.D.2,13, Marisa Stahl, M.D.12, Isabel Flores Garcia, Patricia Gesualdo2,6,11,13, Sierra Hays, Michelle Hoffman11,12,13, Randi Johnson, Ph.D.2,3, Rachel Karban11, Edwin Liu, M.D.12, Leila Loaiza, Jill Norris, Ph.D.2,3,11, Holly O’Donnell, Ph.D.8, Andrea Steck, M.D.3,13, Kathleen Waugh6,7,11. University of Colorado, Anschutz Medical Campus, Barbara Davis Center for Childhood Diabetes, Aurora, CO, USA.
Finland Clinical Center: Jorma Toppari, M.D., Ph.D., PI¥^1,4,10,13,Olli G. Simell, M.D., Ph.D., Annika Adamsson, Ph.D.^11, Suvi Ahonen*±§, Mari Åkerlund*±§, Sirpa Anttilaμ¤, Leena Hakola, Ph.D.*±, Sanni Heikuraμ¤, Tiia Honkanenμ¤, Heikki Hyöty, M.D., Ph.D.*±6, Jorma Ilonen, M.D., Ph.D.¥3, Saori Itoshima, M.D. ¥^, Minna Jokipolvi*±, Sanna Jokipuu^, Taru Karjalainenμ¤, Leena Karlsson^, Pieta Kemppainenμ¤, Jukka Kero, M.D., Ph.D.¥^3, 13, Marika Korpelaμ¤, Jaakko J. Koskenniemi M.D., Ph.D.¥^, Miia Kähönenμ¤11,13, Mikael Knip, M.D., Ph.D.*±, Minna-Liisa Koivikkoμ¤, Katja Kokkonen*±, Merja Koskinen*±, Mirva Koreasalo*±§2, Kalle Kurppa, M.D., Ph.D.*±12, Salla Kuusela, M.D. μ¤, Jarita Kytölä*±, Jutta Laiho, Ph.D.*6, Tiina Latva-ahoμ¤, Siiri Leisku*±, Laura Leppänen^, Katri Lindfors, Ph.D.*12, Maria Lönnrot, M.D., Ph.D.*±6, Elina Mäntymäki^, Markus Mattila, Ph.D.*±2, Maija E. Miettinen, Ph.D.§2, Teija Mykkänenμ¤, Tiina Niininen±*11, Sari Niinistö, Ph.D.§2, Noora Nurminen*±, Sami Oikarinen, Ph.D.*±6, Hanna-Leena Oinas*±, Paula Ollikainenμ¤, Zhian Othmani¥, Sirpa Pohjola μ¤, Jenna Rautanen§, Mia Reinμ¤, Minna Romo^, Juulia Rönkäμ¤, Nelli Rönkäμ¤, Noora Ruotsalainenμ¤, Satu Simell, M.D., Ph.D.¥12, Päivi Tossavainen, M.D.μ¤, Erika Turtinenμ¤, Mari Vähä-Mäkilä¥, Eeva Varjonen^11, Riitta Veijola, M.D., Ph.D.μ¤13, Irene Viinikangasμ¤, Suvi M. Virtanen, M.D., Ph.D.*±§2. ¥University of Turku, Turku, Finland, *Tampere University, Tampere, Finland, μUniversity of Oulu, Oulu, Finland, ^Turku University Hospital, Wellbeing Services County of Southwest Finland, Turku, Finland, ±Tampere University Hospital, Wellbeing Services County of Pirkanmaa, Tampere, Finland, ¤Oulu University Hospital, Wellbeing Services County of North Ostrobothia, Oulu, Finland, §Finnish Institute for Health and Welfare, Helsinki, Finland.
Georgia/Florida Clinical Center: Richard McIndoe, Ph.D., PI^4,10, Desmond Schatz*, M.D.*4,7,8, Diane Hopkins^11, Michael Haller, M.D.*13, Melissa Gardiner^11, Ashok Sharma^, Ph.D.^, Laura Jacobsen, M.D.*13, Percy Gordon^, Jennifer Hosford*,. ^Center for Biotechnology and Genomic Medicine, Augusta University, Augusta, GA, USA. *University of Florida, Pediatric Endocrinology, Gainesville, FL, USA.
Germany Clinical Center: Anette G. Ziegler, M.D., PI1,3,4,10, Ezio Bonifacio Ph.D.*, Cigdem Sanverdi, Anja Heublein, Sandra Hummel, Ph.D.2, Annette Knopff7, Melanie Köger, Sibylle Koletzko, M.D.¶12, Claudia Ramminger11, Roswith Roth, Ph.D.8, Jennifer Schmidt, Marlon Scholz, Joanna Stock8,11,13, Katharina Warncke, M.D.13, Lorena Müller, Christiane Winkler, Ph.D.2,11. Forschergruppe Diabetes e.V. and Institute of Diabetes Research, Helmholtz Zentrum München, Forschergruppe Diabetes, and Klinikum rechts der Isar, Technische Universität München, Neuherberg, Germany. *Center for Regenerative Therapies, TU Dresden, Dresden, Germany, ¶Dr. von Hauner Children’s Hospital, Department of Gastroenterology, Ludwig Maximillians University Munich, Munich, Germany.
Sweden Clinical Center: Åke Lernmark, Ph.D., PI1,3,4,5,6,8,9,10, Daniel Agardh, M.D., Ph.D.6,12, Carin Andrén Aronsson, Ph.D.2,11,12, Rasmus Bennet, Corrado Cilio, Ph.D., M.D.6, Susanne Dahlberg, Malin Goldman Tsubarah, Emelie Ericson-Hallström, Lina Fransson, Emina Halilovic, Susanne Hyberg, Berglind Jonsdottir, M.D., Ph.D.11, Naghmeh Karimi, Helena Elding Larsson, M.D., Ph.D.6,13, Marielle Lindström, Markus Lundgren, M.D., Ph.D.13, Jessica Melin, Ph.D.11, Kobra Rahmati, Anita Ramelius, Falastin Salami, Ph.D., Anette Sjöberg, Evelyn Tekum Amboh, Carina Törn, Ph.D.3, Ulrika Ulvenhag, Terese Wiktorsson, Åsa Wimar13. Lund University, Lund, Sweden.
Past staff: Eva Andersson, Marie Andersson Turpeinen, Rawya Antar, Maria Ask, Jenny Bremer, Sylvia Bianconi Svensson, Ulla-Marie Carlsson, Magdalena Delikat Kulinski, Annika Fors, Ulla Fält, Thomas Gard, Joanna Gerardsson, Monika Hansen, Anna Hansson, Carina Hansson, Gertie Hansson, Elin M. Hård af Segerstad, Ph.D.2, Hanna Jisser, Fredrik Johansen, Linda Jonsson, Silvija Jovic, Sigrid Lenrick Forss, Barbro Lernmark, Ph.D.8, Maria Markan, Theodosia Massadakis, Marlena Maziarz, Ph.D., Zeliha Mestan, Maria Månsson Martinez, Caroline Nilsson, Emma Nilsson, Yohanna Nordh, Karin Ottosson, Sara Rang, Anna Rosenquist, Monika Sedig Järvirova, Sara Sibthorpe, Birgitta Sjöberg, Ulrika Swartling Ph.D.8, Erika Trulsson, Anne Wallin, Ingrid Wigheden, Sofie Åberg.
Washington Clinical Center: William A. Hagopian, M.D., Ph.D., PI^1,3,4,6,7,10,12,13, Michael Killian*6,7,11,12, Claire Cowen Crouch*11,13, Jennifer Skidmore*2, Ben Kim*, Cody McCall*, Arlene Meyer*, Jared Radtke*, Shreya Roy*. ^Indiana University, Indianapolis, IN, USA. *Pacific Northwest Research Institute, Seattle, WA, USA.
Pennsylvania Satellite Center:Dorothy Becker, M.D., Margaret Franciscus, MaryEllen Dalmagro-Elias Smith2, Ashi Daftary, M.D., Mary Beth Klein, Chrystal Yates. Children’s Hospital of Pittsburgh of UPMC, Pittsburgh, PA, USA.
Data Coordinating Center: Jeffrey P. Krischer, Ph.D., PI1,4,5,9,10, Rajesh Adusumali, Sarah Austin-Gonzalez, Maryouri Avendano, Sandra Baethke, Brant Burkhardt, Ph.D.6, Martha Butterworth2, Nicholas Cadigan, Joanna Clasen, Ph.D., Kevin Counts, Laura Gandolfo, Jennifer Garmeson, Veena Gowda, Shu Liu, Xiang Liu, Ph.D.2,3,8,13, Kristian Lynch, Ph.D. 6,8, Jamie Malloy, Lazarus Mramba, Ph.D.2, Cristina McCarthy11, Hemang M. Parikh, Ph.D.3,8, Cassandra Remedios, Chris Shaffer, Susan Smith11, Noah Sulman, Ph.D., Roy Tamura, Ph.D.1,2,11,12,13, Dena Tewey, Henri Thuma, Michael Toth, Ulla Uusitalo, Ph.D.2, Kendra Vehik, Ph.D.4,5,6,8,13, Ponni Vijayakandipan, Melissa Wroble, Jimin Yang, Ph.D., R.D.2, Kenneth Young, Ph.D. Past staff: Michael Abbondondolo, Lori Ballard, Rasheedah Brown, David Cuthbertson, Stephen Dankyi, Christopher Eberhard, Steven Fiske, David Hadley, Ph.D., Kathleen Heyman, Belinda Hsiao, Christina Karges, Francisco Perez Laras, Hye-Seung Lee, Ph.D., Qian Li, Ph.D., Colleen Maguire, Wendy McLeod, Aubrie Merrell, Steven Meulemans, Jose Moreno, Ryan Quigley, Laura Smith, Ph.D. University of South Florida, Tampa, FL, USA.
Project scientist:Beena Akolkar, Ph.D.1,3,4,5,6,7,9,10. National Institutes of Diabetes and Digestive and Kidney Diseases, Bethesda, MD, USA.
Other contributors:Thomas Briese, Ph.D.6, Columbia University, New York, NY, USA. Todd Brusko, Ph.D.5, University of Florida, Gainesville, FL, USA. Teresa Buckner, Ph.D.2, University of Northern Colorado, Greeley, CO, USA. Suzanne Bennett Johnson, Ph.D.8,11, Florida State University, Tallahassee, FL, USA. Eoin McKinney, Ph.D.5, University of Cambridge, Cambridge, UK. Tomi Pastinen, M.D., Ph.D.5, The Children’s Mercy Hospital, Kansas City, MO, USA. Steffen Ullitz Thorsen, M.D., Ph.D.2, Department of Clinical Immunology, University of Copenhagen, Copenhagen, Denmark, and Department of Pediatrics and Adolescents, Copenhagen University Hospital, Herlev, Denmark. Eric Triplett, Ph.D.6, University of Florida, Gainesville, FL, USA.
Committees:
1Ancillary Studies, 2Diet, 3Genetics, 4Human Subjects/Publicity/Publications, 5Immune Markers, 6Infectious Agents, 7Laboratory Implementation, 8Psychosocial, 9Quality Assurance, 10Steering, 11Study Coordinators, 12Celiac Disease, 13Clinical Implementation.
Funding
This work was funded by Leona M. and Harry B. Helmsley Charitable Trust grants 2018PG-T1D017 and G-2103–05121. The TEDDY Study is funded by U01 DK63829, U01 DK63861, U01 DK63821, U01 DK63865, U01 DK63863, U01 DK63836, U01 DK63790, UC4 DK63829, UC4 DK63861, UC4 DK63821, UC4 DK63865, UC4 DK63863, UC4 DK63836, UC4 DK95300, UC4 DK100238, UC4 DK106955, UC4 DK112243, UC4 DK117483, U01 DK124166, U01 DK128847, and Contract No. HHSN267200700014C from the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), National Institute of Allergy and Infectious Diseases (NIAID), Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), National Institute of Environmental Health Sciences (NIEHS), Centers for Disease Control and Prevention (CDC), and Breakthrough T1D (formerly JDRF). This work is supported in part by the NIH/NCATS Clinical and Translational Science Awards to the University of Florida (UL1 TR000064) and the University of Colorado (UL1 TR002535). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Footnotes
Competing interests
The authors declare no competing interests.
Ethics approval and consent to participate
For DAISY cohort, written informed consent was obtained from participants and parents. The Colorado Multiple Institutional Review Board approved all protocols of DAISY cohort.
For TEDDY study, all procedures were approved by the ethics committees / institutional review boards including Colorado Multiple Institutional Review Board (04–0361); Medical College of Georgia Human Assurance Committee (2004–2010)/Georgia Health Sciences University Human Assurance Committee (2011–2012)/Georgia Regents University Institutional Review Board (2013–2017)/Augusta University Institutional Review Board (2017-present) (HAC 0405380); University of Florida Health Center Institutional Review Board (IRB201600277); Washington State Institutional Review Board (2004–2012)/Western Institutional Review Board (2013–present) (20130211); Ethics Committee of the Hospital District of Southwest Finland (Dnro168/2004); Bayerischen Landesärztekammer (Bavarian Medical Association) Ethics Committee (04089); and Regional Ethics Board in Lund, Section 2 (2004–2012)/Lund University Committee for Continuing Ethical Review (2013-present) (217/2004). In addition, TEDDY is monitored by an external evaluation committee formed by the National Institutes of Health, Bethesda, MD, U.S.A.
Contributor Information
Xiaowei Hu, Department of Genome Sciences, University of Virginia.
Bobbie-Jo M Webb-Robertson, Biological Sciences Division, Pacific Northwest National Laboratory.
Hemang M Parikh, Health Informatics Institute, Morsani College of Medicine, University of South Florida.
Ernesto S Nakayasu, Biological Sciences Division, Pacific Northwest National Laboratory.
Suna Onengut-Gumuscu, Department of Genome Sciences, University of Virginia.
Wei-Min Chen, Department of Genome Sciences, University of Virginia.
Ashley Frazer-Abel, Exsera BioLabs, Division of Rheumatology, Department of Medicine, University of Colorado Anschutz Medical Campus.
Thomas O Metz, Biological Sciences Division, Pacific Northwest National Laboratory.
Stephen S Rich, Department of Genome Sciences, University of Virginia.
Marian J Rewers, Barbara Davis Center for Diabetes, School of Medicine, University of Colorado Anschutz Medical Campus.
Ani Manichaikul, Department of Genome Sciences, University of Virginia.
Availability of data and materials
The TEDDY Immunochip (SNP) data that support the findings of this study have been deposited in NCBI’s database of Genotypes and Phenotypes (dbGaP) with the primary accession code phs001442.v4.p3. The TEDDY-T1DExome Array data that support the findings of this study have been deposited in NCBI’s database of Genotypes and Phenotypes (dbGaP) with the primary accession code phs001442.v4.p3. The TEDDY Whole Genome Sequencing (WGS) data that support the findings of this study have been deposited in NCBI’s database of Genotypes and Phenotypes (dbGaP) with the primary accession code phs001442.v4.p3. The TEDDY Proteomics Discovery Phase data that support the findings of this study are available from the Mass Spectrometry Interactive Virtual Environment (MassIVE) repository https://massive.ucsd.edu/ via its dataset identifier MSV000091560 (https://doi.org/doi:10.25345/C5V11VW3F). The TEDDY Proteomics Validation Phase data that support the findings of this study are available from the Mass Spectrometry Interactive Virtual Environment (MassIVE) repository https://massive.ucsd.edu/ via its dataset identifier MSV000091562 (https://doi.org/doi:10.25345/C5KH0F84V).
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The TEDDY Immunochip (SNP) data that support the findings of this study have been deposited in NCBI’s database of Genotypes and Phenotypes (dbGaP) with the primary accession code phs001442.v4.p3. The TEDDY-T1DExome Array data that support the findings of this study have been deposited in NCBI’s database of Genotypes and Phenotypes (dbGaP) with the primary accession code phs001442.v4.p3. The TEDDY Whole Genome Sequencing (WGS) data that support the findings of this study have been deposited in NCBI’s database of Genotypes and Phenotypes (dbGaP) with the primary accession code phs001442.v4.p3. The TEDDY Proteomics Discovery Phase data that support the findings of this study are available from the Mass Spectrometry Interactive Virtual Environment (MassIVE) repository https://massive.ucsd.edu/ via its dataset identifier MSV000091560 (https://doi.org/doi:10.25345/C5V11VW3F). The TEDDY Proteomics Validation Phase data that support the findings of this study are available from the Mass Spectrometry Interactive Virtual Environment (MassIVE) repository https://massive.ucsd.edu/ via its dataset identifier MSV000091562 (https://doi.org/doi:10.25345/C5KH0F84V).