Introduction

Type 1 diabetes (T1D) is characterised by immune-mediated destruction of insulin-producing pancreatic beta cells leading to loss of insulin production and hyperglycaemia. Population-level data has enabled genome-wide association studies (GWAS) that have identified ~60 genetic loci that are associated with the risk of developing T1D1. In addition to the GWAS studies, a number of highly phenotyped prospective birth cohort studies have investigated potential early determinants of T1D risk2,3,4. Notably, the transition from genetic risk to T1D onset is hypothesised to require an environmental trigger event, such as infection, in those individuals who go on to develop the disorder5. However, the mechanisms responsible for this transition remain poorly characterised, limiting strategies for optimising treatment and furthering therapeutic development.

One hindrance to characterising the genetic mechanisms responsible for T1D development is the finding that the majority of SNPs are within intergenic regions of the genome. Previously, we used information on the spatial organisation of the genome (captured by Hi-C) to identify the tissue-specific gene regulatory impacts (i.e. eQTLs) of SNPs associated with T1D6. Consistent with our understanding of T1D pathology, we reported that the differentially expressed genes were enriched for immune activation and response pathways6. However, this still did not provide any important information into the relative contributions of the tissue-specific gene regulatory effects we identified. Therefore, we reasoned that we could use genotypes for T1D cases and controls to machine learn the tissue-specific-expression scores for T1D-associated variants. This approach would enable the ranking of the tissue-specific regulatory changes that contribute to the conversion of genetic risk to T1D pathology.

In the present study, we assigned SNPs associated with T1D to the genes they modulate through Hi-C chromatin interactions captured from primary tissues (i.e. pancreas and spleen) and immortalised cells. We integrated a regularised logistic regression model on European ancestry genotypes of T1D case and control to identify transcriptional changes in the lung involving AP4B1-AS1 and CTLA4 (associated with rs6679677) as the largest individual contributors, through a gene regulatory mechanism, to the conversion of the genetic risk for the development of T1D. Finally, a plasmid-based luciferase reporter expression assay was performed to validate the allele-specific enhancer activity of the locus marked by rs6679677 in lung cells.

Results

T1D SNPs impact an extensive gene regulatory network

The methodology used for the characterisation of the regulatory networks for T1D-associated SNPs is summarised in Fig. 1. Briefly, we used the CoDeS3D7 algorithm to analyse 313 T1D-associated SNPs (Methods section and Supplementary Data 1) using Hi-C chromatin contact libraries (Supplementary Data 2) and GTEx (v7) RNAseq data (Methods section). The Hi-C libraries that were used in this study included immortalized cell lines and primary human tissues (Supplementary Data 2) and were chosen to ensure a range of known possible interactions were included in the analysis. We define a spatial eQTL as a SNP that tags a locus that: (1) physically interacts with a gene; and (2) explains a fraction of the genetic variance of the interacting gene transcription level. According to our definition, the eQTL variant can sit anywhere within the genome. This includes within the boundaries of the gene, as long as the gene is covered by ≥3 restriction fragments in the Hi-C library. This minimum connection distance is determined by Hi-C resolution, which cannot distinguish spatial connections between ligated contiguous restriction sites versus an undigested restriction site. Of the 313 SNPs, 57 SNPs had no identifiable eQTLs, resulting in 256 T1D-associated SNPs connecting to 822 genes (1479 spatial eQTL-eGene associations; FDR q < 0.05; Supplementary Data 3). As expected from our previous study6, the 822 genes were enriched for immune activation and response pathways (Supplementary Data 4).

Fig. 1: Overview of the method used to rank the tissue-specific transcription regulatory effects of genetic variants associated with the development of T1D.
figure 1

SNPs associated with T1D were analysed (CoDeS3D) to identify the interacting loci, which were then tested for eQTLs within GTEx. This resulted in a map of T1D-eGenes across multiple human tissues. The T1D genes were subsequently used to perform logistic regression to identify key tissues for T1D pathogenesis. Luciferase assays were used to test the final predictions.

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The eQTL-eGene interactions were categorised as either: cis, the eQTL and eGene are separated by a linear distance of ≤1 Mb on the same chromosome; or trans, eQTLs and their eGenes were separated by >1 Mb on the same chromosome or located on different chromosomes. Notably, of the 256 T1D-associated SNPs with spatial-eQTLs, 190 affected the trans-regulation of 361 genes, while 201 affected the cis-regulation of 493 genes. Some genes (n = 32) were regulated by different eQTLs in both cis and trans (e.g. TRIM26, RNF5, PSMB9 and NOTCH4; Supplementary Data 3). Notably, the 112 trans-regulated genes (e.g. FOXP1, CAMTA1 and ROBO2) we identified are enriched for being less tolerant of inactivating (i.e. Loss-of-Function) mutations (Supplementary Fig. 1 and Supplementary Data 5).

Machine learning identifies transcriptional changes in the lung as being involved in the conversion of risk to T1D

Based on our eQTL analysis, we determined that T1D SNPs form an integrated gene regulatory network across tissues and immune cell types. We reasoned that we could use a machine learning approach to integrate the tissue-specific spatial eQTL-eGene associations with thousands of individual genotypes from large T1D cohorts to convert population-level risk (i.e. GWAS SNPs) to individualised risk (i.e. the burden for an individual’s genotype).

We trained and validated the predictive accuracy of a regularised logistic regression predictor (Methods section and Supplementary Fig. 2), which predicts the T1D disease status. This model was used to estimate the additive tissue-specific contribution of the spatial eQTLs within genotypes from individuals who developed T1D (WTCCC; 1960 T1D cases and 2933 controls). Individual genotypes were weighted using the tissue-specific spatial eQTL effect sizes from the CoDeS3D analysis (Supplementary Data 3). Of the 313 T1D-associated SNPs, 253 were present within each of the WTCCC genotypes (Supplementary Data 6). Of the 253 SNPs, 224 had identifiable eQTLs, connecting to 758 eGenes (6307 tissue-specific eQTL effects).

Essential feature selection was performed using the Mann–Whitney U test8 (selected 2048 data features from 6307 eQTL features and 29 SNP features with unknown eQTL effects) and lasso regularisation of the logistic regression. The regularisation parameter was optimised by sampling 80% of the WTCCC derived eQTL dataset, identifying optimal hyperparameters from this sample, and evaluating the performance of the algorithm on the remaining 20% using the optimal hyperparameters. In order to identify optimal hyperparameters, the selected 80% of data were divided into 10 subsets, with each subset having approximately the same number of samples. For every hyperparameter value, the prediction model was trained repeatedly on 9 subsets and evaluated on the remaining subset until every subset had been used for evaluation once. The optimal regularisation parameter was selected based on the AUC (model 1). Applying this model to the validation data (20% of the WTCCC derived eQTL dataset) resulted in an AUC of 0.76, which is acceptable for our purpose of developing an interpretable machine learning model, which identifies the top-performing predictors from additive tissue-specific contributions of the spatial eQTLs within genotypes from individuals who developed T1D. In order to quantify the uncertainty of the model performance subjective to different splits of the WTCCC data into training and test sets, we sampled 50 different training and test sets by repeating a 5-fold internal cross-validation of the WTCCC data a total of 10 times. This experiment generated 50 out-of-sample AUC values from 50 different T1D regularised logistic regression predictors created with model 1’s optimised hyperparameters. Each predictor was trained on a different subset comprising 80% of the WTCCC data and evaluated on the remaining 20% (Fig. 2; Supplementary Data 7 and Methods section). The 50 out-of-sample AUCs varied between 0.712 and 0.771 with a mean of 0.747 and a standard deviation of 0.14 (Supplementary Fig. 3). Internal cross-validation is a standard practice in machine learning9 and showed that model 1 predictor, which was fitted with optimised hyperparameters, generalises well across different subsets of WTCCC derived eQTL data.

Fig. 2: Tissue-specific contributions of the 50 T1D regularised logistic regression predictors.
figure 2

The distributions were created from the 50 T1D regularised logistic regression predictors that were created using the optimised hyperparameters of model 1. These regularised logistic regression predictors integrated four different forms of biological information: GWAS or fine-mapping; Hi-C; eQTL and genotype data. SNPs denote T1D-associated SNPs for which no eQTLs were identified. The X axis is the total value of the model weights (no units).

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Tissue-specific contributions to the T1D risk were extracted from each of the 50 T1D regularised logistic regression predictors as the sum of the absolute values of the model weights associated with each tissue. We then ranked the tissue-specific contributions to the 50 regularised logistic regression predictors. This ranking identified the lung as the top average contributor to the relative risk (case:control) of developing T1D. Across all 50 regularised logistic regression predictors, the lung explained a mean of 13.6% (standard deviation of 2.51%) of the relative risk of developing T1D (Fig. 2; Supplementary Data 8).

CTLA4 contributes to the risk associated with the lung and testes

When training our T1D regularised logistic regression predictors, we identified a split distribution for the lung that was dependent upon the lasso regularisation inclusion or exclusion of the rs3087243-CTLA4 cis-acting (i.e. <1 Mb apart) spatial eQTL within the two tissues where it was identified (i.e. lung or testes; Supplementary Fig. 4). Since lasso regularisation retains only one of a group of highly correlated features, we sought to validate the possibility that rs3087243-CTLA4 has notable effects on the contribution of the lung and testis to T1D risk.

To test the effects of specifically removing the rs3087243-CTLA4 feature, we created two alternative weighted WTCCC genotype T1D-eQTL models in which this eQTL was removed from either the lung or the testis within the weighted WTCCC genotype T1D-eQTL matrix. The AUCs and tissue-specific contributions from 50 T1D predictors with the optimised hyperparameters from each of the alterative matrices were evaluated by two-sided t-test and Bayesian methods (Supplementary Figs. 5 and 6; ref. 10). The inclusion or exclusion of the rs3087243-CTLA4 spatial eQTL within the lung or testes had a strong impact on the lungs’ predicted contribution to T1D risk. No other tissues were affected, consistent with the rs3087243-CTLA4 spatial eQTL only being detected in the lung and testes. The lung rs3087243-CTLA4 eQTL contributed an average of 4% to T1D risk (Supplementary Data 9). The lung eQTL involving rs3087243 and CTLA4 is also notable as: (1) rs3087243 has been linked with progression from single to multiple autoantibodies in the TrialNet PTP cohort11; (2) CTLA4 encodes an immunoglobulin protein crucial for modulating T cell function and mediating autoimmunity and (3) immune intervention trials targeting CTLA4 have reported significant but short-term positive metabolic outcomes12.

Predictions from the first model were confirmed using a second model with more features

To enable more precise estimations of relative risk for data features, a second T1D regularised logistic regression predictor (model 2) (134 tissue-specific eQTL effects across GTEx tissues and six SNPs with unknown eQTLs) was created (Methods) and trained with the optimised hyperparameters using the full WTCCC cohort (in-sample AUC = 0.774). (Model 2 differed from model 1, which used an 80:20 split for internal WTCCC validation.) Model 2 was validated against the UK Biobank (UKBB) (30 subsets of T1D datasets of 415 cases and 578 controls). Model 2 achieved a mean AUC = 0.754 (Table 1 and Fig. 3a [Kernel density estimate] and Fig. 3b [posterior estimation results from the Bayesian analysis]; Supplementary Data 10) and was used to rank the eQTLs that impacted the lung contribution to T1D risk (Supplementary Data 11). It should be noted that model 2 excluded the rs3087243-CTLA4 eQTL, which contributed an average of 4% to T1D risk in the 50 T1D regularised logistic regression predictors (calculated with the optimised hyperparameters of model 1; see above). The major contributor eQTL (rs6679677) downregulated AP4B1-AS1 transcript levels in cis (i.e. rs6679677 and AP4B1-AS1 are <1 Mb apart) in the lungs and conferred a 13.3% contribution to the T1D regularised logistic regression predictor. This was comparable to the mean predictor weighting (13.6%) of the 50 models created with model 1’s hyperparameters. Notably, rs6679677 also downregulates AP4B1-AS1 expression in whole blood samples (eQTLGen; Supplementary Data 12) and modulates the expression of genes associated with immune regulation, including FOXP3, CTLA4, IL2RA and SLAMF1 in whole blood (eQTLGen; http://www.eqtlgen.org13; Supplementary Data 12).

Table 1 A summary from the Bayesian analysis of the validation AUCs from the model 2 predictor on the 30 UK Biobank dataset.
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Fig. 3: Validation of AUC results from Model 2 on the 30 UK Biobank test dataset.
figure 3

a Kernel density estimate (KDE) plot of the 30 AUC results (mean = 0.754; SD = 0.0011. [X axis: AUC, Y axis: frequency of AUC values (%)]. b The posterior estimation results from the Bayesian analysis (1000 iterations of model simulation) of the 30 AUCs. From the Bayesian estimation, there was a 95% chance that the mean (µ) AUC was between 0.749 and 0.758, and its SD (σ) was between 0.009 and 0.014. SD standard deviation, AUC area under the curve, HPD highest posterior density interval. Uninformed prior distribution was normal with mean ~ U(0,1) and standard deviation ~ HalfNormal(std = 0.01). [X axis: mean AUC and standard deviation of AUC, Y axis: posterior distribution].

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Regulatory changes in the HLA locus contribute to the risk of developing T1D in both models

The HLA region is strongly associated with the development of T1D, accounting for 40–50% of the familial aggregation14. We observed spatial eQTLs involving HLA (Supplementary Data 13) within models 1 and 2. The notable HLA spatial cis-acting eQTLs involved SNPs rs2251396-PSORS1C1, rs2251396-MICA, rs3129889-HLA-DRB5 and rs9268645-HLA-DQB2 and were observed as contributing to the risk of developing T1D in multiple tissues (e.g. adrenal gland, transverse colon, small intestine, spleen, left heart ventricle, sun-exposed skin, testis and thyroid).

Validation of lung cell allele-specific enhancer activity of locus marked by rs6679677

Our results indicate that eQTL (rs6679677) downregulates AP4B1-AS1 transcript levels in the lung. Therefore, we performed a luciferase enhancer assay to experimentally validate that the top-ranked eQTL (rs6679677) marks an allele and tissue-specific enhancer. DNA sequences flanking rs6679677 (i.e. 74 bp 5′ – ref/alt allele – 75 bp 3′ [chr1:114303734-114303884; GRCh37]) were cloned into the 3′UTR of a minimal TATA-box promoter and luciferase gene construct to test whether the cloned sequence contain enhancer elements for gene expression (Methods section and Supplementary Fig. 7)15. Transient transfection of the plasmid vector containing the reference locus (i.e. the major allele for rs6679677) resulted in a fold increase in luciferase activity when compared to the control vector in A549 (lung) and HepG2 (liver) cells (~11 and ~5 fold increase, respectively). This is consistent with the existence of H3K9ac histone modifications at the locus tagged by rs6679677 in both the lung and liver tissues (see HaploReg; https://pubs.broadinstitute.org/mammals/haploreg/haploreg.php). Notably, a significant allele-specific reduction in enhancer activity (i.e. nucleotide change from C > A) was observed only in the A549 cells (p = 0.005 [two-sided t-test]; Fig. 4), consistent with the identification of an eQTL involving this locus in the lung but not the liver. Collectively, these results support the allele-specific enhancer activity for the locus marked by rs6679677 in the lung.

Fig. 4: rs6679677 is an allele-specific enhancer (i.e. nucleotide change from C>A) in lung (A549) but not liver (HepG2) epithelial cells.
figure 4

The locus tagged by rs6679677 was cloned within the 3′ UTR of a luciferase gene driven by a minimal promoter and transiently transfected into A549 and HepG2 cells. The relative enhancer activity for the ref and alt versions of the rs6679677 locus was calculated compared to the empty control vector (pMRAdonor2). Relative luminescence units (RLU) for the luciferase assay were normalised using the absorbance values for the beta-galactosidase assay. Results were plotted as the percentage of the ref_alleles enhancer activity (mean ± SD). Transfection experiments were repeated three times across n = 3 or n = 9 technical replicates in HepG2 (liver) and A549 (lung) cells, respectively. Representative results are shown for one transient transfection of A549 and HepG2 cells. Two-sided t-test was used for independent mean evaluation.

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Discussion

In the present study, we have used a logistic lasso regression model to integrate T1D case and control genotypes with spatial eQTL data to predict the relative tissue-specific contributions for the conversion of genetic risk to T1D. The regularised logistic regression models generated in this manuscript were validated by both internal cross-validation (model 1) which is standard practice in machine learning9 and external cross-validation using the different cohorts (model 2). Notably, the discussion refers to conclusions drawn from model 2, with the most stringent cross-cohort validation. Model 2 was validated across two independent case–control cohorts and identified the lungs and a cis-acting eQTL involving rs6679677 as the largest single contributor of the risk for developing T1D. Our results agree with observations by Gamazon et al.16, who used LD score regression to identify the lung as the top tissue influencing T1D disease development. These observations are consistent with an environmental event impacting the largest single interface between the body and environment to precipitate the development of T1D17,18,19. Moreover, our results are consistent with the association of respiratory infections (i.e. influenza-like illness) with an increased risk of islet autoantibody seroconversion in young-onset T1D study cohorts19,20. Collectively, our results demonstrate that tissue-specific approaches can improve our understanding of disease aetiology, potentially aiding therapeutic development in preventing T1D onset.

It is widely recognised that the vast majority of measurable genetic risk for T1D is associated with the HLA region and polymorphisms of the class II HLA DQ, DR and DP genes14. Typically these effects are ascribed to polymorphisms within the HLA genes that affect the shape of the peptide-binding grove and the scope of the peptides that can bind to the allele and thus be presented on the cell surface14. Our analysis focused on identifying and ranking the tissue-specific regulatory changes and thus does not capture variants that change the function or structural properties of a gene-encoded protein. Therefore, it is notable that the HLA locus variants that were retained within our models (rs3129889-HLA-DRB5 and rs9268645-HLA-DQB2) regulated the expression of DRB and DQB alleles. In addition, rs2251396 was associated with the expression of PSORS1C1, which has been associated with T1D21. Similarly, rs2251396 was associated with the expression of MICA, which has previously been identified as part of an extended HLA haplotype that associates with TID risk22, Collectively these observations support the hypothesis that changes in HLA gene expression contribute to T1D risk, in addition to the recognised role for HLA polymorphisms.

In the lung, our eQTL analysis revealed a marked association between rs6679677 and the expression of AP4B1-AS1. The allele-specific enhancer activity of the rs6679677 tagged region in lung cells was confirmed using a plasmid-based luciferase assay. However, rs6679677 also: (1) has eQTLs with AP4B1-AS1 and immune regulation genes (i.e. FOXP3, CTLA4, IL2RA and SLAMF123,24,25,26) in whole blood; (2) has been associated with the development of multiple persistent autoantibodies (including islet autoantibody), but not progression to T1D development in the TEDDY prospective cohort27; (3) has been associated with the development of other autoimmune disorders (i.e. juvenile idiopathic arthritis and rheumatoid arthritis)28,29 and (4) was reported as the top non-HLA SNP associated with T1D from WTCCC studies30. Collectively, these results support an important molecular role for the locus tagged by rs6679677 in a lung-specific increase in the risk of T1D development. However, our results do not prove the effect is exclusively due to the impact on lung cells. As such, future work should dissect if the lung-specific regulatory impact of rs6679677 contributes to the mechanism of T1D risk.

AP4B1-AS1 is located in a genomic region that is recognised as being strongly associated with autoimmune disorders1. PTPN22, which is on the antiparallel DNA strand to AP4B1-AS1, encodes a lymphoid-specific intracellular phosphatase (LyP) from the non-receptor class 4 subfamily of the protein-tyrosine phosphatases that acts as a critical negative regulator of T cell activation and T cell receptor signalling pathways31. Notably, rs6679677 downregulates AP4B1-AS1 expression in whole blood samples. Similarly, C1858T-rs2476601 (in complete linkage with rs6679677) has been linked with reduced protection against the influenza virus32. Therefore, we propose that future studies should ascertain the regulatory roles of rs6679677 on AP4B1-AS1 in T cells, particularly in response to viral infections in the lungs. We contend that this will help untangle the genetic mechanisms that connect respiratory infections and the induction of islet autoantibodies that have been observed in young children19,20.

This study has limitations. Firstly, the genetic data used in the analyses is predominantly from people of European ancestry, which limits the immediate translation of our findings to populations with different genetic structures (e.g. variable haplotypes in this region). Secondly, thirty SNPs identified as being associated with T1D did not have identifiable eQTLs in any of the GTEx tissues studied, consistent with the presence of alternative methods or developmental stages through which SNPs can mediate their effects on phenotypes. Thirdly, not all of the eQTLs we identified were represented in the individual genotypes we analysed (i.e. SNPs were unable to be imputed), meaning we could have missed effects. Fourthly, the reporter assay does not take into account the genomic context through which chromatin looping influences the enhancer-promoter interactions that mediate transcriptional activity33. Fifthly, the use of a lung cancer cell line may limit the interpretation of the transcriptional control of genes in normal lung tissue. Lastly, most of the spatial chromatin interactions were identified from immortalized cancer cell lines or primary tissues. By contrast, the eQTL associations were primarily obtained from post-mortem samples taken from a cross-sectional cohort (20–70 years of age). Therefore, it is possible that the Hi-C interactions and eQTL sets were not representative of the tissues in which they were tested. Nevertheless, the reproducibility of the prediction model across independent cohorts supports the utility of the approach and its use with expanded datasets for T1D and other immune and non-immune diseases.

The novelty of the approach we undertook lies in: (1) the integration of T1D-associated SNPs with their tissue-specific eQTLs (in both cis and trans); (2) interpreting individual case and control genotypes in terms of these tissue-specific eQTL effects; (3) including effects from variants that do not have detectable eQTLs in the reference library that is used in the assay and (4) the application of machine learning to select and rank the tissue-specific eQTL effects that confer disease risk. This approach moves T1D research away from a candidate gene approach to include gene regulatory changes, including within the HLA locus, as possible contributors to the risk of developing T1D. However, all results are putative until they are followed up by integrative empirical methods that prove the link between gene expression in the lung and other tissues, and the conversion of T1D risk.

In conclusion, our work provides insights into the role of variation in gene regulation in the risk of developing T1D. The transcriptional changes (including AP4B1-AS1 and CTLA4) we identified in the lung may help explain the reported association between respiratory infections and risk of islet autoantibody seroconversion reported in young children.

Methods

Identification of genetic variants associated with the development of T1D

In total, 313 genotyped and imputed SNPs associated with T1D, and those associated with time-to-event development of islet autoimmunity and T1D, were retrieved from the GWAS catalogue (www.ebi.ac.uk/gwas, downloaded 8 February 2019; p-value 1 × 10−5), prospective studies27, 34,35,36, TrialNet PTP cohort11, adult-onset37 and GRS prediction studies38,39,40 (Supplementary Data 1). All genomic positions for SNPs and genes are annotated according to reference human genome hg19/GRChr37.

Identification of SNP-gene pairs and expression QTL associations in human tissues

The Contextualizing Developmental SNPs in three-dimensions algorithm (CoDeS3D7) was used to identify genes that physically interact with loci marked by the T1D-associated SNPs. Briefly, the CoDeS3D modular python scripts integrate Hi-C contact libraries from published sources (Supplementary Data 2) to identify spatial co-localisation of two DNA fragments, with one fragment marking the queried SNP. Gene-containing restricted fragments that are in physical contact with fragments containing the queried SNPs are identified as spatial pairs to the SNPs. Finally, the resultant spatial SNP-gene pairs are queried in the Genotype-Tissue Expression database (GTEx) to identify SNPs that are associated with transcript levels of genes through physical interaction at FDR < 0.057.

In the present study, spatial interactions were identified in Hi-C chromatin contact libraries captured from: (1) immortalized cell lines that represent the embryonic germ layers (i.e. HUVEC, NHEK, HeLa, HMEC, IMR90, KBM7 and K562); (2) B-cell derived lymphoblastoid cell line (GM12878) and (3) primary human tissues (i.e. spleen and pancreas) (Supplementary Data 2). These published libraries were chosen to ensure a wide range of known interactions and were analysed in aggregate within the pipeline to address a lack of tissue-specific Hi-C interactions captured within ‘relevant’ published Hi-C libraries. The identified putative spatial interactions aggregated from different Hi-C libraries were utilised to select tissue-specific eQTL effects. Of note, the summary statistics for the CoDeS3D run include the Hi-C cell line/tissue in which the interaction was observed (Supplementary Data 3; column = cell lines). The regulatory potential of the spatial connections was identified by incorporating eQTL information from 44 human tissues (Genotype-Tissue Expression database [GTEx] v7; www.gtexportal.org)41. Spatial eQTLs were deemed significant and recorded if the q < 0.05 after correcting for multiple testing using the BH procedure42. Finally, genes whose transcript levels were associated with a spatial-eQTL were denoted as eGenes. The eQTL-eGene interactions were defined as either: cis, the eQTL and eGene are separated by a linear distance of ≤1 Mb on the same chromosome; or trans, eQTLs and their eGenes were separated by >1 Mb on the same chromosome or located on different chromosomes.

Genotype imputation for T1D cases and controls

Genotypes from T1D cases (2000) and controls (3000) were obtained from the Wellcome Trust Case Control Consortium (WTCCC)30. SNPs within individual genotypes were converted to rsIDs and genomic positions mapped (GRCh37, hg19). PLINK (v1.90b6.2, 64-bit) was used for quality control. Genotypes were cleaned using the Method-of-moments F coefficient estimate to remove homozygosity outliers (F values <−0.04 or 0.025 < F values). Related individuals were identified and removed using proportion IBD (PI_HAT > 0.08). Ancestry outliers (identified by principal component analysis [PCA] plotting), individuals with sex genotype errors (identified by PLINK), or individuals with missing genotype data (missing rate > 5%) were also removed. Finally, SNPs that were not in Hardy–Weinberg Equilibrium (p < 10−6) or had a minor allele frequency <1% were removed before SNP data imputation (Sanger imputation server; https://imputation.sanger.ac.uk)43. PLINK and bcftools (version: 1.9) were used in the imputation data preparation specified by Sanger imputation service43. Following imputation, the T1D genotype data was cleaned to remove SNPs with an: impute2 score <0.3; missing data rate >5% or a minor allele frequency <1%.

Creation of a weighted WTCCC genotype T1D-eQTL matrix

The machine learning only used SNPs (quantified or imputed) that did not have any missing data across the cohort and thus were present within each of the genotypes that were used. From the total 313 T1D SNPs included in the study, 253 SNPs were present within each of the WTCCC genotypes (Supplementary Data 6). Of these 253 T1D SNPs, 224 had detectable eQTLs, connecting to 758 eGenes (6307 tissue-specific eQTL effects). The tissue-specific eQTL normalised effect size for each T1D-associated SNP within the imputed WTCCC genotypes was extracted from the GTEx eQTL summary table of significant eQTLs (Supplementary Data 3). The normalised effect size for each tissue-specific eQTL was weighted by the number of alternative alleles at the eQTL SNP position in each individual’s genome. The 30 T1D-associated SNPs that were not eQTLs were unweighted, using solely SNP allele count from the imputed genotype.

Generation, training and validation of the regularised logistic regression models

In order to identify the optimised predictor model parameters, the weighted WTCCC genotype T1D-eQTL matrix was randomly split (80:20) by python numpy-permutation into two groups that contained case and control genotype data for model training and validation. The Mann-Whitney U test8 (tsfresh version 0.12.044) was used to select the individual feature columns within the 80% training dataset that were the most relevant attributes for predicting the T1D status (i.e. the relevant subset; FDR = 0.2)45. The relevant subset was then used to train a multiple logistic algorithm (Scikit-learn version 0.21.346;) implemented with elastic net regularisation using the SAGA solver to predict T1D disease status. The training was optimised using a Grid Search algorithm with 10-fold cross-validation to identify the best predictor with the optimised parameters (model 1). Hence, model 1 was created from 80% of the data with the optimised parameters (hyperparameters: C = 1, l1_ratio = 1, max_iter = 500, penalty = ‘elasticnet’, random_state=1, solver = ‘saga’).

We used prediction performance (AUC) to enable us to identify the best performing models for subsequent use. Prediction performance (measured by area under the curve [AUC]) for model 1 was tested using the relevant subset from within the 20% validation dataset. The optimal hyperparameter l1_ratio=1 effectively reduces the elastic net regularisation to a lasso regularisation. We used Elastic net regularisation and found via hyperparameter optimisation that the limit case of lasso regularisation was the most performant.

To calculate a measure of the variation in AUCs of the modelling with the optimised parameters; we undertook ten repeats of 5-fold cross-validation of model generation and validation using the Scikit-learn RepeatedKFold algorithm46, starting with the random generation of the 80:20 training:validation data sets and without Grid Search optimisation. This resulted in 50 T1D logistic regression predictors derived using the same general parameters as model 1.

Calculation of tissue-specific contributions to T1D risk

The 50 T1D regularised logistic regression predictors created from the 10 repeats of 5-fold cross-validation were used to test the predictive power of tissue-specific eQTL effects on individual genotype risk scores. Tissue-specific contributions to the T1D risk were extracted from each predictor as the sum of the absolute values of the weights associated with each tissue.

Validation of the importance of the lung eQTLs in UK Biobank data (model 2)

A second model (Supplementary Fig. 2) was created and trained using the full WTCCC training dataset with the optimised parameters. This model did not use the 80:20 split that was used in model 1. The predictor was then validated using 30 cohorts of 993 individual samples (415 cases and 578 controls) derived from the UK Biobank (Supplementary Data 9).

The 415 cases were selected, using a modification of Sharp et al.39, from the UK Biobank imputed (487,411 individual samples) BGEN format dataset using the following criteria:

  1. (1)

    European Caucasian by genetic clustering methods

  2. (2)

    Clinical diagnosis of diabetes at ≤ 20 years of age

  3. (3)

    On insulin within 1 year from the time of diagnosis

  4. (4)

    Still on insulin at the time of recruitment

  5. (5)

    Never self-report as having type 2 diabetes (T2D)

  6. (6)

    All SNPs included in the model 2 predictor were present within each individual’s imputed genotypes

The 578 control individual samples, without missing data for any of the SNPs included in the model 2 predictor, were randomly selected from the healthy controls within the UK Biobank data for each of the 30 test datasets. The genotype data for the 993 case and control UK Biobank samples in each test dataset was used to build a weighted eQTL-genotype matrix as outlined for model 1.

Reporter assay methodology for validating the regulatory effects of genetic sequences

Luciferase reporter assays (Supplementary Fig. 7) were performed using a modification of15. Briefly, DNA sequences flanking rs6679677 (i.e. 74 bp 5′–ref/alt allele – 75 bp 3′ [chr1:114303734-114303884; GRCh37]) containing the reference and alternative sequences) were synthesised by Integrated DNA Technologies (IDT). To ensure compatibility with the pMPRA vectors (pMPRA1 [Addgene: plasmid #49349] and pMPRAdonor2 [Addgene: plasmid #49353]), each sequence was designed using the following template: 5′-ACTGGCCGCTTCACTG-var-GGTACCTCTAGAAGATCGGAAGAGCGTCG-3′ (i.e. var denotes the 150 bp sequence to be assayed) (Supplementary Fig. 7). The variable region (var) was separated by a pair of KpnI (GGTACC) and XbaI (TCTAGA) restriction sites to enable directional insertion of a reporter gene. PCR amplification was performed using primer sequences (MPRA_SfiI_F [forward], MPRA_SfiI_R [reverse]; Supplementary Data 13) to add two distinct SfiI (GGCCNNNNNGGCC) tails to enable directional ligation of the oligonucleotide into pMPRA1 (Supplementary Fig. 7). An aliquot of the amplification product was electrophoresed (2% agarose, 100 V, 45 min) to visualise and verify that the product was the correct size (~200 bp) and that there were no non-specific amplification products. PCR amplicons were digested with SfiI (50 °C, 2 h), purified (QIAquick PCR Purification Kit; Qiagen) and quantified by Nanodrop.

pMPRA1 was linearised by digesting with SfiI (50 °C, 2 h), electrophoresed (0.8% agarose, 60 V, 1 h), the 2.5 kb linearised vector backbone excised, gel purified (ZymocleanTM Gel DNA Recovery Kit; Zymo Research) and quantified by Nanodrop.

SfiI-digested oligonucleotides (100 ng) were mixed with linearised pMPRA1 vector backbone (50 ng) and ligated by T4 DNA ligase (1U, 16 °C, min) to create pMPRA1: rs6679677_ref and pMPRA1: rs6679677_alt. The ligation reaction was stopped by heating (65 °C, 20 min).

pMPRA1:rs6679677_ref and pMPRA1:rs6679677_alt were amplified and selected by transformation in competent E. coli DH5-alpha cells (Mix & Go competent cells) according to the manufacturer’s instructions (www.zymoresearch.com/). Briefly, competent E. coli DH5-alpha cells (100 μL) were thawed on ice before the addition of ligation products (1-5 μL), gentle mixing (by flicking) and incubation on ice (5 min). Immediately following incubation on ice, the transformed competent cells (100 μL) were spread onto pre-warmed LB agar plates supplemented with ampicillin (100 µg/mL) and incubated (37 °C, overnight). Single colonies were picked and inoculated into LB:Ampicillin media (5 ml containing 100 μg/mL Ampicillin) and incubated (37 °C, overnight) with shaking (~200 rpm). Plasmid DNA was extracted using a QIAprep Spin Miniprep Kit, according to the manufacturer’s instructions. Plasmids were Sanger sequenced (Massey Genome Service; Massey University) using RVprimer3 (forward) and EBV-rev (reverse) primers in (Supplementary Data 13) to confirm the sequences of the inserts.

pMPRA1:rs6679677_ref, pMPRA1:rs6679677_alt were linearised with KpnI (10 U, 37 °C, 1 h) and purified using the QIAquick PCR Purification Kit (Qiagen). Samples were subsequently digested with XbaI (10 U) in the presence of Shrimp Alkaline Phosphatase (1 U, 37 °C, 2 h) prior to heat-inactivation (65 °C, 5 min) and purification using the QIAquick PCR Purification Kit (according to the manufacturer’s instructions).

A luc2 open reading frame (ORF) was prepared from pMPRAdonor2 (1 μg) by KpnI (20 U) XbaI (20 U) double digestion (37 °C, 1 h), electrophoresis (0.8% agarose, 60 V, 1 h) and gel purification of the 1.7 kb band using the ZymocleanTM Gel DNA Recovery Kit (Zymo Research).

The luc2 open reading frame was cloned into the KpnI and XbaI digested pMPRA1:rs6679677_ref, pMPRA1:rs6679677_alt plasmids, transformed and selected as described earlier. The resultant plasmids (pMPRA1:luc_rs6679677_ref, pMPRA1:luc_rs6679677_alt) were Sanger sequenced (Massey Genome Service; Massey University) using the luciferase primer in (Supplementary Data 14) to confirm the luc2 gene insertion.

HepG2 and A549 cell lines were purchased directly from the American Type Culture Collection (ATCC) and used at an early passage number. All cell lines tested negative for mycoplasma contamination and no commonly misidentified cell lines were used in the study. A549 (lung epithelial carcinoma; ATCC) and HepG2 (human liver carcinoma; ATCC) cells were maintained in DMEM and RPMI 1640 (ThermoFisher), respectively, supplemented with 10% fetal bovine serum, 1% GlutaMAX and 1% penicillin/streptomycin at 37 °C in a humid incubator purged with 5% CO2. Cells were routinely tested for mycoplasma contamination. For transfection, ~1.0 × 105 cells were seeded in a single well of a 24-well plate, followed by the addition of 500 μL of the appropriate complete media. On the day of transfection (24  following cell plating), ~75% confluent wells were co-transfected with luciferase plasmid DNA (i.e. 800 ng of pMPRA1:luc_rs6679677_ref, pMPRA1:luc_rs6679677_alt, or pMPRAdonor2 luciferase control) and a beta-galactosidase control plasmid (200 ng) using lipofectamine 3000 (ThermoFisher; according to the manufacturer’s instructions).

At 48 h following transfection, cells were lysed using the Glo lysis buffer (Promega) and luciferase activity assessed using the ONE-GloTM Luciferase Assay System (Promega) in a VarioskanTM LUX multimode microplate reader (according to the manufacturer’s instructions). For the beta-galactosidase assay, 20 µl beta-galactosidase reagent (i.e. 0.2 M phosphate buffer (pH 7.4), 2 mM MgCl2, 100 mM β-mercaptoethanol and 1.3 mg/ml ortho-Nitrophenyl-β-galactoside) was added to 20 µl of transfection cell lysate (prepared using the Glo lysis buffer) in a 96 well plate and incubated at 37 °C for 30 minutes. The absorbance was then read at 420 nM in a VarioskanTM LUX multimode microplate reader following the manufacturer’s instructions.

Statistics and reproducibility

All statistical testing was performed using R software (version v3.6.3)47, Scikit-learn (version 0.21.346;), tsfresh (version 0.12.044) and pymc3 (version 3.848;). Visualisation for the luciferase luminescence was performed using GraphPad Prism (v8.4.3).

Data security and code management is the foundation of reproducible and reliable data analyses. Datasets that were received or downloaded from original sources were individually maintained in read-only and write-protected directories on secured cloud server. Programme code was preserved after producing validated results and the code was named with appropriate functional and step sequential information. Version control (git) was also employed to protect script integrity across the analysis step directories.

Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.