Skip to main content

Machine learning approaches for the genomic prediction of rheumatoid arthritis and systemic lupus erythematosus

Abstract

Background

Rheumatoid arthritis (RA) and systemic lupus erythematous (SLE) are autoimmune rheumatic diseases that share a complex genetic background and common clinical features. This study’s purpose was to construct machine learning (ML) models for the genomic prediction of RA and SLE.

Methods

A total of 2,094 patients with RA and 2,190 patients with SLE were enrolled from the Taichung Veterans General Hospital cohort of the Taiwan Precision Medicine Initiative. Genome-wide single nucleotide polymorphism (SNP) data were obtained using Taiwan Biobank version 2 array. The ML methods used were logistic regression (LR), random forest (RF), support vector machine (SVM), gradient tree boosting (GTB), and extreme gradient boosting (XGB). SHapley Additive exPlanation (SHAP) values were calculated to clarify the contribution of each SNPs. Human leukocyte antigen (HLA) imputation was performed using the HLA Genotype Imputation with Attribute Bagging package.

Results

Compared with LR (area under the curve [AUC] = 0.8247), the RF approach (AUC = 0.9844), SVM (AUC = 0.9828), GTB (AUC = 0.9932), and XGB (AUC = 0.9919) exhibited significantly better prediction performance. The top 20 genes by feature importance and SHAP values included HLA class II alleles. We found that imputed HLA-DQA1*05:01, DQB1*0201 and DRB1*0301 were associated with SLE; HLA-DQA1*03:03, DQB1*0401, DRB1*0405 were more frequently observed in patients with RA.

Conclusions

We established ML methods for genomic prediction of RA and SLE. Genetic variations at HLA-DQA1, HLA-DQB1, and HLA-DRB1 were crucial for differentiating RA from SLE. Future studies are required to verify our results and explore their mechanistic explanation.

Peer Review reports

Background

Rheumatoid arthritis (RA) and systemic lupus erythematous (SLE) are common autoimmune rheumatic diseases worldwide [1]. RA is characterized by chronic synovial proliferation and cartilage erosion [2]. If left untreated, RA may lead to severe disability and increased mortality [3]. The pathogenesis of SLE is an autoantibody overproduction and the activation of the complement system, leading to systemic manifestations [3]. The etiologies of RA and SLE are complex but may involve an interplay of environmental, hormonal, and genetic factors [2, 3]. In particular, a “rhupus” syndrome has been described in patients with overlapping clinical features of RA and SLE [4]. The SLE-related features in rhupus syndrome are usually mild and involve mucocutaneous, hematologic, and renal involvement; the arthritic component of rhupus can manifest as typical erosive polyarthritis [4]. Moreover, a familial aggregation of SLE and RA in a polygenic additive model was observed, suggesting a familial autoimmunity and susceptibility shared in these two diseases [5].

Despite the distinct clinical features of RA and SLE, abundant evidence suggests that they may share a common genetic component [6]. In genome-wide association studies (GWASs), a considerable amount of single nucleotide polymorphism (SNP) loci have been observed in RA and SLE [7, 8]. Type I interferon (IFN) signature overexpression is a well-established and common feature of SLE and RA [9]. However, a distinct human leukocyte antigen (HLA) inheritance pattern in SLE and RA was reported. A high proportion of patients with RA carry the HLA-DR4 genotype in chromosome 6 [10]; HLA-DR3 determines autoantibody initiation and is involved in the pathogenesis of SLE [11]. These results suggest that patients with SLE and RA may have diverse genetic backgrounds. The study designs of prior GWASs have frequently involved comparisons between patients with genetic variants of autoimmune diseases (SLE or RA) and healthy controls. To explore the genetic differences between patients with RA and SLE, a direct comparison of GWAS data related to RA and SLE is required.

With recent advancements in artificial intelligence (AI), machine learning (ML), a branch of AI, has been widely used in the diagnostic classification and prognostic prediction of systemic autoimmune diseases [12]. Most data types used in ML studies of RA and SLE were electric health records, ultrasound or magnetic resonance images, or data on SNP arrays and transcriptomes [12]. RA risk may be predicted using GWAS data with random forest (RF) algorithms in a regression model [13]. Radiographic progression of patients with RA could also be identified using GWAS data and a support vector machine (SVM) classifier [14]. Moreover, bootstrap aggregation of alternating decision trees has been used to detect SNPs associated with SLE [15]. Decision tree ML models could also identify biomarkers for erosive arthritis phenotypes in SLE [16]. However, an ML model has never been used to classify SLE and RA by using genetic variants.

To investigate genetic variations between individuals with SLE and RA, we conducted a comparative study using five ML models and GWAS data sets to identify the different SNPs associated with both diseases.

Methods

Study population

Study population data were obtained from the Taiwan Precision Medicine Initiative (TPMI), which is a collaboration between Taiwan medical centers nationwide and Academia Sinica. The initial goal of the TPMI was to incorporate genetic information into clinical application. Blood samples of each participant enrolled in the TPMI were collected, extracted for DNA, and genotyped. The genetic profiles of TPMI participants are linked to their electronic health records for case management and implementation of precision medicine.

Between June 2019 and December 2020, 32,728 participants were enrolled at the Taichung Veterans General Hospital site of the TPMI project. In total, RA and SLE were diagnosed in 2,094 and 2,190 patients, respectively, based on the 2010 American College of Rheumatology and the European League Against Rheumatism criteria for the classification of RA and the 2012 Systemic Lupus International Collaborating Clinics classification criteria for SLE [17, 18].

Genotyping

DNA extraction was performed on automated platforms at Taichung Veterans General Hospital. Genotyping of each participant was performed using Taiwan Biobank version 2 (TWBv2) array (Thermo Fisher Scientific, Inc., Santa Clara, CA, USA), which was designed in 2017 for both known-risk-alleles GWAS and testing with a total of 714,431 SNPs, as previously described by Wei and colleagues [19]. To maximize accuracy and prevent batch effects, Academia Sinica conducted genotype calls centrally for batches of 3,000 samples each. In cases and controls, quality control of genotyping for each SNP was further evaluated by determining the total call rate (successful call rate) and minor allele frequency (MAF). Those call rates of samples greater than 95% will be used in subsequent analyses. If only one allele appeared in cases and controls, or the total call rate was less than 95%, or the total MAF was less than 0.01, or departing significantly from Hardy-Weinberg equilibrium (P < 1 × 10−4), the SNPs will be excluded.

Feature selection

Because of the noisy nature of the genetic data, we first had to conduct feature filtering using a univariate test to identify the most relevant SNP markers [20]. Dimensionality reduction is employed in feature selection because the amount of SNP markers is larger than the number of patients [21]. We applied the pre-filtering method of Chi-squared test in the training set to select the SNP features which are best associated with the outcome, where alleles were coded as numerical values between 0 (0/0) and 1 (including 0/1 and 1/1). The SNPs whose p values were smaller than the Bonferroni-corrected, genome-wide significance threshold (5 × 10−8) were considered to be statistically significant features [22]. These relevant SNPs were used as inputs for the ML models.

Supervised ML approaches

In this study, we adopted ML approaches to establish classification models based solely on genetic data. Various ML methods, including logistic regression (LR), RF, SVM, gradient tree boosting (GTB), and extreme gradient boosting (XGB) were applied to classify patients into categories associated with having SLE and RA [23]. The advantages of a supervised ML model over a traditional statistical method are that it can overcome high dimensionality, detect interactions among SNP markers, and explore hidden feature combinations [24]. Initially, the entire data set (n = 4,284) was randomly divided into training (80%) and testing (20%) subsets by using stratification. Whenever SNP data were missing, we imputed the mode of the same disease for each SNP. While tuning the hyperparameters, the hyperparameters are optimized through 5-fold cross-validation only for the training set (Supplementary Table 1) whereas the optimization of hyperparameters was not executed in testing set [25].

The interpretation of results in the classification task in GWASs is a critical concern. SHapley Additive exPlanation (SHAP) values were adopted to calculate the contribution of each given feature [26]. This approach could explain the importance of features for the study outcome, providing visual results for interpreting how the feature value would affect the outcome [27, 28]. All of the data preprocessing was performed in R software v4.0.2 (R Foundation, Vienna, Austria), and the related ML analyses were developed in Python 3.7 language.

Performance evaluation

To robustly evaluate the performance of different ML methods, we adopted metrics of accuracy, precision, sensitivity, specificity, F1 score, and area under curve (AUC) by using the receiver operating characteristics (ROC) analysis for comparing each model with 5-fold cross-validation [23]. Sensitivity and specificity are well-known for their utility in evaluating the classified capabilities of models. Sensitivity is a measure of the true positive rate (patients with RA), also known as recall rate. Specificity is a measure of the true negative rate (patients with SLE). For the binary outcome classification, AUC analysis and the precision-recall curve (PR curve) were used as the primary performance metric that could provide insight into the discriminative power of various ML models [29].

To test the robustness of the models, we utilized the statistical method of bootstrapped resampling to re-construct the original training set into new ones. Then the new ones will be repeatedly trained using 5 machine-learning models for 500 times, and the average of the test result of 500-time training is defined as AUC.

HLA imputation

HLA imputation was performed using the R library HLA Genotype Imputation with the Attribute Bagging (HIBAG) package [30] for HLA genes HLA-DQA1, HLA-DQB1, and HLA-DRB1 by using an ethno-specific imputation model of Asian ancestry. Two-field (4-digit) resolution with allele frequencies (AFs) of 5.0% were displayed.

Patient and public involvement

We did not involve patients or the public in our work.

Statistical analysis

Comparisons of imputed HLA alleles in patients with RA and SLE were performed using Pearson’s chi-squared test. The p value and odds ratio (OR) along with 95% confidence intervals (CIs) were calculated using R version 4.0.2.

Results

Feature selection with analysis of SNP association with RA and SLE

A GWAS was to identify SNPs associated with RA and SLE. As denoted in Fig. 1, several SNPs at chromosome 6, HLA region; chromosome 7, GTF2I region; and 12, CDKN1B region differed considerably between patients with RA and SLE. Genetic variants with a Bonferroni-corrected, genome-wide significance threshold of 5 × 10-8 were selected for ML models.

Fig. 1
figure 1

Manhattan plot of the differences in SNPs between patients with RA and SLE. A whole genome and (B) detailed HLA region. Blue- and red-dotted lines indicate thresholds for significance (p < 1 × 10−5 and p < 5 × 10−8, respectively). Xaxis: chromosome number; yaxis: log10P; SNP: single nucleotide polymorphism; RA: rheumatoid arthritis; SLE: systemic lupus erythematosus

ML model performance in genomic prediction of RA and SLE

Table 1 details the ROC analysis of ML model performance with 5-fold cross-validation in the genomic prediction of RA and SLE. Compared with the LR model (AUC = 0.8247, p < 0.001), the RF approach (AUC = 0.9844, p < 0.001), SVM (AUC = 0.9828, p < 0.001), GTB approach (AUC = 0.9932) and XGB approach (AUC = 0.9919, p = 0.008) all exhibited significantly more accurate predictive performance on the testing set (Fig. 2 A). The PR curve of the five ML models was also presented in Fig. 2B. The GTB model still have the highest performance in average precision (AP = 0.9938) on the testing set. In both 5-fold cross-validation and bootstrapping validation, we can get the similar result with 95% of confidence interval (CI) in AUC (Supplementary Table 2).

Table 1 Comparison of machine learning model performance with 5-fold cross-validation
Fig. 2
figure 2

Data Visualization of machine learning model performance. A ROC curve and (B) PR curve. Comparisons of genomic prediction performances of RA and SLE by machine learning models. RA: rheumatoid arthritis; SLE: systemic lupus erythematosus; LR: logistic regression; RF: random forest; SVM: support vector machine; GTB: gradient tree boosting; XGM: extreme gradient boosting; AUC: area under the curve

Top 20 ranked genes and HLA alleles for predicting RA and SLE

Table 2 lists the top 20 ranked genes for the prediction of RA and SLE in terms of feature importance. We discovered that HLA DQA1 (rs6906021), DRB1 (rs9271858), DQB1 (rs9273505), and DRB5 (Affx-28,477,341 and rs4999342) were the top five ranking SNPs in the GTB model. Moreover, the top five ranking SNPs in the XGB model were HLA DQA1 (rs34965214, rs3104376, rs1391371, and rs9273322) and DRB1 (rs9271662). To investigate the top 20 ranking HLA alleles as features for genomic prediction of RA and SLE, GTB and XGB models with 5-fold cross-validation were performed (Supplementary Table 3). The AUCs for GTB and XGB models were 0.6348 and 0.6382, respectively.

Table 2 Top 20 ranking genes by feature importance for predicting RA and SLE in proposed models

SHAP value–based interpretation of prediction models

To identify attributable SNPs that had the greatest effect on the prediction model, we produced a SHAP summary graph of the top 20 SNPs in the GTB and XGB models (Fig. 3 A, B). According to the prediction model, as the SHAP value of an SNP (shown on x-axis) increases, the probability of a person with this SNP having RA is higher. The lower the x-axis SHAP value of a genetic variant, the more likely SLE development becomes. We discovered that the top 20 genes by feature importance and SHAP values included HLA class II alleles.

Fig. 3
figure 3

SHAP summary graph of top 20 SNPs of machine learning models. A) gradient tree boosting and (B) extreme gradient boosting models. As the SHAP value of an SNP (x-axis) increased, the probability of RA increased; the lower is the x-axis SHAP value of an SNP, the higher is the probability of SLE development. Each dot on the SHAP plot was calculated using the prediction model for each SNP’s attribution value for a participant. Dots are illustrated according to the feature values of each participant and accumulate vertically to indicate density. Blue represents 0/0 and red represents 0/1 or 1/1. SNP: single nucleotide polymorphism; SHAP: SHapley Additive exPlanation

Comparisons of imputed HLA alleles between patients with RA and SLE

Because HLA class II alleles seem to be crucial in the prediction model of RA and SLE, we compared the imputed HLA DQA1, DQB1, and DRB1 alleles with an AF of 5% (Table 3). We ascertained that HLA-DQA1*05:01 (OR = 2.35, p = 1.48 × 10-21), DQB1*0201 (OR = 2.35, p = 2.44 × 10-21), and DRB1*0301 (OR = 2.34, p = 2.87 × 10-21) were associated with SLE. By contrast, HLA-DQA1*03:03 (OR = 0.44, p = 2.84 × 10-29), DQB1*0401 (OR = 0.43, p = 5.20 × 10-29), and DRB1*0405 (OR = 0.41, p = 2.51 × 10-33) were more frequently observed in patients with RA.

Table 3 Associations of imputed HLA alleles with SLE compared with RA

Discussion

In this study, we developed and validated an innovative genomic prediction model by using SNP array data of 2,094 patients with RA and 2,190 patients with SLE to predict RA and SLE. We discovered that ML models of XGB, GTB, SVM, and RF outperformed LR, with GTB demonstrating the highest AUC values among the models we tested. The majority of top-ranking genes by feature importance were at the HLA DQA1, DRB1, and DQB1 regions. We also tested the imputed HLA alleles associated with RA and SLE. Our results elucidated the role of HLA in the pathophysiology of RA and SLE and indicated the feasibility of using ML prediction models for the classification of systemic autoimmune rheumatic diseases.

Previous GWASs have demonstrated that several SNPs are associated with both RA and SLE [31]. For example, rs7574865 in STAT4 and rs2476601 in PTPN22 are well-known genetic variants associated with both diseases [32, 33]. In patients with RA, rs6457617 and rs9275406 in HLA-DQA1; rs9275406 and rs12525220 in HLA-DQB1; and rs6457620, rs615672, rs7765379, rs660895, rs13192471, rs6910071, rs9268839, rs9271348, rs3104413, rs9269234, rs9268839 and rs112112734 in HLA-DRB1 were common genetic loci [31]. Previous studies had reported rs2647012 and rs2187668 in HLA-DQA1; rs3129716 and rs114092478 in HLA DQB1; and rs9271100, rs9270984, and rs3135394 in HLA DRB1 to be associated with SLE [31]. However, such GWASs primarily compared genetic variants in patients with RA or SLE with those in healthy controls. Our study was the first to compare SNPs associated with RA and SLE in a large study cohort consisting of patients with RA and SLE and without healthy controls. We demonstrated that SNPs in HLA DRB1, DQA1, and DQB1 regions markedly influenced susceptibility to RA and SLE. HLA-DR and HLA-DQ are arrayed on the surface of antigen-presenting cells with different coding variations in the peptide-binding groove [34]. Our results indicated that antigen-presenting cells might be involved in disease pathogenesis, reacting differently in patients with RA and those with SLE.

ML and AI applications in the context of autoimmune diseases classification have been widely investigated [31]. However, the most prevalent ML methods in prior RA and SLE studies have been LR, RFs, and SVM [31]. GTB and XGB have rarely been used in related investigations, but our results indicate the remarkable prediction performance of these two models in the classification of RA and SLE. GTB is a tree-based ensemble model that combines numerous weak classifiers to provide accurate classification [35]. It is a marked improvement on the classification performance of RF models and can avoid the problem of multi-collinearity [24, 25]. Furthermore, XGB is an optimized type of GTB model and is more efficient than other conventional models; in particular, it has the ability to prevent overfitting through regularization [24]. Therefore, although all four non-LR ML models had similar AUC values, GTB and XGB represented the most suitable models in this study considering that the overfitting problem and non-linear issues may arise in genomic data.

GWAS data can be used to predict phenotypes and risks of disease progression. Joo et al. investigated genome-wide SNPs among 374 Korean patients with RA by using SVM classifiers in the prediction of radiographic progression [14]. With the combination of clinical information and GWAS data, an AUC value of 0.7481 AUC was achieved for predicting structural damage in the context of RA [14]. In addition, bootstrap aggregation in the alternating decision tree method was used to detect genetic variants associated with SLE by using GWAS data from 1,846 Caucasian patients with lupus and 1,825 ethnically similar controls [15]. However, our study is the first to compare SNPs from a large Taiwanese cohort of patients with RA and SLE by using ML methods. Although our result may not be extrapolatable to non-Asian ethnicities, we contend that our study has provided a robust model for genomic prediction of autoimmune diseases with an optimal AUC of >0.99.

Producing an explainable AI model and correctly interpreting ML prediction models are always challenging. We are the first to use SHAP values in ML studies using GWAS data to provide consistent and attributable results of genetic variations associated with RA and SLE. The advantage of using a SHAP plot is that it helps in the interpretation of black box in ML-associated prediction models. We also observed that SNPs identified by GTB models by using a SHAP summary plot were generally also identified by the XGB model. Future studies to are warranted to provide a mechanistic explanation of how these HLA loci contribute to the development of RA and SLE.

Our studies suggested that the genetic variants of HLA-DQA1, DQB1, and DRB1 are associated with RA and SLE. Consistent with a prior report that DRB1*15:01 and DQB1*06:02 were the most important haplotype in East Asian patients with SLE [36], we confirmed that DRB1*15:01 and DQB1*06:02 were associated with SLE (OR = 1.37 and 1.44, respectively). HLA-DRB1 variants were demonstrated by Kim et al. to better account for the link between major histocompatibility complex and susceptibility to RA and SLE in the Korean population than other HLA DRB variants [37]. A GWAS of a native American group revealed that HLA-DQA1*01:02, DQA1*05:01, DQB1*06:02, DQB1*02:01, DRB1*15:01, and DRB1*03:01 were genetic variants associated with the development of SLE [38], and this result was supported by our findings. By contrast, HLA-DQA1*05:05 and DQB1*03:01 were protective alleles in the native American population but not in the Taiwanese population, which might be explained by the study design and differences in ethnicity. Nonetheless, we maintain that ML models can identify key genetic variations for classification of systemic autoimmune rheumatic diseases.

Although this was the first study to establish prediction models of RA and SLE using GWAS data, five ML models, and SHAP values, some limitations were present. First, our SNP data came from a single center. External validation is required to confirm our findings and avoid overfitting. Second, only genomic data were used in this study. Multiomics data sets would theoretically provide improved predictive performance. However, the study sample size was large, and the AUC of ML algorithms was robust. Finally, a cohort of healthy individuals was not included in the analysis. The SNPs identified in this study by ML models revealed the most significant differences between RA and SLE, and thus, our study design is most relevant to clinical scenarios where a symptomatic patient seeks medical attention and needs to be classified and managed quickly and correctly.

Conclusions

We established ML methods for genomic prediction of RA and SLE using GWAS data sets. We demonstrated that SNPs at HLA-DQA1, HLA-DQB1, and HLA-DRB1 were crucial genetic variations that differentiate RA and SLE with robust performance. Future research is required to confirm our results and explore the mechanistic explanations for them.

Availability of data and materials

The data used for this research comprises confidential patient health information and cannot be released publicly.

References

  1. Wang Y, Chen S, Chen J, Xie X, Gao S, Zhang C, et al. Germline genetic patterns underlying familial rheumatoid arthritis, systemic lupus erythematosus and primary Sjögren’s syndrome highlight T cell-initiated autoimmunity. Ann Rheum Dis. 2020;79:268–75.

    Article  CAS  Google Scholar 

  2. Scott D, Wolfe F, Huizinga T. Rheumatoid arthritis. Lancet [Internet]. Elsevier Ltd 2010.

  3. Tsokos GC. Systemic lupus erythematosus. N Engl J Med. 2011 Dec 1; 365:2110–21.

  4. AntoniHGni L, Le Mauff B, Marcelli C, Aouba A, de Boysson H. Rhupus: a systematic literature review. Autoimmun Rev. 2020:102612.

  5. Alarcón-Segovia D, Alarcón‐Riquelme ME, Cardiel MH, Caeiro F, Massardo L, Villa AR, et al. Familial aggregation of systemic lupus erythematosus, rheumatoid arthritis, and other autoimmune diseases in 1,177 lupus patients from the GLADEL cohort. Arthritis Rheum. 2005;52:1138–47.

    Article  Google Scholar 

  6. Cotsapas C, Voight BF, Rossin E, Lage K, Neale BM, Wallace C, et al. Pervasive sharing of genetic effects in autoimmune disease. PLoS Genet. 2011;7:e1002254.

    Article  CAS  Google Scholar 

  7. Cui Y, Sheng Y, Zhang X. Genetic susceptibility to SLE: recent progress from GWAS. J Autoimmun. 2013;41:25–33.

    Article  CAS  Google Scholar 

  8. Viatte S, Plant D, Raychaudhuri S. Genetics and epigenetics of rheumatoid arthritis. Nature Reviews Rheumatology. 2013;9:141.

    Article  CAS  Google Scholar 

  9. Higgs BW, Liu Z, White B, Zhu W, White WI, Morehouse C, et al. Patients with systemic lupus erythematosus, myositis, rheumatoid arthritis and scleroderma share activation of a common type I interferon pathway. Ann Rheum Dis. 2011;70:2029–36.

    Article  CAS  Google Scholar 

  10. Stastny P. Association of the B-cell alloantigen DRw4 with rheumatoid arthritis. New England journal of medicine. 1978;298:869–71.

    Article  CAS  Google Scholar 

  11. Zhao Z, Ren J, Dai C, Kannapell CC, Wang H, Gaskin F, et al. Nature of T cell epitopes in lupus antigens and HLA-DR determines autoantibody initiation and diversification. Ann Rheum Dis. 2019;78:380–90.

    Article  CAS  Google Scholar 

  12. Stafford I, Kellermann M, Mossotto E, Beattie R, MacArthur B, Ennis S. A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases. NPJ digital medicine. 2020;3:1–11.

    Article  Google Scholar 

  13. Kruppa J, Ziegler A, König IR. Risk estimation and risk prediction using machine-learning methods. Hum Genet. 2012;131:1639–54.

    Article  Google Scholar 

  14. Joo YB, Kim Y, Park Y, Kim K, Ryu JA, Lee S, et al. Biological function integrated prediction of severe radiographic progression in rheumatoid arthritis: a nested case control study. Arthritis research & therapy. 2017;19:1–9.

    Article  Google Scholar 

  15. Guy RT, Santago P, Langefeld CD. Bootstrap Aggregating of Alternating Decision Trees to Detect Sets of SNP s That Associate With Disease. Genet Epidemiol. 2012;36:99–106.

    Article  Google Scholar 

  16. Ceccarelli F, Sciandrone M, Perricone C, Galvan G, Cipriano E, Galligari A, et al. Biomarkers of erosive arthritis in systemic lupus erythematosus: Application of machine learning models. PLoS One. 2018;13:e0207926.

    Article  Google Scholar 

  17. Aletaha D, Neogi T, Silman AJ, Funovits J, Felson DT, Bingham III CO, et al. 2010 rheumatoid arthritis classification criteria: an American College of Rheumatology/European League Against Rheumatism collaborative initiative. Arthritis Rheum. 2010;62:2569–81.

    Article  Google Scholar 

  18. Petri M, Orbai AM, Alarcón GS, Gordon C, Merrill JT, Fortin PR, et al. Derivation and validation of the Systemic Lupus International Collaborating Clinics classification criteria for systemic lupus erythematosus. Arthritis Rheum. 2012;64:2677–86.

    Article  Google Scholar 

  19. Wei C-Y, Yang J-H, Yeh E-C, Tsai M-F, Kao H-J, Lo C-Z, et al. Genetic profiles of 103,106 individuals in the Taiwan Biobank provide insights into the health and history of Han Chinese. NPJ genomic medicine. 2021;6:1–10.

    Article  Google Scholar 

  20. Mieth B, Kloft M, Rodríguez JA, Sonnenburg S, Vobruba R, Morcillo-Suárez C, et al. Combining multiple hypothesis testing with machine learning increases the statistical power of genome-wide association studies. Sci Rep. 2016;6:1–14.

    Article  Google Scholar 

  21. Romero-Rosales B-L, Tamez-Pena J-G, Nicolini H, Moreno-Treviño M-G, Trevino V. Improving predictive models for Alzheimer’s disease using GWAS data by incorporating misclassified samples modeling. PLoS One. 2020;15:e0232103.

    Article  CAS  Google Scholar 

  22. Cheng B, Ning Y, Liang C, Li P, Liu L, Cheng S, et al. Genome-Wide Association Analysis Identified ANXA1 Associated with Shoulder Impingement Syndrome in UK Biobank Samples. G3: Genes, Genomes, Genetics. 2020;10:3279-84.

  23. Verma A, Ranga V. Machine learning based intrusion detection systems for IoT applications. Wireless Personal Communications. 2020;111:2287–310.

    Article  Google Scholar 

  24. Li B, Zhang N, Wang Y-G, George AW, Reverter A, Li Y. Genomic prediction of breeding values using a subset of SNPs identified by three machine learning methods. Frontiers in genetics. 2018;9:237.

    Article  Google Scholar 

  25. Ho WK, Tang B-S, Wong SW. Predicting property prices with machine learning algorithms. Journal of Property Research. 2021;38:48–70.

    Article  Google Scholar 

  26. Lundberg S, Lee S-I. A unified approach to interpreting model predictions. arXiv preprint arXiv:170507874. 2017.

  27. Kang E, Jang J, Choi CH, Kang SB, Bang KB, Kim TO, et al. Development of a Clinical and Genetic Prediction Model for Early Intestinal Resection in Patients with Crohn’s Disease: Results from the IMPACT Study. Journal of clinical medicine. 2021;10:633.

    Article  CAS  Google Scholar 

  28. Behravan H, Hartikainen JM, Tengström M, Kosma VM, Mannermaa A. Predicting breast cancer risk using interacting genetic and demographic factors and machine learning. Sci Rep. 2020;10:1–16.

    Article  Google Scholar 

  29. Jung LC, Wang H, Li X, Wu C. A machine learning method for selection of genetic variants to increase prediction accuracy of type 2 diabetes mellitus using sequencing data. Statistical Analysis and Data Mining: The ASA Data Science Journal. 2020;13:261–81.

    Article  Google Scholar 

  30. Zheng X, Shen J, Cox C, Wakefield JC, Ehm MG, Nelson MR, et al. HIBAG—HLA genotype imputation with attribute bagging. The pharmacogenomics journal. 2014;14:192–200.

    Article  CAS  Google Scholar 

  31. Lu H, Zhang J, Jiang Z, Zhang M, Wang T, Zhao H, et al. Detection of Genetic Overlap Between Rheumatoid Arthritis and Systemic Lupus Erythematosus Using GWAS Summary Statistics. Frontiers in genetics. 2021;12:389.

    Google Scholar 

  32. Remmers EF, Plenge RM, Lee AT, Graham RR, Hom G, Behrens TW, et al. STAT4 and the risk of rheumatoid arthritis and systemic lupus erythematosus. New England Journal of Medicine. 2007;357:977–86.

    Article  CAS  Google Scholar 

  33. Orozco G, Sánchez E, González-Gay MA, López‐Nevot MA, Torres B, Cáliz R, et al. Association of a functional single‐nucleotide polymorphism of PTPN22, encoding lymphoid protein phosphatase, with rheumatoid arthritis and systemic lupus erythematosus. Arthritis & Rheumatism: Official Journal of the American College of Rheumatology. 2005;52:219–24.

    Article  CAS  Google Scholar 

  34. Reche PA, Reinherz EL. Sequence variability analysis of human class I and class II MHC molecules: functional and structural correlates of amino acid polymorphisms. J Mol Biol. 2003;331:623–41.

    Article  CAS  Google Scholar 

  35. Dietterich TG. Ensemble methods in machine learning. International workshop on multiple classifier systems;2000:Springer;2000. p. 1-15.

  36. Molineros JE, Looger LL, Kim K, Okada Y, Terao C, Sun C, et al. Amino acid signatures of HLA Class-I and II molecules are strongly associated with SLE susceptibility and autoantibody production in Eastern Asians. PLoS Genet. 2019;15:e1008092.

    Article  CAS  Google Scholar 

  37. Kim K, Bang S-Y, Yoo DH, Cho S-K, Choi C-B, Sung Y-K, et al. Imputing variants in HLA-DR beta genes reveals that HLA-DRB1 is solely associated with rheumatoid arthritis and systemic lupus erythematosus. PLoS One. 2016;11:e0150283.

    Article  Google Scholar 

  38. Alarcón-Riquelme ME, Ziegler JT, Molineros J, Howard TD, Moreno‐Estrada A, Sánchez‐Rodríguez E, et al. Genome‐wide association study in an Amerindian ancestry population reveals novel systemic lupus erythematosus risk loci and the role of European admixture. Arthritis & rheumatology. 2016;68:932–43.

    Article  Google Scholar 

Download references

Acknowledgements

This manuscript was edited by Wallace Academic Editing. We thank all the participants and investigators from Taiwan Precision Medicine Initiative.

Funding

This study was funded by Academia Sinica 40-05-GMM and AS-GC-110-MD02 and Taichung Veterans General Hospital TCVGH-1107302D, TCVGH-1117302D and VTA111-V2-1-1.

Author information

Authors and Affiliations

Authors

Contributions

C-WC conceived and designed the study, acquired genomic and clinical data, conducted data analysis, and drafted and revised the manuscript. C-WC, S-CC, T-SC, and Y-FC established the ML models and revised the manuscript. C-JH and H-IY performed HLA imputation experiments. T-HH, Y-JC, H-HC, and C-HL revised the manuscript. Y-MC formed the original hypothesis, conceived and designed the study, conducted genetic data analysis, and drafted and revised the manuscript. The author(s) read and approved the final manuscript.

Corresponding author

Correspondence to Yi-Ming Chen.

Ethics declarations

Ethics approval and consent to participate

This study has been carried out in accordance with The Code of Ethics of the World Medical Association (Declaration of Helsinki). The Ethics Committee of Taichung Veterans General Hospital reviewed and approved our study (SF19153A), and informed consent was obtained from each participant.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no conflicts of interest to declare.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chung, CW., Hsiao, TH., Huang, CJ. et al. Machine learning approaches for the genomic prediction of rheumatoid arthritis and systemic lupus erythematosus. BioData Mining 14, 52 (2021). https://doi.org/10.1186/s13040-021-00284-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s13040-021-00284-5

Keywords