Dataset Information

Robust SNP-based prediction of rheumatoid arthritis through machine-learning-optimized polygenic risk score.

ABSTRACT: The popular statistics-based Genome-wide association studies (GWAS) have provided deep insights into the field of complex disorder genetics. However, its clinical applicability to predict disease/trait outcomes remains unclear as statistical models are not designed to make predictions. This study employs statistics-free machine-learning (ML)-optimized polygenic risk score (PRS) to complement existing GWAS and bring the prediction of disease/trait outcomes closer to clinical application. Rheumatoid Arthritis (RA) was selected as a model disease to demonstrate the robustness of ML in disease prediction as RA is a prevalent chronic inflammatory joint disease with high mortality rates, affecting adults at the economic prime. Early identification of at-risk individuals may facilitate measures to mitigate the effects of the disease. This study employs a robust ML feature selection algorithm to identify single nucleotide polymorphisms (SNPs) that can predict RA from a set of training data comprising RA patients and population control samples. Thereafter, selected SNPs were evaluated for their predictive performances across 3 independent, unseen test datasets. The selected SNPs were subsequently used to generate PRS which was also evaluated for its predictive capacity as a sole feature. Through robust ML feature selection, 9 SNPs were found to be the minimum number of features for excellent predictive performance (AUC > 0.9) in 3 independent, unseen test datasets. PRS based on these 9 SNPs was significantly associated with (P < 1 × 10-16) and predictive (AUC > 0.9) of RA in the 3 unseen datasets. A RA ML-PRS calculator of these 9 SNPs was developed ( https://xistance.shinyapps.io/prs-ra/ ) to facilitate individualized clinical applicability. The majority of the predictive SNPs are protective, reside in non-coding regions, and are either predicted to be potentially functional SNPs (pfSNPs) or in high linkage disequilibrium (r2 > 0.8) with un-interrogated pfSNPs. These findings highlight the promise of this ML strategy to identify useful genetic features that can robustly predict disease and amenable to translation for clinical application.

SUBMITTER: Lim AJW

PROVIDER: S-EPMC9903430 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature

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Robust SNP-based prediction of rheumatoid arthritis through machine-learning-optimized polygenic risk score.

Lim Ashley J W AJW Tyniana C Tera CT Lim Lee Jin LJ Tan Justina Wei Lynn JWL Koh Ee Tzun ET Chong Samuel S SS Khor Chiea Chuen CC Leong Khai Pang KP Lee Caroline G CG

Journal of translational medicine 20230207 1

<h4>Background</h4>The popular statistics-based Genome-wide association studies (GWAS) have provided deep insights into the field of complex disorder genetics. However, its clinical applicability to predict disease/trait outcomes remains unclear as statistical models are not designed to make predictions. This study employs statistics-free machine-learning (ML)-optimized polygenic risk score (PRS) to complement existing GWAS and bring the prediction of disease/trait outcomes closer to clinical ap ...[more]

PMID: 36750873

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Project description:BackgroundPatients with rheumatoid arthritis (RA) have an increased risk of developing serious infections (SIs) vs. individuals without RA; efforts to predict SIs in this patient group are ongoing. We assessed the ability of different machine learning modeling approaches to predict SIs using baseline data from the tofacitinib RA clinical trials program.MethodsThis analysis included data from 19 clinical trials (phase 2, n = 10; phase 3, n = 6; phase 3b/4, n = 3). Patients with RA receiving tofacitinib 5 or 10 mg twice daily (BID) were included in the analysis; patients receiving tofacitinib 11 mg once daily were considered as tofacitinib 5 mg BID. All available patient-level baseline variables were extracted. Statistical and machine learning methods (logistic regression, support vector machines with linear kernel, random forest, extreme gradient boosting trees, and boosted trees) were implemented to assess the association of baseline variables with SI (logistic regression only), and to predict SI using selected baseline variables using 5-fold cross-validation. Missing values were handled individually per prediction model.ResultsA total of 8404 patients with RA treated with tofacitinib were eligible for inclusion (15,310 patient-years of total follow-up) of which 473 patients reported SIs. Amongst other baseline factors, age, previous infection, and corticosteroid use were significantly associated with SI. When applying prediction modeling for SI across data from all studies, the area under the receiver operating characteristic (AUROC) curve ranged from 0.656 to 0.739. AUROC values ranged from 0.599 to 0.730 in data from phase 3 and 3b/4 studies, and from 0.563 to 0.643 in data from ORAL Surveillance only.ConclusionsBaseline factors associated with SIs in the tofacitinib RA clinical trial program were similar to established SI risk factors associated with advanced treatments for RA. Furthermore, while model performance in predicting SI was similar to other published models, this did not meet the threshold for accurate prediction (AUROC > 0.85). Thus, predicting the occurrence of SIs at baseline remains challenging and may be complicated by the changing disease course of RA over time. Inclusion of other patient-associated and healthcare delivery-related factors and harmonization of the duration of studies included in the models may be required to improve prediction.Trial registrationClinicalTrials.gov: NCT00147498; NCT00413660; NCT00550446; NCT00603512; NCT00687193; NCT01164579; NCT00976599; NCT01059864; NCT01359150; NCT02147587; NCT00960440; NCT00847613; NCT00814307; NCT00856544; NCT00853385; NCT01039688; NCT02187055; NCT02831855; NCT02092467.

Project description:BackgroundFew studies on rheumatoid arthritis (RA) have generated machine learning models to predict biologic disease-modifying antirheumatic drugs (bDMARDs) responses; however, these studies included insufficient analysis on important features. Moreover, machine learning is yet to be used to predict bDMARD responses in ankylosing spondylitis (AS). Thus, in this study, machine learning was used to predict such responses in RA and AS patients.MethodsData were retrieved from the Korean College of Rheumatology Biologics therapy (KOBIO) registry. The number of RA and AS patients in the training dataset were 625 and 611, respectively. We prepared independent test datasets that did not participate in any process of generating machine learning models. Baseline clinical characteristics were used as input features. Responders were defined as those who met the ACR 20% improvement response criteria (ACR20) and ASAS 20% improvement response criteria (ASAS20) in RA and AS, respectively, at the first follow-up. Multiple machine learning methods, including random forest (RF-method), were used to generate models to predict bDMARD responses, and we compared them with the logistic regression model.ResultsThe RF-method model had superior prediction performance to logistic regression model (accuracy: 0.726 [95% confidence interval (CI): 0.725-0.730] vs. 0.689 [0.606-0.717], area under curve (AUC) of the receiver operating characteristic curve (ROC) 0.638 [0.576-0.658] vs. 0.565 [0.493-0.605], F1 score 0.841 [0.837-0.843] vs. 0.803 [0.732-0.828], AUC of the precision-recall curve 0.808 [0.763-0.829] vs. 0.754 [0.714-0.789]) with independent test datasets in patients with RA. However, machine learning and logistic regression exhibited similar prediction performance in AS patients. Furthermore, the patient self-reporting scales, which are patient global assessment of disease activity (PtGA) in RA and Bath Ankylosing Spondylitis Functional Index (BASFI) in AS, were revealed as the most important features in both diseases.ConclusionsRF-method exhibited superior prediction performance for responses of bDMARDs to a conventional statistical method, i.e., logistic regression, in RA patients. In contrast, despite the comparable size of the dataset, machine learning did not outperform in AS patients. The most important features of both diseases, according to feature importance analysis were patient self-reporting scales.

Project description:BackgroundFatigue is a common and burdensome symptom in Rheumatoid Arthritis (RA), yet is poorly understood. Currently, clinicians rely solely on fatigue questionnaires, which are inherently subjective measures. For the effective development of future therapies and stratification, it is of vital importance to identify biomarkers of fatigue. In this study, we identify brain differences between RA patients who improved and did not improve their levels of fatigue based on Chalder Fatigue Scale variation (ΔCFS≥ 2), and we compared the performance of different classifiers to distinguish between these samples at baseline.MethodsFifty-four fatigued RA patients underwent a magnetic resonance (MR) scan at baseline and 6 months later. At 6 months we identified those whose fatigue levels improved and those for whom it did not. More than 900 brain features across three data sets were assessed as potential predictors of fatigue improvement. These data sets included clinical, structural MRI (sMRI) and diffusion tensor imaging (DTI) data. A genetic algorithm was used for feature selection. Three classifiers were employed in the discrimination of improvers and non-improvers of fatigue: a Least Square Linear Discriminant (LSLD), a linear Support Vector Machine (SVM) and a SVM with Radial Basis Function kernel.ResultsThe highest accuracy (67.9%) was achieved with the sMRI set, followed by the DTI set (63.8%), whereas classification performance using clinical features was at the chance level. The mean curvature of the left superior temporal sulcus was most strongly selected during the feature selection step, followed by the surface are of the right frontal pole and the surface area of the left banks of the superior temporal sulcus.ConclusionsThe results presented evidence a superiority of brain metrics over clinical metrics in predicting fatigue changes. Further exploration of these methods may support clinicians to triage patients towards the most appropriate fatigue alleviating therapies.

Dataset Information

Robust SNP-based prediction of rheumatoid arthritis through machine-learning-optimized polygenic risk score.

Publications

Robust SNP-based prediction of rheumatoid arthritis through machine-learning-optimized polygenic risk score.

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