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Title: Robust SNP-based prediction of rheumatoid arthritis through machine-learning-optimized polygenic risk score
Authors: Lim, Ashley JW 
Tyniana, C Tera
Lim, Lee Jin 
Tan, Justina Wei Lynn
Koh, Ee Tzun
Chong, Samuel S 
Khor, Chiea Chuen 
Leong, Khai Pang G
Lee, Caroline G 
Keywords: Science & Technology
Life Sciences & Biomedicine
Medicine, Research & Experimental
Research & Experimental Medicine
Polygenic risk score
Rheumatoid arthritis
Single nucleotide polymorphisms
Issue Date: 7-Feb-2023
Publisher: BMC
Citation: Lim, Ashley JW, Tyniana, C Tera, Lim, Lee Jin, Tan, Justina Wei Lynn, Koh, Ee Tzun, Chong, Samuel S, Khor, Chiea Chuen, Leong, Khai Pang G, Lee, Caroline G (2023-02-07). Robust SNP-based prediction of rheumatoid arthritis through machine-learning-optimized polygenic risk score. JOURNAL OF TRANSLATIONAL MEDICINE 21 (1). ScholarBank@NUS Repository.
Abstract: Background: 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. Methods: 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. Results: 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 ( 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. Conclusions: 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.
ISSN: 1479-5876,1479-5876
DOI: 10.1186/s12967-023-03939-5
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