Please use this identifier to cite or link to this item:
https://doi.org/10.1186/s12967-023-03939-5
DC Field | Value | |
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dc.title | Robust SNP-based prediction of rheumatoid arthritis through machine-learning-optimized polygenic risk score | |
dc.contributor.author | Lim, Ashley JW | |
dc.contributor.author | Tyniana, C Tera | |
dc.contributor.author | Lim, Lee Jin | |
dc.contributor.author | Tan, Justina Wei Lynn | |
dc.contributor.author | Koh, Ee Tzun | |
dc.contributor.author | Chong, Samuel S | |
dc.contributor.author | Khor, Chiea Chuen | |
dc.contributor.author | Leong, Khai Pang G | |
dc.contributor.author | Lee, Caroline G | |
dc.date.accessioned | 2023-07-24T09:05:29Z | |
dc.date.available | 2023-07-24T09:05:29Z | |
dc.date.issued | 2023-02-07 | |
dc.identifier.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. https://doi.org/10.1186/s12967-023-03939-5 | |
dc.identifier.issn | 1479-5876,1479-5876 | |
dc.identifier.uri | https://scholarbank.nus.edu.sg/handle/10635/243371 | |
dc.description.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 (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. 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. | |
dc.language.iso | en | |
dc.publisher | BMC | |
dc.source | Elements | |
dc.subject | Science & Technology | |
dc.subject | Life Sciences & Biomedicine | |
dc.subject | Medicine, Research & Experimental | |
dc.subject | Research & Experimental Medicine | |
dc.subject | Machine-learning | |
dc.subject | Polygenic risk score | |
dc.subject | Rheumatoid arthritis | |
dc.subject | Single nucleotide polymorphisms | |
dc.subject | WHOLE-GENOME ASSOCIATION | |
dc.subject | PATHOGENESIS | |
dc.subject | GENETICS | |
dc.type | Article | |
dc.date.updated | 2023-07-21T06:17:20Z | |
dc.contributor.department | DEAN'S OFFICE (NGS FOR INTGR SCI & ENGG) | |
dc.contributor.department | BIOCHEMISTRY | |
dc.contributor.department | PAEDIATRICS | |
dc.description.doi | 10.1186/s12967-023-03939-5 | |
dc.description.sourcetitle | JOURNAL OF TRANSLATIONAL MEDICINE | |
dc.description.volume | 21 | |
dc.description.issue | 1 | |
dc.published.state | Published | |
Appears in Collections: | Staff Publications Elements |
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Robust SNP-based prediction of rheumatoid arthritis through machine-learning-optimized polygenic risk score. .pdf | Published version | 5.6 MB | Adobe PDF | OPEN | Published | View/Download |
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