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- Title
Robust SNP-based prediction of rheumatoid arthritis through machine-learning-optimized polygenic risk score.
- Authors
Lim, Ashley J. W.; Tyniana, C. Tera; Lim, Lee Jin; Tan, Justina Wei Lynn; Koh, Ee Tzun; Ang, Andrea Ee Ling; Chan, Grace Yin Lai; Chan, Madelynn Tsu-Li; Chia, Faith Li-Ann; Chng, Hiok Hee; Chua, Choon Guan; Howe, Hwee Siew; Koh, Li Wearn; Kong, Kok Ooi; Law, Weng Giap; Lee, Samuel Shang Ming; Lian, Tsui Yee; Lim, Xin Rong; Loh, Jess Mung Ee; Manghani, Mona
- 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.
- Subjects
DISEASE risk factors; MONOGENIC &; polygenic inheritance (Genetics); RHEUMATOID arthritis; SINGLE nucleotide polymorphisms; FEATURE selection; GENOME-wide association studies; MACHINE learning
- Publication
Journal of Translational Medicine, 2023, Vol 21, Issue 1, p1
- ISSN
1479-5876
- Publication type
Article
- DOI
10.1186/s12967-023-03939-5