In:
Journal of Translational Medicine, Springer Science and Business Media LLC, Vol. 21, No. 1 ( 2023-02-07)
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. 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.
Type of Medium:
Online Resource
ISSN:
1479-5876
DOI:
10.1186/s12967-023-03939-5
Language:
English
Publisher:
Springer Science and Business Media LLC
Publication Date:
2023
detail.hit.zdb_id:
2118570-0
