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Ensemble machine learning identifies genetic loci associated with future worsening of disability in people with multiple sclerosis
Citation
Fuh-Ngwa, V and Zhou, Y and Melton, PE and van der Mei, I and Charlesworth, JC and Lin, X and Zarghami, A and Broadley, SA and Ponsonby, A-L and Simpson-Yap, S and Lechner-Scott, J and Taylor, BV, Ensemble machine learning identifies genetic loci associated with future worsening of disability in people with multiple sclerosis, Scientific Reports, 12, (1) pp. 1-13. ISSN 2045-2322 (2022) [Refereed Article]
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DOI: doi:10.1038/s41598-022-23685-w
Abstract
Limited studies have been conducted to identify and validate multiple sclerosis (MS) genetic loci associated with disability progression. We aimed to identify MS genetic loci associated with worsening of disability over time, and to develop and validate ensemble genetic learning model(s) to identify people with MS (PwMS) at risk of future worsening. We examined associations of 208 previously established MS genetic loci with the risk of worsening of disability; we learned ensemble genetic decision rules and validated the predictions in an external dataset. We found 7 genetic loci (rs7731626: HR 0.92, P = 2.4 × 10–5; rs12211604: HR 1.16, P = 3.2 × 10–7; rs55858457: HR 0.93, P = 3.7 × 10–7; rs10271373: HR 0.90, P = 1.1 × 10–7; rs11256593: HR 1.13, P = 5.1 × 10–57; rs12588969: HR = 1.10, P = 2.1 × 10–10; rs1465697: HR 1.09, P = 1.7 × 10–128) associated with risk worsening of disability; most of which were located near or tagged to 13 genomic regions enriched in peptide hormones and steroids biosynthesis pathways by positional and eQTL mapping. The derived ensembles produced a set of genetic decision rules that can be translated to provide additional prognostic values to existing clinical predictions, with the additional benefit of incorporating relevant genetic information into clinical decision making for PwMS. The present study extends our knowledge of MS progression genetics and provides the basis of future studies regarding the functional significance of the identified loci.
Item Details
| Item Type: | Refereed Article |
|---|---|
| Keywords: | Multiple Sclerosis, disability progression, genetic variations, machine learning, genetic decision rules |
| Research Division: | Biological Sciences |
| Research Group: | Bioinformatics and computational biology |
| Research Field: | Translational and applied bioinformatics |
| Objective Division: | Health |
| Objective Group: | Evaluation of health and support services |
| Objective Field: | Evaluation of health outcomes |
| UTAS Author: | Fuh-Ngwa, V (Mr Valery Fuh Ngwa) |
| UTAS Author: | Zhou, Y (Mr Yuan Zhou) |
| UTAS Author: | Melton, PE (Dr Phillip Melton) |
| UTAS Author: | van der Mei, I (Professor Ingrid van der Mei) |
| UTAS Author: | Charlesworth, JC (Dr Jac Charlesworth) |
| UTAS Author: | Lin, X (Mr Xin Lin) |
| UTAS Author: | Zarghami, A (Mr Amin Zarghami) |
| UTAS Author: | Taylor, BV (Professor Bruce Taylor) |
| ID Code: | 155423 |
| Year Published: | 2022 |
| Deposited By: | Menzies Institute for Medical Research |
| Deposited On: | 2023-02-20 |
| Last Modified: | 2023-02-20 |
| Downloads: | 0 |
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