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Ensemble machine learning identifies genetic loci associated with future worsening of disability in people with
Valery Fuh-Ngwa1, Yuan Zhou1, Phillip E Melton1
1Menzies Institute for Medical Research, University of Tasmania, 17 Liverpool St, Hobart, TAS, 7000, Australia.
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.
Insights
This study identified seven genetic loci linked to multiple sclerosis disability progression. Ensemble genetic models can predict worsening disability in people with MS, aiding clinical decisions.
Area of Science:
- Neurogenetics
- Genomics
- Biostatistics
Background:
- Limited research exists on genetic factors influencing multiple sclerosis (MS) disability progression.
- Identifying genetic markers for MS worsening is crucial for personalized medicine.
Purpose of the Study:
- To identify MS genetic loci associated with disability worsening over time.
- To develop and validate ensemble genetic learning models for predicting MS disability progression risk in individuals.
Main Methods:
- Examined associations between 208 MS genetic loci and disability worsening risk.
- Developed and validated ensemble genetic decision rules using an external dataset.
- Utilized positional and eQTL mapping to identify genomic regions.
Main Results:
- Identified 7 significant genetic loci associated with increased risk of disability worsening in MS.
- These loci are located near or tag 13 genomic regions enriched in peptide hormone and steroid biosynthesis pathways.
- Ensemble models generated genetic decision rules with prognostic value for clinical predictions.
Conclusions:
- The study expands knowledge of MS progression genetics by identifying novel genetic loci.
- The developed genetic decision rules can enhance clinical decision-making for people with MS (PwMS).
- Findings provide a foundation for future research into the functional significance of identified MS genetic loci.
