Ensemble machine learning identifies genetic loci associated with future worsening of disability in people with multiple sclerosis

Abstract

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.