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Mining Complex Genetic Patterns Conferring Multiple Sclerosis Risk

Author

Listed:
  • Farren B. S. Briggs

    (Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, 2103 Cornell Rd, Cleveland, OH 44106, USA
    Authors contributed equally.)

  • Corriene Sept

    (Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
    Authors contributed equally.)

Abstract

(1) Background: Complex genetic relationships, including gene-gene (G × G; epistasis), gene( n ), and gene-environment (G × E) interactions, explain a substantial portion of the heritability in multiple sclerosis (MS). Machine learning and data mining methods are promising approaches for uncovering higher order genetic relationships, but their use in MS have been limited. (2) Methods: Association rule mining (ARM), a combinatorial rule-based machine learning algorithm, was applied to genetic data for non-Latinx MS cases ( n = 207) and controls ( n = 179). The objective was to identify patterns (rules) amongst the known MS risk variants, including HLA-DRB1*15:01 presence, HLA-A*02:01 absence, and 194 of the 200 common autosomal variants. Probabilistic measures (confidence and support) were used to mine rules. (3) Results: 114 rules met minimum requirements of 80% confidence and 5% support. The top ranking rule by confidence consisted of HLA-DRB1*15:01 , SLC30A7 -rs56678847 and AC093277.1 -rs6880809; carriers of these variants had a significantly greater risk for MS (odds ratio = 20.2, 95% CI: 8.5, 37.5; p = 4 × 10 −9 ). Several variants were shared across rules, the most common was INTS8 -rs78727559, which was in 32.5% of rules. (4) Conclusions: In summary, we demonstrate evidence that specific combinations of MS risk variants disproportionately confer elevated risk by applying a robust analytical framework to a modestly sized study population.

Suggested Citation

  • Farren B. S. Briggs & Corriene Sept, 2021. "Mining Complex Genetic Patterns Conferring Multiple Sclerosis Risk," IJERPH, MDPI, vol. 18(5), pages 1-11, March.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:5:p:2518-:d:509891
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    References listed on IDEAS

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    1. Veit Rothhammer & Davis M. Borucki & Emily C. Tjon & Maisa C. Takenaka & Chun-Cheih Chao & Alberto Ardura-Fabregat & Kalil Alves de Lima & Cristina Gutiérrez-Vázquez & Patrick Hewson & Ori Staszewski , 2018. "Microglial control of astrocytes in response to microbial metabolites," Nature, Nature, vol. 557(7707), pages 724-728, May.
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