Author Of 1 Presentation

Machine Learning/Network Science Poster Presentation

P0003 - Combinatorial Genetic Interaction Analysis of Multiple Sclerosis Risk Variants (ID 1040)

Speakers
Authors
Presentation Number
P0003
Presentation Topic
Machine Learning/Network Science

Abstract

Background

Common genetic variation within the major histocompatibility complex (MHC), primarily HLA-DRB1*15:01 and HLA-A*02:01, and 200 non-MHC variants contribute to MS risk. It is unknown if specific combinations of these risk variants disproportionately confer elevated risk, as interactions between risk variants have not been extensively studied.

Objectives

To identify if there are specific combinations of risk variants that disproportionately confer increased MS risk using a novel machine learning approach.

Methods

We applied association rule mining (ARM), a combinatorial rule-based machine learning algorithm, to data from non-Hispanic white MS cases (N=207) and controls (N=179). The genetic data consisted of HLA-DRB1*15:01, HLA-A*02:01, and 200 non-MHC risk variants assuming a dominant model. We identified patterns (rules) of 2 to 5 risk variants that were enriched in MS cases compared to controls. Probabilistic measures (confidence and support) evaluated the strength of rules. Odds ratios (ORs), 95% bias corrected confidence intervals (CIs), and permutation p-values obtained from bootstrapped logistic regression models adjusted for genetic ancestry. A Bonferroni approach adjusted for multiple testing. Hahsler and Karpienko’s grouped matrix method identified rules with similar characteristics.

Results

122 rules met minimum requirements of 80% confidence and 5% support. 3 rules met the Bonferroni threshold for significance, and all consisted of 3 variants. The top 3 rules were: 1. HLA-DRB1*15:01, SLC30A7-rs56678847 and rs6880909– carriers of these variants had 20.2-fold increased odds of MS (95% CI: (8.5, 37.5); p=4x10-9); 2. HLA-DRB1*15:01, ADCY3-rs11125803, and rs13327021 (OR: 6.8, 95% CI: (3.1, 20.9); p=0.0001); and 3. HLA-DRB1*15:01, rs13327021, and LOC105375752-rs735542 (OR: 4.9, 95% CI: (2.4, 12.0); p=0.0002). Interestingly, several variants were shared across several of the 122 rules. In particular, INTS8-rs78727559 was present in 34% of top rules and TNIP3-rs17051321 was present in 32% of top rules. HLA-DRB1*15:01, rs35486093, and SLC30A7-rs56678847 were present in 21% of top rules.

Conclusions

In summary, we identified strong evidence suggesting specific combinations of MS risk variants confer elevated risk by applying a robust and novel analytical framework to a modestly sized study population. Replication analyses are underway. These results have the potential to significantly inform efforts aimed at developing risk prediction models for MS.

Collapse

Presenter Of 1 Presentation

Machine Learning/Network Science Poster Presentation

P0003 - Combinatorial Genetic Interaction Analysis of Multiple Sclerosis Risk Variants (ID 1040)

Speakers
Authors
Presentation Number
P0003
Presentation Topic
Machine Learning/Network Science

Abstract

Background

Common genetic variation within the major histocompatibility complex (MHC), primarily HLA-DRB1*15:01 and HLA-A*02:01, and 200 non-MHC variants contribute to MS risk. It is unknown if specific combinations of these risk variants disproportionately confer elevated risk, as interactions between risk variants have not been extensively studied.

Objectives

To identify if there are specific combinations of risk variants that disproportionately confer increased MS risk using a novel machine learning approach.

Methods

We applied association rule mining (ARM), a combinatorial rule-based machine learning algorithm, to data from non-Hispanic white MS cases (N=207) and controls (N=179). The genetic data consisted of HLA-DRB1*15:01, HLA-A*02:01, and 200 non-MHC risk variants assuming a dominant model. We identified patterns (rules) of 2 to 5 risk variants that were enriched in MS cases compared to controls. Probabilistic measures (confidence and support) evaluated the strength of rules. Odds ratios (ORs), 95% bias corrected confidence intervals (CIs), and permutation p-values obtained from bootstrapped logistic regression models adjusted for genetic ancestry. A Bonferroni approach adjusted for multiple testing. Hahsler and Karpienko’s grouped matrix method identified rules with similar characteristics.

Results

122 rules met minimum requirements of 80% confidence and 5% support. 3 rules met the Bonferroni threshold for significance, and all consisted of 3 variants. The top 3 rules were: 1. HLA-DRB1*15:01, SLC30A7-rs56678847 and rs6880909– carriers of these variants had 20.2-fold increased odds of MS (95% CI: (8.5, 37.5); p=4x10-9); 2. HLA-DRB1*15:01, ADCY3-rs11125803, and rs13327021 (OR: 6.8, 95% CI: (3.1, 20.9); p=0.0001); and 3. HLA-DRB1*15:01, rs13327021, and LOC105375752-rs735542 (OR: 4.9, 95% CI: (2.4, 12.0); p=0.0002). Interestingly, several variants were shared across several of the 122 rules. In particular, INTS8-rs78727559 was present in 34% of top rules and TNIP3-rs17051321 was present in 32% of top rules. HLA-DRB1*15:01, rs35486093, and SLC30A7-rs56678847 were present in 21% of top rules.

Conclusions

In summary, we identified strong evidence suggesting specific combinations of MS risk variants confer elevated risk by applying a robust and novel analytical framework to a modestly sized study population. Replication analyses are underway. These results have the potential to significantly inform efforts aimed at developing risk prediction models for MS.

Collapse