Selecting Genetic Variants and Interactions Associated with Amyotrophic Lateral Sclerosis: A Group LASSO Approach

Author(s)

Feronato, Sofia Galvao ; Matos Silva, Maria Luiza ; Izbicki, Rafael ; Farias, Ticiana D. J. ; Shigunov, Patricia ; Dallagiovanna, Bruno ; Passetti, Fabio ; Dos Santos, Hellen Geremias

• Document type: Journal article
• Source: JOURNAL OF PERSONALIZED MEDICINE; v. 12, n. 8, p. 18-pg., 2022-08-01.

Abstract

Amyotrophic lateral sclerosis (ALS) is a multi-system neurodegenerative disease that affects both upper and lower motor neurons, resulting from a combination of genetic, environmental, and lifestyle factors. Usually, the association between single-nucleotide polymorphisms (SNPs) and this disease is tested individually, which leads to the testing of multiple hypotheses. In addition, this classical approach does not support the detection of interaction-dependent SNPs. We applied a two-step procedure to select SNPs and pairwise interactions associated with ALS. SNP data from 276 ALS patients and 268 controls were analyzed by a two-step group LASSO in 2000 iterations. In the first step, we fitted a group LASSO model to a bootstrap sample and a random subset of predictors (25%) from the original data set aiming to screen for important SNPs and, in the second step, we fitted a hierarchical group LASSO model to evaluate pairwise interactions. An in silico analysis was performed on a set of variables, which were prioritized according to their bootstrap selection frequency. We identified seven SNPs (rs16984239, rs10459680, rs1436918, rs1037666, rs4552942, rs10773543, and rs2241493) and two pairwise interactions (rs16984239:rs2118657 and rs16984239:rs3172469) potentially involved in nervous system conservation and function. These results may contribute to the understanding of ALS pathogenesis, its diagnosis, and therapeutic strategy improvement. (AU)

• FAPESP's process: 19/11321-9 - Neural networks in statistical inference problems
• Grantee: Rafael Izbicki
• Support Opportunities: Regular Research Grants