Computational Methods Dedicated to Neurological Disorder Detection through Epistasis Analysis: A Review

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Author(s): Sridharan Priya*, Radhakrishnan Manavalana

Journal Name: Current Chinese Engineering Science

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Abstract:

Background: Neurological disorders diseases such as ALS, Alzheimer’s, epilepsy, Parkinson’s Disease, Autism, Atrial Fibrillation, and Sclerosis affect the central nervous system, including the brain, nerves, spinal cords, muscles, and Neuromuscular joint. These disorders are investigated by detecting the genetic variations in Single Nucleotide Polymorphism (SNP) in Genome-Wide Association Studies (GWAS). In the human genome sequence, one SNP influence the effects of another SNP. These SNP-SNP interactions or Gene-Gene interaction (Epistasis) significantly increases the risk of disease susceptibility to neurological disorders.

Objective: The manual analyzes of various genetic interactions related to Neurological diseases are cumbersome. Hence, the computational system is effective for the discovery of Epistasis effects in Neurological syndromes. This study aims to explore various techniques of statistical, machine learning, optimization, so far applied to find the epistasis effect for neurological-disorder.

Conclusion: This study finds several genetic interactions models involving different loci, various candidate genes, and SNP interactions involved in numerous neurological diseases. The gene APOE and its polymorphism increase Alzheimer's disease pathology. The gene GAB2 and its SNPs play a vital role in Alzheimer’s disease. The genes GABRA4, ITGB3, and SLC64A highly influence the genetic interactions for Autism disorder. In schizophrenia, the SNPs of NRG1 increases the disease risk. The benefits, limitations, and issues of the various computational techniques implemented for epistasis evaluation of neurological disease are deeply discussed.

Keywords: Epistasis, GWAS, genes, neurological disorders, genetic interactions, Alzheimer, schizophrenia, bipolar.

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(E-pub Ahead of Print)
DOI: 10.2174/1573411016999201110101039