Rheumatoid arthritis identification using epistasis analysis through computational models

Abstract

Rheumatoid arthritis (RA) is an autoimmune disorder that damages joints irreversibly. Many RA illnesses were also related to complex genetic characteristics and genetic interactions as well. Genome-wide association studies (GWASs) analyzing the fundamental RA-related genetic factors over the past two decades. Nonlinear interaction recognition, also known as epistasis identification, plays a crucial part in identifying RA's genetic causes. GWAS recognizes all single nucleotide polymorphisms (SNPs) genetic variants and the interactions between SNPs to identify RA susceptibility. Manual evaluation and interactions of many SNPs were too complicated for physicians. The main objective of this study is to explore various techniques of statistical, machine learning, optimization, so far applied to identify epistasis effect related to arthritis. The challenges behind the computational model and the experimental outcome of various methods were also focused.

Keywords

• epistasis
• gene
• genetic variations
• genome-wide association studies
• interactions
• rheumatoid arthritis
• single nucleotide polymorphisms