Genome-wide Association Studies (GWAS) give special insight into genetic differences
and environmental influences that are part of different human disorders and provide prognostic
help to increase the survival of patients. Lung diseases such as lung cancer, asthma, and tuberculosis
are detected by analyzing Single Nucleotide Polymorphism (SNP) genetic variations. The key
causes of lung-related diseases are genetic factors, environmental and social behaviors.
The epistasis effects act as a blueprint for the researchers to observe the genetic variation associated
with lung diseases. The manual examination of the enormous genetic interactions is complicated
to detect the lung syndromes for diagnosis of acute respiratory diseases. Due to its importance,
several computational approaches have been modeled to infer epistasis effects. This article includes
a comprehensive and multifaceted review of all the relevant genetic studies published between
2006 and 2020. In this critical review, various computational approaches are extensively discussed
in detecting respondent epistasis effects for various lung diseases such as asthma, tuberculosis,
lung cancer, and nicotine drug dependence.
The analysis shows that different computational models identified candidate genes such as
CHRNA4, CHRNB2, BDNF, TAS2R16, TAS2R38, BRCA1, BRCA2, RAD21, IL4Ra, IL-13 and
IL-1β, have important causes for genetic variants linked to pulmonary disease. These computational
approaches' strengths and limitations are described. The issues behind the computational methods
while identifying the lung diseases through epistasis effects and the parameters used by various
researchers for their evaluation are also presented.