Genetic interactions effects for cancer disease identification using computational models: a review

Area of Science:

• Genetics and Genomics
• Computational Biology
• Cancer Research

Background:

• Genome-wide association studies (GWAS) are crucial for understanding genetic and environmental factors in human diseases.
• Identifying genetic interactions (epistasis) is vital for understanding complex diseases like cancer, where multi-locus interactions drive tumor growth.
• Manual analysis of single nucleotide polymorphism (SNP) interactions for cancer detection is impractical for clinicians.

Purpose of the Study:

• To provide a comprehensive review of computational approaches for inferring epistasis effects in cancer research.
• To analyze the strengths, limitations, and evaluation parameters of various computational methods for epistasis detection.
• To guide researchers and medical professionals in selecting and developing robust automatic epistasis detection systems.

Main Methods:

• Review of computational methods published between 2001 and 2020.
• Categorization and analysis of techniques including multifactor dimensionality reduction, statistical strategies, machine learning, and optimization-based methods.
• Evaluation of the performance and applicability of reviewed computational approaches.

Main Results:

• Detailed presentation of various computational methods used for epistasis detection in GWAS.
• Comparative analysis of the strengths and limitations of different approaches.
• Identification of key challenges and evaluation parameters in computational epistasis detection for cancer.

Conclusions:

• Computational methods are essential for identifying complex genetic interactions (epistasis) in cancer.
• The review highlights the need for robust and efficient automatic systems for epistasis detection.
• This work serves as a valuable resource for advancing cancer research through genetic interaction analysis.