Detecting Genetic Interactions in Pathway-Based Genome-Wide Association Studies

Abstract Genetic interactions have been reported to underlie phenotypes in a variety of systems, but the extent to which they contribute to complex disease in humans remains unclear. In principle, genome-wide association studies (GWAS) provide a platform for detecting genetic interactions, but existing methods for identifying them from GWAS data tend to focus on testing individual locus pairs, which undermines statistical power. Importantly, a global genetic network mapped for a model eukaryotic organism revealed that genetic interactions often connect genes between compensatory functional modules in a highly coherent manner. Taking advantage of this expected structure, we developed a computational approach called BridGE that identifies pathways connected by genetic interactions from GWAS data. Applying BridGE broadly, we discover significant interactions in Parkinson’s disease, schizophrenia, hypertension, prostate cancer, breast cancer, and type 2 diabetes. Our novel approach provides a general framework for mapping complex genetic networks underlying human disease from genome-wide genotype data.

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KMeans-NM-SalpEpi: Genetic Interactions Detection through K-Means Clustering with Nelder-Mead and Salp Optimization Techniques in Genome-Wide Association Studies

Artificial Intelligence Evolution ◽

10.37256/aie.2220211099 ◽

2021 ◽

pp. 67-80

Author(s):

S. Priya ◽

R. Manavalan

Keyword(s):

Association Studies ◽

Optimization Techniques ◽

Genetic Interactions ◽

Human Diseases ◽

Genome Wide Association ◽

Local Optimum ◽

Genome Wide Association Studies ◽

Human Beings ◽

Objective Functions ◽

Genome Wide

Complex diseases identification through Gene-Gene Interactions (GGIs) plays a significant challenge in Genome-Wide Association Studies (GWAS). A typical indicator of genetic variations in many human diseases is Single Nucleotide Polymorphisms (SNPs). SNPs are the most prevalent sort of genetic variation seen in human beings. The interactions between various SNPs are called Epistasis or genetic interactions. This research paper proposes a two-stage epistasis detection approach based on K-Means clustering and optimization techniques to detect epistasis effects responsible for complex human diseases. In the screening stage, K-Means clustering is adapted to partition the genotype dataset into various clusters. Traditional K-Means clustering algorithms have the flaw of arbitrary selection of the initial k centroid, which leads to inconsistent solutions and traps in the local optimum. We present a hybridized technique based on the K-Means algorithm and Nelder-Mead (NM) optimization (KMeans-NM) to avoid local optima, and all the genotype data falls into a unique collection of clusters for different runs. In the search stage, Salp Optimization with single objective functions (Salp-SO) and Salp Optimization with multi-objective functions (Salp-MO) are employed over the clusters obtained from the screening stage to find disease correlated SNP combinations. The performance of the various proposed algorithms is tested over the simulated datasets. Experimental findings indicated that the KMeans-NM-SalpEpi-SO and KMeans-NM-SalpEpi-MO method is superior to other techniques.

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Discovering genetic interactions bridging pathways in genome-wide association studies

10.1101/182741 ◽

2017 ◽

Cited By ~ 1

Author(s):

Gang Fang ◽

Wen Wang ◽

Vanja Paunic ◽

Hamed Heydari ◽

Michael Costanzo ◽

...

Keyword(s):

Statistical Power ◽

Complex Disease ◽

Association Studies ◽

Genetic Networks ◽

Genetic Interactions ◽

Gwas Data ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Cancer Breast ◽

Genome Wide

AbstractGenetic interactions have been reported to underlie phenotypes in a variety of systems, but the extent to which they contribute to complex disease in humans remains unclear. In principle, genome-wide association studies (GWAS) provide a platform for detecting genetic interactions, but existing methods for identifying them from GWAS data tend to focus on testing individual locus pairs, which undermines statistical power. Importantly, the global genetic networks mapped for a model eukaryotic organism revealed that genetic interactions often connect genes between compensatory functional modules in a highly coherent manner. Taking advantage of this expected structure, we developed a computational approach called BridGE that identifies pathways connected by genetic interactions from GWAS data. Applying BridGE broadly, we discovered significant interactions in Parkinson’s disease, schizophrenia, hypertension, prostate cancer, breast cancer, and type 2 diabetes. Our novel approach provides a general framework for mapping complex genetic networks underlying human disease from genome-wide genotype data.

Download Full-text