HS-MMGKG: A Fast Multi-objective Harmony Search Algorithm for Two-locus Model Detection in GWAS

Abstract

Background:: Genome-Wide Association Study (GWAS) plays a very important role in identifying the causes of a disease. Because most of the existing methods for genetic-interaction detection in GWAS are designed for a single-correlation model, their performances vary considerably for different disease models. These methods usually have high computation cost and low accuracy. Method:: We present a new multi-objective heuristic optimization methodology named HSMMGKG for detecting genetic interactions. In HS-MMGKG, we use harmony search with five objective functions to improve the efficiency and accuracy. A new strategy based on p-value and MDR is adopted to generate more reasonable results. The Boolean representation in BOOST is modified to calculate the five functions rapidly. These strategies take less time complexity and have higher accuracy while detecting the potential models. Results:: We compared HS-MMGKG with CSE, MACOED and FHSA-SED using 26 simulated datasets. The experimental results demonstrate that our method outperforms others in accuracy and computation time. Our method has identified many two-locus SNP combinations that are associated with seven diseases in WTCCC dataset. Some of the SNPs have direct evidence in CTD database. The results may be helpful to further explain the pathogenesis. Conclusion:: It is anticipated that our proposed algorithm could be used in GWAS which is helpful in understanding disease mechanism, diagnosis and prognosis.

SHEIB-AGM: A Novel Stochastic Approach for Detecting High-Order Epistatic Interactions Using Bioinformation With Automatic Gene Matrix in Genome-Wide Association Studies

“…The computation complexity seems to be very high, but most of the operations are Boolean logic operations, so the speed is very fast. The speed improvement of the storage structure and Boolean operation has been proved in other studies [29], [30], [33].…”

“…In this work, we compared SHEIB-AGM with six other methods, DECMDR [22], SNPHarvester [24], MACOED [25], AntEpiSeeker [26], HS-MMGKG [29] and SEE [30], on three simulated datasets. All seven software packages and their parameter settings are shown in Table 1.…”

“…In the second stage, it conducts an exhaustive search of epistatic interactions within the highly suspected SNP combinations and the reduced set of SNPs with top ranking pheromone levels. HS-MMGKG [29] is also a multi-objective heuristic optimization methodology. It uses harmony search with five objective functions.…”

Identification of Causal Genes of COVID-19 Using the SMR Method

“…First, quality control was conducted on 1980 severe COVID-19 patients, and 1,610 patients remained after removing population outliers. Then, the researchers performed a GWAS on 835 Italian patients and 1,255 control group members ( Sun et al, 2019 ), as well as 775 Spanish patients and 950 control group members. As a result, they obtained severe COVID-19 risk data for 8,582,968 SNPs.…”

Identification of Gingivitis-Related Genes Across Human Tissues Based on the Summary Mendelian Randomization

“…The Genome-wide association study (GWAS) is an observational study that detects the single nucleotide polymorphisms (SNP) of multiple individuals of a specific species to find genetic variations associated with a particular trait ( Li et al, 2015 ; Liu and Jiang, 2016 ; Jiang et al, 2017 ; Bush, 2019 ; Cheng et al, 2019b ; Sun et al, 2019 ). In recent years, there have been many studies that have identified several risk genes associated with gingivitis through the GWAS analysis.…”