A parallel genetic algorithm to discover patterns in genetic markers that indicate predisposition to multifactorial disease

Abstract

This paper describes a novel algorithm to analyze genetic linkage data using pattern recognition techniques and genetic algorithms (GA). The method allows a search for regions of the chromosome that may contain genetic variations that jointly predispose individuals for a particular disease. The method uses correlation analysis, filtering theory and genetic algorithms (GA) to achieve this goal. Because current genome scans use from hundreds to hundreds of thousands of markers, two versions of the method have been implemented. The first is an exhaustive analysis version that can be used to visualize, explore, and analyze small genetic data sets for two marker correlations; the second is a GA version, which uses a parallel implementation allowing searches of higher-order correlations in large data sets. Results on simulated data sets indicate that the method can be informative in the identification of major disease loci and gene-gene interactions in genome-wide linkage data and that further exploration of these techniques is justified. The results presented for both variants of the method show that it can help genetic epidemiologists to identify promising combinations of genetic factors that might predispose to complex disorders. In particular, the correlation analysis of IBD expression patterns might hint to possible gene-gene interactions and the filtering might be a fruitful approach to distinguish true correlation signals from noise.

A novel biclustering with parallel genetic algorithm

“…In order to improve the accuracy of optimization, a parallel genetic algorithm [15] is used to solve this problem.…”

Parallel metaheuristics: recent advances and new trends

“…Among the most important ones in recent years, we can mention the following: Automation and robotics, where accurate and efficient learning algorithms to control and use the information technologies are required to reduce the need for human presence. Here, parallel metaheuristics provide a decisive help to tackle learning problems that handle large volume of data (Bouamama, ), and those control problems that involve complex training procedures (Hereford, , ; Huang et al., ). Bioinformatics, an emergent scientific field where parallel models of metaheuristics are helpful tools to cope with computationally expensive optimization problems in molecular biology that often also need to manage very large amount of data, such as sequence alignment (Gomes et al., ; Zola et al., ), DNA sequencing (Hongwei and Yanhua, ; Wirawan et al., ), gene finding (Rausch et al., ), genome assembly (Alba and Luque, ; Nebro et al., ), drug design (Boisson et al., ), protein structure alignment/prediction (Chu and Zomaya, ; Guo et al., ; Islam and Ngom, ; Tantar et al., ), phylogenetic inference (Blagojevic et al., ; Cancino et al., ; Grouchy et al., ), and other related problems (Guarracino et al., ; Martins et al., ; Nebro et al., ). Engineering design, where systems have many components, a large design space, and they usually involve functions with huge computation demands. These characteristics make parallel metaheuristics one of the most promising alternatives to get accurate solutions in reasonable execution times for complex tasks such as aerodynamic optimization and airfoil design (Asouti and Giannakoglou, ; Lim et al., ), design optimization of turbomachinery blade rows (Sasaki et al., ), electronic circuit and VLSI design (Alba et al., ; Lau et al., ; Sait et al., , ), antenna design (Kalinli et al., ; Weis and Lewis, ), signal processing (Li et al., ), etc. Hydraulic engineering, where parallel metaheuristics have been used to efficiently deal with real‐world scenarios arising in water supply network design optimization (López‐Ibánez, ), groundwater source identification (Babbar and Minsker, ; Mirghania et al., ; Sinha and Minsker, ), and multiobjective groundwater problems (Tang et al., ). Information processing, classification, and data mining, where parallel metaheuristics significantly he...…”

Biclustering Gene Expression Data by an Improved Optimal Algorithm

“…The common drawback of the current GA, PSO and SA optimization algorithm is that local convergence is reached easily and the runtime of optimal algorithm is too long. In order to improve the accuracy of optimization and reduce the runtime, a parallel genetic algorithm [5] and a PSO algorithm mixed to a novel algorithm which is used to solve these two problems. First of all, traditional genetic algorithm is to simulate Darwinian evolution: survive of the fittest, select the best.…”