A new approach to detect epistasis utilizing parallel implementation of ant colony optimization by MapReduce framework

Zhou, Zongtan; Liu, Guixia; Su, Lingtao

doi:10.1080/00207160.2014.1000882

Cited by 10 publications

(14 citation statements)

References 29 publications

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“…Previous study showed that GWAS involve the detection and interpretation of epistasis. However, many epistasis detection algorithms cannot be directly applied into GWAS as many combinations of genetic components are present in only a small amount of samples or even none at all (Zhou et al, 2016). Linear mixed models (LMMs) are widely used in GWAS to account for population structure and relatedness, for both continuous and binary traits.…”

Section: Discussionmentioning

confidence: 99%

Genome-wide association study of bone mineral density trait among three pig breeds

Jiang

Wang

Tang

et al. 2020

Animal

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Leg weakness (LW) issues are a great concern for pig breeding industry. And it also has a serious impact on animal welfare. To dissect the genetic architecture of limb-and-hoof firmness in commercial pigs, a genome-wide association study was conducted on bone mineral density (BMD) in three sow populations, including Duroc, Landrace and Yorkshire. The BMD data were obtained by ultrasound technology from 812 pigs (including Duroc 115, Landrace 243 and Yorkshire 454). In addition, all pigs were genotyped using genome-by-sequencing and a total of 224 162 single-nucleotide polymorphisms (SNPs) were obtained. After quality control, 218 141 SNPs were used for subsequent genome-wide association analysis. Nine significant associations were identified on chromosomes 3, 5, 6, 7, 9, 10, 12 and 18 that passed Bonferroni correction threshold of 0.05/(total SNP numbers). The most significant locus that associated with BMD (P value = 1.92e−14) was detected at approximately 41.7 Mb on SSC6 (SSC stands for Sus scrofa chromosome). CUL7, PTK7, SRF, VEGFA, RHEB, PRKAR1A and TPO that are located near the lead SNP of significant loci were highlighted as functionally plausible candidate genes for sow limb-and-hoof firmness. Moreover, we also applied a new method to measure the BMD data of pigs by ultrasound technology. The results provide an insight into the genetic architecture of LW and can also help to improve animal welfare in pigs.

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Section: Discussionmentioning

confidence: 99%

Genome-wide association study of bone mineral density trait among three pig breeds

Jiang

Wang

Tang

et al. 2020

Animal

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“…These two probability functions and the random selection mechanism increases the diversity of the search, and also has been adopted by other ACO methods, including epiACO(Z) [24], MACOED [25], [26], IACO [27], and epiACO(S) [28]. Zhou et al [29] developed a modified ACO method for detecting epistatic interactions, and implemented it on a hadoop cluster utilizing Google's MapReduce framework. Similarly, they provided another probability function to greedily find the best SNP, which was defined as…”

Section: A Path Selection Strategies 1) Probability Functionsmentioning

confidence: 99%

“…Though requiring more computational costs, the TuRF improves its performance when the data contain a large number of noise SNPs by iterating the ReliefF and deleting SNPs with the lowest ReliefF scores at each iteration. For the method proposed by Zhou et al [29], its heuristic information, they called it distance, was got by the following formula,…”

Section: ) Heuristic Informationmentioning

confidence: 99%

“…For most of discussed methods here, pheromones are normally deposited on SNPs, except the methods [25], [26], [29], [30], [33], which deposit pheromones on connections between SNPs. Specifically, pheromones are stored as a square matrix, whose dimensionality is equal to the SNP number N , to reflect association strengths between two-SNP combinations and the phenotype.…”

Section: B Pheromone Updating Rules 1) Pheromone Depositionmentioning

confidence: 99%

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A Review of Ant Colony Optimization Based Methods for Detecting Epistatic Interactions

Shang

Wang

et al. 2019

IEEE Access

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Detection of epistatic interactions, which are referred to as nonlinear interactive effects of single nucleotide polymorphisms (SNPs), is increasingly being recognized as an important route in capturing the underlying genetic causes of complex diseases. Its methodological and computational challenges have been well understood, and many methods also have been proposed from different perspectives. Among them ant colony optimization (ACO)-based methods are promising due to their controllable time complexities, heuristic positive feedback search, and high detection power. Nevertheless, there is no comprehensive overview of them so far. This paper, therefore, provides a systematic review of 25 ACO-based epistasis detection methods. First, the generic ACO algorithm, as well as how it is applied to detect epistatic interactions, is briefly described. Then, an in-depth review of ACO-based methods for detecting epistatic interactions is discussed from four aspects, including path selection strategies, pheromone updating rules, fitness functions, and two-stage designs. Finally, this paper analyzes the strengths and limitations of involved methods, provides guidelines for applying them, and gives several views on the future directions of epistasis detection methods. INDEX TERMS Ant colony optimization (ACO), epistatic interactions, single nucleotide polymorphisms (SNPs), heuristic information, genome-wide association studies (GWAS).

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“…Face with the current situation of domestic traffic, in order to find a reasonable driving route, is undoubtedly need processing for a huge data transportation. Currently, Developed by the Apache Software Foundation and the open source Hadoop distributed system architecture is very powerful platform for dealing with big date, its main core is a distributed file system (HDFS) to achieve big data storage and computing a MapReduce model [1,8] for parallel computing of massive data.…”

Section: Introductionmentioning

confidence: 99%

A hybrid ant colony optimization algorithm based on MapReduce

Cai¹,

Zuo²

2016

Proceedings of the 2016 3rd International Conference on Materials Engineering, Manufacturing Technology and Control

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Abstract. The traditional ant colony optimization (ACO) had defects in long searching time and convergence in local optima solution. To solve the problem, a hybrid ant colony optimization algorithm (HACO) was proposed in this paper. Two modes in HACO are defined that are DefaultModel and EliteModel. In order to search the optimum result, the two modes will be automatically switched in this algorithm. It also use the parallel calculation model of MapReduce to loop iteration part of ACO and deploy it on the Hadoop cloud computing platform. Finally, simulation results validate the proposed approach on the traveling salesman problem.

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A new approach to detect epistasis utilizing parallel implementation of ant colony optimization by MapReduce framework

Cited by 10 publications

References 29 publications

Genome-wide association study of bone mineral density trait among three pig breeds

Genome-wide association study of bone mineral density trait among three pig breeds

A Review of Ant Colony Optimization Based Methods for Detecting Epistatic Interactions

A hybrid ant colony optimization algorithm based on MapReduce

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