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A Novel Multitasking Ant Colony Optimization Method for Detecting Multiorder SNP Interactions

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Abstract

Motivation

Linear or nonlinear interactions of multiple single-nucleotide polymorphisms (SNPs) play an important role in understanding the genetic basis of complex human diseases. However, combinatorial analytics in high-dimensional space makes it extremely challenging to detect multiorder SNP interactions. Most classic approaches can only perform one task (for detecting k-order SNP interactions) in each run. Since prior knowledge of a complex disease is usually not available, it is difficult to determine the value of k for detecting k-order SNP interactions.

Methods

A novel multitasking ant colony optimization algorithm (named MTACO-DMSI) is proposed to detect multiorder SNP interactions, and it is divided into two stages: searching and testing. In the searching stage, multiple multiorder SNP interaction detection tasks (from 2nd-order to kth-order) are executed in parallel, and two subpopulations that separately adopt the Bayesian network-based K2-score and Jensen–Shannon divergence (JS-score) as evaluation criteria are generated for each task to improve the global search capability and the discrimination ability for various disease models. In the testing stage, the G test statistical test is adopted to further verify the authenticity of candidate solutions to reduce the error rate.

Result

Three multiorder simulated disease models with different interaction effects and three real age-related macular degeneration (AMD), rheumatoid arthritis (RA) and type 1 diabetes (T1D) datasets were used to investigate the performance of the proposed MTACO-DMSI. The experimental results show that the MTACO-DMSI has a faster search speed and higher discriminatory power for diverse simulation disease models than traditional single-task algorithms. The results on real AMD data and RA and T1D datasets indicate that MTACO-DMSI has the ability to detect multiorder SNP interactions at a genome-wide scale. Availability and implementation: https://github.com/shouhengtuo/MTACO-DMSI/

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Acknowledgements

Shouheng Tuo first proposed the idea of MTACO-DMSI algorithm, guided the writing of the paper, and revised the manuscript in detail. Chao Li proposed some ideas for improving the performance of MTACO-DMSI, did all the experiments, and wrote and revised the paper; Fan Liu, Yanling Zhu, Tianrui Chen, Zhenyu Feng, Haiyan Liu, and Aimin Li also proposed many good ideas for this work. This study utilizes data generated by the Wellcome Trust Case–Control Consortium. A full list of the investigators who contributed to the generation of the data is available from http://www.wtccc.org.uk. Funding for the project was provided by the Wellcome Trust under awards 076113, 085475 and 090355.

Funding

This work was supported in part by the Natural Science Foundation of Shaanxi Provincial Education Department (Grant No. 18JK0165) and Natural Science Foundation of China under Grant No. 62002289.

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Authors and Affiliations

  1. School of Computer Science and Technology, Xi’an University of Posts and Telecommunications, Xi’an, 710121, Shaanxi, China

    Shouheng Tuo, Chao Li, Fan Liu, YanLing Zhu, TianRui Chen, ZengYu Feng & Haiyan Liu

  2. Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi’an, 710121, Shaanxi, China

    Shouheng Tuo, Chao Li, Fan Liu, YanLing Zhu, TianRui Chen, ZengYu Feng & Haiyan Liu

  3. Xi’an Key Laboratory of Big Data and Intelligent Computing, Xi’an, 710121, Shaanxi, China

    Shouheng Tuo, Chao Li, Fan Liu, YanLing Zhu, TianRui Chen, ZengYu Feng & Haiyan Liu

  4. School of Computer Science and Engineering, Xi’an University of Technology, Xi’an, 710048, Shaanxi, China

    Aimin Li

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  1. Shouheng Tuo
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Contributions

A novel multitasking ant colony optimization algorithm is proposed for the detection of multiorder SNP interactions. A new multipopulation mechanism is presented for enhancing the global search ability. Two complementary score functions are employed to improve the discrimination ability of various disease models. An effective knowledge transfer mechanism is designed for sharing interacting SNPs among tasks, which can help the current task rapidly detect multiorder SNP interactions.

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Correspondence to Shouheng Tuo.

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Tuo, S., Li, C., Liu, F. et al. A Novel Multitasking Ant Colony Optimization Method for Detecting Multiorder SNP Interactions. Interdiscip Sci Comput Life Sci 14, 814–832 (2022). https://doi.org/10.1007/s12539-022-00530-2

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