Shouheng Tuo1,2,3, Chao Li4,5,6, Fan Liu4,5,6, YanLing Zhu4,5,6, TianRui Chen4,5,6, ZengYu Feng4,5,6, Haiyan Liu4,5,6, Aimin Li7. 1. School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an, 710121, Shaanxi, China. tuo_sh@126.com. 2. Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi'an, 710121, Shaanxi, China. tuo_sh@126.com. 3. Xi'an Key Laboratory of Big Data and Intelligent Computing, Xi'an, 710121, Shaanxi, China. tuo_sh@126.com. 4. School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an, 710121, Shaanxi, China. 5. Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi'an, 710121, Shaanxi, China. 6. Xi'an Key Laboratory of Big Data and Intelligent Computing, Xi'an, 710121, Shaanxi, China. 7. School of Computer Science and Engineering, Xi'an University of Technology, Xi'an, 710048, Shaanxi, China.
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/.
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/.
Authors: Xiang Wan; Can Yang; Qiang Yang; Hong Xue; Xiaodan Fan; Nelson L S Tang; Weichuan Yu Journal: Am J Hum Genet Date: 2010-09-10 Impact factor: 11.025
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Authors: Peter M Visscher; Naomi R Wray; Qian Zhang; Pamela Sklar; Mark I McCarthy; Matthew A Brown; Jian Yang Journal: Am J Hum Genet Date: 2017-07-06 Impact factor: 11.025