HSIC CR: A Lightweight Scoring Criterion Based on Measuring the Degree of Causality for the Detection of SNP Interactions

Junxi Zheng, Juan Zeng, Xinyang Wang (), Gang Li, Jiaxian Zhu, Fanghong Wang and Deyu Tang ()
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Junxi Zheng: School of Medical Information Engineering, Guangdong Pharmaceutical University, Guangzhou 510006, China
Juan Zeng: School of Medical Information Engineering, Guangdong Pharmaceutical University, Guangzhou 510006, China
Xinyang Wang: School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China
Gang Li: School of Information Technology Engineering, Guangzhou College of Commerce, Guangzhou 511363, China
Jiaxian Zhu: School of Computer Science, Zhaoqing University, Zhaoqing 526061, China
Fanghong Wang: School of Bussiness, Zhijiang College of Zhengjiang University of Technology, Shaoxin 310024, China
Deyu Tang: School of Medical Information Engineering, Guangdong Pharmaceutical University, Guangzhou 510006, China

Mathematics, 2022, vol. 10, issue 21, 1-17

Abstract: Recently, research on detecting SNP interactions has attracted considerable attention, which is of great significance for exploring complex diseases. The formulation of effective swarm intelligence optimization algorithms is a primary resolution to this issue. To achieve this goal, an important problem needs to be solved in advance; that is, designing and selecting lightweight scoring criteria that can be calculated in O ( m ) time and can accurately estimate the degree of association between SNP combinations and disease status. In this study, we propose a high-accuracy scoring criterion (HSIC C R ) by measuring the degree of causality dedicated to assessing the degree. First, we approximate two kinds of dependencies according to the structural equation of the causal relationship between epistasis SNP combination and disease status. Then, inspired by these dependencies, we put forward this scoring criterion that integrates a widely used method of measuring statistical dependencies based on kernel functions (HSIC). However, the computing time complexity of HSIC is O ( m 2 ) , which is too costly to be an integral part of the scoring criterion. Since the sizes of the sample space of the disease status, SNP loci and SNP combination are small enough, we propose an efficient method of computing HSIC for variables with a small sample in O ( m ) time. Eventually, HSIC C R can be computed in O ( m ) time in practice. Finally, we compared HSIC C R with five representative high-accuracy scoring criteria that detect SNP interactions for 49 simulation disease models. The experimental results show that the accuracy of our proposed scoring criterion is, overall, state-of-the-art.

Keywords: lightweight scoring criterion; causality; SNP interactions; measuring statistical dependencies (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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