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Entropy
Volume 22
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10.3390/e22030329
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Article

A Two-Stage Mutual Information Based Bayesian Lasso Algorithm for Multi-Locus Genome-Wide Association Studies

by
Hongping Guo
1,2,
Zuguo Yu
1,3,*,
Jiyuan An
4,
Guosheng Han
1,
Yuanlin Ma
1 and
Runbin Tang
1
1
Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education and Hunan Key Laboratory for Computation and Simulation in Science and Engineering, Xiangtan University, Xiangtan 411105, China
2
School of Mathematics and Computer Science, Hanjiang Normal University, Shiyan 442000, China
3
School of Electrical Engineering and Computer Science, Queensland University of Technology, Brisbane, QLD 4001, Australia
4
Centre for Tropical Crops and Biocommodities, Queensland University of Technology, Brisbane, QLD 4001, Australia
*
Author to whom correspondence should be addressed.
Entropy 2020, 22(3), 329; https://doi.org/10.3390/e22030329
Submission received: 1 February 2020 / Revised: 28 February 2020 / Accepted: 10 March 2020 / Published: 13 March 2020
(This article belongs to the Special Issue Statistical Inference from High Dimensional Data)
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Abstract

Genome-wide association study (GWAS) has turned out to be an essential technology for exploring the genetic mechanism of complex traits. To reduce the complexity of computation, it is well accepted to remove unrelated single nucleotide polymorphisms (SNPs) before GWAS, e.g., by using iterative sure independence screening expectation-maximization Bayesian Lasso (ISIS EM-BLASSO) method. In this work, a modified version of ISIS EM-BLASSO is proposed, which reduces the number of SNPs by a screening methodology based on Pearson correlation and mutual information, then estimates the effects via EM-Bayesian Lasso (EM-BLASSO), and finally detects the true quantitative trait nucleotides (QTNs) through likelihood ratio test. We call our method a two-stage mutual information based Bayesian Lasso (MBLASSO). Under three simulation scenarios, MBLASSO improves the statistical power and retains the higher effect estimation accuracy when comparing with three other algorithms. Moreover, MBLASSO performs best on model fitting, the accuracy of detected associations is the highest, and 21 genes can only be detected by MBLASSO in Arabidopsis thaliana datasets.
Keywords: GWAS; Pearson correlation; mutual information; feature screening; Bayesian Lasso GWAS; Pearson correlation; mutual information; feature screening; Bayesian Lasso

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MDPI and ACS Style

Guo, H.; Yu, Z.; An, J.; Han, G.; Ma, Y.; Tang, R. A Two-Stage Mutual Information Based Bayesian Lasso Algorithm for Multi-Locus Genome-Wide Association Studies. Entropy 2020, 22, 329. https://doi.org/10.3390/e22030329

AMA Style

Guo H, Yu Z, An J, Han G, Ma Y, Tang R. A Two-Stage Mutual Information Based Bayesian Lasso Algorithm for Multi-Locus Genome-Wide Association Studies. Entropy. 2020; 22(3):329. https://doi.org/10.3390/e22030329

Chicago/Turabian Style

Guo, Hongping, Zuguo Yu, Jiyuan An, Guosheng Han, Yuanlin Ma, and Runbin Tang. 2020. "A Two-Stage Mutual Information Based Bayesian Lasso Algorithm for Multi-Locus Genome-Wide Association Studies" Entropy 22, no. 3: 329. https://doi.org/10.3390/e22030329

APA Style

Guo, H., Yu, Z., An, J., Han, G., Ma, Y., & Tang, R. (2020). A Two-Stage Mutual Information Based Bayesian Lasso Algorithm for Multi-Locus Genome-Wide Association Studies. Entropy, 22(3), 329. https://doi.org/10.3390/e22030329

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