Detecting Epistasis by LASSO-Penalized-Model Search Algorithm in Human Genome-Wide Association Studies

Abstract:

Extensive studies have shown that many complex diseases are influenced by interaction of certain genes, while due to the limitations and drawbacks of adopting logistic regression (LR) to detect epistasis in human Genome-Wide Association Studies (GWAS), we propose a new method named LASSO-penalized-model search algorithm (LPMA) by restricting it to a tuning constant and combining it with a penalization of the L₁-norm of the complexity parameter, and it is implemented utilizing the idea of multi-step strategy. LASSO penalized regression particularly shows advantageous properties when the number of factors far exceeds the number of samples. We compare the performance of LPMA with its competitors. Through simulated data experiments, LPMA performs better regarding to the identification of epistasis and prediction accuracy.

• Periodical: Advanced Materials Research (Volumes 989-994)
• Pages: 2426-2430
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.989-994.2426
• Online since: July 2014
• Authors: Zhi Hui Zhou, Gui Xia Liu*, Ling Tao Su, Liang Han, Lun Yan
• Keywords: Epistasis, GWAS, LASSO-Penalized, Logistic Regression

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