An adaptive threshold determination method of feature screening for genomic selection

BMC Bioinformatics

Methodology Article

Gang Wang¹  Xiaotian Dai¹  Guifang Fu¹ 

[1] Department of Mathematics and Statistics, Utah State University, 84322, Logan, UT, USA;

关键词: Genomic selection;    Feature screening;    Backward elimination;    FRIGIDA;   

DOI  :  10.1186/s12859-017-1617-9

 received in 2016-11-09, accepted in 2017-03-28,  发布年份 2017

来源: Springer

【 摘 要 】

BackgroundAlthough the dimension of the entire genome can be extremely large, only a parsimonious set of influential SNPs are correlated with a particular complex trait and are important to the prediction of the trait. Efficiently and accurately selecting these influential SNPs from millions of candidates is in high demand, but poses challenges. We propose a backward elimination iterative distance correlation (BE-IDC) procedure to select the smallest subset of SNPs that guarantees sufficient prediction accuracy, while also solving the unclear threshold issue for traditional feature screening approaches.ResultsVerified through six simulations, the adaptive threshold estimated by the BE-IDC performed uniformly better than fixed threshold methods that have been used in the current literature. We also applied BE-IDC to an Arabidopsis thaliana genome-wide data. Out of 216,130 SNPs, BE-IDC selected four influential SNPs, and confirmed the same FRIGIDA gene that was reported by two other traditional methods.ConclusionsBE-IDC accommodates both the prediction accuracy and the computational speed that are highly demanded in the genomic selection.

【 授权许可】

CC BY   
© The Author(s) 2017

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