KNN-MDR: a learning approach for improving interactions mapping performances in genome wide association studies

BMC Bioinformatics

Methodology Article

Frédéric Farnir¹  Sinan Abo Alchamlat¹ 

[1] Department of Biostatistics, Faculty of Veterinary Medicine, FARAH, University of Liège, Sart Tilman B43, 4000, Liege, Belgium;

关键词: Gene-gene interaction;    Epistasis;    Single nucleotide polymorphism;    Genome-wide association study;    Multi dimensional reduction;    K-nearest neighbors;   

DOI  :  10.1186/s12859-017-1599-7

 received in 2016-04-06, accepted in 2017-03-11,  发布年份 2017

来源: Springer

【 摘 要 】

BackgroundFinding epistatic interactions in large association studies like genome-wide association studies (GWAS) with the nowadays-available large volume of genomic data is a challenging and largely unsolved issue. Few previous studies could handle genome-wide data due to the intractable difficulties met in searching a combinatorial explosive search space and statistically evaluating epistatic interactions given a limited number of samples. Our work is a contribution to this field. We propose a novel approach combining K-Nearest Neighbors (KNN) and Multi Dimensional Reduction (MDR) methods for detecting gene-gene interactions as a possible alternative to existing algorithms, e especially in situations where the number of involved determinants is high. After describing the approach, a comparison of our method (KNN-MDR) to a set of the other most performing methods (i.e., MDR, BOOST, BHIT, MegaSNPHunter and AntEpiSeeker) is carried on to detect interactions using simulated data as well as real genome-wide data.ResultsExperimental results on both simulated data and real genome-wide data show that KNN-MDR has interesting properties in terms of accuracy and power, and that, in many cases, it significantly outperforms its recent competitors.ConclusionsThe presented methodology (KNN-MDR) is valuable in the context of loci and interactions mapping and can be seen as an interesting addition to the arsenal used in complex traits analyses.

【 授权许可】

CC BY   
© The Author(s). 2017