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


RESULTS
Experimental 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.


CONCLUSIONS
The 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.
Department of Biostatistics, Faculty of Veterinary Medicine, FARAH, University of Liège, Sart Tilman B43, 4000, Liege, Belgium.
Sinan
S
Abo Alchamlat
Department of Biostatistics, Faculty of Veterinary Medicine, FARAH, University of Liège, Sart Tilman B43, 4000, Liege, Belgium. f.farnir@ulg.ac.be.
Frédéric
http://orcid.org/0000-0003-3430-9930
F
Farnir
D004843:Epistasis, Genetic / Q000235:genetics*; D055106:Genome-Wide Association Study / Q000379:methods*; D006801:Humans; D020641:Polymorphism, Single Nucleotide / Q000235:genetics*
Epistasis; Gene-gene interaction; Genome-wide association study; K-nearest neighbors; Multi dimensional reduction; Single nucleotide polymorphism