Abstract
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.
Highlights
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
Simulation In order to assess the performances of the proposed method, we have simulated various situations and ran Multi Dimensional Reduction (MDR), Boolean operation-based screening (BOOST), MegaSNPHunter, AntEpiSeeker and K-Nearest Neighbors (KNN)-MDR on the same datasets to compare the performances in terms of detection power and accuracy
We have simulated situations where no Single-nucleotide polymorphism (SNP) was involved in the generation of the phenotypes, so that SNP detection by the algorithms would correspond to false positives
Summary
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. Technical improvements in genotyping and sequencing technologies have facilitated the access to the genome sequence and to massive data on genes expression and on proteins This large availability of molecular information has revolutionized the research in many fields of biology. Most variants identified so far have been found to confer relatively small information about the relationship between changes at the genomic locations and phenotypes because of the lack of reproducibility of many of these findings, or because the identified variants most of the time explain only a small proportion of the underlying genetic variation [3] This observation, quoted as the ‘missing heritability’ problem [4] raises the following question: where does the unexplained genetic variation come from? Note that this gene network hypothesis is a potentially credible explanation to the lack of reproducibility of obtained positive results [6], due to situations where different mutations or mutations combinations within the network (within the same genes or on different genes in the networks) could lead to similar phenotypic effects [7]
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