bbc 2015

BeNeLux Bioinformatics Conference – Antwerp, December 7-8 2015
Abstract ID: P
Poster
10th Benelux Bioinformatics Conference bbc 2015
P1. KNN-MDR APPROACH FOR DETECTING GENE-GENE
INTERACTIONS
Sinan Abo alchamlat 1 & Frédéric Farnir 1,* .
Fundamental and Applied Research for Animals & Health (FARAH), Sustainable Animal Production, University of
Liège 1 . * f.farnir@ulg.ac.be
These last years have seen the emergence of a wealth of biological information. Facilitated access to the genome
sequence, along with massive data on genes expression and on proteins have revolutionized the research in many fields
of biology. For example, the identification of up to several millions SNPs in many species and the development of chips
allowing for an effective genotyping of these SNPs in large cohorts have triggered the need for statistical models able to
identify the effects of individual and of interacting SNPs on phenotypic traits in this new high-dimensional landscape.
Our work is a contribution to this field...............................................................................................................
INTRODUCTION
GWAS has allowed the identification of hundreds of
genetic variants associated to complex diseases and traits,
and provided valuable information into their genetic
architecture (Wu M et al., 2010). Nevertheless, most
variants identified so far have been found to confer
relatively small information about the relationship
between changes at the genomic level and phenotypes
because of the lack of reproducibility of the findings, or
because these variants most of the time explain only a
small proportion of the underlying genetic variation (Fang
G et al., 2012). This observation, quoted as the ‘missing
heritability’ problem (Manolio T et al., 2009) of course
raises the question: where does the unexplained genetic
variation come from? A tentative explanation is that genes
do not work in isolation, leading to the idea that sets of
genes (or genes networks) could have a major effect on the
tested traits while almost no marginal – i.e. individual
gene – effect is detectable. Consequently, an important
question concerns the exact relationship between the
genomic configuration, including the interactions between
the involved genes, and the phenotypic expression.
METHODS
To tackle this subject, different statistical methods such as
MDR (Multi Dimensional Reduction) have been proposed
for detecting gene-gene interaction (Ritchie, D., et al.,
2001); their relative performances remain largely unclear,
and their extension to situations combining many variants
turns out to be challenging. So we propose a novel MDR
approach using K-Nearest Neighbors (KNN) methodology
(KNN-MDR) for detecting gene-gene interaction as a
possible alternative, especially when the number of
involved determinants is potentially high. The idea behind
our method is to replace the status allocation used in
classical MDR methods by a KNN approach: the majority
vote occurs in the k (a parameter that must be tuned and
depends on the various possible scenarios) nearest
neighbors instead of within the (potentially empty) cell
determined by the tested attributes of the individual to be
classified. The steps other than classification are identical
in both methods (i.e. cross-validation, attributes selection,
training and tests balanced accuracy computations, best
model selection procedure).
RESULTS & DISCUSSION
Experimental results on both simulated data and real
genome-wide data from Wellcome Trust Case Control
Consortium (WTCCC) (Wellcome Trust Case Control C.,
2007) show that KNN-MDR has interesting properties in
terms of accuracy and power, and that, in many cases, it
significantly outperforms its recent competitors.
FIGURE 1. Comparison of the inter-chromosomal interactions detected
on the WTCCC dataset by KNN-MDR and other interaction methods
using this same dataset as example (Shchetynsky et al. (2015); Zhang et
al. (2012))
The results of this study allow us to draw some
conclusions about the performance of KNN-MDR: on the
one hand, the performance of the KNN-MDR method to
detect gene-gene interactions are similar to the
performance of MDR for small problems. On the other
hand, KNN-MDR has significant advantages in large
samples and large number of markers (such as GWAS) to
detect the existence of genes effect. So KNN-MDR can be
seen as a new and more comprehensive method than MDR
and other competitors for detecting gene-gene interaction.
REFERENCES
Wu M et al. American journal of human genetics 86, 929-942 (2010).
Fang G et al. PloS one 7, 1932-6203 (2012).
Manolio T et al. Nature 461, 747-753 (2009).
Ritchie, D., et al. Am J Hum Genet,69, 138-147 (2001).
Wellcome Trust Case Control C. Nature, 447(7145):661-678 (2007).
Shchetynsky K et al. Clinical immunology 158(1):19-28 (2015).
Zhang J et al. American Medical Journal 3(1) (2015).
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