A survey of statistical methods for gene-gene interaction in case-control genome-wide association studies

Emily, Mathieu

Journal de la société française de statistique, Tome 159 (2018) no. 1, pp. 27-67.

Ces dernières années ont confirmé l’intérêt des études pangénomiques (GWAS) pour l’identification de régions génomiques associées à des maladies complexes. Néanmoins, les études actuelles reposent sur une stratégie simple-point, dans laquelle chaque marqueur biologique est testé individuellement pour l’association avec la maladie. Cependant, il est largement admis que cette approche est trop simpliste pour s’attaquer à la complexité des mécanismes biologiques sous-jacents et qu’il est important d’inclure l’interaction gène-gène dans l’analyse. Malheureusement, la détection de l’interaction gène-gène soulève des défis statistiques complexes, issus de la grande dimension et de l’architecture complexe des données ainsi que de la taille de l’espace des modèles d’interaction. Le but de cette étude est de fournir un aperçu des nombreuses méthodes statistiques proposées pour détecter une interaction gène-gène dans les GWAS. Ces méthodes ont été développées pour détecter l’interaction à différentes échelles des données et nous décomposons notre étude en trois classes principales : les méthodes d’interaction SNP-SNP, les méthodes d’interaction Gene-Gene et les méthodes à grande échelle. Pour chaque classe de méthodes, nous identifions les forces et les faiblesses en termes de puissance statistique et proposons des pistes de développements dans la modélisation statistique de l’interaction gène-gène.

Over the last few years, case-control genome-wide association studies (GWAS) have proven to be a successful tool to identify genomic regions associated with complex diseases. Nevertheless, current GWAS still heavily rely on a single-marker strategy, in which each biological marker (or SNP for single nucleotide polymorphism) is tested individually for association with the disease. However, it is widely admitted that this is an oversimplified approach to tackle the complexity of underlying biological mechanisms and gene-gene interaction must be considered. Unfortunately, gene-gene interaction detection gives rise to complex statistical challenges, arising from the high-dimensionality and the complex architecture of the data as well as the size of the space of interaction models. The purpose of this survey is to provide a critical overview of the numerous statistical methods proposed to detect gene-gene interaction detection in GWAS. Those methods have been developed to detect interaction at various scales of the data and we decompose our survey in three main classes: SNP-SNP interaction methods, Gene-Gene interaction methods and large-scale methods. For each class of methods, we identify relative strengths and weaknesses in terms of statistical power and provide perspectives to the future of statistical strategies in gene-gene interaction analysis.

Keywords: Gene-gene interaction, Regression models, Machine learning, Information theory, Statistical power
Mot clés : Interaction gène-gène, Modèles de régression, Apprentissage, Théorie de l’information, Puissance statistique

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