Preliminary Studies on Biclustering of GWA: A Multiobjective Approach

  • Khedidja Seridi
  • Laetitia JourdanEmail author
  • El-Ghazali Talbi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8752)


Genome-wide association (GWA) studies aim to identify genetic variations (polymorphisms) associated with diseases, and more generally, with traits. Commonly, a Single Nucleotide Polymorphism (SNP) is considered as it is the most common form of genetic variations. In the literature, several statistical and data mining methods have been applied to GWA data analysis. In this article, we present a preliminary study where we examine the possibilities of applying biclustering approaches to detect association between SNP markers and phenotype traits. Therefore, we propose a multiobjective model for biclustering problems in GWA context. Furthermore, we propose an adapted heuristic and metaheuristic to solve it. The performance of our algorithms are assessed using synthetic data sets.


Quantitative Trait Locus Quantitative Trait Locus Analysis Single Nucleotide Polymorphism Marker Data Mining Method Multiobjective Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Khedidja Seridi
    • 1
    • 2
  • Laetitia Jourdan
    • 1
    • 2
    Email author
  • El-Ghazali Talbi
    • 1
    • 2
  1. 1.INRIA Lille - Nord EuropeDOLPHIN Project-TeamVilleneuve d’Ascq CedexFrance
  2. 2.Université Lille 1, LIFL, UMR CNRS 8022Villeneuve d’Ascq CedexFrance

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