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Open Access Highly Accessed Research article

Co-expression network analysis and genetic algorithms for gene prioritization in preeclampsia

Eduardo Tejera1, João Bernardes23 and Irene Rebelo45*

Author Affiliations

1 Institute for Molecular and Cell Biology (IBMC), University of Porto, Porto, Portugal

2 Center for Research in Health Technologies and Information Systems (CINTESIS), Faculty of Medicine, University of Porto, Porto, Portugal

3 Department of Obstetrics and Gynecology, São João Hospital of Porto; INEB — Institute of Biomedical Engineering, Porto, Portugal

4 Laboratory of Biochemistry, Department of Biological Sciences, Faculty of Pharmacy, University of Porto, Porto, Portugal

5 Institute for Molecular and Cell Biology (IBMC), University of Porto, Portugal, Porto, Portugal

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BMC Medical Genomics 2013, 6:51  doi:10.1186/1755-8794-6-51

Published: 12 November 2013

Abstract

Background

In this study, we explored the gene prioritization in preeclampsia, combining co-expression network analysis and genetic algorithms optimization approaches. We analysed five public projects obtaining 1,146 significant genes after cross-platform and processing of 81 and 149 microarrays in preeclamptic and normal conditions, respectively.

Methods

After co-expression network construction, modular and node analysis were performed using several approaches. Moreover, genetic algorithms were also applied in combination with the nearest neighbour and discriminant analysis classification methods.

Results

Significant differences were found in the genes connectivity distribution, both in normal and preeclampsia conditions pointing to the need and importance of examining connectivity alongside expression for prioritization. We discuss the global as well as intra-modular connectivity for hubs detection and also the utility of genetic algorithms in combination with the network information. FLT1, LEP, INHA and ENG genes were identified according to the literature, however, we also found other genes as FLNB, INHBA, NDRG1 and LYN highly significant but underexplored during normal pregnancy or preeclampsia.

Conclusions

Weighted genes co-expression network analysis reveals a similar distribution along the modules detected both in normal and preeclampsia conditions. However, major differences were obtained by analysing the nodes connectivity. All models obtained by genetic algorithm procedures were consistent with a correct classification, higher than 90%, restricting to 30 variables in both classification methods applied.

Combining the two methods we identified well known genes related to preeclampsia, but also lead us to propose new candidates poorly explored or completely unknown in the pathogenesis of preeclampsia, which may have to be validated experimentally.