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Genes and Gene Networks Implicated in Aggression Related Behaviour

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Abstract

Aggressive behaviour is a major cause of mortality and morbidity. Despite of moderate heritability estimates, progress in identifying the genetic factors underlying aggressive behaviour has been limited. There are currently three genetic mouse models of high and low aggression created using selective breeding. This is the first study to offer a global transcriptomic characterization of the prefrontal cortex across all three genetic mouse models of aggression. A systems biology approach has been applied to transcriptomic data across the three pairs of selected inbred mouse strains (Turku Aggressive (TA) and Turku Non-Aggressive (TNA), Short Attack Latency (SAL) and Long Attack Latency (LAL) mice and North Carolina Aggressive (NC900) and North Carolina Non-Aggressive (NC100)), providing novel insight into the neurobiological mechanisms and genetics underlying aggression. First, weighted gene co-expression network analysis (WGCNA) was performed to identify modules of highly correlated genes associated with aggression. Probe sets belonging to gene modules uncovered by WGCNA were carried forward for network analysis using ingenuity pathway analysis (IPA). The RankProd non-parametric algorithm was then used to statistically evaluate expression differences across the genes belonging to modules significantly associated with aggression. IPA uncovered two pathways, involving NF-kB and MAPKs. The secondary RankProd analysis yielded 14 differentially expressed genes, some of which have previously been implicated in pathways associated with aggressive behaviour, such as Adrbk2. The results highlighted plausible candidate genes and gene networks implicated in aggression-related behaviour.

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Acknowledgments

Karim Malki is supported by an MRC grant (G9817803). At the time of writing, Oliver Pain and Ebba Du Rietze were enrolled on the GED programme at the SGDP centre at the Institute of Psychiatry, King′s College London. We are grateful to the Director of the Programme, Dr. Cathy Fernandez for her support with this project.

Conflict of interest

The authors declare no conflict of interest.

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Authors and Affiliations

  1. King′s College London, MRC Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, London, UK

    Karim Malki, Oliver Pain, Ebba Du Rietz, Jose Paya-Cano & Leonard C. Schalkwyk

  2. Department of Psychology, University of York, York, UK

    Maria Grazia Tosto

  3. Laboratory for Cognitive Investigations and Behavioural Genetics, Tomsk State University, Tomsk, Russia

    Maria Grazia Tosto

  4. Department of Psychology and Logopedics, Åbo Akademi University, Turku, Finland

    Kenneth N. Sandnabba

  5. Faculty of Mathematics and Natural Sciences, Groningen, The Netherlands

    Sietse de Boer

  6. Department of Psychology, University of Portsmouth, Portsmouth, UK

    Frans Sluyter

  7. School of Biological Sciences, University of Essex, Colchester, UK

    Leonard C. Schalkwyk

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  1. Karim Malki
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  2. Oliver Pain
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Correspondence to Karim Malki.

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Karim Malki and Oliver Pain has contributed equally to this work

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Supplementary Figure 1

Flash clustered factors to identify any clear outliers. Red line shows height cut off (T = 43) marking outlying factors. (PDF 93 kb)

Supplementary Figure 2

Analysis of network topology for various soft-thresholding powers. Panel A shows the scale-free sit index (y-axis) as a function of the soft-thresholding power (x-axis). Panel B shows the mean connectivity (degree, y-axis) as a function of the soft thresholding power (x-axis). Shows the scale-free topology fit index curve flattens at approximately 0.9, determining a soft threshold of 10. (PDF 131 kb)

Supplementary Figure 3

Screen plot showing the percentage of variance explained by the principle components identified in the data. (PDF 108 kb)

Supplementary Figure 4

PCA was unable to identify any structure in the data representing gene expression differences based on aggression state. This is expected, as more predominant global-transcriptomic changes, dependent on aggression state, would have to occur for it to be detectable via PCA. However no outliers were detected. (PDF 99 kb)

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Malki, K., Pain, O., Du Rietz, E. et al. Genes and Gene Networks Implicated in Aggression Related Behaviour. Neurogenetics 15, 255–266 (2014). https://doi.org/10.1007/s10048-014-0417-x

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