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Challenges in conducting genetic analyses based on data-driven classification of major depressive disorder

Molecular Psychiatry volume 23, page 494 (2018)Cite this article

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Major depressive disorder (MDD) is characterized by the psychiatric symptoms of chronically low mood, low self-esteem and loss of interest or pleasure in life, and additional features such as sleep disturbances and changes in appetite and weight. It is not uncommon for different individuals to have contradicting manifestations on some of these traits, which makes it challenging to find suitable study designs and subclassification strategies for large-scale genetic studies.

Milaneschi and coauthors published a recent article in the Journal1 that used innovative mathematical tools to dissect the associations between genome-wide common polymorphisms and MDD subtypes. This is an appealing strategy since it aims to enrich a specific phenotypic manifestation of MDD in an otherwise unremarkable subgroup, thus greatly enhancing the signal-to-noise ratio. Indeed, genome-wide signals for intermediate traits, which are functionally ‘closer’ to the DNA within a biological system than a clinical phenotype, can reveal novel genetic signals that could not be detected by the aggregate outcome observable in the clinics.2 However, it is not immediately obvious if a data-driven MDD subtype, as defined in the paper by Milaneschi and coauthors, can be used similarly to an intermediate trait in order to reveal new signals.

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References

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Authors and Affiliations

  1. Department of Heart Health, South Australian Health and Medical Research Institute, Adelaide, SA, Australia

    V-P Mäkinen

  2. School of Biological Sciences, Faculty of Sciences, University of Adelaide, Adelaide, SA, Australia

    V-P Mäkinen

  3. Computational Medicine, Faculty of Medicine, University of Oulu and Biocenter Oulu, Oulu, Finland

    V-P Mäkinen

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  1. V-P Mäkinen
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Correspondence to V-P Mäkinen.

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Mäkinen, VP. Challenges in conducting genetic analyses based on data-driven classification of major depressive disorder. Mol Psychiatry 23, 494 (2018). https://doi.org/10.1038/mp.2016.205

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