Lessons from complex trait genetics may help us overcome the neuroimaging replication crisis

Research output: Contribution to journalReview articlepeer-review

Abstract / Description of output

The research fields of Complex Trait (or Statistical) Genetics and Neuroimaging face similar challenges in identifying reliable biological correlates of common traits and diseases. This Viewpoint focuses on five major lessons that allowed population-level genetics research to overcome many of its issues of replicability and may be directly applicable to inter-individual neuroimaging research. First, the failure of candidate gene studies inspires abandoning overly simplistic studies mapping individual brain regions onto traits and diseases. Second, developments in genetics research demonstrate that robust study results can be achieved by increasing sample sizes. Third and fourth, the success of genome-wide association studies motivates the use of mass-univariate testing and sharing summary-level association data to boost large-scale collaboration and meta-analysis. Finally, applying genetics methods dealing with complex data structures to vertex-wise (or voxel-wise) neuroimaging data promises more robust discoveries without the need to develop novel neuroimaging-specific methods. Those practices – that are firmly established in genetics research – should either be further endorsed, or newly adopted by the neuroimaging community, promising to accelerate the evolution of Neuroimaging through robust discovery.
Original languageEnglish
Pages (from-to)76-81
Number of pages6
Early online date12 Aug 2023
Publication statusPublished - Nov 2023

Keywords / Materials (for Non-textual outputs)

  • parallels between research disciplines
  • neuroimaging
  • statistical genetics
  • statistical methods
  • data structures
  • replicability
  • interindividual inference


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