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  • Genetic basis to structural grey matter associations with chronic pain

    Author(s)
    Farrell, Scott F
    Campos, Adrián I
    Kho, Pik-Fang
    de Zoete, Rutger MJ
    Sterling, Michele
    Rentería, Miguel E
    Ngo, Trung Thanh
    Cuéllar-Partida, Gabriel
    Griffith University Author(s)
    Farrell, Scott F.
    Year published
    2021
    Metadata
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    Abstract
    Structural neuroimaging studies of individuals with chronic pain conditions have often observed decreased regional grey matter at a phenotypic level. However, it is not known if this association can be attributed to genetic factors. Here we employed a novel integrative data-driven and hypothesis-testing approach to determine whether there is a genetic basis to grey matter morphology differences in chronic pain. Using publicly available genome-wide association study summary statistics for regional chronic pain conditions (n = 196 963) and structural neuroimaging measures (n = 19 629-34 000), we applied bivariate linkage ...
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    Structural neuroimaging studies of individuals with chronic pain conditions have often observed decreased regional grey matter at a phenotypic level. However, it is not known if this association can be attributed to genetic factors. Here we employed a novel integrative data-driven and hypothesis-testing approach to determine whether there is a genetic basis to grey matter morphology differences in chronic pain. Using publicly available genome-wide association study summary statistics for regional chronic pain conditions (n = 196 963) and structural neuroimaging measures (n = 19 629-34 000), we applied bivariate linkage disequilibrium-score regression and latent causal variable analyses to determine the genetic correlations (rG) and genetic causal proportion (GCP) between these complex traits, respectively. Five a priori brain regions (i.e. prefrontal cortex, cingulate cortex, insula, thalamus and superior temporal gyrus) were selected based on systematic reviews of grey matter morphology studies in chronic pain. Across this evidence-based selection of five brain regions, 10 significant negative genetic correlations (out of 369) were found (false discovery rate < 5%), suggesting a shared genetic basis to both reduced regional grey matter morphology and the presence of chronic pain. Specifically, negative genetic correlations were observed between reduced insula grey matter morphology and chronic pain in the abdomen (mean insula cortical thickness), hips (left insula volume) and neck/shoulders (left and right insula volume). Similarly, a shared genetic basis was found for reduced posterior cingulate cortex volume in chronic pain of the hip (left and right posterior cingulate), neck/shoulder (left posterior cingulate) and chronic pain at any site (left posterior cingulate); and for reduced pars triangularis volume in chronic neck/shoulder (left pars triangularis) and widespread pain (right pars triangularis). Across these negative genetic correlations, a significant genetic causal proportion was only found between mean insula thickness and chronic abdominal pain [rG (standard error, SE) = -0.25 (0.08), P = 1.06 × 10-3; GCP (SE) = -0.69 (0.20), P = 4.96 × 10-4]. This finding suggests that the genes underlying reduced cortical thickness of the insula causally contribute to an increased risk of chronic abdominal pain. Altogether, these results provide independent corroborating evidence for observational reports of decreased grey matter of particular brain regions in chronic pain. Further, we show for the first time that this association is mediated (in part) by genetic factors. These novel findings warrant further investigation into the neurogenetic pathways that underlie the development and prolongation of chronic pain conditions.
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    Journal Title
    Brain
    DOI
    https://doi.org/10.1093/brain/awab334
    Note
    This publication has been entered as an advanced online version in Griffith Research Online.
    Subject
    Psychology
    Health sciences
    chronic pain
    genome-wide association study
    grey matter
    neuroimaging
    statistical genetics
    Publication URI
    http://hdl.handle.net/10072/411405
    Collection
    • Journal articles

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