Genetic heterogeneity shapes brain connectivity in psychiatry

Clara Moreau (1, 2, 3) , Annabelle Harvey (2, 3) , Kuldeep Kumar (2) , Guillaume Huguet (2) , Sebastian Urchs (3, 4) , Elise Douard (2) , Laura Schultz (5) , Hanad Sharmarke (3) , Khadije Jizi (2) , Charles-Olivier Martin (2) , Nadine Younis (2) , Petra Tamer (2) , Thomas Rolland (1) , Jean-Louis Martineau , Pierre Orban (6, 7) , Ana Isabel Silva , Jeremy Hall (8) , Marianne B.M. van den Bree (8) , Michael Owen (8) , David E.J. Linden (8, 9) , Aurelie Labbe (10) , Sarah Lippé , Carrie Bearden (11) , Laura Almasy (12) , David Glahn (13, 14) , Paul Thompson , Thomas Bourgeron (1) , Pierre Bellec (3) , Sebastien Jacquemont (2)

1 GHFC (UMR_3571 / U-Pasteur_1) - Génétique humaine et fonctions cognitives - Human Genetics and Cognitive Functions
2 Centre de recherche du CHU Sainte-Justine / Research Center of the Sainte-Justine University Hospital [Montreal, Canada]
3 CRIUGM - Centre de recherche de l'Institut universitaire de gériatrie de Montreal
4 Montreal Neurological Institute and Hospital
5 CHOP - Children’s Hospital of Philadelphia
6 CRIUSMM - Centre de Recherche de l’Institut Universitaire en Santé Mentale de Montréal
7 UdeM - Université de Montréal
8 Cardiff University
9 Maastricht University [Maastricht]
10 HEC Montréal - HEC Montréal
11 Semel Institute for Neuroscience and Human Behavior [Los Angeles, Ca]
12 University of Pennsylvania
13 HMS - Harvard Medical School [Boston]
14 Boston Children's Hospital

Background Polygenicity and genetic heterogeneity pose great challenges for studying psychiatric conditions. Genetically-informed approaches have been implemented in neuroimaging studies to address this issue. However, the effects on functional connectivity of rare and common genetic risks for psychiatric disorders are largely unknown. Our objectives were to estimate and compare the effect-sizes on brain connectivity of psychiatric genomic risk factors with various levels of complexity: oligo-, multi-genic copy number variants (CNVs), and polygenic risk scores (PRS) as well as idiopathic psychiatric conditions and traits. Methods Resting-state functional-MRI data were processed using the same pipeline across nine datasets. Twenty-nine connectome-wide association studies were performed to characterize the effects of 15 CNVs (1003 carriers), 7 PRS, 4 idiopathic psychiatric conditions (1022 individuals with either autism, schizophrenia, bipolar conditions, or ADHD), and 2 traits (31424 unaffected controls). Results Effect sizes on connectivity were largest for psychiatric CNVs (estimates: 0.2 to 0.65 z-score) followed by psychiatric conditions (0.15 to 0.42), neuroticism and fluid intelligence (0.02 to 0.03), and PRS (0.01 to 0.02). Effect-sizes of CNVs on connectivity were correlated to their effects on cognition and risk for disease (r=0.9, p=5.93e-06). However, effect sizes of CNVs adjusted for the number of genes significantly decreased from small oligogenic to large multigenic CNVs (r=-0.88, p=8.78e-06). PRS had disproportionately low effect sizes on connectivity compared to CNVs conferring similar risk for disease. Conclusion Heterogeneity and polygenicity impact our ability to detect brain connectivity alterations underlying psychiatric manifestations.

Mots clés: Genetic heterogeneity; Copy number variant; Functional connectivity; Autism spectrum disorder; Polygenic score transdiagnostic approach

Domaines: Génétique humaine

Clara Moreau, Annabelle Harvey, Kuldeep Kumar, Guillaume Huguet, Sebastian Urchs, et al.. Genetic heterogeneity shapes brain connectivity in psychiatry. Biological Psychiatry, 2023, 93 (1), pp.54-58. ⟨10.1016/j.biopsych.2022.08.024⟩. ⟨pasteur-03819076⟩