Exploring the molecular basis of the genetic correlation between body mass index and brain morphological traits
Daniela Fusco, Camilla Marinelli, Mathilde André, Lucia Troiani, Martina Noè, Fabrizio Pizzagalli, Davide Marnetto, Paolo Provero
Abstract
Several studies have demonstrated significant phenotypic and genetic correlations between body mass index (BMI) and brain morphological traits derived from structural magnetic resonance imaging (sMRI). We use the sMRI, BMI, and genetic data collected by the UK Biobank to systematically compute the genetic correlations between area, volume, and thickness measurements of hundreds of brain structures on one hand, and BMI on the other. In agreement with previous literature, we find many such measurements to have negative genetic correlation with BMI.
Introduction
In 2022 one out of eight people were diagnosed with obesity worldwide, amounting to over 890 million adults and 160 million children and adolescents living with this chronic complex disease. In the majority of cases, obesity is a combination of environmental, psycho-social, and genetic factors. If the current increasing trend continues, 60% of the entire human population is estimated to be overweight or obese by 2030 [1].
Materials and method
Summary statistics and individual data
We used the GWAS summary statistics of brain imaging-derived phenotypes (IDPs) processed with a sample size of about 33 thousands individuals (sample sizes vary across traits) from the UKB release 2020 [19]. In particular, we selected all the IDPs defined according to the Desikan-Killiany cortical atlas [20] and subcortical volumetric segmentation (ASEG) [21]. After excluding the traits “aseg_lh_number_HolesBeforeFixing” and “aseg_rh_number_HolesBeforeFixing,” which correspond to technical artifacts produced by segmentation rather than morphological measurements, we were left with 435 IDPs (see S1 Data for a full list).
Results
Genetic correlation
In order to assess the shared genetic basis between brain morphology and BMI, we relied on variant-trait associations for 435 cortical and subcortical measurements estimated on more than 30 thousand donors from the UK Biobank [19]. These measurements included volume, thickness, areas from white and pial surfaces, gray-white matter contrast for 54 cortical regions [20]; volumes and mean intensities for subcortical segments [21] (see Methods for details on the selection of traits).
Discussion
We have devised a strategy to dissect the molecular underpinnings of the genetic correlation between a macroscopic complex trait (BMI) and a set of endophenotypes (brain morphology parameters assessed by sMRI). Since the effects of genetic variants on complex traits is thought to be mediated mostly by gene regulation, we first used SMR to generate a catalog of 21 genes whose genetically regulated expression pleiotropically affects both the endophenotypes and BMI.
Acknowledgments
Daniela Fusco and Camilla Marinelli are PhD students enrolled in the National PhD in Artificial Intelligence (resp. XXXVIII and XL cycle) course on Health and Life Sciences, organized by Università Campus Bio-Medico di Roma. UKB data were accessed under application 86275. GTEx data were accessed from dbGAP under project #18670.
Citation: Fusco D, Marinelli C, André M, Troiani L, Noè M, Pizzagalli F, et al. (2025) Exploring the molecular basis of the genetic correlation between body mass index and brain morphological traits. PLoS Genet 21(4): e1011658. https://doi.org/10.1371/journal.pgen.1011658
Editor: Yun Li, University of North Carolina at Chapel Hill, UNITED STATES OFAMERICA
Received: October 17, 2024; Accepted: March 17, 2025; Published: April 10, 2025
Copyright: © 2025 Fusco et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: GWAS summary statistics of brain imaging-derived phenotypes (IDPs) were obtained from the Oxford Brain Imaging Genetics Server – BIG 40 – and specifically from https://open.win.ox.ac.uk/ukbiobank/big40/release2/stats33k/0001.txt.gz. GWAS summary statistics for BMI were obtained from the Neale Lab website (http://www.nealelab.is/uk-biobank), and specifically from https://broad-ukb-sumstats-us-east-1.s3.amazonaws.com/round2/additive-tsvs/21001_raw.gwas.imputed_v3.both_sexes.tsv.bgz Individual-level data were obtained from the UK BioBank (https://www.ukbiobank.ac.uk/) under application 86275. Individual-level data for GTEx subjects were obtained from dbGAP (https://www.ncbi.nlm.nih.gov/gap/) under project #18670. Genetic correlation data, gene/trait associations, and chromatin accessibility predictions produced for this work are available in the Supporting information.
Funding: This work was supported in part by a Fondazione CRT grant (grant 2021.1787 to P.P.). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
