iScienceGenetic & Genomic MedicinePeer-reviewed journal article
Genetic control of body weight by the human brain proteome
Éloi GagnoniD, Arnaud S. GirardiD, Émilie GobeiliD, Jérôme BourgaultiD, Christian Yves CoutureiD, Patricia L. MitchelliD, Claude BouchardiD, Angelo TremblayiD, Patrick MathieuiD, Andréanne MichaudiD, Louis PërusseiD, Benoît J. ArsenaultiD
Abstract
Genome-wide association studies (GWAS) have identified hundreds of genetic variants associated with body weight but the biological relevance of most remains unexplored. Given the critical role of the brain in multiple biological processes associated with body weight regulation, we set out to determine whether genetic variants linked with body mass index (BMI) could be mapped to brain proteins. Using genetic colocalization, we mapped 23 loci from the largest BMI GWAS (n=806,834) to brain proteins (obtained from a dataset of >7000 dorsolateral prefrontal cortex proteins measured by mass spectrometry in >400 individuals). We also performed a proteome-wide Mendelian randomization analysis followed by genetic colocalization, which allowed us to identify an additional 48 brain proteins linked with BMI. Multi-trait colocalization suggested that more than 75% of the protein quantitative trait loci (pQTL)-BMI associations are mediated via protein expression and not via RNA expression. Single-cell sequencing from the human brain cortex revealed that the genes expressing the proteins associated with BMI may be predominantly expressed in oligodendrocytes. In the Quebec Family Study, a genetic risk score (GRS) including these brain pQTLs was associated with higher dietary carbohydrate intake and lower lipid intake whereas a GRS including the 67 variants most strongly associated with BMI was not associated with dietary intake. In conclusions, we identified 71 proteins expressed in the prefrontal cortex that may be critical regulators of body weight and possibly dietary intake in humans.
- organism: human
- assay: proteomics
- research focus: GWAS / association mapping
- approach: statistical model
- Citations
- 6
- FWCI
- 1.00
- Field percentile
- top 27%
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