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[Preprint]. 2023 Feb 14:2023.02.10.23285650.
doi: 10.1101/2023.02.10.23285650.

Genetic mechanisms of 184 neuro-related proteins in human plasma

Affiliations

Genetic mechanisms of 184 neuro-related proteins in human plasma

Linda Repetto et al. medRxiv. .

Update in

  • The genetic landscape of neuro-related proteins in human plasma.
    Repetto L, Chen J, Yang Z, Zhai R, Timmers PRHJ, Feng X, Li T, Yao Y, Maslov D, Timoshchuk A, Tu F, Twait EL, May-Wilson S, Muckian MD, Prins BP, Png G, Kooperberg C, Johansson Å, Hillary RF, Wheeler E, Pan L, He Y, Klasson S, Ahmad S, Peters JE, Gilly A, Karaleftheri M, Tsafantakis E, Haessler J, Gyllensten U, Harris SE, Wareham NJ, Göteson A, Lagging C, Ikram MA, van Duijn CM, Jern C, Landén M, Langenberg C, Deary IJ, Marioni RE, Enroth S, Reiner AP, Dedoussis G, Zeggini E, Sharapov S, Aulchenko YS, Butterworth AS, Mälarstig A, Wilson JF, Navarro P, Shen X. Repetto L, et al. Nat Hum Behav. 2024 Nov;8(11):2222-2234. doi: 10.1038/s41562-024-01963-z. Epub 2024 Aug 29. Nat Hum Behav. 2024. PMID: 39210026

Abstract

Understanding the genetic basis of neuro-related proteins is essential for dissecting the disease etiology of neuropsychiatric disorders and other complex traits and diseases. Here, the SCALLOP Consortium conducted a genome-wide association meta-analysis of over 12,500 individuals for 184 neuro-reiated proteins in human plasma. The analysis identified 117 cis-regulatory protein quantitative trait loci (cis-pQTL) and 166 trans-pQTL. The mapped pQTL capture on average 50% of each protein's heritability. Mendelian randomization analyses revealed multiple proteins showing potential causal effects on neuro-reiated traits as well as complex diseases such as hypertension, high cholesterol, immune-related disorders, and psychiatric disorders. Integrating with established drug information, we validated 13 combinations of protein targets and diseases or side effects with available drugs, while suggesting hundreds of re-purposing and new therapeutic targets for diseases and comorbidities. This consortium effort provides a large-scale proteogenomic resource for biomedical research.

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Conflict of interest statement

Competing interests statement P.R.H.J.T is a salaried employee of BioAge Labs, Inc. The remaining authors declare no competing financial interests. R.E.M has received a speaker fee from Illumina, is an advisor to the Epigenetic Clock Development Foundation, and a scientific consultant for Optima Partners. E.W. is now an employee of AstraZeneca.

Figures

Figure 1:
Figure 1:. Overview of the mapped protein quantitative trait loci (pQTL).
a. Pleiotropictrans-pQTL counts and overlap of the mapped pQTL with existing eQTL. The upper barplot shows the number of proteins share trans-pQTL (gene annotations based on gene closest to the trans-pQTL). The scatterplot shows the genomic location of significant cis-pQTL in red (P < 5 × 10−8), significant trans-pQTL in blue (P < 5 × 10−8/184), and the shading within the dots indicates significance of the corresponding/nearest cis-eQTL for the respective protein. b. Scatterplot of the pQTL lead variants association signals v.s. their distance to the transcription start site (TSS) of the corresponding/nearest coding genes. c. Scatterplot of the absolute estimated genetic effects of the pQTL lead variants v.s. their minor allele frequencies (MAFs). d. The scatterplot in c shown in logarithm scale. e. Number of mapped pQTL per protein v.s. the linear mixed model estimated heritability in the ORCADES cohort. f. The variance explained by the mapped pQTL summed up for each protein v.s. the estimated heritability. g. For the proteins with significant cis-pQTL mapped, the lead variant signal strength v.s. the estimated heritability of each protein.
Figure 2:
Figure 2:. Causality between the proteins and neuro-reiated phenotypes inferred by Mendeiian randomization (MR) analyses.
The forest plot shows the significant MR results (false discovery rate < 0.05) based on LD-pruned (r2 < 0.001) instrumental variants within each cis-pQTL. Inverse-variance weighted (IVW) estimates are provided as the solid round dots, and the whiskers indicate 95% confidence intervals. The numbers of instrumental variants in the cis-pQTL are given to the right of the whiskers. As a colocalization measure, the HEIDI (heterogeneity in dependent instruments) test evidence (p > 0.05) are given as the diamonds, where the largest diamonds correspond to a p-value of 1. The upper part of the plot shows the results where the proteins are known druggable targets, while the lower part shows the results for new protein targets.
Figure 3:
Figure 3:. Regional association patterns of the pQTL and the colocalized neuro-reiated complex traits.
The displayed protein-trait pairs correspond to the Mendeiian randomization discoveries in Figure 2 with the HEIDI p-value > 0.05. Each subfigure shows the pQTL region of 1Mb centered at the lead variant. The vertical dashed line in each subfigure marks the transcription start site of the corresponding protein’s coding gene.
Figure 4:
Figure 4:. Causality between the proteins and UK Biobank disease phenotypes inferred by Mendeiian randomization (MR) analyses.
The forest plot shows the significant MR results (false discovery rate < 0.05) based on LD-pruned (r2 < 0.001) instrumental variants within each cis-pQTL. Inverse-variance weighted (IVW) estimates are provided as the solid round dots, and the whiskers indicate 95% confidence intervals. The numbers of instrumental variants in the cis-pQTL are given to the right of the whiskers. As a colocalization measure, the HEIDI (heterogeneity in dependent instruments) test evidence (p > 0.05) are given as the diamonds, where the largest diamonds correspond to a p-value of 1. The upper part of the plot shows the results where the proteins are known druggable targets, while the lower part shows the results for new protein targets.
Figure 5:
Figure 5:. Drug targets revealed by Mendeiian randomization (MR) analyses.
The MR results with 5% false discovery rate are considered. a. The number of MR inferred pairs of proteins and traits split into four categories: new (drug) targets, druggable targets that have drugs with unclear clinical function, re-purposing targets that have established drugs but for different diseases, and validated known targets where the established drugs have pharmacological effects that match the MR results. b. Numbers of re-purposing and validated drug targets per protein analysed. c. The validated known drug targets, the description of the drugs, and the corresponding consistent MR estimated effects. d. Potential mechanism of the adverse effect of Clenbuterol that targets NGF. e. Potential mechanism of Fostamatinib treating Chronic immune thrombocytopenia through CTSS. f. Potential pharmacology of DPEPl’s re-purposing drug on schizophrenia.

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