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[Preprint]. 2024 Jan 19:2024.01.18.24301455.
doi: 10.1101/2024.01.18.24301455.

Large-Scale Mendelian Randomization Study Reveals Circulating Blood-based Proteomic Biomarkers for Psychopathology and Cognitive Task Performance

Upasana Bhattacharyya  1   2 , Jibin John  1   2 , Max Lam  1   2 , Jonah Fisher  3   4 , Benjamin Sun  3 , Denis Baird  3 , Chia-Yen Chen  3 , Todd Lencz  1   2   5
Affiliations

Large-Scale Mendelian Randomization Study Reveals Circulating Blood-based Proteomic Biomarkers for Psychopathology and Cognitive Task Performance

Upasana Bhattacharyya et al. medRxiv. .

Abstract

Background: Research on peripheral (e.g., blood-based) biomarkers for psychiatric illness has typically been low-throughput in terms of both the number of subjects and the range of assays performed. Moreover, traditional case-control studies examining blood-based biomarkers are subject to potential confounds of treatment and other exposures common to patients with psychiatric illnesses. Our research addresses these challenges by leveraging large-scale, high-throughput proteomics data and Mendelian Randomization (MR) to examine the causal impact of circulating proteins on psychiatric phenotypes and cognitive task performance.

Methods: We utilized plasma proteomics data from the UK Biobank (3,072 proteins assayed in 34,557 European-ancestry individuals) and deCODE Genetics (4,719 proteins measured across 35,559 Icelandic individuals). Significant proteomic quantitative trait loci (both cis-pQTLs and trans-pQTLs) served as MR instruments, with the most recent GWAS for schizophrenia, bipolar disorder, major depressive disorder, and cognitive task performance (all excluding overlapping UK Biobank participants) as phenotypic outcomes.

Results: MR revealed 109 Bonferroni-corrected causal associations (44 novel) involving 88 proteins across the four phenotypes. Several immune-related proteins, including interleukins and complement factors, stood out as pleiotropic across multiple outcome phenotypes. Drug _target enrichment analysis identified several novel potential pharmacologic repurposing opportunities, including anti-inflammatory agents for schizophrenia and bipolar disorder and duloxetine for cognitive performance.

Conclusions: Identification of causal effects for these circulating proteins suggests potential biomarkers for these conditions and offers insights for developing innovative therapeutic strategies. The findings also indicate substantial evidence for the pleiotropic effects of many proteins across different phenotypes, shedding light on the shared etiology among psychiatric conditions and cognitive ability.

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Conflict of interest statement

Competing interests J.F., B.S., D.B., and C.-Y.C. are employees of Biogen. The other authors declare no competing interests.

Figures

Figure 1.
Figure 1.
MR analysis workflow
Figure 2.
Figure 2.
Manhattan plot showing findings from MR analysis for schizophrenia, bipolar disorder, major depressive disorder, and cognitive task performance, employing Cis-pQTLs from UKB-PPP and DECODE dataset as instrumental variables
Figure 3.
Figure 3.
Manhattan plot showing findings from MR analysis for schizophrenia, bipolar disorder, major depressive disorder, and cognitive task performance, employing Trans-pQTLs from UKB-PPP and DECODE datasets as instrumental variables
Figure 4a.
Figure 4a.
CIS-pQTL MR results across traits
Figure 4b.
Figure 4b.
TRANS-pQTL MR results across traits
See this image and copyright information in PMC

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