Genetic estimates of correlation and causality between blood-based biomarkers and psychiatric disorders

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Abstract

There is a long-standing interest in exploring the relationship between blood-based biomarkers and psychiatric disorders, despite their causal role being difficult to resolve in observational studies. In this study, we leverage genome-wide association study data for a large panel of heritable serum biochemical traits to refine our understanding of causal effect in biochemical-psychiatric trait pairings. We observed widespread positive and negative genetic correlation between psychiatric disorders and biochemical traits. Causal inference was then implemented to distinguish causation from correlation, with strong evidence that C-reactive protein (CRP) exerts a causal effect on psychiatric disorders. Notably, CRP demonstrated both protective and risk-increasing effects on different disorders. Multivariable models that conditioned CRP effects on interleukin-6 signaling and body mass index supported that the CRP-schizophrenia relationship was not driven by these factors. Collectively, these data suggest that there are shared pathways that influence both biochemical traits and psychiatric illness.

Abstract

There was extensive genetic overlap between blood-based biochemical traits and psychiatric illness.

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STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets

Damian Szklarczyk, Annika L Gable, David Lyon … (2018)

Abstract Proteins and their functional interactions form the backbone of the cellular machinery. Their connectivity network needs to be considered for the full understanding of biological phenomena, but the available information on protein–protein associations is incomplete and exhibits varying levels of annotation granularity and reliability. The STRING database aims to collect, score and integrate all publicly available sources of protein–protein interaction information, and to complement these with computational predictions. Its goal is to achieve a comprehensive and objective global network, including direct (physical) as well as indirect (functional) interactions. The latest version of STRING (11.0) more than doubles the number of organisms it covers, to 5090. The most important new feature is an option to upload entire, genome-wide datasets as input, allowing users to visualize subsets as interaction networks and to perform gene-set enrichment analysis on the entire input. For the enrichment analysis, STRING implements well-known classification systems such as Gene Ontology and KEGG, but also offers additional, new classification systems based on high-throughput text-mining as well as on a hierarchical clustering of the association network itself. The STRING resource is available online at https://string-db.org/.

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Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression

Jack Bowden, George Davey Smith, Stephen Burgess (2016)

Background: The number of Mendelian randomization analyses including large numbers of genetic variants is rapidly increasing. This is due to the proliferation of genome-wide association studies, and the desire to obtain more precise estimates of causal effects. However, some genetic variants may not be valid instrumental variables, in particular due to them having more than one proximal phenotypic correlate (pleiotropy). Methods: We view Mendelian randomization with multiple instruments as a meta-analysis, and show that bias caused by pleiotropy can be regarded as analogous to small study bias. Causal estimates using each instrument can be displayed visually by a funnel plot to assess potential asymmetry. Egger regression, a tool to detect small study bias in meta-analysis, can be adapted to test for bias from pleiotropy, and the slope coefficient from Egger regression provides an estimate of the causal effect. Under the assumption that the association of each genetic variant with the exposure is independent of the pleiotropic effect of the variant (not via the exposure), Egger’s test gives a valid test of the null causal hypothesis and a consistent causal effect estimate even when all the genetic variants are invalid instrumental variables. Results: We illustrate the use of this approach by re-analysing two published Mendelian randomization studies of the causal effect of height on lung function, and the causal effect of blood pressure on coronary artery disease risk. The conservative nature of this approach is illustrated with these examples. Conclusions: An adaption of Egger regression (which we call MR-Egger) can detect some violations of the standard instrumental variable assumptions, and provide an effect estimate which is not subject to these violations. The approach provides a sensitivity analysis for the robustness of the findings from a Mendelian randomization investigation.

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Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator

Jack Bowden, George Davey Smith, Philip C. Haycock … (2017)

ABSTRACT Developments in genome‐wide association studies and the increasing availability of summary genetic association data have made application of Mendelian randomization relatively straightforward. However, obtaining reliable results from a Mendelian randomization investigation remains problematic, as the conventional inverse‐variance weighted method only gives consistent estimates if all of the genetic variants in the analysis are valid instrumental variables. We present a novel weighted median estimator for combining data on multiple genetic variants into a single causal estimate. This estimator is consistent even when up to 50% of the information comes from invalid instrumental variables. In a simulation analysis, it is shown to have better finite‐sample Type 1 error rates than the inverse‐variance weighted method, and is complementary to the recently proposed MR‐Egger (Mendelian randomization‐Egger) regression method. In analyses of the causal effects of low‐density lipoprotein cholesterol and high‐density lipoprotein cholesterol on coronary artery disease risk, the inverse‐variance weighted method suggests a causal effect of both lipid fractions, whereas the weighted median and MR‐Egger regression methods suggest a null effect of high‐density lipoprotein cholesterol that corresponds with the experimental evidence. Both median‐based and MR‐Egger regression methods should be considered as sensitivity analyses for Mendelian randomization investigations with multiple genetic variants.

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Author and article information

Contributors

William R. Reay:

ORCID: https://orcid.org/0000-0001-7689-2453

Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: SoftwareRole: ValidationRole: VisualizationRole: Writing - original draftRole: Writing - review & editing

Dylan J. Kiltschewskij:

ORCID: https://orcid.org/0000-0003-0855-8757

Role: Formal analysisRole: Writing - review & editing

Michael P. Geaghan:

ORCID: https://orcid.org/0000-0002-4350-8600

Role: Formal analysisRole: SoftwareRole: VisualizationRole: Writing - review & editing

Joshua R. Atkins:

ORCID: https://orcid.org/0000-0003-0821-1112

Role: InvestigationRole: Software

Vaughan J. Carr:

ORCID: https://orcid.org/0000-0002-8907-5804

Role: ConceptualizationRole: Funding acquisition

Melissa J. Green:

ORCID: https://orcid.org/0000-0002-9361-4874

Role: Funding acquisitionRole: SupervisionRole: Writing - review & editing

Murray J. Cairns:

ORCID: https://orcid.org/0000-0003-2490-2538

Role: ConceptualizationRole: Funding acquisitionRole: MethodologyRole: Project administrationRole: ResourcesRole: SupervisionRole: Writing - original draftRole: Writing - review & editing

Journal

Journal ID (nlm-ta): Sci Adv

Journal ID (iso-abbrev): Sci Adv

Journal ID (publisher-id): sciadv

Journal ID (hwp): advances

Title: Science Advances

Publisher: American Association for the Advancement of Science

ISSN (Electronic): 2375-2548

Publication date Collection: April 2022

Publication date (Electronic, pub): 06 April 2022

Volume: 8

Issue: 14

Electronic Location Identifier: eabj8969

Affiliations

[1 ]School of Biomedical Sciences and Pharmacy, The University of Newcastle, Callaghan, NSW, Australia.

[2 ]Centre for Brain and Mental Health Research, Hunter Medical Research Institute, Newcastle, NSW, Australia.

[3 ]School of Psychiatry, University of New South Wales, Randwick, NSW, Australia.

[4 ]Neuroscience Research Australia, Sydney, NSW, Australia.

[5 ]Department of Psychiatry, Monash University, Melbourne, VIC, Australia.

Author notes

[* ]Corresponding author. Email: murray.cairns@ 123456newcastle.edu.au

Author information

William R. Reay https://orcid.org/0000-0001-7689-2453

Dylan J. Kiltschewskij https://orcid.org/0000-0003-0855-8757

Michael P. Geaghan https://orcid.org/0000-0002-4350-8600

Joshua R. Atkins https://orcid.org/0000-0003-0821-1112

Vaughan J. Carr https://orcid.org/0000-0002-8907-5804

Melissa J. Green https://orcid.org/0000-0002-9361-4874

Murray J. Cairns https://orcid.org/0000-0003-2490-2538

Article

Publisher ID: abj8969

DOI: 10.1126/sciadv.abj8969

PMC ID: 8986101

PubMed ID: 35385317

SO-VID: 5bd86604-fe09-4e0f-ad2a-66ad9e829325

Copyright © Copyright © 2022 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution License 4.0 (CC BY).

License:

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 work is properly cited.

History

Date received : 08 June 2021

Date accepted : 16 February 2022

Funding

Funded by: FundRef http://dx.doi.org/10.13039/501100000925, National Health and Medical Research Council;

Award ID: 1188493

Funded by: FundRef http://dx.doi.org/10.13039/501100000925, National Health and Medical Research Council;

Award ID: 1121474

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Genetic estimates of correlation and causality between blood-based biomarkers and psychiatric disorders

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Most cited references 93

STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets

Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression

Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator

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Cited by 24

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