Identifying the potential causal role of insomnia symptoms on 11,409 health-related outcomes: a phenome-wide Mendelian randomisation analysis in UK Biobank

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Abstract

Background

Insomnia symptoms are widespread in the population and might have effects on many chronic conditions and their risk factors but previous research has focused on select hypothesised associations/effects rather than taking a systematic hypothesis-free approach across many health outcomes.

Methods

We performed a Mendelian randomisation (MR) phenome-wide association study (PheWAS) in 336,975 unrelated white-British UK Biobank participants. Self-reported insomnia symptoms were instrumented by a genetic risk score (GRS) created from 129 single-nucleotide polymorphisms (SNPs). A total of 11,409 outcomes from UK Biobank were extracted and processed by an automated pipeline (PHESANT) for the MR-PheWAS. Potential causal effects (those passing a Bonferroni-corrected significance threshold) were followed up with two-sample MR in MR-Base, where possible.

Results

Four hundred thirty-seven potential causal effects of insomnia symptoms were observed for a diverse range of outcomes, including anxiety, depression, pain, body composition, respiratory, musculoskeletal and cardiovascular traits. We were able to undertake two-sample MR for 71 of these 437 and found evidence of causal effects (with directionally concordant effect estimates across main and sensitivity analyses) for 30 of these. These included novel findings (by which we mean not extensively explored in conventional observational studies and not previously explored using MR based on a systematic search) of an adverse effect on risk of spondylosis (OR [95%CI] = 1.55 [1.33, 1.81]) and bronchitis (OR [95%CI] = 1.12 [1.03, 1.22]), among others.

Conclusions

Insomnia symptoms potentially cause a wide range of adverse health-related outcomes and behaviours. This has implications for developing interventions to prevent and treat a number of diseases in order to reduce multimorbidity and associated polypharmacy.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12916-023-02832-8.

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Most cited references 49

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A global reference for human genetic variation

Lachlan Coin, Robert Garry, Oleksyk Taras (2017)

The 1000 Genomes Project set out to provide a comprehensive description of common human genetic variation by applying whole-genome sequencing to a diverse set of individuals from multiple populations. Here we report completion of the project, having reconstructed the genomes of 2,504 individuals from 26 populations using a combination of low-coverage whole-genome sequencing, deep exome sequencing, and dense microarray genotyping. We characterized a broad spectrum of genetic variation, in total over 88 million variants (84.7 million single nucleotide polymorphisms (SNPs), 3.6 million short insertions/deletions (indels), and 60,000 structural variants), all phased onto high-quality haplotypes. This resource includes >99% of SNP variants with a frequency of >1% for a variety of ancestries. We describe the distribution of genetic variation across the global sample, and discuss the implications for common disease studies.

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The UK Biobank resource with deep phenotyping and genomic data

Clare Bycroft, Colin Freeman, Desislava Petkova … (2018)

The UK Biobank project is a prospective cohort study with deep genetic and phenotypic data collected on approximately 500,000 individuals from across the United Kingdom, aged between 40 and 69 at recruitment. The open resource is unique in its size and scope. A rich variety of phenotypic and health-related information is available on each participant, including biological measurements, lifestyle indicators, biomarkers in blood and urine, and imaging of the body and brain. Follow-up information is provided by linking health and medical records. Genome-wide genotype data have been collected on all participants, providing many opportunities for the discovery of new genetic associations and the genetic bases of complex traits. Here we describe the centralized analysis of the genetic data, including genotype quality, properties of population structure and relatedness of the genetic data, and efficient phasing and genotype imputation that increases the number of testable variants to around 96 million. Classical allelic variation at 11 human leukocyte antigen genes was imputed, resulting in the recovery of signals with known associations between human leukocyte antigen alleles and many diseases.

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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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Author and article information

Contributors

Mark J. Gibson:

ORCID: http://orcid.org/0000-0002-6930-4542

mark.gibson@bristol.ac.uk

Journal

Journal ID (nlm-ta): BMC Med

Journal ID (iso-abbrev): BMC Med

Title: BMC Medicine

Publisher: BioMed Central (London )

ISSN (Electronic): 1741-7015

Publication date (Electronic): 3 April 2023

Publication date PMC-release: 3 April 2023

Publication date Collection: 2023

Volume: 21

Electronic Location Identifier: 128

Affiliations

[1 ]GRID grid.5337.2, ISNI 0000 0004 1936 7603, MRC Integrative Epidemiology Unit (IEU), , University of Bristol, ; Bristol, UK

[2 ]GRID grid.5337.2, ISNI 0000 0004 1936 7603, School of Psychological Science, , University of Bristol, ; Bristol, UK

[3 ]GRID grid.5337.2, ISNI 0000 0004 1936 7603, Department of Population Health Sciences, Bristol Medical School, , University of Bristol, ; Bristol, UK

Author information

Mark J. Gibson http://orcid.org/0000-0002-6930-4542

Article

Publisher ID: 2832

DOI: 10.1186/s12916-023-02832-8

PMC ID: 10071698

PubMed ID: 37013595

SO-VID: 9eba9ed2-fdcc-4c69-99e3-bdc56b09b4a5

License:

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

History

Date received : 2 November 2022

Date accepted : 13 March 2023

Funding

Funded by: FundRef http://dx.doi.org/10.13039/501100000265, Medical Research Council;

Award ID: MC_UU_00011/1; MC_UU_00011/6; MC_UU_00011/7; MR/V033867/1

Funded by: Diabetes UK

Award ID: 17/0005700

Funded by: FundRef http://dx.doi.org/10.13039/501100000274, British Heart Foundation;

Award ID: AA/18/1/34219; CH/F/20/90003

Funded by: National Institute of Health

Award ID: NF-0616-10102

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ScienceOpen disciplines: Medicine

Keywords: insomnia,mendelian randomisation,mr-phewas,uk biobank

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ScienceOpen disciplines: Medicine

Keywords: insomnia, mendelian randomisation, mr-phewas, uk biobank

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Identifying the potential causal role of insomnia symptoms on 11,409 health-related outcomes: a phenome-wide Mendelian randomisation analysis in UK Biobank

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Abstract

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Supplementary Information

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IEEE: COVID-19 related research

Most cited references 49

A global reference for human genetic variation

The UK Biobank resource with deep phenotyping and genomic data

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

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